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tap1012
tap1012
Commit 83a24718 authored by Carlos David García Hernández's avatar Carlos David García Hernández
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Matplotlib y palabras1

parent 287eca81
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.ipynb_checkpoints/06-LinearAlgebra&Statistics-checkpoint.ipynb
View file @ 83a24718
...@@ -1619,112 +1619,6 @@ ...@@ -1619,112 +1619,6 @@
"* ¿Las distribuciones son iguales? explique su respuesta justificándola con atributos estadísticos obtenidos con scipy.stats \n" "* ¿Las distribuciones son iguales? explique su respuesta justificándola con atributos estadísticos obtenidos con scipy.stats \n"
] ]
}, },
{
"cell_type": "code",
"execution_count": 206,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'X' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-206-3717f3a3c783>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"PDF from Template\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdensity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhist_dist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'PDF'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
]
},
{
"data": {
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"text/plain": [
"<Figure size 2160x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"\n",
"data = stats.norm.rvs(size=100000, loc=0, scale=1.5, random_state=123)\n",
"hist = np.histogram(data, bins=100)\n",
"hist_dist = stats.rv_histogram(hist)\n",
"\n",
"plt.title(\"PDF from Template\")\n",
"plt.hist(data, density=True, bins=100)\n",
"plt.plot(X, hist_dist.pdf(X), label='PDF')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 274,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[355,\n",
" 230,\n",
" 188,\n",
" 187,\n",
" 168,\n",
" 165,\n",
" 159,\n",
" 136,\n",
" 122,\n",
" 116,\n",
" 110,\n",
" 108,\n",
" 79,\n",
" 68,\n",
" 62,\n",
" 35,\n",
" 34,\n",
" 27,\n",
" 26,\n",
" 25,\n",
" 24,\n",
" 20,\n",
" 18,\n",
" 14,\n",
" 14,\n",
" 12,\n",
" 8,\n",
" 7,\n",
" 5,\n",
" 4,\n",
" 4,\n",
" 4,\n",
" 4,\n",
" 4,\n",
" 3,\n",
" 3,\n",
" 3,\n",
" 2,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1]"
]
},
"execution_count": 274,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[1]"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 319, "execution_count": 319,
......
06-LinearAlgebra&Statistics.ipynb
View file @ 83a24718
...@@ -1619,112 +1619,6 @@ ...@@ -1619,112 +1619,6 @@
"* ¿Las distribuciones son iguales? explique su respuesta justificándola con atributos estadísticos obtenidos con scipy.stats \n" "* ¿Las distribuciones son iguales? explique su respuesta justificándola con atributos estadísticos obtenidos con scipy.stats \n"
] ]
}, },
{
"cell_type": "code",
"execution_count": 206,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'X' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-206-3717f3a3c783>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"PDF from Template\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdensity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhist_dist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'PDF'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
]
},
{
"data": {
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"text/plain": [
"<Figure size 2160x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"\n",
"data = stats.norm.rvs(size=100000, loc=0, scale=1.5, random_state=123)\n",
"hist = np.histogram(data, bins=100)\n",
"hist_dist = stats.rv_histogram(hist)\n",
"\n",
"plt.title(\"PDF from Template\")\n",
"plt.hist(data, density=True, bins=100)\n",
"plt.plot(X, hist_dist.pdf(X), label='PDF')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 274,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[355,\n",
" 230,\n",
" 188,\n",
" 187,\n",
" 168,\n",
" 165,\n",
" 159,\n",
" 136,\n",
" 122,\n",
" 116,\n",
" 110,\n",
" 108,\n",
" 79,\n",
" 68,\n",
" 62,\n",
" 35,\n",
" 34,\n",
" 27,\n",
" 26,\n",
" 25,\n",
" 24,\n",
" 20,\n",
" 18,\n",
" 14,\n",
" 14,\n",
" 12,\n",
" 8,\n",
" 7,\n",
" 5,\n",
" 4,\n",
" 4,\n",
" 4,\n",
" 4,\n",
" 4,\n",
" 3,\n",
" 3,\n",
" 3,\n",
" 2,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 1]"
]
},
"execution_count": 274,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[1]"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 319, "execution_count": 319,
......
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