science/pandas-构造.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pandas 基础"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import Series,DataFrame\n",
    "from numpy import *\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj=Series([1,2,3,4],index=list('abcd'))\n",
    "obj"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "method 填充方法，ffile,bfill\n",
    "fill_value,用固定值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "d    4\n",
       "e    4\n",
       "a    1\n",
       "b    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2=obj.reindex(list('cdeab'),method='ffill')\n",
    "obj2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame.reindex\n",
    "    reindex(self, index=None, columns=None, **kwargs)\n",
    "        Conform DataFrame to new index with optional filling logic, placing\n",
    "        NA/NaN in locations having no value in the previous index. A new object\n",
    "        is produced unless the new index is equivalent to the current one and\n",
    "        copy=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame=DataFrame(arange(9).reshape((3,3)),index=['a','b','c'],columns=['d','e','f'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   d  e  f\n",
      "a  0  1  2\n",
      "b  3  4  5\n",
      "c  6  7  8 \n",
      "     a   b   c\n",
      "a NaN NaN NaN\n",
      "b NaN NaN NaN\n",
      "c NaN NaN NaN\n"
     ]
    }
   ],
   "source": [
    "print(frame,'\\n',frame.reindex(columns=['a','b','c'],copy=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 如果重新构造就不会有哪些问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=DataFrame(array(frame[['d','e','f']]),index=frame.index,columns=['a','b','c'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### drop，axis=0,1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    .dataframe tbody tr 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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "b  3  4  5\n",
       "c  6  7  8"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除每列的a\n",
    "f.drop(['a'],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   b  c\n",
       "a  1  2\n",
       "b  4  5\n",
       "c  7  8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除行的a\n",
    "f.drop('a',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
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      "text/plain": [
       "   a  b  c\n",
       "a  0  0  0\n",
       "b  3  3  3\n",
       "c  6  6  6"
      ]
     },
     "execution_count": 21,
     "metadata": {},
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   ],
   "source": [
    "#对每列应用这个函数\n",
    "app=lambda x: x-x.min()\n",
    "f.apply(app,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.00</td>\n",
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       "      <td>2.00</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3.00</td>\n",
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       "      <td>5.00</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>6.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.00</td>\n",
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      "text/plain": [
       "      a     b     c\n",
       "a  0.00  1.00  2.00\n",
       "b  3.00  4.00  5.00\n",
       "c  6.00  7.00  8.00"
      ]
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     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#格式化\n",
    "form=lambda x:'%.2f'%x\n",
    "f.applymap(form)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "##### sort_index,按照索引对索引排序  axis，对 列 / 行操作，ascendign升序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>8</td>\n",
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       "      <th>b</th>\n",
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      "text/plain": [
       "   a  b  c\n",
       "c  6  7  8\n",
       "b  3  4  5\n",
       "a  0  1  2"
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     "execution_count": 69,
     "metadata": {},
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   ],
   "source": [
    "f.sort_index(axis=0,ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "##### sort_values,按照值对索引排序  axis，对 列 / 行操作，ascendign升序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   a  b  c\n",
       "c  6  7  8\n",
       "b  3  4  5\n",
       "a  0  1  2"
      ]
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     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "f.sort_values(by='a',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     9\n",
       "b    12\n",
       "c    15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对每列求和\n",
    "f.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
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       "   a  b  c\n",
       "a  0  1  2\n",
       "b  3  4  5\n",
       "c  6  7  8"
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     "execution_count": 77,
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   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   a   b   c\n",
       "a  0   1   3\n",
       "b  3   7  12\n",
       "c  6  13  21"
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     "execution_count": 81,
     "metadata": {},
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   ],
   "source": [
    "#累加\n",
    "f.cumsum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### describe , 对DataFrame 描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         a    b    c\n",
       "count  3.0  3.0  3.0\n",
       "mean   3.0  4.0  5.0\n",
       "std    3.0  3.0  3.0\n",
       "min    0.0  1.0  2.0\n",
       "25%    1.5  2.5  3.5\n",
       "50%    3.0  4.0  5.0\n",
       "75%    4.5  5.5  6.5\n",
       "max    6.0  7.0  8.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b    c\n",
       "a  NaN  NaN  NaN\n",
       "b  3.0  3.0  3.0\n",
       "c  3.0  3.0  3.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#一阶差分\n",
    "f.diff(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### corr 相关系数"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "f.corr?\n",
    "Compute pairwise correlation of columns, excluding NA/null values\n",
    "\n",
    "Parameters\n",
    "----------\n",
    "method : {'pearson', 'kendall', 'spearman'}\n",
    "    * pearson : standard correlation coefficient\n",
    "    * kendall : Kendall Tau correlation coefficient\n",
    "    * spearman : Spearman rank correlation\n",
    "min_periods : int, optional\n",
    "    Minimum number of observations required per pair of columns\n",
    "    to have a valid result. Currently only available for pearson\n",
    "    and spearman correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b    c\n",
       "a  1.0  1.0  1.0\n",
       "b  1.0  1.0  1.0\n",
       "c  1.0  1.0  1.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.corr()#coeff"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "f.cov\n",
    "计算列的协方差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b    c\n",
       "a  9.0  9.0  9.0\n",
       "b  9.0  9.0  9.0\n",
       "c  9.0  9.0  9.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.cov()#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    d\n",
       "2    x\n",
       "3    c\n",
       "4    s\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ob=Series(list('adxcsalksdkjxsa'))\n",
    "ob.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 'd', 'x', 'c', 's', 'l', 'k', 'j'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "un=ob.unique()\n",
    "un"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3\n",
       "l    1\n",
       "c    1\n",
       "d    2\n",
       "k    2\n",
       "x    2\n",
       "s    3\n",
       "j    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ob.value_counts(sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     a\n",
       "5     a\n",
       "14    a\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ob[ob.isin(['a'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### nan    ———————— axis: 0 or 'index', 1 or 'columns'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2\n",
       "0  1.0  2.0  3.0\n",
       "1  4.0  NaN  NaN\n",
       "2  NaN  NaN  NaN\n",
       "3  NaN  6.0  3.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da=DataFrame([[1,2,3],[4,nan,nan],\n",
    "             [nan,nan,nan],[nan,6,3]])\n",
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>0</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2\n",
       "0  1.0  2.0  3.0\n",
       "1  4.0  NaN  NaN\n",
       "3  NaN  6.0  3.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "    .dataframe tbody tr th {\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
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       "     0    1    2\n",
       "0  1.0  2.0  3.0\n",
       "1  4.0  NaN  NaN\n",
       "2  NaN  NaN  NaN\n",
       "3  NaN  6.0  3.0"
      ]
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.dropna(how='all',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "     0    1    2\n",
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     "execution_count": 116,
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   "source": [
    "da.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "     0    1    2\n",
       "0  1.0  2.0  3.0\n",
       "1  4.0  0.5  NaN\n",
       "2  2.0  0.5  NaN\n",
       "3  2.0  6.0  3.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按照列方向\n",
    "da.fillna({0:2,1:0.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2\n",
       "0  1.0  2.0  3.0\n",
       "1  4.0  2.0  3.0\n",
       "2  4.0  2.0  3.0\n",
       "3  4.0  6.0  3.0"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.fillna(method='ffill',limit=1,inplace=True)\n",
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### stack ，unstack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[[0, 1, 2], [0, 1, 2, 3]],\n",
       "           labels=[[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da2=da.unstack()\n",
    "da2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0  0    1.0\n",
       "   1    4.0\n",
       "   2    4.0\n",
       "   3    4.0\n",
       "1  0    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2\n",
       "0  1.0  2.0  3.0\n",
       "1  4.0  2.0  3.0\n",
       "2  4.0  2.0  3.0\n",
       "3  4.0  6.0  3.0"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    2\n",
       "1            \n",
       "2.0  1.0  3.0\n",
       "2.0  4.0  3.0\n",
       "2.0  4.0  3.0\n",
       "6.0  4.0  3.0"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用那一列当做新的index\n",
    "da3=da.set_index([1],drop=True)\n",
    "da3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    2\n",
       "0  1.0  3.0\n",
       "1  4.0  3.0\n",
       "2  4.0  3.0\n",
       "3  4.0  3.0"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dro ，是否删除index\n",
    "da3.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 链接  \n",
    "     merge,键 \n",
    "     concat 轴，axis=0"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "merge?\n",
    "merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "concat?\n",
    "concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, copy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key\n",
       "0      0   a\n",
       "1      1   b\n",
       "2      2   d"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1=DataFrame({'key':['a','b','d'],'data1':range(3)})\n",
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data2</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data2 key\n",
       "0      1   c\n",
       "1      2   b\n",
       "2      3   d"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2=DataFrame({'key':['c','b','d'],'data2':range(1,4)})\n",
    "d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key_x</th>\n",
       "      <th>data2</th>\n",
       "      <th>key_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>d</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key_x  data2 key_y\n",
       "0      1     b      1     c\n",
       "1      2     d      2     b"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#应用于连个dataframe 需要对齐的位置，索引不同的情况\n",
    "merge(d1,d2,left_on='data1',right_on='data2',how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key_x</th>\n",
       "      <th>data2</th>\n",
       "      <th>key_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b</td>\n",
       "      <td>1.0</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>d</td>\n",
       "      <td>2.0</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key_x  data2 key_y\n",
       "0    0.0     a    NaN   NaN\n",
       "1    1.0     b    1.0     c\n",
       "2    2.0     d    2.0     b\n",
       "3    NaN   NaN    3.0     d"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#应用于连个dataframe 需要对齐的位置，索引不同的情况\n",
    "merge(d1,d2,left_on='data1',right_on='data2',how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>d</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key  data2\n",
       "0      1   b      2\n",
       "1      2   d      3"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 会删除，在key上不同的东西\n",
    "merge(d1,d2,on='key',how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>b</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>d</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>c</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key  data2\n",
       "0    0.0   a    NaN\n",
       "1    1.0   b    2.0\n",
       "2    2.0   d    3.0\n",
       "3    NaN   c    1.0"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge(d1,d2,on='key',how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data2</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data2 key\n",
       "a      1   c\n",
       "1      2   b\n",
       "2      3   d"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data2</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data2 key\n",
       "a      1   c\n",
       "1      2   b\n",
       "2      3   d"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2.rename(index={0:'a'})\n",
    "d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011,01,12'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp.strftime('%Y,%m,%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 11, 1, 0, 0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dateutil.parser import parse\n",
    "parse('2011-11-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "parse?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dates=date_range('1/1/2000',periods=100,freq='W-WED')\n",
    "long=DataFrame(random.rand(100,4),index=dates,columns=list('abcd'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>0.625324</td>\n",
       "      <td>0.081019</td>\n",
       "      <td>0.056472</td>\n",
       "      <td>0.090580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>0.442830</td>\n",
       "      <td>0.268490</td>\n",
       "      <td>0.809225</td>\n",
       "      <td>0.029676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19</th>\n",
       "      <td>0.976271</td>\n",
       "      <td>0.217780</td>\n",
       "      <td>0.642069</td>\n",
       "      <td>0.404248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-26</th>\n",
       "      <td>0.303100</td>\n",
       "      <td>0.056061</td>\n",
       "      <td>0.497815</td>\n",
       "      <td>0.671508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-02</th>\n",
       "      <td>0.115643</td>\n",
       "      <td>0.228232</td>\n",
       "      <td>0.218574</td>\n",
       "      <td>0.094792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   a         b         c         d\n",
       "2000-01-05  0.625324  0.081019  0.056472  0.090580\n",
       "2000-01-12  0.442830  0.268490  0.809225  0.029676\n",
       "2000-01-19  0.976271  0.217780  0.642069  0.404248\n",
       "2000-01-26  0.303100  0.056061  0.497815  0.671508\n",
       "2000-02-02  0.115643  0.228232  0.218574  0.094792"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-05-02</th>\n",
       "      <td>0.995576</td>\n",
       "      <td>0.530611</td>\n",
       "      <td>0.662965</td>\n",
       "      <td>0.297078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-09</th>\n",
       "      <td>0.498233</td>\n",
       "      <td>0.684180</td>\n",
       "      <td>0.288669</td>\n",
       "      <td>0.537507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-16</th>\n",
       "      <td>0.890172</td>\n",
       "      <td>0.228506</td>\n",
       "      <td>0.892775</td>\n",
       "      <td>0.613714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-23</th>\n",
       "      <td>0.289677</td>\n",
       "      <td>0.856919</td>\n",
       "      <td>0.364465</td>\n",
       "      <td>0.838670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-30</th>\n",
       "      <td>0.394762</td>\n",
       "      <td>0.007050</td>\n",
       "      <td>0.337491</td>\n",
       "      <td>0.835321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-06-06</th>\n",
       "      <td>0.858180</td>\n",
       "      <td>0.962105</td>\n",
       "      <td>0.457871</td>\n",
       "      <td>0.236417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-06-13</th>\n",
       "      <td>0.840386</td>\n",
       "      <td>0.617091</td>\n",
       "      <td>0.406302</td>\n",
       "      <td>0.894596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-06-20</th>\n",
       "      <td>0.637508</td>\n",
       "      <td>0.543856</td>\n",
       "      <td>0.738506</td>\n",
       "      <td>0.620478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-06-27</th>\n",
       "      <td>0.905285</td>\n",
       "      <td>0.476522</td>\n",
       "      <td>0.048184</td>\n",
       "      <td>0.166503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   a         b         c         d\n",
       "2001-05-02  0.995576  0.530611  0.662965  0.297078\n",
       "2001-05-09  0.498233  0.684180  0.288669  0.537507\n",
       "2001-05-16  0.890172  0.228506  0.892775  0.613714\n",
       "2001-05-23  0.289677  0.856919  0.364465  0.838670\n",
       "2001-05-30  0.394762  0.007050  0.337491  0.835321\n",
       "2001-06-06  0.858180  0.962105  0.457871  0.236417\n",
       "2001-06-13  0.840386  0.617091  0.406302  0.894596\n",
       "2001-06-20  0.637508  0.543856  0.738506  0.620478\n",
       "2001-06-27  0.905285  0.476522  0.048184  0.166503"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long.ix['5-2001':'6-2001']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index=Series(date_range('4/1/2000','4/1/2001',freq='M'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2000-04-30\n",
       "1    2000-05-31\n",
       "2    2000-06-30\n",
       "3    2000-07-31\n",
       "4    2000-08-31\n",
       "5    2000-09-30\n",
       "6    2000-10-31\n",
       "7    2000-11-30\n",
       "8    2000-12-31\n",
       "9    2001-01-31\n",
       "10   2001-02-28\n",
       "11   2001-03-31\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007-08', 'M')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "p=pd.Period('2007-8','M')\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006    0.994509\n",
       "2007    0.390422\n",
       "2008    0.702878\n",
       "2009    0.402120\n",
       "2010    0.752779\n",
       "2011    0.048156\n",
       "2012    0.331685\n",
       "Freq: A-DEC, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "rng=pd.period_range('2006','2012',freq='A-DEC')\n",
    "ts=pd.Series(np.random.rand(len(rng)),index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-01    0.994509\n",
       "2007-01    0.390422\n",
       "2008-01    0.702878\n",
       "2009-01    0.402120\n",
       "2010-01    0.752779\n",
       "2011-01    0.048156\n",
       "2012-01    0.331685\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq('M',how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-01-01   -0.541159\n",
       "2006-01-02    1.824539\n",
       "2006-01-03    0.838465\n",
       "2006-01-04   -0.569178\n",
       "2006-01-05   -0.444249\n",
       "2006-01-06   -0.367286\n",
       "2006-01-07   -1.172551\n",
       "2006-01-08   -0.960563\n",
       "2006-01-09   -0.138703\n",
       "2006-01-10   -1.606852\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#resample\n",
    "rng=pd.date_range('1/1/2006',periods=10,freq='D')\n",
    "ts=pd.Series(np.random.randn(len(rng)),index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-01   -0.313754\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M',kind='period').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt5Agg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-0.3137537424344476"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ezh4RDABeAjKBUmyXjx4UkVTgcWPMHBHJAV63vyUE+Lcx5reObH/ixElm7dqC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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1be04020128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab\n",
    "%matplotlib inline\n",
    "ts.plot()\n",
    "ts.apply(np.cumsum)\n",
    "ts.mean()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}