practice4 - US_Crime_Rates_1960_2014 - 函数.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# US_Crime_Rates_1960_2014数据 - 函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/4.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 55 entries, 0 to 54\n",
      "Data columns (total 12 columns):\n",
      "Year                  55 non-null int64\n",
      "Population            55 non-null int64\n",
      "Total                 55 non-null int64\n",
      "Violent               55 non-null int64\n",
      "Property              55 non-null int64\n",
      "Murder                55 non-null int64\n",
      "Forcible_Rape         55 non-null int64\n",
      "Robbery               55 non-null int64\n",
      "Aggravated_assault    55 non-null int64\n",
      "Burglary              55 non-null int64\n",
      "Larceny_Theft         55 non-null int64\n",
      "Vehicle_Theft         55 non-null int64\n",
      "dtypes: int64(12)\n",
      "memory usage: 5.2 KB\n"
     ]
    }
   ],
   "source": [
    "crime = pd.read_csv('data/US_Crime_Rates_1960_2014.csv')\n",
    "crime.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将Year的数据类型转换为 datetime64  显示格式为年"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   1960-01-01\n",
       "1   1961-01-01\n",
       "2   1962-01-01\n",
       "Name: Year, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime.Year = pd.to_datetime(crime.Year, format='%Y')\n",
    "crime.Year.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将列Year设置为数据框的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Total</th>\n",
       "      <th>Violent</th>\n",
       "      <th>Property</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Forcible_Rape</th>\n",
       "      <th>Robbery</th>\n",
       "      <th>Aggravated_assault</th>\n",
       "      <th>Burglary</th>\n",
       "      <th>Larceny_Theft</th>\n",
       "      <th>Vehicle_Theft</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>179323175</td>\n",
       "      <td>3384200</td>\n",
       "      <td>288460</td>\n",
       "      <td>3095700</td>\n",
       "      <td>9110</td>\n",
       "      <td>17190</td>\n",
       "      <td>107840</td>\n",
       "      <td>154320</td>\n",
       "      <td>912100</td>\n",
       "      <td>1855400</td>\n",
       "      <td>328200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>182992000</td>\n",
       "      <td>3488000</td>\n",
       "      <td>289390</td>\n",
       "      <td>3198600</td>\n",
       "      <td>8740</td>\n",
       "      <td>17220</td>\n",
       "      <td>106670</td>\n",
       "      <td>156760</td>\n",
       "      <td>949600</td>\n",
       "      <td>1913000</td>\n",
       "      <td>336000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>185771000</td>\n",
       "      <td>3752200</td>\n",
       "      <td>301510</td>\n",
       "      <td>3450700</td>\n",
       "      <td>8530</td>\n",
       "      <td>17550</td>\n",
       "      <td>110860</td>\n",
       "      <td>164570</td>\n",
       "      <td>994300</td>\n",
       "      <td>2089600</td>\n",
       "      <td>366800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Population    Total  Violent  Property  Murder  Forcible_Rape  \\\n",
       "Year                                                                        \n",
       "1960-01-01   179323175  3384200   288460   3095700    9110          17190   \n",
       "1961-01-01   182992000  3488000   289390   3198600    8740          17220   \n",
       "1962-01-01   185771000  3752200   301510   3450700    8530          17550   \n",
       "\n",
       "            Robbery  Aggravated_assault  Burglary  Larceny_Theft  \\\n",
       "Year                                                               \n",
       "1960-01-01   107840              154320    912100        1855400   \n",
       "1961-01-01   106670              156760    949600        1913000   \n",
       "1962-01-01   110860              164570    994300        2089600   \n",
       "\n",
       "            Vehicle_Theft  \n",
       "Year                       \n",
       "1960-01-01         328200  \n",
       "1961-01-01         336000  \n",
       "1962-01-01         366800  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime = crime.set_index('Year', drop=True) # drop 是否删除该列，默认为True\n",
    "crime.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 删除名为Total的列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Violent</th>\n",
       "      <th>Property</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Forcible_Rape</th>\n",
       "      <th>Robbery</th>\n",
       "      <th>Aggravated_assault</th>\n",
       "      <th>Burglary</th>\n",
       "      <th>Larceny_Theft</th>\n",
       "      <th>Vehicle_Theft</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>179323175</td>\n",
       "      <td>288460</td>\n",
       "      <td>3095700</td>\n",
       "      <td>9110</td>\n",
       "      <td>17190</td>\n",
       "      <td>107840</td>\n",
       "      <td>154320</td>\n",
       "      <td>912100</td>\n",
       "      <td>1855400</td>\n",
       "      <td>328200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>182992000</td>\n",
       "      <td>289390</td>\n",
       "      <td>3198600</td>\n",
       "      <td>8740</td>\n",
       "      <td>17220</td>\n",
       "      <td>106670</td>\n",
       "      <td>156760</td>\n",
       "      <td>949600</td>\n",
       "      <td>1913000</td>\n",
       "      <td>336000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>185771000</td>\n",
       "      <td>301510</td>\n",
       "      <td>3450700</td>\n",
       "      <td>8530</td>\n",
       "      <td>17550</td>\n",
       "      <td>110860</td>\n",
       "      <td>164570</td>\n",
       "      <td>994300</td>\n",
       "      <td>2089600</td>\n",
       "      <td>366800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Population  Violent  Property  Murder  Forcible_Rape  Robbery  \\\n",
       "Year                                                                        \n",
       "1960-01-01   179323175   288460   3095700    9110          17190   107840   \n",
       "1961-01-01   182992000   289390   3198600    8740          17220   106670   \n",
       "1962-01-01   185771000   301510   3450700    8530          17550   110860   \n",
       "\n",
       "            Aggravated_assault  Burglary  Larceny_Theft  Vehicle_Theft  \n",
       "Year                                                                    \n",
       "1960-01-01              154320    912100        1855400         328200  \n",
       "1961-01-01              156760    949600        1913000         336000  \n",
       "1962-01-01              164570    994300        2089600         366800  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del crime['Total']\n",
    "crime.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 按照Year对数据框进行分组并求和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Violent</th>\n",
       "      <th>Property</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Forcible_Rape</th>\n",
       "      <th>Robbery</th>\n",
       "      <th>Aggravated_assault</th>\n",
       "      <th>Burglary</th>\n",
       "      <th>Larceny_Theft</th>\n",
       "      <th>Vehicle_Theft</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>201385000.0</td>\n",
       "      <td>4134930</td>\n",
       "      <td>45160900</td>\n",
       "      <td>106180</td>\n",
       "      <td>236720</td>\n",
       "      <td>1633510</td>\n",
       "      <td>2158520</td>\n",
       "      <td>13321100</td>\n",
       "      <td>26547700</td>\n",
       "      <td>5292100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>220099000.0</td>\n",
       "      <td>9607930</td>\n",
       "      <td>91383800</td>\n",
       "      <td>192230</td>\n",
       "      <td>554570</td>\n",
       "      <td>4159020</td>\n",
       "      <td>4702120</td>\n",
       "      <td>28486000</td>\n",
       "      <td>53157800</td>\n",
       "      <td>9739900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>248239000.0</td>\n",
       "      <td>14074328</td>\n",
       "      <td>117048900</td>\n",
       "      <td>206439</td>\n",
       "      <td>865639</td>\n",
       "      <td>5383109</td>\n",
       "      <td>7619130</td>\n",
       "      <td>33073494</td>\n",
       "      <td>72040253</td>\n",
       "      <td>11935411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>272690813.0</td>\n",
       "      <td>17527048</td>\n",
       "      <td>119053499</td>\n",
       "      <td>211664</td>\n",
       "      <td>998827</td>\n",
       "      <td>5748930</td>\n",
       "      <td>10568963</td>\n",
       "      <td>26750015</td>\n",
       "      <td>77679366</td>\n",
       "      <td>14624418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>307006550.0</td>\n",
       "      <td>13968056</td>\n",
       "      <td>100944369</td>\n",
       "      <td>163068</td>\n",
       "      <td>922499</td>\n",
       "      <td>4230366</td>\n",
       "      <td>8652124</td>\n",
       "      <td>21565176</td>\n",
       "      <td>67970291</td>\n",
       "      <td>11412834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>318857056.0</td>\n",
       "      <td>6072017</td>\n",
       "      <td>44095950</td>\n",
       "      <td>72867</td>\n",
       "      <td>421059</td>\n",
       "      <td>1749809</td>\n",
       "      <td>3764142</td>\n",
       "      <td>10125170</td>\n",
       "      <td>30401698</td>\n",
       "      <td>3569080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "             Population   Violent   Property  Murder  Forcible_Rape  Robbery  \\\n",
       "Year                                                                           \n",
       "1960-01-01  201385000.0   4134930   45160900  106180         236720  1633510   \n",
       "1970-01-01  220099000.0   9607930   91383800  192230         554570  4159020   \n",
       "1980-01-01  248239000.0  14074328  117048900  206439         865639  5383109   \n",
       "1990-01-01  272690813.0  17527048  119053499  211664         998827  5748930   \n",
       "2000-01-01  307006550.0  13968056  100944369  163068         922499  4230366   \n",
       "2010-01-01  318857056.0   6072017   44095950   72867         421059  1749809   \n",
       "2020-01-01          NaN         0          0       0              0        0   \n",
       "\n",
       "            Aggravated_assault  Burglary  Larceny_Theft  Vehicle_Theft  \n",
       "Year                                                                    \n",
       "1960-01-01             2158520  13321100       26547700        5292100  \n",
       "1970-01-01             4702120  28486000       53157800        9739900  \n",
       "1980-01-01             7619130  33073494       72040253       11935411  \n",
       "1990-01-01            10568963  26750015       77679366       14624418  \n",
       "2000-01-01             8652124  21565176       67970291       11412834  \n",
       "2010-01-01             3764142  10125170       30401698        3569080  \n",
       "2020-01-01                   0         0              0              0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关于 .resample 的介绍\n",
    "# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html\n",
    "# https://blog.csdn.net/wangshuang1631/article/details/52314944\n",
    "# 更多关于 Offset Aliases的介绍 \n",
    "# (http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases)\n",
    "\n",
    "# AS = YS = year start\n",
    "# 10AS = 以10年为周期执行重采样，获取每个10年的犯罪率\n",
    "# 10年的重采样求和就是做10年数据的累加\n",
    "crimes = crime.resample('10AS').sum()\n",
    "\n",
    "# 10年的犯罪数可以累加，但是人口累加没有意义\n",
    "# 用resample去得到“Population”列的最大值\n",
    "population = crime['Population'].resample('10AS').max()\n",
    "\n",
    "# 更新 \"Population\" \n",
    "crimes['Population'] = population\n",
    "\n",
    "crimes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 何时是美国历史上生存最危险的年代？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population            2014\n",
       "Total                 1991\n",
       "Violent               1992\n",
       "Property              1991\n",
       "Murder                1991\n",
       "Forcible_Rape         1992\n",
       "Robbery               1991\n",
       "Aggravated_assault    1993\n",
       "Burglary              1980\n",
       "Larceny_Theft         1991\n",
       "Vehicle_Theft         1991\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime = pd.read_csv('data/US_Crime_Rates_1960_2014.csv')\n",
    "crime = crime.set_index('Year', drop=True)\n",
    "crime.idxmax(0)"
   ]
  }
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