C1_Week3.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "---\n",
    "\n",
    "_You are currently looking at **version 1.0** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merging Dataframes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>22.5</td>\n",
       "      <td>Sponge</td>\n",
       "      <td>Chris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>Kevyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Spoon</td>\n",
       "      <td>Filip</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cost Item Purchased   Name\n",
       "Store 1  22.5         Sponge  Chris\n",
       "Store 1   2.5   Kitty Litter  Kevyn\n",
       "Store 2   5.0          Spoon  Filip"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame([{'Name': 'Chris', 'Item Purchased': 'Sponge', 'Cost': 22.50},\n",
    "                   {'Name': 'Kevyn', 'Item Purchased': 'Kitty Litter', 'Cost': 2.50},\n",
    "                   {'Name': 'Filip', 'Item Purchased': 'Spoon', 'Cost': 5.00}],\n",
    "                  index=['Store 1', 'Store 1', 'Store 2'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Name</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>22.5</td>\n",
       "      <td>Sponge</td>\n",
       "      <td>Chris</td>\n",
       "      <td>December 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>Kevyn</td>\n",
       "      <td>January 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Spoon</td>\n",
       "      <td>Filip</td>\n",
       "      <td>mid-May</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cost Item Purchased   Name        Date\n",
       "Store 1  22.5         Sponge  Chris  December 1\n",
       "Store 1   2.5   Kitty Litter  Kevyn   January 1\n",
       "Store 2   5.0          Spoon  Filip     mid-May"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "def add_num_column():\n",
    "    n = 0\n",
    "    for i,r in df.iterrows():\n",
    "        n += 1\n",
    "        df['Number'] = [n,n,n]\n",
    "    return df\n",
    "add_num_column()\n",
    "'''\n",
    "df['Date'] = ['December 1', 'January 1', 'mid-May']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cost</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Name</th>\n",
       "      <th>Date</th>\n",
       "      <th>Delivered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>22.5</td>\n",
       "      <td>Sponge</td>\n",
       "      <td>Chris</td>\n",
       "      <td>December 1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>Kevyn</td>\n",
       "      <td>January 1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Spoon</td>\n",
       "      <td>Filip</td>\n",
       "      <td>mid-May</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cost Item Purchased   Name        Date  Delivered\n",
       "Store 1  22.5         Sponge  Chris  December 1       True\n",
       "Store 1   2.5   Kitty Litter  Kevyn   January 1       True\n",
       "Store 2   5.0          Spoon  Filip     mid-May       True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Delivered'] = True\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Item Purchased</th>\n",
       "      <th>Name</th>\n",
       "      <th>Date</th>\n",
       "      <th>Delivered</th>\n",
       "      <th>Feedback</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>22.5</td>\n",
       "      <td>Sponge</td>\n",
       "      <td>Chris</td>\n",
       "      <td>December 1</td>\n",
       "      <td>True</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>Kevyn</td>\n",
       "      <td>January 1</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Spoon</td>\n",
       "      <td>Filip</td>\n",
       "      <td>mid-May</td>\n",
       "      <td>True</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Cost Item Purchased   Name        Date  Delivered  Feedback\n",
       "Store 1  22.5         Sponge  Chris  December 1       True  Positive\n",
       "Store 1   2.5   Kitty Litter  Kevyn   January 1       True      None\n",
       "Store 2   5.0          Spoon  Filip     mid-May       True  Negative"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Feedback'] = ['Positive', None, 'Negative']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = df.reset_index()\n",
    "adf['Date'] = pd.Series({0: 'December 1', 2: 'mid-May'})\n",
    "adf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Role\n",
      "Name                 \n",
      "Kelly  Director of HR\n",
      "Sally  Course liasion\n",
      "James          Grader\n",
      "\n",
      "            School\n",
      "Name              \n",
      "James     Business\n",
      "Mike           Law\n",
      "Sally  Engineering\n"
     ]
    }
   ],
   "source": [
    "staff_df = pd.DataFrame([{'Name': 'Kelly', 'Role': 'Director of HR'},\n",
    "                         {'Name': 'Sally', 'Role': 'Course liasion'},\n",
    "                         {'Name': 'James', 'Role': 'Grader'}])\n",
    "staff_df = staff_df.set_index('Name')\n",
    "student_df = pd.DataFrame([{'Name': 'James', 'School': 'Business'},\n",
    "                           {'Name': 'Mike', 'School': 'Law'},\n",
    "                           {'Name': 'Sally', 'School': 'Engineering'}])\n",
    "student_df = student_df.set_index('Name')\n",
    "print(staff_df.head())\n",
    "print()\n",
    "print(student_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Kelly</th>\n",
       "      <td>Director of HR</td>\n",
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       "      <th>Mike</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Law</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sally</th>\n",
       "      <td>Course liasion</td>\n",
       "      <td>Engineering</td>\n",
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      "text/plain": [
       "                 Role       School\n",
       "Name                              \n",
       "James          Grader     Business\n",
       "Kelly  Director of HR          NaN\n",
       "Mike              NaN          Law\n",
       "Sally  Course liasion  Engineering"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(staff_df, student_df, how='outer', left_index=True, right_index=True) #outer join 把所有行列都合并进来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sally</th>\n",
       "      <td>Course liasion</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James</th>\n",
       "      <td>Grader</td>\n",
       "      <td>Business</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "                 Role       School\n",
       "Name                              \n",
       "Sally  Course liasion  Engineering\n",
       "James          Grader     Business"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(staff_df, student_df, how='inner', left_index=True, right_index=True) \n",
    "#inner join 只有两个df里面重叠（index名一样）的部分才会被合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>Kelly</th>\n",
       "      <td>Director of HR</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sally</th>\n",
       "      <td>Course liasion</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James</th>\n",
       "      <td>Grader</td>\n",
       "      <td>Business</td>\n",
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      "text/plain": [
       "                 Role       School\n",
       "Name                              \n",
       "Kelly  Director of HR          NaN\n",
       "Sally  Course liasion  Engineering\n",
       "James          Grader     Business"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(staff_df, student_df, how='left', left_index=True, right_index=True) #left join只包含所有左侧df的index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>James</th>\n",
       "      <td>Grader</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mike</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Law</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sally</th>\n",
       "      <td>Course liasion</td>\n",
       "      <td>Engineering</td>\n",
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      "text/plain": [
       "                 Role       School\n",
       "Name                              \n",
       "James          Grader     Business\n",
       "Mike              NaN          Law\n",
       "Sally  Course liasion  Engineering"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(staff_df, student_df, how='right', left_index=True, right_index=True)#right join只包含所有右侧df的index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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       "      <td>Kelly</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sally</td>\n",
       "      <td>Course liasion</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>James</td>\n",
       "      <td>Grader</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name            Role       School\n",
       "0  Kelly  Director of HR          NaN\n",
       "1  Sally  Course liasion  Engineering\n",
       "2  James          Grader     Business"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "staff_df = staff_df.reset_index()\n",
    "student_df = student_df.reset_index()\n",
    "pd.merge(staff_df, student_df, how='left', left_on='Name', right_on='Name') #也可以使用column名，比如Name这个column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Location_x</th>\n",
       "      <th>Name</th>\n",
       "      <th>Role</th>\n",
       "      <th>Location_y</th>\n",
       "      <th>School</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State Street</td>\n",
       "      <td>Kelly</td>\n",
       "      <td>Director of HR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Washington Avenue</td>\n",
       "      <td>Sally</td>\n",
       "      <td>Course liasion</td>\n",
       "      <td>512 Wilson Crescent</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Washington Avenue</td>\n",
       "      <td>James</td>\n",
       "      <td>Grader</td>\n",
       "      <td>1024 Billiard Avenue</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Location_x   Name            Role            Location_y       School\n",
       "0       State Street  Kelly  Director of HR                   NaN          NaN\n",
       "1  Washington Avenue  Sally  Course liasion   512 Wilson Crescent  Engineering\n",
       "2  Washington Avenue  James          Grader  1024 Billiard Avenue     Business"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "staff_df = pd.DataFrame([{'Name': 'Kelly', 'Role': 'Director of HR', 'Location': 'State Street'},\n",
    "                         {'Name': 'Sally', 'Role': 'Course liasion', 'Location': 'Washington Avenue'},\n",
    "                         {'Name': 'James', 'Role': 'Grader', 'Location': 'Washington Avenue'}])\n",
    "student_df = pd.DataFrame([{'Name': 'James', 'School': 'Business', 'Location': '1024 Billiard Avenue'},\n",
    "                           {'Name': 'Mike', 'School': 'Law', 'Location': 'Fraternity House #22'},\n",
    "                           {'Name': 'Sally', 'School': 'Engineering', 'Location': '512 Wilson Crescent'}])\n",
    "pd.merge(staff_df, student_df, how='left', left_on='Name', right_on='Name')\n",
    "# 如果两个df出现了相同column，且这个column不是用来匹配的column，那么结果中会将两个重名的column分别重命名为 ‘原名_x’ 和 ‘原名_y’， \n",
    "# 其中x指左边的，y右边。这个是默认的设置，你也可以自行定义如何重命名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 如果两个df，一个的index名称和另一个df的某一个column名称相同，则可以使用 left_index = True, right_on = '相同的名字'\n",
    "# 假设左边的是index名，右边的是column名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "staff_df = pd.DataFrame([{'First Name': 'Kelly', 'Last Name': 'Desjardins', 'Role': 'Director of HR'},\n",
    "                         {'First Name': 'Sally', 'Last Name': 'Brooks', 'Role': 'Course liasion'},\n",
    "                         {'First Name': 'James', 'Last Name': 'Wilde', 'Role': 'Grader'}])\n",
    "student_df = pd.DataFrame([{'First Name': 'James', 'Last Name': 'Hammond', 'School': 'Business'},\n",
    "                           {'First Name': 'Mike', 'Last Name': 'Smith', 'School': 'Law'},\n",
    "                           {'First Name': 'Sally', 'Last Name': 'Brooks', 'School': 'Engineering'}])\n",
    "staff_df\n",
    "student_df\n",
    "pd.merge(staff_df, student_df, how='inner', left_on=['First Name','Last Name'], right_on=['First Name','Last Name'])\n",
    "# 对于人名相同但实际不是同一个人的情况，使用姓和名两个column来共同做匹配"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Idiomatic Pandas: Making Code Pandorable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you see a ][ you should think carefully about what you are doing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "      <th>POPESTIMATE2010</th>\n",
       "      <th>...</th>\n",
       "      <th>RDOMESTICMIG2011</th>\n",
       "      <th>RDOMESTICMIG2012</th>\n",
       "      <th>RDOMESTICMIG2013</th>\n",
       "      <th>RDOMESTICMIG2014</th>\n",
       "      <th>RDOMESTICMIG2015</th>\n",
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       "      <th>RNETMIG2012</th>\n",
       "      <th>RNETMIG2013</th>\n",
       "      <th>RNETMIG2014</th>\n",
       "      <th>RNETMIG2015</th>\n",
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       "  <tbody>\n",
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       "      <td>Alabama</td>\n",
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       "      <td>4780127</td>\n",
       "      <td>4785161</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.193196</td>\n",
       "      <td>0.381066</td>\n",
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       "      <td>1.030015</td>\n",
       "      <td>0.826644</td>\n",
       "      <td>1.383282</td>\n",
       "      <td>1.724718</td>\n",
       "      <td>0.712594</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>54571</td>\n",
       "      <td>54571</td>\n",
       "      <td>54660</td>\n",
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       "      <td>-2.915927</td>\n",
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       "      <td>7.606016</td>\n",
       "      <td>-2.626146</td>\n",
       "      <td>-2.722002</td>\n",
       "      <td>2.592270</td>\n",
       "      <td>-2.187333</td>\n",
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       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>182265</td>\n",
       "      <td>182265</td>\n",
       "      <td>183193</td>\n",
       "      <td>...</td>\n",
       "      <td>14.832960</td>\n",
       "      <td>17.647293</td>\n",
       "      <td>21.845705</td>\n",
       "      <td>19.243287</td>\n",
       "      <td>17.197872</td>\n",
       "      <td>15.844176</td>\n",
       "      <td>18.559627</td>\n",
       "      <td>22.727626</td>\n",
       "      <td>20.317142</td>\n",
       "      <td>18.293499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>27457</td>\n",
       "      <td>27457</td>\n",
       "      <td>27341</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.728132</td>\n",
       "      <td>-2.500690</td>\n",
       "      <td>-7.056824</td>\n",
       "      <td>-3.904217</td>\n",
       "      <td>-10.543299</td>\n",
       "      <td>-4.874741</td>\n",
       "      <td>-2.758113</td>\n",
       "      <td>-7.167664</td>\n",
       "      <td>-3.978583</td>\n",
       "      <td>-10.543299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>22915</td>\n",
       "      <td>22919</td>\n",
       "      <td>22861</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.527043</td>\n",
       "      <td>-5.068871</td>\n",
       "      <td>-6.201001</td>\n",
       "      <td>-0.177537</td>\n",
       "      <td>0.177258</td>\n",
       "      <td>-5.088389</td>\n",
       "      <td>-4.363636</td>\n",
       "      <td>-5.403729</td>\n",
       "      <td>0.754533</td>\n",
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       "<p>5 rows × 100 columns</p>\n",
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       "   SUMLEV  REGION  DIVISION  STATE  COUNTY   STNAME         CTYNAME  \\\n",
       "0      40       3         6      1       0  Alabama         Alabama   \n",
       "1      50       3         6      1       1  Alabama  Autauga County   \n",
       "2      50       3         6      1       3  Alabama  Baldwin County   \n",
       "3      50       3         6      1       5  Alabama  Barbour County   \n",
       "4      50       3         6      1       7  Alabama     Bibb County   \n",
       "\n",
       "   CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010     ...       \\\n",
       "0        4779736            4780127          4785161     ...        \n",
       "1          54571              54571            54660     ...        \n",
       "2         182265             182265           183193     ...        \n",
       "3          27457              27457            27341     ...        \n",
       "4          22915              22919            22861     ...        \n",
       "\n",
       "   RDOMESTICMIG2011  RDOMESTICMIG2012  RDOMESTICMIG2013  RDOMESTICMIG2014  \\\n",
       "0          0.002295         -0.193196          0.381066          0.582002   \n",
       "1          7.242091         -2.915927         -3.012349          2.265971   \n",
       "2         14.832960         17.647293         21.845705         19.243287   \n",
       "3         -4.728132         -2.500690         -7.056824         -3.904217   \n",
       "4         -5.527043         -5.068871         -6.201001         -0.177537   \n",
       "\n",
       "   RDOMESTICMIG2015  RNETMIG2011  RNETMIG2012  RNETMIG2013  RNETMIG2014  \\\n",
       "0         -0.467369     1.030015     0.826644     1.383282     1.724718   \n",
       "1         -2.530799     7.606016    -2.626146    -2.722002     2.592270   \n",
       "2         17.197872    15.844176    18.559627    22.727626    20.317142   \n",
       "3        -10.543299    -4.874741    -2.758113    -7.167664    -3.978583   \n",
       "4          0.177258    -5.088389    -4.363636    -5.403729     0.754533   \n",
       "\n",
       "   RNETMIG2015  \n",
       "0     0.712594  \n",
       "1    -2.187333  \n",
       "2    18.293499  \n",
       "3   -10.543299  \n",
       "4     1.107861  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('census.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <th rowspan=\"5\" valign=\"top\">Alabama</th>\n",
       "      <th>Autauga County</th>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>54571.0</td>\n",
       "      <td>54660.0</td>\n",
       "      <td>55253.0</td>\n",
       "      <td>55175.0</td>\n",
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       "      <td>2.592270</td>\n",
       "      <td>-2.187333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baldwin County</th>\n",
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       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>182265.0</td>\n",
       "      <td>182265.0</td>\n",
       "      <td>183193.0</td>\n",
       "      <td>186659.0</td>\n",
       "      <td>190396.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.832960</td>\n",
       "      <td>17.647293</td>\n",
       "      <td>21.845705</td>\n",
       "      <td>19.243287</td>\n",
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       "      <td>20.317142</td>\n",
       "      <td>18.293499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbour County</th>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27457.0</td>\n",
       "      <td>27457.0</td>\n",
       "      <td>27341.0</td>\n",
       "      <td>27226.0</td>\n",
       "      <td>27159.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>-3.978583</td>\n",
       "      <td>-10.543299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bibb County</th>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22915.0</td>\n",
       "      <td>22919.0</td>\n",
       "      <td>22861.0</td>\n",
       "      <td>22733.0</td>\n",
       "      <td>22642.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.527043</td>\n",
       "      <td>-5.068871</td>\n",
       "      <td>-6.201001</td>\n",
       "      <td>-0.177537</td>\n",
       "      <td>0.177258</td>\n",
       "      <td>-5.088389</td>\n",
       "      <td>-4.363636</td>\n",
       "      <td>-5.403729</td>\n",
       "      <td>0.754533</td>\n",
       "      <td>1.107861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blount County</th>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>57322.0</td>\n",
       "      <td>57322.0</td>\n",
       "      <td>57373.0</td>\n",
       "      <td>57711.0</td>\n",
       "      <td>57776.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.807375</td>\n",
       "      <td>-1.177622</td>\n",
       "      <td>-1.748766</td>\n",
       "      <td>-2.062535</td>\n",
       "      <td>-1.369970</td>\n",
       "      <td>1.859511</td>\n",
       "      <td>-0.848580</td>\n",
       "      <td>-1.402476</td>\n",
       "      <td>-1.577232</td>\n",
       "      <td>-0.884411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        SUMLEV  REGION  DIVISION  STATE  COUNTY  \\\n",
       "STNAME  CTYNAME                                                   \n",
       "Alabama Autauga County    50.0     3.0       6.0    1.0     1.0   \n",
       "        Baldwin County    50.0     3.0       6.0    1.0     3.0   \n",
       "        Barbour County    50.0     3.0       6.0    1.0     5.0   \n",
       "        Bibb County       50.0     3.0       6.0    1.0     7.0   \n",
       "        Blount County     50.0     3.0       6.0    1.0     9.0   \n",
       "\n",
       "                        CENSUS2010POP  Estimates Base 2010  POPESTIMATE2010  \\\n",
       "STNAME  CTYNAME                                                               \n",
       "Alabama Autauga County        54571.0              54571.0          54660.0   \n",
       "        Baldwin County       182265.0             182265.0         183193.0   \n",
       "        Barbour County        27457.0              27457.0          27341.0   \n",
       "        Bibb County           22915.0              22919.0          22861.0   \n",
       "        Blount County         57322.0              57322.0          57373.0   \n",
       "\n",
       "                        POPESTIMATE2011  POPESTIMATE2012     ...       \\\n",
       "STNAME  CTYNAME                                              ...        \n",
       "Alabama Autauga County          55253.0          55175.0     ...        \n",
       "        Baldwin County         186659.0         190396.0     ...        \n",
       "        Barbour County          27226.0          27159.0     ...        \n",
       "        Bibb County             22733.0          22642.0     ...        \n",
       "        Blount County           57711.0          57776.0     ...        \n",
       "\n",
       "                        RDOMESTICMIG2011  RDOMESTICMIG2012  RDOMESTICMIG2013  \\\n",
       "STNAME  CTYNAME                                                                \n",
       "Alabama Autauga County          7.242091         -2.915927         -3.012349   \n",
       "        Baldwin County         14.832960         17.647293         21.845705   \n",
       "        Barbour County         -4.728132         -2.500690         -7.056824   \n",
       "        Bibb County            -5.527043         -5.068871         -6.201001   \n",
       "        Blount County           1.807375         -1.177622         -1.748766   \n",
       "\n",
       "                        RDOMESTICMIG2014  RDOMESTICMIG2015  RNETMIG2011  \\\n",
       "STNAME  CTYNAME                                                           \n",
       "Alabama Autauga County          2.265971         -2.530799     7.606016   \n",
       "        Baldwin County         19.243287         17.197872    15.844176   \n",
       "        Barbour County         -3.904217        -10.543299    -4.874741   \n",
       "        Bibb County            -0.177537          0.177258    -5.088389   \n",
       "        Blount County          -2.062535         -1.369970     1.859511   \n",
       "\n",
       "                        RNETMIG2012  RNETMIG2013  RNETMIG2014  RNETMIG2015  \n",
       "STNAME  CTYNAME                                                             \n",
       "Alabama Autauga County    -2.626146    -2.722002     2.592270    -2.187333  \n",
       "        Baldwin County    18.559627    22.727626    20.317142    18.293499  \n",
       "        Barbour County    -2.758113    -7.167664    -3.978583   -10.543299  \n",
       "        Bibb County       -4.363636    -5.403729     0.754533     1.107861  \n",
       "        Blount County     -0.848580    -1.402476    -1.577232    -0.884411  \n",
       "\n",
       "[5 rows x 98 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用一个括号括起来，就可以把dataframe后面接续的多个attribution分行写，这种写法非常优美 pandorable\n",
    "(\n",
    "    df.where(df['SUMLEV']==50)\n",
    "    .dropna() #丢掉na值（n/a）\n",
    "    .set_index(['STNAME','CTYNAME']) #将某column设为index\n",
    "    .rename(columns={'ESTIMATESBASE2010': 'Estimates Base 2010'}) #column重命名\n",
    "    .head()\n",
    ") \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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></th>\n",
       "      <th>SUMLEV</th>\n",
       "      <th>REGION</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>CENSUS2010POP</th>\n",
       "      <th>ESTIMATESBASE2010</th>\n",
       "      <th>POPESTIMATE2010</th>\n",
       "      <th>POPESTIMATE2011</th>\n",
       "      <th>POPESTIMATE2012</th>\n",
       "      <th>...</th>\n",
       "      <th>RDOMESTICMIG2011</th>\n",
       "      <th>RDOMESTICMIG2012</th>\n",
       "      <th>RDOMESTICMIG2013</th>\n",
       "      <th>RDOMESTICMIG2014</th>\n",
       "      <th>RDOMESTICMIG2015</th>\n",
       "      <th>RNETMIG2011</th>\n",
       "      <th>RNETMIG2012</th>\n",
       "      <th>RNETMIG2013</th>\n",
       "      <th>RNETMIG2014</th>\n",
       "      <th>RNETMIG2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Alabama</th>\n",
       "      <th>Autauga County</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>54571</td>\n",
       "      <td>54571</td>\n",
       "      <td>54660</td>\n",
       "      <td>55253</td>\n",
       "      <td>55175</td>\n",
       "      <td>...</td>\n",
       "      <td>7.242091</td>\n",
       "      <td>-2.915927</td>\n",
       "      <td>-3.012349</td>\n",
       "      <td>2.265971</td>\n",
       "      <td>-2.530799</td>\n",
       "      <td>7.606016</td>\n",
       "      <td>-2.626146</td>\n",
       "      <td>-2.722002</td>\n",
       "      <td>2.592270</td>\n",
       "      <td>-2.187333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baldwin County</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>182265</td>\n",
       "      <td>182265</td>\n",
       "      <td>183193</td>\n",
       "      <td>186659</td>\n",
       "      <td>190396</td>\n",
       "      <td>...</td>\n",
       "      <td>14.832960</td>\n",
       "      <td>17.647293</td>\n",
       "      <td>21.845705</td>\n",
       "      <td>19.243287</td>\n",
       "      <td>17.197872</td>\n",
       "      <td>15.844176</td>\n",
       "      <td>18.559627</td>\n",
       "      <td>22.727626</td>\n",
       "      <td>20.317142</td>\n",
       "      <td>18.293499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbour County</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>27457</td>\n",
       "      <td>27457</td>\n",
       "      <td>27341</td>\n",
       "      <td>27226</td>\n",
       "      <td>27159</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.728132</td>\n",
       "      <td>-2.500690</td>\n",
       "      <td>-7.056824</td>\n",
       "      <td>-3.904217</td>\n",
       "      <td>-10.543299</td>\n",
       "      <td>-4.874741</td>\n",
       "      <td>-2.758113</td>\n",
       "      <td>-7.167664</td>\n",
       "      <td>-3.978583</td>\n",
       "      <td>-10.543299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bibb County</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>22915</td>\n",
       "      <td>22919</td>\n",
       "      <td>22861</td>\n",
       "      <td>22733</td>\n",
       "      <td>22642</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.527043</td>\n",
       "      <td>-5.068871</td>\n",
       "      <td>-6.201001</td>\n",
       "      <td>-0.177537</td>\n",
       "      <td>0.177258</td>\n",
       "      <td>-5.088389</td>\n",
       "      <td>-4.363636</td>\n",
       "      <td>-5.403729</td>\n",
       "      <td>0.754533</td>\n",
       "      <td>1.107861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blount County</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>57322</td>\n",
       "      <td>57322</td>\n",
       "      <td>57373</td>\n",
       "      <td>57711</td>\n",
       "      <td>57776</td>\n",
       "      <td>...</td>\n",
       "      <td>1.807375</td>\n",
       "      <td>-1.177622</td>\n",
       "      <td>-1.748766</td>\n",
       "      <td>-2.062535</td>\n",
       "      <td>-1.369970</td>\n",
       "      <td>1.859511</td>\n",
       "      <td>-0.848580</td>\n",
       "      <td>-1.402476</td>\n",
       "      <td>-1.577232</td>\n",
       "      <td>-0.884411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        SUMLEV  REGION  DIVISION  STATE  COUNTY  \\\n",
       "STNAME  CTYNAME                                                   \n",
       "Alabama Autauga County      50       3         6      1       1   \n",
       "        Baldwin County      50       3         6      1       3   \n",
       "        Barbour County      50       3         6      1       5   \n",
       "        Bibb County         50       3         6      1       7   \n",
       "        Blount County       50       3         6      1       9   \n",
       "\n",
       "                        CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010  \\\n",
       "STNAME  CTYNAME                                                             \n",
       "Alabama Autauga County          54571              54571            54660   \n",
       "        Baldwin County         182265             182265           183193   \n",
       "        Barbour County          27457              27457            27341   \n",
       "        Bibb County             22915              22919            22861   \n",
       "        Blount County           57322              57322            57373   \n",
       "\n",
       "                        POPESTIMATE2011  POPESTIMATE2012     ...       \\\n",
       "STNAME  CTYNAME                                              ...        \n",
       "Alabama Autauga County            55253            55175     ...        \n",
       "        Baldwin County           186659           190396     ...        \n",
       "        Barbour County            27226            27159     ...        \n",
       "        Bibb County               22733            22642     ...        \n",
       "        Blount County             57711            57776     ...        \n",
       "\n",
       "                        RDOMESTICMIG2011  RDOMESTICMIG2012  RDOMESTICMIG2013  \\\n",
       "STNAME  CTYNAME                                                                \n",
       "Alabama Autauga County          7.242091         -2.915927         -3.012349   \n",
       "        Baldwin County         14.832960         17.647293         21.845705   \n",
       "        Barbour County         -4.728132         -2.500690         -7.056824   \n",
       "        Bibb County            -5.527043         -5.068871         -6.201001   \n",
       "        Blount County           1.807375         -1.177622         -1.748766   \n",
       "\n",
       "                        RDOMESTICMIG2014  RDOMESTICMIG2015  RNETMIG2011  \\\n",
       "STNAME  CTYNAME                                                           \n",
       "Alabama Autauga County          2.265971         -2.530799     7.606016   \n",
       "        Baldwin County         19.243287         17.197872    15.844176   \n",
       "        Barbour County         -3.904217        -10.543299    -4.874741   \n",
       "        Bibb County            -0.177537          0.177258    -5.088389   \n",
       "        Blount County          -2.062535         -1.369970     1.859511   \n",
       "\n",
       "                        RNETMIG2012  RNETMIG2013  RNETMIG2014  RNETMIG2015  \n",
       "STNAME  CTYNAME                                                             \n",
       "Alabama Autauga County    -2.626146    -2.722002     2.592270    -2.187333  \n",
       "        Baldwin County    18.559627    22.727626    20.317142    18.293499  \n",
       "        Barbour County    -2.758113    -7.167664    -3.978583   -10.543299  \n",
       "        Bibb County       -4.363636    -5.403729     0.754533     1.107861  \n",
       "        Blount County     -0.848580    -1.402476    -1.577232    -0.884411  \n",
       "\n",
       "[5 rows x 98 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 不如上面的代码更r\n",
    "df = df[df['SUMLEV']==50]\n",
    "df.set_index(['STNAME','CTYNAME'], inplace=True)\n",
    "df.rename(columns={'ESTIMATESBASE2010': 'Estimates Base 2010'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def min_max(row):\n",
    "    data = row[['POPESTIMATE2010',\n",
    "                'POPESTIMATE2011',\n",
    "                'POPESTIMATE2012',\n",
    "                'POPESTIMATE2013',\n",
    "                'POPESTIMATE2014',\n",
    "                'POPESTIMATE2015']]\n",
    "    return pd.Series({'min': np.min(data), 'max': np.max(data)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Alabama</th>\n",
       "      <th>Autauga County</th>\n",
       "      <td>54660.0</td>\n",
       "      <td>55347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baldwin County</th>\n",
       "      <td>183193.0</td>\n",
       "      <td>203709.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbour County</th>\n",
       "      <td>26489.0</td>\n",
       "      <td>27341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bibb County</th>\n",
       "      <td>22512.0</td>\n",
       "      <td>22861.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blount County</th>\n",
       "      <td>57373.0</td>\n",
       "      <td>57776.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             min       max\n",
       "STNAME  CTYNAME                           \n",
       "Alabama Autauga County   54660.0   55347.0\n",
       "        Baldwin County  183193.0  203709.0\n",
       "        Barbour County   26489.0   27341.0\n",
       "        Bibb County      22512.0   22861.0\n",
       "        Blount County    57373.0   57776.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(min_max, axis=1).head()\n",
    "#min_max('POPESTIMATE2010').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>SUMLEV</th>\n",
       "      <th>REGION</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
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       "      <th>...</th>\n",
       "      <th>RDOMESTICMIG2013</th>\n",
       "      <th>RDOMESTICMIG2014</th>\n",
       "      <th>RDOMESTICMIG2015</th>\n",
       "      <th>RNETMIG2011</th>\n",
       "      <th>RNETMIG2012</th>\n",
       "      <th>RNETMIG2013</th>\n",
       "      <th>RNETMIG2014</th>\n",
       "      <th>RNETMIG2015</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Alabama</th>\n",
       "      <th>Autauga County</th>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54571.0</td>\n",
       "      <td>54571.0</td>\n",
       "      <td>54660.0</td>\n",
       "      <td>55253.0</td>\n",
       "      <td>55175.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.012349</td>\n",
       "      <td>2.265971</td>\n",
       "      <td>-2.530799</td>\n",
       "      <td>7.606016</td>\n",
       "      <td>-2.626146</td>\n",
       "      <td>-2.722002</td>\n",
       "      <td>2.592270</td>\n",
       "      <td>-2.187333</td>\n",
       "      <td>55347.0</td>\n",
       "      <td>54660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baldwin County</th>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>182265.0</td>\n",
       "      <td>182265.0</td>\n",
       "      <td>183193.0</td>\n",
       "      <td>186659.0</td>\n",
       "      <td>190396.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.845705</td>\n",
       "      <td>19.243287</td>\n",
       "      <td>17.197872</td>\n",
       "      <td>15.844176</td>\n",
       "      <td>18.559627</td>\n",
       "      <td>22.727626</td>\n",
       "      <td>20.317142</td>\n",
       "      <td>18.293499</td>\n",
       "      <td>203709.0</td>\n",
       "      <td>183193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbour County</th>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27457.0</td>\n",
       "      <td>27457.0</td>\n",
       "      <td>27341.0</td>\n",
       "      <td>27226.0</td>\n",
       "      <td>27159.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.056824</td>\n",
       "      <td>-3.904217</td>\n",
       "      <td>-10.543299</td>\n",
       "      <td>-4.874741</td>\n",
       "      <td>-2.758113</td>\n",
       "      <td>-7.167664</td>\n",
       "      <td>-3.978583</td>\n",
       "      <td>-10.543299</td>\n",
       "      <td>27341.0</td>\n",
       "      <td>26489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bibb County</th>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22915.0</td>\n",
       "      <td>22919.0</td>\n",
       "      <td>22861.0</td>\n",
       "      <td>22733.0</td>\n",
       "      <td>22642.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.201001</td>\n",
       "      <td>-0.177537</td>\n",
       "      <td>0.177258</td>\n",
       "      <td>-5.088389</td>\n",
       "      <td>-4.363636</td>\n",
       "      <td>-5.403729</td>\n",
       "      <td>0.754533</td>\n",
       "      <td>1.107861</td>\n",
       "      <td>22861.0</td>\n",
       "      <td>22512.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blount County</th>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>57322.0</td>\n",
       "      <td>57322.0</td>\n",
       "      <td>57373.0</td>\n",
       "      <td>57711.0</td>\n",
       "      <td>57776.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.748766</td>\n",
       "      <td>-2.062535</td>\n",
       "      <td>-1.369970</td>\n",
       "      <td>1.859511</td>\n",
       "      <td>-0.848580</td>\n",
       "      <td>-1.402476</td>\n",
       "      <td>-1.577232</td>\n",
       "      <td>-0.884411</td>\n",
       "      <td>57776.0</td>\n",
       "      <td>57373.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        SUMLEV  REGION  DIVISION  STATE  COUNTY  \\\n",
       "STNAME  CTYNAME                                                   \n",
       "Alabama Autauga County    50.0     3.0       6.0    1.0     1.0   \n",
       "        Baldwin County    50.0     3.0       6.0    1.0     3.0   \n",
       "        Barbour County    50.0     3.0       6.0    1.0     5.0   \n",
       "        Bibb County       50.0     3.0       6.0    1.0     7.0   \n",
       "        Blount County     50.0     3.0       6.0    1.0     9.0   \n",
       "\n",
       "                        CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010  \\\n",
       "STNAME  CTYNAME                                                             \n",
       "Alabama Autauga County        54571.0            54571.0          54660.0   \n",
       "        Baldwin County       182265.0           182265.0         183193.0   \n",
       "        Barbour County        27457.0            27457.0          27341.0   \n",
       "        Bibb County           22915.0            22919.0          22861.0   \n",
       "        Blount County         57322.0            57322.0          57373.0   \n",
       "\n",
       "                        POPESTIMATE2011  POPESTIMATE2012    ...     \\\n",
       "STNAME  CTYNAME                                             ...      \n",
       "Alabama Autauga County          55253.0          55175.0    ...      \n",
       "        Baldwin County         186659.0         190396.0    ...      \n",
       "        Barbour County          27226.0          27159.0    ...      \n",
       "        Bibb County             22733.0          22642.0    ...      \n",
       "        Blount County           57711.0          57776.0    ...      \n",
       "\n",
       "                        RDOMESTICMIG2013  RDOMESTICMIG2014  RDOMESTICMIG2015  \\\n",
       "STNAME  CTYNAME                                                                \n",
       "Alabama Autauga County         -3.012349          2.265971         -2.530799   \n",
       "        Baldwin County         21.845705         19.243287         17.197872   \n",
       "        Barbour County         -7.056824         -3.904217        -10.543299   \n",
       "        Bibb County            -6.201001         -0.177537          0.177258   \n",
       "        Blount County          -1.748766         -2.062535         -1.369970   \n",
       "\n",
       "                        RNETMIG2011  RNETMIG2012  RNETMIG2013  RNETMIG2014  \\\n",
       "STNAME  CTYNAME                                                              \n",
       "Alabama Autauga County     7.606016    -2.626146    -2.722002     2.592270   \n",
       "        Baldwin County    15.844176    18.559627    22.727626    20.317142   \n",
       "        Barbour County    -4.874741    -2.758113    -7.167664    -3.978583   \n",
       "        Bibb County       -5.088389    -4.363636    -5.403729     0.754533   \n",
       "        Blount County      1.859511    -0.848580    -1.402476    -1.577232   \n",
       "\n",
       "                        RNETMIG2015       max       min  \n",
       "STNAME  CTYNAME                                          \n",
       "Alabama Autauga County    -2.187333   55347.0   54660.0  \n",
       "        Baldwin County    18.293499  203709.0  183193.0  \n",
       "        Barbour County   -10.543299   27341.0   26489.0  \n",
       "        Bibb County        1.107861   22861.0   22512.0  \n",
       "        Blount County     -0.884411   57776.0   57373.0  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "def min_max(row):\n",
    "    data = row[['POPESTIMATE2010',\n",
    "                'POPESTIMATE2011',\n",
    "                'POPESTIMATE2012',\n",
    "                'POPESTIMATE2013',\n",
    "                'POPESTIMATE2014',\n",
    "                'POPESTIMATE2015']]\n",
    "    row['max'] = np.max(data)\n",
    "    row['min'] = np.min(data)\n",
    "    return row\n",
    "df.apply(min_max, axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "rows = ['POPESTIMATE2010',\n",
    "        'POPESTIMATE2011',\n",
    "        'POPESTIMATE2012',\n",
    "        'POPESTIMATE2013',\n",
    "        'POPESTIMATE2014',\n",
    "        'POPESTIMATE2015']\n",
    "df.apply(lambda x: np.max(x[rows]), axis=1)\n",
    "#上面的类型是： <class 'pandas.core.series.Series'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUMLEV</th>\n",
       "      <th>REGION</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>CENSUS2010POP</th>\n",
       "      <th>ESTIMATESBASE2010</th>\n",
       "      <th>POPESTIMATE2010</th>\n",
       "      <th>...</th>\n",
       "      <th>RDOMESTICMIG2011</th>\n",
       "      <th>RDOMESTICMIG2012</th>\n",
       "      <th>RDOMESTICMIG2013</th>\n",
       "      <th>RDOMESTICMIG2014</th>\n",
       "      <th>RDOMESTICMIG2015</th>\n",
       "      <th>RNETMIG2011</th>\n",
       "      <th>RNETMIG2012</th>\n",
       "      <th>RNETMIG2013</th>\n",
       "      <th>RNETMIG2014</th>\n",
       "      <th>RNETMIG2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>54571</td>\n",
       "      <td>54571</td>\n",
       "      <td>54660</td>\n",
       "      <td>...</td>\n",
       "      <td>7.242091</td>\n",
       "      <td>-2.915927</td>\n",
       "      <td>-3.012349</td>\n",
       "      <td>2.265971</td>\n",
       "      <td>-2.530799</td>\n",
       "      <td>7.606016</td>\n",
       "      <td>-2.626146</td>\n",
       "      <td>-2.722002</td>\n",
       "      <td>2.592270</td>\n",
       "      <td>-2.187333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>182265</td>\n",
       "      <td>182265</td>\n",
       "      <td>183193</td>\n",
       "      <td>...</td>\n",
       "      <td>14.832960</td>\n",
       "      <td>17.647293</td>\n",
       "      <td>21.845705</td>\n",
       "      <td>19.243287</td>\n",
       "      <td>17.197872</td>\n",
       "      <td>15.844176</td>\n",
       "      <td>18.559627</td>\n",
       "      <td>22.727626</td>\n",
       "      <td>20.317142</td>\n",
       "      <td>18.293499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>27457</td>\n",
       "      <td>27457</td>\n",
       "      <td>27341</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.728132</td>\n",
       "      <td>-2.500690</td>\n",
       "      <td>-7.056824</td>\n",
       "      <td>-3.904217</td>\n",
       "      <td>-10.543299</td>\n",
       "      <td>-4.874741</td>\n",
       "      <td>-2.758113</td>\n",
       "      <td>-7.167664</td>\n",
       "      <td>-3.978583</td>\n",
       "      <td>-10.543299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>22915</td>\n",
       "      <td>22919</td>\n",
       "      <td>22861</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.527043</td>\n",
       "      <td>-5.068871</td>\n",
       "      <td>-6.201001</td>\n",
       "      <td>-0.177537</td>\n",
       "      <td>0.177258</td>\n",
       "      <td>-5.088389</td>\n",
       "      <td>-4.363636</td>\n",
       "      <td>-5.403729</td>\n",
       "      <td>0.754533</td>\n",
       "      <td>1.107861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>57322</td>\n",
       "      <td>57322</td>\n",
       "      <td>57373</td>\n",
       "      <td>...</td>\n",
       "      <td>1.807375</td>\n",
       "      <td>-1.177622</td>\n",
       "      <td>-1.748766</td>\n",
       "      <td>-2.062535</td>\n",
       "      <td>-1.369970</td>\n",
       "      <td>1.859511</td>\n",
       "      <td>-0.848580</td>\n",
       "      <td>-1.402476</td>\n",
       "      <td>-1.577232</td>\n",
       "      <td>-0.884411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SUMLEV  REGION  DIVISION  STATE  COUNTY   STNAME         CTYNAME  \\\n",
       "1      50       3         6      1       1  Alabama  Autauga County   \n",
       "2      50       3         6      1       3  Alabama  Baldwin County   \n",
       "3      50       3         6      1       5  Alabama  Barbour County   \n",
       "4      50       3         6      1       7  Alabama     Bibb County   \n",
       "5      50       3         6      1       9  Alabama   Blount County   \n",
       "\n",
       "   CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010     ...       \\\n",
       "1          54571              54571            54660     ...        \n",
       "2         182265             182265           183193     ...        \n",
       "3          27457              27457            27341     ...        \n",
       "4          22915              22919            22861     ...        \n",
       "5          57322              57322            57373     ...        \n",
       "\n",
       "   RDOMESTICMIG2011  RDOMESTICMIG2012  RDOMESTICMIG2013  RDOMESTICMIG2014  \\\n",
       "1          7.242091         -2.915927         -3.012349          2.265971   \n",
       "2         14.832960         17.647293         21.845705         19.243287   \n",
       "3         -4.728132         -2.500690         -7.056824         -3.904217   \n",
       "4         -5.527043         -5.068871         -6.201001         -0.177537   \n",
       "5          1.807375         -1.177622         -1.748766         -2.062535   \n",
       "\n",
       "   RDOMESTICMIG2015  RNETMIG2011  RNETMIG2012  RNETMIG2013  RNETMIG2014  \\\n",
       "1         -2.530799     7.606016    -2.626146    -2.722002     2.592270   \n",
       "2         17.197872    15.844176    18.559627    22.727626    20.317142   \n",
       "3        -10.543299    -4.874741    -2.758113    -7.167664    -3.978583   \n",
       "4          0.177258    -5.088389    -4.363636    -5.403729     0.754533   \n",
       "5         -1.369970     1.859511    -0.848580    -1.402476    -1.577232   \n",
       "\n",
       "   RNETMIG2015  \n",
       "1    -2.187333  \n",
       "2    18.293499  \n",
       "3   -10.543299  \n",
       "4     1.107861  \n",
       "5    -0.884411  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('census.csv')\n",
    "df = df[df['SUMLEV']==50]\n",
    "\n",
    "df.head() #完整的有3142行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "Counties in state Alabama have an average population of 71339.34328358209\n",
      "22.1 ms ± 1.67 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 10 #对下面的代码计时，一共10个loop\n",
    "# 结果(完整版，不含head（））： 1.04 s ± 49.3 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
    "\n",
    "for state in df.head()['STNAME'].unique(): #不重复的  head()是我自己加的，源代码没有\n",
    "    avg = np.average(df.where(df['STNAME']==state).dropna()['CENSUS2010POP'])\n",
    "    print('Counties in state ' + state + ' have an average population of ' + str(avg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alabama \n",
      "\n",
      "    SUMLEV  REGION  DIVISION  STATE  COUNTY   STNAME            CTYNAME  \\\n",
      "1       50       3         6      1       1  Alabama     Autauga County   \n",
      "2       50       3         6      1       3  Alabama     Baldwin County   \n",
      "3       50       3         6      1       5  Alabama     Barbour County   \n",
      "4       50       3         6      1       7  Alabama        Bibb County   \n",
      "5       50       3         6      1       9  Alabama      Blount County   \n",
      "6       50       3         6      1      11  Alabama     Bullock County   \n",
      "7       50       3         6      1      13  Alabama      Butler County   \n",
      "8       50       3         6      1      15  Alabama     Calhoun County   \n",
      "9       50       3         6      1      17  Alabama    Chambers County   \n",
      "10      50       3         6      1      19  Alabama    Cherokee County   \n",
      "11      50       3         6      1      21  Alabama     Chilton County   \n",
      "12      50       3         6      1      23  Alabama     Choctaw County   \n",
      "13      50       3         6      1      25  Alabama      Clarke County   \n",
      "14      50       3         6      1      27  Alabama        Clay County   \n",
      "15      50       3         6      1      29  Alabama    Cleburne County   \n",
      "16      50       3         6      1      31  Alabama      Coffee County   \n",
      "17      50       3         6      1      33  Alabama     Colbert County   \n",
      "18      50       3         6      1      35  Alabama     Conecuh County   \n",
      "19      50       3         6      1      37  Alabama       Coosa County   \n",
      "20      50       3         6      1      39  Alabama   Covington County   \n",
      "21      50       3         6      1      41  Alabama    Crenshaw County   \n",
      "22      50       3         6      1      43  Alabama     Cullman County   \n",
      "23      50       3         6      1      45  Alabama        Dale County   \n",
      "24      50       3         6      1      47  Alabama      Dallas County   \n",
      "25      50       3         6      1      49  Alabama      DeKalb County   \n",
      "26      50       3         6      1      51  Alabama      Elmore County   \n",
      "27      50       3         6      1      53  Alabama    Escambia County   \n",
      "28      50       3         6      1      55  Alabama      Etowah County   \n",
      "29      50       3         6      1      57  Alabama     Fayette County   \n",
      "30      50       3         6      1      59  Alabama    Franklin County   \n",
      "..     ...     ...       ...    ...     ...      ...                ...   \n",
      "38      50       3         6      1      75  Alabama       Lamar County   \n",
      "39      50       3         6      1      77  Alabama  Lauderdale County   \n",
      "40      50       3         6      1      79  Alabama    Lawrence County   \n",
      "41      50       3         6      1      81  Alabama         Lee County   \n",
      "42      50       3         6      1      83  Alabama   Limestone County   \n",
      "43      50       3         6      1      85  Alabama     Lowndes County   \n",
      "44      50       3         6      1      87  Alabama       Macon County   \n",
      "45      50       3         6      1      89  Alabama     Madison County   \n",
      "46      50       3         6      1      91  Alabama     Marengo County   \n",
      "47      50       3         6      1      93  Alabama      Marion County   \n",
      "48      50       3         6      1      95  Alabama    Marshall County   \n",
      "49      50       3         6      1      97  Alabama      Mobile County   \n",
      "50      50       3         6      1      99  Alabama      Monroe County   \n",
      "51      50       3         6      1     101  Alabama  Montgomery County   \n",
      "52      50       3         6      1     103  Alabama      Morgan County   \n",
      "53      50       3         6      1     105  Alabama       Perry County   \n",
      "54      50       3         6      1     107  Alabama     Pickens County   \n",
      "55      50       3         6      1     109  Alabama        Pike County   \n",
      "56      50       3         6      1     111  Alabama    Randolph County   \n",
      "57      50       3         6      1     113  Alabama     Russell County   \n",
      "58      50       3         6      1     115  Alabama   St. Clair County   \n",
      "59      50       3         6      1     117  Alabama      Shelby County   \n",
      "60      50       3         6      1     119  Alabama      Sumter County   \n",
      "61      50       3         6      1     121  Alabama   Talladega County   \n",
      "62      50       3         6      1     123  Alabama  Tallapoosa County   \n",
      "63      50       3         6      1     125  Alabama  Tuscaloosa County   \n",
      "64      50       3         6      1     127  Alabama      Walker County   \n",
      "65      50       3         6      1     129  Alabama  Washington County   \n",
      "66      50       3         6      1     131  Alabama      Wilcox County   \n",
      "67      50       3         6      1     133  Alabama     Winston County   \n",
      "\n",
      "    CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010     ...       \\\n",
      "1           54571              54571            54660     ...        \n",
      "2          182265             182265           183193     ...        \n",
      "3           27457              27457            27341     ...        \n",
      "4           22915              22919            22861     ...        \n",
      "5           57322              57322            57373     ...        \n",
      "6           10914              10915            10887     ...        \n",
      "7           20947              20946            20944     ...        \n",
      "8          118572             118586           118437     ...        \n",
      "9           34215              34170            34098     ...        \n",
      "10          25989              25986            25976     ...        \n",
      "11          43643              43631            43665     ...        \n",
      "12          13859              13858            13841     ...        \n",
      "13          25833              25840            25767     ...        \n",
      "14          13932              13932            13880     ...        \n",
      "15          14972              14972            14973     ...        \n",
      "16          49948              49948            50177     ...        \n",
      "17          54428              54428            54514     ...        \n",
      "18          13228              13228            13208     ...        \n",
      "19          11539              11758            11758     ...        \n",
      "20          37765              37765            37796     ...        \n",
      "21          13906              13906            13853     ...        \n",
      "22          80406              80410            80473     ...        \n",
      "23          50251              50251            50358     ...        \n",
      "24          43820              43820            43803     ...        \n",
      "25          71109              71115            71142     ...        \n",
      "26          79303              79296            79465     ...        \n",
      "27          38319              38319            38309     ...        \n",
      "28         104430             104427           104442     ...        \n",
      "29          17241              17241            17231     ...        \n",
      "30          31704              31709            31734     ...        \n",
      "..            ...                ...              ...     ...        \n",
      "38          14564              14564            14495     ...        \n",
      "39          92709              92709            92747     ...        \n",
      "40          34339              34339            34305     ...        \n",
      "41         140247             140296           140782     ...        \n",
      "42          82782              82782            83163     ...        \n",
      "43          11299              11299            11275     ...        \n",
      "44          21452              21448            21545     ...        \n",
      "45         334811             334811           336138     ...        \n",
      "46          21027              21029            20942     ...        \n",
      "47          30776              30777            30800     ...        \n",
      "48          93019              93019            93146     ...        \n",
      "49         412992             413143           413332     ...        \n",
      "50          23068              23070            22982     ...        \n",
      "51         229363             229363           229723     ...        \n",
      "52         119490             119486           119603     ...        \n",
      "53          10591              10581            10540     ...        \n",
      "54          19746              19746            19716     ...        \n",
      "55          32899              32899            32957     ...        \n",
      "56          22913              22914            22891     ...        \n",
      "57          52947              52951            53297     ...        \n",
      "58          83593              83593            83812     ...        \n",
      "59         195085             195218           196048     ...        \n",
      "60          13763              13763            13740     ...        \n",
      "61          82291              82291            82071     ...        \n",
      "62          41616              41618            41502     ...        \n",
      "63         194656             194653           194977     ...        \n",
      "64          67023              67023            67004     ...        \n",
      "65          17581              17583            17610     ...        \n",
      "66          11670              11665            11557     ...        \n",
      "67          24484              24483            24401     ...        \n",
      "\n",
      "    RDOMESTICMIG2011  RDOMESTICMIG2012  RDOMESTICMIG2013  RDOMESTICMIG2014  \\\n",
      "1           7.242091         -2.915927         -3.012349          2.265971   \n",
      "2          14.832960         17.647293         21.845705         19.243287   \n",
      "3          -4.728132         -2.500690         -7.056824         -3.904217   \n",
      "4          -5.527043         -5.068871         -6.201001         -0.177537   \n",
      "5           1.807375         -1.177622         -1.748766         -2.062535   \n",
      "6         -30.953709         -5.180127         -1.130263         14.354290   \n",
      "7         -14.032727        -11.684234         -5.655413          1.085428   \n",
      "8          -6.155670         -4.611706         -5.524649         -4.463211   \n",
      "9          -2.731639          3.849092          2.872721         -2.287222   \n",
      "10          6.339327          1.113180          5.488706         -0.076806   \n",
      "11         -1.372935         -2.653369          0.480044          0.456017   \n",
      "12        -15.455274         -0.737028         -8.766391         -1.274984   \n",
      "13         -6.194363        -17.667705         -0.318345         -8.686428   \n",
      "14        -10.744102        -13.345130          4.902871          5.702648   \n",
      "15         -3.673524         -5.151880          7.345821          3.654485   \n",
      "16          0.377640          7.675579        -13.146535         -3.602859   \n",
      "17         -0.073423          1.065051          1.762390          1.835688   \n",
      "18         -4.861559         -7.504690         -6.107224        -14.645416   \n",
      "19        -33.930581        -10.291443         -4.313831        -22.958017   \n",
      "20          6.696899         -4.612668          0.740271          3.697932   \n",
      "21          1.729792          3.950156         -1.864936          3.084648   \n",
      "22         -1.404233         -1.019628          4.071247          5.087142   \n",
      "23        -10.749798         -5.277150        -15.236079        -11.979785   \n",
      "24        -15.635599        -11.308243        -16.745678         -9.344789   \n",
      "25          0.294677         -9.302391         -1.748807          0.267830   \n",
      "26          3.235576          0.822717          1.760531         -1.507057   \n",
      "27         -3.449988         -3.855889         -4.822706         -1.189831   \n",
      "28         -1.015919          2.062637         -1.931884         -1.726932   \n",
      "29         -5.015601         -0.646640         -3.725937          0.296745   \n",
      "30         -1.638750         -5.459394         -8.043702         -1.267849   \n",
      "..               ...               ...               ...               ...   \n",
      "38        -10.004516         -0.560538          0.281304         -4.674718   \n",
      "39          0.064737          1.640601          1.952440          5.113245   \n",
      "40         -5.530358         -7.785199         -6.088235         -1.850940   \n",
      "41         13.479880         16.576137         14.902891          9.198122   \n",
      "42         22.558084         16.312081         12.838340         14.976432   \n",
      "43        -13.575065        -28.401074        -11.493720        -16.071429   \n",
      "44        -11.725137        -38.373096        -28.414867        -20.642728   \n",
      "45          4.170416          3.438521          6.367708          4.064107   \n",
      "46        -12.484994        -15.404427        -11.313608         -4.808407   \n",
      "47          0.877663         -4.278110         -5.066373          1.420924   \n",
      "48          3.838505          0.287029         -1.039209         -2.246560   \n",
      "49         -5.242052         -3.722733         -3.181655         -2.311087   \n",
      "50         -8.697933         -6.613611        -17.389749        -10.669451   \n",
      "51          0.793549        -17.053618        -16.353778         -8.910671   \n",
      "52         -0.058443         -0.616520         -6.114295         -1.997409   \n",
      "53         -4.275534        -30.449493        -13.879939        -11.386538   \n",
      "54        -16.788228          0.723253          2.995868         47.451138   \n",
      "55         -6.316429          0.333293         11.688020        -21.462830   \n",
      "56         -2.232289         -6.867709          1.016395         -5.275290   \n",
      "57         24.111089         42.595881         22.802470         -1.728057   \n",
      "58          1.498849          6.643969          9.511583          1.551186   \n",
      "59          1.456731          7.783057          8.805327          5.037501   \n",
      "60        -16.368174         -4.006083         -4.990131        -13.135673   \n",
      "61         -2.318600          1.563340         -5.966492          1.230224   \n",
      "62         -0.337532         -6.705723          1.772834         -0.194404   \n",
      "63          3.983504          4.256278          5.261075          4.787490   \n",
      "64         -4.579296         -4.336533         -0.666096         -3.300481   \n",
      "65        -14.708407        -13.062030        -11.181733          1.067236   \n",
      "66         -7.029724        -10.239356        -12.314507        -13.835235   \n",
      "67          0.410332         -3.916234          2.441548          1.077609   \n",
      "\n",
      "    RDOMESTICMIG2015  RNETMIG2011  RNETMIG2012  RNETMIG2013  RNETMIG2014  \\\n",
      "1          -2.530799     7.606016    -2.626146    -2.722002     2.592270   \n",
      "2          17.197872    15.844176    18.559627    22.727626    20.317142   \n",
      "3         -10.543299    -4.874741    -2.758113    -7.167664    -3.978583   \n",
      "4           0.177258    -5.088389    -4.363636    -5.403729     0.754533   \n",
      "5          -1.369970     1.859511    -0.848580    -1.402476    -1.577232   \n",
      "6         -16.167247   -29.001673    -2.825524     1.507017    17.243790   \n",
      "7          -6.529805   -13.936612   -11.586865    -5.557058     1.184103   \n",
      "8          -3.376322    -5.791579    -4.092677    -5.062836    -3.912834   \n",
      "9           1.349468    -1.821092     4.701181     3.781439    -1.290228   \n",
      "10         -3.239866     6.416167     1.420264     5.757384     0.230419   \n",
      "11         -2.253483    -0.823761    -2.447504     0.868651     0.957636   \n",
      "12         -5.291205   -15.528177    -0.737028    -8.766391    -1.274984   \n",
      "13         -5.613667    -6.077488   -17.509958    -0.159172    -8.486280   \n",
      "14          3.912450   -10.816697   -13.345130     4.977157     5.776708   \n",
      "15         -3.123961    -3.673524    -5.151880     7.345821     3.654485   \n",
      "16          2.214774     2.166460    11.513368   -10.438741    -0.767822   \n",
      "17         -0.110260     0.513964     1.469035     2.276420     2.533249   \n",
      "18          2.684140    -4.861559    -7.504690    -6.107224   -14.645416   \n",
      "19         -5.387581   -34.017138   -10.380162    -4.403703   -23.049483   \n",
      "20         -0.316945     6.881460    -4.559952     0.793147     3.750759   \n",
      "21          3.439504     2.666763     5.099293    -0.502098     4.734577   \n",
      "22          7.915406    -1.031427    -0.634159     4.542916     5.593387   \n",
      "23         -5.107706    -9.575283    -0.776637   -12.640155    -9.503292   \n",
      "24        -14.687232   -15.727573   -11.378047   -16.792849    -9.368689   \n",
      "25          0.028141     1.375159    -8.656001    -1.029539     1.198187   \n",
      "26          2.067820     3.674511     1.558176     2.306047    -0.951175   \n",
      "27          1.190902    -3.397716    -3.803428    -4.769999    -1.136950   \n",
      "28         -2.082234    -0.632554     2.446383    -1.518596    -1.234901   \n",
      "29         -2.797536    -5.132243    -0.705426    -3.785079     0.237396   \n",
      "30         -2.401719     0.063029    -3.471291    -5.700261     1.553115   \n",
      "..               ...          ...          ...          ...          ...   \n",
      "38         -7.588774    -9.865564    -0.490471     0.351630    -4.603889   \n",
      "39         -4.782962     0.593424     2.039957     2.373132     5.597658   \n",
      "40         -7.604334    -5.501097    -7.667242    -5.969440    -1.731524   \n",
      "41          6.255238    17.060473    20.343441    18.496685    13.015964   \n",
      "42          6.824853    23.458985    16.971506    13.621581    15.934746   \n",
      "43         -7.804692   -13.485755   -28.310045   -11.401029   -15.883459   \n",
      "44        -10.599605   -12.005419   -38.421002   -28.464457   -20.642728   \n",
      "45          2.480087     5.739131     4.880766     7.818211     5.734524   \n",
      "46         -8.315905   -11.572629   -14.286756   -10.133058    -3.420413   \n",
      "47         -0.033105     1.527785    -3.853565    -4.605793     1.916595   \n",
      "48          0.423099     5.249877     1.009918    -0.148458    -1.155071   \n",
      "49         -3.873201    -3.751237    -2.281051    -1.753896    -0.759909   \n",
      "50         -5.967409    -8.697933    -6.613611   -17.389749   -10.669451   \n",
      "51         -5.820167     2.441356   -15.057012   -14.486528    -6.784461   \n",
      "52         -3.670246     0.993538     0.024994    -5.288490    -1.036313   \n",
      "53        -16.096786    -3.895487   -30.256162   -13.582511   -11.185006   \n",
      "54         24.852559   -16.788228     0.723253     2.995868    47.451138   \n",
      "55         -6.290069    -3.735196     3.211732    14.804825   -17.865707   \n",
      "56          9.959718    -2.407371    -7.131851     0.795439    -5.496941   \n",
      "57         -6.232617    24.222029    44.584517    23.569942    -1.107299   \n",
      "58          3.121490     2.093630     7.104209    10.013421     2.118411   \n",
      "59          5.986812     3.065733     8.755939     9.897345     6.359052   \n",
      "60        -12.981098   -15.120376    -2.225602    -3.053663   -10.958904   \n",
      "61         -4.231434    -1.879286     2.064097    -5.402922     1.919150   \n",
      "62         -5.978818     0.072328    -6.415222     2.088545     0.218704   \n",
      "63          1.884402     5.306232     5.202679     6.333332     6.057539   \n",
      "64         -3.087245    -4.085450    -3.869753    -0.181663    -2.692097   \n",
      "65         -0.950486   -13.849940   -12.075121   -10.122411     2.193763   \n",
      "66          0.090453    -7.029724   -10.239356   -12.314507   -13.835235   \n",
      "67         -6.669724     0.410332    -3.957457     2.400166     1.036162   \n",
      "\n",
      "    RNETMIG2015  \n",
      "1     -2.187333  \n",
      "2     18.293499  \n",
      "3    -10.543299  \n",
      "4      1.107861  \n",
      "5     -0.884411  \n",
      "6    -13.193961  \n",
      "7     -6.430868  \n",
      "8     -2.806406  \n",
      "9      2.346901  \n",
      "10    -2.931307  \n",
      "11    -1.752709  \n",
      "12    -5.291205  \n",
      "13    -5.411736  \n",
      "14     3.986270  \n",
      "15    -3.123961  \n",
      "16     5.350738  \n",
      "17     0.588052  \n",
      "18     2.684140  \n",
      "19    -5.387581  \n",
      "20    -0.264121  \n",
      "21     5.087600  \n",
      "22     8.417777  \n",
      "23    -1.998668  \n",
      "24   -14.711389  \n",
      "25     0.956790  \n",
      "26     2.757093  \n",
      "27     1.243830  \n",
      "28    -1.588308  \n",
      "29    -2.857058  \n",
      "30     0.442422  \n",
      "..          ...  \n",
      "38    -7.517182  \n",
      "39    -4.308975  \n",
      "40    -7.484108  \n",
      "41    10.134256  \n",
      "42     7.801399  \n",
      "43    -7.614334  \n",
      "44   -10.599605  \n",
      "45     4.180881  \n",
      "46    -6.921621  \n",
      "47     0.463469  \n",
      "48     1.533734  \n",
      "49    -2.288272  \n",
      "50    -5.967409  \n",
      "51    -3.598965  \n",
      "52    -2.700432  \n",
      "53   -15.891731  \n",
      "54    24.852559  \n",
      "55    -2.661183  \n",
      "56     9.738391  \n",
      "57    -5.193848  \n",
      "58     3.685893  \n",
      "59     7.307574  \n",
      "60   -10.855538  \n",
      "61    -3.540587  \n",
      "62    -5.515154  \n",
      "63     3.158710  \n",
      "64    -2.460626  \n",
      "65     0.118811  \n",
      "66     0.090453  \n",
      "67    -6.711409  \n",
      "\n",
      "[67 rows x 100 columns]\n",
      "Alaska \n",
      "\n",
      "    SUMLEV  REGION  DIVISION  STATE  COUNTY  STNAME  \\\n",
      "69      50       4         9      2      13  Alaska   \n",
      "70      50       4         9      2      16  Alaska   \n",
      "71      50       4         9      2      20  Alaska   \n",
      "72      50       4         9      2      50  Alaska   \n",
      "73      50       4         9      2      60  Alaska   \n",
      "74      50       4         9      2      68  Alaska   \n",
      "75      50       4         9      2      70  Alaska   \n",
      "76      50       4         9      2      90  Alaska   \n",
      "77      50       4         9      2     100  Alaska   \n",
      "78      50       4         9      2     105  Alaska   \n",
      "79      50       4         9      2     110  Alaska   \n",
      "80      50       4         9      2     122  Alaska   \n",
      "81      50       4         9      2     130  Alaska   \n",
      "82      50       4         9      2     150  Alaska   \n",
      "83      50       4         9      2     158  Alaska   \n",
      "84      50       4         9      2     164  Alaska   \n",
      "85      50       4         9      2     170  Alaska   \n",
      "86      50       4         9      2     180  Alaska   \n",
      "87      50       4         9      2     185  Alaska   \n",
      "88      50       4         9      2     188  Alaska   \n",
      "89      50       4         9      2     195  Alaska   \n",
      "90      50       4         9      2     198  Alaska   \n",
      "91      50       4         9      2     220  Alaska   \n",
      "92      50       4         9      2     230  Alaska   \n",
      "93      50       4         9      2     240  Alaska   \n",
      "94      50       4         9      2     261  Alaska   \n",
      "95      50       4         9      2     275  Alaska   \n",
      "96      50       4         9      2     282  Alaska   \n",
      "97      50       4         9      2     290  Alaska   \n",
      "\n",
      "                              CTYNAME  CENSUS2010POP  ESTIMATESBASE2010  \\\n",
      "69             Aleutians East Borough           3141               3141   \n",
      "70         Aleutians West Census Area           5561               5561   \n",
      "71             Anchorage Municipality         291826             291826   \n",
      "72                 Bethel Census Area          17013              17013   \n",
      "73                Bristol Bay Borough            997                997   \n",
      "74                     Denali Borough           1826               1826   \n",
      "75             Dillingham Census Area           4847               4847   \n",
      "76       Fairbanks North Star Borough          97581              97581   \n",
      "77                     Haines Borough           2508               2508   \n",
      "78          Hoonah-Angoon Census Area           2150               2147   \n",
      "79            Juneau City and Borough          31275              31275   \n",
      "80            Kenai Peninsula Borough          55400              55400   \n",
      "81          Ketchikan Gateway Borough          13477              13479   \n",
      "82              Kodiak Island Borough          13592              13606   \n",
      "83               Kusilvak Census Area           7459               7459   \n",
      "84         Lake and Peninsula Borough           1631               1635   \n",
      "85          Matanuska-Susitna Borough          88995              88995   \n",
      "86                   Nome Census Area           9492               9492   \n",
      "87                North Slope Borough           9430               9430   \n",
      "88           Northwest Arctic Borough           7523               7523   \n",
      "89                 Petersburg Borough           3815               3207   \n",
      "90  Prince of Wales-Hyder Census Area           5559               6172   \n",
      "91             Sitka City and Borough           8881               8881   \n",
      "92               Skagway Municipality            968                968   \n",
      "93    Southeast Fairbanks Census Area           7029               7029   \n",
      "94         Valdez-Cordova Census Area           9636               9636   \n",
      "95          Wrangell City and Borough           2369               2365   \n",
      "96           Yakutat City and Borough            662                662   \n",
      "97          Yukon-Koyukuk Census Area           5588               5588   \n",
      "\n",
      "    POPESTIMATE2010     ...       RDOMESTICMIG2011  RDOMESTICMIG2012  \\\n",
      "69             3169     ...              -5.604858        -12.221204   \n",
      "70             5546     ...             -11.496318          0.000000   \n",
      "71           293405     ...              -3.788123         -7.563712   \n",
      "72            17080     ...               0.057906         -3.926031   \n",
      "73             1002     ...              23.529412        -54.401583   \n",
      "74             1837     ...              -2.695418          2.647604   \n",
      "75             4856     ...               5.299093        -12.911035   \n",
      "76            98289     ...             -17.339069          5.430293   \n",
      "77             2501     ...              14.595661         -5.059350   \n",
      "78             2147     ...             -20.599251          5.621926   \n",
      "79            31388     ...              14.785919         -0.526080   \n",
      "80            55573     ...               7.860793          3.211689   \n",
      "81            13525     ...              -0.441826         -5.341333   \n",
      "82            13651     ...              -3.933280         -1.578532   \n",
      "83             7464     ...               0.926048         -6.345506   \n",
      "84             1637     ...               3.645200        -20.618557   \n",
      "85            89761     ...              13.050517         11.474315   \n",
      "86             9536     ...               9.395973        -15.023093   \n",
      "87             9468     ...              -7.999579         -3.438575   \n",
      "88             7545     ...              -1.178936        -16.833927   \n",
      "89             3217     ...              -4.952794         -5.536758   \n",
      "90             6212     ...              20.176344         -6.896552   \n",
      "91             8898     ...              -8.421289          3.339270   \n",
      "92              965     ...             -24.960998         22.575680   \n",
      "93             7070     ...              -4.643309        -12.705061   \n",
      "94             9659     ...              -3.300670        -10.381334   \n",
      "95             2370     ...              -0.841220          5.432511   \n",
      "96              662     ...             -31.987814         -6.134969   \n",
      "97             5588     ...               7.421150         -4.885709   \n",
      "\n",
      "    RDOMESTICMIG2013  RDOMESTICMIG2014  RDOMESTICMIG2015  RNETMIG2011  \\\n",
      "69        -17.257039        -27.748454        -16.221087    19.305620   \n",
      "70        -13.711875        -11.179039        -26.717891     1.257410   \n",
      "71         -3.962343        -18.897015        -19.781254    -0.969922   \n",
      "72         -9.731949        -13.525220        -14.441440     0.173717   \n",
      "73        -33.023736        -14.736842        -55.495103    23.529412   \n",
      "74          0.000000        -29.189471         -5.756149     8.625337   \n",
      "75         -8.644954        -11.216825        -11.210089     5.299093   \n",
      "76         -9.147023        -33.436804        -13.859157   -15.791483   \n",
      "77         -3.506721        -13.257945        -16.470588    20.118343   \n",
      "78         -5.599627        -39.688164         19.412879   -19.194757   \n",
      "79         -1.812930        -11.337521         -4.833208    16.893699   \n",
      "80         -4.349694          3.888133          3.370874     8.450352   \n",
      "81        -11.325442         -5.827930        -14.041470     2.282769   \n",
      "82        -16.758983        -28.109878        -28.319472    -0.218516   \n",
      "83          2.540973         -8.997188        -10.140984     1.058341   \n",
      "84         -1.213961        -32.307223        -45.112782     4.252734   \n",
      "85         12.906763         15.008086         18.697964    13.391923   \n",
      "86        -12.046363        -23.122560         -9.146806     9.705731   \n",
      "87         -4.329897        -21.180341        -13.917526    -6.525972   \n",
      "88        -19.691670        -13.579928        -11.870968    -0.392979   \n",
      "89        -14.463764        -39.533074        -12.891055    -1.857298   \n",
      "90         -1.878081         -7.981221         -4.243615    20.335213   \n",
      "91        -12.626682        -24.714829        -11.045987    -4.379070   \n",
      "92         10.994503         16.601562         24.821002   -22.880915   \n",
      "93        -35.389496        -16.163639        -32.568575    -1.829182   \n",
      "94         -7.282798        -45.265781        -23.017767    -1.444043   \n",
      "95          4.990643        -30.620805          6.748208    -0.841220   \n",
      "96        -35.686579        -14.173228        -44.907779   -22.848439   \n",
      "97        -17.029494        -22.095510         -6.669070     7.597844   \n",
      "\n",
      "    RNETMIG2012  RNETMIG2013  RNETMIG2014  RNETMIG2015  \n",
      "69     8.249313     5.752346    -3.920977     7.509763  \n",
      "70    13.314397     0.175793     4.890830   -10.826858  \n",
      "71    -2.652342     0.053320   -14.789980   -15.280810  \n",
      "72    -3.300142    -9.056901   -12.798659   -13.716580  \n",
      "73   -54.401583   -33.023736   -14.736842   -55.495103  \n",
      "74     9.001853     6.774362   -20.849622     2.616431  \n",
      "75   -12.709300    -8.443908   -11.016525   -11.009909  \n",
      "76    10.618791    -4.926084   -29.823633    -8.984834  \n",
      "77     1.167542     2.727450    -5.849093    -9.019608  \n",
      "78     7.027407    -4.199720   -38.270730    20.833333  \n",
      "79     2.506615     0.829646    -8.579746    -1.835396  \n",
      "80     3.864615    -3.788443     4.498378     3.958616  \n",
      "81    -1.170703    -7.379804    -1.602681    -9.894507  \n",
      "82     7.318648    -9.586706   -20.566467   -20.074563  \n",
      "83    -6.086506     2.795071    -8.747266    -9.893643  \n",
      "84   -20.012129    -0.606980   -31.088083   -43.859649  \n",
      "85    12.303913    13.528903    15.605525    19.349985  \n",
      "86   -14.820078   -11.843903   -22.919730    -8.943544  \n",
      "87    -1.667188    -2.577320   -19.123998   -11.855670  \n",
      "88   -16.056976   -18.914367   -12.674599   -10.967742  \n",
      "89    -1.537988    -9.847669   -33.929961    -7.231567  \n",
      "90    -6.739812    -1.721574    -7.824726    -4.086444  \n",
      "91     9.572573    -7.753226   -19.682398    -5.748422  \n",
      "92    25.654182    13.993003    19.531250    27.684964  \n",
      "93    -7.678883   -31.723652   -12.730654   -28.370848  \n",
      "94    -5.961558    -3.487537   -41.320598   -18.668788  \n",
      "95     5.432511     4.990643   -30.201342     7.169970  \n",
      "96    -4.601227   -34.134988   -11.023622   -41.700080  \n",
      "97    -4.536730   -16.678371   -21.739130    -6.308580  \n",
      "\n",
      "[29 rows x 100 columns]\n",
      "Arizona \n",
      "\n",
      "     SUMLEV  REGION  DIVISION  STATE  COUNTY   STNAME          CTYNAME  \\\n",
      "99       50       4         8      4       1  Arizona    Apache County   \n",
      "100      50       4         8      4       3  Arizona   Cochise County   \n",
      "101      50       4         8      4       5  Arizona  Coconino County   \n",
      "102      50       4         8      4       7  Arizona      Gila County   \n",
      "\n",
      "     CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010     ...       \\\n",
      "99           71518              71518            71766     ...        \n",
      "100         131346             131357           131809     ...        \n",
      "101         134421             134437           134626     ...        \n",
      "102          53597              53597            53544     ...        \n",
      "\n",
      "     RDOMESTICMIG2011  RDOMESTICMIG2012  RDOMESTICMIG2013  RDOMESTICMIG2014  \\\n",
      "99           1.775891          1.197250        -20.566049         -5.674114   \n",
      "100          3.353411        -15.621933        -22.883978        -23.290753   \n",
      "101        -12.402905          4.908203         -3.154921         -0.809483   \n",
      "102         -0.766233         -6.628859         -0.056574          4.125692   \n",
      "\n",
      "     RDOMESTICMIG2015  RNETMIG2011  RNETMIG2012  RNETMIG2013  RNETMIG2014  \\\n",
      "99         -11.302431     2.094996     1.500003   -20.220982    -5.326435   \n",
      "100        -13.596166     5.128302   -12.065659   -20.574165   -20.939882   \n",
      "101          2.992043   -11.569596     5.818774    -2.120401     0.444851   \n",
      "102          1.712168    -0.429838    -6.234508     0.396017     4.615501   \n",
      "\n",
      "     RNETMIG2015  \n",
      "99    -10.939637  \n",
      "100   -10.798115  \n",
      "101     4.256795  \n",
      "102     2.220173  \n",
      "\n",
      "[4 rows x 100 columns]\n"
     ]
    }
   ],
   "source": [
    "#%%timeit -n 10\n",
    "#结果（完整版，不含head（））： 20.6 ms ± 2.16 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
    "for group, frame in df.head(100).groupby('STNAME'): # head()是我自己加的，源代码没有\n",
    "    \n",
    "#上面：.groupby() 的第一个key是groupby所针对的那个column的值，即‘STNAME’的value。\n",
    "#     frame则是对应每个group值下的所有内容，包括所有行和列\n",
    "    print(group,'\\n') #另起一行，换行符：\\n\n",
    "    print(frame)\n",
    "#    avg = np.average(frame['CENSUS2010POP'])\n",
    "#    print('Counties in state ' + group + ' have an average population of ' + str(avg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>SUMLEV</th>\n",
       "      <th>REGION</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>CENSUS2010POP</th>\n",
       "      <th>ESTIMATESBASE2010</th>\n",
       "      <th>POPESTIMATE2010</th>\n",
       "      <th>...</th>\n",
       "      <th>RDOMESTICMIG2011</th>\n",
       "      <th>RDOMESTICMIG2012</th>\n",
       "      <th>RDOMESTICMIG2013</th>\n",
       "      <th>RDOMESTICMIG2014</th>\n",
       "      <th>RDOMESTICMIG2015</th>\n",
       "      <th>RNETMIG2011</th>\n",
       "      <th>RNETMIG2012</th>\n",
       "      <th>RNETMIG2013</th>\n",
       "      <th>RNETMIG2014</th>\n",
       "      <th>RNETMIG2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>54571</td>\n",
       "      <td>54571</td>\n",
       "      <td>54660</td>\n",
       "      <td>...</td>\n",
       "      <td>7.242091</td>\n",
       "      <td>-2.915927</td>\n",
       "      <td>-3.012349</td>\n",
       "      <td>2.265971</td>\n",
       "      <td>-2.530799</td>\n",
       "      <td>7.606016</td>\n",
       "      <td>-2.626146</td>\n",
       "      <td>-2.722002</td>\n",
       "      <td>2.592270</td>\n",
       "      <td>-2.187333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>182265</td>\n",
       "      <td>182265</td>\n",
       "      <td>183193</td>\n",
       "      <td>...</td>\n",
       "      <td>14.832960</td>\n",
       "      <td>17.647293</td>\n",
       "      <td>21.845705</td>\n",
       "      <td>19.243287</td>\n",
       "      <td>17.197872</td>\n",
       "      <td>15.844176</td>\n",
       "      <td>18.559627</td>\n",
       "      <td>22.727626</td>\n",
       "      <td>20.317142</td>\n",
       "      <td>18.293499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>27457</td>\n",
       "      <td>27457</td>\n",
       "      <td>27341</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.728132</td>\n",
       "      <td>-2.500690</td>\n",
       "      <td>-7.056824</td>\n",
       "      <td>-3.904217</td>\n",
       "      <td>-10.543299</td>\n",
       "      <td>-4.874741</td>\n",
       "      <td>-2.758113</td>\n",
       "      <td>-7.167664</td>\n",
       "      <td>-3.978583</td>\n",
       "      <td>-10.543299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>22915</td>\n",
       "      <td>22919</td>\n",
       "      <td>22861</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.527043</td>\n",
       "      <td>-5.068871</td>\n",
       "      <td>-6.201001</td>\n",
       "      <td>-0.177537</td>\n",
       "      <td>0.177258</td>\n",
       "      <td>-5.088389</td>\n",
       "      <td>-4.363636</td>\n",
       "      <td>-5.403729</td>\n",
       "      <td>0.754533</td>\n",
       "      <td>1.107861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>57322</td>\n",
       "      <td>57322</td>\n",
       "      <td>57373</td>\n",
       "      <td>...</td>\n",
       "      <td>1.807375</td>\n",
       "      <td>-1.177622</td>\n",
       "      <td>-1.748766</td>\n",
       "      <td>-2.062535</td>\n",
       "      <td>-1.369970</td>\n",
       "      <td>1.859511</td>\n",
       "      <td>-0.848580</td>\n",
       "      <td>-1.402476</td>\n",
       "      <td>-1.577232</td>\n",
       "      <td>-0.884411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SUMLEV  REGION  DIVISION  STATE  COUNTY   STNAME         CTYNAME  \\\n",
       "1      50       3         6      1       1  Alabama  Autauga County   \n",
       "2      50       3         6      1       3  Alabama  Baldwin County   \n",
       "3      50       3         6      1       5  Alabama  Barbour County   \n",
       "4      50       3         6      1       7  Alabama     Bibb County   \n",
       "5      50       3         6      1       9  Alabama   Blount County   \n",
       "\n",
       "   CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010     ...       \\\n",
       "1          54571              54571            54660     ...        \n",
       "2         182265             182265           183193     ...        \n",
       "3          27457              27457            27341     ...        \n",
       "4          22915              22919            22861     ...        \n",
       "5          57322              57322            57373     ...        \n",
       "\n",
       "   RDOMESTICMIG2011  RDOMESTICMIG2012  RDOMESTICMIG2013  RDOMESTICMIG2014  \\\n",
       "1          7.242091         -2.915927         -3.012349          2.265971   \n",
       "2         14.832960         17.647293         21.845705         19.243287   \n",
       "3         -4.728132         -2.500690         -7.056824         -3.904217   \n",
       "4         -5.527043         -5.068871         -6.201001         -0.177537   \n",
       "5          1.807375         -1.177622         -1.748766         -2.062535   \n",
       "\n",
       "   RDOMESTICMIG2015  RNETMIG2011  RNETMIG2012  RNETMIG2013  RNETMIG2014  \\\n",
       "1         -2.530799     7.606016    -2.626146    -2.722002     2.592270   \n",
       "2         17.197872    15.844176    18.559627    22.727626    20.317142   \n",
       "3        -10.543299    -4.874741    -2.758113    -7.167664    -3.978583   \n",
       "4          0.177258    -5.088389    -4.363636    -5.403729     0.754533   \n",
       "5         -1.369970     1.859511    -0.848580    -1.402476    -1.577232   \n",
       "\n",
       "   RNETMIG2015  \n",
       "1    -2.187333  \n",
       "2    18.293499  \n",
       "3   -10.543299  \n",
       "4     1.107861  \n",
       "5    -0.884411  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1177 records in group 0 for processing.\n",
      "There are 1134 records in group 1 for processing.\n",
      "There are 831 records in group 2 for processing.\n"
     ]
    }
   ],
   "source": [
    "df = df.set_index('STNAME')\n",
    "\n",
    "def fun(item):\n",
    "    if item[0]<'M':\n",
    "        return 0\n",
    "    if item[0]<'Q':\n",
    "        return 1\n",
    "    return 2\n",
    "\n",
    "for group, frame in df.groupby(fun):\n",
    "    print('There are ' + str(len(frame)) + ' records in group ' + str(group) + ' for processing.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('census.csv')\n",
    "df = df[df['SUMLEV']==50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>CENSUS2010POP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STNAME</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>71339.343284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>24490.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>426134.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>38878.906667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>642309.586207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>78581.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>446762.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>299311.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District of Columbia</th>\n",
       "      <td>601723.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>280616.567164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>60928.635220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>272060.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>35626.863636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>125790.509804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>70476.108696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>30771.262626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>27172.552381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>36161.391667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>70833.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>83022.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>240564.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>467687.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>119080.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>60964.655172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>36186.548780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>52077.626087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>17668.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>19638.075269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>158855.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>131647.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>418661.619048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>62399.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>312550.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>95354.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>12690.396226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>131096.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>48718.844156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>106418.722222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>189587.746269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>210513.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>100551.391304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>12336.060606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>66801.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>98998.271654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>95306.379310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>44695.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>60111.293233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>172424.102564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>33690.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>78985.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>24505.478261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      CENSUS2010POP\n",
       "STNAME                             \n",
       "Alabama                71339.343284\n",
       "Alaska                 24490.724138\n",
       "Arizona               426134.466667\n",
       "Arkansas               38878.906667\n",
       "California            642309.586207\n",
       "Colorado               78581.187500\n",
       "Connecticut           446762.125000\n",
       "Delaware              299311.333333\n",
       "District of Columbia  601723.000000\n",
       "Florida               280616.567164\n",
       "Georgia                60928.635220\n",
       "Hawaii                272060.200000\n",
       "Idaho                  35626.863636\n",
       "Illinois              125790.509804\n",
       "Indiana                70476.108696\n",
       "Iowa                   30771.262626\n",
       "Kansas                 27172.552381\n",
       "Kentucky               36161.391667\n",
       "Louisiana              70833.937500\n",
       "Maine                  83022.562500\n",
       "Maryland              240564.666667\n",
       "Massachusetts         467687.785714\n",
       "Michigan              119080.000000\n",
       "Minnesota              60964.655172\n",
       "Mississippi            36186.548780\n",
       "Missouri               52077.626087\n",
       "Montana                17668.125000\n",
       "Nebraska               19638.075269\n",
       "Nevada                158855.941176\n",
       "New Hampshire         131647.000000\n",
       "New Jersey            418661.619048\n",
       "New Mexico             62399.363636\n",
       "New York              312550.032258\n",
       "North Carolina         95354.830000\n",
       "North Dakota           12690.396226\n",
       "Ohio                  131096.636364\n",
       "Oklahoma               48718.844156\n",
       "Oregon                106418.722222\n",
       "Pennsylvania          189587.746269\n",
       "Rhode Island          210513.400000\n",
       "South Carolina        100551.391304\n",
       "South Dakota           12336.060606\n",
       "Tennessee              66801.105263\n",
       "Texas                  98998.271654\n",
       "Utah                   95306.379310\n",
       "Vermont                44695.785714\n",
       "Virginia               60111.293233\n",
       "Washington            172424.102564\n",
       "West Virginia          33690.800000\n",
       "Wisconsin              78985.916667\n",
       "Wyoming                24505.478261"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('STNAME').agg({'CENSUS2010POP': np.average}) #agg是aggregate的缩写，agg()内跟一个dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.groupby.DataFrameGroupBy'>\n",
      "<class 'pandas.core.groupby.groupby.SeriesGroupBy'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x1192e56a0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(df.groupby(level=0)['POPESTIMATE2010','POPESTIMATE2011']))\n",
    "print(type(df.groupby(level=0)['POPESTIMATE2010']))\n",
    "\n",
    "#运行结果：\n",
    "#<class 'pandas.core.groupby.groupby.DataFrameGroupBy'>\n",
    "#<class 'pandas.core.groupby.groupby.SeriesGroupBy'>\n",
    "\n",
    "df.groupby(level=1)['POPESTIMATE2010','POPESTIMATE2011']\n",
    "#返回错误：ValueError: level > 0 or level < -1 only valid with  MultiIndex\n",
    "#也就是说level = 0 就是针对单index的df，而上面改成level = 1，不符合实际情况了，所以报错\n",
    "\n",
    "df.groupby(level=0)['POPESTIMATE2010','POPESTIMATE2011']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiucat/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  \n"
     ]
    },
    {
     "data": {
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       "    }\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>avg</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>71339.343284</td>\n",
       "      <td>4779736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>24490.724138</td>\n",
       "      <td>710231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>426134.466667</td>\n",
       "      <td>6392017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>38878.906667</td>\n",
       "      <td>2915918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>642309.586207</td>\n",
       "      <td>37253956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>78581.187500</td>\n",
       "      <td>5029196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>446762.125000</td>\n",
       "      <td>3574097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>299311.333333</td>\n",
       "      <td>897934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District of Columbia</th>\n",
       "      <td>601723.000000</td>\n",
       "      <td>601723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>280616.567164</td>\n",
       "      <td>18801310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>60928.635220</td>\n",
       "      <td>9687653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>272060.200000</td>\n",
       "      <td>1360301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>35626.863636</td>\n",
       "      <td>1567582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>125790.509804</td>\n",
       "      <td>12830632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>70476.108696</td>\n",
       "      <td>6483802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>30771.262626</td>\n",
       "      <td>3046355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>27172.552381</td>\n",
       "      <td>2853118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>36161.391667</td>\n",
       "      <td>4339367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>70833.937500</td>\n",
       "      <td>4533372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>83022.562500</td>\n",
       "      <td>1328361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>240564.666667</td>\n",
       "      <td>5773552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>467687.785714</td>\n",
       "      <td>6547629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>119080.000000</td>\n",
       "      <td>9883640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>60964.655172</td>\n",
       "      <td>5303925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>36186.548780</td>\n",
       "      <td>2967297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>52077.626087</td>\n",
       "      <td>5988927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>17668.125000</td>\n",
       "      <td>989415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>19638.075269</td>\n",
       "      <td>1826341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>158855.941176</td>\n",
       "      <td>2700551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>131647.000000</td>\n",
       "      <td>1316470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>418661.619048</td>\n",
       "      <td>8791894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>62399.363636</td>\n",
       "      <td>2059179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>312550.032258</td>\n",
       "      <td>19378102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>95354.830000</td>\n",
       "      <td>9535483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>12690.396226</td>\n",
       "      <td>672591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>131096.636364</td>\n",
       "      <td>11536504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>48718.844156</td>\n",
       "      <td>3751351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>106418.722222</td>\n",
       "      <td>3831074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>189587.746269</td>\n",
       "      <td>12702379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>210513.400000</td>\n",
       "      <td>1052567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>100551.391304</td>\n",
       "      <td>4625364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>12336.060606</td>\n",
       "      <td>814180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>66801.105263</td>\n",
       "      <td>6346105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>98998.271654</td>\n",
       "      <td>25145561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>95306.379310</td>\n",
       "      <td>2763885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>44695.785714</td>\n",
       "      <td>625741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>60111.293233</td>\n",
       "      <td>7994802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>172424.102564</td>\n",
       "      <td>6724540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>33690.800000</td>\n",
       "      <td>1852994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>78985.916667</td>\n",
       "      <td>5686986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>24505.478261</td>\n",
       "      <td>563626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                avg       sum\n",
       "STNAME                                       \n",
       "Alabama                71339.343284   4779736\n",
       "Alaska                 24490.724138    710231\n",
       "Arizona               426134.466667   6392017\n",
       "Arkansas               38878.906667   2915918\n",
       "California            642309.586207  37253956\n",
       "Colorado               78581.187500   5029196\n",
       "Connecticut           446762.125000   3574097\n",
       "Delaware              299311.333333    897934\n",
       "District of Columbia  601723.000000    601723\n",
       "Florida               280616.567164  18801310\n",
       "Georgia                60928.635220   9687653\n",
       "Hawaii                272060.200000   1360301\n",
       "Idaho                  35626.863636   1567582\n",
       "Illinois              125790.509804  12830632\n",
       "Indiana                70476.108696   6483802\n",
       "Iowa                   30771.262626   3046355\n",
       "Kansas                 27172.552381   2853118\n",
       "Kentucky               36161.391667   4339367\n",
       "Louisiana              70833.937500   4533372\n",
       "Maine                  83022.562500   1328361\n",
       "Maryland              240564.666667   5773552\n",
       "Massachusetts         467687.785714   6547629\n",
       "Michigan              119080.000000   9883640\n",
       "Minnesota              60964.655172   5303925\n",
       "Mississippi            36186.548780   2967297\n",
       "Missouri               52077.626087   5988927\n",
       "Montana                17668.125000    989415\n",
       "Nebraska               19638.075269   1826341\n",
       "Nevada                158855.941176   2700551\n",
       "New Hampshire         131647.000000   1316470\n",
       "New Jersey            418661.619048   8791894\n",
       "New Mexico             62399.363636   2059179\n",
       "New York              312550.032258  19378102\n",
       "North Carolina         95354.830000   9535483\n",
       "North Dakota           12690.396226    672591\n",
       "Ohio                  131096.636364  11536504\n",
       "Oklahoma               48718.844156   3751351\n",
       "Oregon                106418.722222   3831074\n",
       "Pennsylvania          189587.746269  12702379\n",
       "Rhode Island          210513.400000   1052567\n",
       "South Carolina        100551.391304   4625364\n",
       "South Dakota           12336.060606    814180\n",
       "Tennessee              66801.105263   6346105\n",
       "Texas                  98998.271654  25145561\n",
       "Utah                   95306.379310   2763885\n",
       "Vermont                44695.785714    625741\n",
       "Virginia               60111.293233   7994802\n",
       "Washington            172424.102564   6724540\n",
       "West Virginia          33690.800000   1852994\n",
       "Wisconsin              78985.916667   5686986\n",
       "Wyoming                24505.478261    563626"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.set_index('STNAME').groupby(level=0)['CENSUS2010POP']\n",
    "    .agg({'avg': np.average, 'sum': np.sum}))\n",
    "#这个就是针对seires的groupby，SeriesGroupBy\n",
    "#因为groupby的结果只有一个column：'CENSUS2010POP'，所以名字直接被改掉了，改为 avg和sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiucat/anaconda3/lib/python3.6/site-packages/pandas/core/groupby/groupby.py:4658: FutureWarning: using a dict with renaming is deprecated and will be removed in a future version\n",
      "  return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">avg</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>POPESTIMATE2010</th>\n",
       "      <th>POPESTIMATE2011</th>\n",
       "      <th>POPESTIMATE2010</th>\n",
       "      <th>POPESTIMATE2011</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STNAME</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>71420.313433</td>\n",
       "      <td>71658.328358</td>\n",
       "      <td>4785161</td>\n",
       "      <td>4801108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>24621.413793</td>\n",
       "      <td>24921.379310</td>\n",
       "      <td>714021</td>\n",
       "      <td>722720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>427213.866667</td>\n",
       "      <td>431248.800000</td>\n",
       "      <td>6408208</td>\n",
       "      <td>6468732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>38965.253333</td>\n",
       "      <td>39180.506667</td>\n",
       "      <td>2922394</td>\n",
       "      <td>2938538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>643691.017241</td>\n",
       "      <td>650000.586207</td>\n",
       "      <td>37334079</td>\n",
       "      <td>37700034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>78878.968750</td>\n",
       "      <td>79991.875000</td>\n",
       "      <td>5048254</td>\n",
       "      <td>5119480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>447464.625000</td>\n",
       "      <td>448719.875000</td>\n",
       "      <td>3579717</td>\n",
       "      <td>3589759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>299930.333333</td>\n",
       "      <td>302638.666667</td>\n",
       "      <td>899791</td>\n",
       "      <td>907916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District of Columbia</th>\n",
       "      <td>605126.000000</td>\n",
       "      <td>620472.000000</td>\n",
       "      <td>605126</td>\n",
       "      <td>620472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>281341.641791</td>\n",
       "      <td>285157.208955</td>\n",
       "      <td>18849890</td>\n",
       "      <td>19105533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>61090.905660</td>\n",
       "      <td>61712.452830</td>\n",
       "      <td>9713454</td>\n",
       "      <td>9812280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>272796.000000</td>\n",
       "      <td>275645.400000</td>\n",
       "      <td>1363980</td>\n",
       "      <td>1378227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>35704.227273</td>\n",
       "      <td>36003.045455</td>\n",
       "      <td>1570986</td>\n",
       "      <td>1584134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>125894.598039</td>\n",
       "      <td>126096.882353</td>\n",
       "      <td>12841249</td>\n",
       "      <td>12861882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>70549.891304</td>\n",
       "      <td>70835.271739</td>\n",
       "      <td>6490590</td>\n",
       "      <td>6516845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>30815.090909</td>\n",
       "      <td>30963.525253</td>\n",
       "      <td>3050694</td>\n",
       "      <td>3065389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>27226.895238</td>\n",
       "      <td>27332.542857</td>\n",
       "      <td>2858824</td>\n",
       "      <td>2869917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>36232.808333</td>\n",
       "      <td>36399.016667</td>\n",
       "      <td>4347937</td>\n",
       "      <td>4367882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>71014.859375</td>\n",
       "      <td>71490.328125</td>\n",
       "      <td>4544951</td>\n",
       "      <td>4575381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>82980.937500</td>\n",
       "      <td>83016.062500</td>\n",
       "      <td>1327695</td>\n",
       "      <td>1328257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>241183.708333</td>\n",
       "      <td>243507.125000</td>\n",
       "      <td>5788409</td>\n",
       "      <td>5844171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>468931.142857</td>\n",
       "      <td>472271.214286</td>\n",
       "      <td>6565036</td>\n",
       "      <td>6611797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>119004.445783</td>\n",
       "      <td>118995.048193</td>\n",
       "      <td>9877369</td>\n",
       "      <td>9876589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>61044.862069</td>\n",
       "      <td>61472.632184</td>\n",
       "      <td>5310903</td>\n",
       "      <td>5348119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>36223.365854</td>\n",
       "      <td>36317.060976</td>\n",
       "      <td>2970316</td>\n",
       "      <td>2977999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>52139.582609</td>\n",
       "      <td>52265.973913</td>\n",
       "      <td>5996052</td>\n",
       "      <td>6010587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>17690.053571</td>\n",
       "      <td>17816.892857</td>\n",
       "      <td>990643</td>\n",
       "      <td>997746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>19677.688172</td>\n",
       "      <td>19810.569892</td>\n",
       "      <td>1830025</td>\n",
       "      <td>1842383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>159025.882353</td>\n",
       "      <td>159930.529412</td>\n",
       "      <td>2703440</td>\n",
       "      <td>2718819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>131670.800000</td>\n",
       "      <td>131834.400000</td>\n",
       "      <td>1316708</td>\n",
       "      <td>1318344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>419232.428571</td>\n",
       "      <td>421092.095238</td>\n",
       "      <td>8803881</td>\n",
       "      <td>8842934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>62567.909091</td>\n",
       "      <td>62976.545455</td>\n",
       "      <td>2064741</td>\n",
       "      <td>2078226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>312950.322581</td>\n",
       "      <td>314890.354839</td>\n",
       "      <td>19402920</td>\n",
       "      <td>19523202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>95589.790000</td>\n",
       "      <td>96510.250000</td>\n",
       "      <td>9558979</td>\n",
       "      <td>9651025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>12726.981132</td>\n",
       "      <td>12930.679245</td>\n",
       "      <td>674530</td>\n",
       "      <td>685326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>131145.068182</td>\n",
       "      <td>131198.204545</td>\n",
       "      <td>11540766</td>\n",
       "      <td>11545442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>48825.922078</td>\n",
       "      <td>49176.961039</td>\n",
       "      <td>3759596</td>\n",
       "      <td>3786626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>106610.333333</td>\n",
       "      <td>107458.583333</td>\n",
       "      <td>3837972</td>\n",
       "      <td>3868509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>189731.552239</td>\n",
       "      <td>190226.895522</td>\n",
       "      <td>12712014</td>\n",
       "      <td>12745202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>210643.800000</td>\n",
       "      <td>210371.200000</td>\n",
       "      <td>1053219</td>\n",
       "      <td>1051856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>100780.304348</td>\n",
       "      <td>101581.152174</td>\n",
       "      <td>4635894</td>\n",
       "      <td>4672733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>12368.166667</td>\n",
       "      <td>12489.227273</td>\n",
       "      <td>816299</td>\n",
       "      <td>824289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>66911.421053</td>\n",
       "      <td>67351.663158</td>\n",
       "      <td>6356585</td>\n",
       "      <td>6398408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>99387.255906</td>\n",
       "      <td>101001.826772</td>\n",
       "      <td>25244363</td>\n",
       "      <td>25654464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>95704.344828</td>\n",
       "      <td>97118.620690</td>\n",
       "      <td>2775426</td>\n",
       "      <td>2816440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>44713.142857</td>\n",
       "      <td>44763.357143</td>\n",
       "      <td>625984</td>\n",
       "      <td>626687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>60344.263158</td>\n",
       "      <td>60983.330827</td>\n",
       "      <td>8025787</td>\n",
       "      <td>8110783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>172898.974359</td>\n",
       "      <td>174954.589744</td>\n",
       "      <td>6743060</td>\n",
       "      <td>6823229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>33713.181818</td>\n",
       "      <td>33726.327273</td>\n",
       "      <td>1854225</td>\n",
       "      <td>1854948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>79030.611111</td>\n",
       "      <td>79301.666667</td>\n",
       "      <td>5690204</td>\n",
       "      <td>5709720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>24544.173913</td>\n",
       "      <td>24685.565217</td>\n",
       "      <td>564516</td>\n",
       "      <td>567768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 avg                             sum  \\\n",
       "                     POPESTIMATE2010 POPESTIMATE2011 POPESTIMATE2010   \n",
       "STNAME                                                                 \n",
       "Alabama                 71420.313433    71658.328358         4785161   \n",
       "Alaska                  24621.413793    24921.379310          714021   \n",
       "Arizona                427213.866667   431248.800000         6408208   \n",
       "Arkansas                38965.253333    39180.506667         2922394   \n",
       "California             643691.017241   650000.586207        37334079   \n",
       "Colorado                78878.968750    79991.875000         5048254   \n",
       "Connecticut            447464.625000   448719.875000         3579717   \n",
       "Delaware               299930.333333   302638.666667          899791   \n",
       "District of Columbia   605126.000000   620472.000000          605126   \n",
       "Florida                281341.641791   285157.208955        18849890   \n",
       "Georgia                 61090.905660    61712.452830         9713454   \n",
       "Hawaii                 272796.000000   275645.400000         1363980   \n",
       "Idaho                   35704.227273    36003.045455         1570986   \n",
       "Illinois               125894.598039   126096.882353        12841249   \n",
       "Indiana                 70549.891304    70835.271739         6490590   \n",
       "Iowa                    30815.090909    30963.525253         3050694   \n",
       "Kansas                  27226.895238    27332.542857         2858824   \n",
       "Kentucky                36232.808333    36399.016667         4347937   \n",
       "Louisiana               71014.859375    71490.328125         4544951   \n",
       "Maine                   82980.937500    83016.062500         1327695   \n",
       "Maryland               241183.708333   243507.125000         5788409   \n",
       "Massachusetts          468931.142857   472271.214286         6565036   \n",
       "Michigan               119004.445783   118995.048193         9877369   \n",
       "Minnesota               61044.862069    61472.632184         5310903   \n",
       "Mississippi             36223.365854    36317.060976         2970316   \n",
       "Missouri                52139.582609    52265.973913         5996052   \n",
       "Montana                 17690.053571    17816.892857          990643   \n",
       "Nebraska                19677.688172    19810.569892         1830025   \n",
       "Nevada                 159025.882353   159930.529412         2703440   \n",
       "New Hampshire          131670.800000   131834.400000         1316708   \n",
       "New Jersey             419232.428571   421092.095238         8803881   \n",
       "New Mexico              62567.909091    62976.545455         2064741   \n",
       "New York               312950.322581   314890.354839        19402920   \n",
       "North Carolina          95589.790000    96510.250000         9558979   \n",
       "North Dakota            12726.981132    12930.679245          674530   \n",
       "Ohio                   131145.068182   131198.204545        11540766   \n",
       "Oklahoma                48825.922078    49176.961039         3759596   \n",
       "Oregon                 106610.333333   107458.583333         3837972   \n",
       "Pennsylvania           189731.552239   190226.895522        12712014   \n",
       "Rhode Island           210643.800000   210371.200000         1053219   \n",
       "South Carolina         100780.304348   101581.152174         4635894   \n",
       "South Dakota            12368.166667    12489.227273          816299   \n",
       "Tennessee               66911.421053    67351.663158         6356585   \n",
       "Texas                   99387.255906   101001.826772        25244363   \n",
       "Utah                    95704.344828    97118.620690         2775426   \n",
       "Vermont                 44713.142857    44763.357143          625984   \n",
       "Virginia                60344.263158    60983.330827         8025787   \n",
       "Washington             172898.974359   174954.589744         6743060   \n",
       "West Virginia           33713.181818    33726.327273         1854225   \n",
       "Wisconsin               79030.611111    79301.666667         5690204   \n",
       "Wyoming                 24544.173913    24685.565217          564516   \n",
       "\n",
       "                                      \n",
       "                     POPESTIMATE2011  \n",
       "STNAME                                \n",
       "Alabama                      4801108  \n",
       "Alaska                        722720  \n",
       "Arizona                      6468732  \n",
       "Arkansas                     2938538  \n",
       "California                  37700034  \n",
       "Colorado                     5119480  \n",
       "Connecticut                  3589759  \n",
       "Delaware                      907916  \n",
       "District of Columbia          620472  \n",
       "Florida                     19105533  \n",
       "Georgia                      9812280  \n",
       "Hawaii                       1378227  \n",
       "Idaho                        1584134  \n",
       "Illinois                    12861882  \n",
       "Indiana                      6516845  \n",
       "Iowa                         3065389  \n",
       "Kansas                       2869917  \n",
       "Kentucky                     4367882  \n",
       "Louisiana                    4575381  \n",
       "Maine                        1328257  \n",
       "Maryland                     5844171  \n",
       "Massachusetts                6611797  \n",
       "Michigan                     9876589  \n",
       "Minnesota                    5348119  \n",
       "Mississippi                  2977999  \n",
       "Missouri                     6010587  \n",
       "Montana                       997746  \n",
       "Nebraska                     1842383  \n",
       "Nevada                       2718819  \n",
       "New Hampshire                1318344  \n",
       "New Jersey                   8842934  \n",
       "New Mexico                   2078226  \n",
       "New York                    19523202  \n",
       "North Carolina               9651025  \n",
       "North Dakota                  685326  \n",
       "Ohio                        11545442  \n",
       "Oklahoma                     3786626  \n",
       "Oregon                       3868509  \n",
       "Pennsylvania                12745202  \n",
       "Rhode Island                 1051856  \n",
       "South Carolina               4672733  \n",
       "South Dakota                  824289  \n",
       "Tennessee                    6398408  \n",
       "Texas                       25654464  \n",
       "Utah                         2816440  \n",
       "Vermont                       626687  \n",
       "Virginia                     8110783  \n",
       "Washington                   6823229  \n",
       "West Virginia                1854948  \n",
       "Wisconsin                    5709720  \n",
       "Wyoming                       567768  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.set_index('STNAME').groupby(level=0)['POPESTIMATE2010','POPESTIMATE2011']\n",
    "    .agg({'avg': np.average, 'sum': np.sum})) #重新命名：avg，和sum，在结果中是以两级column name的形式呈现的\n",
    "#因为groupby针对的是两个column：'POPESTIMATE2010','POPESTIMATE2011'，所以计算avg和sum的同时，不能丢掉这两个名字，\n",
    "#所以column name变成了两层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\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>POPESTIMATE2010</th>\n",
       "      <th>POPESTIMATE2011</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>71420.313433</td>\n",
       "      <td>4801108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>24621.413793</td>\n",
       "      <td>722720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>427213.866667</td>\n",
       "      <td>6468732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>38965.253333</td>\n",
       "      <td>2938538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>643691.017241</td>\n",
       "      <td>37700034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>78878.968750</td>\n",
       "      <td>5119480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>447464.625000</td>\n",
       "      <td>3589759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>299930.333333</td>\n",
       "      <td>907916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District of Columbia</th>\n",
       "      <td>605126.000000</td>\n",
       "      <td>620472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>281341.641791</td>\n",
       "      <td>19105533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>61090.905660</td>\n",
       "      <td>9812280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>272796.000000</td>\n",
       "      <td>1378227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>35704.227273</td>\n",
       "      <td>1584134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>125894.598039</td>\n",
       "      <td>12861882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>70549.891304</td>\n",
       "      <td>6516845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>30815.090909</td>\n",
       "      <td>3065389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>27226.895238</td>\n",
       "      <td>2869917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>36232.808333</td>\n",
       "      <td>4367882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>71014.859375</td>\n",
       "      <td>4575381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>82980.937500</td>\n",
       "      <td>1328257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>241183.708333</td>\n",
       "      <td>5844171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>468931.142857</td>\n",
       "      <td>6611797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>119004.445783</td>\n",
       "      <td>9876589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>61044.862069</td>\n",
       "      <td>5348119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>36223.365854</td>\n",
       "      <td>2977999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>52139.582609</td>\n",
       "      <td>6010587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>17690.053571</td>\n",
       "      <td>997746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>19677.688172</td>\n",
       "      <td>1842383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>159025.882353</td>\n",
       "      <td>2718819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>131670.800000</td>\n",
       "      <td>1318344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>419232.428571</td>\n",
       "      <td>8842934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>62567.909091</td>\n",
       "      <td>2078226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>312950.322581</td>\n",
       "      <td>19523202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>95589.790000</td>\n",
       "      <td>9651025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>12726.981132</td>\n",
       "      <td>685326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>131145.068182</td>\n",
       "      <td>11545442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>48825.922078</td>\n",
       "      <td>3786626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>106610.333333</td>\n",
       "      <td>3868509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>189731.552239</td>\n",
       "      <td>12745202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>210643.800000</td>\n",
       "      <td>1051856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>100780.304348</td>\n",
       "      <td>4672733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>12368.166667</td>\n",
       "      <td>824289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>66911.421053</td>\n",
       "      <td>6398408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>99387.255906</td>\n",
       "      <td>25654464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>95704.344828</td>\n",
       "      <td>2816440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>44713.142857</td>\n",
       "      <td>626687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>60344.263158</td>\n",
       "      <td>8110783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>172898.974359</td>\n",
       "      <td>6823229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>33713.181818</td>\n",
       "      <td>1854948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>79030.611111</td>\n",
       "      <td>5709720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>24544.173913</td>\n",
       "      <td>567768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      POPESTIMATE2010  POPESTIMATE2011\n",
       "STNAME                                                \n",
       "Alabama                  71420.313433          4801108\n",
       "Alaska                   24621.413793           722720\n",
       "Arizona                 427213.866667          6468732\n",
       "Arkansas                 38965.253333          2938538\n",
       "California              643691.017241         37700034\n",
       "Colorado                 78878.968750          5119480\n",
       "Connecticut             447464.625000          3589759\n",
       "Delaware                299930.333333           907916\n",
       "District of Columbia    605126.000000           620472\n",
       "Florida                 281341.641791         19105533\n",
       "Georgia                  61090.905660          9812280\n",
       "Hawaii                  272796.000000          1378227\n",
       "Idaho                    35704.227273          1584134\n",
       "Illinois                125894.598039         12861882\n",
       "Indiana                  70549.891304          6516845\n",
       "Iowa                     30815.090909          3065389\n",
       "Kansas                   27226.895238          2869917\n",
       "Kentucky                 36232.808333          4367882\n",
       "Louisiana                71014.859375          4575381\n",
       "Maine                    82980.937500          1328257\n",
       "Maryland                241183.708333          5844171\n",
       "Massachusetts           468931.142857          6611797\n",
       "Michigan                119004.445783          9876589\n",
       "Minnesota                61044.862069          5348119\n",
       "Mississippi              36223.365854          2977999\n",
       "Missouri                 52139.582609          6010587\n",
       "Montana                  17690.053571           997746\n",
       "Nebraska                 19677.688172          1842383\n",
       "Nevada                  159025.882353          2718819\n",
       "New Hampshire           131670.800000          1318344\n",
       "New Jersey              419232.428571          8842934\n",
       "New Mexico               62567.909091          2078226\n",
       "New York                312950.322581         19523202\n",
       "North Carolina           95589.790000          9651025\n",
       "North Dakota             12726.981132           685326\n",
       "Ohio                    131145.068182         11545442\n",
       "Oklahoma                 48825.922078          3786626\n",
       "Oregon                  106610.333333          3868509\n",
       "Pennsylvania            189731.552239         12745202\n",
       "Rhode Island            210643.800000          1051856\n",
       "South Carolina          100780.304348          4672733\n",
       "South Dakota             12368.166667           824289\n",
       "Tennessee                66911.421053          6398408\n",
       "Texas                    99387.255906         25654464\n",
       "Utah                     95704.344828          2816440\n",
       "Vermont                  44713.142857           626687\n",
       "Virginia                 60344.263158          8110783\n",
       "Washington              172898.974359          6823229\n",
       "West Virginia            33713.181818          1854948\n",
       "Wisconsin                79030.611111          5709720\n",
       "Wyoming                  24544.173913           567768"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.set_index('STNAME').groupby(level=0)['POPESTIMATE2010','POPESTIMATE2011']\n",
    "    .agg({'POPESTIMATE2010': np.average, 'POPESTIMATE2011': np.sum}))\n",
    "#上面：针对dataframe，没有renaming，所以没有提示futurewarning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Grades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ok</th>\n",
       "      <td>C+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ok</th>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ok</th>\n",
       "      <td>C-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>D+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Grades\n",
       "excellent     A+\n",
       "excellent      A\n",
       "excellent     A-\n",
       "good          B+\n",
       "good           B\n",
       "good          B-\n",
       "ok            C+\n",
       "ok             C\n",
       "ok            C-\n",
       "poor          D+\n",
       "poor           D"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(['A+', 'A', 'A-', 'B+', 'B', 'B-', 'C+', 'C', 'C-', 'D+', 'D'],\n",
    "                  index=['excellent', 'excellent', 'excellent', 'good', 'good', 'good', 'ok', 'ok', 'ok', 'poor', 'poor'])\n",
    "df.rename(columns={0: 'Grades'}, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "excellent    A+\n",
       "excellent     A\n",
       "excellent    A-\n",
       "good         B+\n",
       "good          B\n",
       "Name: Grades, dtype: category\n",
       "Categories (11, object): [A, A+, A-, B, ..., C+, C-, D, D+]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Grades'].astype('category').head() #将Grades的类型设置为category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiucat/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "excellent    A+\n",
       "excellent     A\n",
       "excellent    A-\n",
       "good         B+\n",
       "good          B\n",
       "Name: Grades, dtype: category\n",
       "Categories (11, object): [D < D+ < C- < C ... B+ < A- < A < A+]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#正确的order：\n",
    "#Categories (11, object): [D < D+ < C- < C ... B+ < A- < A < A+]\n",
    "\n",
    "grades = df['Grades'].astype('category',\n",
    "                             categories=['D', 'D+', 'C-', 'C', 'C+', 'B-', 'B', 'B+', 'A-', 'A', 'A+'],\n",
    "                             ordered=True)\n",
    "grades.head()\n",
    "\n",
    "#FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; \n",
    "#pass a CategoricalDtype instead\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiucat/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "excellent    A+\n",
       "excellent     A\n",
       "excellent    A-\n",
       "good         B+\n",
       "good          B\n",
       "Name: Grades, dtype: category\n",
       "Categories (11, object): [A < A+ < A- < B ... C+ < C- < D < D+]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据上面的futurewarning提示，将代码改成直接pass CategoricalDtype()数据：\n",
    "'''下面代码有问题，没有成功排序ordered，错误的order如下：'''\n",
    "# Categories (11, object): [A < A+ < A- < B ... C+ < C- < D < D+]\n",
    "\n",
    "grades = df['Grades'].astype('category',\n",
    "                             pd.api.types.CategoricalDtype(categories=['D', 'D+', 'C-', 'C', 'C+', 'B-', 'B', 'B+', 'A-', 'A', 'A+']\n",
    "                             ),ordered=True)#ordered=True\n",
    "grades.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "excellent     True\n",
       "excellent     True\n",
       "excellent     True\n",
       "good          True\n",
       "good          True\n",
       "good          True\n",
       "ok            True\n",
       "ok           False\n",
       "ok           False\n",
       "poor         False\n",
       "poor         False\n",
       "Name: Grades, dtype: bool"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades > 'C'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiucat/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "STNAME\n",
       "Alabama                   (11706.087, 75333.413]\n",
       "Alaska                    (11706.087, 75333.413]\n",
       "Arizona                 (390320.176, 453317.529]\n",
       "Arkansas                  (11706.087, 75333.413]\n",
       "California              (579312.234, 642309.586]\n",
       "Colorado                 (75333.413, 138330.766]\n",
       "Connecticut             (390320.176, 453317.529]\n",
       "Delaware                (264325.471, 327322.823]\n",
       "District of Columbia    (579312.234, 642309.586]\n",
       "Florida                 (264325.471, 327322.823]\n",
       "Georgia                   (11706.087, 75333.413]\n",
       "Hawaii                  (264325.471, 327322.823]\n",
       "Idaho                     (11706.087, 75333.413]\n",
       "Illinois                 (75333.413, 138330.766]\n",
       "Indiana                   (11706.087, 75333.413]\n",
       "Iowa                      (11706.087, 75333.413]\n",
       "Kansas                    (11706.087, 75333.413]\n",
       "Kentucky                  (11706.087, 75333.413]\n",
       "Louisiana                 (11706.087, 75333.413]\n",
       "Maine                    (75333.413, 138330.766]\n",
       "Maryland                (201328.118, 264325.471]\n",
       "Massachusetts           (453317.529, 516314.881]\n",
       "Michigan                 (75333.413, 138330.766]\n",
       "Minnesota                 (11706.087, 75333.413]\n",
       "Mississippi               (11706.087, 75333.413]\n",
       "Missouri                  (11706.087, 75333.413]\n",
       "Montana                   (11706.087, 75333.413]\n",
       "Nebraska                  (11706.087, 75333.413]\n",
       "Nevada                  (138330.766, 201328.118]\n",
       "New Hampshire            (75333.413, 138330.766]\n",
       "New Jersey              (390320.176, 453317.529]\n",
       "New Mexico                (11706.087, 75333.413]\n",
       "New York                (264325.471, 327322.823]\n",
       "North Carolina           (75333.413, 138330.766]\n",
       "North Dakota              (11706.087, 75333.413]\n",
       "Ohio                     (75333.413, 138330.766]\n",
       "Oklahoma                  (11706.087, 75333.413]\n",
       "Oregon                   (75333.413, 138330.766]\n",
       "Pennsylvania            (138330.766, 201328.118]\n",
       "Rhode Island            (201328.118, 264325.471]\n",
       "South Carolina           (75333.413, 138330.766]\n",
       "South Dakota              (11706.087, 75333.413]\n",
       "Tennessee                 (11706.087, 75333.413]\n",
       "Texas                    (75333.413, 138330.766]\n",
       "Utah                     (75333.413, 138330.766]\n",
       "Vermont                   (11706.087, 75333.413]\n",
       "Virginia                  (11706.087, 75333.413]\n",
       "Washington              (138330.766, 201328.118]\n",
       "West Virginia             (11706.087, 75333.413]\n",
       "Wisconsin                (75333.413, 138330.766]\n",
       "Wyoming                   (11706.087, 75333.413]\n",
       "Name: avg, dtype: category\n",
       "Categories (10, interval[float64]): [(11706.087, 75333.413] < (75333.413, 138330.766] < (138330.766, 201328.118] < (201328.118, 264325.471] ... (390320.176, 453317.529] < (453317.529, 516314.881] < (516314.881, 579312.234] < (579312.234, 642309.586]]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('census.csv')\n",
    "df = df[df['SUMLEV']==50]\n",
    "df = df.set_index('STNAME').groupby(level=0)['CENSUS2010POP'].agg({'avg': np.average})\n",
    "pd.cut(df['avg'],10)\n",
    "#将数值平均分为n等分，n即该代码中的“10”：pd.cut(df['avg'],10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pivot Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64\n",
    "df = pd.read_csv('cars.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "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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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Size</th>\n",
       "      <th>(kW)</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>CITY (kWh/100 km)</th>\n",
       "      <th>HWY (kWh/100 km)</th>\n",
       "      <th>COMB (kWh/100 km)</th>\n",
       "      <th>CITY (Le/100 km)</th>\n",
       "      <th>HWY (Le/100 km)</th>\n",
       "      <th>COMB (Le/100 km)</th>\n",
       "      <th>(g/km)</th>\n",
       "      <th>RATING</th>\n",
       "      <th>(km)</th>\n",
       "      <th>TIME (h)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>MITSUBISHI</td>\n",
       "      <td>i-MiEV</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>49</td>\n",
       "      <td>A1</td>\n",
       "      <td>B</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.4</td>\n",
       "      <td>18.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>NISSAN</td>\n",
       "      <td>LEAF</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>80</td>\n",
       "      <td>A1</td>\n",
       "      <td>B</td>\n",
       "      <td>19.3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>FORD</td>\n",
       "      <td>FOCUS ELECTRIC</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>107</td>\n",
       "      <td>A1</td>\n",
       "      <td>B</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>MITSUBISHI</td>\n",
       "      <td>i-MiEV</td>\n",
       "      <td>SUBCOMPACT</td>\n",
       "      <td>49</td>\n",
       "      <td>A1</td>\n",
       "      <td>B</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.4</td>\n",
       "      <td>18.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>NISSAN</td>\n",
       "      <td>LEAF</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>80</td>\n",
       "      <td>A1</td>\n",
       "      <td>B</td>\n",
       "      <td>19.3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR        Make           Model        Size  (kW) Unnamed: 5 TYPE  \\\n",
       "0  2012  MITSUBISHI          i-MiEV  SUBCOMPACT    49         A1    B   \n",
       "1  2012      NISSAN            LEAF    MID-SIZE    80         A1    B   \n",
       "2  2013        FORD  FOCUS ELECTRIC     COMPACT   107         A1    B   \n",
       "3  2013  MITSUBISHI          i-MiEV  SUBCOMPACT    49         A1    B   \n",
       "4  2013      NISSAN            LEAF    MID-SIZE    80         A1    B   \n",
       "\n",
       "   CITY (kWh/100 km)  HWY (kWh/100 km)  COMB (kWh/100 km)  CITY (Le/100 km)  \\\n",
       "0               16.9              21.4               18.7               1.9   \n",
       "1               19.3              23.0               21.1               2.2   \n",
       "2               19.0              21.1               20.0               2.1   \n",
       "3               16.9              21.4               18.7               1.9   \n",
       "4               19.3              23.0               21.1               2.2   \n",
       "\n",
       "   HWY (Le/100 km)  COMB (Le/100 km)  (g/km)  RATING  (km)  TIME (h)  \n",
       "0              2.4               2.1       0     NaN   100         7  \n",
       "1              2.6               2.4       0     NaN   117         7  \n",
       "2              2.4               2.2       0     NaN   122         4  \n",
       "3              2.4               2.1       0     NaN   100         7  \n",
       "4              2.6               2.4       0     NaN   117         7  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Make</th>\n",
       "      <th>BMW</th>\n",
       "      <th>CHEVROLET</th>\n",
       "      <th>FORD</th>\n",
       "      <th>KIA</th>\n",
       "      <th>MITSUBISHI</th>\n",
       "      <th>NISSAN</th>\n",
       "      <th>SMART</th>\n",
       "      <th>TESLA</th>\n",
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       "    <tr>\n",
       "      <th>YEAR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>280.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>104.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>268.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>125.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>320.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>125.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>409.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Make    BMW  CHEVROLET   FORD   KIA  MITSUBISHI  NISSAN  SMART       TESLA\n",
       "YEAR                                                                      \n",
       "2012    NaN        NaN    NaN   NaN        49.0    80.0    NaN         NaN\n",
       "2013    NaN        NaN  107.0   NaN        49.0    80.0   35.0  280.000000\n",
       "2014    NaN      104.0  107.0   NaN        49.0    80.0   35.0  268.333333\n",
       "2015  125.0      104.0  107.0  81.0        49.0    80.0   35.0  320.666667\n",
       "2016  125.0      104.0  107.0  81.0        49.0    80.0   35.0  409.700000"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(values='(kW)', index='YEAR', columns='Make', aggfunc=np.mean)\n",
    "#以厂商Make和年份YEAR作为index和column，然后求每年对应厂商的 (kW) 这列数值的平均值 ：aggfunc=np.mean\n",
    "\n",
    "#pivot_table 可以把某一列column的值变成新的column header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th colspan=\"9\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"9\" halign=\"left\">amin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Make</th>\n",
       "      <th>BMW</th>\n",
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       "      <th>KIA</th>\n",
       "      <th>MITSUBISHI</th>\n",
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       "      <th>CHEVROLET</th>\n",
       "      <th>FORD</th>\n",
       "      <th>KIA</th>\n",
       "      <th>MITSUBISHI</th>\n",
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       "      <th>YEAR</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>158.444444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>104.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>268.333333</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>125.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>320.666667</td>\n",
       "      <td>181.428571</td>\n",
       "      <td>125.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>125.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>409.700000</td>\n",
       "      <td>252.263158</td>\n",
       "      <td>125.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>125.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>345.478261</td>\n",
       "      <td>190.622642</td>\n",
       "      <td>125.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean                                                             \\\n",
       "Make    BMW CHEVROLET   FORD   KIA MITSUBISHI NISSAN SMART       TESLA   \n",
       "YEAR                                                                     \n",
       "2012    NaN       NaN    NaN   NaN       49.0   80.0   NaN         NaN   \n",
       "2013    NaN       NaN  107.0   NaN       49.0   80.0  35.0  280.000000   \n",
       "2014    NaN     104.0  107.0   NaN       49.0   80.0  35.0  268.333333   \n",
       "2015  125.0     104.0  107.0  81.0       49.0   80.0  35.0  320.666667   \n",
       "2016  125.0     104.0  107.0  81.0       49.0   80.0  35.0  409.700000   \n",
       "All   125.0     104.0  107.0  81.0       49.0   80.0  35.0  345.478261   \n",
       "\n",
       "                   amin                                                        \\\n",
       "Make         All    BMW CHEVROLET   FORD   KIA MITSUBISHI NISSAN SMART  TESLA   \n",
       "YEAR                                                                            \n",
       "2012   64.500000    NaN       NaN    NaN   NaN       49.0   80.0   NaN    NaN   \n",
       "2013  158.444444    NaN       NaN  107.0   NaN       49.0   80.0  35.0  270.0   \n",
       "2014  135.000000    NaN     104.0  107.0   NaN       49.0   80.0  35.0  225.0   \n",
       "2015  181.428571  125.0     104.0  107.0  81.0       49.0   80.0  35.0  280.0   \n",
       "2016  252.263158  125.0     104.0  107.0  81.0       49.0   80.0  35.0  283.0   \n",
       "All   190.622642  125.0     104.0  107.0  81.0       49.0   80.0  35.0  225.0   \n",
       "\n",
       "          \n",
       "Make All  \n",
       "YEAR      \n",
       "2012  49  \n",
       "2013  35  \n",
       "2014  35  \n",
       "2015  35  \n",
       "2016  35  \n",
       "All   35  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(values='(kW)', index='YEAR', columns='Make', aggfunc=[np.mean,np.min], margins=True)\n",
    "#代码中加入：margins=True ，下面就加入了 All这个列\n",
    "#加入了两个function\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Date Functionality in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-09-01 10:05:00')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('9/1/2016 10:05AM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2016-01', 'M')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Period('1/2016')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2016-03-05', 'D')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Period('3/5/2016')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DatetimeIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-09-01    a\n",
       "2016-09-02    b\n",
       "2016-09-03    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#这个代码的隔断很特别，用逗号。。。\n",
    "t1 = pd.Series(list('abc'), [pd.Timestamp('2016-09-01'), pd.Timestamp('2016-09-02'), pd.Timestamp('2016-09-03')])\n",
    "t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(t1.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PeriodIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-09    d\n",
       "2016-10    e\n",
       "2016-11    f\n",
       "Freq: M, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2 = pd.Series(list('def'), [pd.Period('2016-09'), pd.Period('2016-10'), pd.Period('2016-11')])\n",
    "t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.tseries.period.PeriodIndex"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(t2.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to Datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2 June 2013</th>\n",
       "      <td>51</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug 29, 2014</th>\n",
       "      <td>53</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-26</th>\n",
       "      <td>60</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7/12/16</th>\n",
       "      <td>94</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               a   b\n",
       "2 June 2013   51  47\n",
       "Aug 29, 2014  53  39\n",
       "2015-06-26    60  88\n",
       "7/12/16       94  62"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = ['2 June 2013', 'Aug 29, 2014', '2015-06-26', '7/12/16']\n",
    "ts3 = pd.DataFrame(np.random.randint(10, 100, (4,2)), index=d1, columns=list('ab'))\n",
    "ts3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-06-02</th>\n",
       "      <td>51</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-29</th>\n",
       "      <td>53</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-26</th>\n",
       "      <td>60</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-12</th>\n",
       "      <td>94</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             a   b\n",
       "2013-06-02  51  47\n",
       "2014-08-29  53  39\n",
       "2015-06-26  60  88\n",
       "2016-07-12  94  62"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts3.index = pd.to_datetime(ts3.index) #可以把不同格式的日期转换成统一格式，email parsing项目用得到\n",
    "ts3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-07-04 00:00:00')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime('4.7.12', dayfirst=True) #如果源数据的时间格式是欧式：日，月，年，则可以单独进行调整"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Timedeltas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('2 days 00:00:00')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('9/3/2016')-pd.Timestamp('9/1/2016') #两个日期做减法，获得相差时长"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2016-09-24 11:29:00')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp('9/2/2016 8:10AM') + pd.Timedelta('22Days 3Hours 19minutes') #一个日期加上特定时长,时长单位多样\n",
    "#时长单位的格式：不区分大小写；d，day，days都可以；h，hour，hours都可以，m,min,minute,minutes都可以；\n",
    "#不能用月，不能用mins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with Dates in a Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-10-02', '2016-10-16', '2016-10-30', '2016-11-13',\n",
       "               '2016-11-27', '2016-12-11', '2016-12-25', '2017-01-08',\n",
       "               '2017-01-22'],\n",
       "              dtype='datetime64[ns]', freq='2W-SUN')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('10-01-2016', periods=9, freq='2W-SUN')#每两个周日\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count 1</th>\n",
       "      <th>Count 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-10-02</th>\n",
       "      <td>98</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-16</th>\n",
       "      <td>106</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-30</th>\n",
       "      <td>115</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-13</th>\n",
       "      <td>119</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-27</th>\n",
       "      <td>122</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-11</th>\n",
       "      <td>125</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-25</th>\n",
       "      <td>127</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>123</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-22</th>\n",
       "      <td>128</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count 1  Count 2\n",
       "2016-10-02       98      128\n",
       "2016-10-16      106      128\n",
       "2016-10-30      115      118\n",
       "2016-11-13      119      115\n",
       "2016-11-27      122      124\n",
       "2016-12-11      125      119\n",
       "2016-12-25      127      117\n",
       "2017-01-08      123      118\n",
       "2017-01-22      128      117"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Count 1': 100 + np.random.randint(-5, 10, 9).cumsum(),\n",
    "                  'Count 2': 120 + np.random.randint(-5, 10, 9)}, index=dates)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sunday', 'Sunday', 'Sunday', 'Sunday', 'Sunday', 'Sunday', 'Sunday',\n",
       "       'Sunday', 'Sunday'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.weekday_name #自动判断dataframe中的日期是星期几"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Count 1</th>\n",
       "      <th>Count 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-10-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-16</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-30</th>\n",
       "      <td>9.0</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-13</th>\n",
       "      <td>4.0</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-27</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-11</th>\n",
       "      <td>3.0</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-25</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>-4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-22</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count 1  Count 2\n",
       "2016-10-02      NaN      NaN\n",
       "2016-10-16      8.0      0.0\n",
       "2016-10-30      9.0    -10.0\n",
       "2016-11-13      4.0     -3.0\n",
       "2016-11-27      3.0      9.0\n",
       "2016-12-11      3.0     -5.0\n",
       "2016-12-25      2.0     -2.0\n",
       "2017-01-08     -4.0      1.0\n",
       "2017-01-22      5.0     -1.0"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.diff() #下一个row减去上一个row的差值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count 1</th>\n",
       "      <th>Count 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-10-31</th>\n",
       "      <td>106.333333</td>\n",
       "      <td>124.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-30</th>\n",
       "      <td>120.500000</td>\n",
       "      <td>119.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>126.000000</td>\n",
       "      <td>118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>125.500000</td>\n",
       "      <td>117.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Count 1     Count 2\n",
       "2016-10-31  106.333333  124.666667\n",
       "2016-11-30  120.500000  119.500000\n",
       "2016-12-31  126.000000  118.000000\n",
       "2017-01-31  125.500000  117.500000"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample('M').mean() #查看每月数据的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count 1</th>\n",
       "      <th>Count 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>123</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-22</th>\n",
       "      <td>128</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count 1  Count 2\n",
       "2017-01-08      123      118\n",
       "2017-01-22      128      117"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count 1</th>\n",
       "      <th>Count 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-11</th>\n",
       "      <td>125</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-25</th>\n",
       "      <td>127</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count 1  Count 2\n",
       "2016-12-11      125      119\n",
       "2016-12-25      127      117"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2016-12']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count 1</th>\n",
       "      <th>Count 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-11</th>\n",
       "      <td>125</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-25</th>\n",
       "      <td>127</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>123</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-22</th>\n",
       "      <td>128</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count 1  Count 2\n",
       "2016-12-11      125      119\n",
       "2016-12-25      127      117\n",
       "2017-01-08      123      118\n",
       "2017-01-22      128      117"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2016-12':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count 1</th>\n",
       "      <th>Count 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-10-02</th>\n",
       "      <td>98</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-09</th>\n",
       "      <td>98</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-16</th>\n",
       "      <td>106</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-23</th>\n",
       "      <td>106</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-30</th>\n",
       "      <td>115</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-06</th>\n",
       "      <td>115</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-13</th>\n",
       "      <td>119</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-20</th>\n",
       "      <td>119</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-27</th>\n",
       "      <td>122</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-04</th>\n",
       "      <td>122</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-11</th>\n",
       "      <td>125</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-18</th>\n",
       "      <td>125</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-25</th>\n",
       "      <td>127</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>127</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>123</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-15</th>\n",
       "      <td>123</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-22</th>\n",
       "      <td>128</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count 1  Count 2\n",
       "2016-10-02       98      128\n",
       "2016-10-09       98      128\n",
       "2016-10-16      106      128\n",
       "2016-10-23      106      128\n",
       "2016-10-30      115      118\n",
       "2016-11-06      115      118\n",
       "2016-11-13      119      115\n",
       "2016-11-20      119      115\n",
       "2016-11-27      122      124\n",
       "2016-12-04      122      124\n",
       "2016-12-11      125      119\n",
       "2016-12-18      125      119\n",
       "2016-12-25      127      117\n",
       "2017-01-01      127      117\n",
       "2017-01-08      123      118\n",
       "2017-01-15      123      118\n",
       "2017-01-22      128      117"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.asfreq('W', method='ffill') #把日期频率变成每周一个，然后新增的日期对应的数据使用填充的方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e372048>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}