03-UK-MPS-Scandal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 案例：2009年英国国会议员开支丑闻\n",
    "\n",
    "\n",
    "MPs expenses scandal \n",
    "\n",
    "https://github.com/data-journalism/data-journalism.github.io/discussions/54\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T04:26:21.893773Z",
     "start_time": "2021-10-24T04:26:21.558486Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import pylab as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号, 注意['SimHei']对应这句不行.\n",
    "plt.style.use('ggplot')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T03:44:20.129275Z",
     "start_time": "2021-10-24T03:44:19.998948Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MPs' expenses claims, Jul-Dec, 2009.xlsx\r\n",
      "\u001b[31mdata.js\u001b[m\u001b[m*\r\n"
     ]
    }
   ],
   "source": [
    "ls './data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T03:44:57.076097Z",
     "start_time": "2021-10-24T03:44:52.119802Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name of member</th>\n",
       "      <th>Allowance Type</th>\n",
       "      <th>Expenditure Type</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount, £</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adam Afriyie</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>Incidentals</td>\n",
       "      <td>2009-07-03 00:00:00</td>\n",
       "      <td>111.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adam Afriyie</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>Incidentals</td>\n",
       "      <td>2009-11-05 00:00:00</td>\n",
       "      <td>111.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adam Afriyie</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>Incidentals</td>\n",
       "      <td>2009-11-05 00:00:00</td>\n",
       "      <td>51.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adam Holloway</td>\n",
       "      <td>Communications Expenditure</td>\n",
       "      <td>Delivery charges</td>\n",
       "      <td>2009-11-10 00:00:00</td>\n",
       "      <td>258.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adam Holloway</td>\n",
       "      <td>Communications Expenditure</td>\n",
       "      <td>Delivery charges</td>\n",
       "      <td>2009-12-10 00:00:00</td>\n",
       "      <td>879.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name of member                  Allowance Type  Expenditure Type  \\\n",
       "0   Adam Afriyie  Office running costs (IEP/AOE)       Incidentals   \n",
       "1   Adam Afriyie  Office running costs (IEP/AOE)       Incidentals   \n",
       "2   Adam Afriyie  Office running costs (IEP/AOE)       Incidentals   \n",
       "3  Adam Holloway      Communications Expenditure  Delivery charges   \n",
       "4  Adam Holloway      Communications Expenditure  Delivery charges   \n",
       "\n",
       "                  Date Amount, £  \n",
       "0  2009-07-03 00:00:00    111.09  \n",
       "1  2009-11-05 00:00:00    111.09  \n",
       "2  2009-11-05 00:00:00     51.75  \n",
       "3  2009-11-10 00:00:00    258.22  \n",
       "4  2009-12-10 00:00:00    879.66  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"./data/MPs' expenses claims, Jul-Dec, 2009.xlsx\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T03:59:58.065898Z",
     "start_time": "2021-10-24T03:59:58.061704Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name of member', 'Allowance Type', 'Expenditure Type', 'Date',\n",
       "       'Amount, £'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T03:45:03.100896Z",
     "start_time": "2021-10-24T03:45:03.097141Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38925"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T03:45:11.904373Z",
     "start_time": "2021-10-24T03:45:11.745330Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>Name of member</th>\n",
       "      <th>Allowance Type</th>\n",
       "      <th>Expenditure Type</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount, £</th>\n",
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       "      <th>count</th>\n",
       "      <td>38925</td>\n",
       "      <td>38925</td>\n",
       "      <td>38925</td>\n",
       "      <td>38925</td>\n",
       "      <td>38925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>643</td>\n",
       "      <td>5</td>\n",
       "      <td>39</td>\n",
       "      <td>140</td>\n",
       "      <td>14441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Danny Alexander</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>Incidentals</td>\n",
       "      <td>2009-12-21 00:00:00</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>173</td>\n",
       "      <td>25787</td>\n",
       "      <td>8672</td>\n",
       "      <td>828</td>\n",
       "      <td>857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name of member                  Allowance Type Expenditure Type  \\\n",
       "count             38925                           38925            38925   \n",
       "unique              643                               5               39   \n",
       "top     Danny Alexander  Office running costs (IEP/AOE)      Incidentals   \n",
       "freq                173                           25787             8672   \n",
       "\n",
       "                       Date  Amount, £  \n",
       "count                 38925      38925  \n",
       "unique                  140      14441  \n",
       "top     2009-12-21 00:00:00         50  \n",
       "freq                    828        857  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 清洗数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T04:21:28.478254Z",
     "start_time": "2021-10-24T04:21:28.467143Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name of member</th>\n",
       "      <th>Allowance Type</th>\n",
       "      <th>Expenditure Type</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount, £</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27710</th>\n",
       "      <td>Member Name</td>\n",
       "      <td>Allowance Type</td>\n",
       "      <td>Expenditure Type</td>\n",
       "      <td>Date</td>\n",
       "      <td>Amount</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "      Name of member  Allowance Type  Expenditure Type  Date Amount, £\n",
       "27710    Member Name  Allowance Type  Expenditure Type  Date    Amount"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Amount, £']=='Amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T04:21:42.466877Z",
     "start_time": "2021-10-24T04:21:42.458764Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df[df['Amount, £']!='Amount']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T04:21:48.850815Z",
     "start_time": "2021-10-24T04:21:48.839151Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>Name of member</th>\n",
       "      <th>Allowance Type</th>\n",
       "      <th>Expenditure Type</th>\n",
       "      <th>Date</th>\n",
       "      <th>Amount, £</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22221</th>\n",
       "      <td>John Randall</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>Accommodation costs</td>\n",
       "      <td>2009-08-20 00:00:00</td>\n",
       "      <td>-2134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22222</th>\n",
       "      <td>John Randall</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>Other equipment lease</td>\n",
       "      <td>2009-07-01 00:00:00</td>\n",
       "      <td>25.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name of member                  Allowance Type       Expenditure Type  \\\n",
       "22221   John Randall  Office running costs (IEP/AOE)    Accommodation costs   \n",
       "22222   John Randall  Office running costs (IEP/AOE)  Other equipment lease   \n",
       "\n",
       "                      Date Amount, £  \n",
       "22221  2009-08-20 00:00:00     -2134  \n",
       "22222  2009-07-01 00:00:00     25.08  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Name of member']=='John Randall']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T04:21:51.351769Z",
     "start_time": "2021-10-24T04:21:51.332624Z"
    }
   },
   "outputs": [],
   "source": [
    "df['Amount, £'] = [abs(float(i)) for i in df['Amount, £']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 描述数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T04:01:48.554576Z",
     "start_time": "2021-10-24T04:01:48.458532Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amount, £</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name of member</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adam Afriyie</th>\n",
       "      <td>273.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Holloway</th>\n",
       "      <td>7415.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Ingram</th>\n",
       "      <td>18578.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Price</th>\n",
       "      <td>7874.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adrian Bailey</th>\n",
       "      <td>10668.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>William Hague</th>\n",
       "      <td>7370.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>William McCrea</th>\n",
       "      <td>24689.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>William Rennie</th>\n",
       "      <td>27257.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Willie Bain</th>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yvette Cooper</th>\n",
       "      <td>21450.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>643 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Amount, £\n",
       "Name of member          \n",
       "Adam Afriyie      273.93\n",
       "Adam Holloway    7415.06\n",
       "Adam Ingram     18578.90\n",
       "Adam Price       7874.73\n",
       "Adrian Bailey   10668.61\n",
       "...                  ...\n",
       "William Hague    7370.98\n",
       "William McCrea  24689.34\n",
       "William Rennie  27257.33\n",
       "Willie Bain          822\n",
       "Yvette Cooper   21450.30\n",
       "\n",
       "[643 rows x 1 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Name of member', 'Amount, £']].groupby(\"Name of member\").agg('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T04:24:23.132983Z",
     "start_time": "2021-10-24T04:24:23.124399Z"
    }
   },
   "outputs": [
    {
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       "      <td>42753.53</td>\n",
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       "      <td>34471.90</td>\n",
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       "      <th>Joan Humble</th>\n",
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       "      <td>32861.14</td>\n",
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       "      <th>Sian James</th>\n",
       "      <td>32495.32</td>\n",
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       "    <tr>\n",
       "      <th>Helen Jones</th>\n",
       "      <td>32209.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Amount, £\n",
       "Name of member            \n",
       "Graham Allen      42753.53\n",
       "Michael Foster    39999.69\n",
       "Mark Hunter       34779.11\n",
       "Gordon Banks      34471.90\n",
       "Lynda Waltho      34288.55\n",
       "Joan Humble       33395.89\n",
       "Phil Wilson       33031.23\n",
       "Barbara Follett   32861.14\n",
       "Sian James        32495.32\n",
       "Helen Jones       32209.45"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat1 = df[['Name of member', 'Amount, £']].groupby(\"Name of member\").agg('sum')\n",
    "dat1 = dat1.sort_values(by=['Amount, £'], ascending = False)\n",
    "dat1[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T04:24:53.141197Z",
     "start_time": "2021-10-24T04:24:53.137515Z"
    }
   },
   "outputs": [],
   "source": [
    "dat1['Rank'] = range(1, len(dat1)+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T04:25:00.589843Z",
     "start_time": "2021-10-24T04:25:00.582497Z"
    }
   },
   "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>Amount, £</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name of member</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Graham Allen</th>\n",
       "      <td>42753.53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael Foster</th>\n",
       "      <td>39999.69</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mark Hunter</th>\n",
       "      <td>34779.11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gordon Banks</th>\n",
       "      <td>34471.90</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lynda Waltho</th>\n",
       "      <td>34288.55</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joan Humble</th>\n",
       "      <td>33395.89</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phil Wilson</th>\n",
       "      <td>33031.23</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbara Follett</th>\n",
       "      <td>32861.14</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sian James</th>\n",
       "      <td>32495.32</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Helen Jones</th>\n",
       "      <td>32209.45</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Amount, £  Rank\n",
       "Name of member                  \n",
       "Graham Allen      42753.53     1\n",
       "Michael Foster    39999.69     2\n",
       "Mark Hunter       34779.11     3\n",
       "Gordon Banks      34471.90     4\n",
       "Lynda Waltho      34288.55     5\n",
       "Joan Humble       33395.89     6\n",
       "Phil Wilson       33031.23     7\n",
       "Barbara Follett   32861.14     8\n",
       "Sian James        32495.32     9\n",
       "Helen Jones       32209.45    10"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat1[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T04:28:48.838969Z",
     "start_time": "2021-10-24T04:28:48.621907Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(16, 6), dpi = 100)\n",
    "\n",
    "plt.plot(dat1['Rank'], dat1['Amount, £'], 'ro')\n",
    "# plt.yscale('log')\n",
    "# plt.xscale('log')\n",
    "\n",
    "plt.xlabel('Rank')\n",
    "plt.ylabel('Amount, £')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T04:29:56.279966Z",
     "start_time": "2021-10-24T04:29:56.086678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(16, 6), dpi = 100)\n",
    "\n",
    "dat10 = dat1[:10]\n",
    "plt.plot(dat10['Rank'], dat10['Amount, £'], 'ro')\n",
    "# plt.yscale('log')\n",
    "# plt.xscale('log')\n",
    "\n",
    "plt.xlabel('Rank')\n",
    "plt.ylabel('Amount, £')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T05:31:40.777811Z",
     "start_time": "2021-10-24T05:31:40.764136Z"
    }
   },
   "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>Amount, £</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allowance Type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Office running costs (IEP/AOE)</th>\n",
       "      <td>4561001.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cost of staying away from the main home (ACA/PAAE)</th>\n",
       "      <td>3160432.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Communications Expenditure</th>\n",
       "      <td>2510511.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staffing non-payroll</th>\n",
       "      <td>24414.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     Amount, £\n",
       "Allowance Type                                                \n",
       "Office running costs (IEP/AOE)                      4561001.11\n",
       "Cost of staying away from the main home (ACA/PAAE)  3160432.72\n",
       "Communications Expenditure                          2510511.02\n",
       "Staffing non-payroll                                  24414.17"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat2 = df[['Allowance Type', 'Amount, £']].groupby(\"Allowance Type\").agg('sum')\n",
    "dat2 = dat2.sort_values(by=['Amount, £'], ascending = False)\n",
    "dat2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T05:34:12.983235Z",
     "start_time": "2021-10-24T05:34:12.966693Z"
    }
   },
   "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>Amount, £</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expenditure Type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accommodation costs</th>\n",
       "      <td>1809184.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Publications</th>\n",
       "      <td>1097722.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mortgage interest</th>\n",
       "      <td>1056568.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delivery charges</th>\n",
       "      <td>1019891.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rent</th>\n",
       "      <td>969231.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Incidentals</th>\n",
       "      <td>806222.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telephones</th>\n",
       "      <td>677887.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other household costs</th>\n",
       "      <td>463874.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subsistence</th>\n",
       "      <td>373680.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Advertising</th>\n",
       "      <td>261207.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Council tax</th>\n",
       "      <td>226974.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agencies etc</th>\n",
       "      <td>220883.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Constituency Association payments</th>\n",
       "      <td>187676.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>172737.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other equipment lease</th>\n",
       "      <td>141508.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Websites</th>\n",
       "      <td>131689.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT equipment purchase</th>\n",
       "      <td>117466.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professional fees</th>\n",
       "      <td>90381.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other equipment purchase</th>\n",
       "      <td>82802.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maintenance, janitorial</th>\n",
       "      <td>79518.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotel Expenses</th>\n",
       "      <td>70102.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Petty cash</th>\n",
       "      <td>47442.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payments to individuals</th>\n",
       "      <td>42585.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Member travel (AOE)</th>\n",
       "      <td>31841.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff Subsistence</th>\n",
       "      <td>24401.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff office purchases</th>\n",
       "      <td>22137.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff travel rail</th>\n",
       "      <td>8195.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff telephone expenses</th>\n",
       "      <td>6965.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT equipment lease</th>\n",
       "      <td>4382.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff subsistence</th>\n",
       "      <td>4071.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff travel car</th>\n",
       "      <td>2888.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff use of home expenses</th>\n",
       "      <td>1969.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff travel air</th>\n",
       "      <td>800.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff travel parking</th>\n",
       "      <td>697.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff travel taxi</th>\n",
       "      <td>521.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff travel public transport</th>\n",
       "      <td>190.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>General Running Costs</th>\n",
       "      <td>40.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff travel tolls</th>\n",
       "      <td>12.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Amount, £\n",
       "Expenditure Type                             \n",
       "Accommodation costs                1809184.32\n",
       "Publications                       1097722.44\n",
       "Mortgage interest                  1056568.92\n",
       "Delivery charges                   1019891.50\n",
       "Rent                                969231.40\n",
       "Incidentals                         806222.41\n",
       "Telephones                          677887.23\n",
       "Other household costs               463874.76\n",
       "Subsistence                         373680.38\n",
       "Advertising                         261207.10\n",
       "Council tax                         226974.67\n",
       "Agencies etc                        220883.47\n",
       "Constituency Association payments   187676.45\n",
       "Utilities                           172737.37\n",
       "Other equipment lease               141508.52\n",
       "Websites                            131689.98\n",
       "IT equipment purchase               117466.40\n",
       "Professional fees                    90381.10\n",
       "Other equipment purchase             82802.68\n",
       "Maintenance, janitorial              79518.88\n",
       "Hotel Expenses                       70102.59\n",
       "Petty cash                           47442.51\n",
       "Payments to individuals              42585.55\n",
       "Member travel (AOE)                  31841.92\n",
       "Staff Subsistence                    24401.87\n",
       "Staff office purchases               22137.03\n",
       "Staff travel rail                     8195.26\n",
       "Staff telephone expenses              6965.79\n",
       "IT equipment lease                    4382.88\n",
       "Staff subsistence                     4071.69\n",
       "Staff travel car                      2888.88\n",
       "Staff use of home expenses            1969.68\n",
       "Staff travel air                       800.41\n",
       "Staff travel parking                   697.74\n",
       "Staff travel taxi                      521.54\n",
       "Staff travel public transport          190.85\n",
       "General Running Costs                   40.55\n",
       "Staff travel tolls                      12.30"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat3 = df[['Expenditure Type', 'Amount, £']].groupby(\"Expenditure Type\").agg('sum')\n",
    "dat3 = dat3.sort_values(by=['Amount, £'], ascending = False)\n",
    "dat3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T05:35:04.793167Z",
     "start_time": "2021-10-24T05:35:04.010433Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(16, 4), dpi = 100)\n",
    "dat3['Amount, £'].plot(kind = 'bar')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 桑基图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T05:42:03.704414Z",
     "start_time": "2021-10-24T05:42:03.693868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"519b03395ede41a3b9ccfaf970f203e9\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_519b03395ede41a3b9ccfaf970f203e9 = echarts.init(\n",
       "                    document.getElementById('519b03395ede41a3b9ccfaf970f203e9'), 'white', {renderer: 'canvas'});\n",
       "                var option_519b03395ede41a3b9ccfaf970f203e9 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"sankey\",\n",
       "            \"name\": \"sankey\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"category1\"\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"category2\"\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"category3\"\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"category4\"\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"category5\"\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"category6\"\n",
       "                }\n",
       "            ],\n",
       "            \"links\": [\n",
       "                {\n",
       "                    \"source\": \"category1\",\n",
       "                    \"target\": \"category2\",\n",
       "                    \"value\": 10\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"category2\",\n",
       "                    \"target\": \"category3\",\n",
       "                    \"value\": 15\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"category3\",\n",
       "                    \"target\": \"category4\",\n",
       "                    \"value\": 20\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"category5\",\n",
       "                    \"target\": \"category6\",\n",
       "                    \"value\": 25\n",
       "                }\n",
       "            ],\n",
       "            \"left\": \"5%\",\n",
       "            \"top\": \"5%\",\n",
       "            \"right\": \"20%\",\n",
       "            \"bottom\": \"5%\",\n",
       "            \"nodeWidth\": 20,\n",
       "            \"nodeGap\": 8,\n",
       "            \"nodeAlign\": \"justify\",\n",
       "            \"layoutIteration\": 32,\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"draggable\": true,\n",
       "            \"focusNodeAdjacency\": false,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"right\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 0.2,\n",
       "                \"curveness\": 0.5,\n",
       "                \"type\": \"solid\",\n",
       "                \"color\": \"source\"\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"sankey\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"sankey\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"Sankey-\\u57fa\\u672c\\u793a\\u4f8b\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_519b03395ede41a3b9ccfaf970f203e9.setOption(option_519b03395ede41a3b9ccfaf970f203e9);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1265b5610>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Sankey\n",
    "\n",
    "nodes = [\n",
    "    {\"name\": \"category1\"},\n",
    "    {\"name\": \"category2\"},\n",
    "    {\"name\": \"category3\"},\n",
    "    {\"name\": \"category4\"},\n",
    "    {\"name\": \"category5\"},\n",
    "    {\"name\": \"category6\"},\n",
    "]\n",
    "\n",
    "links = [\n",
    "    {\"source\": \"category1\", \"target\": \"category2\", \"value\": 10},\n",
    "    {\"source\": \"category2\", \"target\": \"category3\", \"value\": 15},\n",
    "    {\"source\": \"category3\", \"target\": \"category4\", \"value\": 20},\n",
    "    {\"source\": \"category5\", \"target\": \"category6\", \"value\": 25},\n",
    "]\n",
    "c = (\n",
    "    Sankey()\n",
    "    .add(\n",
    "        \"sankey\",\n",
    "        nodes,\n",
    "        links,\n",
    "        linestyle_opt=opts.LineStyleOpts(opacity=0.2, curve=0.5, color=\"source\"),\n",
    "        label_opts=opts.LabelOpts(position=\"right\"),\n",
    "    )\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"Sankey-基本示例\"))\n",
    "    #.render(\"sankey_base.html\")\n",
    "    .render_notebook()\n",
    ")\n",
    "\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T06:00:16.876612Z",
     "start_time": "2021-10-24T06:00:16.851116Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<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>Expenditure Type</th>\n",
       "      <th>Allowance Type</th>\n",
       "      <th>Amount, £</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accommodation costs</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>1809184.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Publications</td>\n",
       "      <td>Communications Expenditure</td>\n",
       "      <td>1097722.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Mortgage interest</td>\n",
       "      <td>Cost of staying away from the main home (ACA/P...</td>\n",
       "      <td>1056568.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Delivery charges</td>\n",
       "      <td>Communications Expenditure</td>\n",
       "      <td>1019891.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Rent</td>\n",
       "      <td>Cost of staying away from the main home (ACA/P...</td>\n",
       "      <td>969231.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Incidentals</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>806222.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Telephones</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>677887.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Other household costs</td>\n",
       "      <td>Cost of staying away from the main home (ACA/P...</td>\n",
       "      <td>463874.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Subsistence</td>\n",
       "      <td>Cost of staying away from the main home (ACA/P...</td>\n",
       "      <td>373680.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Advertising</td>\n",
       "      <td>Communications Expenditure</td>\n",
       "      <td>261207.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Council tax</td>\n",
       "      <td>Cost of staying away from the main home (ACA/P...</td>\n",
       "      <td>226974.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Agencies etc</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>220883.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Constituency Association payments</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>187676.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Utilities</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>172737.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Other equipment lease</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>141508.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Websites</td>\n",
       "      <td>Communications Expenditure</td>\n",
       "      <td>131689.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>IT equipment purchase</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>117466.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Professional fees</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>90381.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Other equipment purchase</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>82802.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Maintenance, janitorial</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>79518.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hotel Expenses</td>\n",
       "      <td>Cost of staying away from the main home (ACA/P...</td>\n",
       "      <td>70102.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Petty cash</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>47442.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Payments to individuals</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>42585.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Member travel (AOE)</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>31841.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Staff Subsistence</td>\n",
       "      <td>Staffing non-payroll</td>\n",
       "      <td>24401.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Staff office purchases</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>22137.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Staff travel rail</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>8195.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Staff telephone expenses</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>6965.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IT equipment lease</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>4382.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Staff subsistence</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>4071.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Staff travel car</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>2888.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Staff use of home expenses</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>1969.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Staff travel air</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>800.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Staff travel parking</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>697.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Staff travel taxi</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>521.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Staff travel public transport</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>190.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>General Running Costs</td>\n",
       "      <td>Office running costs (IEP/AOE)</td>\n",
       "      <td>40.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Staff travel tolls</td>\n",
       "      <td>Staffing non-payroll</td>\n",
       "      <td>12.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Expenditure Type  \\\n",
       "0                 Accommodation costs   \n",
       "20                       Publications   \n",
       "13                  Mortgage interest   \n",
       "5                    Delivery charges   \n",
       "21                               Rent   \n",
       "10                        Incidentals   \n",
       "35                         Telephones   \n",
       "16              Other household costs   \n",
       "34                        Subsistence   \n",
       "1                         Advertising   \n",
       "4                         Council tax   \n",
       "2                        Agencies etc   \n",
       "3   Constituency Association payments   \n",
       "36                          Utilities   \n",
       "14              Other equipment lease   \n",
       "37                           Websites   \n",
       "9               IT equipment purchase   \n",
       "19                  Professional fees   \n",
       "15           Other equipment purchase   \n",
       "11            Maintenance, janitorial   \n",
       "7                      Hotel Expenses   \n",
       "18                         Petty cash   \n",
       "17            Payments to individuals   \n",
       "12                Member travel (AOE)   \n",
       "22                  Staff Subsistence   \n",
       "23             Staff office purchases   \n",
       "30                  Staff travel rail   \n",
       "25           Staff telephone expenses   \n",
       "8                  IT equipment lease   \n",
       "24                  Staff subsistence   \n",
       "27                   Staff travel car   \n",
       "33         Staff use of home expenses   \n",
       "26                   Staff travel air   \n",
       "28               Staff travel parking   \n",
       "31                  Staff travel taxi   \n",
       "29      Staff travel public transport   \n",
       "6               General Running Costs   \n",
       "32                 Staff travel tolls   \n",
       "\n",
       "                                       Allowance Type   Amount, £  \n",
       "0                      Office running costs (IEP/AOE)  1809184.32  \n",
       "20                         Communications Expenditure  1097722.44  \n",
       "13  Cost of staying away from the main home (ACA/P...  1056568.92  \n",
       "5                          Communications Expenditure  1019891.50  \n",
       "21  Cost of staying away from the main home (ACA/P...   969231.40  \n",
       "10                     Office running costs (IEP/AOE)   806222.41  \n",
       "35                     Office running costs (IEP/AOE)   677887.23  \n",
       "16  Cost of staying away from the main home (ACA/P...   463874.76  \n",
       "34  Cost of staying away from the main home (ACA/P...   373680.38  \n",
       "1                          Communications Expenditure   261207.10  \n",
       "4   Cost of staying away from the main home (ACA/P...   226974.67  \n",
       "2                      Office running costs (IEP/AOE)   220883.47  \n",
       "3                      Office running costs (IEP/AOE)   187676.45  \n",
       "36                     Office running costs (IEP/AOE)   172737.37  \n",
       "14                     Office running costs (IEP/AOE)   141508.52  \n",
       "37                         Communications Expenditure   131689.98  \n",
       "9                      Office running costs (IEP/AOE)   117466.40  \n",
       "19                     Office running costs (IEP/AOE)    90381.10  \n",
       "15                     Office running costs (IEP/AOE)    82802.68  \n",
       "11                     Office running costs (IEP/AOE)    79518.88  \n",
       "7   Cost of staying away from the main home (ACA/P...    70102.59  \n",
       "18                     Office running costs (IEP/AOE)    47442.51  \n",
       "17                     Office running costs (IEP/AOE)    42585.55  \n",
       "12                     Office running costs (IEP/AOE)    31841.92  \n",
       "22                               Staffing non-payroll    24401.87  \n",
       "23                     Office running costs (IEP/AOE)    22137.03  \n",
       "30                     Office running costs (IEP/AOE)     8195.26  \n",
       "25                     Office running costs (IEP/AOE)     6965.79  \n",
       "8                      Office running costs (IEP/AOE)     4382.88  \n",
       "24                     Office running costs (IEP/AOE)     4071.69  \n",
       "27                     Office running costs (IEP/AOE)     2888.88  \n",
       "33                     Office running costs (IEP/AOE)     1969.68  \n",
       "26                     Office running costs (IEP/AOE)      800.41  \n",
       "28                     Office running costs (IEP/AOE)      697.74  \n",
       "31                     Office running costs (IEP/AOE)      521.54  \n",
       "29                     Office running costs (IEP/AOE)      190.85  \n",
       "6                      Office running costs (IEP/AOE)       40.55  \n",
       "32                               Staffing non-payroll       12.30  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat4 = df[['Allowance Type','Expenditure Type', 'Amount, £']].groupby(['Expenditure Type', 'Allowance Type'],  as_index = False).agg('sum')\n",
    "dat4 = dat4.sort_values(by=['Amount, £'], ascending = False)\n",
    "dat4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T06:10:46.376949Z",
     "start_time": "2021-10-24T06:10:46.371798Z"
    }
   },
   "outputs": [],
   "source": [
    "nodes = dat4['Allowance Type'].unique().tolist() + dat4['Expenditure Type'].unique().tolist()\n",
    "nodes = [{'name': i} for i in nodes]\n",
    "links = [{'source': dat4['Allowance Type'][i], 'target': dat4['Expenditure Type'][i], 'value': dat4['Amount, £'][i]}  for i in dat4.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T06:11:21.828986Z",
     "start_time": "2021-10-24T06:11:21.819192Z"
    }
   },
   "outputs": [
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       "                    \"name\": \"Staff travel public transport\"\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"General Running Costs\"\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"Staff travel tolls\"\n",
       "                }\n",
       "            ],\n",
       "            \"links\": [\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Accommodation costs\",\n",
       "                    \"value\": 1809184.3200000054\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Communications Expenditure\",\n",
       "                    \"target\": \"Publications\",\n",
       "                    \"value\": 1097722.4399999988\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Cost of staying away from the main home (ACA/PAAE)\",\n",
       "                    \"target\": \"Mortgage interest\",\n",
       "                    \"value\": 1056568.9200000013\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Communications Expenditure\",\n",
       "                    \"target\": \"Delivery charges\",\n",
       "                    \"value\": 1019891.5000000002\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Cost of staying away from the main home (ACA/PAAE)\",\n",
       "                    \"target\": \"Rent\",\n",
       "                    \"value\": 969231.4000000001\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Incidentals\",\n",
       "                    \"value\": 806222.4099999958\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Telephones\",\n",
       "                    \"value\": 677887.2300000067\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Cost of staying away from the main home (ACA/PAAE)\",\n",
       "                    \"target\": \"Other household costs\",\n",
       "                    \"value\": 463874.7600000004\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Cost of staying away from the main home (ACA/PAAE)\",\n",
       "                    \"target\": \"Subsistence\",\n",
       "                    \"value\": 373680.38\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Communications Expenditure\",\n",
       "                    \"target\": \"Advertising\",\n",
       "                    \"value\": 261207.10000000038\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Cost of staying away from the main home (ACA/PAAE)\",\n",
       "                    \"target\": \"Council tax\",\n",
       "                    \"value\": 226974.67\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Agencies etc\",\n",
       "                    \"value\": 220883.47000000003\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Constituency Association payments\",\n",
       "                    \"value\": 187676.45000000004\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Utilities\",\n",
       "                    \"value\": 172737.3700000001\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Other equipment lease\",\n",
       "                    \"value\": 141508.52\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Communications Expenditure\",\n",
       "                    \"target\": \"Websites\",\n",
       "                    \"value\": 131689.98000000007\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"IT equipment purchase\",\n",
       "                    \"value\": 117466.40000000008\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Professional fees\",\n",
       "                    \"value\": 90381.09999999999\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Other equipment purchase\",\n",
       "                    \"value\": 82802.68\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Maintenance, janitorial\",\n",
       "                    \"value\": 79518.88000000003\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Cost of staying away from the main home (ACA/PAAE)\",\n",
       "                    \"target\": \"Hotel Expenses\",\n",
       "                    \"value\": 70102.58999999998\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Petty cash\",\n",
       "                    \"value\": 47442.51\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Payments to individuals\",\n",
       "                    \"value\": 42585.549999999996\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Member travel (AOE)\",\n",
       "                    \"value\": 31841.920000000013\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Staffing non-payroll\",\n",
       "                    \"target\": \"Staff Subsistence\",\n",
       "                    \"value\": 24401.869999999995\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Staff office purchases\",\n",
       "                    \"value\": 22137.029999999984\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Staff travel rail\",\n",
       "                    \"value\": 8195.26\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Staff telephone expenses\",\n",
       "                    \"value\": 6965.789999999999\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"IT equipment lease\",\n",
       "                    \"value\": 4382.88\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Staff subsistence\",\n",
       "                    \"value\": 4071.6899999999996\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Staff travel car\",\n",
       "                    \"value\": 2888.880000000001\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Staff use of home expenses\",\n",
       "                    \"value\": 1969.6799999999998\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Staff travel air\",\n",
       "                    \"value\": 800.4100000000001\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Staff travel parking\",\n",
       "                    \"value\": 697.7399999999999\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Staff travel taxi\",\n",
       "                    \"value\": 521.54\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"Staff travel public transport\",\n",
       "                    \"value\": 190.85000000000002\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Office running costs (IEP/AOE)\",\n",
       "                    \"target\": \"General Running Costs\",\n",
       "                    \"value\": 40.55\n",
       "                },\n",
       "                {\n",
       "                    \"source\": \"Staffing non-payroll\",\n",
       "                    \"target\": \"Staff travel tolls\",\n",
       "                    \"value\": 12.3\n",
       "                }\n",
       "            ],\n",
       "            \"left\": \"5%\",\n",
       "            \"top\": \"5%\",\n",
       "            \"right\": \"20%\",\n",
       "            \"bottom\": \"5%\",\n",
       "            \"nodeWidth\": 20,\n",
       "            \"nodeGap\": 8,\n",
       "            \"nodeAlign\": \"justify\",\n",
       "            \"layoutIteration\": 32,\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"draggable\": true,\n",
       "            \"focusNodeAdjacency\": false,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"right\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 0.2,\n",
       "                \"curveness\": 0.5,\n",
       "                \"type\": \"solid\",\n",
       "                \"color\": \"source\"\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"sankey\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"sankey\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"Sankey Graph of UK MPS\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_a47752b556b44adab6778945d76dc2ee.setOption(option_a47752b556b44adab6778945d76dc2ee);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1265c4b50>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Sankey\n",
    "\n",
    "s = (\n",
    "    Sankey()\n",
    "    .add(\n",
    "        \"sankey\",\n",
    "        nodes,\n",
    "        links,\n",
    "        linestyle_opt=opts.LineStyleOpts(opacity=0.2, curve=0.5, color=\"source\"),\n",
    "        label_opts=opts.LabelOpts(position=\"right\"),\n",
    "    )\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"Sankey Graph of UK MPS\"))\n",
    ")\n",
    "\n",
    "s.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T06:11:26.935440Z",
     "start_time": "2021-10-24T06:11:26.928825Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/datalab/github/data-journalism.github.io/notebook/sankey_mps.html'"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.render(\"sankey_mps.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://data-journalism.github.io/notebook/sankey_mps.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}