12.2012美国大选献金项目数据分析(1).ipynb

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    "import numpy as np\n",
    "import pandas as pd\n",
    "#方便大家操作，将月份和参选人以及所在政党进行定义：\n",
    "months = {'JAN' : 1, 'FEB' : 2, 'MAR' : 3, 'APR' : 4, 'MAY' : 5, 'JUN' : 6,\n",
    "          'JUL' : 7, 'AUG' : 8, 'SEP' : 9, 'OCT': 10, 'NOV': 11, 'DEC' : 12}\n",
    "\n",
    "parties = {\n",
    "  'Bachmann, Michelle': 'Republican',\n",
    "  'Romney, Mitt': 'Republican',\n",
    "  'Obama, Barack': 'Democrat',\n",
    "  \"Roemer, Charles E. 'Buddy' III\": 'Reform',\n",
    "  'Pawlenty, Timothy': 'Republican',\n",
    "  'Johnson, Gary Earl': 'Libertarian',\n",
    "  'Paul, Ron': 'Republican',\n",
    "  'Santorum, Rick': 'Republican',\n",
    "  'Cain, Herman': 'Republican',\n",
    "  'Gingrich, Newt': 'Republican',\n",
    "  'McCotter, Thaddeus G': 'Republican',\n",
    "  'Huntsman, Jon': 'Republican',\n",
    "  'Perry, Rick': 'Republican'           \n",
    " }"
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    "### 需求\n",
    "- 加载数据\n",
    "- 查看数据的基本信息\n",
    "- 指定数据截取，将如下字段的数据进行提取，其他数据舍弃\n",
    "    - cand_nm ：候选人姓名\n",
    "    - contbr_nm ： 捐赠人姓名\n",
    "    - contbr_st ：捐赠人所在州\n",
    "    - contbr_employer ： 捐赠人所在公司\n",
    "    - contbr_occupation ： 捐赠人职业\n",
    "    - contb_receipt_amt ：捐赠数额（美元）\n",
    "    - contb_receipt_dt ： 捐款的日期\n",
    "- 对新数据进行总览,查看是否存在缺失数据\n",
    "- 用统计学指标快速描述数值型属性的概要。\n",
    "- 空值处理。可能因为忘记填写或者保密等等原因，相关字段出现了空值，将其填充为NOT PROVIDE\n",
    "- 异常值处理。将捐款金额<=0的数据删除\n",
    "- 新建一列为各个候选人所在党派party\n",
    "- 查看party这一列中有哪些不同的元素\n",
    "- 统计party列中各个元素出现次数\n",
    "- 查看各个党派收到的政治献金总数contb_receipt_amt\n",
    "- 查看具体每天各个党派收到的政治献金总数contb_receipt_amt\n",
    "- 将表中日期格式转换为'yyyy-mm-dd'。\n",
    "- 查看老兵(捐献者职业)DISABLED VETERAN主要支持谁\n"
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      "/Users/zhangxiaobo/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (6) have mixed types.Specify dtype option on import or set low_memory=False.\n",
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       "          cmte_id    cand_id             cand_nm               contbr_nm  \\\n",
       "0       C00410118  P20002978  Bachmann, Michelle         HARVEY, WILLIAM   \n",
       "1       C00410118  P20002978  Bachmann, Michelle         HARVEY, WILLIAM   \n",
       "2       C00410118  P20002978  Bachmann, Michelle           SMITH, LANIER   \n",
       "3       C00410118  P20002978  Bachmann, Michelle        BLEVINS, DARONDA   \n",
       "4       C00410118  P20002978  Bachmann, Michelle      WARDENBURG, HAROLD   \n",
       "...           ...        ...                 ...                     ...   \n",
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       "536037  C00500587  P20003281         Perry, Rick      TOLBERT, DARYL MR.   \n",
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       "536040  C00500587  P20003281         Perry, Rick    GORMAN, CHRIS D. MR.   \n",
       "\n",
       "               contbr_city contbr_st   contbr_zip  \\\n",
       "0                   MOBILE        AL   3.6601e+08   \n",
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       "2                   LANETT        AL  3.68633e+08   \n",
       "3                  PIGGOTT        AR  7.24548e+08   \n",
       "4       HOT SPRINGS NATION        AR  7.19016e+08   \n",
       "...                    ...       ...          ...   \n",
       "536036      INFO REQUESTED        XX        99999   \n",
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       "\n",
       "                               contbr_employer  \\\n",
       "0                                      RETIRED   \n",
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       "2                        INFORMATION REQUESTED   \n",
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       "4                                         NONE   \n",
       "...                                        ...   \n",
       "536036  INFORMATION REQUESTED PER BEST EFFORTS   \n",
       "536037                                T.A.C.C.   \n",
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       "536039            DUFFY EQUIPMENT COMPANY INC.   \n",
       "536040  INFORMATION REQUESTED PER BEST EFFORTS   \n",
       "\n",
       "                             contbr_occupation  contb_receipt_amt  \\\n",
       "0                                      RETIRED              250.0   \n",
       "1                                      RETIRED               50.0   \n",
       "2                        INFORMATION REQUESTED              250.0   \n",
       "3                                      RETIRED              250.0   \n",
       "4                                      RETIRED              300.0   \n",
       "...                                        ...                ...   \n",
       "536036  INFORMATION REQUESTED PER BEST EFFORTS             2500.0   \n",
       "536037            LONGWALL MAINTENANCE FOREMAN              500.0   \n",
       "536038  INFORMATION REQUESTED PER BEST EFFORTS              500.0   \n",
       "536039                          BUSINESS OWNER             2500.0   \n",
       "536040  INFORMATION REQUESTED PER BEST EFFORTS             5000.0   \n",
       "\n",
       "       contb_receipt_dt                                       receipt_desc  \\\n",
       "0             20-JUN-11                                                NaN   \n",
       "1             23-JUN-11                                                NaN   \n",
       "2             05-JUL-11                                                NaN   \n",
       "3             01-AUG-11                                                NaN   \n",
       "4             20-JUN-11                                                NaN   \n",
       "...                 ...                                                ...   \n",
       "536036        31-AUG-11                                                NaN   \n",
       "536037        30-SEP-11                                                NaN   \n",
       "536038        29-SEP-11                                                NaN   \n",
       "536039        30-SEP-11                                                NaN   \n",
       "536040        29-SEP-11  REATTRIBUTION / REDESIGNATION REQUESTED (AUTOM...   \n",
       "\n",
       "       memo_cd                                          memo_text form_tp  \\\n",
       "0          NaN                                                NaN   SA17A   \n",
       "1          NaN                                                NaN   SA17A   \n",
       "2          NaN                                                NaN   SA17A   \n",
       "3          NaN                                                NaN   SA17A   \n",
       "4          NaN                                                NaN   SA17A   \n",
       "...        ...                                                ...     ...   \n",
       "536036     NaN                                                NaN   SA17A   \n",
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       "536038     NaN                                                NaN   SA17A   \n",
       "536039     NaN                                                NaN   SA17A   \n",
       "536040     NaN  REATTRIBUTION / REDESIGNATION REQUESTED (AUTOM...   SA17A   \n",
       "\n",
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       "...          ...  \n",
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       "536038    751678  \n",
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   "source": [
    "df = pd.read_csv('./data/usa_election.txt')\n",
    "df"
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       "      <td>BLEVINS, DARONDA</td>\n",
       "      <td>AR</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>01-AUG-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>WARDENBURG, HAROLD</td>\n",
       "      <td>AR</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              cand_nm           contbr_nm contbr_st        contbr_employer  \\\n",
       "0  Bachmann, Michelle     HARVEY, WILLIAM        AL                RETIRED   \n",
       "1  Bachmann, Michelle     HARVEY, WILLIAM        AL                RETIRED   \n",
       "2  Bachmann, Michelle       SMITH, LANIER        AL  INFORMATION REQUESTED   \n",
       "3  Bachmann, Michelle    BLEVINS, DARONDA        AR                   NONE   \n",
       "4  Bachmann, Michelle  WARDENBURG, HAROLD        AR                   NONE   \n",
       "\n",
       "       contbr_occupation  contb_receipt_amt contb_receipt_dt  \n",
       "0                RETIRED              250.0        20-JUN-11  \n",
       "1                RETIRED               50.0        23-JUN-11  \n",
       "2  INFORMATION REQUESTED              250.0        05-JUL-11  \n",
       "3                RETIRED              250.0        01-AUG-11  \n",
       "4                RETIRED              300.0        20-JUN-11  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['cand_nm','contbr_nm','contbr_st','contbr_employer','contbr_occupation','contb_receipt_amt','contb_receipt_dt']]\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm              False\n",
       "contbr_nm            False\n",
       "contbr_st             True\n",
       "contbr_employer       True\n",
       "contbr_occupation     True\n",
       "contb_receipt_amt    False\n",
       "contb_receipt_dt     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对新数据进行总览,查看是否存在缺失数据\n",
    "df.isnull().any(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 536041 entries, 0 to 536040\n",
      "Data columns (total 7 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   cand_nm            536041 non-null  object \n",
      " 1   contbr_nm          536041 non-null  object \n",
      " 2   contbr_st          536040 non-null  object \n",
      " 3   contbr_employer    525088 non-null  object \n",
      " 4   contbr_occupation  530520 non-null  object \n",
      " 5   contb_receipt_amt  536041 non-null  float64\n",
      " 6   contb_receipt_dt   536041 non-null  object \n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 28.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.360410e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.750373e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.564436e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.080000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.500000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.944042e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       contb_receipt_amt\n",
       "count       5.360410e+05\n",
       "mean        3.750373e+02\n",
       "std         3.564436e+03\n",
       "min        -3.080000e+04\n",
       "25%         5.000000e+01\n",
       "50%         1.000000e+02\n",
       "75%         2.500000e+02\n",
       "max         1.944042e+06"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用统计学指标快速描述数值型属性的概要。\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zhangxiaobo/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py:4317: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().fillna(\n"
     ]
    }
   ],
   "source": [
    "#空值处理。可能因为忘记填写或者保密等等原因，相关字段出现了空值，将其填充为NOT PROVIDE\n",
    "df.fillna(value='NOT PROVIDE',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zhangxiaobo/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py:4163: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "#异常值处理。将捐款金额<=0的数据删除\n",
    "df['contb_receipt_amt'] <= 0\n",
    "#将布尔值作为行索引，取出异常值对应的行数据\n",
    "df.loc[df['contb_receipt_amt'] <= 0]\n",
    "#将异常值对应的行索引获取\n",
    "drop_index = df.loc[df['contb_receipt_amt'] <= 0].index\n",
    "#将指定的行进行删除\n",
    "df.drop(labels=drop_index,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检测amt列中异常数据是否被清除干净\n",
    "(df['contb_receipt_amt'] <= 0).sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th>contbr_employer</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th>contb_receipt_dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>AL</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>AL</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>50.0</td>\n",
       "      <td>23-JUN-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>SMITH, LANIER</td>\n",
       "      <td>AL</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>05-JUL-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>BLEVINS, DARONDA</td>\n",
       "      <td>AR</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>01-AUG-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>WARDENBURG, HAROLD</td>\n",
       "      <td>AR</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              cand_nm           contbr_nm contbr_st        contbr_employer  \\\n",
       "0  Bachmann, Michelle     HARVEY, WILLIAM        AL                RETIRED   \n",
       "1  Bachmann, Michelle     HARVEY, WILLIAM        AL                RETIRED   \n",
       "2  Bachmann, Michelle       SMITH, LANIER        AL  INFORMATION REQUESTED   \n",
       "3  Bachmann, Michelle    BLEVINS, DARONDA        AR                   NONE   \n",
       "4  Bachmann, Michelle  WARDENBURG, HAROLD        AR                   NONE   \n",
       "\n",
       "       contbr_occupation  contb_receipt_amt contb_receipt_dt  \n",
       "0                RETIRED              250.0        20-JUN-11  \n",
       "1                RETIRED               50.0        23-JUN-11  \n",
       "2  INFORMATION REQUESTED              250.0        05-JUL-11  \n",
       "3                RETIRED              250.0        01-AUG-11  \n",
       "4                RETIRED              300.0        20-JUN-11  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#新建一列为各个候选人所在党派party\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-80457ca972db>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['party'] = df['cand_nm'].map(parties)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th>contbr_employer</th>\n",
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       "      <th>0</th>\n",
       "      <td>Bachmann, Michelle</td>\n",
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       "      <th>1</th>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
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       "      <td>RETIRED</td>\n",
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       "      <td>Republican</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>SMITH, LANIER</td>\n",
       "      <td>AL</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>05-JUL-11</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>BLEVINS, DARONDA</td>\n",
       "      <td>AR</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>01-AUG-11</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>WARDENBURG, HAROLD</td>\n",
       "      <td>AR</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              cand_nm           contbr_nm contbr_st        contbr_employer  \\\n",
       "0  Bachmann, Michelle     HARVEY, WILLIAM        AL                RETIRED   \n",
       "1  Bachmann, Michelle     HARVEY, WILLIAM        AL                RETIRED   \n",
       "2  Bachmann, Michelle       SMITH, LANIER        AL  INFORMATION REQUESTED   \n",
       "3  Bachmann, Michelle    BLEVINS, DARONDA        AR                   NONE   \n",
       "4  Bachmann, Michelle  WARDENBURG, HAROLD        AR                   NONE   \n",
       "\n",
       "       contbr_occupation  contb_receipt_amt contb_receipt_dt       party  \n",
       "0                RETIRED              250.0        20-JUN-11  Republican  \n",
       "1                RETIRED               50.0        23-JUN-11  Republican  \n",
       "2  INFORMATION REQUESTED              250.0        05-JUL-11  Republican  \n",
       "3                RETIRED              250.0        01-AUG-11  Republican  \n",
       "4                RETIRED              300.0        20-JUN-11  Republican  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['party'] = df['cand_nm'].map(parties)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Republican', 'Democrat', 'Reform', 'Libertarian'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看party这一列中有哪些不同的元素\n",
    "df['party'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Democrat       289999\n",
       "Republican     234300\n",
       "Reform           5313\n",
       "Libertarian       702\n",
       "Name: party, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计party列中各个元素出现次数\n",
    "df['party'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "party\n",
       "Democrat       8.259441e+07\n",
       "Libertarian    4.132769e+05\n",
       "Reform         3.429658e+05\n",
       "Republican     1.251181e+08\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看各个党派收到的政治献金总数contb_receipt_amt\n",
    "df.groupby(by='party')['contb_receipt_amt'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8.259441e+07</td>\n",
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       "      <th>Libertarian</th>\n",
       "      <td>4.132769e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reform</th>\n",
       "      <td>3.429658e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republican</th>\n",
       "      <td>1.251181e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             contb_receipt_amt\n",
       "party                         \n",
       "Democrat          8.259441e+07\n",
       "Libertarian       4.132769e+05\n",
       "Reform            3.429658e+05\n",
       "Republican        1.251181e+08"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index='party',values='contb_receipt_amt',aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "contb_receipt_dt  party      \n",
       "01-APR-11         Reform             50.00\n",
       "                  Republican      12635.00\n",
       "01-AUG-11         Democrat       182198.00\n",
       "                  Libertarian      1000.00\n",
       "                  Reform           1847.00\n",
       "                                   ...    \n",
       "31-MAY-11         Republican     313839.80\n",
       "31-OCT-11         Democrat       216971.87\n",
       "                  Libertarian      4250.00\n",
       "                  Reform           3205.00\n",
       "                  Republican     751542.36\n",
       "Name: contb_receipt_amt, Length: 1183, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看具体每天各个党派收到的政治献金总数contb_receipt_amt\n",
    "df.groupby(by=['contb_receipt_dt','party'])['contb_receipt_amt'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "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></th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contb_receipt_dt</th>\n",
       "      <th>party</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">01-APR-11</th>\n",
       "      <th>Reform</th>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republican</th>\n",
       "      <td>12635.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">01-AUG-11</th>\n",
       "      <th>Democrat</th>\n",
       "      <td>182198.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Libertarian</th>\n",
       "      <td>1000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reform</th>\n",
       "      <td>1847.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31-MAY-11</th>\n",
       "      <th>Republican</th>\n",
       "      <td>313839.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">31-OCT-11</th>\n",
       "      <th>Democrat</th>\n",
       "      <td>216971.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Libertarian</th>\n",
       "      <td>4250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reform</th>\n",
       "      <td>3205.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republican</th>\n",
       "      <td>751542.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1183 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              contb_receipt_amt\n",
       "contb_receipt_dt party                         \n",
       "01-APR-11        Reform                   50.00\n",
       "                 Republican            12635.00\n",
       "01-AUG-11        Democrat             182198.00\n",
       "                 Libertarian            1000.00\n",
       "                 Reform                 1847.00\n",
       "...                                         ...\n",
       "31-MAY-11        Republican           313839.80\n",
       "31-OCT-11        Democrat             216971.87\n",
       "                 Libertarian            4250.00\n",
       "                 Reform                 3205.00\n",
       "                 Republican           751542.36\n",
       "\n",
       "[1183 rows x 1 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index=['contb_receipt_dt','party'],values='contb_receipt_amt',aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-b52ba6806f9a>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['contb_receipt_dt'] = df['contb_receipt_dt'].map(transformData)\n"
     ]
    }
   ],
   "source": [
    "#将表中日期格式转换为'yyyy-mm-dd'\n",
    "def transformData(d):\n",
    "    day,month,year = d.split('-')\n",
    "    return '20'+year+'-'+str(months[month])+'-'+day\n",
    "df['contb_receipt_dt'] = df['contb_receipt_dt'].map(transformData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#查看老兵(捐献者职业)DISABLED VETERAN主要支持谁"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.可以将职业为DISABLED VETERAN的行数据取出\n",
    "df['contbr_occupation'] == 'DISABLED VETERAN'\n",
    "old_bing_df = df.loc[df['contbr_occupation'] == 'DISABLED VETERAN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm\n",
       "Cain, Herman       300.00\n",
       "Obama, Barack     4205.00\n",
       "Paul, Ron         2425.49\n",
       "Santorum, Rick     250.00\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2.基于候选人分组，聚合捐赠金额即可\n",
    "old_bing_df.groupby(by='cand_nm')['contb_receipt_amt'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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