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@zeikomi552
Created March 10, 2021 07:51
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"from sklearn import linear_model\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>number of create articles</th>\n",
" <th>number of articles</th>\n",
" <th>number of users</th>\n",
" <th>number of page views</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>46.000000</td>\n",
" <td>46.000000</td>\n",
" <td>46.000000</td>\n",
" <td>46.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4.586957</td>\n",
" <td>82.543478</td>\n",
" <td>1761.717391</td>\n",
" <td>3174.804348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>8.012701</td>\n",
" <td>42.255680</td>\n",
" <td>965.613073</td>\n",
" <td>2218.860785</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>6.000000</td>\n",
" <td>18.000000</td>\n",
" <td>31.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>64.750000</td>\n",
" <td>1229.000000</td>\n",
" <td>2197.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.000000</td>\n",
" <td>77.000000</td>\n",
" <td>1564.000000</td>\n",
" <td>2778.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.000000</td>\n",
" <td>80.750000</td>\n",
" <td>2160.000000</td>\n",
" <td>3705.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>32.000000</td>\n",
" <td>211.000000</td>\n",
" <td>4627.000000</td>\n",
" <td>14965.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" number of create articles number of articles number of users \\\n",
"count 46.000000 46.000000 46.000000 \n",
"mean 4.586957 82.543478 1761.717391 \n",
"std 8.012701 42.255680 965.613073 \n",
"min 0.000000 6.000000 18.000000 \n",
"25% 0.000000 64.750000 1229.000000 \n",
"50% 1.000000 77.000000 1564.000000 \n",
"75% 6.000000 80.750000 2160.000000 \n",
"max 32.000000 211.000000 4627.000000 \n",
"\n",
" number of page views \n",
"count 46.000000 \n",
"mean 3174.804348 \n",
"std 2218.860785 \n",
"min 31.000000 \n",
"25% 2197.500000 \n",
"50% 2778.000000 \n",
"75% 3705.750000 \n",
"max 14965.000000 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# CSVファイルの読み込み\n",
"df = pd.read_csv('blog access.csv')\n",
"\n",
"# データの外観確認\n",
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>month</th>\n",
" <th>number of create articles</th>\n",
" <th>number of articles</th>\n",
" <th>number of users</th>\n",
" <th>number of page views</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017年5月</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017年6月</td>\n",
" <td>24</td>\n",
" <td>30</td>\n",
" <td>3813</td>\n",
" <td>14965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017年7月</td>\n",
" <td>7</td>\n",
" <td>37</td>\n",
" <td>244</td>\n",
" <td>1096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017年8月</td>\n",
" <td>0</td>\n",
" <td>37</td>\n",
" <td>376</td>\n",
" <td>896</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017年9月</td>\n",
" <td>0</td>\n",
" <td>37</td>\n",
" <td>754</td>\n",
" <td>1428</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" month number of create articles number of articles number of users \\\n",
"0 2017年5月 6 6 18 \n",
"1 2017年6月 24 30 3813 \n",
"2 2017年7月 7 37 244 \n",
"3 2017年8月 0 37 376 \n",
"4 2017年9月 0 37 754 \n",
"\n",
" number of page views \n",
"0 31 \n",
"1 14965 \n",
"2 1096 \n",
"3 896 \n",
"4 1428 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# データイメージ\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x23b5b7c3280>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.loc[:,[\"number of create articles\", \"number of articles\"]].plot()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x23b5beca550>"
]
},
"execution_count": 5,
"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.loc[:,[\"number of articles\", \"number of page views\",\"number of users\"]].plot()"
]
},
{
"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>number of articles</th>\n",
" <th>number of page views</th>\n",
" <th>number of users</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>number of articles</th>\n",
" <td>1.000000</td>\n",
" <td>0.292594</td>\n",
" <td>0.683353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of page views</th>\n",
" <td>0.292594</td>\n",
" <td>1.000000</td>\n",
" <td>0.807473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of users</th>\n",
" <td>0.683353</td>\n",
" <td>0.807473</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" number of articles number of page views \\\n",
"number of articles 1.000000 0.292594 \n",
"number of page views 0.292594 1.000000 \n",
"number of users 0.683353 0.807473 \n",
"\n",
" number of users \n",
"number of articles 0.683353 \n",
"number of page views 0.807473 \n",
"number of users 1.000000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[:,[\"number of articles\", \"number of page views\", \"number of users\"]].corr()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x23b5bfae580>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.loc[:,[\"number of articles\", \"number of page views\", \"number of users\"]].drop(df.index[[1]]).plot()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>number of articles</th>\n",
" <th>number of page views</th>\n",
" <th>number of users</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>number of articles</th>\n",
" <td>1.000000</td>\n",
" <td>0.752562</td>\n",
" <td>0.798818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of page views</th>\n",
" <td>0.752562</td>\n",
" <td>1.000000</td>\n",
" <td>0.971323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of users</th>\n",
" <td>0.798818</td>\n",
" <td>0.971323</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" number of articles number of page views \\\n",
"number of articles 1.000000 0.752562 \n",
"number of page views 0.752562 1.000000 \n",
"number of users 0.798818 0.971323 \n",
"\n",
" number of users \n",
"number of articles 0.798818 \n",
"number of page views 0.971323 \n",
"number of users 1.000000 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[:,[\"number of articles\", \"number of page views\", \"number of users\"]].drop(df.index[[1]]).corr()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"回帰係数: [[24.09179397]]\n",
"切片: [896.04915835]\n"
]
},
{
"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": [
"reg = linear_model.LinearRegression()\n",
"\n",
"df_basic = df.loc[:,[\"number of articles\", \"number of page views\"]].drop(df.index[[1]])\n",
"X = df_basic.loc[:,['number of articles']].values\n",
"Y = df_basic.loc[:,['number of page views']].values\n",
"\n",
"reg.fit(X,Y)\n",
"\n",
"print('回帰係数:', reg.coef_)\n",
"print('切片:',reg.intercept_)\n",
"\n",
"\n",
"plt.scatter(X,Y)\n",
"plt.xlabel('number of articles')\n",
"plt.ylabel('number of page views')\n",
"\n",
"plt.plot(X, reg.predict(X))\n",
"plt.grid(True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"回帰係数: [[17.60534304]]\n",
"切片: [242.37050602]\n"
]
},
{
"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": [
"reg = linear_model.LinearRegression()\n",
"\n",
"df_basic = df.loc[:,[\"number of articles\", \"number of users\"]].drop(df.index[[1]])\n",
"X = df_basic.loc[:,['number of articles']].values\n",
"Y = df_basic.loc[:,['number of users']].values\n",
"\n",
"reg.fit(X,Y)\n",
"\n",
"print('回帰係数:', reg.coef_)\n",
"print('切片:',reg.intercept_)\n",
"\n",
"plt.scatter(X,Y)\n",
"plt.xlabel('number of articles')\n",
"plt.ylabel('number of users')\n",
"\n",
"plt.plot(X, reg.predict(X))\n",
"plt.grid(True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000023B5C0E75B0>]],\n",
" dtype=object)"
]
},
"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": [
"df.loc[:,[\"number of create articles\"]].hist()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000023B5C1643A0>]],\n",
" dtype=object)"
]
},
"execution_count": 12,
"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.loc[:,[\"number of page views\"]].hist()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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