Python-100-Days/Day76-90/code/1-pandas入门.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import Series,DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Math       120\n",
       "Python     136\n",
       "En         128\n",
       "Chinese     99\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建\n",
    "# Series是一维的数据\n",
    "s = Series(data = [120,136,128,99],index = ['Math','Python','En','Chinese'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([120, 136, 128,  99], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = s.values\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120.75"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.903353943953666"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Math       14400\n",
       "Python     18496\n",
       "En         16384\n",
       "Chinese     9801\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.pow(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Python</th>\n",
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      "text/plain": [
       "   Python   En  Math\n",
       "a     113  116    75\n",
       "b      19  145    23\n",
       "c      57  107   113\n",
       "d      95    3    66\n",
       "e      28  121   120\n",
       "f     141   85   132\n",
       "h     124   39    10\n",
       "i      80   35    17\n",
       "j      68   99    31\n",
       "k      74   12    11"
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     },
     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "# DataFrame是二维的数据\n",
    "# excel就非常相似\n",
    "# 所有进行数据分析，数据挖掘的工具最基础的结果：行和列，行表示样本，列表示的是属性\n",
    "df = DataFrame(data = np.random.randint(0,150,size = (10,3)),index = list('abcdefhijk'),columns=['Python','En','Math'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[113, 116,  75],\n",
       "       [ 19, 145,  23],\n",
       "       [ 57, 107, 113],\n",
       "       [ 95,   3,  66],\n",
       "       [ 28, 121, 120],\n",
       "       [141,  85, 132],\n",
       "       [124,  39,  10],\n",
       "       [ 80,  35,  17],\n",
       "       [ 68,  99,  31],\n",
       "       [ 74,  12,  11]])"
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "v = df.values\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python    79.9\n",
       "En        76.2\n",
       "Math      59.8\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.mean()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python    141\n",
       "En        145\n",
       "Math      132\n",
       "dtype: int32"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Python   En  Math\n",
       "a     113  116    75\n",
       "b      19  145    23\n",
       "c      57  107   113\n",
       "d      95    3    66\n",
       "e      28  121   120\n",
       "f     141   85   132\n",
       "h     124   39    10\n",
       "i      80   35    17\n",
       "j      68   99    31\n",
       "k      74   12    11"
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     "execution_count": 18,
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python    79.9\n",
       "En        76.2\n",
       "Math      59.8\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis = 0)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    101.333333\n",
       "b     62.333333\n",
       "c     92.333333\n",
       "d     54.666667\n",
       "e     89.666667\n",
       "f    119.333333\n",
       "h     57.666667\n",
       "i     44.000000\n",
       "j     66.000000\n",
       "k     32.333333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.mean(axis = 1)"
   ]
  }
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