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@kshirsagarsiddharth
Created January 3, 2020 18:09
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Created on Cognitive Class Labs
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"ts = pd.Series(np.random.randn(1000),\n",
" ...: index=pd.date_range('1/1/2000', periods=1000))\n",
" ...: "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"ts = ts.cumsum()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5f5ac7f0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ts.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.randn(1000, 4),\n",
" ...: index=ts.index, columns=list('ABCD'))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"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>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>-1.074327</td>\n",
" <td>-2.073962</td>\n",
" <td>-1.284284</td>\n",
" <td>1.336217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>0.384338</td>\n",
" <td>1.884437</td>\n",
" <td>0.545026</td>\n",
" <td>0.749925</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>1.518895</td>\n",
" <td>1.315738</td>\n",
" <td>1.247766</td>\n",
" <td>-1.693696</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-04</th>\n",
" <td>-0.321861</td>\n",
" <td>1.030530</td>\n",
" <td>-0.630585</td>\n",
" <td>-0.422316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-05</th>\n",
" <td>-0.629984</td>\n",
" <td>-0.118042</td>\n",
" <td>0.557189</td>\n",
" <td>0.661179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-09-22</th>\n",
" <td>-0.502281</td>\n",
" <td>0.115113</td>\n",
" <td>-1.840905</td>\n",
" <td>-0.007652</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-09-23</th>\n",
" <td>-0.331362</td>\n",
" <td>-1.678390</td>\n",
" <td>0.428507</td>\n",
" <td>0.733709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-09-24</th>\n",
" <td>-2.010006</td>\n",
" <td>0.992666</td>\n",
" <td>-0.880225</td>\n",
" <td>-0.018718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-09-25</th>\n",
" <td>-0.473632</td>\n",
" <td>-0.237611</td>\n",
" <td>-0.274669</td>\n",
" <td>-0.690122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-09-26</th>\n",
" <td>1.748430</td>\n",
" <td>0.821674</td>\n",
" <td>0.193628</td>\n",
" <td>-1.508308</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2000-01-01 -1.074327 -2.073962 -1.284284 1.336217\n",
"2000-01-02 0.384338 1.884437 0.545026 0.749925\n",
"2000-01-03 1.518895 1.315738 1.247766 -1.693696\n",
"2000-01-04 -0.321861 1.030530 -0.630585 -0.422316\n",
"2000-01-05 -0.629984 -0.118042 0.557189 0.661179\n",
"... ... ... ... ...\n",
"2002-09-22 -0.502281 0.115113 -1.840905 -0.007652\n",
"2002-09-23 -0.331362 -1.678390 0.428507 0.733709\n",
"2002-09-24 -2.010006 0.992666 -0.880225 -0.018718\n",
"2002-09-25 -0.473632 -0.237611 -0.274669 -0.690122\n",
"2002-09-26 1.748430 0.821674 0.193628 -1.508308\n",
"\n",
"[1000 rows x 4 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"df = df.cumsum()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5d3ad780>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"df3 = pd.DataFrame(np.random.randn(1000, 2), columns=['B', 'C']).cumsum()\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5d2b5dd8>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df3.plot()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"df3['A'] = pd.Series(list(range(len(df))))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"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>B</th>\n",
" <th>C</th>\n",
" <th>A</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.440984</td>\n",
" <td>-0.123727</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.862848</td>\n",
" <td>-1.518675</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.177719</td>\n",
" <td>-1.264024</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.344882</td>\n",
" <td>-2.240111</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.358408</td>\n",
" <td>-3.179949</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>7.064242</td>\n",
" <td>27.397046</td>\n",
" <td>995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>7.942224</td>\n",
" <td>28.539919</td>\n",
" <td>996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>6.648845</td>\n",
" <td>29.030879</td>\n",
" <td>997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>5.333406</td>\n",
" <td>27.547027</td>\n",
" <td>998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>4.047255</td>\n",
" <td>28.688064</td>\n",
" <td>999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" B C A\n",
"0 1.440984 -0.123727 0\n",
"1 0.862848 -1.518675 1\n",
"2 0.177719 -1.264024 2\n",
"3 -0.344882 -2.240111 3\n",
"4 1.358408 -3.179949 4\n",
".. ... ... ...\n",
"995 7.064242 27.397046 995\n",
"996 7.942224 28.539919 996\n",
"997 6.648845 29.030879 997\n",
"998 5.333406 27.547027 998\n",
"999 4.047255 28.688064 999\n",
"\n",
"[1000 rows x 3 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5d230710>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df3.plot(x='A',y='B')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"df3 = pd.DataFrame(np.random.randn(1000, 2), columns=['B', 'C']).cumsum()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"df3['A'] = pd.Series(list(range(len(df3))))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5d229240>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7WpsurYy8QtGdqcyXy8hEOLIW6qsh8yT/9slb9BoH2Zvgqz/AK6e779M0c73sGITFND2/9AhE6VVJP74Bait819fWMIx8j+FyudkSIimE+7Gh4TDqEqgubtOllZFXKLozpfooVmswVRtThvitO14lMqH5EoDWqJuqYncJ5QZdCqEkC5IGmu37l3q/j23FCGsNi3Jvv/arpscCxPZu8zyCMvIKRXelvgZy9OpJddVyEg+kgegOOOLdt21hMPkmuV6Rb7bXlEkf/qwH5HbhfhlGevBHmRtww2LZXn7M931ujtKjcnnS/7m3953q+fjwGHDWukcLNYMy8gpFd2T5s/BIqrmdvxM2vitL57UjkSagqa923/7ldzBotly3+rhrSqVRnH4b3LISNCe8cLLcF5lkukjKcnzfZ08462HBHTLZafAZkDig9XNceQJlrR6qjLxC0d3QNPjmPsDilz6yVi7P/odfuuQT7BFyOeJCGfffc5QZPfSKRdelpkwaeYDkwWC3uETComR+QEQilPvJyJdlS0XNwXPkhHIvvdDe+KuaP8f4fQ6vbvXy3ijknSGE+F4IsV0IsVUI8Vu9PVEIsVAIsVtfJnT2XgqFog2UeXA7GFmuCf26ti++ZOqtcOFLcPErcOo9ss0w8mDGwVuNfIgNJt/Q9FrRqX408vrnNVwPmTznKTj7STjnmebPMSp4vdd66Ks3RvL1wG2apg0DpgC/EkIMB+4CFmmaNghYpG8rFApfU2hJ8hl1iVwaVZQa+7GDmdBwGV5ojT6x6rvk75ZLq5EHd7+3kWUakwp7vnWPyukqjLmAmJ5yGR4DE65rGlVjZcCpbb58p428pmnZmqat09fLgO1Ab+A84HX9sNeB8zt7L4VC0QaMkeH138L5z7vvMzJFuytWaQYjPr6xkXfEwsz75bph5PcvlT7+bZ90TT+tGJ9XdM+2n2Ozw4m/gVBHq4d61ScvhMgExgGrgFRN07JBPgiAHs2cc4MQYo0QYk1eXp6nQxQKRVuoLIQv/2Am+ST2a5rkFGxJTx3h/nxAwOFV8NIsqK8yJyoNEvvrS32S0xgZ+2PyteyYlDSISm79WCth0fLB5Gy5iIjXjLwQIhr4CPidpmltFnnWNO1FTdMmaJo2ISUlpfUTFAqFSe4OeO1s6Y757hFY9W9ZTQggQp8Gu/w9//XPH9jskJApo4mOrJFt4Y2SoUZcAFd/YU5uXqI7Hdqiaultyo5BdA85X9AejJj6h1sumuKVylBCCDvSwL+tadr/9OYcIUSapmnZQog0IEBEmxWKbsQHV0PeDilb0Fgn3jAamdPlz6iLu75//iJ9AhTtN7cbG3khoN90y/5o+VD0NGnta0oOQ0xa+89rnDjVDN6IrhHAy8B2TdOs8VmfAVfr61cDn3b2XgrFcce+HzwrKxoYqfjlOc3Lz4ZHwzVfmKJkxwMxjfzbjY28J+L7uD8YugJNk5FPaaPbf25teZsO84a7Zhrwc2CmEGKD/nMm8DgwWwixG5itbysUiraiafDGeS2HyRnRIAeWwf4lZvuNSzwff7wQ0Shiuy1GPmUY7P0OSrpQX748R2rQ9BjR/nPbkjSFF9w1mqYtA5qL9ZnV2esrFMctrQlQaZqZ2bnxXbmccL0UIEsb49u+BTqNjXxsG6peZUyETe/B0XVdVwrReKB0pL7ukLlw+x54dx7wXbOHecUnr1AofECVxcjXVZkZngYV+WbpPgARAnP/cnxE0LRG44gTqxBZc/Q7RS67ooDI6+fIiJ+MyXK7Iw8VISA6RZ9rUUZeoQg+rCP58hwZMWKlVA+VtIVLYx+ZrAy8wYCZUl74xFuh1/i2Ra4YD1FfG/nqEtO1tuML+fm15SHUHFNuBm5pdrcy8gpFoGJkqQKU53ow8oZy4e9g8V+aRtcczyQPhHuyWj/OSlcZeSMT12DyjW2OlOkISqBMoQhUrO4aT6F9xkNg5EVyOXqe7/vUnXEZ+Urf3sdq5M98Ambc6dPbqZG8QhGoVBaY657Es4yQyegecNvOppONivZhSAT4eiRvfK6/2wLxGb69F2okr1AELlZNdI9GXs/ODI+VceHdRSfeXwgB9kgpg2ClLEeKlznrodwL0iuGBnxbIn68gDLyCkWgUp4rJ1Mjk2HJ32QMt5XqEmng25sOr2gee0TTkfyH18FbF8ns4icGQm0n3Tk1ZXJSuIs+N2XkFYpApSJPumKMYtwfXue+v6oIHHFd36/ujD2yaUFvQ81yxxdy+ec0ObLvKDUlbUvO8hLKJ69QBCrlubIIRt+T4OAyiLIIuX70S9j8Pgyc7b/+dUdi0mDzh3KUrSEjmqz6/AY/vQwDT+vYPRpLH/sYZeQVikClIhfSJ8oC1G9eAA11sr26VBp4gMxp/utfdyRpAGSthvVvtXyc6IQTpKasS3X9lbtGoQhUyvPk6D2+Dww/H4oPyQLdTwwyj1HuGu8y7FwIaUNCWWf86dWlXTqSV0ZeoQhEqoqhrkKWpQNZAKShXhborq82jwuL9k//uitDz4T78+ChZhQ9DZx1Hb9HF7trlJFXKAKRL/8gl6kj5TLKY2G1zrkNFJ5prrZqRCL8/BNZVSp7U8evX1PWtFKVD1H/IQpFoNHQANv08gvpE+VSJTp1PZNulMsxl8P/bYc/7JdlAifdIHWDDFmJ1sjZCiufh43vwUNxUHa0S428mnhVKAKNo+tkQs6FL5kTdJGJno/NmNR1/TremPM4nPFnsDUyk8aD9x/D4IEi+PAaqe1+2oOer/PuPDmfYkVF1ygUxzFH18tl5klmW+ORfGgE3OeHUnXHEyEheHR29Bpnrv/J8rk0Z+Q9JU+Fd91cinLXKBT+oixHxmRb2fkVLLhdrlv98I54uT33bzD7T3DD913XT4U7ITa4+JW2H2+dKDcYdan3+tMKaiSvUPiLF0+R/tk+U82iEcssZZKtboKQELijkUStwn9YR/MG+xZD/xnubbWVnmuxGlFTXYBXRvJCiFeEELlCiC2WtkQhxEIhxG59qWaOFAqD7Z9LAw9m2jzQfCVNRUCR2B9Shrq3vXFu0+MCQOPfW+6a14A5jdruAhZpmjYIWKRvKxSKwv3w35+Z24aRd9bD4ZVy/Xiv0RoMGBo3/Syj909vdT+mQtcdOucZmPMXuGY+3Ly8a/qn4xUjr2naEqCwUfN5wOv6+uvA+d64l0IR9BhSswZGQZDig3LZYzhcs6Br+6RoPzPvk8loU24229a/CXUWH3y5PpJPGw1TbpKT6akjurSbvpx4TdU0LRtAX3rM5hBC3CCEWCOEWJOX5wWtZoUi0KlqNB4qOiBH9ps/kNtn/b1Loy8UHWTMPLjnSNOYd+NhDWYdgKiUrutXI/weXaNp2ouapk3QNG1CSor//hAKRZdhvMIbrHxe+uh/eExud6aos6Lr6TNVjurnvSu3iw6Y+w6vkuGvMWl+6Rr4NromRwiRpmlathAiDfD/DIRC4W80DZY/06jNadkQUkNeETyEhMDJd5hVowr3Q32tNPa7F0pJYj8WdvHlSP4z4Gp9/WrgUx/eS6EIDspzIXujuW1o0xhc91XX9kfhPaKSwR4ljfvC++GfE2XBl8zpfu2Wt0Io3wVWAEOEEFlCiOuBx4HZQojdwGx9u8vYcLiYr7aojEBFgGEU3z75DjjhGjj/eXPf6MugzxS/dEvhBYSQaqFFB6SbxsDP7jevuGs0Tbu8mV2zvHH9tlJZW0+dUyMuws75//wRgF2PzCUs1O9TDwqFxIis6T0BhsyB+hroOQr6nwKnP+LPnim8QVSKXrbRkuyU0Nd//aGbZbzOfGIxx0qrOfD4Wa623blljOilCisoAgBNkwYATIGq0HC4aZn/+qTwLpFJMromKtlsi+3tv/4QANE13qK4spZjpTI+9T9L9xEaIjMHCytq/dkthcJk9Yvw7mVyXYVIdk8iE6GyAAr2yO0ptzSvT99FdBsjv2i7GbzzyPztRITJ2ew3Vhxs7hSFomuxipF1odSsoguJSJTzLgV7pPttzmP+7lH3MfI5Ze5KbxF2aeQXbsvB2aD5o0sKhYvqOic/FUWaDeHKhdgtScg01yff5LduWOkWRj6/vIZjJaaRDw8NcZtsfXrRbq55dTXzN2V7Ol2h8ClbjpRw6hM/UF2az+HQvnDFBxCV5O9uKXyBoUI5836wtaEgeBcQ1BOv9c4G3l+TxT0fbwagb1IkEXYbtfUNlNfU0zcpkoMFlaw9WMiPewr4YWceZ40+q5WrKhTe5Za315FdUk1yWClFjgwyBp/u7y4pfEVsL7hzf0CVawzqkfzfvt7pMvAABwsqmdI/ibyyGqrqnJw6pAczh/bgxz0Ffuyl4rgjfzcsuFMWBUEORgDiRTkNjsD58it8RGSi3ydbrQS1kV++t6nxTokJp6ymnrLqehx2G73jI1z74iMD4/VJ0c1Z9zqsfgG2yIlWhx4EkEAZlbY4bnt/I7tyylq6gkLhNYLayNfpIySD0BBBcnSYazsuwk6PmHDXdnFlHZ9tbGOFdYWioxgJT/myklN5dT3zxibjEHXsLrPz0bos7vhgYwsXUCi8R1AbeYfdXfTnrxePJsVi1JOiwoiNcB+9/+bd9SraRuEzdh4ro7JQn+DP30VOaTW5ZTUMjpH5GttK5P9jeKj/BKsUxxdBbeQrauqZO7Indpv0f104Pp3kaNPIJ0SFMSEzgZjwUEb1NkPWthwp6fK+KronR4ur+HTDEdf2GU8tYcfevXLj4I/Ynx3DG/bHOCG5HoBiTSZBGXkcCoWvCWojX1pdR1yEnZV3z2L5XTMB3EbyiVFhjOgVx+Y/nsGfzjOrsXy6oXu4bJwNGpl3zef5H/b6uyvHLVf+ZxW/fW8DVbVSLvgV+18ZH7LHtT+x7hgn2zYzxiGT9Yp0I794Vx6apt4oFb4nMI18dQn8uTfsmN/iYSVVdcRG2EmKDqeXPsGaFGUa+YEpZur4uD4JLL7jFABe+XG/17t8uLCSVfsKqK1vaP1gL5FfXgPAs9/t7rJ7Ktw5UCDrfBZUyM9ipm0DAI/UXUnRaX83Dyw5DEAhZqbrliOlXdRLxfFMYMbJP95HLr+5D4Z6jmvfm1dOdV0DCZFhbu1hoSH8+2fj6RHrIK5RNE1ClHnst9tyOG14Kt5g57EyznhqCQAXju/NPy4d65Xrtsbfvt4JNJ2bUHQ9s/+xhOG2Q3ykby/RxuEsmsqDxgF6taCpIwdRdrCBnNIa1h4sZFS6ynxV+JbAHMkbFO6Dje+5NuudDWw5UoKzQWOFHj55+oimhnrOyDTG92kajxwTbj7TfvHGGq918/01h13rGw8Xe+26rfHh2iwAwmyB/TF2R44WV5F513wMj4tWV8n72p0ALHGO4rAtg1d/PGCeUHwIgIfnTWfFXbOwhQjy9DcxhcKXBL51+PhG1+r8zdmc/ewybnt/g0tdMj0horkzmyCEYFK/RNf2rL//wBsrDnS6iy8vM90/e/MqqKip7/Q120LfJKmFUlxVS2l1XZfc83in9tAaFr/w+yYFae4PfQubkBb/7d73UVUnffTzau+TBxQflAWfbXZCQgSJUWHklymFVIXvCXwjb2FfnvR/frLhKOsPFRHjCG13KNqlEzJc63vzKnjg063t7sfmrBKOFFcBuE2epcbK+QDDjeJrauqk/7+6roG7P9rcytGKTvPfnxP2yixmZL/C2/MXuu0aFCLfqv5Y93NOm2BO8l99+mS5UnTQLdU9OTqcgooaznx6KXN0V59C4QsC08hHJLpvl8q44+ySKlfT8r0FJEW5++PbgrOh6cRoZW37Rt7nPLeMaY9/B0CZPmq/58yhTB+UAsChwsp296u9aJpGYaU5EtxxTE3i+ZSyHNj+mWtzQdg9AHx32wySKWFSiHyw33b/ky4/+ylDUpg70TD4miwooZOREMGe3HK2ZZey45jKflX4Dp8beSHEHCHETiHEHiHEXW06qa4Sxl8Fg+fI7fVvoWka6w8VM7SnjE6oqW8gNdbR7v6cO6Y3MwanuLW9/9PhZo5uSrX+Gg6wPbuU3FLpV02JCefh80bSK85BWRe4TvLLa6mtb+DeM4cBMlxU4UM+cZeNDRd1pMfa6Z8Szc/6V7jaox12hqTG8NwV4/jXlV4itakAACAASURBVOOljomBxchPyEzgQEHnBgNPf7vbTbtJofCET428EMIG/BOYCwwHLhdCDG/2hOoSqCqG+mpZMutyfdK1soAHPt3K7txyLp/Ux+WHH9Cj/dV1IsJs/PHcEW5tD32+rc3nZxWZX8w7PtzIYX3UnpEQSUSYjRMyE8kt8+2E2g87c5n46LcADO8VC8BPB4rYm1fu0/se1+gTp1Ym1q8F4JaJ8jPgtIcAOfdz9uheRIaFugtVOc3/C0+BAe2h3tnAk9/u4p1VTfulUFjx9Uh+ErBH07R9mqbVAu8B5zV7dOE+nB9cK9ftkfILkjQQyrJ5c6Ws8DS0Z4wrbHJQB4w8QG8Pk7UNbZQ6yLNMlm05Usq1r/0EQL/kKABSosPJ87GRf3e1+cUemxHPjTP6A/D9jtzmTmmWq15ZzVnPLOWLTU0TxF5cspfB933Z5r9Nt6WqCAr2whAZzvuv+nMBSHXKydewCl3GYOIvPJ8/4Tq5TBroahqTEe8mnvePb3a2mhy1K6eMtQeLAFi2J9/V3ljDSaGw4msj3xuw+kKy9DYXQogbhBBrhBBrAKr2rwKgUuhVdKJ7wrZPiEL648dkxLsM6rC02A51ym4JObxgnOyO1b/dEiVV8rjfzBrk1m64S9ITIqisdZJbWt3kXG8RGSZDQR+9YCRR4aHcPXcYPWLCWX+4uM2++bUHC3l/zWGW7Mpj69FSbn1nPWsOFDL+4YXs1H3ET3yzi9r6BuUz/uZ+QIMTb+WBjNf4NPEXOIWdG1O3w1f3yIeALaz5kn5nPykLhZz+iKvJbgvhx7tm8lv9/+iZ7/awN6/C8/lAblk1pz+5hIueXw7gViQnX4ViKlrA10bek6iy23BF07QXNU2boGnaBIBoTf6j52l6ksiAUwHoK3K4cnIfHHbpbrnvrGFMymw0QdsBztDj7H/19roWj9M0GZtfVCn97eeP7eW2X+iv5SN098nWo76bCM0vr2FMRjxXTu7rauufEsX8TdnMeWop+/ObNxYGFz2/gjs/3OTWdvG/V1BYUcsz3+3msQXbXRPb+/I77wbKLa12yycIFkqKCmD9m3Kj9wnsaUgjJsKOLTaNhNxVsPKfUHZMhke2xODTISyqSbPx/wK0+Lk9On+723ZxlTnvc6iTvn1F98bXRj4LyLBspwNtEo7JdcZQXedkd9R4AE4I2cWsg0+Bs56EqDB+Mb0/ISEdF+Y/a1QadptgSv8kkqPDWLW/sMUR0dLd+Vz+0kru/p+c6EqNdXDuGGnop/Q3HzYZifINZFdOmSthy9sUVtQ2iSyKcZjZvUeLqxqfAsCrP+5n7J++YcIjCz3uN5i/KZsXluwjWx8t5nfC/VRQXsMHaw5z7Ws/ceeHm4Ju1Ln/ydlyZcqvIDScsup6YhyhENPTPKjoADg69lZ5+oie/HTvaQDsa2FOpd5pjo0u/fcKiitNI789W0VWKZrH10b+J2CQEKKfECIMmAd81so5AGwrDWfo/V9x1Qcy/vhh+2vMLP4Q8r0Tg/7cFePY/eiZxEeG8dwV8kGy+UhJs/7NxsYpMszGM5ePY/ejc3n7F1Nc7cZ8wWNf7uDyl1ZSUun9SJuiitom0TTWrNfCilqW7c5vEjX0x8+3UVxZR365u2vqisl9WsyabXx8e3hhyT7u+HCT683m2205Hb6WPxguDsiVSb8EoKy6Tj5QYyyZ1odXyjmkDmKI6j325Q43RUsr4Xbz81l9oJBdOWUkR4cTHR7a6SgdRffGp0Ze07R64Fbga2A78L6maW3KPvrXejmKzCOOBs0yYq/I80rfhCXqwZgAu/bVnxh075dux+WWVVNRU+9y0zQ+324LwWZ5o3DY3YuIP/jZFq8WKtE0jQIPRv6uuUOZ0FdGbBRW1PKzl1dx50ebPF3CjaE9Y/jzBaO4/YzBzR6TXSL/Bnd91L6ReL2zgdX7C937+b/Nzb5pBBo1tbUI4Ln689ASMgHMkbyzUW5FzpZO3at/inTlvLnioMf91tBdgO925JIQaadXvCNo/p4K/+DzOHlN0xZomjZY07QBmqY92tKxm7X+nF/zJ4ZUv0ZOhRxR1xNKiLC48cu8PxLsERve7L5Jjy7ikn+vYNvRUiLsNn45vR9/v2RMs8cLIYi3FCr5ZMNRrxYqqapzUlPfVJgtIzGS926Ygt0m+HqrmXLfWsz+ObrL6bKJfbhwXG+evXxck2M2ZRXzxoqDvPfTYd5YfqBN/dyUVczAe79kgwctHyNCJFDQNI1NWcVNoltK87OxCyfHtETyymrQNI1SYyQ/9gqwN/Wxd5TnrzwBgDUHi1zZ1FbKqpsm7PVNiiItLoLskmpW7C3g0fnbmjwMFIqAy3jdoA0kOsoMjWziRijL9vo9w0NtbiPjOmcDdc4Gcsvk28S27FKW7M5j9vBU7j1rOBedkN7i9TzFyVvj6ztDge468ZTtG2oLYd7EPm61bw2DqmkaEXYbYzPiXfumDUzi6hMzAVkq8R+XjeVkS6JYrzgHN80YwP78ClcMfuNKW83x7fbmwzlzfBh51BF+2JnHuc/9SL+7F7An14wkKs+To+psLZENh4upqW+gzqkRGxEKw8+F31pK+P3f9saXbRdDepqROVkeMqYra50MbBQy3C85kl7xERwtruKJb3by0tL9LN2d3+RcxfFNwBl5gMGp8h/+zFE9WXXPLD5ynmTuLPeNT9eaPTvo3i8ZdO+XTHp0kautoLzGJQjWGsbo2Iq3EpWK9FDPhGYyXBvLJz+5cBfL9+Szcl8hVXVOzhxlThj+68oTiA53V5uOi7Azc2gPbpzRnwW/nU5chJ36Bs1VTaumjXr5nt4gpg2UGZ+lVS2/XTz25Xb+1I4Etc5iLap93WumOmnB0QMAHNOSuOHNta6wRdckt0WLhtimn3lHyfEwSKioqadfchR3zR3Kk5eNYWJmAldO7kuvOAcFFbWuoiWB9pak8D8BZeQH9Yjmqql9XVmcSVHhJESFkXDlK5Rd+wPEpMHhVT65t7XgtycaNNxKC7bE3y4ezbf/N4On543lH5dK186+FmKg20OBrr7ZnIyBVZWzR0w4G7NKuOI/qzhWKl0Apw0zHwKxDs/lBF65ZiJ3zx1GfGSYHLWCK1a+uLKWhz7bypMLd7XYz9Kqpu6FSydkEOsIpaQFI69pGi8s3ueTwi7NsSfXfAAfKa5yuW0+XyYNfrYmo6du1sNsXX83m3fLMVyjv1Vle3DXVNY6iQqzcdOMAVwwLp0PbjqRzOQoV7Gc3fobSLBFLyl8T0AZeYfdxp/Okwk+gMvAzByaSkzfcdJVc2QtlGR5/d4prRh5gKTotunDOOw2BvaI5ryxvblwfDqJUWFeG8kbMgopzTxwrFmUZ45Kc60fKpCGIz4yjPF9pMvGOvncHNbQTIAlu/J5bfkBnl60m5r65v2/1pH8rKE9+Op30zlrVBpxkXa+2JTN0t15HCup5j9L97n5wrOKTANX3MYEtc6y41gZ0wclc99Zw3A2aJTq/u/eIp8azc4l0+WD2ghVtP6NvcmD5wzHYQ/xaKgra51EhDV9qBgJgXV6iOWHa7PYlNV1NQ0UgU9AGXkDw+A29kEy4w9yecz7okx3zR3KpRNMX/s7v5zc5JiOCKKBnBS1Gq/OsHhnHplJkWQkejY0DruNv108mu9vP4U4i//8yW/lyDvWEco7v5zCxgdPb9P9rKP9aQOT2GlxbRjibJ4oq65nfJ94fnXqAB67cBRDe8YSagshLsJOQUUtP395NTe+uYZH5m/ncKH5t1l3yHQ37MrpGi2erKJK+iZFEq9PZhdX1lJd52SEOMAurTd3nzWcC8eZidqj0815Dea9A9d+5ZV+CCFIjg5vErKaW1ZNfnmNq2C9laE9Y9w+Z4Bzn/vRK/1RdA8C0shfckI6H940lfPH9nbfMfVWECGQ9ZPX75kcHc69Zw4n1hHKb2YO5MQByex4eA4v/vwE1zEZCR2LhY6PsLfoomgP+/IrGNErrsVR+CUTMuiXHNXkntHhoYTaQnDYbU0MQ3NYj7v2xH5u+7JLmp9ALamqIyEyjDvOGEoPy8Oxp2V9Y5b085/1zFJXmzUaZ2cXySdLV0goiVHydy2qrKPs6G6m2bYS1m8qYIY4Am4hsgw9C/pO9VpfpJF3f3j++4d9AE3CUQFCQkSzc0V788op76ICNorAJSCNvMNuY0JmYlND5oiFvtNg73c+uW9cpJ2198/m97MHu/px+oieJOi1Ylvz2zd73U4YeU3TeH/NYXJKq6l3NnC4sLLNE8BXTO5DSkw44bpR6kgY53BL2r0xIW7Qkm5+XnmNRxdYDw9vQ2U19by58iBPLtzFuoNFTOibQFJUmOsh4EsaGjRq6htw2G30iJF9yyqqpCpbJt0VZ0pRsltOGdjsNbxJsgeBuwbdnfXw+SM9nmMoWlofPpqmMevviznvuWU+6qkiWAjMQt4tkdgfdnnn9dgTdg+Znwv/bwYHCyo7LKNgGPlPNxzhlWX7+dfPTmizX3fhthzu/HAT103rx02n9Ke+QaNnXNvcRoNTY/jp3tNYviefK/6zylWSrj2Eh9pY8JvpxDhCmxRGt0alWKl3NpBfXuPxoXjyoGSP8rj3f2ImE103rR9R4aFdkq5frc8rRIbZGJwaQ3hoCLe+s54LQ5bzjzAIT5DzGiEhgjevn9SmuZvOkBIT1iS34GBBBUN7xjCxGa2mu+YOZUjPGAakRHPpCyuICrO5wnj35lXQ0KB1SgJEEdwEn5GPSpZhlHu/gwEzu+SWydHhbY6s8URchJ3iyjp++94GAB5bsJ0nLxvr8YHSmN165Ed1vdNV17a9BUIm9Uukd3wEN+mSxO3FGM1bJ0j7J0e51CobU1BRi6ZBiodR+5yRaRx4/Cy2Hi0hPNRGZlIk7/502M3Ij06Po6rOydajvh/JG6GHEWE2wkJDXCGiiUL+bjGJZsipUfnLlyRHh1NYUYOzQcMWImSxnMPFnD68acF6A4fdxuWT+gAyQufDtVkcsIid7c0rZ1BqMwqZim5PQLprWsSmG7g3L5Aa30FAn0bulS82ZfNEG+vAGolD1XVOCl2JUO174ITqsrY/n5rZrvMaY3WfjcmIb9bIG+JZLZVnHNErjoE9ogm1hfCzyX3c9o1OjyM1Vk5AHmiDomZnqNSNvMNug9UvMSVExucnijJqNRtxCck+vX9jkqPDadDMfIhle/IprqxjeBtltfsmRVJeU8/zi83vhrcm/RXBSfAZ+Sm3mOuf3NL8cQHEKUOajgAX7chlYRvEuowEnKW7pcsF2h7K6QtmDe3BjSf3p19yFMdKq6mtb6Cytt7N319eI41840Sr5mg899IvOcoVGvjogs5lkraGIQMQaQcW3M57YY/goIYR4gBFxLgibroKw8WVU1rNsZJqfv7yagDS2zjpP0Qfsf+w09R48nURG0VgE3xG3hELp/1Rrod3rDJUV5NoMRQf3CQjMfbklvPLN9a0mrxiuGisX9T+yd7TTGkvL18zkbvPHOZyGc1+cjHDH/iaAfcsQNM0Pl6fxUXPrwAguplkK0+suHsmf7t4NK9eOxEhBKcPT2XW0B4s35PfRFPm7VUHvaahbsxTjNzxrKvtFfvfmGHbRE1YIqFtcKl5k1R9viWntJqH55tZv8ltnAuYOsCsI3vnnCEAHCz07duQIrAJPiMPcOKv5TKxYz7mrsZqKCZmJvK/W050bZd7EJ6yUtwoKifWEdrlhscThpE/aDG2NfUNPPCpKTIa08aRPEBaXASXTMjg1CE9ADm6P214KhW1Tl5aus91XH55Dfd+vIWb3lrb2V8BwBVi2PPQfFfbiTZpXPukdq2rBswQ0+teW8N+PUv61zMHMiY9rk3nW9+Kbp4xgLQ4Bws2H2vhjOOTtQcLeWnJvtYP7Ab431p0hBCbLPRdG5w62uMsImGlrahEFjeSOA6UKAlPk79Very5QXtG8p64aHy6q+KVwW49Qaq+wTt1TYsq6kiglPCKI5AxpdHerq9ta02425Zdytmj07jt9CFtyk42eOWaCdw9dyhCCM4Z04tDhZU4GzQqauoDThzOX1z0/AoeXbCd2jZqMQUzwWnkQRZpqO2ajEhv8OuZA7n/7OGA+2jLk4SsgaZprpqyBn0TO16cwpt4CgGtrncSGWZzbTeWRGgvYaEhDEuLpcyS0HOwQI5uvRXKeLS4istt3yHQYO7j7jutAmRdhC1E8O4vzYdNR6K6Zg5N5cYZAwDISIjA2aBRUF7DL15fw+Q/L2L9ISViZnDYS+qwgUzwGvmwSKgLng/ottOHcP1JZsbox7rL5qstzb9KV9Y6XZokAKcN68FLV03wXSfbgVUIzdDCqap1uhmlKIvB7yiRdhuVNWZ8/zFXtFEDn288ypJdnSsi8+iC7QwLOYSW2B96jYPBc82dU/wzsW+dWE/u5CS78WZwuKiKFfukBPW1r7WeMb5yXwENXqqBEIgYmdw7smWE2OHCSjLvmt8tVTyD2MhHB627BnCpB7658qBrYvF/67L4cK0pvmZMtj5w9nAeOX8kL101wWPGqD8QQvD97aew+t5Z3HCynBuprmvAHirfUp68bEy7XAzNERUeSmWtOZI33A1rDxbx63fXc9UrMvqksra+VUGzv3+zk9P+sdgVsfTlZukG6ikKEbG6hIYhGTx4jquIfFcTb0k6s06kdgQjce6i55e72owKYs2xfE8+815cyQvd2GdtvAmuOSilIlbpkhEvLgmOsOz2EHzJUAZhUVDq/QIiXUVqrINTh6Tw/c483lp1iMU7c12FNi7Wi5IYWYsDe0S7FfMIFPrpUT7hdjlir6pzUl5dz4zBKVwwruXCKm0lIszmlql7zINeTkODxtnPLmNfXgUHHj/L43XqnQ08+90eAKY8toild57qkg4e4iiFWL0ilmHkQ/z31UiOCufSCemM75PACX09Z7m2lZ4eBgVGuOsjX2zjP8v2u/5mzgaN059czF59wvcvX+3g2mmZMoegG7Fgc7ZLXnq/nodh/I95S2MqkOjUSF4IcYkQYqsQokEIMaHRvruFEHuEEDuFEGd0rpseSBsDuVuhsqloU7DwxCVjCBEypd9TJSVjJN9SecJAIEI3AocKK9iYVeLRsLSbumrYt5hIu406p+aaIPMkilZQUevS628cbmnQuFrX/M3Z9E2KJIoqYmuPQdIguSNGz3B1+u/LHhIi+OvFY5g3qU/rB7dCkgeffnlNPUeKq/jPMqnZn1Nazbfbcvhs4xGXgTdoyZ0YrNyiP9zBlO72VuW2QKSz7potwIXAEmujEGI4MA8YAcwB/iWE8O5wYPAc0Bpgz6LWjw1QkqLDmdSv+ZGaMSmUFucb/XJvYYz0fv9fWQ6vcYZvh3j3MnjjXFI06XM35AdySquZNzHDTSE0u8TM6Gws02tQu/o1Xrc/Tgzyb1paVUdheS3/N0p/aPTUxb/C9ByEtNGd/x0CAFujaCwh5GT/tMdNkb/Jf17EL95Y4/r8rFgLqnQnJmYmcNOMARwqrCSrqJIXFkvXlJGX0p3olJHXNG27pmme8vPPA97TNK1G07T9wB5gUmfu1YRe4wAB+S1XKAp0LhjXu0lbbX0DDQ0a//p+Dz1jHW2WBfYXEY1e5y/v7Ai0thL2/QDAvGVzSaGI8tp6ausbKKqso1d8BI6SfQyok5+9Vchsiye9G2cdmcvvYoZtE9fYpLjdv37YS3RNDnPzX5Py1X30iJZh58K5z8LJd3budwggNjwwG4Dpg5I5f2xvV5UvT1ilKFJjw9lf0L0SqYwM51OG9GBsRhx1To03Vx507W9ukBDM+GritTdw2LKdpbc1QQhxgxBijRBiTV5eOyIlQmzgiIOq4J4Nv2xiH9bdP9utLb+8ht255ZRW1zO6jUkw/iTBMlF4/Un92i2g1oQ1L7ttDg05THFlrWtiNTEqDJ6bwIBPzgHgwc/MBKw7P9zU9HrFpurlOX1qGSiy2BF+NSscv6ZX0U+QOsIMlwyxwfirINR/0hHeJj4yjAOPn8Wb108mppXchYKKWlehmD6JkRR0s3KCRshyjCOUfskyY36FXvj++pP6UVRZy968cj7beNRvffQ2rRp5IcS3QogtHn7Oa+k0D20enaWapr2oadoETdMmpKS0c3IxIgGqg7/UWWOjmF1S5Srhduecof7oUruw9v+3pw3q/AX3fu828ZlAGXllNRTpiWEJFpkIWwjU1NVzTsQmFkY/iFZZ2NQvX7DHtTrYUczvQv+HQ1h87qMv63yfgwRr2cjmwjO//v3JvH7dJJKjwynoZiNboyxljCPUFaq6KauEpKgwMpOj0DSY9ffF/Obd9d0mUarVEAJN007rwHWzgAzLdjrg/UdjRHzQj+QNxmTEs1HXEc8qqnJFkVjj0QMVq8xCbCcToADI3y3nXHZ8AUA/cYy8shqX79/65nCd+IIbwr8gRSuFeujbkMUHa7K4dKL+7+eshx+fBmBL6AhG7l/C2Y1nh6be2vk+BwnXntSPspp6fjNrENHhoWTeNd9t/9I7TyUtLoK0uAgWbMpmd245hwsryQiQJLzO4hrJh9tJiAxDCNA0qd6Z0uihl1Na3S1+b1+5az4D5gkhwoUQ/YBBwGqv3yUiASryTMnh/N2Qtcbrt+kKPrnlRLb96QxsIYI9ueUcK60mLsIeVOFrXnkgFeyFkkPQc5RrhD0mZC/55dJdkyFymPLuCNfh99rfIUWYPvkEUc7LetQIANs/g4Oy5uma+Dme7+mFeP5gITo8lHvOHOZSCJ2Y6R4zbzVqk/vLoIAFm4M3VLkx+/LlRHJavANbiHC9FfZJjGxSw/locfeQaO5sCOUFQogsYCowXwjxNYCmaVuB94FtwFfArzRNa39ZotboPQGyN8Kz42HBHfDcBPjPLK/fpisQQhAZFsrg1Bie/W4Pb686RIDI1LSJTQ+dzsLfz+j8hbZ9KpejLoELX4RxP2NsyB7+8tV2soqquC30A0KczeuvJIgyRva2zGPUyS/qv7ULOZBxIUQ1cgle93Xn+xzEvHGdWbB+35/PdNt34fh0esdH8NiXO1wTlsGMs0Hj/Z+ySIoKY1hPKWXdR3+opcSEM9aiKQXdJ9Kms9E1H2ualq5pWrimaamapp1h2feopmkDNE0bomnal53vqgcmXGeur37RXC/JanpskGDVpjFqjgYDsQ47EV6QMaBgD0T3hCSpvUKv8SSKcnpRQPa3/2SwONLklLK0qTD9dgAGx9S5i77pczb/qplLv5RouGMPlSPmUdJnNtz0oxlVc5wSEWbjmcvH8fmtJ3kUvzuij2afXrS7q7vmddYfKmLFvgKun97P9bv21t8+4yPDEEK4SY8YBWWCneDNeAWITfPcvuoFOP3hru2LlzAmg8JsIbx+nXejToOCogPuEtK63MByx2/ktmGHZj0Ai/4EQMyNX0nH6o9PkWyroNTIWizcB9/K2gNlRDAgRUZTRF7ygq9/i6Di3DG9Wj2mq9QrK2vrWbQ9l7NHp3lFFsOKMXE/faD5NmeEjIbp80pplvrJVjmNYCZ4tWsM+p/StG35M0GdCQvw+9mD21ywu1tRkQfRPcztSA/aLSGhMP02aehv1jVZhICIRBJDyk1lz2fGgVOGAGqEuEZtig7QRVpl9368hV+/u56tR71fxL1CVzONDDffOA1BPaOuQHioaRLfXnWoS+oM+5rgN/JXfQq3WiZbB+keoyBNkjKEkzorTNVhKguhvpXY6PI82L+k5WM6SkWeu9880swIrtf0f9dE3ZUz/TYZ4245NoFyiitrqalqmvDTw0vyxMcjXaVHuU6XQfZFpm2FPjK3lqW8Zlom54/txVVT+wIwqZ/5vdtxrIy7Ptrs9X50NcFv5MF9tHfKH+QySEfyN80YwGe3TmsyCdRlPDEInjcrV+Gshy0fuYeqvn6O/PH23IezTt7Hg5EviR/OwJo32T7uAbjiPc/nRyQSTzkRpXs580/vNtkd1Y5KVQrJU5eNBUwdfytPfL2zTdWVtmeXsiunDE3TXKNpT6w7VIRNd9F4w8hrmkad04x1d43kLXNHsQ47T80b59L4GdIzhr2WCejNR0pcyVLBSvf4r3fEQ5+pMOJCiNRLtlUG2Qez4V345CYcd+5ndHrnlAc7jLMOGurl5Of2LyAhEw6vhPm3wciLYcYf4PAqyNOLax9cAaMv8d79jaSleIssQkQCXPQycZnTORCTCpzd/PkRCfQ5NJ9F4evI02T0RGnqZK48dA6jegd+5nAgcv643mzLLuXVH/fjbNC45tXVDE+L5e4zh/Hc9/LzykiMZM7Ins1eY+7TSwG4/fTBPPHNLjY+cDpxke75FP9YuItnLJO73ijmcd8nW3h71SH2/flMQkIEFXpdgsiwls1eY72fj9ZlMXVAEqc/uZjR6fE8cckY177csmpSosO9Pn/gTbrHSD4kBK77CibfYI7qq4JsJL/mFbnM2drycb7kfzeY6/+9Ev49DY5tkdsHf4QXZ8BnlsShUi+P5I/odVvTJ7q3j7oYYlJbP9/i2jFi5zcOvpXNWn+3L6aifWQkRFDn1MguqWLp7nxeWLLPLav4t++tb9N1nvhGulDzK0x3YE5pNQ99ttXNwIOpDtlRlu/J5+1VUs7iq63HyC6p4v01h4mw25oY8ZaYObQHm7KKWXuwkF055W71HvbkljHp0UW8seJgC1fwP93DyFsJi5Ije0sqe1BguCj8Gf659X9N2/Z9L5dl2U0rcZV6OYnZuF58RsvHNUdU08LbL26QUSGZycGfuegvjCShpbvzXW3lFrdLTX1DsxWVPEk/l1TVoWkab6w4wK/eXsdryw80OaZxAfv2cuObZqH3N1Yc4LznfiS7pLrNukqf/moaS+88laE9Y9ibV8HDX2x37TN+p2telRW2At2d0/2MvBCQPgGy1rZ+bCARqWceFvqpMo2mea5pWnTA8/GJ/aXhby/1NfDuCBPCwgAAGkpJREFUFZDdSEisogAW/0X2IbSDE6T6W9whYYYELs9z4LCHEB4aPJnDgYZRxezu/5mTkDt1JUtDIdVaecpKgYeEouLKWqb/9Xse+HQrazw8HMJsIS3WPm6N6jonZTX1pMSEMykzkQ2Hi131BK4+sW+brjEmI56MxEh6xITjbNDYcLjYNWG79mARNfVOsopkDkFnC9b7mu5n5EFmwuZth5rmJVUDDj0zk3w/JZ2U58pJzzl/gYxWEoRGXQoxaVDZAd2gnC2wc767awjgw2vlfEB1J0LWdCPfp3c6hxKn0qAJnNi458xhHb+mguFpsU0kK5btkaP6lkoJltfU88l6mbx2oiVaLK+sxmUgrRgKmQN7RLuExDrCwQL5xnnH6UM4e0wa1XXm5OtVUzPbda1Ei6DbL6fL/I2NWSWuIjXgHq0TiHRPI99zlCwo4i+D2REM41bgp5H8ATk5Rq+xcPHLMOH65o+NSJA/ZUdbr6CUu8OlDQ9Ahf7KX9/oS35Er9ajdUL5L22MzJadeD0rJj/PkJrXARU62VlCQoQrWeiWU2T46lPfyu/WmBaiwEY++DWPzN9OQqTdVdISPEfOpCdEcINuREf0iqW6rsEtMqatHC2u4oynZHhvSky4S7YA5MRve7WgrPr6Jw5MItYRytdbj/GHj8w30bDQwDajgd27jmL4t7d/JmO6Ax1NMydcC/dCgx8kTrd/BjG9IH0SxKXD2f+AC/8D4XFw8wrzuOieMPM+OWou3Ce1go604Bp77wp44zxp7MHUdq9rlEFpVGS64EU6TOoIuH0njJlHUkwkdXrwWM8Ar6wVDBzTM14bR9FYjbdROxZwK6o+tGcsEzPNSXFPRUuW3HEqt84cyPK7ZjK8l4yM6ojLZsku8/uekRhJ36Qo1/bYjJYLmHsi2TKSH50ex+DUGFbvL2RTlvnGGeiZsd3TyBsRNsuehDcvkOurX/Jv5EpLlOdI/3bKMDm52Zyv+8en4Y+JsPlD7/fh8E+QOU1GKhmMvgTuPgSpw2H4eTDlFmlEHbFg10dI2RvhpZnmm0jJEVj5vLltzDH8azJseAd2fyO3ayvkww3ksqoITvw1jPGOtvuMISmc0DeBs0anMVqFT3aaSyfIyfBhabFMHyQnuB8+bwS94iN47MJRgLtq40frTI2hIT1jyEiM5Me7ZgLmBO5F480HREiIQAhBr/gIl1x1aTsmX3NLq7n5rbWsPmBG1fVPjqJ3vPmAn5DZfiNvdVOFh9qYPTzVbV9anMOlcVNSVec2IR0oBLYzqaNYQunI3QYNTlhwuyzz9mAA6c9nb4Iew6BaT+HOmCTnEgp2Q5yHQlor/gmaEz66XoYVepOqIohuIUzx0jfct8dcBqueN7d3LICxl8P7V8GRNVJX5t5sCLFDg/5l/eRm8/jaMlmfd9BpMvbeWQPxbZsUawt2Wwgf3Xxi6wcq2sT9Zw/njjOGYLeF8MZ1k9ziwg1NoD155S6p4v35pkvGqGNsNbgA15yYybSBSdQ73SNwEnX9psLKWjKJctvX0KCx5WgJo9Pd3URvrDjIl3rR8T6JkbxyzQTpZgoRXDstkxG94jok2904ge76k/oxsnccI3vHEesI5YynllBZ46ShQWPMH79hTHocn956EnXOBt5aeRC7LYSRveN4dtFunv/ZCX5x7XRPI++w/AOERZnZmloDFO6X7pzwaP/0zaBwP7wwXY6OR14k23qNhXWvS798+iTZX2s/I5PlqB9kJqrNSx9fg1P6yMNj2n5Or3FwXy4se0q+YWz5UBr5I7rERF2lHKF7UpgeMAv2LoJN70FMT1j7uizlOGaed34fhdexhQiXwWuc+DMsLQaHPYTvtudy6hCpO3S4sIqRvWP57w1T3TJMrcRF2LnQMpo3SI6SLpJ9eRWMy4h3u98LS/bxl6928NHNJ3KCZdI3NdZ0q0wbmMTAHub/8oPnWKQvOsBfLx7tEi4LtYUwbaAZqhsXYae4qpZ9+XIidmNWCY99uZ23Vx5yjer7JUexP7+CtQeL/CJX0j3dNVaXQ1iUe/brM2Phsd7+8XtbMeLhDyyDGn0knzQIENKQPzNO9tOKsPxepU0ldztMrR4pEBbV8nGNCQ2XMhKDToOig1BqcTNFJusuGQ9/57l/hdAI2PyBTLjK3wW9T2jfQ0YRMMQ47IzsFcfePHP0nltWQ89YB1HhoR6zQU8dktKsAF9yjBzJ3/7BRu79ZIvbvvW6tk1jVUyb5Ts/VNeK9xaXTshg+iDPpUmTomSJxCxLhu4Li/e55xHoWvyXv7TSLyUFu6eRt1JfDSuea9qul5bzG4aRP7bJnDeISJDzCeU5UH6s6Tl1FXIiFGTIo7eo1b+cYR18u4nvC8UH3ZOjKvM9199NGwvJA90Tl3K2uL99KYKOuAg7JRYfemlVHbERTUtBxkfaCRHw6rWTmnVdJEWZo/J3Vh1y21ff4FkqzTr5aUzcdgXJMWEcLKh0JUZ5SqY9WmI+kM5+dmlXdc1F9zfyVUWw7o2m7Ybbw1/kepgEdsRKQ7/2NbOtymIoayshQdd1+fpu7/XFNZLvoJFPyARnran8OUCvzvWk5TXZFgY9hsPcv+j3srw11FfLer2KoCUuwk5xpbuRj/Ng5H/8w0w2P3RGk3YrYaEhbvH3zgaNkso6/vn9HtdIuKTRpKxRueq22YNbjN33NinRDmotoZ7GMyiqGRfVrpzyJn33Nd3fyDdH0QE4vFr6g/3BkXXSZWElIkFOxFqxRtrUVZmTk1k/wXMTYYsHKYL2YsSut9ddY5CQKZc5+qv19Nvc918zH/5wAG5ZYVZiqm8UQulQETDBTGyEndKqOnLLqql3NlBWU++xqHtUeGib1EAfOGe4a/1YaTUPz9/G377e6UrCKqp0z6StqnMSGiL49axBXSoWdu5YM7v65MGmS+elqydwwTjT3ToszXy7OOIhEcyXdLbG69+EEDuEEJuEEB8LIeIt++4WQuwRQuwUQrT86PYFt6ySxuWGxU33RfeU2jYvz4bPf+OqHtSlFOyFoWe5t4XHwAWNqhZZ3zjqKkyDCnLk/NlvOt+X9W/KZeP6p23F6NMxPUEkpif0P9Xcnzyk6QOk9wnu28rIBzXxkXbKauqZ9OgiBt4rq312phzk6PR43v6FrD97qKCS/HL3Gge5pe7b+WW1bgU/uop+yVGM0N1DoRZfzcTMRB45f6Rr+845Q3hY3+6qKlsGnf2rLARGapo2GtgF3A0ghBgOzANGAHOAfwkhulY8pMdQyDxJRqzM+Qtcv1DWAb19j9S22fWVeeyyf3Qunb691FVJn3vKkKb7wiJNvztI33v+bji6Xqb9O+Kl5K/r+A6OvgHWvCpLJdZVyWpL6RM6dp24DDkpbChWhse4h7F6EA7jnKfhZx9ZjungA0YREJw0sOlnbB29dgQjW/Xyl1Y2EUDbll3qMpYVNfX8d81hKvxUk/X16yax7v7Z/PFc6Z688eT+2G0hRIWHugTR0uIczBwqI4+yS7rWyHcqBk/TtG8smysBI3j7POA9TdNqgP1CiD3AJGAF/mDKTXKZoddM7Tut6cTrweUwZG77rntknTRm1tF1WzCkCxL7SxdNVRFc97W536r2WJ4Dz1mMryNWSvEu1n3bHfVlO+vhi9+Z270nSHG3jhAaBrHpUKJPkoVFQ4Ru5Kff7vm64TEw8DRz21rXVRF0DLUY9JjwUFbeM6vTRVrS4hz0SYzkUGFlk+zX1fsLmfznRSz4zXTOfEZOZjbnB/c1RlZsYlQYBx53fzsfnR7HDzvziI8IIyUmnAi7jYXbjvHRuiwSIsP43WmDGNmOZL0XFu9ly9FSnr18nKtty5GWB6jefL+5DvhSX+8NHLbsy9LbmiCEuEEIsUYIsSYvr4skCMZfZY6GDe3yz3/b/uu8dCo83QGdcsN3nToSbtsF9+ebvmowk4eg6QRxRCL0Hg8PFMlInI66OUoOu29HdrJQSf8ZchnVQ76N2PXwOHsrdWp76w+wpIGdu7/Cr1gN7KaHTvdKFa5QWwiL7ziFiY0yVa33mveiOW5cc9/sTt/T2zw9bxwv/vwEesY5sIUIBveM4fudeaw9WMS323M4+9ll5JW1Um7TwmNf7uDzjUeZ/f/t3XuUlHUZwPHvs7vswl5gEVa57rIaieCNlQjRSMGMpMDMVMQLncrqoKfCStSOWUneyjrRxZCiixihZhJ08Xos9Yhk4mXTFZC7m6DACmgLC09//N6X953ZmZ2Zndmd2Znnc86eed935p1557e7z/zmd3l+dzzBnU+sZ932vXx8wZMdnpMwyIvIIyLycoyfGaHHXA+0AUv8QzGeKubYJ1VdqKrjVHVcTU03fWUvq4Qzr4M5z8Kse11TRaqjbQ6GahYrr3bj3ZO16Sko6+sCW0kpFEd1UI05z307qK5tP1TST0lcVAT1k9xs0U1Pw4JTUuuE3Rm1bFusNMOpGOx92PX3OoarvA6pXQkWVLh8ues/CS/ebXqccGdnJjs+RYTpJw2JOBau+b4TquGn0wfQVfr16cXZY4J8PyOPbD+CrTNt9Gu37+WWv77KWXfE6HOMkjDIq+pZqnp8jJ8HAUTkctyabLM0WCFgKxBe+WEYkOEVJjKg5lgX3E6fC0iQSyUZ4VEvqxfBr6clf/6W1VA3Mf6M1fN/BVc979IM7H0zchJUn1CNu8j7cFj8MdeR/MRtyV9/OENnWd+gRt1Zfrt7L2/E0HHeMn3HTe/4vNIK139iTBwzx9cy4ejg735sbfsKyQs3nN2dl9RpY2vbN6/ujJFzP5PSHV0zFbgGmK6q4WWDlgMXiUiZiNQDI4Fn03mtLlVWCWgwXjwZW1e3P+any+3IwTaXtKumg8Am4mrqlUe55wzPGg3nlzkUNd62pINVb/a/GznL960md/uFf8C1W9zSieko8Zplir1rqK6FG1vg/T3jn8+kr7hIGH5E5jN+lhQXsfSKUw/vR9fsgXZrxuYqP9EbwPIrTwPaDwftSHV5r5jvvyPptsn/BKgCHhaRNSJyJ4CqNgLLgP8AfwPmqMZKYpIj/On0yS4y0rLNjbHvFTWyZdFkaEvwC3tnm5s4NOCYxK9TVBKkPDj7Jpj7amTisuhc7q17YEcTtIbydTc+4CZX3VYPD10fHN/xGgz/YNDMkq5+Xg6S+kmZeT7T4zR++6M8OveMLn+dYwdV0XTT1C5/na7Qq7iI2RNHMHviCIb3d6OH7lm1mQfXJE5TcuiQ0vLeAUYMSG0py3RH18TtLVPV+cD8dJ6/25R6Qf6eC1w+ls89Ev+xbzbCz73shjWjYNZ9rjnlh97kjV0boeb98c/f46UrqEri09gP8OBWa+o7OPL+uomRo4TeaYafjofR58IFv4FXV8K9s4P7X/wDTL3Zbb+9FkZmsKNq0AkwZ7V1oBawzmR5TMXPZjWwbdd7FBcJxUXBay2e/YEOzso9N3pDLQ8eUkRg1YadrNqwkxknxxybwt7WNva3HaLIa1HuVx58Y68oLU44dDQ/s1Cmyv+S4U/mWXk1TPtB7MfuCeWUkaJg0emJV8HTC1zQ3X1C/ADq56RJpqOxZhSsf8xtR8+EBZfBsvkFF7whWG2peY27XXNP5OPffdul9z1mstuuyHBnZ0cfbsak6ZwTIis5AypKeXvffsYM7b5cNZlUXCRxu/Fa3j1AZe8SigTG3fQw/ztwiEHegur9y3vx+NfOYOe+Vgb168Ouffs54db4r1O4aQ3C6k6L3F+9KPaKUqrwyp/ddu9qmHJDcN+kb7jbR78NS86P32zjj5apGhT7/rAp3wq2Y6VGFoFz72x/vMz7ow+PoPG/rdx9npv8dKjNsj6aHu23nx3PosvGcWRVgmG6PUSLl/unte0gJ33nIW5c3siKF5sPr1Hrr87Vv7yU+oEVnFJ3BEOr+yQcZ29BHoLaeJif170pNDO28QF4brHb/tJTkZOnekfVJsJNLT7VIIVCeRJ5pRONMYfItMq+fTvca+3a6PZLq+BTdwX3f8+rEVmQNz3YmCH9OGt0Bwvd9DBzl7lv4G/sdsH8d89s4un1b6X9vBbk43nsu7CgAX5/oRuHDrD1X8H9sSYhXXh3sB0rTULzGrciEkBRku2X03/i0jKkYk8zLJ3lZs5+5Dswb5P7QKo7PfJxnc06aYzJiJ9e3HA45fLGt/dF3EL7Rc8HVJSmvPCIBXnfkAZ3+80d7cd27/AWoW4NBe5YAfK4T8D4L7jtBQ1ujdlwo1tnFhVvuDRIyxDP+YvhA58P9itqoGml2y4fGHygRI/oOZj8TDtjTOZNO3Ewz17nUnOv37GPxjda+OU/Nxy+f/XGyJw9v7j0lJQ7uC3I+2avgKub3Fjz6DHsuze74Yrh2afxZvUd94lg+5EbXbOPz88hf0QSwydTcfx5rsbuGzY+2A43C1VFjc5JZfKXMaZLVIdGy0z78ZOH0ynHclTf1PsfLMj7SiuCztCBUaNEdm+B298Ha718bLPuJ67otvktoTlgjX9yQw2vei79641WWg5nz3eTm0aEmmXC2R3991da6TqKT74489dhjEnZh0ZGZvEMz/AN5+4Z1j/1yWY2hDKWWpfHmsqjXK179+ZgKbuTL3FrmsYTnZEy3Hb/zjY49pzOZ3tMZOKV7nbQifCP2+G9ne5DxefX5KUYJl/f/nxjTFb84NMnMf57jx7eP35IP555fSf1Ayu467JxPL95N2/tbe1UXiAL8rFU18JXG90wyb98HV4IjTevm9jxub37wScXujH0j893uWf+1wJLLnCjXpIZOpkuEfjKS26oZDjVgZ8jprUbc+cbYxI6sm9vll4xgYsWPgPAZaeOYN/+Nq6ZOorq8lLOHNX5OS0W5OPxp+kffUYQ5CfMSa6J46QL3e1L98L6x+GW2uC+ym4a8lVW2X5svb904Cmzu+cajDFJm3B00H9WO6Ccm887MSPPa0E+kXD79qQ4C2DEU/tBWPv3yGPppvRNhwh8c3uQvdIYk1OemjeZ1gOZTfNlQT6R8NJ1qS6sMWRs+2NlWZ6CXVKW3dc3xsQ1tDrzWTxtdE0i6QTF+g+3X9bOZpkaY7qRBflkTLkBLl6W+nlFxZH5bcCCvDGmW1lzTTI+dHXnzx0U1XkSK9GYMcZ0EavJd7UBx8A1G4O2eMsXY4zpRlaT7w59+sPnH3MLbWdzdI0xpuBYkO8uA0fCGddk+yqMMQUm3YW8vysiL3rruz4kIkNC910rIutEpElEPpr+pRpjjElVum3yt6vqiap6MrACuAFAREYDFwFjgKnAz0SkaxeANMYY005aQV5Vw8sfVQB+7toZwFJVbVXVDcA6YHz0+cYYY7pW2m3yIjIfuAxoAc70Dg8Fngk9bKt3LNb5VwBXANTW1sZ6iDHGmE5KWJMXkUdE5OUYPzMAVPV6VR0OLAGu9E+L8VQxV6hQ1YWqOk5Vx9XU1MR6iDHGmE5KWJNX1Q6Sp0e4B1gJfAtXcw+vjj0MeCPlqzPGGJOWdEfXjAztTge8xVBZDlwkImUiUg+MBJ6NPt8YY0zXSrdN/hYRORY4BGwCvgigqo0isgz4D9AGzFHVzObPNMYYk5BoDi3mLCJ7gKZsX0eOGAjEX9G3sFhZBKwsAlYWgTpVjdmpmWszXptUdVy2LyIXiMi/rCwcK4uAlUXAyiI5lqDMGGPymAV5Y4zJY7kW5Bdm+wJyiJVFwMoiYGURsLJIQk51vBpjjMmsXKvJG2OMySAL8sYYk8dyJsiLyFQv9/w6EZmX7evpaiIyXEQeF5FXRKRRRL7sHT9CRB4WkbXebf/QOXmbo19EikXkeRFZ4e0XZDkAiEi1iNwnIq96fx+nFmJ5iMhXvf+Nl0Xk9yLSuxDLIW2qmvUfoBhYDxwNlAIvAKOzfV1d/J4HAw3edhXwGjAauA2Y5x2fB9zqbY/2yqUMqPfKqzjb7yOD5TEXl/9ohbdfkOXgvcffAJ/ztkuB6kIrD1zW2g1AH29/GTC70MohEz+5UpMfD6xT1ddVdT+wFJeTPm+parOq/tvb3gO8gvvDnoH7J8e7Pdfbztsc/SIyDJgGLAodLrhyABCRvsAk4JcAqrpfVXdTmOVRAvQRkRKgHJfksBDLIS25EuSHAltC+3Hzz+cjERkBjAVWAUepajO4DwLgSO9h+VxGPwK+gcuB5CvEcgD3bXYHsNhrvlokIhUUWHmo6jbg+8BmoBloUdWHKLByyIRcCfJJ55/PNyJSCdwPfEUjV9pq99AYx3p8GYnIx4HtqvpcsqfEONbjyyGkBGgAfq6qY4F9uGaJePKyPLy29hm4ppchQIWIXNLRKTGO9fhyyIRcCfIFmX9eRHrhAvwSVf2jd/hNERns3T8Y2O4dz9cyOg2YLiIbcc10k0XkbgqvHHxbga2qusrbvw8X9AutPM4CNqjqDlU9APwRmEjhlUPaciXIrwZGiki9iJTiFgFfnuVr6lIiIrh211dU9Y7QXcuBy73ty4EHQ8fzLke/ql6rqsNUdQTu9/6Yql5CgZWDT1X/C2zxUngDTMGl7C608tgMTBCRcu9/ZQqu36rQyiFtOZGFUlXbRORK4O+4kTa/UtXGLF9WVzsNuBR4SUTWeMeuA24BlonIZ3F/6J+GgszRX8jlcBWwxKvwvA58BlchK5jyUNVVInIf8G/c+3oel8agkgIqh0ywtAbGGJPHcqW5xhhjTBewIG+MMXnMgrwxxuQxC/LGGJPHLMgbY0wesyBvTAIi8kkRUREZle1rMSZVFuSNSWwm8CRuspYxPYqNkzemA15uoSbgTGC5qlpt3vQoVpM3pmPnAn9T1deAnSLSkO0LMiYVFuSN6dhMXOI0vNuZWbwWY1JmzTXGxCEiA3DZDbfj0tYWe7d1av84poewmrwx8Z0P/FZV61R1hKoOxy1Jd3qWr8uYpFmQNya+mcADUcfuBy7OwrUY0ynWXGOMMXnMavLGGJPHLMgbY0wesyBvjDF5zIK8McbkMQvyxhiTxyzIG2NMHrMgb4wxeez/8wpiiX1TlTAAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df3.plot(x='A',y=['B','C'])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"A -0.819304\n",
"B 1.884131\n",
"C 0.194834\n",
"D 1.253771\n",
"Name: 2000-01-06 00:00:00, dtype: float64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"df.iloc[5]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"A -0.819304\n",
"B 1.884131\n",
"C 0.194834\n",
"D 1.253771\n",
"Name: 2000-01-06 00:00:00, dtype: float64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[5,:]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"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>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>-1.074327</td>\n",
" <td>-2.073962</td>\n",
" <td>-1.284284</td>\n",
" <td>1.336217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>-0.689989</td>\n",
" <td>-0.189525</td>\n",
" <td>-0.739258</td>\n",
" <td>2.086142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>0.828906</td>\n",
" <td>1.126212</td>\n",
" <td>0.508507</td>\n",
" <td>0.392446</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-04</th>\n",
" <td>0.507045</td>\n",
" <td>2.156742</td>\n",
" <td>-0.122078</td>\n",
" <td>-0.029870</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-05</th>\n",
" <td>-0.122940</td>\n",
" <td>2.038700</td>\n",
" <td>0.435111</td>\n",
" <td>0.631309</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2000-01-01 -1.074327 -2.073962 -1.284284 1.336217\n",
"2000-01-02 -0.689989 -0.189525 -0.739258 2.086142\n",
"2000-01-03 0.828906 1.126212 0.508507 0.392446\n",
"2000-01-04 0.507045 2.156742 -0.122078 -0.029870\n",
"2000-01-05 -0.122940 2.038700 0.435111 0.631309"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[0:5,:]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"A -1.074327\n",
"B -2.073962\n",
"C -1.284284\n",
"D 1.336217\n",
"Name: 2000-01-01 00:00:00, dtype: float64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"A -0.819304\n",
"B 1.884131\n",
"C 0.194834\n",
"D 1.253771\n",
"Name: 2000-01-06 00:00:00, dtype: float64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[5]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5d18dd30>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.iloc[5].plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-24-a2a07ea03950>, line 2)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-24-a2a07ea03950>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m df.plot.<TAB>\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"df = pd.DataFrame()\n",
"df.plot.<TAB>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame()\n",
"\n",
"df.plot.<TAB> # noqa: E225, E999"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax = df.plot.bar(x='lab',y='val',rot=29)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
">>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]\n",
">>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]\n",
">>> index = ['snail', 'pig', 'elephant',\n",
"... 'rabbit', 'giraffe', 'coyote', 'horse']\n",
">>> df = pd.DataFrame({'speed': speed,\n",
"... 'lifespan': lifespan}, index=index)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax = df.plot.bar(rot=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"axes = df.plot.bar(rot=35,subplots=True)\n",
"axes[1].legend(loc=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax = df.plot.bar(y='speed',rot=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax = df.plot.bar(y='lifespan',rot=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax = df.plot.bar(subplots=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax = df.plot.barh(x='lab',y='val')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
">>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]\n",
">>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]\n",
">>> index = ['snail', 'pig', 'elephant',\n",
"... 'rabbit', 'giraffe', 'coyote', 'horse']\n",
">>> df = pd.DataFrame({'speed': speed,\n",
"... 'lifespan': lifespan}, index=index)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.plot.barh()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax = df.plot.barh(y='speed')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax = df.plot.barh(y='speed')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax = df.plot.barh(y='lifespan')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"df.iloc[5].plot.bar()\n",
"plt.axhline(0,color='k')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"df.iloc[0].plot.bar()\n",
"plt.axhline(0,color='k')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df2.plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
">>> df = pd.DataFrame(\n",
"... np.random.randint(1, 7, 6000),\n",
"... columns = ['one'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['two'] = df['one'] + np.random.randint(1,7,6000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax = df.plot.hist(bins=12,alpha=0.2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" df4 = pd.DataFrame({'a': np.random.randn(1000) + 1, 'b': np.random.randn(1000),\n",
" ....: 'c': np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])\n",
" ....: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df4.plot.hist(bins=13,alpha=0.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df4.plot.hist(bins=20,stacked=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df4['a'].plot.hist(orientation='horizontal',cumulative=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df2['a'].diff().hist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df2['a'].hist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df2 = pd.DataFrame(np.random.rand(1000, 4), columns=['a', 'b', 'c', 'd'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df2['a'].diff().hist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df2['a']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df2['a'].diff()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df2.diff().hist(color='k',alpha=0.5,bins=50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = pd.Series(np.random.randn(1000))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data.hist(by = np.random.randint(0,4,1000))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data.hist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = np.random.randn(25,4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(data,columns=list('ABCD'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ax = df.plot.box()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
">>> np.random.seed(1234)\n",
">>> df = pd.DataFrame(np.random.randn(10,4),\n",
"... columns=['Col1', 'Col2', 'Col3', 'Col4'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"boxplot = df.boxplot(column=['Col1','Col2','Col3','Col4'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.randn(10, 2),\n",
"... columns=['Col1', 'Col2'])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"df['X'] = pd.Series(['A','A','A','A','A','B','B','B','C','C',])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"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>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>X</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>-1.074327</td>\n",
" <td>-2.073962</td>\n",
" <td>-1.284284</td>\n",
" <td>1.336217</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>-0.689989</td>\n",
" <td>-0.189525</td>\n",
" <td>-0.739258</td>\n",
" <td>2.086142</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>0.828906</td>\n",
" <td>1.126212</td>\n",
" <td>0.508507</td>\n",
" <td>0.392446</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-04</th>\n",
" <td>0.507045</td>\n",
" <td>2.156742</td>\n",
" <td>-0.122078</td>\n",
" <td>-0.029870</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-05</th>\n",
" <td>-0.122940</td>\n",
" <td>2.038700</td>\n",
" <td>0.435111</td>\n",
" <td>0.631309</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-09-22</th>\n",
" <td>71.861553</td>\n",
" <td>-49.109186</td>\n",
" <td>15.809916</td>\n",
" <td>38.765663</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-09-23</th>\n",
" <td>71.530190</td>\n",
" <td>-50.787577</td>\n",
" <td>16.238423</td>\n",
" <td>39.499372</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-09-24</th>\n",
" <td>69.520185</td>\n",
" <td>-49.794911</td>\n",
" <td>15.358198</td>\n",
" <td>39.480654</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-09-25</th>\n",
" <td>69.046552</td>\n",
" <td>-50.032522</td>\n",
" <td>15.083529</td>\n",
" <td>38.790532</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-09-26</th>\n",
" <td>70.794982</td>\n",
" <td>-49.210848</td>\n",
" <td>15.277156</td>\n",
" <td>37.282224</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" A B C D X\n",
"2000-01-01 -1.074327 -2.073962 -1.284284 1.336217 NaN\n",
"2000-01-02 -0.689989 -0.189525 -0.739258 2.086142 NaN\n",
"2000-01-03 0.828906 1.126212 0.508507 0.392446 NaN\n",
"2000-01-04 0.507045 2.156742 -0.122078 -0.029870 NaN\n",
"2000-01-05 -0.122940 2.038700 0.435111 0.631309 NaN\n",
"... ... ... ... ... ...\n",
"2002-09-22 71.861553 -49.109186 15.809916 38.765663 NaN\n",
"2002-09-23 71.530190 -50.787577 16.238423 39.499372 NaN\n",
"2002-09-24 69.520185 -49.794911 15.358198 39.480654 NaN\n",
"2002-09-25 69.046552 -50.032522 15.083529 38.790532 NaN\n",
"2002-09-26 70.794982 -49.210848 15.277156 37.282224 NaN\n",
"\n",
"[1000 rows x 5 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "not enough values to unpack (expected 2, got 0)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-27-e739b9cc46c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mboxplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'X'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#the grouping of boxplot is done by X in this case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mboxplot_frame\u001b[0;34m(self, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 420\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 421\u001b[0m )\n\u001b[1;32m 422\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36mboxplot_frame\u001b[0;34m(self, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 356\u001b[0m )\n\u001b[1;32m 357\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(data, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 303\u001b[0m )\n\u001b[1;32m 304\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36m_grouped_plot_by_column\u001b[0;34m(plotf, data, columns, by, numeric_only, grid, figsize, ax, layout, return_type, **kwargs)\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_axes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 205\u001b[0m \u001b[0mgp_col\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrouped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mgp_col\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 207\u001b[0m \u001b[0mre_plotf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplotf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 2, got 0)"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"boxplot = df.boxplot(by='X') #the grouping of boxplot is done by X in this case"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
">>> df = pd.DataFrame(np.random.randn(10,3),\n",
"... columns=['Col1', 'Col2', 'Col3'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['X'] = pd.Series(['A','A','A','A','A','B','B','B','B','B'])\n",
"df['Y'] = pd.Series(['A','B','A','B','A','B','A','B','A','B'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"boxplot = df.boxplot(column=['Col1','Col2'],by=['X','Y'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"boxplot = df.boxplot(column = ['Col1','Col2'],by='X',layout=(2,1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"boxplot = df.boxplot(column = ['Col1','Col2'],by=['X','Y'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"boxplot = df.boxplot(grid=False,rot=45,fontsize=15)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"boxplot = df.boxplot(column = ['Col1','Col2'],return_type='axes')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'boxplot' 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-28-97dac2864b94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'boxplot' is not defined"
]
}
],
"source": [
"type(boxplot)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'Column not found: Col1'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-29-1cfa244c7d94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mboxplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Col1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Col2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'X'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mboxplot_frame\u001b[0;34m(self, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 420\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 421\u001b[0m )\n\u001b[1;32m 422\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36mboxplot_frame\u001b[0;34m(self, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 356\u001b[0m )\n\u001b[1;32m 357\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(data, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 303\u001b[0m )\n\u001b[1;32m 304\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36m_grouped_plot_by_column\u001b[0;34m(plotf, data, columns, by, numeric_only, grid, figsize, ax, layout, return_type, **kwargs)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_axes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m \u001b[0mgp_col\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrouped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 206\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mgp_col\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 207\u001b[0m \u001b[0mre_plotf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplotf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Column not found: {key}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 269\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'Column not found: Col1'"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAN3klEQVR4nO3dXYhc93nH8e+vUgWNk8am3oREkqla5NhqsYu9cUzoi9PQWlIuRMAXttOaGgchiEN6U2xa+gK5aQiFEOxYCCNMbqKbmFQpSt3SkrjgutEa/CI52Kxlam1k0Do2KThQV/bTi5m209FKe6Q5s7PS//uBhTnn/HWeZ7TP/nR23pSqQpJ0+fu5WTcgSVobBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiNWDfwkB5OcTnLsHMeT5OtJFpM8n+Sm/tuUJE2qyxX+Y8DO8xzfBWwffu0FHpm8LUlS31YN/Kp6EnjzPEv2AN+sgaeBK5N8pK8GJUn92NjDOTYDJ0e2l4b7Xh9fmGQvg98CuOKKK26+7rrreigvne2ZZ555o6rm1qqes621Msls9xH4WWHfip/XUFUHgAMA8/PztbCw0EN56WxJ/n0t6znbWiuTzHYfr9JZAraObG8BTvVwXklSj/oI/MPAPcNX69wK/LSqzno4R5I0W6s+pJPkW8BtwNVJloC/BH4eoKr2A0eA3cAi8DPg3mk1K0m6eKsGflXdtcrxAr7QW0eSpKnwnbaS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjOgV+kp1JXkqymOTBFY5/MMl3kzyX5HiSe/tvVZI0iVUDP8kG4GFgF7ADuCvJjrFlXwBerKobgduAv0myqedeJUkT6HKFfwuwWFUnquod4BCwZ2xNAR9IEuD9wJvAmV47lSRNpEvgbwZOjmwvDfeNegi4HjgFvAB8qareGz9Rkr1JFpIsLC8vX2TL0vrjbOtS0CXws8K+Gtu+HXgW+CjwG8BDSX7xrD9UdaCq5qtqfm5u7oKbldYrZ1uXgi6BvwRsHdnewuBKftS9wOM1sAi8ClzXT4uSpD50CfyjwPYk24ZPxN4JHB5b8xrwaYAkHwY+Bpzos1FJ0mQ2rragqs4kuR94AtgAHKyq40n2DY/vB74MPJbkBQYPAT1QVW9MsW9J0gVaNfABquoIcGRs3/6R26eA3++3NUlSn3ynrSQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGdAr8JDuTvJRkMcmD51hzW5JnkxxP8oN+25QkTWrjaguSbAAeBn4PWAKOJjlcVS+OrLkS+Aaws6peS/KhaTUsSbo4Xa7wbwEWq+pEVb0DHAL2jK25G3i8ql4DqKrT/bYpSZpUl8DfDJwc2V4a7ht1LXBVku8neSbJPSudKMneJAtJFpaXly+uY2kdcrZ1KegS+FlhX41tbwRuBj4D3A78eZJrz/pDVQeqar6q5ufm5i64WWm9crZ1KVj1MXwGV/RbR7a3AKdWWPNGVb0NvJ3kSeBG4OVeupQkTazLFf5RYHuSbUk2AXcCh8fW/C3wW0k2Jnkf8AngR/22KkmaxKpX+FV1Jsn9wBPABuBgVR1Psm94fH9V/SjJ3wPPA+8Bj1bVsWk2Lkm6MF0e0qGqjgBHxvbtH9v+KvDV/lqTJPXJd9pKUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mN6BT4SXYmeSnJYpIHz7Pu40neTXJHfy1KkvqwauAn2QA8DOwCdgB3JdlxjnVfAZ7ou0lJ0uS6XOHfAixW1Ymqegc4BOxZYd0XgW8Dp3vsT5LUky6Bvxk4ObK9NNz3v5JsBj4L7D/fiZLsTbKQZGF5eflCe5XWLWdbl4IugZ8V9tXY9teAB6rq3fOdqKoOVNV8Vc3Pzc117VFa95xtXQo2dlizBGwd2d4CnBpbMw8cSgJwNbA7yZmq+k4vXUqSJtYl8I8C25NsA34M3AncPbqgqrb9z+0kjwF/Z9hL0vqyauBX1Zkk9zN49c0G4GBVHU+yb3j8vI/bS5LWhy5X+FTVEeDI2L4Vg76q/mjytiRJffOdtpLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5Ia0Snwk+xM8lKSxSQPrnD8c0meH349leTG/luVJE1i1cBPsgF4GNgF7ADuSrJjbNmrwO9U1Q3Al4EDfTcqSZpMlyv8W4DFqjpRVe8Ah4A9owuq6qmqemu4+TSwpd82JUmT6hL4m4GTI9tLw33nch/wvZUOJNmbZCHJwvLycvcupXXO2daloEvgZ4V9teLC5FMMAv+BlY5X1YGqmq+q+bm5ue5dSuucs61LwcYOa5aArSPbW4BT44uS3AA8Cuyqqp/0054kqS9drvCPAtuTbEuyCbgTODy6IMk1wOPAH1bVy/23KUma1KpX+FV1Jsn9wBPABuBgVR1Psm94fD/wF8AvAd9IAnCmquan17Yk6UJ1eUiHqjoCHBnbt3/k9ueBz/fbmiSpT77TVpIaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJakSnwE+yM8lLSRaTPLjC8ST5+vD480lu6r9VSdIkVg38JBuAh4FdwA7griQ7xpbtArYPv/YCj/TcpyRpQl2u8G8BFqvqRFW9AxwC9oyt2QN8swaeBq5M8pGee5UkTWBjhzWbgZMj20vAJzqs2Qy8ProoyV4GvwEA/GeSYxfUbX+uBt5oqO4sa8+q7sfWstg6mW3nq43aFz3bXQI/K+yri1hDVR0ADgAkWaiq+Q71ezer2t7nta27lvXWw2w7X23UnmS2uzykswRsHdneApy6iDWSpBnqEvhHge1JtiXZBNwJHB5bcxi4Z/hqnVuBn1bV6+MnkiTNzqoP6VTVmST3A08AG4CDVXU8yb7h8f3AEWA3sAj8DLi3Q+0DF9315GZV2/t8+dedZW3vcxu1L7puqs56qF2SdBnynbaS1AgDX5IaMfXAn9XHMnSo+7lhveeTPJXkxj7qdqk9su7jSd5Ncsda1U1yW5JnkxxP8oM+6napneSDSb6b5Llh7S7P83SpezDJ6XO97n2G8zW1jxuZ1WzPaq671p7GbF92c11VU/ti8CTvK8CvAJuA54AdY2t2A99j8Fr+W4F/W6O6nwSuGt7e1UfdrrVH1v0zgye871ij+3wl8CJwzXD7Q2v4ff5T4CvD23PAm8CmHmr/NnATcOwcx2c1X73XneVsz2quZznbl+NcT/sKf1Yfy7Bq3ap6qqreGm4+zeC9A33ocp8Bvgh8Gzi9hnXvBh6vqtcAqmotaxfwgSQB3s/gB+PMpIWr6snhuc5lJvM1pbqdak9ptmc1111rT2O2L7u5nnbgn+sjFy50zTTqjrqPwb+WfVi1dpLNwGeB/T3V7FQXuBa4Ksn3kzyT5J41rP0QcD2DN+S9AHypqt7rqf6kvU3jnNOoezHn7Wu2ZzXXnWozndm+7Oa6y0crTKK3j2WYQt3BwuRTDH4ofnPCmhdS+2vAA1X17uDCYM3qbgRuBj4N/ALwr0merqqX16D27cCzwO8Cvwr8Y5J/qar/mLB2H71N45zTqHtB5+15tmc1111rT2O2L7u5nnbgz+pjGTqdM8kNwKPArqr6yYQ1L6T2PHBo+ENxNbA7yZmq+s6U6y4Bb1TV28DbSZ4EbgQmDfwute8F/roGD0AuJnkVuA744YS1++htGuec1seNzGq2ZzXXXWtPY7Yvv7me9MmFVZ542AicALbxf096/NrYms/w/598+OEa1b2GwTuDP7nW93ls/WP086Rtl/t8PfBPw7XvA44Bv75GtR8B/mp4+8PAj4Gre/o7/2XO/eTWrOar97qznO1ZzfUsZ/tynOtehmGVpncz+Ff2FeDPhvv2AfuGt8PgP1h5hcFjYPNrVPdR4C0Gv449Cyys1X0eW9vnD8aqdYE/YfBqhmPAH6/h9/mjwD8Mv8fHgD/oqe63GHwM938xuOq5b53M11TqznK2ZzXXs5zty22u/WgFSWqE77SVpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakR/w3I5gzuBe2C1wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"boxplot = df.boxplot(column=['Col1','Col2'],by='X',return_type='axes')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"type(boxplot)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'Column not found: Col1'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-30-87205eade01e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mboxplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Col1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Col2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'X'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mboxplot_frame\u001b[0;34m(self, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 420\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 421\u001b[0m )\n\u001b[1;32m 422\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36mboxplot_frame\u001b[0;34m(self, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 356\u001b[0m )\n\u001b[1;32m 357\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(data, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 303\u001b[0m )\n\u001b[1;32m 304\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/boxplot.py\u001b[0m in \u001b[0;36m_grouped_plot_by_column\u001b[0;34m(plotf, data, columns, by, numeric_only, grid, figsize, ax, layout, return_type, **kwargs)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_axes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m \u001b[0mgp_col\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrouped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 206\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mgp_col\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 207\u001b[0m \u001b[0mre_plotf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplotf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Column not found: {key}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 269\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'Column not found: Col1'"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAN3klEQVR4nO3dXYhc93nH8e+vUgWNk8am3oREkqla5NhqsYu9cUzoi9PQWlIuRMAXttOaGgchiEN6U2xa+gK5aQiFEOxYCCNMbqKbmFQpSt3SkrjgutEa/CI52Kxlam1k0Do2KThQV/bTi5m209FKe6Q5s7PS//uBhTnn/HWeZ7TP/nR23pSqQpJ0+fu5WTcgSVobBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiNWDfwkB5OcTnLsHMeT5OtJFpM8n+Sm/tuUJE2qyxX+Y8DO8xzfBWwffu0FHpm8LUlS31YN/Kp6EnjzPEv2AN+sgaeBK5N8pK8GJUn92NjDOTYDJ0e2l4b7Xh9fmGQvg98CuOKKK26+7rrreigvne2ZZ555o6rm1qqes621Msls9xH4WWHfip/XUFUHgAMA8/PztbCw0EN56WxJ/n0t6znbWiuTzHYfr9JZAraObG8BTvVwXklSj/oI/MPAPcNX69wK/LSqzno4R5I0W6s+pJPkW8BtwNVJloC/BH4eoKr2A0eA3cAi8DPg3mk1K0m6eKsGflXdtcrxAr7QW0eSpKnwnbaS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjOgV+kp1JXkqymOTBFY5/MMl3kzyX5HiSe/tvVZI0iVUDP8kG4GFgF7ADuCvJjrFlXwBerKobgduAv0myqedeJUkT6HKFfwuwWFUnquod4BCwZ2xNAR9IEuD9wJvAmV47lSRNpEvgbwZOjmwvDfeNegi4HjgFvAB8qareGz9Rkr1JFpIsLC8vX2TL0vrjbOtS0CXws8K+Gtu+HXgW+CjwG8BDSX7xrD9UdaCq5qtqfm5u7oKbldYrZ1uXgi6BvwRsHdnewuBKftS9wOM1sAi8ClzXT4uSpD50CfyjwPYk24ZPxN4JHB5b8xrwaYAkHwY+Bpzos1FJ0mQ2rragqs4kuR94AtgAHKyq40n2DY/vB74MPJbkBQYPAT1QVW9MsW9J0gVaNfABquoIcGRs3/6R26eA3++3NUlSn3ynrSQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGdAr8JDuTvJRkMcmD51hzW5JnkxxP8oN+25QkTWrjaguSbAAeBn4PWAKOJjlcVS+OrLkS+Aaws6peS/KhaTUsSbo4Xa7wbwEWq+pEVb0DHAL2jK25G3i8ql4DqKrT/bYpSZpUl8DfDJwc2V4a7ht1LXBVku8neSbJPSudKMneJAtJFpaXly+uY2kdcrZ1KegS+FlhX41tbwRuBj4D3A78eZJrz/pDVQeqar6q5ufm5i64WWm9crZ1KVj1MXwGV/RbR7a3AKdWWPNGVb0NvJ3kSeBG4OVeupQkTazLFf5RYHuSbUk2AXcCh8fW/C3wW0k2Jnkf8AngR/22KkmaxKpX+FV1Jsn9wBPABuBgVR1Psm94fH9V/SjJ3wPPA+8Bj1bVsWk2Lkm6MF0e0qGqjgBHxvbtH9v+KvDV/lqTJPXJd9pKUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mN6BT4SXYmeSnJYpIHz7Pu40neTXJHfy1KkvqwauAn2QA8DOwCdgB3JdlxjnVfAZ7ou0lJ0uS6XOHfAixW1Ymqegc4BOxZYd0XgW8Dp3vsT5LUky6Bvxk4ObK9NNz3v5JsBj4L7D/fiZLsTbKQZGF5eflCe5XWLWdbl4IugZ8V9tXY9teAB6rq3fOdqKoOVNV8Vc3Pzc117VFa95xtXQo2dlizBGwd2d4CnBpbMw8cSgJwNbA7yZmq+k4vXUqSJtYl8I8C25NsA34M3AncPbqgqrb9z+0kjwF/Z9hL0vqyauBX1Zkk9zN49c0G4GBVHU+yb3j8vI/bS5LWhy5X+FTVEeDI2L4Vg76q/mjytiRJffOdtpLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5Ia0Snwk+xM8lKSxSQPrnD8c0meH349leTG/luVJE1i1cBPsgF4GNgF7ADuSrJjbNmrwO9U1Q3Al4EDfTcqSZpMlyv8W4DFqjpRVe8Ah4A9owuq6qmqemu4+TSwpd82JUmT6hL4m4GTI9tLw33nch/wvZUOJNmbZCHJwvLycvcupXXO2daloEvgZ4V9teLC5FMMAv+BlY5X1YGqmq+q+bm5ue5dSuucs61LwcYOa5aArSPbW4BT44uS3AA8Cuyqqp/0054kqS9drvCPAtuTbEuyCbgTODy6IMk1wOPAH1bVy/23KUma1KpX+FV1Jsn9wBPABuBgVR1Psm94fD/wF8AvAd9IAnCmquan17Yk6UJ1eUiHqjoCHBnbt3/k9ueBz/fbmiSpT77TVpIaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJakSnwE+yM8lLSRaTPLjC8ST5+vD480lu6r9VSdIkVg38JBuAh4FdwA7griQ7xpbtArYPv/YCj/TcpyRpQl2u8G8BFqvqRFW9AxwC9oyt2QN8swaeBq5M8pGee5UkTWBjhzWbgZMj20vAJzqs2Qy8ProoyV4GvwEA/GeSYxfUbX+uBt5oqO4sa8+q7sfWstg6mW3nq43aFz3bXQI/K+yri1hDVR0ADgAkWaiq+Q71ezer2t7nta27lvXWw2w7X23UnmS2uzykswRsHdneApy6iDWSpBnqEvhHge1JtiXZBNwJHB5bcxi4Z/hqnVuBn1bV6+MnkiTNzqoP6VTVmST3A08AG4CDVXU8yb7h8f3AEWA3sAj8DLi3Q+0DF9315GZV2/t8+dedZW3vcxu1L7puqs56qF2SdBnynbaS1AgDX5IaMfXAn9XHMnSo+7lhveeTPJXkxj7qdqk9su7jSd5Ncsda1U1yW5JnkxxP8oM+6napneSDSb6b5Llh7S7P83SpezDJ6XO97n2G8zW1jxuZ1WzPaq671p7GbF92c11VU/ti8CTvK8CvAJuA54AdY2t2A99j8Fr+W4F/W6O6nwSuGt7e1UfdrrVH1v0zgye871ij+3wl8CJwzXD7Q2v4ff5T4CvD23PAm8CmHmr/NnATcOwcx2c1X73XneVsz2quZznbl+NcT/sKf1Yfy7Bq3ap6qqreGm4+zeC9A33ocp8Bvgh8Gzi9hnXvBh6vqtcAqmotaxfwgSQB3s/gB+PMpIWr6snhuc5lJvM1pbqdak9ptmc1111rT2O2L7u5nnbgn+sjFy50zTTqjrqPwb+WfVi1dpLNwGeB/T3V7FQXuBa4Ksn3kzyT5J41rP0QcD2DN+S9AHypqt7rqf6kvU3jnNOoezHn7Wu2ZzXXnWozndm+7Oa6y0crTKK3j2WYQt3BwuRTDH4ofnPCmhdS+2vAA1X17uDCYM3qbgRuBj4N/ALwr0merqqX16D27cCzwO8Cvwr8Y5J/qar/mLB2H71N45zTqHtB5+15tmc1111rT2O2L7u5nnbgz+pjGTqdM8kNwKPArqr6yYQ1L6T2PHBo+ENxNbA7yZmq+s6U6y4Bb1TV28DbSZ4EbgQmDfwute8F/roGD0AuJnkVuA744YS1++htGuec1seNzGq2ZzXXXWtPY7Yvv7me9MmFVZ542AicALbxf096/NrYms/w/598+OEa1b2GwTuDP7nW93ls/WP086Rtl/t8PfBPw7XvA44Bv75GtR8B/mp4+8PAj4Gre/o7/2XO/eTWrOar97qznO1ZzfUsZ/tynOtehmGVpncz+Ff2FeDPhvv2AfuGt8PgP1h5hcFjYPNrVPdR4C0Gv449Cyys1X0eW9vnD8aqdYE/YfBqhmPAH6/h9/mjwD8Mv8fHgD/oqe63GHwM938xuOq5b53M11TqznK2ZzXXs5zty22u/WgFSWqE77SVpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakR/w3I5gzuBe2C1wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"boxplot = df.boxplot(column=['Col1','Col2'],by='X',return_type=None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"type(boxplot)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <th>C</th>\n",
" <th>D</th>\n",
" <th>X</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>-1.074327</td>\n",
" <td>-2.073962</td>\n",
" <td>-1.284284</td>\n",
" <td>1.336217</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>-0.689989</td>\n",
" <td>-0.189525</td>\n",
" <td>-0.739258</td>\n",
" <td>2.086142</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td>0.828906</td>\n",
" <td>1.126212</td>\n",
" <td>0.508507</td>\n",
" <td>0.392446</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-04</th>\n",
" <td>0.507045</td>\n",
" <td>2.156742</td>\n",
" <td>-0.122078</td>\n",
" <td>-0.029870</td>\n",
" <td>NaN</td>\n",
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" <th>2000-01-05</th>\n",
" <td>-0.122940</td>\n",
" <td>2.038700</td>\n",
" <td>0.435111</td>\n",
" <td>0.631309</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>2002-09-22</th>\n",
" <td>71.861553</td>\n",
" <td>-49.109186</td>\n",
" <td>15.809916</td>\n",
" <td>38.765663</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-09-23</th>\n",
" <td>71.530190</td>\n",
" <td>-50.787577</td>\n",
" <td>16.238423</td>\n",
" <td>39.499372</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-09-24</th>\n",
" <td>69.520185</td>\n",
" <td>-49.794911</td>\n",
" <td>15.358198</td>\n",
" <td>39.480654</td>\n",
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" <th>2002-09-25</th>\n",
" <td>69.046552</td>\n",
" <td>-50.032522</td>\n",
" <td>15.083529</td>\n",
" <td>38.790532</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2002-09-26</th>\n",
" <td>70.794982</td>\n",
" <td>-49.210848</td>\n",
" <td>15.277156</td>\n",
" <td>37.282224</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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],
"text/plain": [
" A B C D X\n",
"2000-01-01 -1.074327 -2.073962 -1.284284 1.336217 NaN\n",
"2000-01-02 -0.689989 -0.189525 -0.739258 2.086142 NaN\n",
"2000-01-03 0.828906 1.126212 0.508507 0.392446 NaN\n",
"2000-01-04 0.507045 2.156742 -0.122078 -0.029870 NaN\n",
"2000-01-05 -0.122940 2.038700 0.435111 0.631309 NaN\n",
"... ... ... ... ... ...\n",
"2002-09-22 71.861553 -49.109186 15.809916 38.765663 NaN\n",
"2002-09-23 71.530190 -50.787577 16.238423 39.499372 NaN\n",
"2002-09-24 69.520185 -49.794911 15.358198 39.480654 NaN\n",
"2002-09-25 69.046552 -50.032522 15.083529 38.790532 NaN\n",
"2002-09-26 70.794982 -49.210848 15.277156 37.282224 NaN\n",
"\n",
"[1000 rows x 5 columns]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/numpy/lib/function_base.py:3826: RuntimeWarning: Invalid value encountered in percentile\n",
" interpolation=interpolation)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5cde88d0>"
]
},
"execution_count": 32,
"metadata": {},
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{
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot.box()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
" color = {'boxes': 'DarkGreen', 'whiskers': 'DarkOrange',\n",
" ....: 'medians': 'DarkBlue', 'caps': 'Gray'}\n",
" ....: "
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'boxes': 'DarkGreen',\n",
" 'whiskers': 'DarkOrange',\n",
" 'medians': 'DarkBlue',\n",
" 'caps': 'Gray'}"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"color"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/numpy/lib/function_base.py:3826: RuntimeWarning: Invalid value encountered in percentile\n",
" interpolation=interpolation)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5ccd6a20>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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r6nVj1zK2hX8X3fRLgK8Ax9Y6DJIkzwFuAz4KXAi8sqp+OG5VB/MZrwecB3y2qr6R5LEkr6qq28cuaiTPT3IH8DzgBOD1I9czpjOY/4+sp6iqb3bDNccBj4xdz6FWVT9K8l7gs8Ab12LAg8M1C70DuLF7f2M3vV7t/1m+GTgH+Fg3bCE91Xr/u/g1YA/znYE1yZ48kOQnme+tnpGkgA1AJXnfevwZulBV/UuSY4FJ4NGx6xnBLuDtYxexFnXDNU+wPv8uSPIK5o9NvBr4QpIbq2rPyGUdxJ78vLcDH6uqU6pqU1WdDNwP/NLIdY2uO9NoA/DdsWsZyT8BRya5cP+MJL+Q5JdHrGl0SSaBvwI+uB47Qt0v2w8Dl1TVg8DVwDXjVrU4Q37eO5g/g2KhTwK/MUIta8Hzu1Pl7gD+DthSVU+MXdQYugB7G3B2dwrlLmAr8PCohY1j/9/FLuBW4HPAzMg1jeVC4MGquqWb/hCweS1++Xt2jSQ1zJ68JDXMkJekhhnyktQwQ16SGmbIS1LDDHlJapghL0kN+3916PYCvInVrgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot.box(color=color,sym='r+')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/numpy/lib/function_base.py:3826: RuntimeWarning: Invalid value encountered in percentile\n",
" interpolation=interpolation)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5cc1ee10>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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FpG9HxMfU8NyHFZ/H523/XLHqYknfU478z0k63/ZbivfPxWod61h09iW5Z70QEfGU7a+p9aaYlPTxiDhYc6zpvijpOEkPFd8MvhMRv5she0RM2v6EpL9T6wj5nRHxVM2x5nKBpN+W9KTtJ4p110vaJulrtjeq9eG8vKZ885Up92ZJdxV/2J+R9Dtq7Vw2On8xbHOvpD1qfRa/q9ZU8xO0yOxMNweABLpqGAQAsqKsASAByhoAEqCsASAByhoAEqCsASAByhoAEvh/E7TmrZV0VVkAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot.box(vert=False)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.rand(10,5))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"bp = df.boxplot()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
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" <td>0.962325</td>\n",
" <td>0.848476</td>\n",
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" <td>0.578791</td>\n",
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"text/plain": [
" 0 1 2 3 4\n",
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"4 0.618213 0.348243 0.465993 0.410037 0.075490\n",
"5 0.451304 0.370856 0.321950 0.473036 0.323191\n",
"6 0.000056 0.885483 0.794796 0.341066 0.382864\n",
"7 0.925979 0.083713 0.308857 0.578791 0.486285\n",
"8 0.315289 0.058612 0.204225 0.331911 0.921956\n",
"9 0.004338 0.108532 0.366977 0.247110 0.939185"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
" df = pd.DataFrame(np.random.rand(10, 2), columns=['Col1', 'Col2'])\n",
"\n",
"df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'])\n",
"\n",
"plt.figure();"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <tbody>\n",
" <tr>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>0.163425</td>\n",
" <td>0.512852</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>0.738050</td>\n",
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" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
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" <td>0.198357</td>\n",
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" <tr>\n",
" <th>4</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.315328</td>\n",
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" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.785805</td>\n",
" <td>0.827681</td>\n",
" <td>B</td>\n",
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" <tr>\n",
" <th>7</th>\n",
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" <td>0.390828</td>\n",
" <td>B</td>\n",
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" <tr>\n",
" <th>8</th>\n",
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],
"text/plain": [
" Col1 Col2 X\n",
"0 0.263057 0.743977 A\n",
"1 0.163425 0.512852 A\n",
"2 0.738050 0.059489 A\n",
"3 0.099973 0.198357 A\n",
"4 0.761382 0.332644 A\n",
"5 0.315328 0.419373 B\n",
"6 0.785805 0.827681 B\n",
"7 0.081719 0.390828 B\n",
"8 0.585437 0.793020 B\n",
"9 0.670875 0.972819 B"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"bp = df.boxplot(by='X')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = pd.DataFrame(np.random.rand(10, 3), columns=['Col1', 'Col2', 'Col3'])\n",
"\n",
"df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'])\n",
"\n",
"df['Y'] = pd.Series(['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B'])\n",
"\n",
"plt.figure();"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"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>Col1</th>\n",
" <th>Col2</th>\n",
" <th>Col3</th>\n",
" <th>X</th>\n",
" <th>Y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.429453</td>\n",
" <td>0.041325</td>\n",
" <td>0.012199</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.652866</td>\n",
" <td>0.474267</td>\n",
" <td>0.561382</td>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.263634</td>\n",
" <td>0.444230</td>\n",
" <td>0.880844</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.514548</td>\n",
" <td>0.945724</td>\n",
" <td>0.017133</td>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.498570</td>\n",
" <td>0.502478</td>\n",
" <td>0.196871</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.376915</td>\n",
" <td>0.904495</td>\n",
" <td>0.033951</td>\n",
" <td>B</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.977275</td>\n",
" <td>0.447494</td>\n",
" <td>0.399318</td>\n",
" <td>B</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.189293</td>\n",
" <td>0.414839</td>\n",
" <td>0.113125</td>\n",
" <td>B</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.377717</td>\n",
" <td>0.690788</td>\n",
" <td>0.920750</td>\n",
" <td>B</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.154215</td>\n",
" <td>0.435503</td>\n",
" <td>0.667964</td>\n",
" <td>B</td>\n",
" <td>B</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Col1 Col2 Col3 X Y\n",
"0 0.429453 0.041325 0.012199 A A\n",
"1 0.652866 0.474267 0.561382 A B\n",
"2 0.263634 0.444230 0.880844 A A\n",
"3 0.514548 0.945724 0.017133 A B\n",
"4 0.498570 0.502478 0.196871 A A\n",
"5 0.376915 0.904495 0.033951 B B\n",
"6 0.977275 0.447494 0.399318 B A\n",
"7 0.189293 0.414839 0.113125 B B\n",
"8 0.377717 0.690788 0.920750 B A\n",
"9 0.154215 0.435503 0.667964 B B"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"bp = df.boxplot(column=['Col1','Col2'],by=['X','Y'])"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(1234)\n",
"df_box = pd.DataFrame(np.random.randn(50, 2))\n",
"\n",
"df_box['g'] = np.random.choice(['A', 'B'], size=50)\n",
"\n",
"df_box.loc[df_box['g'] == 'B', 1] += 3"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>2</th>\n",
" <td>-0.720589</td>\n",
" <td>3.887163</td>\n",
" <td>B</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>0.859588</td>\n",
" <td>-0.636524</td>\n",
" <td>A</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>0.015696</td>\n",
" <td>0.757315</td>\n",
" <td>B</td>\n",
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" <th>5</th>\n",
" <td>1.150036</td>\n",
" <td>0.991946</td>\n",
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" <th>6</th>\n",
" <td>0.953324</td>\n",
" <td>-2.021255</td>\n",
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" <th>7</th>\n",
" <td>-0.334077</td>\n",
" <td>0.002118</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>0.405453</td>\n",
" <td>0.289092</td>\n",
" <td>A</td>\n",
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" <tr>\n",
" <th>9</th>\n",
" <td>1.321158</td>\n",
" <td>-1.546906</td>\n",
" <td>A</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>-0.202646</td>\n",
" <td>2.344031</td>\n",
" <td>B</td>\n",
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" <th>11</th>\n",
" <td>0.193421</td>\n",
" <td>3.553439</td>\n",
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" </tr>\n",
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" <th>12</th>\n",
" <td>1.318152</td>\n",
" <td>2.530695</td>\n",
" <td>B</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>0.675554</td>\n",
" <td>1.182973</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>-0.183109</td>\n",
" <td>4.058969</td>\n",
" <td>B</td>\n",
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" <th>15</th>\n",
" <td>-0.397840</td>\n",
" <td>0.337438</td>\n",
" <td>A</td>\n",
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" <tr>\n",
" <th>16</th>\n",
" <td>1.047579</td>\n",
" <td>4.045938</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.863717</td>\n",
" <td>2.877908</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.124713</td>\n",
" <td>2.677205</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.841675</td>\n",
" <td>5.390961</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.076200</td>\n",
" <td>-0.566446</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.036142</td>\n",
" <td>0.925022</td>\n",
" <td>B</td>\n",
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" <tr>\n",
" <th>22</th>\n",
" <td>0.247792</td>\n",
" <td>2.102843</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>-0.136795</td>\n",
" <td>3.018289</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.755414</td>\n",
" <td>3.215269</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.841009</td>\n",
" <td>1.554190</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-1.401973</td>\n",
" <td>-0.100918</td>\n",
" <td>A</td>\n",
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" <tr>\n",
" <th>27</th>\n",
" <td>-0.548242</td>\n",
" <td>2.855380</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.354020</td>\n",
" <td>2.964487</td>\n",
" <td>B</td>\n",
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" <tr>\n",
" <th>29</th>\n",
" <td>0.565738</td>\n",
" <td>1.545659</td>\n",
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" <th>30</th>\n",
" <td>-0.974236</td>\n",
" <td>2.929655</td>\n",
" <td>B</td>\n",
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" <tr>\n",
" <th>31</th>\n",
" <td>0.307969</td>\n",
" <td>-0.208499</td>\n",
" <td>A</td>\n",
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" <tr>\n",
" <th>32</th>\n",
" <td>1.033801</td>\n",
" <td>-2.400454</td>\n",
" <td>A</td>\n",
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" <tr>\n",
" <th>33</th>\n",
" <td>2.030604</td>\n",
" <td>-1.142631</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>0.211883</td>\n",
" <td>0.704721</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>-0.785435</td>\n",
" <td>3.462060</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>0.704228</td>\n",
" <td>3.523508</td>\n",
" <td>B</td>\n",
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" <tr>\n",
" <th>37</th>\n",
" <td>-0.926254</td>\n",
" <td>5.007843</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>0.226963</td>\n",
" <td>1.847341</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>0.631979</td>\n",
" <td>3.039513</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>0.464392</td>\n",
" <td>-3.563517</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>1.321106</td>\n",
" <td>3.152631</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>0.164530</td>\n",
" <td>-0.430096</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>0.767369</td>\n",
" <td>0.984920</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>0.270836</td>\n",
" <td>1.391986</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>0.079842</td>\n",
" <td>2.600035</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>-1.027851</td>\n",
" <td>2.415282</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>0.816594</td>\n",
" <td>2.918053</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>-0.344766</td>\n",
" <td>0.528288</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>-1.068989</td>\n",
" <td>-0.511881</td>\n",
" <td>A</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 g\n",
"0 0.471435 -1.190976 A\n",
"1 1.432707 -0.312652 A\n",
"2 -0.720589 3.887163 B\n",
"3 0.859588 -0.636524 A\n",
"4 0.015696 0.757315 B\n",
"5 1.150036 0.991946 A\n",
"6 0.953324 -2.021255 A\n",
"7 -0.334077 0.002118 A\n",
"8 0.405453 0.289092 A\n",
"9 1.321158 -1.546906 A\n",
"10 -0.202646 2.344031 B\n",
"11 0.193421 3.553439 B\n",
"12 1.318152 2.530695 B\n",
"13 0.675554 1.182973 B\n",
"14 -0.183109 4.058969 B\n",
"15 -0.397840 0.337438 A\n",
"16 1.047579 4.045938 B\n",
"17 0.863717 2.877908 B\n",
"18 0.124713 2.677205 B\n",
"19 0.841675 5.390961 B\n",
"20 0.076200 -0.566446 A\n",
"21 0.036142 0.925022 B\n",
"22 0.247792 2.102843 B\n",
"23 -0.136795 3.018289 B\n",
"24 0.755414 3.215269 B\n",
"25 0.841009 1.554190 B\n",
"26 -1.401973 -0.100918 A\n",
"27 -0.548242 2.855380 B\n",
"28 0.354020 2.964487 B\n",
"29 0.565738 1.545659 A\n",
"30 -0.974236 2.929655 B\n",
"31 0.307969 -0.208499 A\n",
"32 1.033801 -2.400454 A\n",
"33 2.030604 -1.142631 A\n",
"34 0.211883 0.704721 A\n",
"35 -0.785435 3.462060 B\n",
"36 0.704228 3.523508 B\n",
"37 -0.926254 5.007843 B\n",
"38 0.226963 1.847341 B\n",
"39 0.631979 3.039513 B\n",
"40 0.464392 -3.563517 A\n",
"41 1.321106 3.152631 B\n",
"42 0.164530 -0.430096 A\n",
"43 0.767369 0.984920 A\n",
"44 0.270836 1.391986 A\n",
"45 0.079842 2.600035 B\n",
"46 -1.027851 2.415282 B\n",
"47 0.816594 2.918053 B\n",
"48 -0.344766 0.528288 A\n",
"49 -1.068989 -0.511881 A"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_box"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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3jiRlxDN9KROOuhJkHPqbaldw/vVXdFy29/q9HdYHOH7EijQoHHUlyDj0H25e7X8ASdmxT1+SMmLoS1JGDH1Jyki2ffpaPy+CS4OrlNCPiEuA/wScBPxhSunqMrar/uRFcGlwdd29ExEnAR8CXg48F6hHxHO73a4kqXxlnOm/EPhGSumbABFxE7AL+GoJ2+6pzmegwWWznT+oIkmDrozQPwe4r236fuBFJWy3pzp1T0DrjWCpZZI06MoI/egw77gfi4yI3cBugC1btjAzM1PCrnujn2vrF52O0fz8/JLHLrdj2q/t3detPMsdt76WUurqBrwY+NO26b3A3uUec9FFF6V+tfXyqapL6HtLHaP9+/evaf2NBBxIXbb19d76pb0P4uvWz5Y6bv1iqTZfxjj9LwHPiojtEfEk4DXArSVsV5JUsq67d1JKcxFxGfCntIZsfiSl9JWuK5Mkla6UcfoppU8BnypjWxoMjnySBpOfyNWaOfJJGlyGvpQJvz5DYOhL2fDrMwR+y6YkZcXQl6SMGPqSlBFDX5IyYuhLUkYMfUnKiKEvSRkx9CUpI4a+JGXE0JekjBj6kpQRQ1+SMmLoS1JGDH1JyohfrayuRMSx09ccvd/6bWb1E3/xTIY+Blc32o/PUt/Lrv7gL54J7N4BWsG1cNu/f/8x05J0IjH0JSkjhr4kZcTQl6SMGPqFyclJhoeH2bVrF8PDw0xOTlZdkiSVztE7tAJ/fHyciYkJDh8+zObNmxkbGwOgXq9XXJ0klcczfaDRaDAxMcHo6ChDQ0OMjo4yMTFBo9GoujRJKpWhDzSbTUZGRo6ZNzIyQrPZrKgiSeoNQx+o1WrMzs4eM292dpZarVZRRZLUG4Y+MD4+ztjYGNPT08zNzTE9Pc3Y2Bjj4+NVlyZJpfJCLkcv1u7Zs4dms0mtVqPRaHgRV9IJx9Av1Ot16vW63x8j6YRm944kZcTQl6SMGPqSlBFDX5Iy0lXoR8T7I+JrEfGXEXFLRDy1rMIkSeXr9kz/M8BwSul5wNeBvd2XJEnqla5CP6V0e0pprpj8PHBu9yVJknqlzD79fwb8txK3J0kq2YofzoqIzwJndVg0nlL6k2KdcWAO+Ngy29kN7AbYsmULMzMz66m35+bn5/u2tn7nsTtqUNr7gn6vrx8NbHtv/xHw9dyANwJ/AZy22sdcdNFFqV/t37+/6hIGVj8fO+BA6rKtr/fWz+09pZS2Xj5VdQkDqZ/be0pLt/muvoYhIi4BLgd+PqX0WLdvQJKk3uq2T/+DwCbgMxFxd0T8fgk1SZJ6pKsz/ZTSM8sqRJLUe34iV5IyYuhLUkYMfUnKiD+iImUoIo6dvubo/dZoP52oPNOXMtQ+bnv//v2LP3ujE5ihL0kZMfQlKSOGviRlxNCXpIwY+pKUEUNfkjJi6EtSRgx9ScqIoS9JGTH0JSkjhr4kZcTQl6SMGPqSlBFDX5IyYuhLUkYMfUnKiKEvSRkx9CUpI4a+JGXE0JekjBj6kpQRQ1/K1OTkJMPDw+zatYvh4WEmJyerLkkbYKjqAiRtvMnJScbHx5mYmODw4cNs3ryZsbExAOr1esXVqZc805cy1Gg0mJiYYHR0lKGhIUZHR5mYmKDRaFRdmnrM0Jcy1Gw2GRkZOWbeyMgIzWazooq0UQx9KUO1Wo3Z2dlj5s3OzlKr1SqqSBvF0JcyND4+ztjYGNPT08zNzTE9Pc3Y2Bjj4+NVl6Ye80KulKGFi7V79uyh2WxSq9VoNBpexM2AoS9lql6vU6/XmZqaYufOnVWXow1i944kZaSU0I+I34qIFBFnlrE9SVJvdB36EXEe8I+A/9N9OZKkXirjTP8/AO8AUgnbkiT1UFehHxG/AHwrpXRPSfVIknpoxdE7EfFZ4KwOi8aBdwIvW82OImI3sLuYfCQi/mq1RW6w7cDfVF3EgOrnY7d1I3c2QO0d+vt162f9ftw6tvlIaX29MhFxPvA54LFi1rnAIeCFKaXvrGujfSAiHk0pnV51HYPIYzeYfN3WZ1CP27rH6aeU/hfwYwvTEXEQuDil9EAJdUmSesBx+pKUkdI+kZtS2lbWtip2c9UFDDCP3WDydVufgTxu6+7TlyQNHrt3JCkjhn6biPgnxddJ/FTVtQyKiJiPiLsj4p6IuCsi/n7VNWl1bO/rM+ht3tA/Vh2YBV5TdSED5EhK6cKU0gXAXuB9VRekVbO9r89At3lDvxARZwAvAcbwP8F6PQV4qOoitDLbe2kGrs37ffpHvQr4dErp6xHxYES8IKV0V9VFDYBTI+Ju4BTgx4GXVlyPVsf2vn4D3eY90z+qDtxU3L+pmNbKFv7U/SngEuCjERFVF6UV2d7Xb6DbvEM2gYh4OnA/8D1a3xZ6UvHv1uQBWlZEPJJSOqNt+rvA+Sml71VYlpZhe+/OoLd5z/RbXg18NKW0NaW0LaV0Hq0vUhqpuK6BUowCOQn4ftW1aFm295IMYpu3T7+lDly9aN4ngdcC/2PjyxkoC/2bAAG8MaU0X2VBWpHtvTsD3ebt3pGkjNi9I0kZMfQlKSOGviRlxNCXpIwY+pKUEUO/D0XEtog40jYsbKn13h8R34mI39qo2qResM1vHMfp96+/TilduNwKKaV/HRGPblRBUo/Z5jeAoT8AIuLdwOuA+4AHgDtTSr9XbVVS79jme8fQ73MRcTHwS8Dzab1edwF3VlqU1EO2+d4y9PvfCPAnKaUjABGxv+J6pF6zzfeQF3L738B8ZatUEtt8Dxn6/W8WeGVEnFL82tGlVRck9Zhtvofs3ulzKaUvRcStwD3AvcAB4HC1VUm9Y5vvLc/0B8PvpZSeQ+sn7p6DF7V04rPN94ih35/mgc1tH1S5trh/F/DJhd8yjYj3A68HHLesQWeb3yB+n74kZcQzfUnKiKEvSRkx9CUpI4a+JGXE0JekjBj6kpSR/w+JmnR0qdKFdwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"bp = df_box.boxplot(by='g')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"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>0</th>\n",
" <th>1</th>\n",
" <th>g</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.471435</td>\n",
" <td>-1.190976</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.432707</td>\n",
" <td>-0.312652</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.720589</td>\n",
" <td>3.887163</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.859588</td>\n",
" <td>-0.636524</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.015696</td>\n",
" <td>0.757315</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1.150036</td>\n",
" <td>0.991946</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.953324</td>\n",
" <td>-2.021255</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.334077</td>\n",
" <td>0.002118</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.405453</td>\n",
" <td>0.289092</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1.321158</td>\n",
" <td>-1.546906</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>-0.202646</td>\n",
" <td>2.344031</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.193421</td>\n",
" <td>3.553439</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1.318152</td>\n",
" <td>2.530695</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.675554</td>\n",
" <td>1.182973</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>-0.183109</td>\n",
" <td>4.058969</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>-0.397840</td>\n",
" <td>0.337438</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1.047579</td>\n",
" <td>4.045938</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.863717</td>\n",
" <td>2.877908</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.124713</td>\n",
" <td>2.677205</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.841675</td>\n",
" <td>5.390961</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.076200</td>\n",
" <td>-0.566446</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.036142</td>\n",
" <td>0.925022</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.247792</td>\n",
" <td>2.102843</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>-0.136795</td>\n",
" <td>3.018289</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.755414</td>\n",
" <td>3.215269</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.841009</td>\n",
" <td>1.554190</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-1.401973</td>\n",
" <td>-0.100918</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>-0.548242</td>\n",
" <td>2.855380</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.354020</td>\n",
" <td>2.964487</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.565738</td>\n",
" <td>1.545659</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>-0.974236</td>\n",
" <td>2.929655</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>0.307969</td>\n",
" <td>-0.208499</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>1.033801</td>\n",
" <td>-2.400454</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>2.030604</td>\n",
" <td>-1.142631</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>0.211883</td>\n",
" <td>0.704721</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>-0.785435</td>\n",
" <td>3.462060</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>0.704228</td>\n",
" <td>3.523508</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>-0.926254</td>\n",
" <td>5.007843</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>0.226963</td>\n",
" <td>1.847341</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>0.631979</td>\n",
" <td>3.039513</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>0.464392</td>\n",
" <td>-3.563517</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>1.321106</td>\n",
" <td>3.152631</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>0.164530</td>\n",
" <td>-0.430096</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>0.767369</td>\n",
" <td>0.984920</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>0.270836</td>\n",
" <td>1.391986</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>0.079842</td>\n",
" <td>2.600035</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>-1.027851</td>\n",
" <td>2.415282</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>0.816594</td>\n",
" <td>2.918053</td>\n",
" <td>B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>-0.344766</td>\n",
" <td>0.528288</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>-1.068989</td>\n",
" <td>-0.511881</td>\n",
" <td>A</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 g\n",
"0 0.471435 -1.190976 A\n",
"1 1.432707 -0.312652 A\n",
"2 -0.720589 3.887163 B\n",
"3 0.859588 -0.636524 A\n",
"4 0.015696 0.757315 B\n",
"5 1.150036 0.991946 A\n",
"6 0.953324 -2.021255 A\n",
"7 -0.334077 0.002118 A\n",
"8 0.405453 0.289092 A\n",
"9 1.321158 -1.546906 A\n",
"10 -0.202646 2.344031 B\n",
"11 0.193421 3.553439 B\n",
"12 1.318152 2.530695 B\n",
"13 0.675554 1.182973 B\n",
"14 -0.183109 4.058969 B\n",
"15 -0.397840 0.337438 A\n",
"16 1.047579 4.045938 B\n",
"17 0.863717 2.877908 B\n",
"18 0.124713 2.677205 B\n",
"19 0.841675 5.390961 B\n",
"20 0.076200 -0.566446 A\n",
"21 0.036142 0.925022 B\n",
"22 0.247792 2.102843 B\n",
"23 -0.136795 3.018289 B\n",
"24 0.755414 3.215269 B\n",
"25 0.841009 1.554190 B\n",
"26 -1.401973 -0.100918 A\n",
"27 -0.548242 2.855380 B\n",
"28 0.354020 2.964487 B\n",
"29 0.565738 1.545659 A\n",
"30 -0.974236 2.929655 B\n",
"31 0.307969 -0.208499 A\n",
"32 1.033801 -2.400454 A\n",
"33 2.030604 -1.142631 A\n",
"34 0.211883 0.704721 A\n",
"35 -0.785435 3.462060 B\n",
"36 0.704228 3.523508 B\n",
"37 -0.926254 5.007843 B\n",
"38 0.226963 1.847341 B\n",
"39 0.631979 3.039513 B\n",
"40 0.464392 -3.563517 A\n",
"41 1.321106 3.152631 B\n",
"42 0.164530 -0.430096 A\n",
"43 0.767369 0.984920 A\n",
"44 0.270836 1.391986 A\n",
"45 0.079842 2.600035 B\n",
"46 -1.027851 2.415282 B\n",
"47 0.816594 2.918053 B\n",
"48 -0.344766 0.528288 A\n",
"49 -1.068989 -0.511881 A"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_box"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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cpw8AgeR2R667z+e1roI9UXQBFUbbVRP7bTKVbLeJx/QBANXD8A4ABELoX8XMTpjZN83sJTM7WXQ9VWFmnzWzV83sn4quBeOjv0+m6v2d0N9mZjOSPi3p/ZKOS2qY2fFiq6qMJyWdKLoIjI/+nsmTqnB/J/R33CvpJXf/lrv/n6SnJT1YcE2V4O5flvRfRdeBfaG/T6jq/Z3Q33GHpO9cNf3y9jzgIKK/B0Xo77Ah87i0CQcV/T0oQn/Hy5LuvGr68mMlgIOI/h4Uob/jq5LebWbHzOwdkh6S9IWCawKmhf4eFKG/zd03JD0i6VlJqaTPu/uLxVZVDWbWlfR3kn7UzF42s2bRNWFv9PfJVb2/c0cuAATCkT4ABELoA0AghD4ABELoA0AghD4ABELoA0AghD4ABELoA0Ag/w9POBT/g1gofAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"bp = df_box.groupby('g').boxplot()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
">>> df = pd.DataFrame({\n",
"... 'sales': [3, 2, 3, 9, 10, 6],\n",
"... 'signups': [5, 5, 6, 12, 14, 13],\n",
"... 'visits': [20, 42, 28, 62, 81, 50],\n",
"... }, index=pd.date_range(start='2018/01/01', end='2018/07/01',\n",
"... freq='M'))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"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>sales</th>\n",
" <th>signups</th>\n",
" <th>visits</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2018-01-31</th>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-02-28</th>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-03-31</th>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-04-30</th>\n",
" <td>9</td>\n",
" <td>12</td>\n",
" <td>62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-05-31</th>\n",
" <td>10</td>\n",
" <td>14</td>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-06-30</th>\n",
" <td>6</td>\n",
" <td>13</td>\n",
" <td>50</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sales signups visits\n",
"2018-01-31 3 5 20\n",
"2018-02-28 2 5 42\n",
"2018-03-31 3 6 28\n",
"2018-04-30 9 12 62\n",
"2018-05-31 10 14 81\n",
"2018-06-30 6 13 50"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = df.plot.area()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = df.plot.area(stacked=False)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = df.plot.area(y='sales')"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
">>> df = pd.DataFrame({\n",
"... 'sales': [3, 2, 3],\n",
"... 'visits': [20, 42, 28],\n",
"... 'day': [1, 2, 3],\n",
"... })"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"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>sales</th>\n",
" <th>visits</th>\n",
" <th>day</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" <td>20</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>42</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>28</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sales visits day\n",
"0 3 20 1\n",
"1 2 42 2\n",
"2 3 28 3"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5c5b1dd8>"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot.area(x='day')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])\n"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.556895</td>\n",
" <td>0.084774</td>\n",
" <td>0.333002</td>\n",
" <td>0.728429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.142435</td>\n",
" <td>0.552469</td>\n",
" <td>0.273043</td>\n",
" <td>0.974495</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.667787</td>\n",
" <td>0.255653</td>\n",
" <td>0.108311</td>\n",
" <td>0.776181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.782478</td>\n",
" <td>0.761604</td>\n",
" <td>0.914403</td>\n",
" <td>0.658623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.568368</td>\n",
" <td>0.201756</td>\n",
" <td>0.698296</td>\n",
" <td>0.952195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.889963</td>\n",
" <td>0.993567</td>\n",
" <td>0.818704</td>\n",
" <td>0.545122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.451254</td>\n",
" <td>0.890557</td>\n",
" <td>0.973265</td>\n",
" <td>0.593411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.366074</td>\n",
" <td>0.323095</td>\n",
" <td>0.871423</td>\n",
" <td>0.215634</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.734945</td>\n",
" <td>0.365619</td>\n",
" <td>0.801603</td>\n",
" <td>0.782736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.701355</td>\n",
" <td>0.622777</td>\n",
" <td>0.493683</td>\n",
" <td>0.840538</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 0.556895 0.084774 0.333002 0.728429\n",
"1 0.142435 0.552469 0.273043 0.974495\n",
"2 0.667787 0.255653 0.108311 0.776181\n",
"3 0.782478 0.761604 0.914403 0.658623\n",
"4 0.568368 0.201756 0.698296 0.952195\n",
"5 0.889963 0.993567 0.818704 0.545122\n",
"6 0.451254 0.890557 0.973265 0.593411\n",
"7 0.366074 0.323095 0.871423 0.215634\n",
"8 0.734945 0.365619 0.801603 0.782736\n",
"9 0.701355 0.622777 0.493683 0.840538"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5c5a1dd8>"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot.area()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5c4ac438>"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot.area(stacked=False)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
">>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],\n",
"... [6.4, 3.2, 1], [5.9, 3.0, 2]],\n",
"... columns=['length', 'width', 'species'])"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"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>length</th>\n",
" <th>width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7.0</td>\n",
" <td>3.2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6.4</td>\n",
" <td>3.2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.9</td>\n",
" <td>3.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" length width species\n",
"0 5.1 3.5 0\n",
"1 4.9 3.0 0\n",
"2 7.0 3.2 1\n",
"3 6.4 3.2 1\n",
"4 5.9 3.0 2"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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s39T2PJLeUVcNAICZ8c5iACgcQQAAhSMIAKBwBAEAFI4gAIDCEQQAUDiCAAAKRxAAQOFqm2KiLrabkh7t08uvkPSTPr32fED/TI/+mR79M71ftn9WJek6a+e8C4J+sj021VwdoH9mQv9Mj/6ZXp39w6khACgcQQAAhSMITs3mfhdwhqN/pkf/TI/+mV5t/cM1AgAoHEcEAFA4ggAACkcQdGF73Pa3be+x/bTPxXTLx20/Ynuv7Uv7UWe/zKJ/XmF7otq+x/ZN/aizX2yfZ/sLth+2/ZDtyzu2lz5+ZuqfYseP7ee3/dx7bD9p+70dbeZ8/NT5UZXz3SuTTPXmjaslPa96vFTSzdXXkkzXP5J0d5Jre1bNmeVjkr6a5HrbSyR1fr5l6eNnpv6RCh0/Sf5b0hpJqj7m938k3dbRbM7HD0cEp+c6SZ9Jyw5J59le2e+i0H+2l0l6uaRRSUoymWR/R7Nix88s+wctr5L03SSdMynM+fghCLqLpDts77a9vsv2CyT9oG35sWpdKWbqH0m63PYDtrfZflEvi+uz50pqSvq07W/Z/pTtczralDx+ZtM/Urnjp92bJG3psn7Oxw9B0N1vJ7lUrUOwd9h+ecd2d/meku7Dnal/vqnWvCYXS/qEpC/1usA+WizpUkk3J7lE0v9K+mBHm5LHz2z6p+TxI0mqTpn9gaTPd9vcZd0vNX4Igi6S/LD6uk+t83NrO5o8JulZbcsXSvphb6rrv5n6J8mTSQ5Uz2+XNGB7Rc8L7Y/HJD2WZGe1/AW1/vB1til1/MzYP4WPnxOulvTNJD/usm3Oxw9B0MH2ObbPPfFc0mskfaej2Zclvbm6ev8ySRNJHu9xqX0xm/6xfb5tV8/XqjXOnuh1rf2Q5EeSfmD7+dWqV0n6r45mxY6f2fRPyeOnzQ3qflpIqmH8cNfQ0/26pNuqcbhY0meTfNX2BklKsknS7ZKukfSIpIOSbuxTrf0wm/65XtLbbR+VdEjSm1LWW9jfJelfq8P770m6kfFzkpn6p+jxY3uppFdL+qO2dbWOH6aYAIDCcWoIAApHEABA4QgCACgcQQAAhSMIAKBwBAHQwfaBGva5xvY1bcsfsv2+uX4d4HQQBEBvrFHr3m/gjEMQANOw/X7bu6p53zdW64aqefQ/aftB23fYHqy2XVa1vc/2R2x/p3rj1F9KemM1x/wbq92/0PZdtr9n+919+hEBggCYiu3XqDXn+1q1/qN/SdsEe8+T9PdJXiRpv6Q3VOs/LWlDksslHZNaUy1LuknS55KsSfK5qu1FktZV+/8L2wM9+LGApyEIgKm9pnp8S60ZMS9SKwAk6ftJ9lTPd0sasn2epHOT3Fut/+wM+/9KksPVB/zsU2v6DqDnmGsImJol/U2SfzxppT0k6XDbqmOSBtV9euDpdO6D30f0BUcEwNS2S3qr7WdIku0LbP/aVI2T/EzSU9WMkFLrg0VOeErSubVVCvwSCAJgCknuUOv0zn22v63W3Pkz/TEfkbTZ9n1qHSFMVOv/Q62Lw+0Xi4EzArOPAnPI9jNOfKiK7Q9KWpnkPX0uC5gW5ySBufVa23+m1u/Wo5Le0t9ygJlxRAAAheMaAQAUjiAAgMIRBABQOIIAAApHEABA4f4fnhFK/Q9+1u4AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax1 = df.plot.scatter(x='length',y='width',c='darkblue')"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax2 = df.plot.scatter(x='length',y='width',c='species',colormap='viridis')"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
" df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.753544</td>\n",
" <td>0.270584</td>\n",
" <td>0.522303</td>\n",
" <td>0.098329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.713637</td>\n",
" <td>0.884041</td>\n",
" <td>0.567054</td>\n",
" <td>0.994482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.178740</td>\n",
" <td>0.012200</td>\n",
" <td>0.456998</td>\n",
" <td>0.931752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.846025</td>\n",
" <td>0.473330</td>\n",
" <td>0.902555</td>\n",
" <td>0.225996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.304154</td>\n",
" <td>0.714994</td>\n",
" <td>0.724091</td>\n",
" <td>0.018676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.285813</td>\n",
" <td>0.580486</td>\n",
" <td>0.930787</td>\n",
" <td>0.338997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.120083</td>\n",
" <td>0.516273</td>\n",
" <td>0.699207</td>\n",
" <td>0.298641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.861610</td>\n",
" <td>0.905807</td>\n",
" <td>0.768583</td>\n",
" <td>0.261232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.938456</td>\n",
" <td>0.938642</td>\n",
" <td>0.745045</td>\n",
" <td>0.910735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.237225</td>\n",
" <td>0.494967</td>\n",
" <td>0.809878</td>\n",
" <td>0.954566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.637483</td>\n",
" <td>0.910850</td>\n",
" <td>0.692137</td>\n",
" <td>0.042943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.833587</td>\n",
" <td>0.369949</td>\n",
" <td>0.936557</td>\n",
" <td>0.483053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.125332</td>\n",
" <td>0.964454</td>\n",
" <td>0.017026</td>\n",
" <td>0.676571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.140440</td>\n",
" <td>0.155313</td>\n",
" <td>0.649558</td>\n",
" <td>0.981654</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.694807</td>\n",
" <td>0.761974</td>\n",
" <td>0.425209</td>\n",
" <td>0.138893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.216186</td>\n",
" <td>0.764700</td>\n",
" <td>0.054606</td>\n",
" <td>0.490123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.180690</td>\n",
" <td>0.630925</td>\n",
" <td>0.551242</td>\n",
" <td>0.567081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.818345</td>\n",
" <td>0.938987</td>\n",
" <td>0.192901</td>\n",
" <td>0.712844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.697940</td>\n",
" <td>0.258296</td>\n",
" <td>0.915808</td>\n",
" <td>0.532358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.557964</td>\n",
" <td>0.322772</td>\n",
" <td>0.338631</td>\n",
" <td>0.332302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.978818</td>\n",
" <td>0.203215</td>\n",
" <td>0.667026</td>\n",
" <td>0.574784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.051975</td>\n",
" <td>0.542838</td>\n",
" <td>0.207949</td>\n",
" <td>0.091090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.869856</td>\n",
" <td>0.027370</td>\n",
" <td>0.968625</td>\n",
" <td>0.327500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.410278</td>\n",
" <td>0.135544</td>\n",
" <td>0.127066</td>\n",
" <td>0.413984</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.617444</td>\n",
" <td>0.132267</td>\n",
" <td>0.974481</td>\n",
" <td>0.180635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.760350</td>\n",
" <td>0.482162</td>\n",
" <td>0.768079</td>\n",
" <td>0.302898</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.015175</td>\n",
" <td>0.463369</td>\n",
" <td>0.703337</td>\n",
" <td>0.066041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.669011</td>\n",
" <td>0.345479</td>\n",
" <td>0.591721</td>\n",
" <td>0.229053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.267568</td>\n",
" <td>0.932827</td>\n",
" <td>0.826145</td>\n",
" <td>0.145443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.647004</td>\n",
" <td>0.883951</td>\n",
" <td>0.741361</td>\n",
" <td>0.515711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>0.135252</td>\n",
" <td>0.039884</td>\n",
" <td>0.834322</td>\n",
" <td>0.117058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>0.523102</td>\n",
" <td>0.873058</td>\n",
" <td>0.038708</td>\n",
" <td>0.692391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>0.476268</td>\n",
" <td>0.169264</td>\n",
" <td>0.063172</td>\n",
" <td>0.032224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>0.242431</td>\n",
" <td>0.112707</td>\n",
" <td>0.404990</td>\n",
" <td>0.042073</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>0.926565</td>\n",
" <td>0.935263</td>\n",
" <td>0.810427</td>\n",
" <td>0.188343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>0.587318</td>\n",
" <td>0.903511</td>\n",
" <td>0.967342</td>\n",
" <td>0.067539</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>0.317586</td>\n",
" <td>0.696863</td>\n",
" <td>0.640265</td>\n",
" <td>0.632623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>0.828751</td>\n",
" <td>0.713110</td>\n",
" <td>0.335480</td>\n",
" <td>0.167695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>0.764690</td>\n",
" <td>0.359315</td>\n",
" <td>0.088005</td>\n",
" <td>0.729568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>0.881050</td>\n",
" <td>0.041264</td>\n",
" <td>0.174868</td>\n",
" <td>0.420097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>0.340607</td>\n",
" <td>0.044769</td>\n",
" <td>0.112891</td>\n",
" <td>0.438041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>0.071073</td>\n",
" <td>0.816245</td>\n",
" <td>0.866116</td>\n",
" <td>0.131120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>0.193219</td>\n",
" <td>0.437493</td>\n",
" <td>0.970925</td>\n",
" <td>0.773861</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>0.131776</td>\n",
" <td>0.927180</td>\n",
" <td>0.827797</td>\n",
" <td>0.392732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>0.140571</td>\n",
" <td>0.052136</td>\n",
" <td>0.092729</td>\n",
" <td>0.886990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>0.168151</td>\n",
" <td>0.362887</td>\n",
" <td>0.654282</td>\n",
" <td>0.604822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>0.394993</td>\n",
" <td>0.374605</td>\n",
" <td>0.063073</td>\n",
" <td>0.428959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>0.719721</td>\n",
" <td>0.768031</td>\n",
" <td>0.194368</td>\n",
" <td>0.862871</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>0.028722</td>\n",
" <td>0.902129</td>\n",
" <td>0.236546</td>\n",
" <td>0.128584</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>0.317876</td>\n",
" <td>0.848761</td>\n",
" <td>0.832298</td>\n",
" <td>0.345651</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 0.753544 0.270584 0.522303 0.098329\n",
"1 0.713637 0.884041 0.567054 0.994482\n",
"2 0.178740 0.012200 0.456998 0.931752\n",
"3 0.846025 0.473330 0.902555 0.225996\n",
"4 0.304154 0.714994 0.724091 0.018676\n",
"5 0.285813 0.580486 0.930787 0.338997\n",
"6 0.120083 0.516273 0.699207 0.298641\n",
"7 0.861610 0.905807 0.768583 0.261232\n",
"8 0.938456 0.938642 0.745045 0.910735\n",
"9 0.237225 0.494967 0.809878 0.954566\n",
"10 0.637483 0.910850 0.692137 0.042943\n",
"11 0.833587 0.369949 0.936557 0.483053\n",
"12 0.125332 0.964454 0.017026 0.676571\n",
"13 0.140440 0.155313 0.649558 0.981654\n",
"14 0.694807 0.761974 0.425209 0.138893\n",
"15 0.216186 0.764700 0.054606 0.490123\n",
"16 0.180690 0.630925 0.551242 0.567081\n",
"17 0.818345 0.938987 0.192901 0.712844\n",
"18 0.697940 0.258296 0.915808 0.532358\n",
"19 0.557964 0.322772 0.338631 0.332302\n",
"20 0.978818 0.203215 0.667026 0.574784\n",
"21 0.051975 0.542838 0.207949 0.091090\n",
"22 0.869856 0.027370 0.968625 0.327500\n",
"23 0.410278 0.135544 0.127066 0.413984\n",
"24 0.617444 0.132267 0.974481 0.180635\n",
"25 0.760350 0.482162 0.768079 0.302898\n",
"26 0.015175 0.463369 0.703337 0.066041\n",
"27 0.669011 0.345479 0.591721 0.229053\n",
"28 0.267568 0.932827 0.826145 0.145443\n",
"29 0.647004 0.883951 0.741361 0.515711\n",
"30 0.135252 0.039884 0.834322 0.117058\n",
"31 0.523102 0.873058 0.038708 0.692391\n",
"32 0.476268 0.169264 0.063172 0.032224\n",
"33 0.242431 0.112707 0.404990 0.042073\n",
"34 0.926565 0.935263 0.810427 0.188343\n",
"35 0.587318 0.903511 0.967342 0.067539\n",
"36 0.317586 0.696863 0.640265 0.632623\n",
"37 0.828751 0.713110 0.335480 0.167695\n",
"38 0.764690 0.359315 0.088005 0.729568\n",
"39 0.881050 0.041264 0.174868 0.420097\n",
"40 0.340607 0.044769 0.112891 0.438041\n",
"41 0.071073 0.816245 0.866116 0.131120\n",
"42 0.193219 0.437493 0.970925 0.773861\n",
"43 0.131776 0.927180 0.827797 0.392732\n",
"44 0.140571 0.052136 0.092729 0.886990\n",
"45 0.168151 0.362887 0.654282 0.604822\n",
"46 0.394993 0.374605 0.063073 0.428959\n",
"47 0.719721 0.768031 0.194368 0.862871\n",
"48 0.028722 0.902129 0.236546 0.128584\n",
"49 0.317876 0.848761 0.832298 0.345651"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5c2dd550>"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot.scatter(x='a',y='b')"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5c292198>"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = df.plot.scatter(x='a',y='b',color='DarkBlue',label='Group 1')\n",
"df.plot.scatter(x='c',y='d',color='DarkGreen',label='Group 2',ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5c123b00>"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot.scatter(x='a',y='b',c='c',s=50)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5c831f28>"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot.scatter(x='a',y='b',s=df['c']*200)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [],
"source": [
">>> n = 10000\n",
">>> df = pd.DataFrame({'x': np.random.randn(n),\n",
"... 'y': np.random.randn(n)})"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"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>x</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.218905</td>\n",
" <td>1.588921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1.590539</td>\n",
" <td>-1.404046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.365772</td>\n",
" <td>1.358838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.301151</td>\n",
" <td>-0.241061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.290636</td>\n",
" <td>0.793198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9995</th>\n",
" <td>1.153491</td>\n",
" <td>0.331849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9996</th>\n",
" <td>-0.385311</td>\n",
" <td>-0.214749</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9997</th>\n",
" <td>0.055015</td>\n",
" <td>0.074507</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9998</th>\n",
" <td>-0.956206</td>\n",
" <td>-0.087480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9999</th>\n",
" <td>-0.288227</td>\n",
" <td>0.043489</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10000 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" x y\n",
"0 0.218905 1.588921\n",
"1 -1.590539 -1.404046\n",
"2 0.365772 1.358838\n",
"3 2.301151 -0.241061\n",
"4 -1.290636 0.793198\n",
"... ... ...\n",
"9995 1.153491 0.331849\n",
"9996 -0.385311 -0.214749\n",
"9997 0.055015 0.074507\n",
"9998 -0.956206 -0.087480\n",
"9999 -0.288227 0.043489\n",
"\n",
"[10000 rows x 2 columns]"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = df.plot.hexbin(x='x',y='y',gridsize=20)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
">>> n = 500\n",
">>> df = pd.DataFrame({\n",
"... 'coord_x': np.random.uniform(-3, 3, size=n),\n",
"... 'coord_y': np.random.uniform(30, 50, size=n),\n",
"... 'observations': np.random.randint(1,5, size=n)\n",
"... })"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"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>coord_x</th>\n",
" <th>coord_y</th>\n",
" <th>observations</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-2.557690</td>\n",
" <td>32.295721</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.566970</td>\n",
" <td>41.520797</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1.022102</td>\n",
" <td>43.482772</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.203928</td>\n",
" <td>32.086235</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-2.098471</td>\n",
" <td>30.782389</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495</th>\n",
" <td>1.943187</td>\n",
" <td>45.139737</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>496</th>\n",
" <td>0.031662</td>\n",
" <td>48.049942</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>497</th>\n",
" <td>-2.656090</td>\n",
" <td>38.762819</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>498</th>\n",
" <td>-2.726567</td>\n",
" <td>32.537402</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>499</th>\n",
" <td>2.793479</td>\n",
" <td>43.268417</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>500 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" coord_x coord_y observations\n",
"0 -2.557690 32.295721 1\n",
"1 1.566970 41.520797 3\n",
"2 -1.022102 43.482772 1\n",
"3 0.203928 32.086235 1\n",
"4 -2.098471 30.782389 3\n",
".. ... ... ...\n",
"495 1.943187 45.139737 4\n",
"496 0.031662 48.049942 3\n",
"497 -2.656090 38.762819 2\n",
"498 -2.726567 32.537402 4\n",
"499 2.793479 43.268417 3\n",
"\n",
"[500 rows x 3 columns]"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"observations\n",
"1 AxesSubplot(0.125,0.125;0.775x0.755)\n",
"2 AxesSubplot(0.125,0.125;0.775x0.755)\n",
"3 AxesSubplot(0.125,0.125;0.775x0.755)\n",
"4 AxesSubplot(0.125,0.125;0.775x0.755)\n",
"dtype: object"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.groupby('observations').plot.scatter(x='coord_x',y='coord_y')"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = df.plot.hexbin(x='coord_x',y='coord_y',C='observations',reduce_C_function=np.sum,gridsize=10,cmap='viridis')"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])\n",
"df['b'] = df['b'] + np.arange(1000)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"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>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.565735</td>\n",
" <td>0.498756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.611110</td>\n",
" <td>2.537175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.669799</td>\n",
" <td>2.280152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.921951</td>\n",
" <td>3.249606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.073289</td>\n",
" <td>4.287813</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>-0.387933</td>\n",
" <td>995.852138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>-1.205753</td>\n",
" <td>996.638920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>-0.244064</td>\n",
" <td>996.839804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>0.557995</td>\n",
" <td>998.205084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>-0.173457</td>\n",
" <td>999.956801</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" a b\n",
"0 1.565735 0.498756\n",
"1 0.611110 2.537175\n",
"2 0.669799 2.280152\n",
"3 1.921951 3.249606\n",
"4 1.073289 4.287813\n",
".. ... ...\n",
"995 -0.387933 995.852138\n",
"996 -1.205753 996.638920\n",
"997 -0.244064 996.839804\n",
"998 0.557995 998.205084\n",
"999 -0.173457 999.956801\n",
"\n",
"[1000 rows x 2 columns]"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.565735</td>\n",
" <td>0.498756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.611110</td>\n",
" <td>2.537175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.669799</td>\n",
" <td>2.280152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.921951</td>\n",
" <td>3.249606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.073289</td>\n",
" <td>4.287813</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>-0.387933</td>\n",
" <td>995.852138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>-1.205753</td>\n",
" <td>996.638920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>-0.244064</td>\n",
" <td>996.839804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>0.557995</td>\n",
" <td>998.205084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>-0.173457</td>\n",
" <td>999.956801</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" a b\n",
"0 1.565735 0.498756\n",
"1 0.611110 2.537175\n",
"2 0.669799 2.280152\n",
"3 1.921951 3.249606\n",
"4 1.073289 4.287813\n",
".. ... ...\n",
"995 -0.387933 995.852138\n",
"996 -1.205753 996.638920\n",
"997 -0.244064 996.839804\n",
"998 0.557995 998.205084\n",
"999 -0.173457 999.956801\n",
"\n",
"[1000 rows x 2 columns]"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5bb9c4e0>"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot.hexbin(x='a',y='b',gridsize=25)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [],
"source": [
"In [72]: df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])\n",
"\n",
"In [73]: df['b'] = df['b'] = df['b'] + np.arange(1000)\n",
"\n",
"In [74]: df['z'] = np.random.uniform(0, 3, 1000)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"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>a</th>\n",
" <th>b</th>\n",
" <th>z</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.655297</td>\n",
" <td>-0.146557</td>\n",
" <td>1.847875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.344382</td>\n",
" <td>1.029792</td>\n",
" <td>1.346556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.323828</td>\n",
" <td>2.431772</td>\n",
" <td>2.600857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.096341</td>\n",
" <td>3.590487</td>\n",
" <td>1.960842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.803216</td>\n",
" <td>4.761059</td>\n",
" <td>1.561176</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>1.039997</td>\n",
" <td>995.766861</td>\n",
" <td>1.072278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>-1.006554</td>\n",
" <td>995.347934</td>\n",
" <td>0.985423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>0.016243</td>\n",
" <td>999.254952</td>\n",
" <td>0.327488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>-0.521683</td>\n",
" <td>998.119409</td>\n",
" <td>2.420683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>-1.351125</td>\n",
" <td>998.131652</td>\n",
" <td>0.039982</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" a b z\n",
"0 -0.655297 -0.146557 1.847875\n",
"1 -0.344382 1.029792 1.346556\n",
"2 -0.323828 2.431772 2.600857\n",
"3 0.096341 3.590487 1.960842\n",
"4 0.803216 4.761059 1.561176\n",
".. ... ... ...\n",
"995 1.039997 995.766861 1.072278\n",
"996 -1.006554 995.347934 0.985423\n",
"997 0.016243 999.254952 0.327488\n",
"998 -0.521683 998.119409 2.420683\n",
"999 -1.351125 998.131652 0.039982\n",
"\n",
"[1000 rows x 3 columns]"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5c96def0>"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.plot.hexbin(x='a',y='b',C='z',reduce_C_function=np.max,gridsize=25)"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [],
"source": [
">>> df = pd.DataFrame({'mass': [0.330, 4.87 , 5.97],\n",
"... 'radius': [2439.7, 6051.8, 6378.1]},\n",
"... index=['Mercury', 'Venus', 'Earth'])"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"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>mass</th>\n",
" <th>radius</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Mercury</th>\n",
" <td>0.33</td>\n",
" <td>2439.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Venus</th>\n",
" <td>4.87</td>\n",
" <td>6051.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Earth</th>\n",
" <td>5.97</td>\n",
" <td>6378.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mass radius\n",
"Mercury 0.33 2439.7\n",
"Venus 4.87 6051.8\n",
"Earth 5.97 6378.1"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fad5bd4e940>"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot.pie(y='mass')"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x216 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot = df.plot.pie(subplots=True,figsize=(6,3))"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [],
"source": [
"In [76]: series = pd.Series(3 * np.random.rand(4),\n",
" ....: index=['a', 'b', 'c', 'd'], name='series')\n",
" ....: "
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1.384287\n",
"b 2.941502\n",
"c 1.149064\n",
"d 1.204506\n",
"Name: series, dtype: float64"
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"series.plot.pie(figsize=(6,6))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"In [76]: series = pd.Series(3 * np.random.rand(4),\n",
" ....: index=['a', 'b', 'c', 'd'], name='series')\n",
" ....: "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 2.477664\n",
"b 0.165769\n",
"c 2.580148\n",
"d 0.755987\n",
"Name: series, dtype: float64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'series' 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-2-f00b6bd37ffc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'series' is not defined"
]
}
],
"source": [
"series.plot.pie(figsize=(6,6))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from pandas.plotting import scatter_matrix\n",
"\n",
"df = pd.DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd'])\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.062741</td>\n",
" <td>-2.276163</td>\n",
" <td>0.591181</td>\n",
" <td>3.337321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.577191</td>\n",
" <td>1.669506</td>\n",
" <td>-0.081322</td>\n",
" <td>2.000010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.154028</td>\n",
" <td>0.978481</td>\n",
" <td>-1.219969</td>\n",
" <td>0.453383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.808191</td>\n",
" <td>-0.987684</td>\n",
" <td>0.117777</td>\n",
" <td>-1.591473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.296968</td>\n",
" <td>-0.718517</td>\n",
" <td>-0.910116</td>\n",
" <td>0.885661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>1.563998</td>\n",
" <td>-0.759901</td>\n",
" <td>1.088857</td>\n",
" <td>-1.065195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>-1.255323</td>\n",
" <td>-0.011111</td>\n",
" <td>0.605096</td>\n",
" <td>0.712969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>0.757298</td>\n",
" <td>-0.918231</td>\n",
" <td>-0.166054</td>\n",
" <td>0.318441</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>0.511578</td>\n",
" <td>0.187296</td>\n",
" <td>-1.420958</td>\n",
" <td>0.807462</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>-0.129998</td>\n",
" <td>-0.634672</td>\n",
" <td>-0.388047</td>\n",
" <td>-0.541843</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 0.062741 -2.276163 0.591181 3.337321\n",
"1 0.577191 1.669506 -0.081322 2.000010\n",
"2 -0.154028 0.978481 -1.219969 0.453383\n",
"3 -0.808191 -0.987684 0.117777 -1.591473\n",
"4 0.296968 -0.718517 -0.910116 0.885661\n",
".. ... ... ... ...\n",
"995 1.563998 -0.759901 1.088857 -1.065195\n",
"996 -1.255323 -0.011111 0.605096 0.712969\n",
"997 0.757298 -0.918231 -0.166054 0.318441\n",
"998 0.511578 0.187296 -1.420958 0.807462\n",
"999 -0.129998 -0.634672 -0.388047 -0.541843\n",
"\n",
"[1000 rows x 4 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f341eaf7160>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f341ea6dc50>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f341d224240>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f341d1d37f0>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x7f341d185da0>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f341d142390>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f341d172940>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f341d126f28>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x7f341d126f60>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f341d094a90>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f341d053080>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f341d001630>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x7f341d036be0>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f341cff41d0>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f341cfa0780>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f341cf54d30>]],\n",
" dtype=object)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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teHGzxeW1mKI2U8POdBRn2cTzhoeHpaiHFLKzvOZoVpBWNa5lDtzrW62PHb73Jzm3xylNrUjKmv1xzt/fPsW1LX7vQhfftdnsBMyLioNJzp1RCmj+vw+GnCYVceBwoRfx8nab7W5AWioWZcMoKUgKiW1ZtEOHTmiCcydyHwlcliUIrOcnIADnxAOljfo48hwOZX5uZnc8L7CE6cmstz+kKReNRGlDAz6c5Nw4SXhlu0N7WVa6e5piLVXh/di4/FaN4mCaM8kqbg8TJHBvlDJJayLfJi1rPDtimlcoDYfjnHvjnG7kMitqrq61nmgV/1lwOCvQ2vy+CgpfA540L+GbhH7smWAg+NTG8OMwyUzDLq8k6iEBTzd0z5vBx/OSQeRRS4e2b2NbPgeTHCFgllbYtkUncBgtSk7TgnFisoBJUvHmlQFpWXNcNDQqoBN47PR8JmmFZQmOZhkIzSD2EQIWhRG61Y3iNCnZaBuK7NkZ+zzbRHcjl10VIPWjNszDRcE4Kdmf5lzdiPFs+5GgcYbYt9nt+twdZYySknFak+Y1WaNZa3n86aubbLRMxnBjmBD5FmWtDJVVa6Z5zW43pBM4bHUCfn53yjgtiTwbIQQXeiE9y8GyQCgIl8yyqlGcpiW+LZBa0A6c5yZb2Gz754f+ODXamci1ScuG07RkljU8mGQMWh5FJdGYEupeL6AXuaRVzfXtNkotJ85ZAscyNu2N1Ly40eLFjRiFIClqPjiec2OY0A1dlBJklSkHlrXFKClRwOsXuvRDj7/+4IRGmUDy8labwLW4PHh6c70noRO4zPKadvDpx/4qKKzwWDztxCipDPXxrO7Zjz3SypRzepFHJ3QYxB6zvObGccJvD+YMYo+sbLi0FvHe4YLTrOR0UXJ5rYVA8MZmm37kEvsu/3BLMskWWEKw2wuoGk1a1gghuHGc0o8qIt8hDmyYgevapKVECIXvWstafM1wXnKyKJkXjWk8d3wqqdhcZgmfJnB7lnjcjc4Wgp/cPCUpG4TQ/OmrO49lIO31Q9q+h9QZoWvT8l0aaTKxslE8mJT0Ip/Ytbm61kIrWIt9GqVplEIguD1KuDtOCRwLz7FIigYpNVJrRknBVjfgxY0W7x4tOE1qhMjJK8k4LfnN/pyW77LXD/j+5QGuLViUDb5jnTe7v25YlmBz2Q/ohaZ8dHeUcuMkoWgkketgCYEFvLU/JXAdxknJB8eLc3GkAC4OwnPV8sVBROhZ7I9zTpKSv71xSuw7vH0w57cHc2Z5QyM167FnRH+WQGrF0bzgV/dnvLTVYrcfstcLTUBNSspGYgmB5whmeU3Ldz4mIvysuLQWGfr1E8auroLCCp8bRW1q0Fqbpu00q9Fas9MNUcqUP5KiIaskB5OMUVJxmpRYFry63eZwWvDByYKyllTS0PQcy9w050VDUtYEvs2V9ZikbLg4iAk9m7XY5YOTFN+zWG/7BK7DZsen5bvcOE4IPIvTpOLt/Tl3/YwfXh1wOM05ntVsdcyYQse2OE0rPLvCd2zuTzJ8x+LaRuup3nxFLTmeF8S+Q8t3OF02Bb8Or6V26BK6Np5tUTX6EeriaFHyztGcTuCy3fHpRQ5rsQcIfnC5x3ARcXdsSh37k4xGSk6Tin7k8tJWmxc3W/zi7pisqmlsm98cLAhsmGTmYLo0iAk9i1FSc2UtZpE3bLT8cwvooonwHYv3j1OO5gUvbDiktWSWVxzPS6TSOLbg5a32I3YnzwKWJRjNSt47XgCCna7PXj9AacXhtCByHW6dppzMbWwb0rLGssyoUiEEWmtuj1JmWc3ltZCf3ZmQlRKlNf3I5d5pQl43bHUCAlcQeg7d0MO2YZY1OJZFL3T5f98/YX9SoLXi+5e6nCwqtroRea24NUqpGk3k27zwJZSSnmYO9yoorPBEnN2ix2mFgPPGc1HL87r30aw4/zhzDN2zbOSS8lihlLGmuLQWMVpU3BwmdAKbpGiYZDXXNmJagc1gSQ3ccQK6occL69DxHSaZoU3mpUQBb+x1WI89Lg4iDmYFWSm51A84nOXcO804mue0fZfdvpm7cDzPmedmwphlCW6cLNifFFgWvLzVXnrbKDOj+AlsmTNn16JWzPMG2wKpDNf91Z3OF77RPQnrLZ/vX+5xb5zx+m4X1/7w5/3T3THH04LjpKQfOpSNohU4bLfNRLrjhekHvLHTIpOag2lBrTRx4PCvXt7gaJqzPys4nub0W96yXp4ReBZt3+XVnS6+62BZDceLnBc21ykbxf1Jyv6kIK8bvrvX49IgpB3YdAKPy+shR7OS/WmG51hstAIapXlGycIjyCrJoOXx7uGCQewySiosYdGNXLKq4TWvQ7bsXQWuxVbLZ5xV/OLumH7k8X/86pAHk5TrGy18z2Z/mrPbCZhbgn7k0Y09tmKftw5m3DhZIDX86NqA1y/EuI7Fe8czfnF3ilq+eX7/Qp9XtgJsW3BlzWhofnswZZrVVC9v8OrOx0d8SqW/1BkYq6CwwqdinFYcTHOKeqmcXapXe5FHJ3Dphsa3Zb3l8WBSmFtS7NGLNO8czTmal2SlxHcFFwcRh7OcyLOZF5J3DxfUUrPdDfjOxR4XeiG/PZhT1orNlo/tGtFb4NrUUjNKjJJWWFBVkqlV81rgIqYFlVQczGsOZwV3xiloqGVNN3K5e7pgUSh6kXduVXzWQA9dm9Cz0ZUkcG2iJ9S+R0nJ4bTgNC3pBC6hZ27sWWXS/a+rAPWHL6zzo6uP+hKdLArSomF/mnOaFOSlT+xbXF1rISzB39wY8vb+gryShK7Fj69v4Ds286xmltXcPs1YFBWLoqGQiklasdsNaKRmOK9wu2avjmcFUio2Wj5rLZ9RUtILPRa52cPQd7CXw2YuDUJmecOd0YTTpMR3bb57sf/c9Bh2ugGNVLy01aYbGv2LsWQxFhWF1ByMM5JK8uJGi9i3mWTGr+hwlvOrBxPKRhvvKdtGCDPBLvRsHCvg0lrIVifg3iTn1jDFteD+acZLmy2GSckkrbEtGCc1290AqRWv7vbZ7YXYlmCclPw/71akRcNfvX1M7LlcWvuwz3A0y3lrf047cLg0iM6Fd18Eq6CwwqfirHF8ZlD3cC3YssQjD+irgYvW5u/fPphxd5hxZ5Tiu4IrYQwC3rjQ5e8+GCGXFMtepPBsm1e2OwSuxSKvuT9OyErJ9y/3uHOa4dkWLd/mb28sWOQN/cilUYaSOklLLg9aZFXDg9OUe6cZ06widBxe2Y74zl6PduCy3TXMkzO3z+/sdfFti2le0fIdLq99KBQapxXTrGK9/fFpWPnSjXQt9tnq+OdN+UVRE3nOIwZoXzWKWjLJKhzL0G2rRnFtPaYTOtw4WTBKa6SCw3mOLeBgXDDLK9Ky4Z/ujEnqht1ORFLW7HR93j6YEfk2vm3R9h1C1yKpFFvtgHFa0YtdGqU4mhXLGdomwA4ijx9c6Z8HCc8WaG3oqnfHuckG05qkkCSFZJyUTzVa9etAO3B5bbfLybxgUdaUtaIbuux0QyLf5eVBxOs7He6NMzRwNM1xLYkQgvXY/H+H85LjSYpl2QgLuscu//FrW3RDl0aai8RW26NqJLNa4k9ybg4TikpzY5ggNLyy28a1bPanBS9nhjYa+w7t0GW97TFMSgaO6c2dZe61VBxMC5KioWrkl6YpWgWF5wSfxl6686//7GtcyaNYb/kcNDkXByFt3xx6HxVSlY1kkpp5CGellzMGymhR4DgWtmWRN4rtdsDvXexR1spQURvJbjc8ZwG9fbTgzihhs5Px3vGC717qEfs2zTJFPklM7Tr2bOZFTd0oNtoB/dhjmlaUtaTju3z3Upc/fW2bne5yoHqt2Op+aE/s2JaZ5VBJ/vnelD9+eQPHtozK98xRVOZ0PuIoutE2hme+a503LIHPLS57HMpGMstq2stM5AwnC1Mm2+4GaA3vHS24NUxYa/lc21A0UjHOjE/U67t9bp8uEAgcIXhrf8oordiIPSppBhK9dX/GLS/FcyzunjoUjSL2LAJXMC9AI/iDKz3eerDAdQX9yOf+JMMWAt+zCf1l1mUJLg1iLg1iGql453ABGJfRqjGvaytwOJjm+K7Nwazgxa02WmuO5yWNUmx3gi+lx6C1Zp43+K71mbKRzU5ANpKMs5JJWnFl3dhWNFJxY5gwW/bLAtdhvS1453DORtvnj1/a4K/fPSYpfUZJwWY7YBA43B/n/IejE7qxR9tzyGtJpTRpaXpRt4Ypnm0TOhbt0GNtScZQKP79b454eafN9/b6XOiHfO9Snwu9CKkMa+yMEOFYhtm11vLwHYud7pcjxlwFhRU+Fd3Qpe07JFVD6NqPVVfeH2fklWKUlLy208GyBGWjmOQVCjicFiileWG9xUbbZ6cb8ldvHxpOuCWgC6OkYJRUxL6Na1k0UrGoak7mOe3AuHw2SiO08eqppWCcVTgCbpyk/Nnv7fDuvrnpVo3iQi/CtS1uDBM2Wj7XN2OyWjHNKoaLkp/cGPHBccJ6y+eV3fZ5GcayBKFnLamKH397BK79ldsP3D3NKGvFKKl4bbdDUUtuDRMOpgWbbd84nXaMYvtwVpBVDVfWIsapGQRTK4nSCinh7f0pjiNIigalFK7r8eMX1jhcFNwf5xzOc2Lfpm5cTpISrTm/AYeexaKUbLRc4tBGSs1oXjLLK8ZpzeZSBNUJjAgr9m22OwGBY5FWDS3fI2zZjLOKH14Z0AlcFmXD7vLwmhcNw4URktmWYKf75NLHcFHiu9YnNvSP5gWjhVHjX99qPZHlNEkrfvVgSmupKxjODREi8hxuDRN6kUtZKw5nBYFrYdsNBxMzGOq9dE4vdFFgSAbh8vnuhRwnJadpxfGiYhDZuEvzwlJKshKEUuwvSgLX4YW2T+w53BunDBeGyReMLCZJxY+uDtjrR1zsR+dU4DMIIRjEHmkpWWt5n2u40uOwCgorPBEPJvnSGsCwRj5aIjmrz5/9nlWGWXF1rYVSmmgpZLsxTNhq+9w4Sfj5nVPuLQ+5rbbHojAit42Wx9ZLa0zTmrSSPJhkIAQt3+X1nS6X12LKqmGS16S14sGsoD9OeP9kxmzpZ2QJQVorfrs/J/ZdFllNUtQoBeOs4mBWMM9q8rpB43FtPaJsFP7SbfTaeuvcZ+kMeWVueJFvs9n+au0xznY3rxuKWjJclFRSk1UNee2w0fFp+c45Y8gSmqRsOF4UvHc4J68a8kpyc5TSSAg9i9izWYs9rm22eXEjZisNsRHcmxjF9KKoKCpJow1v/9J6zMVByHtHCw4nGa5tcXUtJmsUea2omob7k5zL6xUPJhnDeUU3dIk9h0oqNMbvqhu658SEH14ZUDSScHmD92wLIUBrnsqltpbqXGn/OLEeQLN0KtX6UfHfGbTW3BtnlI3iQi/k3aM5k7RmktbsdAPWWx7CgjvDlHnREPkWL260CFyjtC8qSVkrjuY5s6zi7+clsWfzxk6bjW7AXi+iaiR3Tw2bLbJMiWqSldhYuEJQNpp3j1MuDiIGLR9bwDgrqRpNXjWsL5l8aSX56a1Tfu9Cww8u988DQrFU5bd8h0lWYVuCaVaz1Xky3fRpsAoKKzwRlTR19DNNgvWRduqlQbR0INUczgvavk0c2FRS4dg2nZBz3vXP7o65PUr52b0pRS1JC8l2N6QbOixKyYvrMZ4j+LvZKR+cLKikZrsdsDcQ9CKHH13uE/su/9uv9xlE7lJZa/PWgzlJ2ZDXirXIoxu42BbcOV2Q5JLdfsiiqOnHPqEjmKJwbYEtNG89mDHNG9ZbPi9stJbZwqMHzuEsJy0li8L4/Xz0QPoydQ6X12JujxKqxriVDmIPWwgur8UEjkVWNbxzOCdwbbY6HqPEaECmecXhvGS4KLCXlMlFXjHNBRttw9S62A9phQ7zoqYdOvRLD9+xKZuG/dmMrJL4TsODccqltZCyljyYFTiWRdZIeoFxAF2UkllW8ct7E+6OEoRlc3U9JPZt7o9zNjs+Sdmcix/nhfGlevg2G3o2L262kOrJY1ufFtvdYOm7ZD+WjZNWknluBI2nScVGy+doVgLaWFZbgtCx+A/vnnAyL7m2EbPbi9gbRPz63oSf3hpTNZLdbsAsL6mkQhaaoqMXcTQAACAASURBVFH85sGM//2XB9gCbGERBTavbrVYlIq01CgBpdQISyO1wrIgcASb7YCf3Bhxd5SRVDV7vZC9bkC6nL1wMCvYmRXs9aNH1PhbXZ9e5JFXxXKU7Zfz/K2CwgpPxF7fjMPsBO5j676ObWEJ+MmtU0LHaAYuDyJ+41pc32wzXOT0Io87pxlSKRa58Z4XAhwHeoHNJK1olOHDIzSTvGaWSQJfMMsrosRirR3wzvGC7U7Ai+sxg9BjntW8sBFTSoUQpn6+1Qm4uBaxHrn0Ipe/uzHmeF6YIGAJ1loeWS1Z5DVZpTheHnqebT1yuNdSnZfLQs+I4hxbfKyEdn+cMc1q1tveU5VAngTPsWgHLlVj3Epj32Gj7XM8L5ikNbdGKe3AcN43OyG2ZeM7FrOyxgI8R+DbNp3lVLpxako0w6TkcFZwa5RwMi/ZaHm8tN2mkYoPjhN2uyHjtKKWiklW86s7UwYtj27oIpUmdGwiz+VK6LHbDSiahoNpwbSQ5KWhnB7Py+XhpZb6COO5c3DG2AodLg+MxbbSHwaDWVYzzoxe4pP6M65tsdML8J1P7heY2R2f/BqcifBqqeiEDr0oohu53B/nTDLDAIp8h6vrEbFvAsvdcUrkOowz4/lUN5JXd9qstz3keyPmecX9Sc4kM+LIomqoGkUc2Ox0fLqxxzDR9CIXqRSxZ3pv1zdbjJKKn94+JW8atNa0Aof70xzbsgg9i17gMoi981kNSWGEoa3ApWoUe/2Itdj7UoWXq6CwwhMRuPbHZhp8FGe+KtOsomqMgjOpGrKyYrvts9MP8V2brU5AWknuTVJGSYGN4GBeshb7RJ7FJC25shEjlca2NbLRDPohZaN4Z3+K0ppu5DNOSnqRyw8uD9jsGHO7+5OUpJR0Q5t/vjvGtizWWy67PfP5lu+wFvuUTYNjWyzKhsh3MMQqgVQfBoQHk4xJaiwBrqzH7HRDeqF3HljOoLVmmtUAyxLEFw8KYOwYjueFaWovhwGVtWKYFKRlzSyvkUqz0/XJ64a0bFDKiKbWYlOy2e4GvH0wRwP10gE1LRp+cvOUeV7SDlz2BhGWNmW1slF0AhuF8fgZZwUI2O76LHLJyzttXt3uMisapmmJjWBRVLR8i8D1qBpFWkhsW3BxEPJgkrPTDZBKo9FM0hrftrk9Ss9r/ZcG5lB+MM1Qyri6flrT/osybBzb4qWt1jlLDsB/qFfWKFNG7EU+UmH6J0lFHWguDWImaY3bD2n5Dr98MEMpY9HeaAydOdAc1g0KTVkr7o4zXnRt2oGHzEr6kYcjBJHr8tfvjcye+w7Xt2MejHPGaQ0SXlhvMUpqgoGDVprdnqHOHs0LpIKqVudOrV+2En8VFFb4wrg/zjiem5LFlfUYgWZe1HQDj2ma8ne3Ttnp+vz4+gbbvZDXd9r8w61TQtdFYxTQndDhNClxXItfPZhSVJJ+7OHbAq00i7xGC4HA1E81Aqk1H5zMefdQoNCklTns88oMRdnuhkgFP7g8wLMtprmxQL53kHI8K3lhPeLl7S4S2OtFppndSO5NcuM+6tjLcYkmWDyuHCGEYL3tMUlNpvBloWwUjmXhWHAyL9ns+GSVGQZU+HJZu1a8e7QgKRvuTzKUhv1ZQegIXtps4zo21zZboAWxb5FVCokkKWqmec2saEAIfNtivCi50I/Z6poG82/250zymv1ZxWbb44XNNqHrcDDLScuGX92fMs9rtroBP76+QejanCzMPIJraxG3RynzvOH+OOWPXtpEY+r9rm3mDsilM10pJeAat9vCDKv5qiGE4OFztBO4bHcDykZiL0WaSVEzXBRMspqJXfPKTpuXt3tsdHz+8fYpf397zD/eHiOlpmgkL2132e663DhaGMfYRi7N53LqRtHyHfJGoRQspKJsFI2SaCx8z0IpQTdy8RyT9R3MM6QSdAoXz7ZwLDPxbrqkq7aeYKl9uswKDd06+kyBYxUUVvjCmBe1cS3VDVsdn4NpjpSKyLN553BOXkuOZgWd0MOxBWtxQDvwQEMrcM9nAysFbx/OcC2LrV7AJK3ZjF1KqUkr039QQOxbbLd9ilpxMs8ZJebn+55N6DqErk1ZG+//jj/AFgLLFvRCl7cPZ/z83hjHsmiHLXZ6Eb3IwV2WW/7XXx6wP84opOLNS33e2Os+8Q210w3Z6YamBJKaEsgXvb15zoeHZ+CZ5rlUiqwythLCglFakpYNw0VF5NvkVUNRNjSNxYNZxutxj7fHGVpoqkbTCmwc26EdOozSEkdoytoMeHntQofItdnqBbxzMMe1ISuWzd15Qcu3mec1WS05mOTnTXCB5sE04zt7ff5ws0XVaBAWv92fkVaSi/0QqcytdqtjsgZLcD4vej02N/8ra6bkFDzFuNavAhttn3unGcdpyb1xim0JDic5R4uSbujyo2sDRknJ+ycJJ/OCGycp47SiUZpOYHN5LaATuvzNB6csSonU0PYtlBa4lkUlNQqotTTqY9dCYBF4Nr5tc2EQMkwKjmYp6y2Pq2tGT2PbghvDlGlRn5dvI89mr//pGemZvmhRmFKt56yCwpeOb5ML6pcBrfXSx8ZiqxNwe5hSSsm/e+uQQeQhtWYQe1xbj7k5zLAsaPk2v3owo6wk/cghci1e3ekwzkoOJ0YQFXk2Za1oeQ6BYzHPG1zbUBA922Kr7dMJXYaJ8RoapxVprbAswQ93ulRa0ws93jueg5b8/a0RR4uCfugtbbQzJqmxLPAcixc2Y0LXJq8l86zm3jhlmlQ4tkU/9gie0othXtTcGxsmj1T6c9txn5UIHMvi+maM1JzXz0PPJq8/dBzNa8XtUUpRSy55EW/sdngwyclrybtHCaeJue1e3YioleKDBwmn6dmcDI1lWfRjl91ugBCCP3l5g2HasB8UCNSyBCgZxD67vcjcgEuj0SgaUFJTSZMBGB2J5uZwwXY3YH+aUTSKrbbHaVrRCU02cFZ62/4Ip/6TMrGvErOsppSS9djHssS506y7PHinecUsr6mV4v/67RHXt9ocLJl4gWex1ws5mOX0QpdGatqezTQpkdr4UQ1ij0aZPWwFDr4tqCoQwiKpJDtdDwubRdVwZ5giMAZ4se9SNJKrazGh55BVknFScfc0Y6Pls9cLz7MEpUxv7qOXkEHsc1DntAPnMzOSVkFhhc8MqTQ3hwlJ0bDbC9juhuz2An57sGCSVsyzejltS/Pjlzb53uWGVmCTVYr9sTHGU9IMMjlZlPzdzRGny0lv37vYw3Ucdrs+p1nNMKkAM+Q8cm0s22J/mlHViqO0JK0aXMc2c4cFoDRvH045mZXMihpHCBpphtz/+gBko6ibho12yJuX+9SN4t7YqKAPZyWR65A6Ndc2WkSeQ+Q//qDKqgalORfrPexcrfn8E2LvnmbcHCZEvoMQZk7veVBwHVq+Rggj5LMF5FVDUkqO5yXdyOWPrq/zi3sTjmY5P7szQSpFIyVH85LDWYlG0wts0z/BiMc6gcugFfD2wYJ50fCbBxMs21hCb7c9FmWNAF7aNDbds6zmn+5OeCBz8qYhLxvWYpc744RaaX51b0qjoGmM/fbJvGSUVOeWJc8Dsqp5JIjvdEMu9E2j/dIg5P4k59KaudAkpaRsFFWjuDVMz+2v96fmc5O0JqsVw3nLXIJGKaFjxp7WlaSSktFCstU1GhOloagUR1NDNTbqcNhsB1zsh5RSce80I1lODrzQC5nn8nw8rWmIK7Ja8sHxgsCxub7VfuTwH8Te51aNP5dBQQjxb4A3gV9orf/iWa9nhUcxy2pGC2NFPUxKOstxg28fztkbGLOzK4MW7cDh4iBGLx/mXuDwUw0HsxxhC2ql6YUOWaWY5RUa+Ke7E9Yjl9HC4/p2l71ugLYgyRuOpgU7fZ+yVtyfGJ+e9djFsgzP+5f7M05mOVWjcB2LulEIz2VR1JSu8W2qpaQX+VwcxBzPKyaZYSadTYfbaPu8st3m8lrM1fX4sbYVSdlwe2jm917om7GL3dBlrx8itT5n3XweTLKKdEn3bKSkF/psdwM6oWP+H42k5Tks6oYbJwmuYxFIw1nfaodcWw+5c5ryi7sTjucZlsB462QV9ZLhUjimdKGAwLMpKsntkwX7jqBRgIBFIdG6IbQtKqV4/zgBIXh1u0OlNKFv2DF5ITlNK/bHOZXSNFLRicye+47FCxstThaGurnTDZ5pUHh48tjDVuNnjlXuMusFk4VZwmKt7VFVko7ncLwomC1LaEpKwMK2FEUtqaXm9mlCURkNR9VYzArj/qu1wrVtstL4hJVSg2xItRFlTrMKf8k42+6G1I3k/qTgYFZwdV1hC5ur6xH3xzn+MsO1BLx3OOPmKCX2HLa7wacOzqmlMqXIp9j/5y4oCCG+D8Ra6x8LIf4HIcQPtdb/9KzXtYLB0axguCg5SUpsy9Tp88p4wbiWjUbz0k7MZisAwXK4S0UncClryQsbEW89mLDZ8rEs2G6HXN+ImOc1UkocS6CEMPVWW7DeD3Fdm5/fHnOSlOS1xLGhlpKm0SSV5nsXY04WJdO0YpKbw6jjWPQiF7SFbUMndBlEHo1StEOPy4OI2Dd8/W7ocTDL6IQurmVS+KRsmGTVY99ozUODeeqHPv68Y0sfxlbHWFicLArGSc0il/Rjl0lmbpi+Y7Ooah5MMm6eJGg0r+12uLYZM2h5lFJz9zTjcFFgW6CVIK3OpqR5tH2HXugsSyAmmO8OQu4MEzMmUgHa6FHagU3TKDqhx6I0FujvHS/Y7Phc7Mc4wtiPV1Lz8/szrq6HNI3m9d02eaVM49YSOLYg8lyO56Z0NfiSKZRPi4cnj71xocuV9YhaGsbWR7HdDdjoerxQtDic5cSBw2hUYqGYFxWhY7EWO1TKQUqJaxl/sHlRUUmomhoEtDwHpU22VytNo6Dl2YSOh+2YBjLaZJq+bfPSVpu0ahhnDWkl6UUukW/TjV3SyugjNjs+7x8n/Pze1GTrSyfgT8LD2oa9fvjE5/S5CwrAHwJ/tfz4r4B/AayCwjNE1SiUNreMrDLCnyuDGMtaPsyOxc1hSuBa5JXkNClBmxrt8Szn1mnGdy92mecW758suHmS0goshrOafuTz2l6PjZbHbw8XtAMX2WjGecV7Rwmv7nawpckEkqI2M3Ujm7xWCMvQTNuBh+s4lE3CpX6Ea9tcGgT0Wz43TxJavsMLGy1eu9Bltxuy0/WxLYuDmRm9uNUJKGqJa1vUStINjfDq3cMFL+8YGuQ4ragaxUbbzPOtOupjE9HABAyln863/nHYW2YenYnDcFFSLgcBqSXd17UtNtoRv7o/xbIFoWMjFMSew7tHcwLb4t5pRllrbMtaNhgFL24Y9WxW1twaZliW4EdX+1zdaHF/lJGWDVoLNlouCI/DqQkQrm0TuBaxb7PTDamlpGw0Ld/lO3sBd8cLwKLnC+6OUtZaAbZtcXktopGabuSy3gpIyprx0qDvzBn360bLtzmclawvD8X2p8y+UErTWpYPe6FH3kgi3+HWKEEocCyLV3c7WMJmllXszzJGSQFCmDo/4Fk2vcCUPJNSMlo0+J7DmudwYRDRDzy2Oj5Z1XDntMB1LTbbPqX0yErJZtvjB5d6/Itr60yyiklS8+v7c04WBS3fpReantdOx2g3Pgll/eGkwbyW9J+wT19JUBBC9IHrwPkrr7X+m6f89h5wc/nxDHj9Mf/+nwN/DnDp0qUvtNYVPo6ilni2hWUJY5ewHKRzcWBYNkfzgtA1t0Sl4Nf7M04XJQeznFpKlA5opDnEzmwJ6kaTNCV/+8GIcVoRZoKNTsjNkzmN0sSBi9QQeg5+JMjHklopbgxTOr7NMCkpGwVCILVD23dYLGvZL261uT1M6McutiX4/b0eg8jjwSQHYd4Ib1zo8i+vb5jZAZ5t/OkbxSD26UYur+92GC5K1loxay2P4aKgG7kcTgt82zo3yZNac6EXPmKG9/C+3RwmKGX4958HQhgdhGfbrLd8tjpm2tzRvGAQG3Hc/XHGG7s9ThcVed3guRa/vDc9LwVZtjCT5aqGRsM0b8gazY93O/zDzTFZLQk9m3vjjGFSMk1rFkXNojSDjYQQ1E2D59jYtkMtoZLwwfFsqdR1lgKygH/54iYfDBOaRtMNLS72I9ZaPpfXYg4mOaFvE7o2Wx2f/Yl5Fr5Iz+WLIHAdfKemlCaL+SRfJKX08vMOm50Az7aNid0owbNtqqYhKxumaUO/ZbE/yThclCyKmjiw2e2FZJWkEzi0Qo/jec40b3AsQVY0zBybXlHT9h2muem/WUIhpaJoFO3QBQS1FLx7lNAKPDbbPrdPU26cLBhnIa/tdLi2ETPPG1qBw81hyktbrcdmYJ3QYdAy5n5Po/P40oOCEOI/B/4C2AN+ibnp/xT4k6f8J6ZAZ/lxZ/nnR6C1/kvgLwHefPPNZ/OEfUtxMM05TSp81+L6ZotyybcGc7j2Io+r6zFKaUZJRVIaU7OqkTRSs9UJ2Z/ktAKHbuTSKE1amuE2i1lthr74Np3QYy30+GCYsNZyGScVLd8mK2tmuSarG5Ky4Y2L5rY/zYz2YDcOCJcMJde2iH2PWVZxtMgpa2N5scglvi1Zb7vYx4JO4DHJau6cmilWgWscUm+cJFzfbLHZ8bmyHrM3iIg9G8e2kMqI0kLPwnU+9OixP6Xs8fDQoXSZUX0enA3wEcsZ2TeGCVWjGC0q1ls+rm3hOxabHZ9pbtEoiFwLz7WYJiUd30VKaHUD3j9JCF2LrdjlYi/ib9UIqQGtTV8oKZEKiqohdCykbdHUNY5l47sOe/2IWV5TNQ1HiwbPtuhpzUZsyn+1VCRFw+W1CEsIXt/rsNsLuXmS8sFJiusIdro1/cjDc2zWWt7HsquvC2cK9U/yRQJjx3LzJEVpTSd0qKRrhIFo+pHH/tiMNu2GLnuDgHvjAi2gaiSh54A2+pnAsdjshKRVjW8bhbljayo0eSV559AMebrQC2gFLofzwjSts5qXt9s8aPsM05JpVnNrmGAJjFZHm7nivdjlynrMaVLRSE29zFAf53RxNk/7afFVZAp/AfwQ+Hut9b8SQrwC/Lef4ft/CvwXwP8C/CnwP37pK1zhE5GW5jAra0WjNN3QJWvJ8/kHZ7AswYV+wFv356zFHknZEHtGUNMJTWobuRaX12IcyzKW01rx+k4HqTWXBhH3xhmX12LGaYlWinkhiTzDz1da4LmCRVazqBqUEiA0jgWXBxEaQw1stOLBLAMtsJZCMs8VXBgEKCW4tGaag0eznPWWj+9a/OYgY3+csd72DDMkKTmZl+x0faRrYf//7L1HjF1Zmuf3O9eb598Lb2iTzMyqyqyqzmoLDbp7RoBGrVkIgrSWNr3SRosRIGij3YwwELTRqiEIWkiAFnIQIEAYSWjNNKZNTZfvqqxKQxtkuOffu94cLc6NxwhmkAxm0ma+P5BIkhHke3Hfvec75/v+Rkp2Oh4r9UcnpqsrPlmuFoonoeGYtLyc/LFr9bw4GW5rlciq6ZocZQmerWNogvWmg21qPJyEBKnKVPi9q13SsmAUpOzPYmqWTs1Si3pWlKw3XY7nCbrQuLnqs9awmScl4yhnnqshv0TQcjQcwyPKJbtdl0ttn1mS8XAccm8YESEJ0xLH0MgGksNJjGPq3O6H/PG7K6zUHEoJeam48VkhF7bqeqmU2ZomCJJ84VH0qnDii2QbGq6l83CshHjrTWfRSgqTnKNpTCElDbdO3THp+SWfHqjY2BJBURRMI7g3DKnZJrLuqEyDQg2UpZS4poYmJUWu3Gqvr3isNRw+3p/RD1IEEllCnBdMxoWag7kWspRMY6X3sYwmYVYQpQVZUfLR5TaDIKPjGzQci8NJUpkdGjTcr57hfIKXURRiKWWsVIPCllL+Wghx86J/WUr5YyFELIT4C+BnUsofvoT3uMQTsNFyOZzG1E8pJp/kJVOzTTo1q2oteWy3XGUXPE0YhymlKWjbJusth72hyi743Ws9TB1+fn/KKFCBL371Wo5lsNlSgS7Kg0+qHr9j8HAc45mK+21bBjVLyf+3WspOQSCoWSZXez6zuCCIczZaLr97tceP748RQvCbwymGBgeTlHGUYejw/Usd0lz5Jn1+HCxcVt/fbJCXktv9Gbahcbnr41lnH7ogyTmcKtXoasNB08Qz7UAugkuVwaBvK6vktYZD17fO+E61PYvfvtLFMXSSvKRTU37+AI4umEZqIf77762wN4qIs5JRlNH2LQwd2o7BlZ5FSclgbhIkGUixaFf1g5C/ezDF1gWjKCfJlKpb8eLVKXGnbXAwjbmx1sDQJZ5lcGcw53s7bXa7KszeNQ3mccoozBGewDX1hReSEHBtpfbK9AmnfZHiTGVTAxzNkkVRkBLCTM3F/GqecDhLaHoWv9yfYGowi0uiVGJPYkKnwLF0Lvc8ZCEJsoJb/YBc6oRZQcuxaNc0bqzXadgmrm3y870xRSEpS0mclex0fAxN8FuXOnyw3eJolnBnEGKbGroQ3Fyt41oGjqWzUpdsNB2OKstxzzJeSNraabyMorAnhGgB/zvwfwshRsDD5/kHljTU14eabVC7YEC4QC1OmgZrdbUorjbUruuXDybcG0Q80GMOZypI5fOjgM2Ww/405Mf3R1i6Trdm41k6f/HpMcN5woORzXe22vTqauHJcuVXs1Y3MXUdhM7f3R8zjNRuShMal3s+vm2gATfXGvzo7phBkGObKQ3HwjN1xb6xTQpZMk0UnfRar05RFPzo3hjPMri64vNgFJEVEs/SsQ1dCbOKgiDNvzCY3J/E9GcJ93OVSeDbTx5cPg8MXatM0EpVNG3jXCPClbrDH7+3xsf7U+ZxjqsrlotpaERhSn+W0J8p/UWSKyVyUO08zcqf6N/+9iazKON/++keQZzTD2KSTFKzdLKy5HCaMglTgkxZXpumRcdTGQKmIbi+WsfSBQ3X5pMjpVO5dRzwrc0mO22PvVHErX7Iw0nE93ZazOKMQj6yt06LEpdXT1O1dA3H1IizkrrzaBkcBCl7w7CiBmcYuvqeLCvQ0PBtk7ptMolzCikYRhnpNCHOClZqNpMoo+malBKVRz5NyIqSe/0qqEeoE600JLbQaXsWozCmYdvYhkaJ8rcaBjGXux6WadBwLeZJhmPqyhGgZuHZ1WbgBYY7neCFFwUp5b9b/fK/EEL8OdAE/q8X/TpLvH7cHSrWyoNRyPXVGldX6lVLQMN3DIKkIJMlRaG8kA4mEf15jGUIPEPgOQYfbjX513cGBEkBmkY/zPn14ZSapY7hUVZi6BqWbmCZGv1ZXOU1qAHmDy63mSYZD0eRUnFOIg6mMZ8eKerkO6t1vrVVx9R1Vmo2ozBRPk2aRoHkVj8kTEoMreRwHBNmBTsdn7yUrHom04pvf5rXfgJTF+xPlPtqf5a+sKIAqv/9yeGMslQWDI+zddIqnU1ZYaiWU5AXlYtniZCKaz+YJ8o80DXJNKqTUkRSlMyTnG7NwdKFYgjFAcMgx9E1EJL1lsdm02Hf0BmFKb6ls9P2kAJMQ2ez7nA4j5U2wTG5148YVmK1LCv51f6E/iwFSpCKBZXk5cIkz9S1hbX2q4YQ4NuKWXVitZEXJXFW0qtbxHmpTkzzBN/WiLOcbs0ilxJHF2yVSkS2X1GH01wN6g0NdE2xtVZqNsN5ShIV/GaSqGzzio3kGxq6pnNl1eeTgxlRXvC//vQBfxSvcjiJMXWN4yDl3/veKg3X4mAScThLuNMPiLKcayv1Fxa/+TheKiVVSvkvXua/v8SLxzTOmMU5Xd/6Qr83zgqmUUbDVXkCUkp+vT/l53sTPj8O+Le+rXaO/XmKZ+pc6nkcVtm3UZqp/yc5/WqQbRo6f3V7yCRK0TSBLVkkpw1zFS+50nAW1MYkl2iaooVKBL99pUPXVxqDeZxzMEmoOzpZXjCPU5ASS9fYbNlc6vj0ag5tX9kkB4nKDa5VC4MENtoeAlitW6w0HDzLoLnVfOK12mi67HYTDE2jkJKH4wjfUgP2r4q8kIthaJIXlKWs+vTK9vmf//KAW8chbd/kUldFNepCCQNrjs79UYFZlNwdBKSFpFd32Go6/OzBGFMTxElJGBf84t6IdzYaKsErztmfxji2TtMxabgGhq6yGFquzv40JZMSDXAMnb1JRF6UzE90KoZGyzVAwizOOBoodftW2+HGao2WayCEoqRuty/WZsuLEk2IF559PQxSBnPlbuuY6cKWJExz6rbJ71310AT8Hz/b5zcHATfW6mx7FpdaHvuzWJ1G5xmTMCejoBBgaQJZQi4L6rbNp0czZnEldiuVViTMShqGTpxLbqw5fLjZJMsldwcBWVbw+eGcMFU2LFmh9CFrTZfNlsutfgASjmcpHT99aTnXb6JOYYnXiHuDUPVVk3wRcn+C2/2AvJAMw5R31xvsdDx+9XBKy7UoK/55XJnfAbimroJ0koLBLGESppSFRNcEQVIwNQuKvEDTNX6w26JEMk8KjmYJTcfA0HUudV3uDkKivMC3NWZJyTDI+c52nZZjcTBTwfCHswRT15gnKg94GGo4tkZalIzDjI/3Z+x2CwQgNBUFud60sUydP36vjWcZFZOjZBLnZGXM1Scomk9gGRrvrqu4zHGYMZinDEi5adW/cgKWa+ms1lVLJitKjucJtq7YO371XkEtvlmu/Ih+fG/IYJZyPM/YbHqUZck8LfFt9f3f3mqy1fI5nqUkRUCY5YS5zmie8o8+3ORfcUxSyootI5gnBcezlA93WhxPQnp1W5kUNpSP1HCeVgl1OR9dahHnilmz2XKYxBmT+4q91fVtmr7N/VFMP0hZa2S8v9FQSue8ZL3pnOv4OQpS9kYRhi64vnqxlubTUJaSMK0yMarPJ0xz+nMVsvPJ4ZzjecJ2y+FSt0aS5xRSMgoz/ubWgB9c7qGZOr5joAmoOTqTWMMpdWLUYprIOEqaAwAAIABJREFUkjwvudWP6Po6mtCoOQYNz0JWjKYgLlhrOrQ8i1Gcc6njk+claCqW9FLN5TdHAbqmWHLXV+toQrBSsyt7Er6Sav5ZWBaFJc5A1wR5Ic/tYS/YmBImlXL4j26uIqUKg9lpuxiaqP4N5eiZZiXTKGWaFGy0PLJcxRneG4RkRU7bc9huO8R5wV9+PsAxDTZaDh9stTA0wSf7cw6mEYauYwqo2yaZVjIKcx5MI4SUzJICQ4PrK77i2aPRcA3WGg5lKXFtZeuc5AWzJGc8z8jKAoGg7Vk0HJPVKlv4//34iHmSc22lxnbbxdGe3u/2bQPfVqZlYVogBHyVTW2al4yjlLpt4juGEk6lBeMoxbMN9icRmy2XD3eb/Hp/znbbxdQE//LTY356f0TDtdisO0S5ypjuBxmzKKPjq8yCm+s1jucRmiYYBjGlFJQSfnRvxJ1hyCxK0V2Trq8yKEZByi8fjGn7Nn41Z/lgq8kkKrg/DGl5Ft/aanB1tUGc5swre+/Nlkvbs5mEKZstB13TCZOQNJeV/XnKp4dzbEOxkc6jTM4rJlxebTa+CspS8suH6kTbck0+2GlxdcXj44MZeSH55HCGLjRkCQ/HMaN5jq6rU1HHsyjKAsfS+M3+BM9SdtSmIVhvqEjPwTyhFJISdWIToiRONYoyx7N11homozDD0nXmFMRpga7rVda0oO1bTGIV4jOcpXQ9k5ZrYuuC3xxMGQTKCuPmep2tlnvu8/misCwKS5zBtZUaUeXq+Diu9JRYZp7k3BuECKH+7OqKOlHsT2LsKpnK1AVHs0QF7WQldcekZhu8v17nz39zTLtmVS0bB9c2+LwfkGQl8yTh9693+IPrXf6nH97n0/6cJJNoGnzvSofjWcooSGi5Bjttlzv9gHmSK3OwozlJVvDuegNN19jp+gSxsiqOs4wgyRiHGZe6Ht2az/d3O/iOQcNRXPRfPpxyZ6CG0A3XfKpKFJSFR1aUrNYdtloudceohoFf/oG9NwyJ0oJjLalYJzp5UdCr2cR5QdtTbK8rvTrf3ekA8L/86D5/c2vINE5xTJ31jkevrvr29jBiECToQtByFUvqD673+OGtER3PIClY2FccTGOmVVZzIdXJL8kLkqzg6moNx1BsIl3T6M8DorzESgsud2o0XIO7/YA4L3HbOgg1yP7+bhvX0qvevI5laPRqNg/GEf15oqwXOuezZ3o1eyEyOzEe/LJIi5J5ojQ3UVYQZQUd36VuqwSzbqX/8C2du6OQYZQwCjJ++0qbYZBhGzrDMCbKcqZxwY3VGh/uNrl1NONgEjGNc/JcparlBbimUCfOvCQpSiZxxoqvlPF11yBJc/qziN61Lt/dbfObwym/uJ9wbxBjG4KGpwKQbNOgP085miZc6flYhnah+0tKubAif15LkWVRWOIMrCqu8DzYhs5KXV9oGaSkin/UVODL/pSPD2Y4hsa1Knu3V7NwLY2NhldZHlhsHs1U1rGrbH1HQUqaq0LUdC1ajs1ffd6nH6QUZUnDs9hs2JhV6MxvXeoQpDkagq5vM40zZKniJsuiZBimGLrg9nGAa6rdWJYLolwNZpuewQfbLTaq3ekoSHkwDhEatDwLx9J4b6N+5mGahMpC+ST6cBpn7I/jxdc3mu5TE8O+DEZRSpjmjKOcumPgW+o/z9apWTqfH8+Js4JhkKh0Nk1nveni2ybjIKPp6LQ8g2mi8pEFGpMoJUwLdrous9jEAH54Z4RratQsA0sI+mFaLeKGEutpqp3U822CtGSe5sySDKTKaLix3uCzoxn3hyGOpROlJlp17U6IbL2afWYwamoaOx2XolSisPOgMpzr537teeGYqhUpkXQ8pRAGuLriEybq3tM1UamZS+6kAaUsCZJyEQC0P0kYzlUvf57mavNk27y31cQxdPpBTM0xMTUldrw7CBiGysTQMnQkAtsQzKKMaVxgGSk/2xtzY6PB/UHEj++NOZwmrDVsPthu8+56nYajsjlaXknN0S88R7jdDwiqn+tKz3+ua7UsCks8NzZbSgjlmTqubXB9tUac5XxyOCVOC/ZGIWtNh+/tqBZQUpTkBazULfqzhHfXG7Rcm5qjown4u4cTfMtAayrh2McHU4SAtmeqgXXX4+E44vYgoOEaxHlB3daxTR2R5AzDjME8QdNUuP2lrgNSkEnFEHJtDd/WmMYlN9cbbLV8ahVTSFkgR0gp0IXGH727gm3obJ5i+8xOZSVkuRL1xVlBIVXEpaG9uKP8pa7HKFTto71RSJKpmcjRLKLhWOx2PfRc8PMHk8pKweRbm02lppZwueeT5iUHk4RvbzcByXfinN8czPj0aM7P9wqSXGUIZ4Wy4E7yghLJbtdjfxqzP4uJ0oytpsuVFZU38eGlFrKQTOKc9YaLZ+v0pwnfv9Sh6ZlEWQFCtb82Gg7TRZ71+bvUzZYS4Dmm/swT2YvCZstjs3V2wG3qGk3v0etrmuC7uy1FhNBglhZV/oGa3RgVldXQBJ8dzTGEIEpUgp1nGRhCYOgal9ouXd/iaJ4wnGcUUs1ONAnTOFU2K2XJJ0cz/vxX+3xyHLA3jtEFSCH4/uUGH263MTSI8xLb0J9LnBamRfX/51fWL4vCW4BnBfzc+ad/8oreiYJlaGd6wLqmgkGurdSYRhkt12S75VGrguPTXFkhfHww5WiSLELTfdtUEZyahmXq5FKxQtIypevbNGyNh3HBrx7OAOU7FGsFmVMyLSSfHs8YzBPSTLnprNZs3l2ro2sqB9jQBd/dXmMWF1UMpEaYFEzjjL1RRCkl3ZqNZaj8427NJM5KorTEMrRzM3AnUbYIUW97Kgu58RRjteeFqWus1tXrtn2rim0s+fX+nF7lX5NUBoW+bdB0Tb611eJ3rvb49cGUvWHE50cTPEvHt3Qu9Xz+4pNjRkHK4SwiSArqtkpec03lvSOQmJqOpWtYmgZCoOvq9x9d7nA0TbnfD9mfxnxnq0GSK82Jo+voQiClZK1howsNx9LY7flVnKh4YqvjJJxpEmX88qHaBGw0Hdreq3dQTfOSw2mMXaW+PRxF/PWtAYfTmCgt2Gp7fHengS40gjRjGuV8cjhnu6VmN7M4Z5qo2U3NMWkYGodBym7HY7Pj8fP7IzxLtU+FgEGssr9rps44zPmLzwZ4ls61FYfjmZpn6cIgTHP2RhGOqXG193yD9u22q9LtkIyC9LkcfJdFYYnnwjTOOJ6pYJaVuk2cKZuFumPywXaLUirvnnGY8ssHyn664Sjb5FGYkRYFszhDSmVJ0XJNvr/T5i9vDZSfT92hKEsudz3mcc7D+xM8U8ezdbbrSm2b5iVJpgLTm65FmBVowuXyis9Wx1u4mdq6hmXoXF2xeTAOcU2DqHJDBRa2bFstl8+P5+Q5VVykICsUJTbMVH7BbtcjL1So+52BOjV0fOupTptfFb2aTVlKfna/XCRo+bbB/mROkpd8sOPw7ccos2muMpg90yAvJMfTmH99d8jdfkjdMWj7Bl3PpOlZTBNFFjjRKaw3HYQuGIQpGqAbQinUJxHHQcpwnmAbOt1aTpKp/vm9UUDNMUmLgpZvsNVSecAXta+YRhlFqSiZ8zivaL5fXRX+JGRFuTj17XY8TF3jcBozDjPiPEeWQhXXccTRNOFSZTWta8oyJM5ybvUDjLxE1zWKpGCr7ZIVkp5vMI4KZKkyFEaBcrWtOyqZbbvlcWPDx7V0frM/YxCmpLnEN100TdD1PVbrAtfUOJhENF1TMQHTQhUHS1NRrBcomi3PYhwqevneKDoT1vQsLIvCEs+F/XFMmpeESUHHt9gbhURpyWCecrXnY+oaV3o++9OIfpByPE2YxTm6Luj4FvdHAXmh2BlKyOQyiTN8y2C75dJr2LiGxtE84af3xyDVg3xjpYWua/ziwZi6pdN0LcZRxk7bRUOw2/V4f6PBH95Y4dOjOfMko+urU8BK3WG1biMQrDRsdjoeddtY9GfVbllQSOVfVEhJwzb47HhOlssv9GVvrNWRyCe6bL5IpEXJbtcjyguu92psth1+sTchL1W+sqySvO4PQ6RUdF/d0JTFs5Bc7nkEaY4hVCvnvY0GN9ZqmEJwdxTx6eEMKUpMTbDedPnubpt3V+v8zZ0hQZzxi70x/UApo7s1izhTrbvtjotnGlzt1ZTQ7mCOUQnp3ttoXLjV0fEtFchjqkUrLb4ay+hZGIUpYaJeYxyqUKWT9pWpaVDNlRqOiWsqg8ZxmDKK1H0RZyXXVmv4loZnG3Q8i5/cGzOLcvYnMZ6lc7lbq/K0CxquRpTqWI7OZtvh5lqDh+OEWwczpmFOLksmkc47azXWWz5hklECaSlJCtWCS3LJOExJZ6rQXF2pnUvhfRwntFsheK7W07IoLPFc8CtxmWup9KeTgaJWZez26hZBUnC543M4HdLyTQwNkODaBn9/dY07g4BZUrDVdlhtOBhhSjvKcQyNb282+On9MftVVGHTN2k7FhsNRwXHCIhyiYgz1poOgyBFB0ZBxsNJwDBIEELw3Z0OQkjeWavxcByz1fZwLZ1vbTa/YHtdsw1GQYquqZ/heJZwexCS5qUKRynOWmp+VQ3C8+CkhXVzvc5q3eGzoznbHZdJmFOzlQvtPM6ZxTlURe+9tTp/c3tYsa4KdlserpFSszUcQ/DD20NW6za+rawsBjPV6lhvOry/0UAX8NnxnJ5v8nAck+WKZFCzDC731DxmpeFg6Rrf3mrxs70Rs0QFyw+DlKJUSuATs7nz1OAn8G2D9zebbDQd5knx0lS6J6jZBkdCqYtPGE29ms3xPEEi2G57dGoWq3W16ZhGOWV1etQ1yWbLxTE1FcIU5Hx2GNDxbSwzol2zqFk6222XaZzTqUGUKWvrXs2hlJK7w1DNuSwdNNBKgaEbTKOcjp/TsE0yKSkq59MPt9tMoow7/YAHo4he3cbQQ65dwIpms+lQsw0cU7tQETnBsigs8VzYbnv0aso9VAg12J1GGV5l3rbRdAnTnJ/cG3O54zOJUixD42d7E2xdw9QFH13uqIVZgKkJ9kbK9nqr5Sq2BYLBPGGtYTGLc0xNMK8ykQ1dpyxLCiBICjZbNvM04+EgJM5yiuIhQlNtKNfS2en4C5O7tYZ7Lnuj6ZrUNhoIFIMJVJFbazpKKFR79Hdmccb0CYrvlwFT186of9ebDrOoxizNEZrg3jBU/fwqeP6d1TqTMKXlmWSl5MZag+urDT45nHF3EHB3GDGLMn59MKNu69Rti+2uR9tXWcz/z8dHTKOEWnWSutLz+du7Y3o1i+/tttlpe/TnCWGqPJSur9bZ6XgcThKiNGen7VJKydE0YZ4oYdj3d9tPpVEezWIOJwk1x3jp5nieZfD+hnLmPxEmBmlOWapYzv5M5Xb4tolhaEziDITk5mqdmmswjZSV9V9+ruYAYVpwMIkYBilNz2K94XBtxWcSK8X89dUamhDEWcHt4zm3+yF744iWb3FtxUdIWGk4lQ19Qcu3uL5aY63hLGZLTdfkctcjzgs80ziTB/40CCG+lI3Isigs8dw4vRjqmjgzxCqKkr/+fMC9Sth0ueuzN1YPgmdq3Nyosd50uDsM0TXBdstbKKDHodIR7I0iur7Jbsfn/ijE0AX7k4RvbTWxDOUcmWQ5Ddfm2oqHpmloaBxPY8KsVDkKmmC1bjONMrbbLs1Kd/CkxenkeN2rVKMnf/90/1ZKFXUppXJIvbH2YuiSz4OabfC9S21uHc/5yb0xt4/n/OByh3fX68jq51ipu+y2VS5ylBUcVqZsqw2nEo4lqv1l6jQ9g+trNW6sNbg/DMmKgl8dzNlqucySgssdj3/zvXU2Oy6+pRakmmNw+zjEtjRu9+cIoeJLm66JoatdqURyMIlpV6eNp80JxqGym5jHeeVLJRbU35eBx1XqnmVgm4pW3fJNjqZKP9H1LWxdzWx0XWBqGlku+dX+lIfjiLJUquZSlkgp2Gg6NHyLIC3ZbLm8t9FYaC1+vT/lFoJxkJBmBRtNjw92Wnxvp8U//9URs3jK/jSm7ppVmqDOLMoZzCesNx16NZvvbDWZx/kLpz4/jmVRWOJcDOYJ+xNloX2pe3Ges0rvKqjZJq6l8/5mg6Is2GjYJFmJa6rEtLJUdND7o5B5mnM4SbAMJTBLshLHUovVbtfjJ/fGbLeVAd7vXO5g6Dq3+zMmsRLG/Rvv9PjLz4bstFwcS+fd1RrNalGxDeVyWbONxUCzqCy3k6Ks3DLPFrnNlkt/nvDLh1MajnlmQTtRfL8o7/ovi6yy7wjTnP1JxG7XW8w4NlsOhxOHEsmvHk5xLQNDh5pjslJz+GCnzp//+phSCtYbDr5lsD8OiTK1s1+tW1Voj46ua6zVHa6t1PAs1Tqs2wa2oRNlBZahrodj6hXDSyna31mtE6cFh9OYv/q8zzxpKkfVc1pvKzWbg2kMEo6m6qQmJQs/opeJWaUivrFWR0plD67yxBVD7mimHFB9y2BvFBGmGaMgU2Z6lVHiMMzUqcvR2Wo4mIaObxvsdrwqrClVbqyuQSktbm40mCc5Ld9iEuWUUmkj1hu2UoL7FnmpNiDrTYdxmNL2LD49nHEwibm2UuP6S9yQLIvCEudiGKRICdNIcdov2kd3TJ3djss8KXh3XS0CnSqe8WAakxUFdwcB/XnKesNhq+0TZwVZXtLyVFhPUaiw+Hc3Ghia4Gim+tRCgGFoJHnBhzvtRQ7CRtPh791QVsL7k4gwKwjHEQ3X5POjOdstl+trddK8ZBop3vbHB1N0oURGV1d84qzEt/XFwnry8ysKqkrsEkJwbaWmTNNeIuvoIthouVzpecRZScu1EDwqUnXHZLOt2nhRVnA8TYnznJW6Q8OBlbrLn3y4xSTI+Ns7A271A0xDY7ftVuwvSV6qQrrdcbna9elUvX7PUhsG1Se36NZUsVczCmMxE1CtO49BkJIWquXSreJFH0fbt2j7FtM4425fMYNeBSv1YBJzPFPzhRtrjwqWYz5i6py83xPLDc8y+YffWa9mOhpHs4R7w4g0K3BMg0+PAq6u+rRcJTr7y8+PGcwz7gzmXOn6XOvVkEgG8wxDCI5nCVe6Pqah0/Mt3lmtoVfpcC1PXYdJlPHXtwbsTyJ0obE3itjt+i9ttrUsCkuci45vLU4Kz3PzOabOt7eai/D6OCswhMbNtRq7HQ/H1JmEGZc6iprX8W3qjqmOxlUal6FpZ+T539lq8mAcoQv177um8hsSIufhOKaQJVstj3mccW9QMokKgrhgEo3RNZ2ilFzq+bimppZOoZgmZal2pLeP55RSYFTsHOXMCXlZ0vXtM0M6pfh+ucf3J+GkrQWKJfWHN1cZhSmupZ/5jBxT50olYvtgu8kvH0y5OwwRqBCg9zca3OoHPBiG6JqGppWAoFtziDKVfieEopnOogz/lMVElpf8fG+CADZaDltt9ZmedoYtSsnt/pwgUXYnszjDrCJOn4aTU1lZyufi1X9ZpLkiEEipPusyk9zuB2hCcLmnTl5lKVVinab+LMlLur7Sali64Ff7MxxT504/5GAS45kGUazYTScGfElekBQlhYSkKNhouovNTtsz8SydKxWj6NqKT1p5PTVdkyDNudNXLUtDCIQGqw37pZId3qiiIIT4R8B/jqKQ/89Syv/qNb+lbyy6NZvul2SC6Jrg4TAizgtmsbIodgydyys+UqqIz7QoF06PJ4vueb3SSZgRZjmrdZvLXZcHkwQpJetNhyQrOJ4lPBzFBIli43TrFuOjgJZnME8ybFOj4yna46fTBEvXuNzxcC2Dg4mK9BwGyg68lGpuoPyHFPNo5wm+PBfFreM5RaniPb/KYHp/EtGfpWfosZomnvgZKaM+9et31xv0g0SJmDzV999peyRZiWXoDIOEd9Zq2IZBxzcpSsmP747YGyoH2nuDgBvrTb6702QW5wRpTpbLMwP4JFfuuLahNCVRqiyvu3Wb36+p3bZ3gSHyq8pXiNKChmugaaqIepbB0TQmVwHWzOKc3FTW8FFWApI4L+lV92yvZrM/icgKpTZ+b7PBLx5MOJ7FJHnBWstBCME7qzV+cm/Mu6t1NE3Qn6Z0PJtuzcGz1OyrU7POiEFdncXA/cTWRNcEf/juKrUnBC69SLxRRQH4GfAHQAn8f0KI/1ZKOXnN72mJ50SYFkwiNTy8N4gAQZQVfOS38Z5gbJYXJXeHIUUpFycKgEGQUJaQlCWFFGf0Ag9HEXujkOE848PdFlGWU7ctfnDFVr40rokG7PZ87g5CHoxDfEtH0+A7Wy0sQwdyJewxdHa6LkKIxY62KJX76+lWxiTMMA3xVJrlCYpSqvAgVDvqq8QmnlzPeZyfOTFcBFKo92IbBh8fTKnZBq5lsNNxWW0oT6K7A+WVM08KGo5Bt2bxiwdT0rzA0DUabsznrtKSqNzngkunZi0H47hyHFVZwq6lk+QF19qqNadp4qUPSC+KWZxxp/9IwHZyymm4JsMwRSCoOwb3BiFhVvBgpIbKQpMgoe4qB9P+LMXQNNaaDl3fJs1K9iYBo7nSLDRdg08O52iaIMxOrFk0JlHGdsdltW6TFuVTabiaJi5EP32ReKOKgpTy3smvhRAFqjgs8ZbBMdXNn2Ql767XmcS5ird8yk55GucLUdEoTNloqgW041v05zOmcY5nKWaLZeisNWyyUnK5V8MxY3xL50qvRikldUcJz1ZqNkfTmJ/eH1GzTOK0rFxcle+/W70fx1QF4WSh3+l4DIOUhmueYaocTWMOp6oHfX219sydv+K2K6po4yvugFfrDkezmJZrPVdBGIcpwyCllGomk2Ql90cRuia4uVbH0Es+Pphytx/QdC3avklaSHRd43LPQxNiMa/oehYtz+IdXc1iThfGEnVNNaE2BY6pkZeKlfO4LuR146RtBEoceALH1Hl3vXHm9y3XwtBUhOYszrFNjZW6jaGrdLUgKej6KhlvMFdCzCAuycqSo1mCJgT745i6o3M4K9jtuFxd8ZhGGYfTeJG/DU//TE88uvKKJPGNaR+dQAjxD4HPpJSzJ3z9T4E/Bdjd3X2Vb22JC0CxT2qUUv06THOsipnyJPi2yp8tSoln6hxNY1xLp+VZSnNg5twZhDRcA8dQR+rttottaFxd8el4Z4Ptr/Zq3O7PSbKSrJSE5Pze9e7CHkMJiEzeMWsIwRl1smPq5+7q8/JRtnD+jP44qBPGext15Sb7FdlKHd967qQtKSV7owgpYbfjs9qwuX0cEMTKPbOUath6b6DaZb5VstvxFuluutBoeaZiI1kGtWq47p9z2ttpe0yjHCkldcdc0IyP58kLSaJ7kVD511J5Xz3lmm63Xbo1C9vQKwvxkpZnLmZdV1dqZ05t39pq0vYtbvcDarZBkGQ0HJX9XUpJWUp6NQdD09gbhhzNVEhR3THPHcCfxjTOFtTd/jz5SqfOZ+GNKApCiH8M/Anw3wP/EvhPgX/nSd8vpfwz4M8APvroowtKOZZ4lRBCcGKQeV6rZRymGLq2UJXahs6764pmd28YMo1yhFCOrHmpDOAajoGp64DErPx1djrn898tQ2Oz5XJ/GDFPcnY6Hu+s1oizElN/ZNT2PH3+tYaDVtlFXNTfXwjxSpg0T3ptx9SIUpWhLKW6zuNQpadN4wzH1LB1jdKSrDZs5VarqTZdyzNpuubi88uL8qkGdx/utJCVsmoSZURp8doymJ8GIcQXMq+f9H0nP7tnGTze/QrTnDBVGRcnhWGz5eKaOveGAZ5lomnw/maDUZgyiXIMXfl1eXaCmKsCm5eSSZg9tXg6pmp7lqWaM7xMvBFFQUr5z4B/JoSoA/8n8B9KKYNX+R6e5US6xIvDiYIVFB30ZOd5sgM7aVekecm9gYolbPsmu50W/WnMX98ecHcY8rtXuvSewmWvOyZ/70ZP8cxttcP7KopZXbvYYvIm4WqvVjGKdH5yb8zeKKTpqhSwMC1puia/f71LXkhcS0erMgU+P56rWU5Wcrmn+uuTKKPlmU8sxPDoM7y+WlP2EK9Zz/GykBUlt46DhZDRtw2OpupUtNVySfKS41nCPM759Gi+IAi4ltJzfHenxbvrdcK04OE4ZkjKeuE8UZvhmDo31+oLVt/LxBtRFE7hPwauAP9ddXP9R1LK26/3LS3xolGemhQV1bH69OKx1XaxDMHDccSDcazcO1G7qEmco5IZVY/1aUUBwDL0aqD8zYSmCXzbIM1LjMqU0DaUYE9KkEjcx3aeeVHycBwRpyWXV1QBmMaqdTGJMnae47W/CZCSyvojr6i4OVe6HqYuOJzFFAVkuVRUaP3EpE7gWgbZqTZkecq/4uTEdVrV/bJZR4vXeSWvckFIKf8J8E9e9/tY4uVitW6jCXXDH0yU6+pWpeQEtSNXdgk6Xd/GPdXj32g63B7MyQvFUlriYjB1Qb3yldpoOdiGpiIpH+uJDOYJnx/PmUY5be9RJOlaw2EYpGdoqN9kmLrG5Z5PmOR0fItBkHIwjrF0nSQrmSUF3ZqNZxnsjUIsQyXbPY6GY7LZcqqUQrXBidKCW/05oOJxX4XH1mm8UUVhiW8GtMoKYRSmBEmOoSma3mnBUs1W8Yh1VzlznvRsPdvgH7y3/rre+muBlJLjWUIpq4L6JXbgQogvxDKep8qeRBmWodhjDddcmLKt1O1XYjvxNqFmG4vZ0lpDOZLe7quW0ixWORErdZt3nmFJ8bjWZBZni9P0LM6XRWGJrz/CVCmRpZQkWYnnG19oAzmmznsb6mF61UlcbxpGYcZh5QmkDO9e3uLcq7jzH2632Gw66K+oZfF1gG8bfGuzwf4kYjBXttu2qT13Ml/LsxbtutcxqF8WhSVeOXTthJEjuNzznzi8fZHFICtKilK+8l3Xi4BxKuf4SZnHLwoNx8Tp6ZRSLgvCl4AQomLIqUXd/BL53ZahcX31/NNFnBVoQnzzdApLfL1hG8qb59OCF6A0AAAgAElEQVTD2cJc7VkUzygtyMvySxnRxVnBZ0dzpFRD7Ofl+79uNByTqys+Ei5Mhf2yOH2tdjruU1XIcVaQFuVTd8LTOOP+MFR+TF3/rRw+PxhHjIKU1bp9ISHeSt3GNjVMTbsQ2y3OCpK8pOEYT90IjcOU+8PowuLJL4vlVmCJ14K8lOiaRilhFKRP/d4wzfnsaM6dfki/CsF5HiR5uQgmibKXG/f4suDbzy6cLwJxVlzoWp0Uj7v9kKNp/MTvG85TyhLCpHgrr72UkuE8rRhGT79PT6PhmBcqCFlR8tnRnHuDkP3Jk68jPPo8pFTX/2VheVL4GuBZGos7//RPXtE7uTh8S8cxNdKi/IIj5omv/Qmy4hFVLy+eX6t44uWTFSUrLznu8W3E6evddE2CWkFRyKd68uSVLxSctYp4HG1f2aErd9s3u3V3nq5CCEHbNxmH2UthXhWnruPjsa+Po1ezyXKJrn+5RLWLYlkUlngtMHTtXFbGNM64NwgXNsKGrtF0Tdaa9oLN8bwQQrxUW4C3GbM44+5Apdtdq+ybty5wrWq2wUbLIc1VlsKT0HRNmlvNF/mWXwoejiMG8/Rccd5222O7/XJe1zF1ttouUVY8c8Ni6tpTE+xeFJZFYYk3CpMwQ0qlZg6zgkY17DyhRi7xYjGJ1PXOckmYFDS9i3eUn3aSeNtw4is0DjN2Oq/2td+0GddyprDEG4VuzcIylAPleWKfJV4sur4KbPFsnZrzzb3eyvn05dJ93xZ8c++CJd5IeJbBzfWXlz+7xFm4lr683izFeaexPCks8bXHYJ5wux8wqwRBSzwdR7OY2/2AMM1f91t5q3E0VdcxSt8u1tU35qSwdEH9ZqIoJQ/HiuqX5iU31988K+c3CWleLhxsi1JyffXVpn59XRBnxUKFfkD8BYuRNxlfm6KwXPSfjLeRsvqioAlwTI04Ky+UEfxNh6EJTEOQ5XJ5vb4CDE1g6IK8ePuu49emKCyxxHkQQlEtk7zEMZfd0mdB0wTvrNZJ8/IrZU9802HoGu+s1siqnIq3CULKtzu4rNfrycuXL7/ut/FUpJWi1jQ03mSV/507d3jTr+XbhLfpepZSkuUqx/llh7h8WbxN1/M0kioT2qqyrd8U/OhHP5JSyi982G/9SeHy5cv87d/+7et+G0RpwSzJaLnWmYdqGmfc7YcAZ4Qx4zDlaJYoYdYbEmz+0UcfvRHX8uuCV3U9Z3FGnJV0fOupOdgnKEvJ/VFImpdstz1cS+dOP2AWq8HytVX/3AjV143XeX/GWcE0zmi65pk878eR5iX3RyEC2Ol4zOKcB6MIUAynNym5Twjx4/P+/M3cErxlKEvJrf6cw0nCvWF45muuqQLpheCMcdjhNCHJSo6mCUUpSfOSefJktsckzBiHF/deWeKbgTgruNMPOZjEPBxH5EXJYJ6cYbxIKZknOXllozBPc6ZRTpyVCy+pRmWbYBnaUxe9bypu9wMOJwl3+uFTv28cpoRJQZAUTKIMz9JJcmXm+CwdSJjmDOZqPXidePO2A287pCQrykXsnqlrvLuuslVP7+LqjsFgnuLbOlJKPj2aUZbQq1tsNM/aDEzCbFFsSvnmKSCXeL0QQpmkCQF7o4hZnCMEvLfRQNcEe6OIcZhhGoKba/XFRqUo5WKj0vEtGo4KNiqlYh5d5NSxxFnUHIOjWYIQ4FsGszjHrNpGJ7bneVGiCXHGZ+l05nOYFk/NwX7ZWBaFFwBNE1zt1ZjFGaMw5Rd7EwxNcHnFp+GowPjHbfA3Wy6rdRtD14izYpG0lGRfNMU6nd1avuUzoIvgm8yWel44ps6lrrdoH90dBIuvqXmhIMnVqSHLJUUpn7hRMXSNIMm53Q8QAq72ahRSMo9z2v7T2yZfd2w2HfZG0VN9nkCJL9/baCBQ60J/nqBVg4SskMSZshLXhOD6am3Raj79WL/uZ3xZFL4kpJTcGYQESc5606FXs1VQ9zThaJao3YAmuLleX5waHsdJELdj6qw3HeKsYLXxxZuu7Vv05wlZUdJ6DUlMS7zZqDsmJ9ZQ222PUZji28bi/tpsufRnKXXn0Z+dt1EBCJIcKdUiNY0zjmcJUsIoTGm6Jr5tnOvQOQpSHowjXOvtzU14Go6qONQH44imaz715zsptA9GEZ8fz5XeY6VGzTZ4MI6QEgopidJiURQsQ+NS1yNKi9feCVgWhS+JrFA7KFB9xF5N7fpX6jajIMWsjuWP3zr3hyGTKGO1YZ8xeXuaxH5aDRIBjufJF9pLS3z9UJaS24OAOCvYbnsXtkq2DO0LxAXPMtjtXuxRb3kWsyRHAG3XpD9XRUFtdCTDIMU7Z6MzClXmQJiowJi3jYb5LJwUAU2ICzGIpnHG7X7AKMhoeyZ2ZRve9S2itMDUBfVqxiClrBTkBdttd1G4XxeWReEZCNOcLJc0vUcP5YNxxDhMycsSy9Do+o8W9PWmw0rdXgyZTn/ARSkXbozDIL2Q8+csztgbqULSdM1ln/cbgigrCBPV9hlXu/QXgfvDkGmcsdZwznU5tQyN7bbL/jimH6RcW6kRpgU1x2BezSo0IShLyTTOcEwdx9Tp+jZxFuFVORlfN1zqeEzjHN/WibOSNC/RNbg/ijB1jSs9/2wrThP4ls7E0LBNnVa1fjimzvXVGlJK9icxaV7S9iyC6rMeBulT0+5eBZZFAUUnfTiJlLf5KS/5KC34/Ej1aNdyFcVXliqJCU7C5Rtf+Pd0TZx7BNQ1QcszmUTZhY+I+5OYogDbFG9llOQSXw6uqePZOnFWvLBFIi9KlRsQpIzDjN7180+nR9NkQU+tOwYd36LlmkzjDNfS0TXB/WHIOMzQNLi5VqfpmWc2Tl83GLpGx7dI8oLPj1VcaVYUmLpOXhTMk3xRuB+MI+KsYLfrcXW1dm5Bnyc5g2od0TXwbZ3wDWgdwbIoAMoALEzUzqxV9U0B8vLR0DevaGJatbCPwnRB43sWpJQkeYmla+x0PHae8H1lKdmvog03Gg6aJnBNnSQrabkW7a/xQ7fEWWiaUmJ/GZzcb7ahnUmw04QgygrSvCQvS5K8OHd47Fr6YsE/+bq67x8tWCfPQ1nCLM4XHP7Xvct92SjLR0NhzzIopBrc+1W7LEhyhvOUwTzh2NT5rcttpJTk1YD/BAJBKSWaEHiWQfcNyqZYFgVU/u00yjF0gX1KeFZ3TDZbDllxNvFro+kQpDnDeYZrps+s7ieUQNfSuL76ZJviYZguTiFWNZ/Ybrv0avYXHvAllngS7g5CZlWr42pVWNJcZQELAWt1h27dwtTOb/P0ajY126j8e87/nq2WS3+e4NsGB1UbZBbnNJynD2HfdriWznbbJc5VUtrj18c2NJK8YBRm9GqC/UlMnBUkWbloLZ8IWqWUbHe8L8TRvm4siwLqIWg4JoYmvnBDn1fB47wky9V2YR7nzywKQWVBHKXluTmwJzhdkE76skKIr93Q7qtiSVl9Ok7ut/CUgC3KCopS0vFsfFvn8jMYQs4z8pQtQ1tEnI7DlDRX87Wvc0E4wdMWcUPXeL/ShxiasrU5oZnPk5yVur2YFemaxptIMH/jioIQ4neA/xoogL+VUv4nr+J1n+X3IqViXmhCtY9ankmSF+eyhqK0YG+kcoZ3Ox6bLZfjWULrGVS2umPyzpra2T3roVxiiSdhu+XRDxI6p1o59YpKmhYlmy33qfdhmCprBtvQ2em4zzyh7nY8JlFGlBUESb5ov34dcFF7ixMMg5TjWcJKzaZTsyq7kJA4e7RWdGtqNqEJ8UZSzN/ET+8u8MdSylgI8T8KIb4jpfzFq34TkzBjGKZ0PIumZ9KfpxxMVL9fE+KpisNBkBBnJXFWMktyXFPH0jWKJ4hShkFKUO0ilsVgia+K84a+mibY7XpIKTmcJhzP57imzkbT+cIi/uv9GYMgpetbdGoWtWcs8kIIxmHGLFbD03fX66+dVvmicOs4WLAGb6zVyYuSg2mMrgnWG84XCubhNCYvlG3NVludpLbb6rqffK+pa1zq+hzPEh5OIlbrzoVMCIMk52iWUHeMl5qP/cYVBSnlwanf5qgTwytBmpccTGIsQ1vws8M0p+k1Afnog33GCbnumIzDDF0TeJbOwSRmHGakRUEpJWt1hyRXsvasKMmKEtvQyYpy0QMGFj4ovZr9jTiWL/FiMIszTF1bbDCSvODWcUApJW3X4nAWc6cf0nANjmcJ7282FgyZIMmJs4J5nBMkGU3XQG+5uG+gQd6rwOOHpEGQMgoUrdw1dVqeRRDnTCszzJpj8MnBDM/SKUqJoQumcca9QYhlaGy1HAoJumCxyZSSC9la7E8iorRkHium05NEsV8Vb+wnLYT4AOhJKX/1ql7zaBYziU4iG5VFgGvqBEnO4TQhTAuurdQwNMEszqg7iqZ3f6AcJzs1i522t/CUWW1YmLqGbWjkZcmDUYRABW8kecGv9sc8GMVsNBze22x+wV31JDFMwhvjpLrE60VWlISJ0g2cp1k5msUcTpT3zvXVGo6pM5inJHnJJEw5mETME+XoO4tTmp5FbWBwY72GbeiYukbDVdYsSZbzk3tj/tVnAz7cbvHepqJfn7iA7na8xYlgu+3y6dF84e/zdcGVnq8G6K5aKk/mfnFWcLs/ZxrljMOU1bpLy1f6j7ptkOQlP7o7pFd3mMfqFBVnBZ8dzdhueTQ8c+FZdbK4p3nJ/iRCAno1SzR1jbxQFiauZRClKZahob9E0skbWRSEEB3gvwH+gyd8/U+BPwXY3d19Ya+rdlYZQsC1lRoScAydw1mMlIqlFOUFBxVtdKvl8OP7Iz49nCMQ/O7VLq6VcDCO6c9THow1fvtKl9WGg66JxQ1wuz8nLyT/4jd9Wp
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