{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Counting museums in China\n",
"\n",
"In this section we'll reproduce the graphics from [this piece here](https://datanews.caixin.com/mobile/museum/), where Caixin does a per-capita analysis of the museums in China."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p class=\"reading-options\">\n <a class=\"btn\" href=\"/caixin-museum-word-count/chinese-museums-per-capita-analysis\">\n <i class=\"fa fa-sm fa-book\"></i>\n Read online\n </a>\n <a class=\"btn\" href=\"/caixin-museum-word-count/notebooks/Chinese museums per capita analysis.ipynb\">\n <i class=\"fa fa-sm fa-download\"></i>\n Download notebook\n </a>\n <a class=\"btn\" href=\"https://colab.research.google.com/github/littlecolumns/ds4j-notebooks/blob/master/caixin-museum-word-count/notebooks/Chinese museums per capita analysis.ipynb\" target=\"_new\">\n <i class=\"fa fa-sm fa-laptop\"></i>\n Interactive version\n </a>\n</p>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prep work: Downloading necessary files\n",
"Before we get started, we need to download all of the data we'll be using.\n",
"* **museums-cleaned.csv:** cleaned museums - A cleaned list of museums\n",
"* **population-cleaned.csv:** population data - Cleaned population data with column names matching museum data\n"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# Make data directory if it doesn't exist\n",
"!mkdir -p data\n",
"!wget -nc https://nyc3.digitaloceanspaces.com/ml-files-distro/v1/caixin-museum-word-count/data/museums-cleaned.csv -P data\n",
"!wget -nc https://nyc3.digitaloceanspaces.com/ml-files-distro/v1/caixin-museum-word-count/data/population-cleaned.csv -P data"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"We'll import pandas as usual, but we'll also need to **do some special matplotlib setup**. This allows us to **have graphs with Chinese characters** - if we don't, every time we graph we'll get a lot of errors and the text won't look right."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# So Chinese characters can appear correctly\n",
"plt.rcParams['font.sans-serif'] = ['SimHei', 'SimSun', 'Microsoft YaHei New', 'Microsoft YaHei', 'Arial Unicode MS']\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing our data\n",
"\n",
"We'll be using the data we previously cleaned."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"</style>\n",
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" <th>\u8d28\u91cf\u7b49\u7ea7</th>\n",
" <th>\u662f\u5426\u514d\u8d39\u5f00\u653e</th>\n",
" <th>\u5730\u5740</th>\n",
" <th>region</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>\u6545\u5bab\u535a\u7269\u9662</td>\n",
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" <td>\u5426</td>\n",
" <td>\u4e1c\u57ce\u533a\u666f\u5c71\u524d\u88574\u53f7</td>\n",
" <td>\u5317\u4eac\u5e02</td>\n",
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" <tr>\n",
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" <td>\u5317\u4eac\u5e02</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>\u4e2d\u56fd\u4eba\u6c11\u9769\u547d\u519b\u4e8b\u535a\u7269\u9986</td>\n",
" <td>\u884c\u4e1a</td>\n",
" <td>\u4e00\u7ea7</td>\n",
" <td>\u662f</td>\n",
" <td>\u6d77\u6dc0\u533a\u590d\u5174\u8def9\u53f7</td>\n",
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" <td>\u6587\u7269</td>\n",
" <td>\u4e00\u7ea7</td>\n",
" <td>\u662f</td>\n",
" <td>\u961c\u6210\u95e8\u5185\u5bab\u95e8\u53e3\u4e8c\u676119\u53f7\\r\u4e1c\u57ce\u533a\u4e94\u56db\u5927\u885729\u53f7</td>\n",
" <td>\u5317\u4eac\u5e02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>\u4e2d\u56fd\u5730\u8d28\u535a\u7269\u9986</td>\n",
" <td>\u884c\u4e1a</td>\n",
" <td>\u4e00\u7ea7</td>\n",
" <td>\u5426</td>\n",
" <td>\u897f\u57ce\u533a\u897f\u56db\u7f8a\u8089\u80e1\u540c15\u53f7</td>\n",
" <td>\u5317\u4eac\u5e02</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" \u535a\u7269\u9986\u540d\u79f0 \u535a\u7269\u9986\u6027\u8d28 \u8d28\u91cf\u7b49\u7ea7 \u662f\u5426\u514d\u8d39\u5f00\u653e \u5730\u5740 region\n",
"0 \u6545\u5bab\u535a\u7269\u9662 \u6587\u7269 \u4e00\u7ea7 \u5426 \u4e1c\u57ce\u533a\u666f\u5c71\u524d\u88574\u53f7 \u5317\u4eac\u5e02\n",
"1 \u4e2d\u56fd\u56fd\u5bb6\u535a\u7269\u9986 \u6587\u7269 \u4e00\u7ea7 \u662f \u5317\u4eac\u5e02\u4e1c\u57ce\u533a\u4e1c\u957f\u5b89\u885716\u53f7 \u5317\u4eac\u5e02\n",
"2 \u4e2d\u56fd\u4eba\u6c11\u9769\u547d\u519b\u4e8b\u535a\u7269\u9986 \u884c\u4e1a \u4e00\u7ea7 \u662f \u6d77\u6dc0\u533a\u590d\u5174\u8def9\u53f7 \u5317\u4eac\u5e02\n",
"3 \u5317\u4eac\u9c81\u8fc5\u535a\u7269\u9986(\u5317\u4eac\u65b0\u6587\u5316\u8fd0\u52a8\\r\u7eaa\u5ff5\u9986) \u6587\u7269 \u4e00\u7ea7 \u662f \u961c\u6210\u95e8\u5185\u5bab\u95e8\u53e3\u4e8c\u676119\u53f7\\r\u4e1c\u57ce\u533a\u4e94\u56db\u5927\u885729\u53f7 \u5317\u4eac\u5e02\n",
"4 \u4e2d\u56fd\u5730\u8d28\u535a\u7269\u9986 \u884c\u4e1a \u4e00\u7ea7 \u5426 \u897f\u57ce\u533a\u897f\u56db\u7f8a\u8089\u80e1\u540c15\u53f7 \u5317\u4eac\u5e02"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"data/museums-cleaned.csv\")\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many museums do we have?"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4469, 6)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great. 4469 rows, 6 columns. 4471 museums, 6 pieces of data about each.\n",
"\n",
"## Counting values\n",
"\n",
"How many museums are in each province?"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u5c71\u4e1c\u7701 339\n",
"\u6d59\u6c5f\u7701 277\n",
"\u6c5f\u82cf\u7701 274\n",
"\u6cb3\u5357\u7701 265\n",
"\u5e7f\u4e1c\u7701 252\n",
"\u9655\u897f\u7701 236\n",
"\u56db\u5ddd\u7701 216\n",
"\u6e56\u5317\u7701 197\n",
"\u9ed1\u9f99\u6c5f\u7701 193\n",
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"\u5317\u4eac\u5e02 146\n",
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"\u5409\u6797 104\n",
"\u4e91\u5357\u7701 101\n",
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"\u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a 99\n",
"\u6cb3\u5317\u7701 97\n",
"\u8fbd\u5b81\u7701 93\n",
"\u8d35\u5dde\u7701 81\n",
"\u91cd\u5e86\u5e02 69\n",
"\u5929\u6d25\u5e02 56\n",
"\u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a 39\n",
"\u9752\u6d77\u7701 32\n",
"\u6d77\u5357\u7701 24\n",
"\u897f\u85cf\u81ea\u6cbb\u533a 8\n",
"Name: region, dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.region.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Honestly we use `value_counts()` to count _everything_ in _almost every column_."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u6587\u7269 2701\n",
"\u975e\u56fd\u6709 1058\n",
"\u884c\u4e1a 710\n",
"Name: \u535a\u7269\u9986\u6027\u8d28, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.\u535a\u7269\u9986\u6027\u8d28.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u65e0\u7ea7\u522b 3772\n",
"\u4e09\u7ea7 393\n",
"\u4e8c\u7ea7 212\n",
"\u4e00\u7ea7 92\n",
"Name: \u8d28\u91cf\u7b49\u7ea7, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.\u8d28\u91cf\u7b49\u7ea7.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u662f 3844\n",
"\u5426 625\n",
"Name: \u662f\u5426\u514d\u8d39\u5f00\u653e, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.\u662f\u5426\u514d\u8d39\u5f00\u653e.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Crosstab for combinations\n",
"\n",
"What if we want to see how many of a _combination_? `pd.crosstab` to the rescue!"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"\u6587\u7269 313 2388\n",
"\u884c\u4e1a 160 550\n",
"\u975e\u56fd\u6709 152 906"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(df.\u535a\u7269\u9986\u6027\u8d28, df.\u662f\u5426\u514d\u8d39\u5f00\u653e)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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" <tr>\n",
" <th>\u535a\u7269\u9986\u6027\u8d28</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>\u6587\u7269</th>\n",
" <td>313</td>\n",
" <td>2388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u884c\u4e1a</th>\n",
" <td>160</td>\n",
" <td>550</td>\n",
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" <tr>\n",
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" <td>152</td>\n",
" <td>906</td>\n",
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"</table>\n",
"</div>"
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"\u662f\u5426\u514d\u8d39\u5f00\u653e \u5426 \u662f\n",
"\u535a\u7269\u9986\u6027\u8d28 \n",
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"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(df.\u535a\u7269\u9986\u6027\u8d28, df.\u662f\u5426\u514d\u8d39\u5f00\u653e)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
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" </tr>\n",
" <tr>\n",
" <th>region</th>\n",
" <th></th>\n",
" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>\u4e0a\u6d77\u5e02</th>\n",
" <td>22</td>\n",
" <td>94</td>\n",
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" <tr>\n",
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" <td>8</td>\n",
" <td>93</td>\n",
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" <td>85</td>\n",
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" <tr>\n",
" <th>\u5929\u6d25\u5e02</th>\n",
" <td>22</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a</th>\n",
" <td>10</td>\n",
" <td>29</td>\n",
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" <tr>\n",
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" <td>35</td>\n",
" <td>304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5c71\u897f\u7701</th>\n",
" <td>16</td>\n",
" <td>105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5e7f\u4e1c\u7701</th>\n",
" <td>18</td>\n",
" <td>234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a</th>\n",
" <td>18</td>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u65b0\u7586\u7ef4\u543e\u5c14\u81ea\u6cbb\u533a</th>\n",
" <td>1</td>\n",
" <td>99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6c5f\u82cf\u7701</th>\n",
" <td>44</td>\n",
" <td>230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6c5f\u897f\u7701</th>\n",
" <td>0</td>\n",
" <td>137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6cb3\u5317\u7701</th>\n",
" <td>13</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6cb3\u5357\u7701</th>\n",
" <td>26</td>\n",
" <td>239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6d59\u6c5f\u7701</th>\n",
" <td>11</td>\n",
" <td>266</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6d77\u5357\u7701</th>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6e56\u5317\u7701</th>\n",
" <td>12</td>\n",
" <td>185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6e56\u5357\u7701</th>\n",
" <td>11</td>\n",
" <td>118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u7518\u8083\u7701</th>\n",
" <td>9</td>\n",
" <td>174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u798f\u5efa\u7701</th>\n",
" <td>6</td>\n",
" <td>105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u897f\u85cf\u81ea\u6cbb\u533a</th>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u8d35\u5dde\u7701</th>\n",
" <td>3</td>\n",
" <td>78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u8fbd\u5b81\u7701</th>\n",
" <td>10</td>\n",
" <td>83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u91cd\u5e86\u5e02</th>\n",
" <td>12</td>\n",
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u9655\u897f\u7701</th>\n",
" <td>86</td>\n",
" <td>150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u9752\u6d77\u7701</th>\n",
" <td>3</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u9ed1\u9f99\u6c5f\u7701</th>\n",
" <td>0</td>\n",
" <td>193</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"\u662f\u5426\u514d\u8d39\u5f00\u653e \u5426 \u662f\n",
"region \n",
"\u4e0a\u6d77\u5e02 22 94\n",
"\u4e91\u5357\u7701 8 93\n",
"\u5185\u8499\u53e4\u81ea\u6cbb\u533a 1 191\n",
"\u5317\u4eac\u5e02 77 69\n",
"\u5409\u6797 15 89\n",
"\u56db\u5ddd\u7701 131 85\n",
"\u5929\u6d25\u5e02 22 34\n",
"\u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a 10 29\n",
"\u5b89\u5fbd\u7701 3 179\n",
"\u5c71\u4e1c\u7701 35 304\n",
"\u5c71\u897f\u7701 16 105\n",
"\u5e7f\u4e1c\u7701 18 234\n",
"\u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a 18 81\n",
"\u65b0\u7586\u7ef4\u543e\u5c14\u81ea\u6cbb\u533a 1 99\n",
"\u6c5f\u82cf\u7701 44 230\n",
"\u6c5f\u897f\u7701 0 137\n",
"\u6cb3\u5317\u7701 13 84\n",
"\u6cb3\u5357\u7701 26 239\n",
"\u6d59\u6c5f\u7701 11 266\n",
"\u6d77\u5357\u7701 0 24\n",
"\u6e56\u5317\u7701 12 185\n",
"\u6e56\u5357\u7701 11 118\n",
"\u7518\u8083\u7701 9 174\n",
"\u798f\u5efa\u7701 6 105\n",
"\u897f\u85cf\u81ea\u6cbb\u533a 2 6\n",
"\u8d35\u5dde\u7701 3 78\n",
"\u8fbd\u5b81\u7701 10 83\n",
"\u91cd\u5e86\u5e02 12 57\n",
"\u9655\u897f\u7701 86 150\n",
"\u9752\u6d77\u7701 3 29\n",
"\u9ed1\u9f99\u6c5f\u7701 0 193"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(df.region, df.\u662f\u5426\u514d\u8d39\u5f00\u653e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Percentage crosstabs\n",
"\n",
"Instead of pure counts, sometimes you want crosstab to return a percentage. In this case, we'll just pass `normalize='index'` to have each column be a percentage of the row."
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>\u535a\u7269\u9986\u6027\u8d28</th>\n",
" <th>\u6587\u7269</th>\n",
" <th>\u884c\u4e1a</th>\n",
" <th>\u975e\u56fd\u6709</th>\n",
" </tr>\n",
" <tr>\n",
" <th>region</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>\u4e0a\u6d77\u5e02</th>\n",
" <td>0.370690</td>\n",
" <td>0.387931</td>\n",
" <td>0.241379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u4e91\u5357\u7701</th>\n",
" <td>0.762376</td>\n",
" <td>0.148515</td>\n",
" <td>0.089109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5185\u8499\u53e4\u81ea\u6cbb\u533a</th>\n",
" <td>0.614583</td>\n",
" <td>0.119792</td>\n",
" <td>0.265625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5317\u4eac\u5e02</th>\n",
" <td>0.363014</td>\n",
" <td>0.479452</td>\n",
" <td>0.157534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5409\u6797</th>\n",
" <td>0.644231</td>\n",
" <td>0.221154</td>\n",
" <td>0.134615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u56db\u5ddd\u7701</th>\n",
" <td>0.675926</td>\n",
" <td>0.069444</td>\n",
" <td>0.254630</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5929\u6d25\u5e02</th>\n",
" <td>0.321429</td>\n",
" <td>0.375000</td>\n",
" <td>0.303571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a</th>\n",
" <td>0.487179</td>\n",
" <td>0.333333</td>\n",
" <td>0.179487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5b89\u5fbd\u7701</th>\n",
" <td>0.582418</td>\n",
" <td>0.115385</td>\n",
" <td>0.302198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5c71\u4e1c\u7701</th>\n",
" <td>0.415929</td>\n",
" <td>0.197640</td>\n",
" <td>0.386431</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5c71\u897f\u7701</th>\n",
" <td>0.752066</td>\n",
" <td>0.107438</td>\n",
" <td>0.140496</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5e7f\u4e1c\u7701</th>\n",
" <td>0.634921</td>\n",
" <td>0.099206</td>\n",
" <td>0.265873</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a</th>\n",
" <td>0.787879</td>\n",
" <td>0.030303</td>\n",
" <td>0.181818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u65b0\u7586\u7ef4\u543e\u5c14\u81ea\u6cbb\u533a</th>\n",
" <td>0.900000</td>\n",
" <td>0.060000</td>\n",
" <td>0.040000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6c5f\u82cf\u7701</th>\n",
" <td>0.437956</td>\n",
" <td>0.350365</td>\n",
" <td>0.211679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6c5f\u897f\u7701</th>\n",
" <td>0.759124</td>\n",
" <td>0.065693</td>\n",
" <td>0.175182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6cb3\u5317\u7701</th>\n",
" <td>0.752577</td>\n",
" <td>0.113402</td>\n",
" <td>0.134021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6cb3\u5357\u7701</th>\n",
" <td>0.641509</td>\n",
" <td>0.071698</td>\n",
" <td>0.286792</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6d59\u6c5f\u7701</th>\n",
" <td>0.422383</td>\n",
" <td>0.158845</td>\n",
" <td>0.418773</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6d77\u5357\u7701</th>\n",
" <td>0.291667</td>\n",
" <td>0.083333</td>\n",
" <td>0.625000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6e56\u5317\u7701</th>\n",
" <td>0.629442</td>\n",
" <td>0.131980</td>\n",
" <td>0.238579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u6e56\u5357\u7701</th>\n",
" <td>0.806202</td>\n",
" <td>0.054264</td>\n",
" <td>0.139535</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u7518\u8083\u7701</th>\n",
" <td>0.765027</td>\n",
" <td>0.120219</td>\n",
" <td>0.114754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u798f\u5efa\u7701</th>\n",
" <td>0.810811</td>\n",
" <td>0.036036</td>\n",
" <td>0.153153</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u897f\u85cf\u81ea\u6cbb\u533a</th>\n",
" <td>0.750000</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u8d35\u5dde\u7701</th>\n",
" <td>0.790123</td>\n",
" <td>0.074074</td>\n",
" <td>0.135802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u8fbd\u5b81\u7701</th>\n",
" <td>0.655914</td>\n",
" <td>0.118280</td>\n",
" <td>0.225806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u91cd\u5e86\u5e02</th>\n",
" <td>0.579710</td>\n",
" <td>0.231884</td>\n",
" <td>0.188406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u9655\u897f\u7701</th>\n",
" <td>0.601695</td>\n",
" <td>0.148305</td>\n",
" <td>0.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u9752\u6d77\u7701</th>\n",
" <td>0.625000</td>\n",
" <td>0.156250</td>\n",
" <td>0.218750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>\u9ed1\u9f99\u6c5f\u7701</th>\n",
" <td>0.580311</td>\n",
" <td>0.191710</td>\n",
" <td>0.227979</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"\u535a\u7269\u9986\u6027\u8d28 \u6587\u7269 \u884c\u4e1a \u975e\u56fd\u6709\n",
"region \n",
"\u4e0a\u6d77\u5e02 0.370690 0.387931 0.241379\n",
"\u4e91\u5357\u7701 0.762376 0.148515 0.089109\n",
"\u5185\u8499\u53e4\u81ea\u6cbb\u533a 0.614583 0.119792 0.265625\n",
"\u5317\u4eac\u5e02 0.363014 0.479452 0.157534\n",
"\u5409\u6797 0.644231 0.221154 0.134615\n",
"\u56db\u5ddd\u7701 0.675926 0.069444 0.254630\n",
"\u5929\u6d25\u5e02 0.321429 0.375000 0.303571\n",
"\u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a 0.487179 0.333333 0.179487\n",
"\u5b89\u5fbd\u7701 0.582418 0.115385 0.302198\n",
"\u5c71\u4e1c\u7701 0.415929 0.197640 0.386431\n",
"\u5c71\u897f\u7701 0.752066 0.107438 0.140496\n",
"\u5e7f\u4e1c\u7701 0.634921 0.099206 0.265873\n",
"\u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a 0.787879 0.030303 0.181818\n",
"\u65b0\u7586\u7ef4\u543e\u5c14\u81ea\u6cbb\u533a 0.900000 0.060000 0.040000\n",
"\u6c5f\u82cf\u7701 0.437956 0.350365 0.211679\n",
"\u6c5f\u897f\u7701 0.759124 0.065693 0.175182\n",
"\u6cb3\u5317\u7701 0.752577 0.113402 0.134021\n",
"\u6cb3\u5357\u7701 0.641509 0.071698 0.286792\n",
"\u6d59\u6c5f\u7701 0.422383 0.158845 0.418773\n",
"\u6d77\u5357\u7701 0.291667 0.083333 0.625000\n",
"\u6e56\u5317\u7701 0.629442 0.131980 0.238579\n",
"\u6e56\u5357\u7701 0.806202 0.054264 0.139535\n",
"\u7518\u8083\u7701 0.765027 0.120219 0.114754\n",
"\u798f\u5efa\u7701 0.810811 0.036036 0.153153\n",
"\u897f\u85cf\u81ea\u6cbb\u533a 0.750000 0.000000 0.250000\n",
"\u8d35\u5dde\u7701 0.790123 0.074074 0.135802\n",
"\u8fbd\u5b81\u7701 0.655914 0.118280 0.225806\n",
"\u91cd\u5e86\u5e02 0.579710 0.231884 0.188406\n",
"\u9655\u897f\u7701 0.601695 0.148305 0.250000\n",
"\u9752\u6d77\u7701 0.625000 0.156250 0.218750\n",
"\u9ed1\u9f99\u6c5f\u7701 0.580311 0.191710 0.227979"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(df.region, df.\u535a\u7269\u9986\u6027\u8d28, normalize='index')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can even graph it..."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1152fe9e8>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pd.crosstab(df.region, df.\u535a\u7269\u9986\u6027\u8d28, normalize='index').plot(kind='barh', figsize=(5,10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"...but it looks much better stacked! We can do this because **each row adds up to 100%.**"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11476de80>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pd.crosstab(df.region, df.\u535a\u7269\u9986\u6027\u8d28, normalize='index').plot(kind='barh', figsize=(5,10), stacked=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Per capita adjustments\n",
"\n",
"To judge the number of museums per person (or the number of people per museum), we'll need to combine the province counts with population counts."
]
},
{
"cell_type": "code",
"execution_count": 15,
"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>index</th>\n",
" <th>museums</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>\u5c71\u4e1c\u7701</td>\n",
" <td>339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>\u6d59\u6c5f\u7701</td>\n",
" <td>277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>\u6c5f\u82cf\u7701</td>\n",
" <td>274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>\u6cb3\u5357\u7701</td>\n",
" <td>265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>\u5e7f\u4e1c\u7701</td>\n",
" <td>252</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index museums\n",
"0 \u5c71\u4e1c\u7701 339\n",
"1 \u6d59\u6c5f\u7701 277\n",
"2 \u6c5f\u82cf\u7701 274\n",
"3 \u6cb3\u5357\u7701 265\n",
"4 \u5e7f\u4e1c\u7701 252"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regions = df.region.value_counts().to_frame('museums').reset_index()\n",
"regions.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This dataset has been cleaned a little bit to make sure the columns match."
]
},
{
"cell_type": "code",
"execution_count": 16,
"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>ISO</th>\n",
" <th>Province</th>\n",
" <th>Chinese</th>\n",
" <th>Population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>CN-AH</td>\n",
" <td>Anhui Province</td>\n",
" <td>\u5b89\u5fbd\u7701</td>\n",
" <td>59500510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>CN-BJ</td>\n",
" <td>Beijing Municipality</td>\n",
" <td>\u5317\u4eac\u5e02</td>\n",
" <td>19612368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>CN-CQ</td>\n",
" <td>Chongqing Municipality</td>\n",
" <td>\u91cd\u5e86\u5e02</td>\n",
" <td>28846170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>CN-FJ</td>\n",
" <td>Fujian Province[e]</td>\n",
" <td>\u798f\u5efa\u7701</td>\n",
" <td>36894216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>CN-GD</td>\n",
" <td>Guangdong Province</td>\n",
" <td>\u5e7f\u4e1c\u7701</td>\n",
" <td>104303132</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ISO Province Chinese Population\n",
"0 CN-AH Anhui Province \u5b89\u5fbd\u7701 59500510\n",
"1 CN-BJ Beijing Municipality \u5317\u4eac\u5e02 19612368\n",
"2 CN-CQ Chongqing Municipality \u91cd\u5e86\u5e02 28846170\n",
"3 CN-FJ Fujian Province[e] \u798f\u5efa\u7701 36894216\n",
"4 CN-GD Guangdong Province \u5e7f\u4e1c\u7701 104303132"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pop = pd.read_csv(\"data/population-cleaned.csv\")\n",
"pop.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll merge on the Chinese name for each region."
]
},
{
"cell_type": "code",
"execution_count": 17,
"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>index</th>\n",
" <th>museums</th>\n",
" <th>ISO</th>\n",
" <th>Province</th>\n",
" <th>Chinese</th>\n",
" <th>Population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>\u5c71\u4e1c\u7701</td>\n",
" <td>339</td>\n",
" <td>CN-SD</td>\n",
" <td>Shandong Province</td>\n",
" <td>\u5c71\u4e1c\u7701</td>\n",
" <td>95793065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>\u6d59\u6c5f\u7701</td>\n",
" <td>277</td>\n",
" <td>CN-ZJ</td>\n",
" <td>Zhejiang Province</td>\n",
" <td>\u6d59\u6c5f\u7701</td>\n",
" <td>54426891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>\u6c5f\u82cf\u7701</td>\n",
" <td>274</td>\n",
" <td>CN-JS</td>\n",
" <td>Jiangsu Province</td>\n",
" <td>\u6c5f\u82cf\u7701</td>\n",
" <td>78659903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>\u6cb3\u5357\u7701</td>\n",
" <td>265</td>\n",
" <td>CN-HA</td>\n",
" <td>Henan Province</td>\n",
" <td>\u6cb3\u5357\u7701</td>\n",
" <td>94023567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>\u5e7f\u4e1c\u7701</td>\n",
" <td>252</td>\n",
" <td>CN-GD</td>\n",
" <td>Guangdong Province</td>\n",
" <td>\u5e7f\u4e1c\u7701</td>\n",
" <td>104303132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>\u9655\u897f\u7701</td>\n",
" <td>236</td>\n",
" <td>CN-SN</td>\n",
" <td>Shaanxi Province</td>\n",
" <td>\u9655\u897f\u7701</td>\n",
" <td>37327378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>\u56db\u5ddd\u7701</td>\n",
" <td>216</td>\n",
" <td>CN-SC</td>\n",
" <td>Sichuan Province</td>\n",
" <td>\u56db\u5ddd\u7701</td>\n",
" <td>80418200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>\u6e56\u5317\u7701</td>\n",
" <td>197</td>\n",
" <td>CN-HB</td>\n",
" <td>Hubei Province</td>\n",
" <td>\u6e56\u5317\u7701</td>\n",
" <td>57237740</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>\u9ed1\u9f99\u6c5f\u7701</td>\n",
" <td>193</td>\n",
" <td>CN-HL</td>\n",
" <td>Heilongjiang Province</td>\n",
" <td>\u9ed1\u9f99\u6c5f\u7701</td>\n",
" <td>38312224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>\u7518\u8083\u7701</td>\n",
" <td>183</td>\n",
" <td>CN-GS</td>\n",
" <td>Gansu Province</td>\n",
" <td>\u7518\u8083\u7701</td>\n",
" <td>25575254</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>\u5b89\u5fbd\u7701</td>\n",
" <td>182</td>\n",
" <td>CN-AH</td>\n",
" <td>Anhui Province</td>\n",
" <td>\u5b89\u5fbd\u7701</td>\n",
" <td>59500510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>\u5317\u4eac\u5e02</td>\n",
" <td>146</td>\n",
" <td>CN-BJ</td>\n",
" <td>Beijing Municipality</td>\n",
" <td>\u5317\u4eac\u5e02</td>\n",
" <td>19612368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>\u6c5f\u897f\u7701</td>\n",
" <td>137</td>\n",
" <td>CN-JX</td>\n",
" <td>Jiangxi Province</td>\n",
" <td>\u6c5f\u897f\u7701</td>\n",
" <td>44567475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>\u6e56\u5357\u7701</td>\n",
" <td>129</td>\n",
" <td>CN-HN</td>\n",
" <td>Hunan Province</td>\n",
" <td>\u6e56\u5357\u7701</td>\n",
" <td>65683722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>\u5c71\u897f\u7701</td>\n",
" <td>121</td>\n",
" <td>CN-SX</td>\n",
" <td>Shanxi Province</td>\n",
" <td>\u5c71\u897f\u7701</td>\n",
" <td>35712111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>\u4e0a\u6d77\u5e02</td>\n",
" <td>116</td>\n",
" <td>CN-SH</td>\n",
" <td>Shanghai Municipality</td>\n",
" <td>\u4e0a\u6d77\u5e02</td>\n",
" <td>23019148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>\u798f\u5efa\u7701</td>\n",
" <td>111</td>\n",
" <td>CN-FJ</td>\n",
" <td>Fujian Province[e]</td>\n",
" <td>\u798f\u5efa\u7701</td>\n",
" <td>36894216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>\u4e91\u5357\u7701</td>\n",
" <td>101</td>\n",
" <td>CN-YN</td>\n",
" <td>Yunnan Province</td>\n",
" <td>\u4e91\u5357\u7701</td>\n",
" <td>45966239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>\u65b0\u7586\u7ef4\u543e\u5c14\u81ea\u6cbb\u533a</td>\n",
" <td>100</td>\n",
" <td>CN-XJ</td>\n",
" <td>Xinjiang Uyghur Autonomous Region</td>\n",
" <td>\u65b0\u7586\u7ef4\u543e\u5c14\u81ea\u6cbb\u533a</td>\n",
" <td>21813334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>\u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a</td>\n",
" <td>99</td>\n",
" <td>CN-GX</td>\n",
" <td>Guangxi Zhuang Autonomous Region</td>\n",
" <td>\u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a</td>\n",
" <td>46026629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>\u6cb3\u5317\u7701</td>\n",
" <td>97</td>\n",
" <td>CN-HE</td>\n",
" <td>Hebei Province</td>\n",
" <td>\u6cb3\u5317\u7701</td>\n",
" <td>71854202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>\u8fbd\u5b81\u7701</td>\n",
" <td>93</td>\n",
" <td>CN-LN</td>\n",
" <td>Liaoning Province</td>\n",
" <td>\u8fbd\u5b81\u7701</td>\n",
" <td>43746323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>\u8d35\u5dde\u7701</td>\n",
" <td>81</td>\n",
" <td>CN-GZ</td>\n",
" <td>Guizhou Province</td>\n",
" <td>\u8d35\u5dde\u7701</td>\n",
" <td>34746468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>\u91cd\u5e86\u5e02</td>\n",
" <td>69</td>\n",
" <td>CN-CQ</td>\n",
" <td>Chongqing Municipality</td>\n",
" <td>\u91cd\u5e86\u5e02</td>\n",
" <td>28846170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>\u5929\u6d25\u5e02</td>\n",
" <td>56</td>\n",
" <td>CN-TJ</td>\n",
" <td>Tianjin Municipality</td>\n",
" <td>\u5929\u6d25\u5e02</td>\n",
" <td>12938224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>\u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a</td>\n",
" <td>39</td>\n",
" <td>CN-NX</td>\n",
" <td>Ningxia Hui Autonomous Region</td>\n",
" <td>\u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a</td>\n",
" <td>6301350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>\u9752\u6d77\u7701</td>\n",
" <td>32</td>\n",
" <td>CN-QH</td>\n",
" <td>Qinghai Province</td>\n",
" <td>\u9752\u6d77\u7701</td>\n",
" <td>5626722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>\u6d77\u5357\u7701</td>\n",
" <td>24</td>\n",
" <td>CN-HI</td>\n",
" <td>Hainan Province</td>\n",
" <td>\u6d77\u5357\u7701</td>\n",
" <td>9171300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>\u897f\u85cf\u81ea\u6cbb\u533a</td>\n",
" <td>8</td>\n",
" <td>CN-XZ</td>\n",
" <td>Tibet Autonomous Region</td>\n",
" <td>\u897f\u85cf\u81ea\u6cbb\u533a</td>\n",
" <td>3002166</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index museums ISO Province Chinese \\\n",
"0 \u5c71\u4e1c\u7701 339 CN-SD Shandong Province \u5c71\u4e1c\u7701 \n",
"1 \u6d59\u6c5f\u7701 277 CN-ZJ Zhejiang Province \u6d59\u6c5f\u7701 \n",
"2 \u6c5f\u82cf\u7701 274 CN-JS Jiangsu Province \u6c5f\u82cf\u7701 \n",
"3 \u6cb3\u5357\u7701 265 CN-HA Henan Province \u6cb3\u5357\u7701 \n",
"4 \u5e7f\u4e1c\u7701 252 CN-GD Guangdong Province \u5e7f\u4e1c\u7701 \n",
"5 \u9655\u897f\u7701 236 CN-SN Shaanxi Province \u9655\u897f\u7701 \n",
"6 \u56db\u5ddd\u7701 216 CN-SC Sichuan Province \u56db\u5ddd\u7701 \n",
"7 \u6e56\u5317\u7701 197 CN-HB Hubei Province \u6e56\u5317\u7701 \n",
"8 \u9ed1\u9f99\u6c5f\u7701 193 CN-HL Heilongjiang Province \u9ed1\u9f99\u6c5f\u7701 \n",
"9 \u7518\u8083\u7701 183 CN-GS Gansu Province \u7518\u8083\u7701 \n",
"10 \u5b89\u5fbd\u7701 182 CN-AH Anhui Province \u5b89\u5fbd\u7701 \n",
"11 \u5317\u4eac\u5e02 146 CN-BJ Beijing Municipality \u5317\u4eac\u5e02 \n",
"12 \u6c5f\u897f\u7701 137 CN-JX Jiangxi Province \u6c5f\u897f\u7701 \n",
"13 \u6e56\u5357\u7701 129 CN-HN Hunan Province \u6e56\u5357\u7701 \n",
"14 \u5c71\u897f\u7701 121 CN-SX Shanxi Province \u5c71\u897f\u7701 \n",
"15 \u4e0a\u6d77\u5e02 116 CN-SH Shanghai Municipality \u4e0a\u6d77\u5e02 \n",
"16 \u798f\u5efa\u7701 111 CN-FJ Fujian Province[e] \u798f\u5efa\u7701 \n",
"17 \u4e91\u5357\u7701 101 CN-YN Yunnan Province \u4e91\u5357\u7701 \n",
"18 \u65b0\u7586\u7ef4\u543e\u5c14\u81ea\u6cbb\u533a 100 CN-XJ Xinjiang Uyghur Autonomous Region \u65b0\u7586\u7ef4\u543e\u5c14\u81ea\u6cbb\u533a \n",
"19 \u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a 99 CN-GX Guangxi Zhuang Autonomous Region \u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a \n",
"20 \u6cb3\u5317\u7701 97 CN-HE Hebei Province \u6cb3\u5317\u7701 \n",
"21 \u8fbd\u5b81\u7701 93 CN-LN Liaoning Province \u8fbd\u5b81\u7701 \n",
"22 \u8d35\u5dde\u7701 81 CN-GZ Guizhou Province \u8d35\u5dde\u7701 \n",
"23 \u91cd\u5e86\u5e02 69 CN-CQ Chongqing Municipality \u91cd\u5e86\u5e02 \n",
"24 \u5929\u6d25\u5e02 56 CN-TJ Tianjin Municipality \u5929\u6d25\u5e02 \n",
"25 \u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a 39 CN-NX Ningxia Hui Autonomous Region \u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a \n",
"26 \u9752\u6d77\u7701 32 CN-QH Qinghai Province \u9752\u6d77\u7701 \n",
"27 \u6d77\u5357\u7701 24 CN-HI Hainan Province \u6d77\u5357\u7701 \n",
"28 \u897f\u85cf\u81ea\u6cbb\u533a 8 CN-XZ Tibet Autonomous Region \u897f\u85cf\u81ea\u6cbb\u533a \n",
"\n",
" Population \n",
"0 95793065 \n",
"1 54426891 \n",
"2 78659903 \n",
"3 94023567 \n",
"4 104303132 \n",
"5 37327378 \n",
"6 80418200 \n",
"7 57237740 \n",
"8 38312224 \n",
"9 25575254 \n",
"10 59500510 \n",
"11 19612368 \n",
"12 44567475 \n",
"13 65683722 \n",
"14 35712111 \n",
"15 23019148 \n",
"16 36894216 \n",
"17 45966239 \n",
"18 21813334 \n",
"19 46026629 \n",
"20 71854202 \n",
"21 43746323 \n",
"22 34746468 \n",
"23 28846170 \n",
"24 12938224 \n",
"25 6301350 \n",
"26 5626722 \n",
"27 9171300 \n",
"28 3002166 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged = regions.merge(pop, right_on='Chinese', left_on='index')\n",
"merged"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then perform some small calculations to build two new columns relating the number of people to the number of museums."
]
},
{
"cell_type": "code",
"execution_count": 18,
"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>index</th>\n",
" <th>museums</th>\n",
" <th>ISO</th>\n",
" <th>Province</th>\n",
" <th>Chinese</th>\n",
" <th>Population</th>\n",
" <th>people_per_museum</th>\n",
" <th>museums_per_1m</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>\u5c71\u4e1c\u7701</td>\n",
" <td>339</td>\n",
" <td>CN-SD</td>\n",
" <td>Shandong Province</td>\n",
" <td>\u5c71\u4e1c\u7701</td>\n",
" <td>95793065</td>\n",
" <td>282575.412979</td>\n",
" <td>3.538878</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>\u6d59\u6c5f\u7701</td>\n",
" <td>277</td>\n",
" <td>CN-ZJ</td>\n",
" <td>Zhejiang Province</td>\n",
" <td>\u6d59\u6c5f\u7701</td>\n",
" <td>54426891</td>\n",
" <td>196486.971119</td>\n",
" <td>5.089396</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>\u6c5f\u82cf\u7701</td>\n",
" <td>274</td>\n",
" <td>CN-JS</td>\n",
" <td>Jiangsu Province</td>\n",
" <td>\u6c5f\u82cf\u7701</td>\n",
" <td>78659903</td>\n",
" <td>287079.937956</td>\n",
" <td>3.483350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>\u6cb3\u5357\u7701</td>\n",
" <td>265</td>\n",
" <td>CN-HA</td>\n",
" <td>Henan Province</td>\n",
" <td>\u6cb3\u5357\u7701</td>\n",
" <td>94023567</td>\n",
" <td>354805.913208</td>\n",
" <td>2.818442</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>\u5e7f\u4e1c\u7701</td>\n",
" <td>252</td>\n",
" <td>CN-GD</td>\n",
" <td>Guangdong Province</td>\n",
" <td>\u5e7f\u4e1c\u7701</td>\n",
" <td>104303132</td>\n",
" <td>413901.317460</td>\n",
" <td>2.416035</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index museums ISO Province Chinese Population \\\n",
"0 \u5c71\u4e1c\u7701 339 CN-SD Shandong Province \u5c71\u4e1c\u7701 95793065 \n",
"1 \u6d59\u6c5f\u7701 277 CN-ZJ Zhejiang Province \u6d59\u6c5f\u7701 54426891 \n",
"2 \u6c5f\u82cf\u7701 274 CN-JS Jiangsu Province \u6c5f\u82cf\u7701 78659903 \n",
"3 \u6cb3\u5357\u7701 265 CN-HA Henan Province \u6cb3\u5357\u7701 94023567 \n",
"4 \u5e7f\u4e1c\u7701 252 CN-GD Guangdong Province \u5e7f\u4e1c\u7701 104303132 \n",
"\n",
" people_per_museum museums_per_1m \n",
"0 282575.412979 3.538878 \n",
"1 196486.971119 5.089396 \n",
"2 287079.937956 3.483350 \n",
"3 354805.913208 2.818442 \n",
"4 413901.317460 2.416035 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged['people_per_museum'] = merged.Population / merged.museums\n",
"merged['museums_per_1m'] = merged.museums / merged.Population * 1000000\n",
"merged.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Viewing our results\n",
"\n",
"We only looked at the first five above because **we're probably interested in the sorted version**."
]
},
{
"cell_type": "code",
"execution_count": 110,
"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>index</th>\n",
" <th>museums</th>\n",
" <th>ISO</th>\n",
" <th>Province</th>\n",
" <th>Chinese</th>\n",
" <th>Population</th>\n",
" <th>people_per_museum</th>\n",
" <th>museums_per_1m</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>\u5317\u4eac\u5e02</td>\n",
" <td>146</td>\n",
" <td>CN-BJ</td>\n",
" <td>Beijing Municipality</td>\n",
" <td>\u5317\u4eac\u5e02</td>\n",
" <td>19612368</td>\n",
" <td>134331.287671</td>\n",
" <td>7.444282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>\u7518\u8083\u7701</td>\n",
" <td>183</td>\n",
" <td>CN-GS</td>\n",
" <td>Gansu Province</td>\n",
" <td>\u7518\u8083\u7701</td>\n",
" <td>25575254</td>\n",
" <td>139755.486339</td>\n",
" <td>7.155354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>\u9655\u897f\u7701</td>\n",
" <td>236</td>\n",
" <td>CN-SN</td>\n",
" <td>Shaanxi Province</td>\n",
" <td>\u9655\u897f\u7701</td>\n",
" <td>37327378</td>\n",
" <td>158166.855932</td>\n",
" <td>6.322437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>\u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a</td>\n",
" <td>39</td>\n",
" <td>CN-NX</td>\n",
" <td>Ningxia Hui Autonomous Region</td>\n",
" <td>\u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a</td>\n",
" <td>6301350</td>\n",
" <td>161573.076923</td>\n",
" <td>6.189150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>\u9752\u6d77\u7701</td>\n",
" <td>32</td>\n",
" <td>CN-QH</td>\n",
" <td>Qinghai Province</td>\n",
" <td>\u9752\u6d77\u7701</td>\n",
" <td>5626722</td>\n",
" <td>175835.062500</td>\n",
" <td>5.687148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>\u6d59\u6c5f\u7701</td>\n",
" <td>277</td>\n",
" <td>CN-ZJ</td>\n",
" <td>Zhejiang Province</td>\n",
" <td>\u6d59\u6c5f\u7701</td>\n",
" <td>54426891</td>\n",
" <td>196486.971119</td>\n",
" <td>5.089396</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>\u4e0a\u6d77\u5e02</td>\n",
" <td>116</td>\n",
" <td>CN-SH</td>\n",
" <td>Shanghai Municipality</td>\n",
" <td>\u4e0a\u6d77\u5e02</td>\n",
" <td>23019148</td>\n",
" <td>198440.931034</td>\n",
" <td>5.039283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>\u9ed1\u9f99\u6c5f\u7701</td>\n",
" <td>193</td>\n",
" <td>CN-HL</td>\n",
" <td>Heilongjiang Province</td>\n",
" <td>\u9ed1\u9f99\u6c5f\u7701</td>\n",
" <td>38312224</td>\n",
" <td>198508.932642</td>\n",
" <td>5.037557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>\u65b0\u7586\u7ef4\u543e\u5c14\u81ea\u6cbb\u533a</td>\n",
" <td>100</td>\n",
" <td>CN-XJ</td>\n",
" <td>Xinjiang Uyghur Autonomous Region</td>\n",
" <td>\u65b0\u7586\u7ef4\u543e\u5c14\u81ea\u6cbb\u533a</td>\n",
" <td>21813334</td>\n",
" <td>218133.340000</td>\n",
" <td>4.584352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>\u5929\u6d25\u5e02</td>\n",
" <td>56</td>\n",
" <td>CN-TJ</td>\n",
" <td>Tianjin Municipality</td>\n",
" <td>\u5929\u6d25\u5e02</td>\n",
" <td>12938224</td>\n",
" <td>231039.714286</td>\n",
" <td>4.328260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>\u5c71\u4e1c\u7701</td>\n",
" <td>339</td>\n",
" <td>CN-SD</td>\n",
" <td>Shandong Province</td>\n",
" <td>\u5c71\u4e1c\u7701</td>\n",
" <td>95793065</td>\n",
" <td>282575.412979</td>\n",
" <td>3.538878</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>\u6c5f\u82cf\u7701</td>\n",
" <td>274</td>\n",
" <td>CN-JS</td>\n",
" <td>Jiangsu Province</td>\n",
" <td>\u6c5f\u82cf\u7701</td>\n",
" <td>78659903</td>\n",
" <td>287079.937956</td>\n",
" <td>3.483350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>\u6e56\u5317\u7701</td>\n",
" <td>197</td>\n",
" <td>CN-HB</td>\n",
" <td>Hubei Province</td>\n",
" <td>\u6e56\u5317\u7701</td>\n",
" <td>57237740</td>\n",
" <td>290546.903553</td>\n",
" <td>3.441785</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>\u5c71\u897f\u7701</td>\n",
" <td>121</td>\n",
" <td>CN-SX</td>\n",
" <td>Shanxi Province</td>\n",
" <td>\u5c71\u897f\u7701</td>\n",
" <td>35712111</td>\n",
" <td>295141.413223</td>\n",
" <td>3.388206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>\u6c5f\u897f\u7701</td>\n",
" <td>137</td>\n",
" <td>CN-JX</td>\n",
" <td>Jiangxi Province</td>\n",
" <td>\u6c5f\u897f\u7701</td>\n",
" <td>44567475</td>\n",
" <td>325310.036496</td>\n",
" <td>3.073991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>\u5b89\u5fbd\u7701</td>\n",
" <td>182</td>\n",
" <td>CN-AH</td>\n",
" <td>Anhui Province</td>\n",
" <td>\u5b89\u5fbd\u7701</td>\n",
" <td>59500510</td>\n",
" <td>326925.879121</td>\n",
" <td>3.058797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>\u798f\u5efa\u7701</td>\n",
" <td>111</td>\n",
" <td>CN-FJ</td>\n",
" <td>Fujian Province[e]</td>\n",
" <td>\u798f\u5efa\u7701</td>\n",
" <td>36894216</td>\n",
" <td>332380.324324</td>\n",
" <td>3.008602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>\u6cb3\u5357\u7701</td>\n",
" <td>265</td>\n",
" <td>CN-HA</td>\n",
" <td>Henan Province</td>\n",
" <td>\u6cb3\u5357\u7701</td>\n",
" <td>94023567</td>\n",
" <td>354805.913208</td>\n",
" <td>2.818442</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>\u56db\u5ddd\u7701</td>\n",
" <td>216</td>\n",
" <td>CN-SC</td>\n",
" <td>Sichuan Province</td>\n",
" <td>\u56db\u5ddd\u7701</td>\n",
" <td>80418200</td>\n",
" <td>372306.481481</td>\n",
" <td>2.685959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>\u897f\u85cf\u81ea\u6cbb\u533a</td>\n",
" <td>8</td>\n",
" <td>CN-XZ</td>\n",
" <td>Tibet Autonomous Region</td>\n",
" <td>\u897f\u85cf\u81ea\u6cbb\u533a</td>\n",
" <td>3002166</td>\n",
" <td>375270.750000</td>\n",
" <td>2.664743</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>\u6d77\u5357\u7701</td>\n",
" <td>24</td>\n",
" <td>CN-HI</td>\n",
" <td>Hainan Province</td>\n",
" <td>\u6d77\u5357\u7701</td>\n",
" <td>9171300</td>\n",
" <td>382137.500000</td>\n",
" <td>2.616859</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>\u5e7f\u4e1c\u7701</td>\n",
" <td>252</td>\n",
" <td>CN-GD</td>\n",
" <td>Guangdong Province</td>\n",
" <td>\u5e7f\u4e1c\u7701</td>\n",
" <td>104303132</td>\n",
" <td>413901.317460</td>\n",
" <td>2.416035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>\u91cd\u5e86\u5e02</td>\n",
" <td>69</td>\n",
" <td>CN-CQ</td>\n",
" <td>Chongqing Municipality</td>\n",
" <td>\u91cd\u5e86\u5e02</td>\n",
" <td>28846170</td>\n",
" <td>418060.434783</td>\n",
" <td>2.391999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>\u8d35\u5dde\u7701</td>\n",
" <td>81</td>\n",
" <td>CN-GZ</td>\n",
" <td>Guizhou Province</td>\n",
" <td>\u8d35\u5dde\u7701</td>\n",
" <td>34746468</td>\n",
" <td>428968.740741</td>\n",
" <td>2.331172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>\u4e91\u5357\u7701</td>\n",
" <td>101</td>\n",
" <td>CN-YN</td>\n",
" <td>Yunnan Province</td>\n",
" <td>\u4e91\u5357\u7701</td>\n",
" <td>45966239</td>\n",
" <td>455111.277228</td>\n",
" <td>2.197265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>\u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a</td>\n",
" <td>99</td>\n",
" <td>CN-GX</td>\n",
" <td>Guangxi Zhuang Autonomous Region</td>\n",
" <td>\u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a</td>\n",
" <td>46026629</td>\n",
" <td>464915.444444</td>\n",
" <td>2.150929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>\u8fbd\u5b81\u7701</td>\n",
" <td>93</td>\n",
" <td>CN-LN</td>\n",
" <td>Liaoning Province</td>\n",
" <td>\u8fbd\u5b81\u7701</td>\n",
" <td>43746323</td>\n",
" <td>470390.569892</td>\n",
" <td>2.125893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>\u6e56\u5357\u7701</td>\n",
" <td>129</td>\n",
" <td>CN-HN</td>\n",
" <td>Hunan Province</td>\n",
" <td>\u6e56\u5357\u7701</td>\n",
" <td>65683722</td>\n",
" <td>509176.139535</td>\n",
" <td>1.963957</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>\u6cb3\u5317\u7701</td>\n",
" <td>97</td>\n",
" <td>CN-HE</td>\n",
" <td>Hebei Province</td>\n",
" <td>\u6cb3\u5317\u7701</td>\n",
" <td>71854202</td>\n",
" <td>740764.969072</td>\n",
" <td>1.349956</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index museums ISO Province Chinese \\\n",
"11 \u5317\u4eac\u5e02 146 CN-BJ Beijing Municipality \u5317\u4eac\u5e02 \n",
"9 \u7518\u8083\u7701 183 CN-GS Gansu Province \u7518\u8083\u7701 \n",
"5 \u9655\u897f\u7701 236 CN-SN Shaanxi Province \u9655\u897f\u7701 \n",
"25 \u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a 39 CN-NX Ningxia Hui Autonomous Region \u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a \n",
"26 \u9752\u6d77\u7701 32 CN-QH Qinghai Province \u9752\u6d77\u7701 \n",
"1 \u6d59\u6c5f\u7701 277 CN-ZJ Zhejiang Province \u6d59\u6c5f\u7701 \n",
"15 \u4e0a\u6d77\u5e02 116 CN-SH Shanghai Municipality \u4e0a\u6d77\u5e02 \n",
"8 \u9ed1\u9f99\u6c5f\u7701 193 CN-HL Heilongjiang Province \u9ed1\u9f99\u6c5f\u7701 \n",
"18 \u65b0\u7586\u7ef4\u543e\u5c14\u81ea\u6cbb\u533a 100 CN-XJ Xinjiang Uyghur Autonomous Region \u65b0\u7586\u7ef4\u543e\u5c14\u81ea\u6cbb\u533a \n",
"24 \u5929\u6d25\u5e02 56 CN-TJ Tianjin Municipality \u5929\u6d25\u5e02 \n",
"0 \u5c71\u4e1c\u7701 339 CN-SD Shandong Province \u5c71\u4e1c\u7701 \n",
"2 \u6c5f\u82cf\u7701 274 CN-JS Jiangsu Province \u6c5f\u82cf\u7701 \n",
"7 \u6e56\u5317\u7701 197 CN-HB Hubei Province \u6e56\u5317\u7701 \n",
"14 \u5c71\u897f\u7701 121 CN-SX Shanxi Province \u5c71\u897f\u7701 \n",
"12 \u6c5f\u897f\u7701 137 CN-JX Jiangxi Province \u6c5f\u897f\u7701 \n",
"10 \u5b89\u5fbd\u7701 182 CN-AH Anhui Province \u5b89\u5fbd\u7701 \n",
"16 \u798f\u5efa\u7701 111 CN-FJ Fujian Province[e] \u798f\u5efa\u7701 \n",
"3 \u6cb3\u5357\u7701 265 CN-HA Henan Province \u6cb3\u5357\u7701 \n",
"6 \u56db\u5ddd\u7701 216 CN-SC Sichuan Province \u56db\u5ddd\u7701 \n",
"28 \u897f\u85cf\u81ea\u6cbb\u533a 8 CN-XZ Tibet Autonomous Region \u897f\u85cf\u81ea\u6cbb\u533a \n",
"27 \u6d77\u5357\u7701 24 CN-HI Hainan Province \u6d77\u5357\u7701 \n",
"4 \u5e7f\u4e1c\u7701 252 CN-GD Guangdong Province \u5e7f\u4e1c\u7701 \n",
"23 \u91cd\u5e86\u5e02 69 CN-CQ Chongqing Municipality \u91cd\u5e86\u5e02 \n",
"22 \u8d35\u5dde\u7701 81 CN-GZ Guizhou Province \u8d35\u5dde\u7701 \n",
"17 \u4e91\u5357\u7701 101 CN-YN Yunnan Province \u4e91\u5357\u7701 \n",
"19 \u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a 99 CN-GX Guangxi Zhuang Autonomous Region \u5e7f\u897f\u58ee\u65cf\u81ea\u6cbb\u533a \n",
"21 \u8fbd\u5b81\u7701 93 CN-LN Liaoning Province \u8fbd\u5b81\u7701 \n",
"13 \u6e56\u5357\u7701 129 CN-HN Hunan Province \u6e56\u5357\u7701 \n",
"20 \u6cb3\u5317\u7701 97 CN-HE Hebei Province \u6cb3\u5317\u7701 \n",
"\n",
" Population people_per_museum museums_per_1m \n",
"11 19612368 134331.287671 7.444282 \n",
"9 25575254 139755.486339 7.155354 \n",
"5 37327378 158166.855932 6.322437 \n",
"25 6301350 161573.076923 6.189150 \n",
"26 5626722 175835.062500 5.687148 \n",
"1 54426891 196486.971119 5.089396 \n",
"15 23019148 198440.931034 5.039283 \n",
"8 38312224 198508.932642 5.037557 \n",
"18 21813334 218133.340000 4.584352 \n",
"24 12938224 231039.714286 4.328260 \n",
"0 95793065 282575.412979 3.538878 \n",
"2 78659903 287079.937956 3.483350 \n",
"7 57237740 290546.903553 3.441785 \n",
"14 35712111 295141.413223 3.388206 \n",
"12 44567475 325310.036496 3.073991 \n",
"10 59500510 326925.879121 3.058797 \n",
"16 36894216 332380.324324 3.008602 \n",
"3 94023567 354805.913208 2.818442 \n",
"6 80418200 372306.481481 2.685959 \n",
"28 3002166 375270.750000 2.664743 \n",
"27 9171300 382137.500000 2.616859 \n",
"4 104303132 413901.317460 2.416035 \n",
"23 28846170 418060.434783 2.391999 \n",
"22 34746468 428968.740741 2.331172 \n",
"17 45966239 455111.277228 2.197265 \n",
"19 46026629 464915.444444 2.150929 \n",
"21 43746323 470390.569892 2.125893 \n",
"13 65683722 509176.139535 1.963957 \n",
"20 71854202 740764.969072 1.349956 "
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged.sort_values(by='museums_per_1m', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11a0a1940>"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"merged.sort_values(by='museums_per_1m').plot(x='Chinese', y='museums_per_1m', kind='barh', figsize=(5,10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The [actual Caixin piece graphic](https://datanews.caixin.com/mobile/museum/) gives you the people per museum, and draws a line at 300,000 people."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img 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\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can easily reproduce that one with matplotlib. If your characters are showing up weird, make sure you ran the font-setting code up at the top!"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.lines.Line2D at 0x11969a588>"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"merged.sort_values(by='people_per_museum', ascending=False).plot(x='Chinese', y='people_per_museum', kind='bar', figsize=(10, 5), color='#8b70b1')\n",
"\n",
"plt.axhline(300000, color='black')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Discussion topics\n",
"\n",
"Why do we look at per capita museums in each province instead of the raw numbers?\n",
"\n",
"When you talk about a \"bigger\" province, you could talk about either popular _or_ how physically large the area is. Why does per capita make more sense here?\n",
"\n",
"\u5b81\u590f\u56de\u65cf\u81ea\u6cbb\u533a and \u9752\u6d77\u7701\thave a large number of museums, per-capita, but not very musuems overall (around 40, compared to 100-250 in the other high per-capita museums). Does it seem reasonable that they're listed between plces like \u9655\u897f\u7701 and \u9655\u897f\u7701 which both have over 200 museums each?\n",
"\n",
"We calculated two numbers for this data - people per museum, and museums per person. What are the different feelings associated with each angle? How would the chart look different if it were presented as museums per person instead of people per museum?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
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"language": "python",
"name": "python3"
},
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"toc": {
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"title_cell": "Table of Contents",
"title_sidebar": "Contents",
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},
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}