Text used the wrong parameter name for hist() call, and I misattributed
a SciPy function to NumPy.
This commit is contained in:
Roger Labbe committed
1 parent
423f3e80fe
commit
15e8574e2e
1 file changed
+69
-128
@@ -3,9 +3,7 @@
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"outputs": [
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@@ -14,52 +12,18 @@
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"\n",
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"\n",
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" //font-family: 'Chivo',verdana,arial,sans-serif;\n",
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" //font-family: 'Cardo',verdana,arial,sans-serif;\n",
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" //font-family: 'Arvo',verdana,arial,sans-serif;\n",
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@@ -67,17 +31,16 @@
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" font-weight: 400;\n",
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" font-size: 12pt;\n",
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"\t}\n",
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"\t\n",
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" div.input_area {\n",
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" border: 1px solid #586e75; \n",
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" border: 1px solid #586e75;\n",
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"\n",
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" .text_cell_render h1 {\n",
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@@ -90,7 +53,7 @@
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" display: block;\n",
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" white-space: wrap;\n",
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@@ -106,7 +69,7 @@
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" display: block;\n",
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" white-space: wrap;\n",
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" text-align: left;\n",
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@@ -149,13 +112,13 @@
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" }\n",
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@@ -173,42 +136,38 @@
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@@ -216,10 +175,10 @@
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@@ -232,7 +191,7 @@
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@@ -266,11 +225,14 @@
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" },\n",
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@@ -312,17 +274,15 @@
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{
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{
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"name": "stdout",
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"text": [
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"50000\n",
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@@ -352,14 +312,14 @@
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"cell_type": "code",
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{
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"data": {
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||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7cbf160>"
|
||||
"<matplotlib.figure.Figure at 0x1c191ba47b8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -392,15 +352,14 @@
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAD1CAYAAAAWAiGpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x73eacc0>"
|
||||
"<matplotlib.figure.Figure at 0x1c1911d7400>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -416,21 +375,19 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"that is not very useful to us - we want the PDF, not bin counts. Fortunately `hist` includes a `density` parameter which will plot the PDF for us."
|
||||
"that is not very useful to us - we want the PDF, not bin counts. Fortunately `hist` includes a `normed` parameter which will plot the PDF for us."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8b7bc88>"
|
||||
"<matplotlib.figure.Figure at 0x1c190171390>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -452,15 +409,13 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8bf5e48>"
|
||||
"<matplotlib.figure.Figure at 0x1c19202c668>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -482,15 +437,13 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8f18898>"
|
||||
"<matplotlib.figure.Figure at 0x1c1937a32b0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -508,21 +461,19 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"There is another way to get the approximate distribution of a set of data. There is a technique called *kernel density estimate* that uses a kernel to estimate the probability distribution of a set of data. NumPy implements it with the function `gaussian_kde`. Do not be mislead by the name - Gaussian refers to the type of kernel used in the computation. This works for any distribution, not just Gaussians. In this section we have a Gaussian distribution, but soon we will not, and this same function will work."
|
||||
"There is another way to get the approximate distribution of a set of data. There is a technique called *kernel density estimate* that uses a kernel to estimate the probability distribution of a set of data. SciPy implements it with the function `gaussian_kde`. Do not be mislead by the name - Gaussian refers to the type of kernel used in the computation. This works for any distribution, not just Gaussians. In this section we have a Gaussian distribution, but soon we will not, and this same function will work."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9f10c88>"
|
||||
"<matplotlib.figure.Figure at 0x1c193822e80>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -552,15 +503,13 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8f02cf8>"
|
||||
"<matplotlib.figure.Figure at 0x1c19381c1d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -570,8 +519,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"mean = 14.00\n",
|
||||
"std = 2.81\n"
|
||||
"mean = 13.99\n",
|
||||
"std = 2.80\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -616,15 +565,13 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x8f06320>"
|
||||
"<matplotlib.figure.Figure at 0x1c1938f79e8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -634,7 +581,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"mean = -1.60\n",
|
||||
"mean = -1.59\n",
|
||||
"std = 2.10\n"
|
||||
]
|
||||
}
|
||||
@@ -671,9 +618,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
@@ -704,9 +649,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
@@ -734,15 +677,13 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x89b7160>"
|
||||
"<matplotlib.figure.Figure at 0x1c19419d5c0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -781,9 +722,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.1"
|
||||
"version": "3.6.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
Reference in new issue
Block a user