pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-01T14:19:38.017047Z",
     "start_time": "2020-09-01T14:19:36.348508Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入\n",
    "习惯上，我们这样导入pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:45:25.520115Z",
     "start_time": "2018-01-13T07:45:25.301417Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创建对象\n",
    "通过列表可以创建Series，Pandas会自动创建整型索引。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:46.841237Z",
     "start_time": "2018-01-13T07:46:46.833237Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.0\n",
      "1    3.0\n",
      "2    5.0\n",
      "3    NaN\n",
      "4    6.0\n",
      "5    8.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "s=pd.Series([1,3,5,np.nan,6,8])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过NumPy的数组可以创建一个DataFrame。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:47.105287Z",
     "start_time": "2018-01-13T07:46:47.093258Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
      "               '2013-01-05', '2013-01-06'],\n",
      "              dtype='datetime64[ns]', freq='D')\n",
      "                   A         B         C         D\n",
      "2013-01-01 -0.653658 -0.412076  0.289240 -0.813057\n",
      "2013-01-02 -0.000286 -1.257760 -1.232549 -0.899191\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  1.141695\n",
      "2013-01-04  0.100815  1.143710  0.467192  0.014952\n",
      "2013-01-05  0.944031 -0.653508  0.005439  0.316151\n",
      "2013-01-06 -0.305994 -1.372941 -2.900295  0.220700\n"
     ]
    }
   ],
   "source": [
    "dates=pd.date_range('20130101',periods=6)\n",
    "print(dates)\n",
    "df=pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以通过字典来创建DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:47.426719Z",
     "start_time": "2018-01-13T07:46:47.412682Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A          B    C  D      E    F\n",
      "0  1.0 2013-01-02  1.0  3   test  foo\n",
      "1  1.0 2013-01-02  1.0  3  train  foo\n",
      "2  1.0 2013-01-02  1.0  3   test  foo\n",
      "3  1.0 2013-01-02  1.0  3  train  foo\n"
     ]
    }
   ],
   "source": [
    "df2=pd.DataFrame({'A':1.,\n",
    "                 'B':pd.Timestamp('20130102'),\n",
    "                 'C':pd.Series(1,index=list(range(4)),dtype='float32'),\n",
    "                 'D':np.array([3]*4,dtype='int32'),\n",
    "                 'E':pd.Categorical(['test','train','test','train']),\n",
    "                 'F':'foo'})\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设置了特定的dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:47.722553Z",
     "start_time": "2018-01-13T07:46:47.714530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-09T03:20:11.361273Z",
     "start_time": "2018-01-09T03:20:11.356261Z"
    }
   },
   "source": [
    "### 查看数据\n",
    "查看数据的顶部和底部"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:48.033330Z",
     "start_time": "2018-01-13T07:46:48.024337Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C         D\n",
      "2013-01-01 -0.653658 -0.412076  0.289240 -0.813057\n",
      "2013-01-02 -0.000286 -1.257760 -1.232549 -0.899191\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  1.141695\n",
      "2013-01-04  0.100815  1.143710  0.467192  0.014952\n",
      "2013-01-05  0.944031 -0.653508  0.005439  0.316151\n",
      "                   A         B         C         D\n",
      "2013-01-04  0.100815  1.143710  0.467192  0.014952\n",
      "2013-01-05  0.944031 -0.653508  0.005439  0.316151\n",
      "2013-01-06 -0.305994 -1.372941 -2.900295  0.220700\n"
     ]
    }
   ],
   "source": [
    "print(df.head())\n",
    "print(df.tail(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看索引，列名，以及纯的NumPy数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:48.406942Z",
     "start_time": "2018-01-13T07:46:48.399922Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
      "               '2013-01-05', '2013-01-06'],\n",
      "              dtype='datetime64[ns]', freq='D')\n",
      "Index(['A', 'B', 'C', 'D'], dtype='object')\n",
      "[[ -6.53658042e-01  -4.12076297e-01   2.89239645e-01  -8.13057346e-01]\n",
      " [ -2.86361403e-04  -1.25775963e+00  -1.23254938e+00  -8.99191307e-01]\n",
      " [  4.04229751e-01  -8.80146291e-01  -1.59352637e+00   1.14169486e+00]\n",
      " [  1.00815285e-01   1.14370966e+00   4.67192499e-01   1.49516032e-02]\n",
      " [  9.44030631e-01  -6.53507646e-01   5.43886068e-03   3.16151200e-01]\n",
      " [ -3.05994200e-01  -1.37294092e+00  -2.90029481e+00   2.20700202e-01]]\n"
     ]
    }
   ],
   "source": [
    "print(df.index)\n",
    "print(df.columns)\n",
    "print(df.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```DataFrame.describe()```提供了便捷的数据统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:48.714876Z",
     "start_time": "2018-01-13T07:46:48.693822Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              A         B         C         D\n",
      "count  6.000000  6.000000  6.000000  6.000000\n",
      "mean   0.081523 -0.572120 -0.827417 -0.003125\n",
      "std    0.556019  0.914615  1.316394  0.764447\n",
      "min   -0.653658 -1.372941 -2.900295 -0.899191\n",
      "25%   -0.229567 -1.163356 -1.503282 -0.606055\n",
      "50%    0.050264 -0.766827 -0.613555  0.117826\n",
      "75%    0.328376 -0.472434  0.218289  0.292288\n",
      "max    0.944031  1.143710  0.467192  1.141695\n"
     ]
    }
   ],
   "source": [
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "转置矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:49.018446Z",
     "start_time": "2018-01-13T07:46:49.008420Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06\n",
      "A   -0.653658   -0.000286    0.404230    0.100815    0.944031   -0.305994\n",
      "B   -0.412076   -1.257760   -0.880146    1.143710   -0.653508   -1.372941\n",
      "C    0.289240   -1.232549   -1.593526    0.467192    0.005439   -2.900295\n",
      "D   -0.813057   -0.899191    1.141695    0.014952    0.316151    0.220700\n"
     ]
    }
   ],
   "source": [
    "print(df.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "沿某一轴排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:49.328897Z",
     "start_time": "2018-01-13T07:46:49.319897Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   D         C         B         A\n",
      "2013-01-01 -0.813057  0.289240 -0.412076 -0.653658\n",
      "2013-01-02 -0.899191 -1.232549 -1.257760 -0.000286\n",
      "2013-01-03  1.141695 -1.593526 -0.880146  0.404230\n",
      "2013-01-04  0.014952  0.467192  1.143710  0.100815\n",
      "2013-01-05  0.316151  0.005439 -0.653508  0.944031\n",
      "2013-01-06  0.220700 -2.900295 -1.372941 -0.305994\n"
     ]
    }
   ],
   "source": [
    "print(df.sort_index(axis=1,ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按照值进行排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:49.682760Z",
     "start_time": "2018-01-13T07:46:49.672733Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C         D\n",
      "2013-01-06 -0.305994 -1.372941 -2.900295  0.220700\n",
      "2013-01-02 -0.000286 -1.257760 -1.232549 -0.899191\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  1.141695\n",
      "2013-01-05  0.944031 -0.653508  0.005439  0.316151\n",
      "2013-01-01 -0.653658 -0.412076  0.289240 -0.813057\n",
      "2013-01-04  0.100815  1.143710  0.467192  0.014952\n"
     ]
    }
   ],
   "source": [
    "print(df.sort_values(by='B'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据选择\n",
    "**注意：**尽管Python标准库和NumPy的语句在选择和设置数据时更直观和方便交互。但是在生产环境中，我们更建议使用Pandas的数据访问函数：```.at, .iat, .loc, .iloc, .ix```\n",
    "\n",
    "#### 数据获取\n",
    "选择一列，返回Series。等同于df.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:50.050583Z",
     "start_time": "2018-01-13T07:46:50.038517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01   -0.653658\n",
       "2013-01-02   -0.000286\n",
       "2013-01-03    0.404230\n",
       "2013-01-04    0.100815\n",
       "2013-01-05    0.944031\n",
       "2013-01-06   -0.305994\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-09T06:24:49.301694Z",
     "start_time": "2018-01-09T06:24:49.293642Z"
    }
   },
   "source": [
    "使用```[]```来切分多行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:50.386679Z",
     "start_time": "2018-01-13T07:46:50.375687Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C         D\n",
      "2013-01-01 -0.653658 -0.412076  0.289240 -0.813057\n",
      "2013-01-02 -0.000286 -1.257760 -1.232549 -0.899191\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  1.141695\n",
      "                   A         B         C         D\n",
      "2013-01-02 -0.000286 -1.257760 -1.232549 -0.899191\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  1.141695\n",
      "2013-01-04  0.100815  1.143710  0.467192  0.014952\n"
     ]
    }
   ],
   "source": [
    "print(df[0:3])\n",
    "print(df['20130102':'20130104'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 根据标签选择\n",
    "使用标签来得到特定的一行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:50.716513Z",
     "start_time": "2018-01-13T07:46:50.710495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.653658\n",
       "B   -0.412076\n",
       "C    0.289240\n",
       "D   -0.813057\n",
       "Name: 2013-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用标签来得到多个指定的列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:51.029548Z",
     "start_time": "2018-01-13T07:46:51.022530Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B\n",
      "2013-01-01 -0.653658 -0.412076\n",
      "2013-01-02 -0.000286 -1.257760\n",
      "2013-01-03  0.404230 -0.880146\n",
      "2013-01-04  0.100815  1.143710\n",
      "2013-01-05  0.944031 -0.653508\n",
      "2013-01-06 -0.305994 -1.372941\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[:,['A','B']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用标签来切片时，端点的值都是被包含在内的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:51.384300Z",
     "start_time": "2018-01-13T07:46:51.375251Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B\n",
      "2013-01-02 -0.000286 -1.257760\n",
      "2013-01-03  0.404230 -0.880146\n",
      "2013-01-04  0.100815  1.143710\n"
     ]
    }
   ],
   "source": [
    "print(df.loc['20130102':'20130104',['A','B']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-09T06:50:46.866941Z",
     "start_time": "2018-01-09T06:50:46.851903Z"
    }
   },
   "source": [
    "选择的返回值可以降低维度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:51.710760Z",
     "start_time": "2018-01-13T07:46:51.701723Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.000286\n",
       "B   -1.257760\n",
       "Name: 2013-01-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20130102',['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-09T06:51:10.599086Z",
     "start_time": "2018-01-09T06:51:10.589060Z"
    }
   },
   "source": [
    "选择一个标量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:52.070121Z",
     "start_time": "2018-01-13T07:46:52.065107Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.65365804231862046"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0],'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-09T06:52:11.528501Z",
     "start_time": "2018-01-09T06:52:11.524460Z"
    }
   },
   "source": [
    "快速获取一个标量（与上面相同）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:52.427089Z",
     "start_time": "2018-01-13T07:46:52.421105Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.65365804231862046"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0],'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 根据位置进行选择\n",
    "根据整数下标来选择数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:52.745211Z",
     "start_time": "2018-01-13T07:46:52.739198Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.100815\n",
       "B    1.143710\n",
       "C    0.467192\n",
       "D    0.014952\n",
       "Name: 2013-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用整数来切片，类似于Python标准库和NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:53.074663Z",
     "start_time": "2018-01-13T07:46:53.067643Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B\n",
      "2013-01-04  0.100815  1.143710\n",
      "2013-01-05  0.944031 -0.653508\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[3:5,0:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-09T07:25:26.762911Z",
     "start_time": "2018-01-09T07:25:26.756895Z"
    }
   },
   "source": [
    "用整数列表来进行选择，与Python标准库和NumPy类似"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:53.452416Z",
     "start_time": "2018-01-13T07:46:53.444912Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         C\n",
      "2013-01-02 -0.000286 -1.232549\n",
      "2013-01-03  0.404230 -1.593526\n",
      "2013-01-05  0.944031  0.005439\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[[1,2,4],[0,2]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对行进行切片选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:53.766568Z",
     "start_time": "2018-01-13T07:46:53.759571Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C         D\n",
      "2013-01-02 -0.000286 -1.257760 -1.232549 -0.899191\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  1.141695\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[1:3,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对列进行切片选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:54.090242Z",
     "start_time": "2018-01-13T07:46:54.082250Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   B         C\n",
      "2013-01-01 -0.412076  0.289240\n",
      "2013-01-02 -1.257760 -1.232549\n",
      "2013-01-03 -0.880146 -1.593526\n",
      "2013-01-04  1.143710  0.467192\n",
      "2013-01-05 -0.653508  0.005439\n",
      "2013-01-06 -1.372941 -2.900295\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[:,1:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取某个标量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:54.427372Z",
     "start_time": "2018-01-13T07:46:54.423365Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.257759630175423"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "快速地获取某个标量（与上面的函数相同）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:54.796667Z",
     "start_time": "2018-01-13T07:46:54.791652Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.257759630175423"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 布尔索引\n",
    "使用某列值来选择数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:55.132799Z",
     "start_time": "2018-01-13T07:46:55.123799Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C         D\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  1.141695\n",
      "2013-01-04  0.100815  1.143710  0.467192  0.014952\n",
      "2013-01-05  0.944031 -0.653508  0.005439  0.316151\n"
     ]
    }
   ],
   "source": [
    "print(df[df.A>0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从DataFrame中选择满足某条件的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:55.502012Z",
     "start_time": "2018-01-13T07:46:55.492005Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A        B         C         D\n",
      "2013-01-01       NaN      NaN  0.289240       NaN\n",
      "2013-01-02       NaN      NaN       NaN       NaN\n",
      "2013-01-03  0.404230      NaN       NaN  1.141695\n",
      "2013-01-04  0.100815  1.14371  0.467192  0.014952\n",
      "2013-01-05  0.944031      NaN  0.005439  0.316151\n",
      "2013-01-06       NaN      NaN       NaN  0.220700\n"
     ]
    }
   ],
   "source": [
    "print(df[df>0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用```isin()```函数来过滤数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:55.855504Z",
     "start_time": "2018-01-13T07:46:55.839958Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C         D      E\n",
      "2013-01-01 -0.653658 -0.412076  0.289240 -0.813057    one\n",
      "2013-01-02 -0.000286 -1.257760 -1.232549 -0.899191    one\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  1.141695    two\n",
      "2013-01-04  0.100815  1.143710  0.467192  0.014952  three\n",
      "2013-01-05  0.944031 -0.653508  0.005439  0.316151   four\n",
      "2013-01-06 -0.305994 -1.372941 -2.900295  0.220700  three\n",
      "                   A         B         C         D     E\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  1.141695   two\n",
      "2013-01-05  0.944031 -0.653508  0.005439  0.316151  four\n"
     ]
    }
   ],
   "source": [
    "df2=df.copy()\n",
    "df2['E']=['one','one','two','three','four','three']\n",
    "print(df2)\n",
    "print(df2[df2['E'].isin(['two','four'])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 设置数据\n",
    "设置一个新列并设置索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:56.254550Z",
     "start_time": "2018-01-13T07:46:56.235969Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-02    1\n",
       "2013-01-03    2\n",
       "2013-01-04    3\n",
       "2013-01-05    4\n",
       "2013-01-06    5\n",
       "2013-01-07    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1=pd.Series([1,2,3,4,5,6],index=pd.date_range('20130102',periods=6))\n",
    "df['F']=s1\n",
    "s1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据标签来设置值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:56.548368Z",
     "start_time": "2018-01-13T07:46:56.539358Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C         D    F\n",
      "2013-01-01  0.000000 -0.412076  0.289240 -0.813057  NaN\n",
      "2013-01-02 -0.000286 -1.257760 -1.232549 -0.899191  1.0\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  1.141695  2.0\n",
      "2013-01-04  0.100815  1.143710  0.467192  0.014952  3.0\n",
      "2013-01-05  0.944031 -0.653508  0.005439  0.316151  4.0\n",
      "2013-01-06 -0.305994 -1.372941 -2.900295  0.220700  5.0\n"
     ]
    }
   ],
   "source": [
    "df.at[dates[0],'A']=0\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据位置的来设置值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:56.911348Z",
     "start_time": "2018-01-13T07:46:56.903352Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C         D    F\n",
      "2013-01-01  0.000000  0.000000  0.289240 -0.813057  NaN\n",
      "2013-01-02 -0.000286 -1.257760 -1.232549 -0.899191  1.0\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  1.141695  2.0\n",
      "2013-01-04  0.100815  1.143710  0.467192  0.014952  3.0\n",
      "2013-01-05  0.944031 -0.653508  0.005439  0.316151  4.0\n",
      "2013-01-06 -0.305994 -1.372941 -2.900295  0.220700  5.0\n"
     ]
    }
   ],
   "source": [
    "df.iat[0,1]=0\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用NumPy数组来赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:57.296301Z",
     "start_time": "2018-01-13T07:46:57.286273Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C  D    F\n",
      "2013-01-01  0.000000  0.000000  0.289240  5  NaN\n",
      "2013-01-02 -0.000286 -1.257760 -1.232549  5  1.0\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  5  2.0\n",
      "2013-01-04  0.100815  1.143710  0.467192  5  3.0\n",
      "2013-01-05  0.944031 -0.653508  0.005439  5  4.0\n",
      "2013-01-06 -0.305994 -1.372941 -2.900295  5  5.0\n"
     ]
    }
   ],
   "source": [
    "df.loc[:,'D']=np.array([5]*len(df))\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在过滤的同时进行赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:57.636646Z",
     "start_time": "2018-01-13T07:46:57.622610Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C  D    F\n",
      "2013-01-01  0.000000  0.000000 -0.289240 -5  NaN\n",
      "2013-01-02 -0.000286 -1.257760 -1.232549 -5 -1.0\n",
      "2013-01-03 -0.404230 -0.880146 -1.593526 -5 -2.0\n",
      "2013-01-04 -0.100815 -1.143710 -0.467192 -5 -3.0\n",
      "2013-01-05 -0.944031 -0.653508 -0.005439 -5 -4.0\n",
      "2013-01-06 -0.305994 -1.372941 -2.900295 -5 -5.0\n"
     ]
    }
   ],
   "source": [
    "df2=df.copy()\n",
    "df2[df2>0]=-df2\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 处理缺失数据\n",
    "Pandas主要使用```np.nan```来表示缺失数据。这个值不会加入在计算中。\n",
    "重新索引允许你对某一轴的索引进行更改/添加/删除操作。这会返回操作数据的拷贝对象。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:58.037021Z",
     "start_time": "2018-01-13T07:46:58.024986Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C  D    F    E\n",
      "2013-01-01  0.000000  0.000000  0.289240  5  NaN  1.0\n",
      "2013-01-02 -0.000286 -1.257760 -1.232549  5  1.0  1.0\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  5  2.0  NaN\n",
      "2013-01-04  0.100815  1.143710  0.467192  5  3.0  NaN\n"
     ]
    }
   ],
   "source": [
    "df1=df.reindex(index=dates[0:4],columns=list(df.columns)+['E'])\n",
    "df1.loc[dates[0]:dates[1],'E']=1\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除含有缺失数据的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:58.417311Z",
     "start_time": "2018-01-13T07:46:58.409315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A        B         C  D    F    E\n",
      "2013-01-02 -0.000286 -1.25776 -1.232549  5  1.0  1.0\n"
     ]
    }
   ],
   "source": [
    "print(df1.dropna(how='any'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "填充缺失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:58.746563Z",
     "start_time": "2018-01-13T07:46:58.738533Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C  D    F    E\n",
      "2013-01-01  0.000000  0.000000  0.289240  5  5.0  1.0\n",
      "2013-01-02 -0.000286 -1.257760 -1.232549  5  1.0  1.0\n",
      "2013-01-03  0.404230 -0.880146 -1.593526  5  2.0  5.0\n",
      "2013-01-04  0.100815  1.143710  0.467192  5  3.0  5.0\n"
     ]
    }
   ],
   "source": [
    "print(df1.fillna(value=5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取数据中是```nan```的位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:59.081650Z",
     "start_time": "2018-01-13T07:46:59.073646Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                A      B      C      D      F      E\n",
      "2013-01-01  False  False  False  False   True  False\n",
      "2013-01-02  False  False  False  False  False  False\n",
      "2013-01-03  False  False  False  False  False   True\n",
      "2013-01-04  False  False  False  False  False   True\n"
     ]
    }
   ],
   "source": [
    "print(pd.isnull(df1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基础操作\n",
    "#### 基本的统计\n",
    "操作默认排除了缺失的数据  \n",
    "执行一个描述性的统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:59.497992Z",
     "start_time": "2018-01-13T07:46:59.491970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.190466\n",
       "B   -0.503441\n",
       "C   -0.827417\n",
       "D    5.000000\n",
       "F    3.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-09T08:11:20.821022Z",
     "start_time": "2018-01-09T08:11:20.811020Z"
    }
   },
   "source": [
    "在另外的轴执行该操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:46:59.877399Z",
     "start_time": "2018-01-13T07:46:59.863830Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    1.322310\n",
       "2013-01-02    0.701881\n",
       "2013-01-03    0.986111\n",
       "2013-01-04    1.942343\n",
       "2013-01-05    1.859192\n",
       "2013-01-06    1.084154\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对不同维度数据的操作需要进行校准。Pandas会自动的在指定的维数上应用广播。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:00.234625Z",
     "start_time": "2018-01-13T07:47:00.224598Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2013-01-01    NaN\n",
      "2013-01-02    NaN\n",
      "2013-01-03    1.0\n",
      "2013-01-04    3.0\n",
      "2013-01-05    5.0\n",
      "2013-01-06    NaN\n",
      "Freq: D, dtype: float64\n",
      "                   A         B         C    D    F\n",
      "2013-01-01       NaN       NaN       NaN  NaN  NaN\n",
      "2013-01-02       NaN       NaN       NaN  NaN  NaN\n",
      "2013-01-03 -0.595770 -1.880146 -2.593526  4.0  1.0\n",
      "2013-01-04 -2.899185 -1.856290 -2.532808  2.0  0.0\n",
      "2013-01-05 -4.055969 -5.653508 -4.994561  0.0 -1.0\n",
      "2013-01-06       NaN       NaN       NaN  NaN  NaN\n"
     ]
    }
   ],
   "source": [
    "s=pd.Series([1,3,5,np.nan,6,8],index=dates).shift(2)\n",
    "print(s)\n",
    "print(df.sub(s,axis='index'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 应用函数\n",
    "对数据应用函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:00.621408Z",
     "start_time": "2018-01-13T07:47:00.605386Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C   D     F\n",
      "2013-01-01  0.000000  0.000000  0.289240   5   NaN\n",
      "2013-01-02 -0.000286 -1.257760 -0.943310  10   1.0\n",
      "2013-01-03  0.403943 -2.137906 -2.536836  15   3.0\n",
      "2013-01-04  0.504759 -0.994196 -2.069644  20   6.0\n",
      "2013-01-05  1.448789 -1.647704 -2.064205  25  10.0\n",
      "2013-01-06  1.142795 -3.020645 -4.964500  30  15.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "A    1.250025\n",
       "B    2.516651\n",
       "C    3.367487\n",
       "D    0.000000\n",
       "F    4.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.apply(np.cumsum))\n",
    "df.apply(lambda x:x.max()-x.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:01.247458Z",
     "start_time": "2018-01-13T07:47:01.239436Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0\n",
      "1    0\n",
      "2    4\n",
      "3    1\n",
      "4    6\n",
      "5    6\n",
      "6    4\n",
      "7    3\n",
      "8    5\n",
      "9    1\n",
      "dtype: int32\n",
      "6    2\n",
      "4    2\n",
      "1    2\n",
      "0    2\n",
      "5    1\n",
      "3    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "s=pd.Series(np.random.randint(0,7,size=10))\n",
    "print(s)\n",
    "print(s.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 字符串函数\n",
    "在下面的代码中，Series可以在```str```属性中应用许多字符处理函数，可以更方便的处理数据中的每个元素。需要注意的是模式识别默认使用正则表达式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:01.648120Z",
     "start_time": "2018-01-13T07:47:01.640099Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       a\n",
       "1       b\n",
       "2       c\n",
       "3    aaba\n",
       "4    baca\n",
       "5     NaN\n",
       "6    caba\n",
       "7     dog\n",
       "8     cat\n",
       "dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series(['A','B','C','Aaba','Baca',np.nan,'CABA','dog','cat'])\n",
    "s.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据合并\n",
    "#### 数据拼接\n",
    "Pandas提供了许多工具来根据索引逻辑和线性代数关系组合Series、DataFrame和Panel。\n",
    "用```concat()```组合Pandas数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:02.215026Z",
     "start_time": "2018-01-13T07:47:02.199983Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2         3\n",
      "0  0.868667 -1.127783 -1.339226 -0.018955\n",
      "1  2.154827 -0.183290  0.346383 -0.535674\n",
      "2  0.539159  0.598349 -0.959137 -1.371542\n",
      "3  0.526160 -0.675516  0.329515  2.741801\n",
      "4 -0.672261  0.277084  0.246108 -0.058672\n",
      "5  1.345204  0.267244 -0.976797  0.258268\n",
      "6 -1.134643  0.736464  0.632367  0.355589\n",
      "7 -0.388383  0.573309 -0.920222  1.232245\n",
      "8 -0.394164 -0.492838 -0.036512  0.965029\n",
      "9 -0.219411  1.003898 -0.033570  1.059801\n",
      "          0         1         2         3\n",
      "0  0.868667 -1.127783 -1.339226 -0.018955\n",
      "1  2.154827 -0.183290  0.346383 -0.535674\n",
      "2  0.539159  0.598349 -0.959137 -1.371542\n",
      "3  0.526160 -0.675516  0.329515  2.741801\n",
      "4 -0.672261  0.277084  0.246108 -0.058672\n",
      "5  1.345204  0.267244 -0.976797  0.258268\n",
      "6 -1.134643  0.736464  0.632367  0.355589\n",
      "7 -0.388383  0.573309 -0.920222  1.232245\n",
      "8 -0.394164 -0.492838 -0.036512  0.965029\n",
      "9 -0.219411  1.003898 -0.033570  1.059801\n"
     ]
    }
   ],
   "source": [
    "df=pd.DataFrame(np.random.randn(10,4))\n",
    "print(df)\n",
    "pieces=[df[:3],df[3:7],df[7:]]\n",
    "print(pd.concat(pieces))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数据关联\n",
    "SQL形式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:02.608433Z",
     "start_time": "2018-01-13T07:47:02.591388Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   key  lval\n",
      "0  foo     1\n",
      "1  foo     2\n",
      "   key  rval\n",
      "0  foo     4\n",
      "1  foo     5\n",
      "   key  lval  rval\n",
      "0  foo     1     4\n",
      "1  foo     1     5\n",
      "2  foo     2     4\n",
      "3  foo     2     5\n"
     ]
    }
   ],
   "source": [
    "left=pd.DataFrame({'key':['foo','foo'],'lval':[1,2]})\n",
    "right=pd.DataFrame({'key':['foo','foo'],'rval':[4,5]})\n",
    "print(left)\n",
    "print(right)\n",
    "print(pd.merge(left,right,on='key'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:02.660585Z",
     "start_time": "2018-01-13T07:47:02.643528Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   key  lval\n",
      "0  foo     1\n",
      "1  bar     2\n",
      "   key  rval\n",
      "0  foo     4\n",
      "1  bar     5\n",
      "   key  lval  rval\n",
      "0  foo     1     4\n",
      "1  bar     2     5\n"
     ]
    }
   ],
   "source": [
    "left=pd.DataFrame({'key':['foo','bar'],'lval':[1,2]})\n",
    "right=pd.DataFrame({'key':['foo','bar'],'rval':[4,5]})\n",
    "print(left)\n",
    "print(right)\n",
    "print(pd.merge(left,right,on='key'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 添加数据（Append）\n",
    "给DataFrame添加行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:03.070830Z",
     "start_time": "2018-01-13T07:47:03.053277Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          A         B         C         D\n",
      "0 -0.379925 -1.017542 -0.189007 -0.428325\n",
      "1 -0.276408 -0.715901 -1.319698 -0.222887\n",
      "2  1.107723  0.353094  0.815195 -1.440487\n",
      "3 -0.033881  0.514738  0.290243 -0.573637\n",
      "4  0.425776 -0.074674 -0.002662  0.963586\n",
      "5  1.982210  1.058390 -0.605711  0.338158\n",
      "6  0.444923  0.802376  1.287365 -0.076757\n",
      "7  1.079877  0.937892  1.511644 -1.344911\n",
      "          A         B         C         D\n",
      "0 -0.379925 -1.017542 -0.189007 -0.428325\n",
      "1 -0.276408 -0.715901 -1.319698 -0.222887\n",
      "2  1.107723  0.353094  0.815195 -1.440487\n",
      "3 -0.033881  0.514738  0.290243 -0.573637\n",
      "4  0.425776 -0.074674 -0.002662  0.963586\n",
      "5  1.982210  1.058390 -0.605711  0.338158\n",
      "6  0.444923  0.802376  1.287365 -0.076757\n",
      "7  1.079877  0.937892  1.511644 -1.344911\n",
      "8 -0.033881  0.514738  0.290243 -0.573637\n"
     ]
    }
   ],
   "source": [
    "df=pd.DataFrame(np.random.randn(8,4),columns=['A','B','C','D'])\n",
    "print(df)\n",
    "s=df.iloc[3]\n",
    "print(df.append(s,ignore_index=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分组\n",
    "分组包含以下的一个或多个流程：\n",
    " - 分组 根据某种标准分组数据\n",
    " - 应用 对每个组应用一个函数\n",
    " - 组合 把分组的结果组合成一个整体结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:03.520375Z",
     "start_time": "2018-01-13T07:47:03.510323Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A      B         C         D\n",
      "0  foo    one  0.596282  0.822393\n",
      "1  bar    one -1.426678  0.106148\n",
      "2  foo    two -0.776830 -0.496158\n",
      "3  bar  three  0.954765  1.579897\n",
      "4  foo    two -0.127376 -0.793390\n",
      "5  bar    two -0.231712 -0.055958\n",
      "6  foo    one  0.576690  0.757426\n",
      "7  foo  three -1.365990 -1.358562\n"
     ]
    }
   ],
   "source": [
    "df=pd.DataFrame({'A':['foo','bar','foo','bar','foo','bar','foo','foo'],'B' : ['one', 'one', 'two', 'three',\n",
    "                              'two', 'two', 'one', 'three'],\n",
    "                       'C' : np.random.randn(8),\n",
    "                      'D' : np.random.randn(8)})\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分组并对分组后的结果求和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:03.930178Z",
     "start_time": "2018-01-13T07:47:03.922156Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            C         D\n",
      "A                      \n",
      "bar -0.703626  1.630087\n",
      "foo -1.097224 -1.068291\n"
     ]
    }
   ],
   "source": [
    "print(df.groupby('A').sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据多个列进行分组可以如下操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:04.352567Z",
     "start_time": "2018-01-13T07:47:04.341537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  C         D\n",
      "A   B                        \n",
      "bar one   -1.426678  0.106148\n",
      "    three  0.954765  1.579897\n",
      "    two   -0.231712 -0.055958\n",
      "foo one    1.172971  1.579818\n",
      "    three -1.365990 -1.358562\n",
      "    two   -0.904206 -1.289548\n"
     ]
    }
   ],
   "source": [
    "print(df.groupby(['A','B']).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 改变数据形状\n",
    "#### 压缩"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:04.749020Z",
     "start_time": "2018-01-13T07:47:04.735984Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('bar', 'one'), ('bar', 'two'), ('baz', 'one'), ('baz', 'two'), ('foo', 'one'), ('foo', 'two'), ('qux', 'one'), ('qux', 'two')]\n",
      "                     A         B\n",
      "first second                    \n",
      "bar   one    -0.482216 -1.720165\n",
      "      two    -1.788449 -0.475704\n",
      "baz   one    -0.757266 -0.026047\n",
      "      two    -1.799689 -0.641557\n"
     ]
    }
   ],
   "source": [
    "tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',\n",
    "                         'foo', 'foo', 'qux', 'qux'],\n",
    "                        ['one', 'two', 'one', 'two',\n",
    "                         'one', 'two', 'one', 'two']]))\n",
    "print(tuples)\n",
    "index=pd.MultiIndex.from_tuples(tuples,names=['first','second'])\n",
    "df=pd.DataFrame(np.random.randn(8,2),index=index,columns=['A','B'])\n",
    "df2 = df[:4]\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```stack()```函数压缩了这个DataFrame中的一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:05.126082Z",
     "start_time": "2018-01-13T07:47:05.118030Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first  second   \n",
       "bar    one     A   -0.482216\n",
       "               B   -1.720165\n",
       "       two     A   -1.788449\n",
       "               B   -0.475704\n",
       "baz    one     A   -0.757266\n",
       "               B   -0.026047\n",
       "       two     A   -1.799689\n",
       "               B   -0.641557\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked=df2.stack()\n",
    "stacked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于压缩之后的DataFrame，能够使用和```stack()```函数相反的```unstack()```函数，默认会解压缩最后一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:05.555850Z",
     "start_time": "2018-01-13T07:47:05.538831Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     A         B\n",
      "first second                    \n",
      "bar   one    -0.482216 -1.720165\n",
      "      two    -1.788449 -0.475704\n",
      "baz   one    -0.757266 -0.026047\n",
      "      two    -1.799689 -0.641557\n",
      "second        one       two\n",
      "first                      \n",
      "bar   A -0.482216 -1.788449\n",
      "      B -1.720165 -0.475704\n",
      "baz   A -0.757266 -1.799689\n",
      "      B -0.026047 -0.641557\n",
      "first          bar       baz\n",
      "second                      \n",
      "one    A -0.482216 -0.757266\n",
      "       B -1.720165 -0.026047\n",
      "two    A -1.788449 -1.799689\n",
      "       B -0.475704 -0.641557\n"
     ]
    }
   ],
   "source": [
    "print(stacked.unstack())\n",
    "print(stacked.unstack(1))\n",
    "print(stacked.unstack(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数据透视表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:05.963870Z",
     "start_time": "2018-01-13T07:47:05.950849Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        A  B    C         D         E\n",
      "0     one  A  foo -0.652781 -0.720116\n",
      "1     one  B  foo -0.515266  1.109243\n",
      "2     two  C  foo  0.724732 -0.012487\n",
      "3   three  A  bar  1.591435 -0.949670\n",
      "4     one  B  bar  0.596480 -0.403556\n",
      "5     one  C  bar -2.191866  0.291499\n",
      "6     two  A  foo -0.835479 -0.749321\n",
      "7   three  B  foo  0.147051 -0.299110\n",
      "8     one  C  foo  0.519447 -0.016910\n",
      "9     one  A  bar  0.482567 -2.322476\n",
      "10    two  B  bar  0.350926  0.135206\n",
      "11  three  C  bar  0.731305 -1.064031\n"
     ]
    }
   ],
   "source": [
    "df=pd.DataFrame({'A':['one','one','two','three']*3,\n",
    "                'B':['A','B','C']*4,\n",
    "                'C':['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n",
    "                'D':np.random.randn(12),\n",
    "                'E':np.random.randn(12)})\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以非常简单的用上面的数据生成一张数据透视表:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:06.489300Z",
     "start_time": "2018-01-13T07:47:06.472752Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C             bar       foo\n",
      "A     B                    \n",
      "one   A  0.482567 -0.652781\n",
      "      B  0.596480 -0.515266\n",
      "      C -2.191866  0.519447\n",
      "three A  1.591435       NaN\n",
      "      B       NaN  0.147051\n",
      "      C  0.731305       NaN\n",
      "two   A       NaN -0.835479\n",
      "      B  0.350926       NaN\n",
      "      C       NaN  0.724732\n"
     ]
    }
   ],
   "source": [
    "print(pd.pivot_table(df,values='D',index=['A','B'],columns=['C']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 时间序列\n",
    "Pandas拥有易用、强大且高效的方法来在频率变换中执行重采样操作（例如：把秒级别的数据转换成5分钟级别的数据）。这通常在金融应用中使用，但不仅限于金融应用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:06.995285Z",
     "start_time": "2018-01-13T07:47:06.979238Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01    25308\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng=pd.date_range('1/1/2012',periods=100,freq='S')\n",
    "ts=pd.Series(np.random.randint(0,500,len(rng)),index=rng)\n",
    "ts.resample('5Min').sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "表现时区"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:07.442870Z",
     "start_time": "2018-01-13T07:47:07.423820Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012-03-06    1.228351\n",
      "2012-03-07   -0.699147\n",
      "2012-03-08   -0.094176\n",
      "2012-03-09    1.070865\n",
      "2012-03-10    2.647150\n",
      "Freq: D, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2012-03-06 00:00:00+00:00    1.228351\n",
       "2012-03-07 00:00:00+00:00   -0.699147\n",
       "2012-03-08 00:00:00+00:00   -0.094176\n",
       "2012-03-09 00:00:00+00:00    1.070865\n",
       "2012-03-10 00:00:00+00:00    2.647150\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng=pd.date_range('3/6/2012 00:00',periods=5,freq='D')\n",
    "ts=pd.Series(np.random.randn(len(rng)),rng)\n",
    "print(ts)\n",
    "ts_utc=ts.tz_localize('UTC')\n",
    "ts_utc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "转换到其他时区"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:08.002107Z",
     "start_time": "2018-01-13T07:47:07.892132Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-05 19:00:00-05:00    1.228351\n",
       "2012-03-06 19:00:00-05:00   -0.699147\n",
       "2012-03-07 19:00:00-05:00   -0.094176\n",
       "2012-03-08 19:00:00-05:00    1.070865\n",
       "2012-03-09 19:00:00-05:00    2.647150\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert('US/Eastern')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "转换时间间隔的表现形式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:08.496275Z",
     "start_time": "2018-01-13T07:47:08.472708Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012-01-31   -0.046642\n",
      "2012-02-29    0.164101\n",
      "2012-03-31    0.249671\n",
      "2012-04-30    0.499200\n",
      "2012-05-31    0.008723\n",
      "Freq: M, dtype: float64\n",
      "2012-01   -0.046642\n",
      "2012-02    0.164101\n",
      "2012-03    0.249671\n",
      "2012-04    0.499200\n",
      "2012-05    0.008723\n",
      "Freq: M, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2012-01-01   -0.046642\n",
       "2012-02-01    0.164101\n",
       "2012-03-01    0.249671\n",
       "2012-04-01    0.499200\n",
       "2012-05-01    0.008723\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng=pd.date_range('1/1/2012',periods=5,freq='M')\n",
    "ts=pd.Series(np.random.randn(len(rng)),index=rng)\n",
    "print(ts)\n",
    "ps=ts.to_period()\n",
    "print(ps)\n",
    "ps.to_timestamp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 标签数据\n",
    "Pandas可以在DataFrame中包含标签数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:08.855260Z",
     "start_time": "2018-01-13T07:47:08.849230Z"
    }
   },
   "outputs": [],
   "source": [
    "df=pd.DataFrame({'id':[1,2,3,4,5,6],'raw_grade':['a','b','b','a','a','e']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将原始数据转换成标签数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:09.365220Z",
     "start_time": "2018-01-13T07:47:09.358201Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    a\n",
       "4    a\n",
       "5    e\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): [a, b, e]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['grade']=df['raw_grade'].astype('category')\n",
    "df['grade']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将标签重命名成更有意义的名字（对```Series.cat.categories```的赋值是没有申请新内存的）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:09.883685Z",
     "start_time": "2018-01-13T07:47:09.876669Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    very good\n",
       "1         good\n",
       "2         good\n",
       "3    very good\n",
       "4    very good\n",
       "5     very bad\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): [very good, good, very bad]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['grade'].cat.categories=['very good','good','very bad']\n",
    "df['grade']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "重排序标签并且同时增加缺失的标签（在```Series.cat```包下的函数默认返回新的```Series```）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:10.426981Z",
     "start_time": "2018-01-13T07:47:10.418958Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    very good\n",
       "1         good\n",
       "2         good\n",
       "3    very good\n",
       "4    very good\n",
       "5     very bad\n",
       "Name: grade, dtype: category\n",
       "Categories (5, object): [very bad, bad, medium, good, very good]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['grade']=df['grade'].cat.set_categories(['very bad','bad','medium','good','very good'])\n",
    "df['grade']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "排序是根据标签排序的，而不是字典序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:11.013855Z",
     "start_time": "2018-01-13T07:47:11.006835Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id raw_grade      grade\n",
      "5   6         e   very bad\n",
      "1   2         b       good\n",
      "2   3         b       good\n",
      "0   1         a  very good\n",
      "3   4         a  very good\n",
      "4   5         a  very good\n"
     ]
    }
   ],
   "source": [
    "print(df.sort_values(by='grade'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T06:42:24.713437Z",
     "start_time": "2018-01-13T06:42:24.700894Z"
    }
   },
   "source": [
    "对标签数据分组时同样会显示空标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:11.597351Z",
     "start_time": "2018-01-13T07:47:11.590358Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "very bad     1\n",
       "bad          0\n",
       "medium       0\n",
       "good         2\n",
       "very good    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('grade').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:12.401288Z",
     "start_time": "2018-01-13T07:47:12.186099Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18a4171d240>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PLTChs9eN8x/8FG9sPZHiWWUWtVJY1Jze6ZgSZPBzEL3Y8vBrMWNHknQtthrQ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\nAw3qECGRP0GhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18a416e6630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts=pd.Series(np.random.randn(1000),index=pd.date_range('1/1/2000',periods=1000))\n",
    "ts=ts.cumsum()\n",
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在DataFrame上，```plot()```是一个函数可以方便地对数据的每个列进行绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:13.098274Z",
     "start_time": "2018-01-13T07:47:12.787162Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18a447b0ba8>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ghWXhITIHQgRUMVKJpDYmDMhmQI6d7/c28fH6Ssb+5fMYUT6tPlNJjrQy7uhK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XMzJr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18a447a6f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.DataFrame(np.random.randn(1000,4),index=ts.index,columns=['A','B','C','D'])\n",
    "df=df.cumsum()\n",
    "df.plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据获取与导出\n",
    "#### CSV\n",
    "保存数据到csv文件中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:13.440814Z",
     "start_time": "2018-01-13T07:47:13.418780Z"
    }
   },
   "outputs": [],
   "source": [
    "df.to_csv('foo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从csv中读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:14.067226Z",
     "start_time": "2018-01-13T07:47:14.055193Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0         A         B         C         D\n",
      "0  2000-01-01  0.573685  0.254583 -1.007374  0.475982\n",
      "1  2000-01-02  0.027795  2.640388 -0.866092 -1.642860\n",
      "2  2000-01-03 -1.298852  2.205553 -1.189777 -1.829773\n",
      "3  2000-01-04 -0.350564  0.717657 -1.497301 -3.179510\n",
      "4  2000-01-05  1.732001  1.939669 -2.658425 -4.195840\n",
      "5  2000-01-06  0.457319  2.281939 -1.612770 -4.582275\n",
      "6  2000-01-07  0.571856  2.053347 -3.937807 -5.300862\n",
      "7  2000-01-08  3.242640  2.280455 -4.378306 -5.054595\n",
      "8  2000-01-09  2.884421  2.906835 -3.156276 -5.133953\n",
      "9  2000-01-10  2.486527  3.205170 -3.407170 -5.450735\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv('foo.csv')\n",
    "print(df[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### HDF5\n",
    "生成HDF5存储（需要安装tables库 ```pip3 install tables```）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:14.639399Z",
     "start_time": "2018-01-13T07:47:14.572526Z"
    }
   },
   "outputs": [],
   "source": [
    "df.to_hdf('foo.h5','df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从HDF5存储中读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:20.537861Z",
     "start_time": "2018-01-13T07:47:20.519813Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0         A         B         C         D\n",
      "0  2000-01-01  0.573685  0.254583 -1.007374  0.475982\n",
      "1  2000-01-02  0.027795  2.640388 -0.866092 -1.642860\n",
      "2  2000-01-03 -1.298852  2.205553 -1.189777 -1.829773\n",
      "3  2000-01-04 -0.350564  0.717657 -1.497301 -3.179510\n",
      "4  2000-01-05  1.732001  1.939669 -2.658425 -4.195840\n",
      "5  2000-01-06  0.457319  2.281939 -1.612770 -4.582275\n",
      "6  2000-01-07  0.571856  2.053347 -3.937807 -5.300862\n",
      "7  2000-01-08  3.242640  2.280455 -4.378306 -5.054595\n",
      "8  2000-01-09  2.884421  2.906835 -3.156276 -5.133953\n",
      "9  2000-01-10  2.486527  3.205170 -3.407170 -5.450735\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_hdf('foo.h5','df')\n",
    "print(df[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Excel\n",
    "生成Excel文件（需要安装openpyxl库```pip3 install openpyxl```）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:21.522778Z",
     "start_time": "2018-01-13T07:47:20.692815Z"
    }
   },
   "outputs": [],
   "source": [
    "df.to_excel('foo.xlsx',sheet_name='Sheet01')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从Excel中读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T07:47:21.631120Z",
     "start_time": "2018-01-13T07:47:21.524783Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0         A         B         C         D\n",
      "0  2000-01-01  0.573685  0.254583 -1.007374  0.475982\n",
      "1  2000-01-02  0.027795  2.640388 -0.866092 -1.642860\n",
      "2  2000-01-03 -1.298852  2.205553 -1.189777 -1.829773\n",
      "3  2000-01-04 -0.350564  0.717657 -1.497301 -3.179510\n",
      "4  2000-01-05  1.732001  1.939669 -2.658425 -4.195840\n",
      "5  2000-01-06  0.457319  2.281939 -1.612770 -4.582275\n",
      "6  2000-01-07  0.571856  2.053347 -3.937807 -5.300862\n",
      "7  2000-01-08  3.242640  2.280455 -4.378306 -5.054595\n",
      "8  2000-01-09  2.884421  2.906835 -3.156276 -5.133953\n",
      "9  2000-01-10  2.486527  3.205170 -3.407170 -5.450735\n"
     ]
    }
   ],
   "source": [
    "print(pd.read_excel('foo.xlsx', 'Sheet01',\n",
    "                    index_col=None, na_values=['NA'])[:10])"
   ]
  }
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