Updated for Python 3.6
This commit is contained in:
Roger Labbe committed
1 parent
8302572e60
commit
3f468dd913
1 file changed
+31
-26
+31
-26
@@ -20,7 +20,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from __future__ import division, print_function\n",
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"%matplotlib inline"
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]
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},
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@@ -36,7 +35,7 @@
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" <style>\n",
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" .output_wrapper, .output {\n",
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" height:auto !important;\n",
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" max-height:100000px; \n",
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" max-height:100000px;\n",
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" }\n",
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" .output_scroll {\n",
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" box-shadow:none !important;\n",
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@@ -326,7 +325,7 @@
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{
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"data": {
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"text/plain": [
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"1.49924"
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"1.499952"
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]
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},
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"execution_count": 7,
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@@ -472,7 +471,7 @@
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}
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],
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"source": [
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"print(\"{:.2f} meters squared\".format(np.var(X)))"
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"print(f\"{np.var(X):.2f} meters squared\")"
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]
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},
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{
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@@ -514,8 +513,8 @@
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}
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],
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"source": [
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"print('std {:.4f}'.format(np.std(X)))\n",
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"print('var {:.4f}'.format(np.std(X)**2))"
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"print(f\"std {np.std(X):.4f}\")\n",
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"print(f\"var {np.std(X)**2:.4f}\")"
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]
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},
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{
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@@ -570,7 +569,7 @@
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"image/png": "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 truncated
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"text/plain": [
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"<Figure size 900x400 with 1 Axes>"
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]
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@@ -582,8 +581,8 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"mean = 1.796\n",
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"std = 0.143\n"
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"mean = 1.811\n",
|
||||
"std = 0.130\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -593,8 +592,8 @@
|
||||
"mean, std = data.mean(), data.std()\n",
|
||||
"\n",
|
||||
"plot_height_std(data, lw=2)\n",
|
||||
"print('mean = {:.3f}'.format(mean))\n",
|
||||
"print('std = {:.3f}'.format(std))"
|
||||
"print(f'mean = {mean:.3f}')\n",
|
||||
"print(f'std = {std:.3f}')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -612,7 +611,7 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"71.0"
|
||||
"69.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
@@ -657,7 +656,7 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print('std of Y is {:.2f} m'.format(np.std(Y)))"
|
||||
"print(f'std of Y is {np.std(Y):.2f} m')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -770,8 +769,8 @@
|
||||
],
|
||||
"source": [
|
||||
"X = [1, -1, 1, -2, -1, 2, 1, 2, -1, 1, -1, 2, 1, -2, 100]\n",
|
||||
"print('Variance of X with outlier = {:6.2f}'.format(np.var(X)))\n",
|
||||
"print('Variance of X without outlier = {:6.2f}'.format(np.var(X[:-1])))"
|
||||
"print(f'Variance of X with outlier = {np.var(X):6.2f}')\n",
|
||||
"print(f'Variance of X without outlier = {np.var(X[:-1]):6.2f}')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -801,7 +800,9 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
@@ -1241,9 +1242,13 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "a07afca115cb406abe449c6fdd78d44f",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"<Figure size 900x400 with 1 Axes>"
|
||||
"interactive(children=(FloatSlider(value=5.0, description='mu', max=7.0, min=3.0), FloatSlider(value=0.03, desc…"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -1638,9 +1643,9 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[-0.08 2.024 1.4 3.024 5.799 0.989 2.083\n",
|
||||
" 0.978 7.542 -2.22 4.984 0.626 4.387 3.676\n",
|
||||
" -0.12 ]\n"
|
||||
"[ 3.026 7.51 -2.588 6.081 -3.413 -1.11 6.484\n",
|
||||
" 5.935 2.313 1.912 1.895 7.964 4.876 -0.841\n",
|
||||
" -0.174]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1781,7 +1786,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 900x400 with 1 Axes>"
|
||||
]
|
||||
@@ -1830,7 +1835,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 900x400 with 1 Axes>"
|
||||
]
|
||||
@@ -1874,7 +1879,7 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"DescribeResult(nobs=5000, minmax=(-0.0070992153027660265, 18.502660722626167), mean=9.967146957530227, variance=2.8318603858557094, skewness=-0.1075382179126775, kurtosis=1.846893680503677)"
|
||||
"DescribeResult(nobs=5000, minmax=(1.7707248080804092, 17.824961707265643), mean=10.004201301507253, variance=2.721973140216924, skewness=-0.03534037712630867, kurtosis=1.583968849611276)"
|
||||
]
|
||||
},
|
||||
"execution_count": 46,
|
||||
@@ -1905,9 +1910,9 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"DescribeResult(nobs=10, minmax=(-1.8106190910322406, 1.7202801709655346), mean=0.03998695860303425, variance=1.2099810612140205, skewness=0.054824114606583485, kurtosis=-0.8322079773586668)\n",
|
||||
"DescribeResult(nobs=10, minmax=(-1.1170056026895896, 1.0389413316321683), mean=-0.14317693801053674, variance=0.63137454114842, skewness=0.23966670696111078, kurtosis=-1.3702119526058378)\n",
|
||||
"\n",
|
||||
"DescribeResult(nobs=300000, minmax=(-5.136201903633123, 4.498934900223554), mean=0.0016752908705450242, variance=1.0019122279656631, skewness=0.002460339180965745, kurtosis=-0.0022807108788165387)\n"
|
||||
"DescribeResult(nobs=300000, minmax=(-4.262303362805642, 4.603650808299195), mean=-0.00040808441709135014, variance=0.998953112076118, skewness=-0.0010979017079029859, kurtosis=-0.00030025117171517124)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
Reference in new issue
Block a user