Pandas_dataframe.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "89821fe6",
   "metadata": {},
   "source": [
    "表格型数据结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75e2da71",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76343bd9",
   "metadata": {},
   "source": [
    "用字典创建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52902170",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "0    1    4\n",
       "1    2    5\n",
       "2    3    6"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'one':[1,2,3],'two':[4,5,6]})#指定列的名称，行名自动生成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bed8a14e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "a    1    4\n",
       "b    2    5\n",
       "c    3    6"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'one':[1,2,3],'two':[4,5,6]},index=['a','b','c'])#指定了行的名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9d927d5",
   "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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   one  two\n",
       "0    3    4\n",
       "1    4    5\n",
       "2    5    6\n",
       "3    6    7"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'one':pd.Series(np.arange(3,7)),'two':pd.Series(np.arange(4,8))})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "deafa946",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.DataFrame({'one':pd.Series(np.arange(3,7),index=['a','b','c','d']),'two':pd.Series(np.arange(4,8),index=['a','b','c','d'])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "84ea7349",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   one  two\n",
       "a    3    4\n",
       "b    4    5\n",
       "c    5    6\n",
       "d    6    7"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0ce53a5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({'one':pd.Series(np.arange(3,7),index=['a','b','c','d']),'two':pd.Series(np.arange(4,8),index=['a','b','c','d'])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "525c88bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('test.csv')#保存到csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "90dc8b4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=pd.read_csv('test.csv')#读取csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f56da77c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    4\n",
       "2    5\n",
       "3    6\n",
       "Name: one, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a['one']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c4c3e97c",
   "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>Unnamed: 0</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
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      "text/plain": [
       "  Unnamed: 0  one  two\n",
       "0          a    3    4\n",
       "1          b    4    5\n",
       "2          c    5    6\n",
       "3          d    6    7"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4825e147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5226d9f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['a', 3, 4],\n",
       "       ['b', 4, 5],\n",
       "       ['c', 5, 6],\n",
       "       ['d', 6, 7]], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7ec57354",
   "metadata": {},
   "outputs": [
    {
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       "      <th>two</th>\n",
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       "  <tbody>\n",
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       "      <th>a</th>\n",
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       "      <th>c</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
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      "text/plain": [
       "   one  two\n",
       "a    3    4\n",
       "b    4    5\n",
       "c    5    6\n",
       "d    6    7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "69771166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.index #返回了行索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3989d4b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 4],\n",
       "       [4, 5],\n",
       "       [5, 6],\n",
       "       [6, 7]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.values#返回了值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7e574a78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['one', 'two'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns#返回列索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6f4fe449",
   "metadata": {},
   "outputs": [
    {
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       "      <th>c</th>\n",
       "      <th>d</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>4</td>\n",
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       "      <td>7</td>\n",
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      "text/plain": [
       "     a  b  c  d\n",
       "one  3  4  5  6\n",
       "two  4  5  6  7"
      ]
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df1.T#转置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c373ce19",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   one  two\n",
       "a    1    4\n",
       "b    2    7\n",
       "c    3    8"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({{'one':[1,2,3],'two':[4,7,8]},index=['a','b','c']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "108f2adb",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "a    1    4\n",
       "b    2    7\n",
       "c    3    8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'one':pd.Series(np.arange(1,4),index=['a','b','c']),'two':pd.Series([4,7,8],index=['a','b','c'])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "389116b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "t    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,2],index=['a','t'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fd07b9e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,2],index=['a','b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4feb31f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    1\n",
       "two    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series({'one':1,'two':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "951e5c55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.290994</td>\n",
       "      <td>1.290994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.750000</td>\n",
       "      <td>4.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.250000</td>\n",
       "      <td>6.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            one       two\n",
       "count  4.000000  4.000000\n",
       "mean   4.500000  5.500000\n",
       "std    1.290994  1.290994\n",
       "min    3.000000  4.000000\n",
       "25%    3.750000  4.750000\n",
       "50%    4.500000  5.500000\n",
       "75%    5.250000  6.250000\n",
       "max    6.000000  7.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()#返回两列的一些统计值"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76531be9",
   "metadata": {},
   "source": [
    "Dataframe属性\n",
    "index行索引\n",
    "columns列索引\n",
    "values值（二维数组\n",
    "T转置"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "917a9902",
   "metadata": {},
   "source": [
    "索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6c28261a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
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       "      <th>b</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "a    3    4\n",
       "b    4    5\n",
       "c    5    6\n",
       "d    6    7"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2e0696f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['one']['a']#先取列指标再取行指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1a296f23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "a    3    4\n",
       "b    4    5\n",
       "c    5    6\n",
       "d    6    7"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[['one','two']]#对列的花式索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0cf2caf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc['a','two']#用loc,\",\"隔开左边是行右边是列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d5c0ed74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ee573b11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    3\n",
       "two    4\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[0,:]#dataframe的本质是几列拼在一起，要访问某一行可以用切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1a22f1bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    3\n",
       "two    4\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc['a',:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8b7d5788",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>3</td>\n",
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       "      <th>c</th>\n",
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       "      <td>6</td>\n",
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      "text/plain": [
       "   one  two\n",
       "a    3    4\n",
       "c    5    6"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[['a','c'],:]#行是花式索引，右边是切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e2f987db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "   one  two\n",
       "a    3    4\n",
       "c    5    6"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[['a','c']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4a53cc40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    4\n",
       "b    5\n",
       "c    6\n",
       "Name: two, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[\"a\":\"c\",'two']#切片和普通索引"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83213eeb",
   "metadata": {},
   "source": [
    "数据对齐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0fda8dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
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       "      <th>c</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
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       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   one  two\n",
       "a    3    4\n",
       "b    4    5\n",
       "c    5    6\n",
       "d    6    7"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4b6ac3cf",
   "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",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>two</th>\n",
       "      <th>one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   two  one\n",
       "c    1    1\n",
       "d    2    1\n",
       "b    3    4\n",
       "a    4    8"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.DataFrame({'two':[1,2,3,4],'one':[1,1,4,8]},index=['c','d','b','a'])\n",
    "df2\n",
    "#行列索引对其"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a5be1ed8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>c</th>\n",
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       "      <td>9</td>\n",
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     "execution_count": 34,
     "metadata": {},
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   "source": [
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  {
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   "id": "e213b128",
   "metadata": {},
   "source": [
    "数据缺失"
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   "execution_count": 35,
   "id": "5298680a",
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f4ff8114",
   "metadata": {},
   "outputs": [
    {
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    "df3=pd.DataFrame({'one':[1,2,3],'two':[3,6,7]},index=['a','b','c'])\n",
    "df3"
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  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "61c49abe",
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   "source": [
    "df4=df1+df3\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e036d68e",
   "metadata": {},
   "outputs": [
    {
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       "   one   two\n",
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   "source": [
    "df4.fillna(0)#用0补充缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d6478322",
   "metadata": {},
   "outputs": [
    {
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       "   one   two\n",
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  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "63af7603",
   "metadata": {},
   "outputs": [
    {
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  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "798f7f60",
   "metadata": {},
   "outputs": [
    {
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       "   one  two\n",
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    "df3"
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  {
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   "execution_count": 42,
   "id": "6e1bf7dc",
   "metadata": {},
   "outputs": [
    {
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       "   one   two\n",
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       "b  6.0  11.0\n",
       "c  8.0  13.0\n",
       "d  NaN   NaN"
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     "execution_count": 42,
     "metadata": {},
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   ],
   "source": [
    "df5=df4\n",
    "df5.loc['a','one']=np.nan\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "88953126",
   "metadata": {},
   "outputs": [
    {
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       "   one   two\n",
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   "source": [
    "df5.dropna()#会删除所有包含nan的行，默认参数为any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1d655261",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   one   two\n",
       "a  NaN   7.0\n",
       "b  6.0  11.0\n",
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     "execution_count": 44,
     "metadata": {},
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   "source": [
    "df5.dropna(how='all')#只删除所有值都为nan的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bf7db019",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [a, b, c, d]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.dropna(axis=1)#按列为单位，删除所有含nan的列"
   ]
  }
 ],
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