From c2c0c392835c71da7dfd999331e34a1783b52f93 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Tue, 4 Jun 2024 18:14:04 -0700
Subject: [PATCH] BF: missing Auto files

---
 Ch02-statlearn-lab.ipynb | 4752 +++++++++++++++++++++++++++++++-------
 1 file changed, 3941 insertions(+), 811 deletions(-)

diff --git a/Ch02-statlearn-lab.ipynb b/Ch02-statlearn-lab.ipynb
index 6439d24..65a0191 100644
--- a/Ch02-statlearn-lab.ipynb
+++ b/Ch02-statlearn-lab.ipynb
@@ -107,10 +107,10 @@
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@@ -140,10 +140,10 @@
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    "outputs": [],
@@ -165,10 +165,10 @@
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@@ -205,10 +205,10 @@
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@@ -254,10 +254,10 @@
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@@ -295,10 +295,10 @@
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@@ -369,10 +369,10 @@
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@@ -580,10 +580,10 @@
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@@ -659,10 +659,10 @@
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@@ -901,10 +901,10 @@
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@@ -980,10 +980,10 @@
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@@ -1038,10 +1038,10 @@
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@@ -1122,10 +1122,10 @@
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@@ -1159,10 +1159,10 @@
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@@ -1195,10 +1195,10 @@
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-     "iopub.execute_input": "2024-06-04T23:23:01.286690Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.286620Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.288595Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.288384Z"
+     "iopub.execute_input": "2024-06-05T01:13:21.848925Z",
+     "iopub.status.busy": "2024-06-05T01:13:21.848841Z",
+     "iopub.status.idle": "2024-06-05T01:13:21.850823Z",
+     "shell.execute_reply": "2024-06-05T01:13:21.850640Z"
     },
     "lines_to_next_cell": 2
    },
@@ -1242,26 +1242,26 @@
    "id": "e2756bb9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.289713Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.289642Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.291852Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.291616Z"
+     "iopub.execute_input": "2024-06-05T01:13:21.852136Z",
+     "iopub.status.busy": "2024-06-05T01:13:21.852038Z",
+     "iopub.status.idle": "2024-06-05T01:13:21.854307Z",
+     "shell.execute_reply": "2024-06-05T01:13:21.854071Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.41226418,  1.64793369, -1.0811617 ,  2.68393401,  0.37921749,\n",
-       "       -0.10425743, -0.91976782, -1.39269564,  0.48974288, -0.89149319,\n",
-       "        0.907972  , -1.19353366, -0.55424777, -0.72800649,  1.40724945,\n",
-       "       -0.84585257,  0.75994765,  0.87186972, -0.96670824, -0.91749601,\n",
-       "       -2.67627945,  2.0831284 , -1.03668468, -1.35263625,  2.64611566,\n",
-       "        0.31286968,  0.45982632,  0.43684444, -0.99861792, -0.09345691,\n",
-       "       -0.15268236,  0.62074588,  0.59112151,  1.19624629,  1.22069198,\n",
-       "        1.27517759,  0.08521397,  1.37804766,  1.56868645, -0.05006845,\n",
-       "        0.64252904,  0.04994592,  1.41862954,  0.23173335,  2.20286736,\n",
-       "       -2.18280483, -0.50792686, -1.16284315,  0.32957924, -1.15313795])"
+       "array([ 0.63214394,  0.34288365,  0.85005043, -0.47385094,  0.32864279,\n",
+       "       -0.78796854,  0.51125391, -1.12947776, -0.71391649, -1.92367418,\n",
+       "       -0.42609273, -0.7309404 ,  0.83090301, -0.47570241, -0.5852834 ,\n",
+       "        0.89788488, -1.27638353,  2.59421427,  1.25973331, -0.61486026,\n",
+       "        1.3979914 ,  0.79368659, -1.14506316, -2.00869422,  0.19502313,\n",
+       "       -0.45956661,  0.65944298,  0.69723473, -1.09211872,  0.15972909,\n",
+       "       -0.22530418,  0.32276968,  0.50374192,  0.80506415, -0.48224507,\n",
+       "       -0.53214642, -1.09662644, -1.52722433, -2.10241781, -0.42403843,\n",
+       "        1.32310673,  2.00932483, -0.16726754,  2.30113584, -0.68976675,\n",
+       "       -0.67621251, -0.78371139, -0.62709974, -1.39453964,  0.25195549])"
       ]
      },
      "execution_count": 28,
@@ -1288,10 +1288,10 @@
    "id": "d336dde1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.293141Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.293072Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.294575Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.294325Z"
+     "iopub.execute_input": "2024-06-05T01:13:21.856931Z",
+     "iopub.status.busy": "2024-06-05T01:13:21.856831Z",
+     "iopub.status.idle": "2024-06-05T01:13:21.858521Z",
+     "shell.execute_reply": "2024-06-05T01:13:21.858270Z"
     },
     "lines_to_next_cell": 0
    },
@@ -1315,18 +1315,18 @@
    "id": "cad26f2b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.295836Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.295770Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.297930Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.297702Z"
+     "iopub.execute_input": "2024-06-05T01:13:21.859935Z",
+     "iopub.status.busy": "2024-06-05T01:13:21.859837Z",
+     "iopub.status.idle": "2024-06-05T01:13:21.861977Z",
+     "shell.execute_reply": "2024-06-05T01:13:21.861747Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[1.        , 0.78663971],\n",
-       "       [0.78663971, 1.        ]])"
+       "array([[1.        , 0.71781515],\n",
+       "       [0.71781515, 1.        ]])"
       ]
      },
      "execution_count": 30,
@@ -1355,10 +1355,10 @@
    "id": "a621aa6c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.299259Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.299172Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.301264Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.301014Z"
+     "iopub.execute_input": "2024-06-05T01:13:21.863299Z",
+     "iopub.status.busy": "2024-06-05T01:13:21.863206Z",
+     "iopub.status.idle": "2024-06-05T01:13:21.865015Z",
+     "shell.execute_reply": "2024-06-05T01:13:21.864730Z"
     },
     "lines_to_next_cell": 0
    },
@@ -1367,8 +1367,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-4.06035487 -3.04947906]\n",
-      "[-1.9555141   3.81545067]\n"
+      "[-0.97181857  3.2315884 ]\n",
+      "[ 0.73365791 -7.23683363]\n"
      ]
     }
    ],
@@ -1406,10 +1406,10 @@
    "id": "8b4cf04d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.302644Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.302565Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.304644Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.304413Z"
+     "iopub.execute_input": "2024-06-05T01:13:21.866527Z",
+     "iopub.status.busy": "2024-06-05T01:13:21.866407Z",
+     "iopub.status.idle": "2024-06-05T01:13:21.868523Z",
+     "shell.execute_reply": "2024-06-05T01:13:21.868290Z"
     }
    },
    "outputs": [
@@ -1452,10 +1452,10 @@
    "id": "4b80757c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.305940Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.305869Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.307972Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.307754Z"
+     "iopub.execute_input": "2024-06-05T01:13:21.869817Z",
+     "iopub.status.busy": "2024-06-05T01:13:21.869734Z",
+     "iopub.status.idle": "2024-06-05T01:13:21.871794Z",
+     "shell.execute_reply": "2024-06-05T01:13:21.871580Z"
     },
     "lines_to_next_cell": 0
    },
@@ -1491,10 +1491,10 @@
    "id": "1e06f53d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.309164Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.309085Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.311053Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.310846Z"
+     "iopub.execute_input": "2024-06-05T01:13:21.873319Z",
+     "iopub.status.busy": "2024-06-05T01:13:21.873222Z",
+     "iopub.status.idle": "2024-06-05T01:13:21.875364Z",
+     "shell.execute_reply": "2024-06-05T01:13:21.875114Z"
     },
     "lines_to_next_cell": 2
    },
@@ -1529,10 +1529,10 @@
    "id": "2499a37e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.312228Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.312159Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.314146Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.313943Z"
+     "iopub.execute_input": "2024-06-05T01:13:21.876542Z",
+     "iopub.status.busy": "2024-06-05T01:13:21.876445Z",
+     "iopub.status.idle": "2024-06-05T01:13:21.878423Z",
+     "shell.execute_reply": "2024-06-05T01:13:21.878185Z"
     }
    },
    "outputs": [
@@ -1566,10 +1566,10 @@
    "id": "3c308346",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.315285Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.315218Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.317217Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.317036Z"
+     "iopub.execute_input": "2024-06-05T01:13:21.879714Z",
+     "iopub.status.busy": "2024-06-05T01:13:21.879626Z",
+     "iopub.status.idle": "2024-06-05T01:13:21.881633Z",
+     "shell.execute_reply": "2024-06-05T01:13:21.881427Z"
     }
    },
    "outputs": [
@@ -1612,10 +1612,10 @@
    "id": "7b5e8fcd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.318331Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.318263Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.320121Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.319916Z"
+     "iopub.execute_input": "2024-06-05T01:13:21.882917Z",
+     "iopub.status.busy": "2024-06-05T01:13:21.882831Z",
+     "iopub.status.idle": "2024-06-05T01:13:21.884797Z",
+     "shell.execute_reply": "2024-06-05T01:13:21.884581Z"
     }
    },
    "outputs": [
@@ -1648,10 +1648,10 @@
    "id": "157d4a21",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.321267Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.321200Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.323059Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.322851Z"
+     "iopub.execute_input": "2024-06-05T01:13:21.886134Z",
+     "iopub.status.busy": "2024-06-05T01:13:21.886054Z",
+     "iopub.status.idle": "2024-06-05T01:13:21.888025Z",
+     "shell.execute_reply": "2024-06-05T01:13:21.887817Z"
     },
     "lines_to_next_cell": 0
    },
@@ -1718,10 +1718,10 @@
    "id": "8b11e94e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.324220Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.324151Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.554777Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.554525Z"
+     "iopub.execute_input": "2024-06-05T01:13:21.889264Z",
+     "iopub.status.busy": "2024-06-05T01:13:21.889168Z",
+     "iopub.status.idle": "2024-06-05T01:13:22.181786Z",
+     "shell.execute_reply": "2024-06-05T01:13:22.181539Z"
     }
    },
    "outputs": [
@@ -1760,10 +1760,10 @@
    "id": "04eb327c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.556332Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.556194Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.605207Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.604941Z"
+     "iopub.execute_input": "2024-06-05T01:13:22.183312Z",
+     "iopub.status.busy": "2024-06-05T01:13:22.183171Z",
+     "iopub.status.idle": "2024-06-05T01:13:22.234220Z",
+     "shell.execute_reply": "2024-06-05T01:13:22.233942Z"
     }
    },
    "outputs": [
@@ -1798,10 +1798,10 @@
    "id": "e678d266",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.606568Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.606495Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.657229Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.656997Z"
+     "iopub.execute_input": "2024-06-05T01:13:22.235936Z",
+     "iopub.status.busy": "2024-06-05T01:13:22.235832Z",
+     "iopub.status.idle": "2024-06-05T01:13:22.288490Z",
+     "shell.execute_reply": "2024-06-05T01:13:22.288148Z"
     },
     "lines_to_next_cell": 0
    },
@@ -1846,10 +1846,10 @@
    "id": "b4cff8b7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.658803Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.658695Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.713296Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.713053Z"
+     "iopub.execute_input": "2024-06-05T01:13:22.290146Z",
+     "iopub.status.busy": "2024-06-05T01:13:22.290040Z",
+     "iopub.status.idle": "2024-06-05T01:13:22.347902Z",
+     "shell.execute_reply": "2024-06-05T01:13:22.347605Z"
     }
    },
    "outputs": [
@@ -1886,10 +1886,10 @@
    "id": "9e59d427",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.714643Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.714567Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.768778Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.768502Z"
+     "iopub.execute_input": "2024-06-05T01:13:22.349518Z",
+     "iopub.status.busy": "2024-06-05T01:13:22.349396Z",
+     "iopub.status.idle": "2024-06-05T01:13:22.406731Z",
+     "shell.execute_reply": "2024-06-05T01:13:22.406399Z"
     },
     "lines_to_next_cell": 0
    },
@@ -1897,7 +1897,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x110f8a4e0>"
+       "<matplotlib.collections.PathCollection at 0x122230260>"
       ]
      },
      "execution_count": 43,
@@ -1949,10 +1949,10 @@
    "id": "c7408ab0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.770270Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.770200Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.836846Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.836604Z"
+     "iopub.execute_input": "2024-06-05T01:13:22.408463Z",
+     "iopub.status.busy": "2024-06-05T01:13:22.408338Z",
+     "iopub.status.idle": "2024-06-05T01:13:22.474709Z",
+     "shell.execute_reply": "2024-06-05T01:13:22.474398Z"
     }
    },
    "outputs": [
@@ -1990,10 +1990,10 @@
    "id": "52bac447",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.838278Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.838190Z",
-     "iopub.status.idle": "2024-06-04T23:23:01.879089Z",
-     "shell.execute_reply": "2024-06-04T23:23:01.878827Z"
+     "iopub.execute_input": "2024-06-05T01:13:22.476314Z",
+     "iopub.status.busy": "2024-06-05T01:13:22.476169Z",
+     "iopub.status.idle": "2024-06-05T01:13:22.519810Z",
+     "shell.execute_reply": "2024-06-05T01:13:22.519505Z"
     },
     "lines_to_next_cell": 0
    },
@@ -2044,10 +2044,10 @@
    "id": "0e4d4d4e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:01.880627Z",
-     "iopub.status.busy": "2024-06-04T23:23:01.880551Z",
-     "iopub.status.idle": "2024-06-04T23:23:02.034759Z",
-     "shell.execute_reply": "2024-06-04T23:23:02.034504Z"
+     "iopub.execute_input": "2024-06-05T01:13:22.521455Z",
+     "iopub.status.busy": "2024-06-05T01:13:22.521350Z",
+     "iopub.status.idle": "2024-06-05T01:13:22.684396Z",
+     "shell.execute_reply": "2024-06-05T01:13:22.684070Z"
     },
     "lines_to_next_cell": 0
    },
@@ -2084,10 +2084,10 @@
    "id": "12d9932f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:02.036153Z",
-     "iopub.status.busy": "2024-06-04T23:23:02.036078Z",
-     "iopub.status.idle": "2024-06-04T23:23:02.141852Z",
-     "shell.execute_reply": "2024-06-04T23:23:02.141588Z"
+     "iopub.execute_input": "2024-06-05T01:13:22.685979Z",
+     "iopub.status.busy": "2024-06-05T01:13:22.685854Z",
+     "iopub.status.idle": "2024-06-05T01:13:22.795331Z",
+     "shell.execute_reply": "2024-06-05T01:13:22.795034Z"
     },
     "lines_to_next_cell": 0
    },
@@ -2137,10 +2137,10 @@
    "id": "17d46f32",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:02.143385Z",
-     "iopub.status.busy": "2024-06-04T23:23:02.143270Z",
-     "iopub.status.idle": "2024-06-04T23:23:02.934879Z",
-     "shell.execute_reply": "2024-06-04T23:23:02.934627Z"
+     "iopub.execute_input": "2024-06-05T01:13:22.797136Z",
+     "iopub.status.busy": "2024-06-05T01:13:22.796912Z",
+     "iopub.status.idle": "2024-06-05T01:13:23.632356Z",
+     "shell.execute_reply": "2024-06-05T01:13:23.632085Z"
     },
     "lines_to_next_cell": 2
    },
@@ -2164,10 +2164,10 @@
    "id": "24a361a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:02.936438Z",
-     "iopub.status.busy": "2024-06-04T23:23:02.936308Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.072057Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.071817Z"
+     "iopub.execute_input": "2024-06-05T01:13:23.634325Z",
+     "iopub.status.busy": "2024-06-05T01:13:23.634128Z",
+     "iopub.status.idle": "2024-06-05T01:13:23.779590Z",
+     "shell.execute_reply": "2024-06-05T01:13:23.779218Z"
     }
    },
    "outputs": [
@@ -2214,10 +2214,10 @@
    "id": "facce2ec",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.073398Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.073322Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.140192Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.139949Z"
+     "iopub.execute_input": "2024-06-05T01:13:23.781448Z",
+     "iopub.status.busy": "2024-06-05T01:13:23.781337Z",
+     "iopub.status.idle": "2024-06-05T01:13:23.857834Z",
+     "shell.execute_reply": "2024-06-05T01:13:23.857499Z"
     },
     "lines_to_next_cell": 0
    },
@@ -2255,10 +2255,10 @@
    "id": "1683862d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.141512Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.141434Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.251215Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.250933Z"
+     "iopub.execute_input": "2024-06-05T01:13:23.859532Z",
+     "iopub.status.busy": "2024-06-05T01:13:23.859411Z",
+     "iopub.status.idle": "2024-06-05T01:13:23.979913Z",
+     "shell.execute_reply": "2024-06-05T01:13:23.979583Z"
     },
     "lines_to_next_cell": 0
    },
@@ -2301,10 +2301,10 @@
    "id": "abd902e8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.252741Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.252632Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.321147Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.320859Z"
+     "iopub.execute_input": "2024-06-05T01:13:23.981732Z",
+     "iopub.status.busy": "2024-06-05T01:13:23.981600Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.056222Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.055931Z"
     },
     "lines_to_next_cell": 2
    },
@@ -2349,10 +2349,10 @@
    "id": "5d59f1a1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.323059Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.322935Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.325379Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.325144Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.058049Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.057942Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.060262Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.060017Z"
     },
     "lines_to_next_cell": 2
    },
@@ -2389,10 +2389,10 @@
    "id": "43939bf3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.326647Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.326553Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.328853Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.328462Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.061789Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.061666Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.063954Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.063674Z"
     }
    },
    "outputs": [
@@ -2430,10 +2430,10 @@
    "id": "8004cba3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.330261Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.330160Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.332018Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.331813Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.065591Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.065473Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.067662Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.067371Z"
     },
     "lines_to_next_cell": 0
    },
@@ -2468,10 +2468,10 @@
    "id": "ed4fd312",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.333301Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.333220Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.335119Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.334894Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.069190Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.069066Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.071066Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.070825Z"
     }
    },
    "outputs": [
@@ -2535,10 +2535,10 @@
    "id": "a98b539d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.336405Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.336311Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.338303Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.338061Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.072570Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.072457Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.074648Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.074370Z"
     }
    },
    "outputs": [
@@ -2576,10 +2576,10 @@
    "id": "db2991e9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.339708Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.339596Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.341615Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.341348Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.076007Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.075893Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.078027Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.077777Z"
     }
    },
    "outputs": [
@@ -2617,10 +2617,10 @@
    "id": "9a28347c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.342899Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.342798Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.344843Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.344555Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.079352Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.079256Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.081380Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.081110Z"
     }
    },
    "outputs": [
@@ -2656,10 +2656,10 @@
    "id": "8dd02706",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.346107Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.346007Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.347928Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.347714Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.082810Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.082704Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.084763Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.084520Z"
     }
    },
    "outputs": [
@@ -2697,10 +2697,10 @@
    "id": "90e43051",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.349257Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.349153Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.351024Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.350801Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.086007Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.085913Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.087984Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.087713Z"
     }
    },
    "outputs": [
@@ -2733,10 +2733,10 @@
    "id": "6715713f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.352441Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.352336Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.354479Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.354209Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.089374Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.089274Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.091441Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.091187Z"
     }
    },
    "outputs": [
@@ -2769,10 +2769,10 @@
    "id": "0a2ec981",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.355715Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.355624Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.369198Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.368964Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.092746Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.092650Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.108005Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.107740Z"
     }
    },
    "outputs": [
@@ -2808,10 +2808,10 @@
    "id": "7af33bcc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.370568Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.370494Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.372728Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.372504Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.109699Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.109581Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.111728Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.111490Z"
     },
     "lines_to_next_cell": 0
    },
@@ -2857,10 +2857,10 @@
    "id": "ec304448",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.373941Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.373868Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.375933Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.375688Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.113315Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.113196Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.115420Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.115148Z"
     },
     "lines_to_next_cell": 2
    },
@@ -2900,10 +2900,10 @@
    "id": "1953fd6f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.377177Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.377107Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.379175Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.378969Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.116829Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.116730Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.118831Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.118589Z"
     },
     "lines_to_next_cell": 0
    },
@@ -2970,10 +2970,10 @@
    "id": "49d86ff4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.380411Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.380341Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.382274Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.382073Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.120208Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.120116Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.122067Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.121820Z"
     },
     "lines_to_next_cell": 0
    },
@@ -3008,10 +3008,10 @@
    "id": "9bc2e603",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.383480Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.383410Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.385362Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.385160Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.123503Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.123411Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.125341Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.125066Z"
     }
    },
    "outputs": [
@@ -3047,10 +3047,10 @@
    "id": "2c381dab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.386527Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.386459Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.388547Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.388318Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.126860Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.126762Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.128900Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.128625Z"
     }
    },
    "outputs": [
@@ -3093,10 +3093,10 @@
    "id": "d0aebd92",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.389869Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.389803Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.391766Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.391564Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.130396Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.130289Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.132282Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.132070Z"
     }
    },
    "outputs": [
@@ -3132,10 +3132,10 @@
    "id": "e6532bac",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.392891Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.392822Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.394868Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.394671Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.133653Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.133556Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.135615Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.135315Z"
     }
    },
    "outputs": [
@@ -3179,10 +3179,10 @@
    "id": "1d5d984c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.396102Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.396040Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.398136Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.397931Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.137042Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.136932Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.139277Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.138995Z"
     }
    },
    "outputs": [
@@ -3219,10 +3219,10 @@
    "id": "4057cec1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.399427Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.399347Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.401561Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.401289Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.140843Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.140734Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.142836Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.142595Z"
     },
     "lines_to_next_cell": 0
    },
@@ -3311,28 +3311,216 @@
    "id": "d13a6775",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.403020Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.402927Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.596296Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.596041Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.144284Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.144189Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.323344Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.323105Z"
     }
    },
    "outputs": [
     {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'Auto.csv'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[74], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m Auto \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mAuto.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m Auto\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Auto.csv'"
-     ]
+     "data": {
+      "text/html": [
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+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>1</td>\n",
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+       "    </tr>\n",
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+       "      <th>1</th>\n",
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+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>buick skylark 320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
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+       "      <td>318.0</td>\n",
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+       "      <td>70</td>\n",
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+       "      <td>plymouth satellite</td>\n",
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+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>amc rebel sst</td>\n",
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+       "      <td>70</td>\n",
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+       "      <td>ford torino</td>\n",
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+       "      <td>27.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>86</td>\n",
+       "      <td>2790</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ford mustang gl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>2130</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2</td>\n",
+       "      <td>vw pickup</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>84</td>\n",
+       "      <td>2295</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>dodge rampage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>79</td>\n",
+       "      <td>2625</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ford ranger</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>119.0</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chevy s-10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>392 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
+       "0    18.0          8         307.0         130    3504          12.0    70   \n",
+       "1    15.0          8         350.0         165    3693          11.5    70   \n",
+       "2    18.0          8         318.0         150    3436          11.0    70   \n",
+       "3    16.0          8         304.0         150    3433          12.0    70   \n",
+       "4    17.0          8         302.0         140    3449          10.5    70   \n",
+       "..    ...        ...           ...         ...     ...           ...   ...   \n",
+       "387  27.0          4         140.0          86    2790          15.6    82   \n",
+       "388  44.0          4          97.0          52    2130          24.6    82   \n",
+       "389  32.0          4         135.0          84    2295          11.6    82   \n",
+       "390  28.0          4         120.0          79    2625          18.6    82   \n",
+       "391  31.0          4         119.0          82    2720          19.4    82   \n",
+       "\n",
+       "     origin                       name  \n",
+       "0         1  chevrolet chevelle malibu  \n",
+       "1         1          buick skylark 320  \n",
+       "2         1         plymouth satellite  \n",
+       "3         1              amc rebel sst  \n",
+       "4         1                ford torino  \n",
+       "..      ...                        ...  \n",
+       "387       1            ford mustang gl  \n",
+       "388       2                  vw pickup  \n",
+       "389       1              dodge rampage  \n",
+       "390       1                ford ranger  \n",
+       "391       1                 chevy s-10  \n",
+       "\n",
+       "[392 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3355,10 +3543,10 @@
    "id": "f7857be6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.597638Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.597570Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.630020Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.629750Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.324726Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.324623Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.327567Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.327331Z"
     },
     "lines_to_next_cell": 0
    },
@@ -3367,25 +3555,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_1206/1967952672.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
+      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_3787/1967952672.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
       "  Auto = pd.read_csv('Auto.data', delim_whitespace=True)\n"
      ]
-    },
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'Auto.data'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[75], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m Auto \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mAuto.data\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdelim_whitespace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Auto.data'"
-     ]
     }
    ],
    "source": [
@@ -3417,24 +3589,34 @@
    "id": "7912c27e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.631450Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.631380Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.636918Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.636679Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.328873Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.328782Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.331187Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.330966Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[76], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhorsepower\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "0      130.0\n",
+       "1      165.0\n",
+       "2      150.0\n",
+       "3      150.0\n",
+       "4      140.0\n",
+       "       ...  \n",
+       "392    86.00\n",
+       "393    52.00\n",
+       "394    84.00\n",
+       "395    79.00\n",
+       "396    82.00\n",
+       "Name: horsepower, Length: 397, dtype: object"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3458,24 +3640,36 @@
    "id": "f8078632",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.638074Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.638008Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.643204Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.642993Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.332465Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.332343Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.334500Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.334302Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[77], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m np\u001b[38;5;241m.\u001b[39munique(\u001b[43mAuto\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhorsepower\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "array(['100.0', '102.0', '103.0', '105.0', '107.0', '108.0', '110.0',\n",
+       "       '112.0', '113.0', '115.0', '116.0', '120.0', '122.0', '125.0',\n",
+       "       '129.0', '130.0', '132.0', '133.0', '135.0', '137.0', '138.0',\n",
+       "       '139.0', '140.0', '142.0', '145.0', '148.0', '149.0', '150.0',\n",
+       "       '152.0', '153.0', '155.0', '158.0', '160.0', '165.0', '167.0',\n",
+       "       '170.0', '175.0', '180.0', '190.0', '193.0', '198.0', '200.0',\n",
+       "       '208.0', '210.0', '215.0', '220.0', '225.0', '230.0', '46.00',\n",
+       "       '48.00', '49.00', '52.00', '53.00', '54.00', '58.00', '60.00',\n",
+       "       '61.00', '62.00', '63.00', '64.00', '65.00', '66.00', '67.00',\n",
+       "       '68.00', '69.00', '70.00', '71.00', '72.00', '74.00', '75.00',\n",
+       "       '76.00', '77.00', '78.00', '79.00', '80.00', '81.00', '82.00',\n",
+       "       '83.00', '84.00', '85.00', '86.00', '87.00', '88.00', '89.00',\n",
+       "       '90.00', '91.00', '92.00', '93.00', '94.00', '95.00', '96.00',\n",
+       "       '97.00', '98.00', '?'], dtype=object)"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3507,10 +3701,10 @@
    "id": "aad623ae",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.644358Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.644290Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.676612Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.676376Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.335798Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.335712Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.338898Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.338695Z"
     },
     "lines_to_next_cell": 2
    },
@@ -3519,25 +3713,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_1206/134681464.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
+      "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_3787/134681464.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
       "  Auto = pd.read_csv('Auto.data',\n"
      ]
     },
     {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'Auto.data'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[78], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m Auto \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mAuto.data\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                   \u001b[49m\u001b[43mna_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m?\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m                   \u001b[49m\u001b[43mdelim_whitespace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m Auto[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhorsepower\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39msum()\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
-      "File \u001b[0;32m~/anaconda3/envs/ISLP_v22_312/lib/python3.12/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Auto.data'"
-     ]
+     "data": {
+      "text/plain": [
+       "40952.0"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3562,23 +3750,22 @@
    "id": "ae1cad4e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.677805Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.677733Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.682675Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.682446Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.340150Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.340081Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.341961Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.341744Z"
     }
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[79], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto\u001b[49m\u001b[38;5;241m.\u001b[39mshape\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "(397, 9)"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3602,24 +3789,23 @@
    "id": "52eb7971",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.683862Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.683797Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.689132Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.688915Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.343152Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.343088Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.345337Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.345111Z"
     },
     "lines_to_next_cell": 2
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[80], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m Auto_new \u001b[38;5;241m=\u001b[39m \u001b[43mAuto\u001b[49m\u001b[38;5;241m.\u001b[39mdropna()\n\u001b[1;32m      2\u001b[0m Auto_new\u001b[38;5;241m.\u001b[39mshape\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "(392, 9)"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3643,24 +3829,25 @@
    "id": "44829ab6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.690324Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.690256Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.695506Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.695274Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.346569Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.346495Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.348509Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.348265Z"
     },
     "lines_to_next_cell": 2
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto_new' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[81], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m Auto \u001b[38;5;241m=\u001b[39m \u001b[43mAuto_new\u001b[49m \u001b[38;5;66;03m# overwrite the previous value\u001b[39;00m\n\u001b[1;32m      2\u001b[0m Auto\u001b[38;5;241m.\u001b[39mcolumns\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto_new' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n",
+       "       'acceleration', 'year', 'origin', 'name'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3686,24 +3873,102 @@
    "id": "3aad1bdb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.696652Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.696584Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.701779Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.701553Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.349852Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.349784Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.353582Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.353356Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[82], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto\u001b[49m[:\u001b[38;5;241m3\u001b[39m]\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chevrolet chevelle malibu</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>buick skylark 320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>plymouth satellite</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
+       "0  18.0          8         307.0       130.0  3504.0          12.0    70   \n",
+       "1  15.0          8         350.0       165.0  3693.0          11.5    70   \n",
+       "2  18.0          8         318.0       150.0  3436.0          11.0    70   \n",
+       "\n",
+       "   origin                       name  \n",
+       "0       1  chevrolet chevelle malibu  \n",
+       "1       1          buick skylark 320  \n",
+       "2       1         plymouth satellite  "
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3724,24 +3989,872 @@
    "id": "ae1e9a3a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.702956Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.702892Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.708074Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.707883Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.354951Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.354871Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.366222Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.365980Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[83], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m idx_80 \u001b[38;5;241m=\u001b[39m \u001b[43mAuto\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myear\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m80\u001b[39m\n\u001b[1;32m      2\u001b[0m Auto[idx_80]\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>27.2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>2490.0</td>\n",
+       "      <td>15.7</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>plymouth reliant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>26.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>2635.0</td>\n",
+       "      <td>16.4</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>buick skylark</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>25.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>156.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>2620.0</td>\n",
+       "      <td>14.4</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>dodge aries wagon (sw)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>23.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>173.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>2725.0</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chevrolet citation</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>342</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>2385.0</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>plymouth reliant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>343</th>\n",
+       "      <td>39.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>1755.0</td>\n",
+       "      <td>16.9</td>\n",
+       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>toyota starlet</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>344</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>1875.0</td>\n",
+       "      <td>16.4</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>plymouth champ</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>345</th>\n",
+       "      <td>35.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>1760.0</td>\n",
+       "      <td>16.1</td>\n",
+       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>honda civic 1300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>32.3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>2065.0</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>subaru</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>1975.0</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>datsun 210 mpg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>37.7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>2050.0</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>toyota tercel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>34.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>1985.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>mazda glc 4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350</th>\n",
+       "      <td>34.7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>2215.0</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>plymouth horizon 4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>34.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>2045.0</td>\n",
+       "      <td>16.2</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ford escort 4w</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>29.9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>2380.0</td>\n",
+       "      <td>20.7</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ford escort 2h</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>2190.0</td>\n",
+       "      <td>14.2</td>\n",
+       "      <td>81</td>\n",
+       "      <td>2</td>\n",
+       "      <td>volkswagen jetta</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>33.7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>2210.0</td>\n",
+       "      <td>14.4</td>\n",
+       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>honda prelude</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>32.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>2350.0</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>toyota corolla</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>32.9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>119.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2615.0</td>\n",
+       "      <td>14.8</td>\n",
+       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>datsun 200sx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>31.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>2635.0</td>\n",
+       "      <td>18.3</td>\n",
+       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>mazda 626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>28.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>3230.0</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>81</td>\n",
+       "      <td>2</td>\n",
+       "      <td>peugeot 505s turbo diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>30.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>145.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>3160.0</td>\n",
+       "      <td>19.6</td>\n",
+       "      <td>81</td>\n",
+       "      <td>2</td>\n",
+       "      <td>volvo diesel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>25.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>168.0</td>\n",
+       "      <td>116.0</td>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>toyota cressida</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>24.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>146.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>2930.0</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>datsun 810 maxima</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>22.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>231.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>3415.0</td>\n",
+       "      <td>15.8</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>buick century</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>26.6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>3725.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>oldsmobile cutlass ls</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>20.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>3060.0</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ford granada gl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366</th>\n",
+       "      <td>17.6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>225.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>3465.0</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chrysler lebaron salon</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>2605.0</td>\n",
+       "      <td>19.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chevrolet cavalier</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>368</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>2640.0</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chevrolet cavalier wagon</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>369</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>2395.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chevrolet cavalier 2-door</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>370</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>2575.0</td>\n",
+       "      <td>16.2</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>pontiac j2000 se hatchback</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>371</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>2525.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>dodge aries se</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>2735.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>pontiac phoenix</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>373</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>2865.0</td>\n",
+       "      <td>16.4</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ford fairmont futura</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>36.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>1980.0</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2</td>\n",
+       "      <td>volkswagen rabbit l</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>375</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>2025.0</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>82</td>\n",
+       "      <td>3</td>\n",
+       "      <td>mazda glc custom l</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>1970.0</td>\n",
+       "      <td>17.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>3</td>\n",
+       "      <td>mazda glc custom</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>2125.0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>plymouth horizon miser</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>36.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>2125.0</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>mercury lynx l</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>36.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>2160.0</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>82</td>\n",
+       "      <td>3</td>\n",
+       "      <td>nissan stanza xe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>36.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>2205.0</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>82</td>\n",
+       "      <td>3</td>\n",
+       "      <td>honda accord</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>2245.0</td>\n",
+       "      <td>16.9</td>\n",
+       "      <td>82</td>\n",
+       "      <td>3</td>\n",
+       "      <td>toyota corolla</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>1965.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>82</td>\n",
+       "      <td>3</td>\n",
+       "      <td>honda civic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>1965.0</td>\n",
+       "      <td>15.7</td>\n",
+       "      <td>82</td>\n",
+       "      <td>3</td>\n",
+       "      <td>honda civic (auto)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>384</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>16.2</td>\n",
+       "      <td>82</td>\n",
+       "      <td>3</td>\n",
+       "      <td>datsun 310 gx</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>2945.0</td>\n",
+       "      <td>16.4</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>buick century limited</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>262.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>3015.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>oldsmobile cutlass ciera (diesel)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>26.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>156.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>2585.0</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chrysler lebaron medallion</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>232.0</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>2835.0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ford granada l</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>144.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2665.0</td>\n",
+       "      <td>13.9</td>\n",
+       "      <td>82</td>\n",
+       "      <td>3</td>\n",
+       "      <td>toyota celica gt</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>36.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>2370.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>dodge charger 2.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>2950.0</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chevrolet camaro</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>2790.0</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ford mustang gl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>2130.0</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2</td>\n",
+       "      <td>vw pickup</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>394</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>2295.0</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>dodge rampage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>2625.0</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ford ranger</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>119.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>2720.0</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chevy s-10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
+       "338  27.2          4         135.0        84.0  2490.0          15.7    81   \n",
+       "339  26.6          4         151.0        84.0  2635.0          16.4    81   \n",
+       "340  25.8          4         156.0        92.0  2620.0          14.4    81   \n",
+       "341  23.5          6         173.0       110.0  2725.0          12.6    81   \n",
+       "342  30.0          4         135.0        84.0  2385.0          12.9    81   \n",
+       "343  39.1          4          79.0        58.0  1755.0          16.9    81   \n",
+       "344  39.0          4          86.0        64.0  1875.0          16.4    81   \n",
+       "345  35.1          4          81.0        60.0  1760.0          16.1    81   \n",
+       "346  32.3          4          97.0        67.0  2065.0          17.8    81   \n",
+       "347  37.0          4          85.0        65.0  1975.0          19.4    81   \n",
+       "348  37.7          4          89.0        62.0  2050.0          17.3    81   \n",
+       "349  34.1          4          91.0        68.0  1985.0          16.0    81   \n",
+       "350  34.7          4         105.0        63.0  2215.0          14.9    81   \n",
+       "351  34.4          4          98.0        65.0  2045.0          16.2    81   \n",
+       "352  29.9          4          98.0        65.0  2380.0          20.7    81   \n",
+       "353  33.0          4         105.0        74.0  2190.0          14.2    81   \n",
+       "355  33.7          4         107.0        75.0  2210.0          14.4    81   \n",
+       "356  32.4          4         108.0        75.0  2350.0          16.8    81   \n",
+       "357  32.9          4         119.0       100.0  2615.0          14.8    81   \n",
+       "358  31.6          4         120.0        74.0  2635.0          18.3    81   \n",
+       "359  28.1          4         141.0        80.0  3230.0          20.4    81   \n",
+       "360  30.7          6         145.0        76.0  3160.0          19.6    81   \n",
+       "361  25.4          6         168.0       116.0  2900.0          12.6    81   \n",
+       "362  24.2          6         146.0       120.0  2930.0          13.8    81   \n",
+       "363  22.4          6         231.0       110.0  3415.0          15.8    81   \n",
+       "364  26.6          8         350.0       105.0  3725.0          19.0    81   \n",
+       "365  20.2          6         200.0        88.0  3060.0          17.1    81   \n",
+       "366  17.6          6         225.0        85.0  3465.0          16.6    81   \n",
+       "367  28.0          4         112.0        88.0  2605.0          19.6    82   \n",
+       "368  27.0          4         112.0        88.0  2640.0          18.6    82   \n",
+       "369  34.0          4         112.0        88.0  2395.0          18.0    82   \n",
+       "370  31.0          4         112.0        85.0  2575.0          16.2    82   \n",
+       "371  29.0          4         135.0        84.0  2525.0          16.0    82   \n",
+       "372  27.0          4         151.0        90.0  2735.0          18.0    82   \n",
+       "373  24.0          4         140.0        92.0  2865.0          16.4    82   \n",
+       "374  36.0          4         105.0        74.0  1980.0          15.3    82   \n",
+       "375  37.0          4          91.0        68.0  2025.0          18.2    82   \n",
+       "376  31.0          4          91.0        68.0  1970.0          17.6    82   \n",
+       "377  38.0          4         105.0        63.0  2125.0          14.7    82   \n",
+       "378  36.0          4          98.0        70.0  2125.0          17.3    82   \n",
+       "379  36.0          4         120.0        88.0  2160.0          14.5    82   \n",
+       "380  36.0          4         107.0        75.0  2205.0          14.5    82   \n",
+       "381  34.0          4         108.0        70.0  2245.0          16.9    82   \n",
+       "382  38.0          4          91.0        67.0  1965.0          15.0    82   \n",
+       "383  32.0          4          91.0        67.0  1965.0          15.7    82   \n",
+       "384  38.0          4          91.0        67.0  1995.0          16.2    82   \n",
+       "385  25.0          6         181.0       110.0  2945.0          16.4    82   \n",
+       "386  38.0          6         262.0        85.0  3015.0          17.0    82   \n",
+       "387  26.0          4         156.0        92.0  2585.0          14.5    82   \n",
+       "388  22.0          6         232.0       112.0  2835.0          14.7    82   \n",
+       "389  32.0          4         144.0        96.0  2665.0          13.9    82   \n",
+       "390  36.0          4         135.0        84.0  2370.0          13.0    82   \n",
+       "391  27.0          4         151.0        90.0  2950.0          17.3    82   \n",
+       "392  27.0          4         140.0        86.0  2790.0          15.6    82   \n",
+       "393  44.0          4          97.0        52.0  2130.0          24.6    82   \n",
+       "394  32.0          4         135.0        84.0  2295.0          11.6    82   \n",
+       "395  28.0          4         120.0        79.0  2625.0          18.6    82   \n",
+       "396  31.0          4         119.0        82.0  2720.0          19.4    82   \n",
+       "\n",
+       "     origin                               name  \n",
+       "338       1                   plymouth reliant  \n",
+       "339       1                      buick skylark  \n",
+       "340       1             dodge aries wagon (sw)  \n",
+       "341       1                 chevrolet citation  \n",
+       "342       1                   plymouth reliant  \n",
+       "343       3                     toyota starlet  \n",
+       "344       1                     plymouth champ  \n",
+       "345       3                   honda civic 1300  \n",
+       "346       3                             subaru  \n",
+       "347       3                     datsun 210 mpg  \n",
+       "348       3                      toyota tercel  \n",
+       "349       3                        mazda glc 4  \n",
+       "350       1                 plymouth horizon 4  \n",
+       "351       1                     ford escort 4w  \n",
+       "352       1                     ford escort 2h  \n",
+       "353       2                   volkswagen jetta  \n",
+       "355       3                      honda prelude  \n",
+       "356       3                     toyota corolla  \n",
+       "357       3                       datsun 200sx  \n",
+       "358       3                          mazda 626  \n",
+       "359       2          peugeot 505s turbo diesel  \n",
+       "360       2                       volvo diesel  \n",
+       "361       3                    toyota cressida  \n",
+       "362       3                  datsun 810 maxima  \n",
+       "363       1                      buick century  \n",
+       "364       1              oldsmobile cutlass ls  \n",
+       "365       1                    ford granada gl  \n",
+       "366       1             chrysler lebaron salon  \n",
+       "367       1                 chevrolet cavalier  \n",
+       "368       1           chevrolet cavalier wagon  \n",
+       "369       1          chevrolet cavalier 2-door  \n",
+       "370       1         pontiac j2000 se hatchback  \n",
+       "371       1                     dodge aries se  \n",
+       "372       1                    pontiac phoenix  \n",
+       "373       1               ford fairmont futura  \n",
+       "374       2                volkswagen rabbit l  \n",
+       "375       3                 mazda glc custom l  \n",
+       "376       3                   mazda glc custom  \n",
+       "377       1             plymouth horizon miser  \n",
+       "378       1                     mercury lynx l  \n",
+       "379       3                   nissan stanza xe  \n",
+       "380       3                       honda accord  \n",
+       "381       3                     toyota corolla  \n",
+       "382       3                        honda civic  \n",
+       "383       3                 honda civic (auto)  \n",
+       "384       3                      datsun 310 gx  \n",
+       "385       1              buick century limited  \n",
+       "386       1  oldsmobile cutlass ciera (diesel)  \n",
+       "387       1         chrysler lebaron medallion  \n",
+       "388       1                     ford granada l  \n",
+       "389       3                   toyota celica gt  \n",
+       "390       1                  dodge charger 2.2  \n",
+       "391       1                   chevrolet camaro  \n",
+       "392       1                    ford mustang gl  \n",
+       "393       2                          vw pickup  \n",
+       "394       1                      dodge rampage  \n",
+       "395       1                        ford ranger  \n",
+       "396       1                         chevy s-10  "
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3763,24 +4876,120 @@
    "id": "2b543957",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.709212Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.709147Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.714353Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.714153Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.367542Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.367463Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.371446Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.371187Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[84], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto\u001b[49m[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmpg\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhorsepower\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>horsepower</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>130.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>165.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>150.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>150.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>140.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>86.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>52.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>394</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>84.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>79.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>82.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>392 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      mpg  horsepower\n",
+       "0    18.0       130.0\n",
+       "1    15.0       165.0\n",
+       "2    18.0       150.0\n",
+       "3    16.0       150.0\n",
+       "4    17.0       140.0\n",
+       "..    ...         ...\n",
+       "392  27.0        86.0\n",
+       "393  44.0        52.0\n",
+       "394  32.0        84.0\n",
+       "395  28.0        79.0\n",
+       "396  31.0        82.0\n",
+       "\n",
+       "[392 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3802,24 +5011,26 @@
    "id": "077b30b4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.715512Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.715435Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.721035Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.720814Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.372753Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.372683Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.374830Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.374522Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[85], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto\u001b[49m\u001b[38;5;241m.\u001b[39mindex\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       387, 388, 389, 390, 391, 392, 393, 394, 395, 396],\n",
+       "      dtype='int64', length=392)"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3841,23 +5052,217 @@
    "id": "601c3700",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.722251Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.722182Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.759273Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.759044Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.376370Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.376285Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.381671Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.381414Z"
     }
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[86], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m Auto_re \u001b[38;5;241m=\u001b[39m \u001b[43mAuto\u001b[49m\u001b[38;5;241m.\u001b[39mset_index(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      2\u001b[0m Auto_re\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
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+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>year</th>\n",
+       "      <th>origin</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>chevrolet chevelle malibu</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>buick skylark 320</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plymouth satellite</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>amc rebel sst</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford torino</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449.0</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford mustang gl</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>2790.0</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vw pickup</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>2130.0</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dodge rampage</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>2295.0</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford ranger</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>2625.0</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevy s-10</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>119.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>2720.0</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>392 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            mpg  cylinders  displacement  horsepower  weight  \\\n",
+       "name                                                                           \n",
+       "chevrolet chevelle malibu  18.0          8         307.0       130.0  3504.0   \n",
+       "buick skylark 320          15.0          8         350.0       165.0  3693.0   \n",
+       "plymouth satellite         18.0          8         318.0       150.0  3436.0   \n",
+       "amc rebel sst              16.0          8         304.0       150.0  3433.0   \n",
+       "ford torino                17.0          8         302.0       140.0  3449.0   \n",
+       "...                         ...        ...           ...         ...     ...   \n",
+       "ford mustang gl            27.0          4         140.0        86.0  2790.0   \n",
+       "vw pickup                  44.0          4          97.0        52.0  2130.0   \n",
+       "dodge rampage              32.0          4         135.0        84.0  2295.0   \n",
+       "ford ranger                28.0          4         120.0        79.0  2625.0   \n",
+       "chevy s-10                 31.0          4         119.0        82.0  2720.0   \n",
+       "\n",
+       "                           acceleration  year  origin  \n",
+       "name                                                   \n",
+       "chevrolet chevelle malibu          12.0    70       1  \n",
+       "buick skylark 320                  11.5    70       1  \n",
+       "plymouth satellite                 11.0    70       1  \n",
+       "amc rebel sst                      12.0    70       1  \n",
+       "ford torino                        10.5    70       1  \n",
+       "...                                 ...   ...     ...  \n",
+       "ford mustang gl                    15.6    82       1  \n",
+       "vw pickup                          24.6    82       2  \n",
+       "dodge rampage                      11.6    82       1  \n",
+       "ford ranger                        18.6    82       1  \n",
+       "chevy s-10                         19.4    82       1  \n",
+       "\n",
+       "[392 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3871,24 +5276,25 @@
    "id": "8ef352e3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.760598Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.760527Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.765917Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.765696Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.383087Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.382999Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.385187Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.384964Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto_re' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[87], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto_re\u001b[49m\u001b[38;5;241m.\u001b[39mcolumns\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto_re' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n",
+       "       'acceleration', 'year', 'origin'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3913,24 +5319,98 @@
    "id": "04e94eee",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.767095Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.767029Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.772346Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.772137Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.386485Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.386407Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.390332Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.390075Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto_re' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[88], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m rows \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mamc rebel sst\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mford torino\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 2\u001b[0m \u001b[43mAuto_re\u001b[49m\u001b[38;5;241m.\u001b[39mloc[rows]\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto_re' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>amc rebel sst</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433.0</td>\n",
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+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford torino</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
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+       "      <td>70</td>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                mpg  cylinders  displacement  horsepower  weight  \\\n",
+       "name                                                               \n",
+       "amc rebel sst  16.0          8         304.0       150.0  3433.0   \n",
+       "ford torino    17.0          8         302.0       140.0  3449.0   \n",
+       "\n",
+       "               acceleration  year  origin  \n",
+       "name                                       \n",
+       "amc rebel sst          12.0    70       1  \n",
+       "ford torino            10.5    70       1  "
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3952,24 +5432,98 @@
    "id": "5cd1291d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.773527Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.773457Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.778489Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.778279Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.391646Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.391572Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.395313Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.395048Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto_re' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[89], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto_re\u001b[49m\u001b[38;5;241m.\u001b[39miloc[[\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m4\u001b[39m]]\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto_re' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
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+       "    <tr>\n",
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+       "      <th>amc rebel sst</th>\n",
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+       "    <tr>\n",
+       "      <th>ford torino</th>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                mpg  cylinders  displacement  horsepower  weight  \\\n",
+       "name                                                               \n",
+       "amc rebel sst  16.0          8         304.0       150.0  3433.0   \n",
+       "ford torino    17.0          8         302.0       140.0  3449.0   \n",
+       "\n",
+       "               acceleration  year  origin  \n",
+       "name                                       \n",
+       "amc rebel sst          12.0    70       1  \n",
+       "ford torino            10.5    70       1  "
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3990,24 +5544,139 @@
    "id": "5f1f3970",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.779694Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.779629Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.784822Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.784585Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.396633Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.396547Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.400604Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.400364Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto_re' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[90], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto_re\u001b[49m\u001b[38;5;241m.\u001b[39miloc[:,[\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m]]\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto_re' is not defined"
-     ]
+     "data": {
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
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+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>chevrolet chevelle malibu</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>buick skylark 320</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plymouth satellite</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>amc rebel sst</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford torino</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford mustang gl</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>86.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vw pickup</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>52.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dodge rampage</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>84.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford ranger</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>79.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevy s-10</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>119.0</td>\n",
+       "      <td>82.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>392 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            mpg  displacement  horsepower\n",
+       "name                                                     \n",
+       "chevrolet chevelle malibu  18.0         307.0       130.0\n",
+       "buick skylark 320          15.0         350.0       165.0\n",
+       "plymouth satellite         18.0         318.0       150.0\n",
+       "amc rebel sst              16.0         304.0       150.0\n",
+       "ford torino                17.0         302.0       140.0\n",
+       "...                         ...           ...         ...\n",
+       "ford mustang gl            27.0         140.0        86.0\n",
+       "vw pickup                  44.0          97.0        52.0\n",
+       "dodge rampage              32.0         135.0        84.0\n",
+       "ford ranger                28.0         120.0        79.0\n",
+       "chevy s-10                 31.0         119.0        82.0\n",
+       "\n",
+       "[392 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -4029,24 +5698,73 @@
    "id": "a9429e4b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.786086Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.786021Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.791204Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.791001Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.401897Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.401825Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.405021Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.404756Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto_re' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[91], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto_re\u001b[49m\u001b[38;5;241m.\u001b[39miloc[[\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m4\u001b[39m],[\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m]]\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto_re' is not defined"
-     ]
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>amc rebel sst</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford torino</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                mpg  displacement  horsepower\n",
+       "name                                         \n",
+       "amc rebel sst  16.0         304.0       150.0\n",
+       "ford torino    17.0         302.0       140.0"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -4067,24 +5785,75 @@
    "id": "1fd20b7d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.792327Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.792261Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.797911Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.797701Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.406268Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.406198Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.409290Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.409058Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto_re' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[92], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto_re\u001b[49m\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mford galaxie 500\u001b[39m\u001b[38;5;124m'\u001b[39m, [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmpg\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124morigin\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto_re' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>origin</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ford galaxie 500</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford galaxie 500</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford galaxie 500</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   mpg  origin\n",
+       "name                          \n",
+       "ford galaxie 500  15.0       1\n",
+       "ford galaxie 500  14.0       1\n",
+       "ford galaxie 500  14.0       1"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -4109,24 +5878,405 @@
    "id": "df6fa9de",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.799061Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.798992Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.804320Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.804103Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.410553Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.410479Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.415558Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.415322Z"
     },
     "lines_to_next_cell": 2
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto_re' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[93], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m idx_80 \u001b[38;5;241m=\u001b[39m \u001b[43mAuto_re\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myear\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m80\u001b[39m\n\u001b[1;32m      2\u001b[0m Auto_re\u001b[38;5;241m.\u001b[39mloc[idx_80, [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124morigin\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto_re' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>weight</th>\n",
+       "      <th>origin</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>plymouth reliant</th>\n",
+       "      <td>2490.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>buick skylark</th>\n",
+       "      <td>2635.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dodge aries wagon (sw)</th>\n",
+       "      <td>2620.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevrolet citation</th>\n",
+       "      <td>2725.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plymouth reliant</th>\n",
+       "      <td>2385.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota starlet</th>\n",
+       "      <td>1755.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plymouth champ</th>\n",
+       "      <td>1875.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda civic 1300</th>\n",
+       "      <td>1760.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>subaru</th>\n",
+       "      <td>2065.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 210 mpg</th>\n",
+       "      <td>1975.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota tercel</th>\n",
+       "      <td>2050.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mazda glc 4</th>\n",
+       "      <td>1985.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plymouth horizon 4</th>\n",
+       "      <td>2215.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford escort 4w</th>\n",
+       "      <td>2045.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford escort 2h</th>\n",
+       "      <td>2380.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>volkswagen jetta</th>\n",
+       "      <td>2190.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda prelude</th>\n",
+       "      <td>2210.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota corolla</th>\n",
+       "      <td>2350.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 200sx</th>\n",
+       "      <td>2615.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mazda 626</th>\n",
+       "      <td>2635.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>peugeot 505s turbo diesel</th>\n",
+       "      <td>3230.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>volvo diesel</th>\n",
+       "      <td>3160.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota cressida</th>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 810 maxima</th>\n",
+       "      <td>2930.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>buick century</th>\n",
+       "      <td>3415.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oldsmobile cutlass ls</th>\n",
+       "      <td>3725.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford granada gl</th>\n",
+       "      <td>3060.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chrysler lebaron salon</th>\n",
+       "      <td>3465.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevrolet cavalier</th>\n",
+       "      <td>2605.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevrolet cavalier wagon</th>\n",
+       "      <td>2640.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevrolet cavalier 2-door</th>\n",
+       "      <td>2395.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pontiac j2000 se hatchback</th>\n",
+       "      <td>2575.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dodge aries se</th>\n",
+       "      <td>2525.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pontiac phoenix</th>\n",
+       "      <td>2735.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford fairmont futura</th>\n",
+       "      <td>2865.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>volkswagen rabbit l</th>\n",
+       "      <td>1980.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mazda glc custom l</th>\n",
+       "      <td>2025.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mazda glc custom</th>\n",
+       "      <td>1970.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plymouth horizon miser</th>\n",
+       "      <td>2125.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mercury lynx l</th>\n",
+       "      <td>2125.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nissan stanza xe</th>\n",
+       "      <td>2160.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda accord</th>\n",
+       "      <td>2205.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota corolla</th>\n",
+       "      <td>2245.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda civic</th>\n",
+       "      <td>1965.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda civic (auto)</th>\n",
+       "      <td>1965.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 310 gx</th>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>buick century limited</th>\n",
+       "      <td>2945.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oldsmobile cutlass ciera (diesel)</th>\n",
+       "      <td>3015.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chrysler lebaron medallion</th>\n",
+       "      <td>2585.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford granada l</th>\n",
+       "      <td>2835.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota celica gt</th>\n",
+       "      <td>2665.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dodge charger 2.2</th>\n",
+       "      <td>2370.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevrolet camaro</th>\n",
+       "      <td>2950.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford mustang gl</th>\n",
+       "      <td>2790.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vw pickup</th>\n",
+       "      <td>2130.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dodge rampage</th>\n",
+       "      <td>2295.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford ranger</th>\n",
+       "      <td>2625.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevy s-10</th>\n",
+       "      <td>2720.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   weight  origin\n",
+       "name                                             \n",
+       "plymouth reliant                   2490.0       1\n",
+       "buick skylark                      2635.0       1\n",
+       "dodge aries wagon (sw)             2620.0       1\n",
+       "chevrolet citation                 2725.0       1\n",
+       "plymouth reliant                   2385.0       1\n",
+       "toyota starlet                     1755.0       3\n",
+       "plymouth champ                     1875.0       1\n",
+       "honda civic 1300                   1760.0       3\n",
+       "subaru                             2065.0       3\n",
+       "datsun 210 mpg                     1975.0       3\n",
+       "toyota tercel                      2050.0       3\n",
+       "mazda glc 4                        1985.0       3\n",
+       "plymouth horizon 4                 2215.0       1\n",
+       "ford escort 4w                     2045.0       1\n",
+       "ford escort 2h                     2380.0       1\n",
+       "volkswagen jetta                   2190.0       2\n",
+       "honda prelude                      2210.0       3\n",
+       "toyota corolla                     2350.0       3\n",
+       "datsun 200sx                       2615.0       3\n",
+       "mazda 626                          2635.0       3\n",
+       "peugeot 505s turbo diesel          3230.0       2\n",
+       "volvo diesel                       3160.0       2\n",
+       "toyota cressida                    2900.0       3\n",
+       "datsun 810 maxima                  2930.0       3\n",
+       "buick century                      3415.0       1\n",
+       "oldsmobile cutlass ls              3725.0       1\n",
+       "ford granada gl                    3060.0       1\n",
+       "chrysler lebaron salon             3465.0       1\n",
+       "chevrolet cavalier                 2605.0       1\n",
+       "chevrolet cavalier wagon           2640.0       1\n",
+       "chevrolet cavalier 2-door          2395.0       1\n",
+       "pontiac j2000 se hatchback         2575.0       1\n",
+       "dodge aries se                     2525.0       1\n",
+       "pontiac phoenix                    2735.0       1\n",
+       "ford fairmont futura               2865.0       1\n",
+       "volkswagen rabbit l                1980.0       2\n",
+       "mazda glc custom l                 2025.0       3\n",
+       "mazda glc custom                   1970.0       3\n",
+       "plymouth horizon miser             2125.0       1\n",
+       "mercury lynx l                     2125.0       1\n",
+       "nissan stanza xe                   2160.0       3\n",
+       "honda accord                       2205.0       3\n",
+       "toyota corolla                     2245.0       3\n",
+       "honda civic                        1965.0       3\n",
+       "honda civic (auto)                 1965.0       3\n",
+       "datsun 310 gx                      1995.0       3\n",
+       "buick century limited              2945.0       1\n",
+       "oldsmobile cutlass ciera (diesel)  3015.0       1\n",
+       "chrysler lebaron medallion         2585.0       1\n",
+       "ford granada l                     2835.0       1\n",
+       "toyota celica gt                   2665.0       3\n",
+       "dodge charger 2.2                  2370.0       1\n",
+       "chevrolet camaro                   2950.0       1\n",
+       "ford mustang gl                    2790.0       1\n",
+       "vw pickup                          2130.0       2\n",
+       "dodge rampage                      2295.0       1\n",
+       "ford ranger                        2625.0       1\n",
+       "chevy s-10                         2720.0       1"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -4148,24 +6298,405 @@
    "id": "b57f8503",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.805548Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.805477Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.810926Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.810707Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.416807Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.416735Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.421885Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.421649Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto_re' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[94], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto_re\u001b[49m\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;28;01mlambda\u001b[39;00m df: df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myear\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m80\u001b[39m, [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124morigin\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto_re' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>weight</th>\n",
+       "      <th>origin</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>plymouth reliant</th>\n",
+       "      <td>2490.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>buick skylark</th>\n",
+       "      <td>2635.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dodge aries wagon (sw)</th>\n",
+       "      <td>2620.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevrolet citation</th>\n",
+       "      <td>2725.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plymouth reliant</th>\n",
+       "      <td>2385.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota starlet</th>\n",
+       "      <td>1755.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plymouth champ</th>\n",
+       "      <td>1875.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda civic 1300</th>\n",
+       "      <td>1760.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>subaru</th>\n",
+       "      <td>2065.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 210 mpg</th>\n",
+       "      <td>1975.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota tercel</th>\n",
+       "      <td>2050.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mazda glc 4</th>\n",
+       "      <td>1985.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plymouth horizon 4</th>\n",
+       "      <td>2215.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford escort 4w</th>\n",
+       "      <td>2045.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford escort 2h</th>\n",
+       "      <td>2380.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>volkswagen jetta</th>\n",
+       "      <td>2190.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda prelude</th>\n",
+       "      <td>2210.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota corolla</th>\n",
+       "      <td>2350.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 200sx</th>\n",
+       "      <td>2615.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mazda 626</th>\n",
+       "      <td>2635.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>peugeot 505s turbo diesel</th>\n",
+       "      <td>3230.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>volvo diesel</th>\n",
+       "      <td>3160.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota cressida</th>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 810 maxima</th>\n",
+       "      <td>2930.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>buick century</th>\n",
+       "      <td>3415.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oldsmobile cutlass ls</th>\n",
+       "      <td>3725.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford granada gl</th>\n",
+       "      <td>3060.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chrysler lebaron salon</th>\n",
+       "      <td>3465.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevrolet cavalier</th>\n",
+       "      <td>2605.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevrolet cavalier wagon</th>\n",
+       "      <td>2640.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevrolet cavalier 2-door</th>\n",
+       "      <td>2395.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pontiac j2000 se hatchback</th>\n",
+       "      <td>2575.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dodge aries se</th>\n",
+       "      <td>2525.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pontiac phoenix</th>\n",
+       "      <td>2735.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford fairmont futura</th>\n",
+       "      <td>2865.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>volkswagen rabbit l</th>\n",
+       "      <td>1980.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mazda glc custom l</th>\n",
+       "      <td>2025.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mazda glc custom</th>\n",
+       "      <td>1970.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plymouth horizon miser</th>\n",
+       "      <td>2125.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mercury lynx l</th>\n",
+       "      <td>2125.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nissan stanza xe</th>\n",
+       "      <td>2160.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda accord</th>\n",
+       "      <td>2205.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota corolla</th>\n",
+       "      <td>2245.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda civic</th>\n",
+       "      <td>1965.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda civic (auto)</th>\n",
+       "      <td>1965.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 310 gx</th>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>buick century limited</th>\n",
+       "      <td>2945.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oldsmobile cutlass ciera (diesel)</th>\n",
+       "      <td>3015.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chrysler lebaron medallion</th>\n",
+       "      <td>2585.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford granada l</th>\n",
+       "      <td>2835.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota celica gt</th>\n",
+       "      <td>2665.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dodge charger 2.2</th>\n",
+       "      <td>2370.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevrolet camaro</th>\n",
+       "      <td>2950.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford mustang gl</th>\n",
+       "      <td>2790.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vw pickup</th>\n",
+       "      <td>2130.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dodge rampage</th>\n",
+       "      <td>2295.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford ranger</th>\n",
+       "      <td>2625.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevy s-10</th>\n",
+       "      <td>2720.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   weight  origin\n",
+       "name                                             \n",
+       "plymouth reliant                   2490.0       1\n",
+       "buick skylark                      2635.0       1\n",
+       "dodge aries wagon (sw)             2620.0       1\n",
+       "chevrolet citation                 2725.0       1\n",
+       "plymouth reliant                   2385.0       1\n",
+       "toyota starlet                     1755.0       3\n",
+       "plymouth champ                     1875.0       1\n",
+       "honda civic 1300                   1760.0       3\n",
+       "subaru                             2065.0       3\n",
+       "datsun 210 mpg                     1975.0       3\n",
+       "toyota tercel                      2050.0       3\n",
+       "mazda glc 4                        1985.0       3\n",
+       "plymouth horizon 4                 2215.0       1\n",
+       "ford escort 4w                     2045.0       1\n",
+       "ford escort 2h                     2380.0       1\n",
+       "volkswagen jetta                   2190.0       2\n",
+       "honda prelude                      2210.0       3\n",
+       "toyota corolla                     2350.0       3\n",
+       "datsun 200sx                       2615.0       3\n",
+       "mazda 626                          2635.0       3\n",
+       "peugeot 505s turbo diesel          3230.0       2\n",
+       "volvo diesel                       3160.0       2\n",
+       "toyota cressida                    2900.0       3\n",
+       "datsun 810 maxima                  2930.0       3\n",
+       "buick century                      3415.0       1\n",
+       "oldsmobile cutlass ls              3725.0       1\n",
+       "ford granada gl                    3060.0       1\n",
+       "chrysler lebaron salon             3465.0       1\n",
+       "chevrolet cavalier                 2605.0       1\n",
+       "chevrolet cavalier wagon           2640.0       1\n",
+       "chevrolet cavalier 2-door          2395.0       1\n",
+       "pontiac j2000 se hatchback         2575.0       1\n",
+       "dodge aries se                     2525.0       1\n",
+       "pontiac phoenix                    2735.0       1\n",
+       "ford fairmont futura               2865.0       1\n",
+       "volkswagen rabbit l                1980.0       2\n",
+       "mazda glc custom l                 2025.0       3\n",
+       "mazda glc custom                   1970.0       3\n",
+       "plymouth horizon miser             2125.0       1\n",
+       "mercury lynx l                     2125.0       1\n",
+       "nissan stanza xe                   2160.0       3\n",
+       "honda accord                       2205.0       3\n",
+       "toyota corolla                     2245.0       3\n",
+       "honda civic                        1965.0       3\n",
+       "honda civic (auto)                 1965.0       3\n",
+       "datsun 310 gx                      1995.0       3\n",
+       "buick century limited              2945.0       1\n",
+       "oldsmobile cutlass ciera (diesel)  3015.0       1\n",
+       "chrysler lebaron medallion         2585.0       1\n",
+       "ford granada l                     2835.0       1\n",
+       "toyota celica gt                   2665.0       3\n",
+       "dodge charger 2.2                  2370.0       1\n",
+       "chevrolet camaro                   2950.0       1\n",
+       "ford mustang gl                    2790.0       1\n",
+       "vw pickup                          2130.0       2\n",
+       "dodge rampage                      2295.0       1\n",
+       "ford ranger                        2625.0       1\n",
+       "chevy s-10                         2720.0       1"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -4191,24 +6722,261 @@
    "id": "965b5b74",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.812102Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.812035Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.817480Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.817272Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.423165Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.423091Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.427651Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.427390Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto_re' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[95], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto_re\u001b[49m\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;28;01mlambda\u001b[39;00m df: (df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124myear\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m80\u001b[39m) \u001b[38;5;241m&\u001b[39m (df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmpg\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m30\u001b[39m),\n\u001b[1;32m      2\u001b[0m             [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124morigin\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      3\u001b[0m            ]\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto_re' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
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+       "      <th>origin</th>\n",
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+       "    <tr>\n",
+       "      <th>name</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>toyota starlet</th>\n",
+       "      <td>1755.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plymouth champ</th>\n",
+       "      <td>1875.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda civic 1300</th>\n",
+       "      <td>1760.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>subaru</th>\n",
+       "      <td>2065.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 210 mpg</th>\n",
+       "      <td>1975.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota tercel</th>\n",
+       "      <td>2050.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mazda glc 4</th>\n",
+       "      <td>1985.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plymouth horizon 4</th>\n",
+       "      <td>2215.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford escort 4w</th>\n",
+       "      <td>2045.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>volkswagen jetta</th>\n",
+       "      <td>2190.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda prelude</th>\n",
+       "      <td>2210.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota corolla</th>\n",
+       "      <td>2350.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 200sx</th>\n",
+       "      <td>2615.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mazda 626</th>\n",
+       "      <td>2635.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>volvo diesel</th>\n",
+       "      <td>3160.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevrolet cavalier 2-door</th>\n",
+       "      <td>2395.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pontiac j2000 se hatchback</th>\n",
+       "      <td>2575.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>volkswagen rabbit l</th>\n",
+       "      <td>1980.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mazda glc custom l</th>\n",
+       "      <td>2025.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mazda glc custom</th>\n",
+       "      <td>1970.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plymouth horizon miser</th>\n",
+       "      <td>2125.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mercury lynx l</th>\n",
+       "      <td>2125.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nissan stanza xe</th>\n",
+       "      <td>2160.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda accord</th>\n",
+       "      <td>2205.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota corolla</th>\n",
+       "      <td>2245.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda civic</th>\n",
+       "      <td>1965.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>honda civic (auto)</th>\n",
+       "      <td>1965.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 310 gx</th>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oldsmobile cutlass ciera (diesel)</th>\n",
+       "      <td>3015.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>toyota celica gt</th>\n",
+       "      <td>2665.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dodge charger 2.2</th>\n",
+       "      <td>2370.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vw pickup</th>\n",
+       "      <td>2130.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dodge rampage</th>\n",
+       "      <td>2295.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chevy s-10</th>\n",
+       "      <td>2720.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   weight  origin\n",
+       "name                                             \n",
+       "toyota starlet                     1755.0       3\n",
+       "plymouth champ                     1875.0       1\n",
+       "honda civic 1300                   1760.0       3\n",
+       "subaru                             2065.0       3\n",
+       "datsun 210 mpg                     1975.0       3\n",
+       "toyota tercel                      2050.0       3\n",
+       "mazda glc 4                        1985.0       3\n",
+       "plymouth horizon 4                 2215.0       1\n",
+       "ford escort 4w                     2045.0       1\n",
+       "volkswagen jetta                   2190.0       2\n",
+       "honda prelude                      2210.0       3\n",
+       "toyota corolla                     2350.0       3\n",
+       "datsun 200sx                       2615.0       3\n",
+       "mazda 626                          2635.0       3\n",
+       "volvo diesel                       3160.0       2\n",
+       "chevrolet cavalier 2-door          2395.0       1\n",
+       "pontiac j2000 se hatchback         2575.0       1\n",
+       "volkswagen rabbit l                1980.0       2\n",
+       "mazda glc custom l                 2025.0       3\n",
+       "mazda glc custom                   1970.0       3\n",
+       "plymouth horizon miser             2125.0       1\n",
+       "mercury lynx l                     2125.0       1\n",
+       "nissan stanza xe                   2160.0       3\n",
+       "honda accord                       2205.0       3\n",
+       "toyota corolla                     2245.0       3\n",
+       "honda civic                        1965.0       3\n",
+       "honda civic (auto)                 1965.0       3\n",
+       "datsun 310 gx                      1995.0       3\n",
+       "oldsmobile cutlass ciera (diesel)  3015.0       1\n",
+       "toyota celica gt                   2665.0       3\n",
+       "dodge charger 2.2                  2370.0       1\n",
+       "vw pickup                          2130.0       2\n",
+       "dodge rampage                      2295.0       1\n",
+       "chevy s-10                         2720.0       1"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -4234,24 +7002,363 @@
    "id": "37ba9d44",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.818641Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.818575Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.824081Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.823872Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.428873Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.428804Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.434121Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.433857Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto_re' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[96], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto_re\u001b[49m\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;28;01mlambda\u001b[39;00m df: (df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdisplacement\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m300\u001b[39m)\n\u001b[1;32m      2\u001b[0m                        \u001b[38;5;241m&\u001b[39m (df\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mcontains(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mford\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      3\u001b[0m                        \u001b[38;5;241m|\u001b[39m df\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mcontains(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdatsun\u001b[39m\u001b[38;5;124m'\u001b[39m)),\n\u001b[1;32m      4\u001b[0m             [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124morigin\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      5\u001b[0m            ]\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto_re' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>weight</th>\n",
+       "      <th>origin</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ford maverick</th>\n",
+       "      <td>2587.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun pl510</th>\n",
+       "      <td>2130.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun pl510</th>\n",
+       "      <td>2130.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford torino 500</th>\n",
+       "      <td>3302.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford mustang</th>\n",
+       "      <td>3139.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 1200</th>\n",
+       "      <td>1613.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford pinto runabout</th>\n",
+       "      <td>2226.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford pinto (sw)</th>\n",
+       "      <td>2395.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 510 (sw)</th>\n",
+       "      <td>2288.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford maverick</th>\n",
+       "      <td>3021.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 610</th>\n",
+       "      <td>2379.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford pinto</th>\n",
+       "      <td>2310.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun b210</th>\n",
+       "      <td>1950.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford pinto</th>\n",
+       "      <td>2451.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 710</th>\n",
+       "      <td>2003.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford maverick</th>\n",
+       "      <td>3158.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford pinto</th>\n",
+       "      <td>2639.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 710</th>\n",
+       "      <td>2545.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford pinto</th>\n",
+       "      <td>2984.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford maverick</th>\n",
+       "      <td>3012.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford granada ghia</th>\n",
+       "      <td>3574.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun b-210</th>\n",
+       "      <td>1990.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford pinto</th>\n",
+       "      <td>2565.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun f-10 hatchback</th>\n",
+       "      <td>1945.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford granada</th>\n",
+       "      <td>3525.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford mustang ii 2+2</th>\n",
+       "      <td>2755.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 810</th>\n",
+       "      <td>2815.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford fiesta</th>\n",
+       "      <td>1800.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun b210 gx</th>\n",
+       "      <td>2070.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford fairmont (auto)</th>\n",
+       "      <td>2965.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford fairmont (man)</th>\n",
+       "      <td>2720.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 510</th>\n",
+       "      <td>2300.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 200-sx</th>\n",
+       "      <td>2405.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford fairmont 4</th>\n",
+       "      <td>2890.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 210</th>\n",
+       "      <td>2020.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 310</th>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford fairmont</th>\n",
+       "      <td>2870.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 510 hatchback</th>\n",
+       "      <td>2434.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 210</th>\n",
+       "      <td>2110.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 280-zx</th>\n",
+       "      <td>2910.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 210 mpg</th>\n",
+       "      <td>1975.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford escort 4w</th>\n",
+       "      <td>2045.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford escort 2h</th>\n",
+       "      <td>2380.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 200sx</th>\n",
+       "      <td>2615.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 810 maxima</th>\n",
+       "      <td>2930.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford granada gl</th>\n",
+       "      <td>3060.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford fairmont futura</th>\n",
+       "      <td>2865.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datsun 310 gx</th>\n",
+       "      <td>1995.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford granada l</th>\n",
+       "      <td>2835.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford mustang gl</th>\n",
+       "      <td>2790.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ford ranger</th>\n",
+       "      <td>2625.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       weight  origin\n",
+       "name                                 \n",
+       "ford maverick          2587.0       1\n",
+       "datsun pl510           2130.0       3\n",
+       "datsun pl510           2130.0       3\n",
+       "ford torino 500        3302.0       1\n",
+       "ford mustang           3139.0       1\n",
+       "datsun 1200            1613.0       3\n",
+       "ford pinto runabout    2226.0       1\n",
+       "ford pinto (sw)        2395.0       1\n",
+       "datsun 510 (sw)        2288.0       3\n",
+       "ford maverick          3021.0       1\n",
+       "datsun 610             2379.0       3\n",
+       "ford pinto             2310.0       1\n",
+       "datsun b210            1950.0       3\n",
+       "ford pinto             2451.0       1\n",
+       "datsun 710             2003.0       3\n",
+       "ford maverick          3158.0       1\n",
+       "ford pinto             2639.0       1\n",
+       "datsun 710             2545.0       3\n",
+       "ford pinto             2984.0       1\n",
+       "ford maverick          3012.0       1\n",
+       "ford granada ghia      3574.0       1\n",
+       "datsun b-210           1990.0       3\n",
+       "ford pinto             2565.0       1\n",
+       "datsun f-10 hatchback  1945.0       3\n",
+       "ford granada           3525.0       1\n",
+       "ford mustang ii 2+2    2755.0       1\n",
+       "datsun 810             2815.0       3\n",
+       "ford fiesta            1800.0       1\n",
+       "datsun b210 gx         2070.0       3\n",
+       "ford fairmont (auto)   2965.0       1\n",
+       "ford fairmont (man)    2720.0       1\n",
+       "datsun 510             2300.0       3\n",
+       "datsun 200-sx          2405.0       3\n",
+       "ford fairmont 4        2890.0       1\n",
+       "datsun 210             2020.0       3\n",
+       "datsun 310             2019.0       3\n",
+       "ford fairmont          2870.0       1\n",
+       "datsun 510 hatchback   2434.0       3\n",
+       "datsun 210             2110.0       3\n",
+       "datsun 280-zx          2910.0       3\n",
+       "datsun 210 mpg         1975.0       3\n",
+       "ford escort 4w         2045.0       1\n",
+       "ford escort 2h         2380.0       1\n",
+       "datsun 200sx           2615.0       3\n",
+       "datsun 810 maxima      2930.0       3\n",
+       "ford granada gl        3060.0       1\n",
+       "ford fairmont futura   2865.0       1\n",
+       "datsun 310 gx          1995.0       3\n",
+       "ford granada l         2835.0       1\n",
+       "ford mustang gl        2790.0       1\n",
+       "ford ranger            2625.0       1"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -4286,10 +7393,10 @@
    "id": "2e945229",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.825260Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.825191Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.826879Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.826683Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.435453Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.435372Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.437109Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.436843Z"
     },
     "lines_to_next_cell": 0
    },
@@ -4330,10 +7437,10 @@
    "id": "a8042d85",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.828030Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.827962Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.829476Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.829264Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.438521Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.438449Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.440428Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.440188Z"
     },
     "lines_to_next_cell": 0
    },
@@ -4382,10 +7489,10 @@
    "id": "25f92ff6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.830608Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.830541Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.832210Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.832018Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.441636Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.441560Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.443474Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.443262Z"
     }
    },
    "outputs": [
@@ -4437,10 +7544,10 @@
    "id": "35700d0c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.833330Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.833242Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.837819Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.837604Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.444779Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.444708Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.448724Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.448517Z"
     },
     "lines_to_next_cell": 2
    },
@@ -4533,10 +7640,10 @@
    "id": "dc8b3ab5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.838970Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.838901Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.840878Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.840678Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.450038Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.449931Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.452280Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.452065Z"
     },
     "lines_to_next_cell": 0
    },
@@ -4590,10 +7697,10 @@
    "id": "06c63b21",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.842112Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.842047Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.896044Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.895801Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.453702Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.453623Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.518273Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.518032Z"
     },
     "lines_to_next_cell": 0
    },
@@ -4639,28 +7746,17 @@
    "id": "dae69229",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.897274Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.897202Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.949779Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.949549Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.519916Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.519789Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.583555Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.583240Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[103], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m subplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(\u001b[43mAuto\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhorsepower\u001b[39m\u001b[38;5;124m'\u001b[39m], Auto[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmpg\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m);\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -4692,24 +7788,23 @@
    "id": "9fbba9c1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.951144Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.951072Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.957697Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.957417Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.585300Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.585174Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.652899Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.652654Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[104], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[43mAuto\u001b[49m\u001b[38;5;241m.\u001b[39mplot\u001b[38;5;241m.\u001b[39mscatter(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhorsepower\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmpg\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      2\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHorsepower vs. MPG\u001b[39m\u001b[38;5;124m'\u001b[39m);\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -4733,10 +7828,10 @@
    "id": "f04edd65",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.959155Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.959049Z",
-     "iopub.status.idle": "2024-06-04T23:23:03.997637Z",
-     "shell.execute_reply": "2024-06-04T23:23:03.997404Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.654736Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.654609Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.686673Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.686397Z"
     }
    },
    "outputs": [],
@@ -4764,27 +7859,16 @@
    "id": "c0e67313",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:03.998919Z",
-     "iopub.status.busy": "2024-06-04T23:23:03.998836Z",
-     "iopub.status.idle": "2024-06-04T23:23:04.097152Z",
-     "shell.execute_reply": "2024-06-04T23:23:04.096910Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.688535Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.688413Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.803975Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.803707Z"
     }
    },
    "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[106], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m fig, axes \u001b[38;5;241m=\u001b[39m subplots(ncols\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m15\u001b[39m, \u001b[38;5;241m5\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m \u001b[43mAuto\u001b[49m\u001b[38;5;241m.\u001b[39mplot\u001b[38;5;241m.\u001b[39mscatter(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhorsepower\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmpg\u001b[39m\u001b[38;5;124m'\u001b[39m, ax\u001b[38;5;241m=\u001b[39maxes[\u001b[38;5;241m1\u001b[39m]);\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
-    },
     {
      "data": {
-      "image/png": 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vvB2Pxx57TOPHj1f//v2VkpKi22+/XV9//XWQog2sbdu26bLLLtPIkSNlMpn0+uuvu71m69atmj59umJjY5Wenq7nnnsu4HH2JD//+c9lMpnsviZMmGA7/vXXX2vFihVKSkrSwIEDddVVV+nYsWMhjDi8uPs7axiG7rvvPo0YMUL9+/fXokWLVFJSYnfO8ePHde211yo+Pl6DBw/W8uXL1djYGMSnCC/uxvSGG27o8nf2oosusjuHMf3GunXrNHPmTA0aNEhDhw7VkiVLVFxcbHeOJ/8/P3z4sC699FLFxcVp6NCh+slPfqJTp04F81EQQLzPdMU7jT3eaezxPtMV7zTfCNk7jREGXnrpJaNv377GM888Y/zzn/80brrpJmPw4MHGsWPHHJ5fUFBgREdHGw899JDx6aefGvfcc48RExNj7N+/P8iRB4a343HNNdcYGzZsMAoLC40DBw4YN9xwg5GQkGAcPXo0yJEHhrfjYVVeXm6cccYZxrx584wrrrgiOMEGibdjcvLkSeOcc84xLrnkEmPHjh1GeXm5sXXrVqOoqCjIkQeGt+PxwgsvGLGxscYLL7xglJeXG++++64xYsQI4/bbbw9y5IHx9ttvG3fffbfx6quvGpKM1157zeX5ZWVlRlxcnLF69Wrj008/NXJzc43o6GjjnXfeCU7APcD9999vnH322caXX35p+7JYLLbjP/jBD4yUlBRj8+bNxkcffWSce+65xty5c0MYcXhx93d2/fr1RkJCgvH6668b//jHP4zLL7/cSEtLM7766ivbORdddJExdepU44MPPjC2b99upKenGzk5OUF+kvDhbkyvv/5646KLLrL7O3v8+HG7cxjTbyxevNh49tlnjU8++cQoKioyLrnkEmP06NFGY2Oj7Rx3/z8/deqUMWnSJGPRokVGYWGh8fbbbxtms9lYs2ZNKB4Jfsb7TFe809jjncYe7zNd8U5jL1TvNGGRJJs1a5axYsUK2/dtbW3GyJEjjXXr1jk8/3vf+55x6aWX2rXNnj3b+P73vx/QOIPF2/Ho7NSpU8agQYOM559/PlAhBpUv43Hq1Clj7ty5xh/+8Afj+uuv71G/UAzD+zF58sknjbFjxxotLS3BCjGovB2PFStWGOeff75d2+rVq42srKyAxhkKnvxC+elPf2qcffbZdm1XX321sXjx4gBG1rPcf//9xtSpUx0eq6urM2JiYoxXXnnF1nbgwAFDkrFr164gRRg5Ov+dbW9vN4YPH2785je/sbXV1dUZsbGxRl5enmEYhvHpp58akow9e/bYzvl//+//GSaTyfj888+DFnu4cpYkc/W7kTF1raqqypBk5OfnG4bh2f/P3377bSMqKsqorKy0nfPkk08a8fHxxsmTJ4P7APA73me64p3GHu809nif6Yp3GueC+U4T8uWWLS0t2rt3rxYtWmRri4qK0qJFi7Rr1y6H1+zatcvufElavHix0/MjiS/j0Vlzc7NaW1s1ZMiQQIUZNL6OxwMPPKChQ4dq+fLlwQgzqHwZkzfeeENz5szRihUrNGzYME2aNEm/+tWv1NbWFqywA8aX8Zg7d6727t1rm75cVlamt99+W5dccklQYg43PfkzNZhKSko0cuRIjR07Vtdee60OHz4sSdq7d69aW1vtxnjChAkaPXo0Y+yB8vJyVVZW2o1fQkKCZs+ebRu/Xbt2afDgwTrnnHNs5yxatEhRUVH68MMPgx5zpNi6dauGDh2q8ePH65ZbblFNTY3tGGPqWn19vSTZ/lvLk/+f79q1S5MnT9awYcNs5yxevFgNDQ365z//GcTo4W+8z3TFO4093mns8T7TFe803eevz9U+/gzKF9XV1Wpra7P7DwZJGjZsmP71r385vKaystLh+ZWVlQGLM1h8GY/O7rzzTo0cObLLX5BI5Mt47NixQxs3blRRUVEQIgw+X8akrKxMf//733Xttdfq7bffVmlpqX74wx+qtbVV999/fzDCDhhfxuOaa65RdXW1zjvvPBmGoVOnTukHP/iBfvaznwUj5LDj7DO1oaFBX331lfr37x+iyCLH7Nmz9dxzz2n8+PH68ssvtXbtWs2bN0+ffPKJKisr1bdvXw0ePNjump7yeyvQrGPk6vd+ZWWlhg4dane8T58+GjJkCGPsxEUXXaQrr7xSaWlpOnjwoH72s5/p4osv1q5duxQdHc2YutDe3q7bbrtNWVlZmjRpkiR59P9zZ5+11mOIXLzPdMU7jT3eaezxPtMV7zTd5693mpAnyeBf69ev10svvaStW7eqX79+oQ4n6E6cOKHrrrtOTz/9tMxmc6jDCRvt7e0aOnSo/ud//kfR0dGaMWOGPv/8c/3mN7/pEb9UvLV161b96le/0hNPPKHZs2ertLRUP/rRj/SLX/xC9957b6jDQwS6+OKLbX+eMmWKZs+erTFjxuiPf/wjSUaEpf/4j/+w/Xny5MmaMmWKxo0bp61bt+qCCy4IYWThb8WKFfrkk0+0Y8eOUIcC9Fi80/BO0xnvM13xThMYIU+Smc1mRUdHd9n959ixYxo+fLjDa4YPH+7V+ZHEl/Gwevjhh7V+/Xr97W9/05QpUwIZZtB4Ox4HDx5URUWFLrvsMltbe3u7pNP/Al5cXKxx48YFNugA8+XvyIgRIxQTE6Po6Ghb21lnnaXKykq1tLSob9++AY05kHwZj3vvvVfXXXedbrzxRkmnXxCbmpp088036+6771ZUVMhXogeVs8/U+Ph4Ejw+Gjx4sM4880yVlpbqW9/6llpaWlRXV2c3y6Sn/N4KNOsYHTt2TCNGjLC1Hzt2TNOmTbOdU1VVZXfdqVOndPz4ccbYQ2PHjpXZbFZpaakuuOACxtSJW2+9VW+++aa2bdumUaNG2dqHDx/u9v/nw4cP77JLmfWztzePaU/A+0xXvNPY453GHu8zXfFO033+eqcJ+aj17dtXM2bM0ObNm21t7e3t2rx5s+bMmePwmjlz5tidL0nvv/++0/MjiS/jIUkPPfSQfvGLX+idd96xqx8S6bwdjwkTJmj//v0qKiqyfV1++eVauHChioqKlJKSEszwA8KXvyNZWVkqLS21/XKVpM8++0wjRoyI+F8ovoxHc3Nzl18a1l+4p+tC9i49+TM1VBobG3Xw4EGNGDFCM2bMUExMjN0YFxcX6/Dhw4yxB9LS0jR8+HC78WtoaNCHH35oG785c+aorq5Oe/futZ3z97//Xe3t7Zo9e3bQY45ER48eVU1NjS0RyZjaMwxDt956q1577TX9/e9/V1pamt1xT/5/PmfOHO3fv98u+fj+++8rPj5eEydODM6DICB4n+mKdxp7vNPY432mK95pus9vn6telfkPkJdeesmIjY01nnvuOePTTz81br75ZmPw4MG23X+uu+4646677rKdX1BQYPTp08d4+OGHjQMHDhj3339/j9oy2dvxWL9+vdG3b1/jT3/6k91W7idOnAjVI/iVt+PRWU/bCcYwvB+Tw4cPG4MGDTJuvfVWo7i42HjzzTeNoUOHGg8++GCoHsGvvB2P+++/3xg0aJCRl5dnlJWVGe+9954xbtw443vf+16oHsGvTpw4YRQWFhqFhYWGJOPRRx81CgsLjUOHDhmGYRh33XWXcd1119nOt26X/JOf/MQ4cOCAsWHDBp+2S+7NfvzjHxtbt241ysvLjYKCAmPRokWG2Ww2qqqqDMMwjB/84AfG6NGjjb///e/GRx99ZMyZM8eYM2dOiKMOH+7+zq5fv94YPHiw8Ze//MX4+OOPjSuuuMJIS0szvvrqK1sfF110kZGZmWl8+OGHxo4dO4yMjAwjJycnVI8Ucq7G9MSJE8Ydd9xh7Nq1yygvLzf+9re/GdOnTzcyMjKMr7/+2tYHY/qNW265xUhISDC2bt1q999azc3NtnPc/f/81KlTxqRJk4wLL7zQKCoqMt555x0jOTnZWLNmTSgeCX7G+0xXvNPY453GHu8zXfFOYy9U7zRhkSQzDMPIzc01Ro8ebfTt29eYNWuW8cEHH9iOzZ8/37j++uvtzv/jH/9onHnmmUbfvn2Ns88+23jrrbeCHHFgeTMeY8aMMSR1+br//vuDH3iAePv3o6Oe9gvFytsx2blzpzF79mwjNjbWGDt2rPHLX/7SOHXqVJCjDhxvxqO1tdX4+c9/bowbN87o16+fkZKSYvzwhz80amtrgx94AGzZssXhZ4J1DK6//npj/vz5Xa6ZNm2a0bdvX2Ps2LHGs88+G/S4I9nVV19tjBgxwujbt69xxhlnGFdffbVRWlpqO/7VV18ZP/zhD43ExEQjLi7O+Pa3v218+eWXIYw4vLj7O9ve3m7ce++9xrBhw4zY2FjjggsuMIqLi+36qKmpMXJycoyBAwca8fHxxrJly3rMi5UvXI1pc3OzceGFFxrJyclGTEyMMWbMGOOmm26y/Ue4FWP6DUdjKcnus9KT/59XVFQYF198sdG/f3/DbDYbP/7xj43W1tYgPw0ChfeZrninscc7jT3eZ7rineYboXqnMRlGL5yHBwAAAAAAAHQQ8ppkAAAAAAAAQKiRJAMAAAAAAECvR5IMAAAAAAAAvR5JMgAAAAAAAPR6JMkAAAAAAADQ65EkAwAAAAAAQK9HkgwAAAAAAAC9HkkyAAAAAAAA9HokyQAAADpYsGCBbrvttlCHAQAAgCAjSQYAAAAAAIBejyQZAABAALW0tIQ6hKDpTc8KAAB6HpJkAAAAnbS3t+unP/2phgwZouHDh+vnP/+57djhw4d1xRVXaODAgYqPj9f3vvc9HTt2zHb85z//uaZNm6Y//OEPSktLU79+/SRJf/rTnzR58mT1799fSUlJWrRokZqammzX/eEPf9BZZ52lfv36acKECXriiSdsxyoqKmQymfTSSy9p7ty56tevnyZNmqT8/Hy7uPPz8zVr1izFxsZqxIgRuuuuu3Tq1ClJ0ptvvqnBgwerra1NklRUVCSTyaS77rrLdv2NN96o//zP/7R9v2PHDs2bN0/9+/dXSkqKVq1aZRdzamqqfvGLX2jp0qWKj4/XzTff3J1hBwAACCmSZAAAAJ08//zzGjBggD788EM99NBDeuCBB/T++++rvb1dV1xxhY4fP678/Hy9//77Kisr09VXX213fWlpqf785z/r1VdfVVFRkb788kvl5OTov/7rv3TgwAFt3bpVV155pQzDkCS98MILuu+++/TLX/5SBw4c0K9+9Svde++9ev755+36/clPfqIf//jHKiws1Jw5c3TZZZeppqZGkvT555/rkksu0cyZM/WPf/xDTz75pDZu3KgHH3xQkjRv3jydOHFChYWFkk4n1Mxms7Zu3WrrPz8/XwsWLJAkHTx4UBdddJGuuuoqffzxx3r55Ze1Y8cO3XrrrXYxPfzww5o6daoKCwt17733+u1nAAAAEGwmw/pfZwAAANCCBQvU1tam7du329pmzZql888/XxdccIEuvvhilZeXKyUlRZL06aef6uyzz9bu3bs1c+ZM/fznP9evfvUrff7550pOTpYk7du3TzNmzFBFRYXGjBnT5Z7p6en6xS9+oZycHFvbgw8+qLfffls7d+5URUWF0tLStH79et15552SpFOnTiktLU0rV67UT3/6U919993685//rAMHDshkMkmSnnjiCd15552qr69XVFSUZsyYoZycHN1xxx369re/rZkzZ2rt2rWqqalRfX29Ro0apc8++0wZGRm68cYbFR0drd///ve2mHbs2KH58+erqalJ/fr1U2pqqjIzM/Xaa6/5/wcBAAAQZMwkAwAA6GTKlCl2348YMUJVVVU6cOCAUlJSbAkySZo4caIGDx6sAwcO2NrGjBljS5BJ0tSpU3XBBRdo8uTJ+u53v6unn35atbW1kqSmpiYdPHhQy5cv18CBA21fDz74oA4ePGgXx5w5c2x/7tOnj8455xzbfQ8cOKA5c+bYEmSSlJWVpcbGRh09elSSNH/+fG3dulWGYWj79u268sorddZZZ2nHjh3Kz8/XyJEjlZGRIUn6xz/+oeeee84upsWLF6u9vV3l5eW2e5xzzjm+DTIAAECY6RPqAAAAAMJNTEyM3fcmk0nt7e0eXz9gwAC776Ojo/X+++9r586deu+995Sbm6u7775bH374oeLi4iRJTz/9tGbPnt3lOn9asGCBnnnmGf3jH/9QTEyMJkyYoAULFmjr1q2qra3V/Pnzbec2Njbq+9//vlatWtWln9GjR9v+3PlZAQAAIhUzyQAAADx01lln6ciRIzpy5Iit7dNPP1VdXZ0mTpzo8lqTyaSsrCytXbtWhYWF6tu3r1577TUNGzZMI0eOVFlZmdLT0+2+0tLS7Pr44IMPbH8+deqU9u7dq7POOssW265du9SxkkZBQYEGDRqkUaNGSfqmLtlvf/tbW0LMmiTbunWrrR6ZJE2fPl2ffvppl5jS09PVt29f3wYQAAAgjDGTDAAAwEOLFi3S5MmTde211+qxxx7TqVOn9MMf/lDz5893uezwww8/1ObNm3XhhRdq6NCh+vDDD2WxWGwJrrVr12rVqlVKSEjQRRddpJMnT+qjjz5SbW2tVq9ebetnw4YNysjI0FlnnaXf/va3qq2t1X/9139Jkn74wx/qscce08qVK3XrrbequLhY999/v1avXq2oqNP/LpqYmKgpU6bohRde0OOPPy5Jys7O1ve+9z21trbazSS78847de655+rWW2/VjTfeqAEDBujTTz/V+++/b7sWAACgJyFJBgAA4CGTyaS//OUvWrlypbKzsxUVFaWLLrpIubm5Lq+Lj4/Xtm3b9Nhjj6mhoUFjxozRI488oosvvliSdOONNyouLk6/+c1v9JOf/EQDBgzQ5MmTddttt9n1s379eq1fv15FRUVKT0/XG2+8IbPZLEk644wz9Pbbb+snP/mJpk6dqiFDhmj58uW655577PqYP3++ioqKbLPGhgwZookTJ+rYsWMaP3687bwpU6YoPz9fd999t+bNmyfDMDRu3LguO3kCAAD0FOxuCQAAEOasu1sWFhZq2rRpoQ4HAACgR6ImGQAAAAAAAHo9kmQAAAAAAADo9VhuCQAAAAAAgF6PmWQAAAAAAADo9UiSAQAAAAAAoNcjSQYAAAAAAIBejyQZAAAAAAAAej2SZAAAAAAAAOj1SJIBAAAAAACg1yNJBgAAAAAAgF6PJBkAAAAAAAB6vf8f972lammgdBgAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -4823,24 +7907,23 @@
    "id": "660482a9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:04.098571Z",
-     "iopub.status.busy": "2024-06-04T23:23:04.098495Z",
-     "iopub.status.idle": "2024-06-04T23:23:04.104280Z",
-     "shell.execute_reply": "2024-06-04T23:23:04.104052Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.805818Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.805711Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.808717Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.808447Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[107], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m Auto\u001b[38;5;241m.\u001b[39mcylinders \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mSeries(\u001b[43mAuto\u001b[49m\u001b[38;5;241m.\u001b[39mcylinders, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcategory\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      2\u001b[0m Auto\u001b[38;5;241m.\u001b[39mcylinders\u001b[38;5;241m.\u001b[39mdtype\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "CategoricalDtype(categories=[3, 4, 5, 6, 8], ordered=False, categories_dtype=int64)"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -4863,27 +7946,16 @@
    "id": "9eb5befc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:04.105502Z",
-     "iopub.status.busy": "2024-06-04T23:23:04.105432Z",
-     "iopub.status.idle": "2024-06-04T23:23:04.157828Z",
-     "shell.execute_reply": "2024-06-04T23:23:04.157587Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.810317Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.810186Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.890120Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.889777Z"
     }
    },
    "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[108], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m subplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m \u001b[43mAuto\u001b[49m\u001b[38;5;241m.\u001b[39mboxplot(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmpg\u001b[39m\u001b[38;5;124m'\u001b[39m, by\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcylinders\u001b[39m\u001b[38;5;124m'\u001b[39m, ax\u001b[38;5;241m=\u001b[39max);\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -4911,28 +7983,17 @@
    "id": "f529de12",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:04.159152Z",
-     "iopub.status.busy": "2024-06-04T23:23:04.159073Z",
-     "iopub.status.idle": "2024-06-04T23:23:04.212595Z",
-     "shell.execute_reply": "2024-06-04T23:23:04.212375Z"
+     "iopub.execute_input": "2024-06-05T01:13:24.891763Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.891650Z",
+     "iopub.status.idle": "2024-06-05T01:13:25.003998Z",
+     "shell.execute_reply": "2024-06-05T01:13:25.003699Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[109], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m subplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m \u001b[43mAuto\u001b[49m\u001b[38;5;241m.\u001b[39mhist(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmpg\u001b[39m\u001b[38;5;124m'\u001b[39m, ax\u001b[38;5;241m=\u001b[39max);\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
-    },
     {
      "data": {
-      "image/png": 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pCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApDSiUF23bl3MmDEjqqqqor6+PrZv3/6e1m3cuDEmTJgQ8+fPH8nDAgBwGik5VDdt2hTNzc3R0tISO3fujJkzZ0ZTU1O88cYb77ru9ddfj3/4h3+Iq6++esSbBQDg9FFyqD788MNx6623xpIlS+ITn/hErF+/Ps4+++x44oknTrimv78/brrpprj//vvjggsueF8bBgDg9FBSqPb19cWOHTuisbHxD3dQVhaNjY3R3t5+wnVf+cpXYvLkyXHzzTe/p8fp7e2Nnp6eITcAAE4vJYXqoUOHor+/P2pra4eM19bWRmdn57BrXnjhhXj88cdjw4YN7/lxWltbo6amZvBWV1dXyjYBADgFjOmn/g8fPhwLFy6MDRs2xKRJk97zuhUrVkR3d/fg7cCBA2O4SwAAMjqjlMmTJk2K8vLy6OrqGjLe1dUVU6ZMOW7+L37xi3j99ddj3rx5g2MDAwO/f+AzzohXXnklLrzwwuPWVVZWRmVlZSlbAwDgFFPSFdWKioqYPXt2tLW1DY4NDAxEW1tbNDQ0HDf/4osvjpdeeik6OjoGb5/97Gfj2muvjY6ODi/pAwBwQiVdUY2IaG5ujsWLF8ecOXNi7ty5sXbt2jhy5EgsWbIkIiIWLVoU06dPj9bW1qiqqopLLrlkyPpzzz03IuK4cQAA+P9KDtUFCxbEwYMHY9WqVdHZ2RmzZs2KrVu3Dn7Aav/+/VFW5g+8AgDg/ZlQFEVxsjfxx/T09ERNTU10d3dHdXX1yd4OAADvMBa95tInAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJRGFKrr1q2LGTNmRFVVVdTX18f27dtPOHfDhg1x9dVXx8SJE2PixInR2Nj4rvMBACBiBKG6adOmaG5ujpaWlti5c2fMnDkzmpqa4o033hh2/rZt2+KGG26IH//4x9He3h51dXXxmc98Jn71q1+9780DAHDqmlAURVHKgvr6+rjiiivikUceiYiIgYGBqKurizvvvDOWL1/+R9f39/fHxIkT45FHHolFixa9p8fs6emJmpqa6O7ujurq6lK2CwDAOBiLXivpimpfX1/s2LEjGhsb/3AHZWXR2NgY7e3t7+k+3nrrrTh69Gicd955J5zT29sbPT09Q24AAJxeSgrVQ4cORX9/f9TW1g4Zr62tjc7Ozvd0H3fffXdMmzZtSOy+U2tra9TU1Aze6urqStkmAACngHH91P+aNWti48aN8cwzz0RVVdUJ561YsSK6u7sHbwcOHBjHXQIAkMEZpUyeNGlSlJeXR1dX15Dxrq6umDJlyruufeihh2LNmjXxwx/+MC677LJ3nVtZWRmVlZWlbA0AgFNMSVdUKyoqYvbs2dHW1jY4NjAwEG1tbdHQ0HDCdQ8++GA88MADsXXr1pgzZ87IdwsAwGmjpCuqERHNzc2xePHimDNnTsydOzfWrl0bR44ciSVLlkRExKJFi2L69OnR2toaERH//M//HKtWrYqnnnoqZsyYMfhe1g996EPxoQ99aBSfCgAAp5KSQ3XBggVx8ODBWLVqVXR2dsasWbNi69atgx+w2r9/f5SV/eFC7Te+8Y3o6+uLz33uc0Pup6WlJb785S+/v90DAHDKKvl7VE8G36MKAJDbSf8eVQAAGC9CFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgpRGF6rp162LGjBlRVVUV9fX1sX379ned/93vfjcuvvjiqKqqiksvvTS2bNkyos0CAHD6KDlUN23aFM3NzdHS0hI7d+6MmTNnRlNTU7zxxhvDzn/xxRfjhhtuiJtvvjl27doV8+fPj/nz58fPf/7z9715AABOXROKoihKWVBfXx9XXHFFPPLIIxERMTAwEHV1dXHnnXfG8uXLj5u/YMGCOHLkSPzgBz8YHPuLv/iLmDVrVqxfv/49PWZPT0/U1NREd3d3VFdXl7JdAADGwVj02hmlTO7r64sdO3bEihUrBsfKysqisbEx2tvbh13T3t4ezc3NQ8aampri2WefPeHj9Pb2Rm9v7+DP3d3dEfH7vwEAAOTzdqeVeA30XZUUqocOHYr+/v6ora0dMl5bWxt79uwZdk1nZ+ew8zs7O0/4OK2trXH//fcfN15XV1fKdgEAGGf//d//HTU1NaNyXyWF6nhZsWLFkKuwb775Znz4wx+O/fv3j9oTJ6+enp6oq6uLAwcOeKvHacB5n16c9+nFeZ9euru74/zzz4/zzjtv1O6zpFCdNGlSlJeXR1dX15Dxrq6umDJlyrBrpkyZUtL8iIjKysqorKw8brympsY/6KeR6upq530acd6nF+d9enHep5eystH79tOS7qmioiJmz54dbW1tg2MDAwPR1tYWDQ0Nw65paGgYMj8i4vnnnz/hfAAAiBjBS//Nzc2xePHimDNnTsydOzfWrl0bR44ciSVLlkRExKJFi2L69OnR2toaERF33XVXXHPNNfG1r30trr/++ti4cWP87Gc/i8cee2x0nwkAAKeUkkN1wYIFcfDgwVi1alV0dnbGrFmzYuvWrYMfmNq/f/+QS75XXnllPPXUU3HvvffGPffcEx/96Efj2WefjUsuueQ9P2ZlZWW0tLQM+3YATj3O+/TivE8vzvv04rxPL2Nx3iV/jyoAAIyH0Xu3KwAAjCKhCgBASkIVAICUhCoAACmlCdV169bFjBkzoqqqKurr62P79u3vOv+73/1uXHzxxVFVVRWXXnppbNmyZZx2ymgo5bw3bNgQV199dUycODEmTpwYjY2Nf/SfD3Ip9ff7bRs3bowJEybE/Pnzx3aDjKpSz/vNN9+MpUuXxtSpU6OysjIuuugi/07/ACn1vNeuXRsf+9jH4qyzzoq6urpYtmxZ/O53vxun3TJSP/nJT2LevHkxbdq0mDBhQjz77LN/dM22bdviU5/6VFRWVsZHPvKRePLJJ0t/4CKBjRs3FhUVFcUTTzxR/Od//mdx6623Fueee27R1dU17Pyf/vSnRXl5efHggw8WL7/8cnHvvfcWZ555ZvHSSy+N884ZiVLP+8YbbyzWrVtX7Nq1q9i9e3fxt3/7t0VNTU3xX//1X+O8c0ai1PN+22uvvVZMnz69uPrqq4u//uu/Hp/N8r6Vet69vb3FnDlziuuuu6544YUXitdee63Ytm1b0dHRMc47ZyRKPe9vf/vbRWVlZfHtb3+7eO2114rnnnuumDp1arFs2bJx3jml2rJlS7Fy5cri6aefLiKieOaZZ951/r59+4qzzz67aG5uLl5++eXi61//elFeXl5s3bq1pMdNEapz584tli5dOvhzf39/MW3atKK1tXXY+Z///OeL66+/fshYfX198Xd/93djuk9GR6nn/U7Hjh0rzjnnnOJb3/rWWG2RUTSS8z527Fhx5ZVXFt/85jeLxYsXC9UPkFLP+xvf+EZxwQUXFH19feO1RUZRqee9dOnS4i//8i+HjDU3NxdXXXXVmO6T0fVeQvVLX/pS8clPfnLI2IIFC4qmpqaSHuukv/Tf19cXO3bsiMbGxsGxsrKyaGxsjPb29mHXtLe3D5kfEdHU1HTC+eQxkvN+p7feeiuOHj0a55133lhtk1Ey0vP+yle+EpMnT46bb755PLbJKBnJeX//+9+PhoaGWLp0adTW1sYll1wSq1evjv7+/vHaNiM0kvO+8sorY8eOHYNvD9i3b19s2bIlrrvuunHZM+NntFqt5D+ZarQdOnQo+vv7B/9kq7fV1tbGnj17hl3T2dk57PzOzs4x2yejYyTn/U533313TJs27bhfAPIZyXm/8MIL8fjjj0dHR8c47JDRNJLz3rdvX/zoRz+Km266KbZs2RJ79+6NO+64I44ePRotLS3jsW1GaCTnfeONN8ahQ4fi05/+dBRFEceOHYvbb7897rnnnvHYMuPoRK3W09MTv/3tb+Oss856T/dz0q+oQinWrFkTGzdujGeeeSaqqqpO9nYYZYcPH46FCxfGhg0bYtKkSSd7O4yDgYGBmDx5cjz22GMxe/bsWLBgQaxcuTLWr19/srfGGNi2bVusXr06Hn300di5c2c8/fTTsXnz5njggQdO9tZI6qRfUZ00aVKUl5dHV1fXkPGurq6YMmXKsGumTJlS0nzyGMl5v+2hhx6KNWvWxA9/+MO47LLLxnKbjJJSz/sXv/hFvP766zFv3rzBsYGBgYiIOOOMM+KVV16JCy+8cGw3zYiN5Pd76tSpceaZZ0Z5efng2Mc//vHo7OyMvr6+qKioGNM9M3IjOe/77rsvFi5cGLfccktERFx66aVx5MiRuO2222LlypVRVub62aniRK1WXV39nq+mRiS4olpRURGzZ8+Otra2wbGBgYFoa2uLhoaGYdc0NDQMmR8R8fzzz59wPnmM5LwjIh588MF44IEHYuvWrTFnzpzx2CqjoNTzvvjii+Oll16Kjo6OwdtnP/vZuPbaa6OjoyPq6urGc/uUaCS/31dddVXs3bt38H9IIiJeffXVmDp1qkhNbiTn/dZbbx0Xo2//T8rvP6PDqWLUWq20z3mNjY0bNxaVlZXFk08+Wbz88svFbbfdVpx77rlFZ2dnURRFsXDhwmL58uWD83/6058WZ5xxRvHQQw8Vu3fvLlpaWnw91QdIqee9Zs2aoqKiovje975X/PrXvx68HT58+GQ9BUpQ6nm/k0/9f7CUet779+8vzjnnnOLv//7vi1deeaX4wQ9+UEyePLn4p3/6p5P1FChBqefd0tJSnHPOOcW//du/Ffv27Sv+/d//vbjwwguLz3/+8yfrKfAeHT58uNi1a1exa9euIiKKhx9+uNi1a1fxy1/+siiKoli+fHmxcOHCwflvfz3VP/7jPxa7d+8u1q1b98H9eqqiKIqvf/3rxfnnn19UVFQUc+fOLf7jP/5j8K9dc801xeLFi4fM/853vlNcdNFFRUVFRfHJT36y2Lx58zjvmPejlPP+8Ic/XETEcbeWlpbx3zgjUurv9/8nVD94Sj3vF198saivry8qKyuLCy64oPjqV79aHDt2bJx3zUiVct5Hjx4tvvzlLxcXXnhhUVVVVdTV1RV33HFH8T//8z/jv3FK8uMf/3jY/xa/fb6LFy8urrnmmuPWzJo1q6ioqCguuOCC4l//9V9LftwJReFaOwAA+Zz096gCAMBwhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKT0f4FLbKWuTRs3AAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -4960,28 +8021,17 @@
    "id": "9a897cee",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:04.213894Z",
-     "iopub.status.busy": "2024-06-04T23:23:04.213823Z",
-     "iopub.status.idle": "2024-06-04T23:23:04.267912Z",
-     "shell.execute_reply": "2024-06-04T23:23:04.267667Z"
+     "iopub.execute_input": "2024-06-05T01:13:25.005602Z",
+     "iopub.status.busy": "2024-06-05T01:13:25.005490Z",
+     "iopub.status.idle": "2024-06-05T01:13:25.073490Z",
+     "shell.execute_reply": "2024-06-05T01:13:25.073206Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[110], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m subplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m \u001b[43mAuto\u001b[49m\u001b[38;5;241m.\u001b[39mhist(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmpg\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m'\u001b[39m, bins\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m12\u001b[39m, ax\u001b[38;5;241m=\u001b[39max);\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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SEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMkMLvcCgF6Wy5V7BQAcRjyzCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyRxSbN56662Ry+ViwYIFhW27d++O5ubmGDlyZAwbNiyampqira3tUNcJAEA/dNCx+cwzz8Q3v/nNmDp1apftCxcujMceeyxWrFgRa9asiS1btsS8efMOeaEAAPQ/BxWbb775ZlxyySVx3333xVFHHVXY3t7eHvfff3984xvfiJkzZ8a0adNi+fLl8dRTT8W6det6bNEAAPQPBxWbzc3N8dGPfjQaGxu7bF+/fn3k8/ku26dMmRITJkyItWvXHtpKAQDodwaXeoWHH344nnvuuXjmmWe6XbZ169YYMmRIjBgxosv20aNHx9atW/d5e52dndHZ2Vk439HRERER+Xw+8vl8qcvrdXvXWLa1VleX535LlP/zOvP9ZL3lZFbF61ezKvP3s7J/r+pHzKp4ZlW8gTarUh5HSbG5efPmuPbaa6O1tTWqqqpKXti+3HLLLbF06dJu23/yk5/E0KFDe+Q+ekNra2t57vi73y3P/R6k1gceKPcS+g2zKl6/mNWPflTuFUREGb9X9UNmVTyzKt5AmdXOnTuL3jeXZVlW7M6PPvpofOITn4gjjjiisO3tt9+OXC4XgwYNiieeeCIaGxvjjTfe6PLs5sSJE2PBggWxcOHCbre5r2c2x48fH3/84x+jpqam6AdSLvl8PlpbW2P27NlRUVHR+wuore39+zwI+erqaH3ggZh92WVRsWtXuZfTp5lV8frVrNrby3r3Zf9e1Y+YVfHMqngDbVYdHR0xatSoaG9vP2CvlfTM5qxZs+L555/vsm3+/PkxZcqUuO6662L8+PFRUVERq1atiqampoiI2LhxY2zatCkaGhr2eZuVlZVRWVnZbXtFRUW/+mKUbb19/Qfsu1Ts2tX3o6CPMKvi9YtZ9ZHvZ/3te2s5mVXxzKp4A2VWpTyGkmJz+PDhcfLJJ3fZduSRR8bIkSML2y+//PJYtGhR1NXVRU1NTVx99dXR0NAQp59+eil3BQDAAFDyG4QO5I477ohBgwZFU1NTdHZ2xty5c+Puu+/u6bsBAKAfOOTYXL16dZfzVVVVsWzZsli2bNmh3jQAAP2cz0YHACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBekMuV95Tbe0766itLf26AIdAbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQzOByLwCAPq4/f4pQlpV7BXDY88wmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyZQUm/fcc09MnTo1ampqoqamJhoaGuLHP/5x4fLdu3dHc3NzjBw5MoYNGxZNTU3R1tbW44sGAKB/KCk2x40bF7feemusX78+nn322Zg5c2acf/758etf/zoiIhYuXBiPPfZYrFixItasWRNbtmyJefPmJVk4AAB93+BSdj7vvPO6nP/a174W99xzT6xbty7GjRsX999/f7S0tMTMmTMjImL58uVx4oknxrp16+L000/vuVUDANAvHPRrNt9+++14+OGHY8eOHdHQ0BDr16+PfD4fjY2NhX2mTJkSEyZMiLVr1/bIYgEA6F9KemYzIuL555+PhoaG2L17dwwbNixWrlwZJ510UmzYsCGGDBkSI0aM6LL/6NGjY+vWre95e52dndHZ2Vk439HRERER+Xw+8vl8qcvrdXvXWLa1VleX535LlP/zOvP9ZL3lZFbFM6viHbazOojvzWX/vt6PmFXxBtqsSnkcuSzLslJu/K233opNmzZFe3t7fO9734tvf/vbsWbNmtiwYUPMnz+/SzhGREyfPj3OOeecuO222/Z5e0uWLImlS5d2297S0hJDhw4tZWkAAPSCnTt3xsUXXxzt7e1RU1Oz331Ljs13a2xsjOOOOy4+9alPxaxZs+KNN97o8uzmxIkTY8GCBbFw4cJ9Xn9fz2yOHz8+/vjHPx5w8X1BPp+P1tbWmD17dlRUVPT+Ampre/8+D0K+ujpaH3ggZl92WVTs2lXu5fRpZlU8syreYTur9vaSr1L27+v9iFkVb6DNqqOjI0aNGlVUbJb8a/R327NnT3R2dsa0adOioqIiVq1aFU1NTRERsXHjxti0aVM0NDS85/UrKyujsrKy2/aKiop+9cUo23r72Q+Nil27Dq8fdIfArIpnVsU77GZ1CN+X+9vPoXIyq+INlFmV8hhKis3FixfHueeeGxMmTIjt27dHS0tLrF69Op544omora2Nyy+/PBYtWhR1dXVRU1MTV199dTQ0NHgnOgDAYaqk2Hz11Vfjc5/7XLzyyitRW1sbU6dOjSeeeCJmz54dERF33HFHDBo0KJqamqKzszPmzp0bd999d5KFAwDQ95UUm/fff/9+L6+qqoply5bFsmXLDmlRAAAMDD4bHQCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgADVy5X+qm29p3r1tYe3PV76gQDhNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACCZkmLzlltuidNOOy2GDx8exxxzTFxwwQWxcePGLvvs3r07mpubY+TIkTFs2LBoamqKtra2Hl00AAD9Q0mxuWbNmmhubo5169ZFa2tr5PP5mDNnTuzYsaOwz8KFC+Oxxx6LFStWxJo1a2LLli0xb968Hl84AAB93+BSdn788ce7nH/wwQfjmGOOifXr18dZZ50V7e3tcf/990dLS0vMnDkzIiKWL18eJ554Yqxbty5OP/30nls5AAB9Xkmx+W7t7e0REVFXVxcREevXr498Ph+NjY2FfaZMmRITJkyItWvX7jM2Ozs7o7Ozs3C+o6MjIiLy+Xzk8/lDWV6v2LvGsq21uro891ui/J/Xme8n6y0nsyqeWRXPrIrXZ2blZ+CAMtBmVcrjyGVZlh3MnezZsyc+/vGPx7Zt2+LnP/95RES0tLTE/Pnzu8RjRMT06dPjnHPOidtuu63b7SxZsiSWLl3abXtLS0sMHTr0YJYGAEBCO3fujIsvvjja29ujpqZmv/se9DObzc3N8atf/aoQmgdr8eLFsWjRosL5jo6OGD9+fMyZM+eAi+8L8vl8tLa2xuzZs6OioqL3F1Bb2/v3eRDy1dXR+sADMfuyy6Ji165yL6dPM6vimVXxzKp4fWZWf/7tYV9W9p+B/chAm9Xe30QX46Bi86qrroof/vCH8eSTT8a4ceMK2+vr6+Ott96Kbdu2xYgRIwrb29raor6+fp+3VVlZGZWVld22V1RU9KsvRtnW289+aFTs2uUHXZHMqnhmVTyzKl7ZZ+Vn4IA0UGZVymMo6d3oWZbFVVddFStXroyf/vSnMWnSpC6XT5s2LSoqKmLVqlWFbRs3boxNmzZFQ0NDKXcFAMAAUNIzm83NzdHS0hLf//73Y/jw4bF169aIiKitrY3q6uqora2Nyy+/PBYtWhR1dXVRU1MTV199dTQ0NHgnOgDAYaik2LznnnsiIuLss8/usn358uXx+c9/PiIi7rjjjhg0aFA0NTVFZ2dnzJ07N+6+++4eWSwAAP1LSbFZzBvXq6qqYtmyZbFs2bKDXhQAAAODz0YHACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzY3CuXO7hTbe0716+tPfjbOJQTAEAfJjYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQB9US7X90+1te+stba263b4P8QmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJBMybH55JNPxnnnnRdjx46NXC4Xjz76aJfLsyyLG264IcaMGRPV1dXR2NgYL7zwQk+tFwCAfqTk2NyxY0eccsopsWzZsn1efvvtt8ddd90V9957bzz99NNx5JFHxty5c2P37t2HvFgAAPqXwaVe4dxzz41zzz13n5dlWRZ33nlnfPWrX43zzz8/IiIeeuihGD16dDz66KPx6U9/+tBWCwBAv1JybO7Pyy+/HFu3bo3GxsbCttra2pgxY0asXbt2n7HZ2dkZnZ2dhfMdHR0REZHP5yOfz/fk8vavuvqgrpb/8/XyB3n9w4U5Fc+simdWxTOr4plV8d5zVr3587uf2Ns0vdo2CZXyOHJZlmUHe0e5XC5WrlwZF1xwQUREPPXUU3HGGWfEli1bYsyYMYX9LrroosjlcvHII490u40lS5bE0qVLu21vaWmJoUOHHuzSAABIZOfOnXHxxRdHe3t71NTU7HffHn1m82AsXrw4Fi1aVDjf0dER48ePjzlz5hxw8T1q70dulShfXR2tDzwQsy+7LCp27erhRQ0c5lQ8syqeWRXPrIpnVsV7z1m1t5dvUX1UPp+P1tbWmD17dlRUVJR7OYds72+ii9GjsVlfXx8REW1tbV2e2Wxra4sPfvCD+7xOZWVlVFZWdtteUVHRu1+MQ/yGUrFrl29KRTCn4plV8cyqeGZVPLMqXrdZDYCYSqXX+yaRUh5Dj/6dzUmTJkV9fX2sWrWqsK2joyOefvrpaGho6Mm7AgCgHyj5mc0333wzXnzxxcL5l19+OTZs2BB1dXUxYcKEWLBgQdx8881x/PHHx6RJk+L666+PsWPHFl7XCQDA4aPk2Hz22WfjnHPOKZzf+3rLSy+9NB588MH48pe/HDt27Igrr7wytm3bFmeeeWY8/vjjUVVV1XOrBgCgXyg5Ns8+++zY3xvYc7lc3HTTTXHTTTcd0sIAAOj/fDY6AADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQD0rFzO6d2n2tp3ZlNbm+4++iixCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAySSLzWXLlsWxxx4bVVVVMWPGjPjFL36R6q4AAOijksTmI488EosWLYobb7wxnnvuuTjllFNi7ty58eqrr6a4OwAA+qgksfmNb3wjrrjiipg/f36cdNJJce+998bQoUPjgQceSHF3AAD0UYN7+gbfeuutWL9+fSxevLiwbdCgQdHY2Bhr167ttn9nZ2d0dnYWzre3t0dExOuvvx75fL6nl/feqqoO6mr5qqrYuXNnvFZVFRVZ1sOLGjjMqXhmVTyzKp5ZFc+simdWxeuVWb32Wprb3Yft27dHRERWzGPJetgf/vCHLCKyp556qsv2L33pS9n06dO77X/jjTdmEeHk5OTk5OTk5NTPTps3bz5gG/b4M5ulWrx4cSxatKhwfs+ePfH666/HyJEjI5fLlXFlxeno6Ijx48fH5s2bo6amptzL6bPMqXhmVTyzKp5ZFc+simdWxRtos8qyLLZv3x5jx4494L49HpujRo2KI444Itra2rpsb2tri/r6+m77V1ZWRmVlZZdtI0aM6OllJVdTUzMgDp7UzKl4ZlU8syqeWRXPrIpnVsUbSLOqra0tar8ef4PQkCFDYtq0abFq1arCtj179sSqVauioaGhp+8OAIA+LMmv0RctWhSXXnppnHrqqTF9+vS48847Y8eOHTF//vwUdwcAQB+VJDY/9alPxf/+7//GDTfcEFu3bo0PfvCD8fjjj8fo0aNT3F1ZVVZWxo033tjtpQB0ZU7FM6vimVXxzKp4ZlU8syre4TyrXJb5WwUAAKThs9EBAEhGbAIAkIzYBAAgGbEJAEAyYrMITz75ZJx33nkxduzYyOVy8eijj3a5PMuyuOGGG2LMmDFRXV0djY2N8cILL5RnsWV2oFl9/vOfj1wu1+X0kY98pDyLLbNbbrklTjvttBg+fHgcc8wxccEFF8TGjRu77LN79+5obm6OkSNHxrBhw6KpqanbByYcDoqZ1dlnn93t2PqHf/iHMq24fO65556YOnVq4Q9HNzQ0xI9//OPC5Y6pvzjQrBxT+3brrbdGLpeLBQsWFLY5rvZtX7M6HI8rsVmEHTt2xCmnnBLLli3b5+W333573HXXXXHvvffG008/HUceeWTMnTs3du/e3csrLb8DzSoi4iMf+Ui88sorhdN3v/vdXlxh37FmzZpobm6OdevWRWtra+Tz+ZgzZ07s2LGjsM/ChQvjscceixUrVsSaNWtiy5YtMW/evDKuujyKmVVExBVXXNHl2Lr99tvLtOLyGTduXNx6662xfv36ePbZZ2PmzJlx/vnnx69//euIcEz9XweaVYRj6t2eeeaZ+OY3vxlTp07tst1x1d17zSriMDyuDvjp6XQREdnKlSsL5/fs2ZPV19dn//Iv/1LYtm3btqyysjL77ne/W4YV9h3vnlWWZdmll16anX/++WVZT1/36quvZhGRrVmzJsuyd46jioqKbMWKFYV9fvOb32QRka1du7Zcy+wT3j2rLMuy//f//l927bXXlm9RfdhRRx2Vffvb33ZMFWHvrLLMMfVu27dvz44//vistbW1y2wcV92916yy7PA8rjyzeYhefvnl2Lp1azQ2Nha21dbWxowZM2Lt2rVlXFnftXr16jjmmGPihBNOiH/8x3+M1157rdxL6hPa29sjIqKuri4iItavXx/5fL7LsTVlypSYMGHCYX9svXtWe33nO9+JUaNGxcknnxyLFy+OnTt3lmN5fcbbb78dDz/8cOzYsSMaGhocU/vx7lnt5Zj6i+bm5vjoRz/a5fiJ8L1qX95rVnsdbsdVkk8QOpxs3bo1IqLbpyONHj26cBl/8ZGPfCTmzZsXkyZNipdeeim+8pWvxLnnnhtr166NI444otzLK5s9e/bEggUL4owzzoiTTz45It45toYMGRIjRozosu/hfmzta1YRERdffHFMnDgxxo4dG7/85S/juuuui40bN8a///u/l3G15fH8889HQ0ND7N69O4YNGxYrV66Mk046KTZs2OCYepf3mlWEY+r/evjhh+O5556LZ555pttlvld1tb9ZRRyex5XYpFd9+tOfLvz7Ax/4QEydOjWOO+64WL16dcyaNauMKyuv5ubm+NWvfhU///nPy72UPu+9ZnXllVcW/v2BD3wgxowZE7NmzYqXXnopjjvuuN5eZlmdcMIJsWHDhmhvb4/vfe97cemll8aaNWvKvaw+6b1mddJJJzmm/mzz5s1x7bXXRmtra1RVVZV7OX1aMbM6HI8rv0Y/RPX19RER3d5119bWVriM9/b+978/Ro0aFS+++GK5l1I2V111Vfzwhz+Mn/3sZzFu3LjC9vr6+njrrbdi27ZtXfY/nI+t95rVvsyYMSMi4rA8toYMGRKTJ0+OadOmxS233BKnnHJK/Ou//qtjah/ea1b7crgeU+vXr49XX301PvShD8XgwYNj8ODBsWbNmrjrrrti8ODBMXr0aMfVnx1oVm+//Xa36xwOx5XYPESTJk2K+vr6WLVqVWFbR0dHPP30011e98O+/f73v4/XXnstxowZU+6l9Losy+Kqq66KlStXxk9/+tOYNGlSl8unTZsWFRUVXY6tjRs3xqZNmw67Y+tAs9qXDRs2REQclsfWu+3Zsyc6OzsdU0XYO6t9OVyPqVmzZsXzzz8fGzZsKJxOPfXUuOSSSwr/dly940Cz2tfLxQ6H48qv0Yvw5ptvdvk/jpdffjk2bNgQdXV1MWHChFiwYEHcfPPNcfzxx8ekSZPi+uuvj7Fjx8YFF1xQvkWXyf5mVVdXF0uXLo2mpqaor6+Pl156Kb785S/H5MmTY+7cuWVcdXk0NzdHS0tLfP/734/hw4cXXttUW1sb1dXVUVtbG5dffnksWrQo6urqoqamJq6++upoaGiI008/vcyr710HmtVLL70ULS0t8bd/+7cxcuTI+OUvfxkLFy6Ms846a59/dmQgW7x4cZx77rkxYcKE2L59e7S0tMTq1avjiSeecEy9y/5m5Zj6i+HDh3d5fXRExJFHHhkjR44sbHdcveNAszpsj6tyvx2+P/jZz36WRUS306WXXppl2Tt//uj666/PRo8enVVWVmazZs3KNm7cWN5Fl8n+ZrVz585szpw52dFHH51VVFRkEydOzK644ops69at5V52WexrThGRLV++vLDPrl27si984QvZUUcdlQ0dOjT7xCc+kb3yyivlW3SZHGhWmzZtys4666ysrq4uq6yszCZPnpx96Utfytrb28u78DK47LLLsokTJ2ZDhgzJjj766GzWrFnZT37yk8Lljqm/2N+sHFP79+4/3+O4em//d1aH63GVy7Is6824BQDg8OE1mwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgmf8P4biV52JAgFEAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -5013,24 +8063,23 @@
    "id": "5e003b22",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:04.269235Z",
-     "iopub.status.busy": "2024-06-04T23:23:04.269156Z",
-     "iopub.status.idle": "2024-06-04T23:23:04.274767Z",
-     "shell.execute_reply": "2024-06-04T23:23:04.274533Z"
+     "iopub.execute_input": "2024-06-05T01:13:25.074969Z",
+     "iopub.status.busy": "2024-06-05T01:13:25.074887Z",
+     "iopub.status.idle": "2024-06-05T01:13:25.692705Z",
+     "shell.execute_reply": "2024-06-05T01:13:25.692389Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[111], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m pd\u001b[38;5;241m.\u001b[39mplotting\u001b[38;5;241m.\u001b[39mscatter_matrix(\u001b[43mAuto\u001b[49m);\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 49 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -5052,24 +8101,23 @@
    "id": "87f2d391",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:04.276000Z",
-     "iopub.status.busy": "2024-06-04T23:23:04.275927Z",
-     "iopub.status.idle": "2024-06-04T23:23:04.281644Z",
-     "shell.execute_reply": "2024-06-04T23:23:04.281421Z"
+     "iopub.execute_input": "2024-06-05T01:13:25.694496Z",
+     "iopub.status.busy": "2024-06-05T01:13:25.694384Z",
+     "iopub.status.idle": "2024-06-05T01:13:25.844894Z",
+     "shell.execute_reply": "2024-06-05T01:13:25.844571Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[112], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m pd\u001b[38;5;241m.\u001b[39mplotting\u001b[38;5;241m.\u001b[39mscatter_matrix(\u001b[43mAuto\u001b[49m[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmpg\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      2\u001b[0m                                  \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdisplacement\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      3\u001b[0m                                  \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m]]);\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -5092,24 +8140,99 @@
    "id": "33cc3b75",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:04.283059Z",
-     "iopub.status.busy": "2024-06-04T23:23:04.282969Z",
-     "iopub.status.idle": "2024-06-04T23:23:04.289173Z",
-     "shell.execute_reply": "2024-06-04T23:23:04.288949Z"
+     "iopub.execute_input": "2024-06-05T01:13:25.846556Z",
+     "iopub.status.busy": "2024-06-05T01:13:25.846449Z",
+     "iopub.status.idle": "2024-06-05T01:13:25.851555Z",
+     "shell.execute_reply": "2024-06-05T01:13:25.851286Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[113], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto\u001b[49m[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmpg\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m]]\u001b[38;5;241m.\u001b[39mdescribe()\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>392.000000</td>\n",
+       "      <td>392.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>23.445918</td>\n",
+       "      <td>2977.584184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>7.805007</td>\n",
+       "      <td>849.402560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>1613.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>2225.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>22.750000</td>\n",
+       "      <td>2803.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>3614.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>46.600000</td>\n",
+       "      <td>5140.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              mpg       weight\n",
+       "count  392.000000   392.000000\n",
+       "mean    23.445918  2977.584184\n",
+       "std      7.805007   849.402560\n",
+       "min      9.000000  1613.000000\n",
+       "25%     17.000000  2225.250000\n",
+       "50%     22.750000  2803.500000\n",
+       "75%     29.000000  3614.750000\n",
+       "max     46.600000  5140.000000"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -5130,24 +8253,31 @@
    "id": "398d24d3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-04T23:23:04.290410Z",
-     "iopub.status.busy": "2024-06-04T23:23:04.290336Z",
-     "iopub.status.idle": "2024-06-04T23:23:04.296008Z",
-     "shell.execute_reply": "2024-06-04T23:23:04.295791Z"
+     "iopub.execute_input": "2024-06-05T01:13:25.853203Z",
+     "iopub.status.busy": "2024-06-05T01:13:25.853071Z",
+     "iopub.status.idle": "2024-06-05T01:13:25.856902Z",
+     "shell.execute_reply": "2024-06-05T01:13:25.856620Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'Auto' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[114], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mAuto\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcylinders\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mdescribe()\n\u001b[1;32m      2\u001b[0m Auto[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmpg\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mdescribe()\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'Auto' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "count    392.000000\n",
+       "mean      23.445918\n",
+       "std        7.805007\n",
+       "min        9.000000\n",
+       "25%       17.000000\n",
+       "50%       22.750000\n",
+       "75%       29.000000\n",
+       "max       46.600000\n",
+       "Name: mpg, dtype: float64"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [