pandas基础运算和合并示例

本文将介绍Pandas库的基础数据操作,包括数据读取、数据清洗、数据筛选,以及如何进行数据合并和连接操作。通过实例展示如何利用Pandas进行数据处理,为数据分析工作打下坚实基础。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

{
   
 "cells": [
  {
   
   "cell_type": "markdown",
   "metadata": {
   },
   "source": [
    "### Python数据分析的三剑客"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# pip install matplotlib\n",
    "# 画图,可视化!\n",
    "# 头号玩家,虚拟现实游戏,可视化,立体化\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   
   "cell_type": "markdown",
   "metadata": {
   },
   "source": [
    "### 生成对象"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
   
    "collapsed": true
   },
   "outputs": [
    {
   
     "data": {
   
      "text/plain": [
       "张三       88.0\n",
       "李四      103.0\n",
       "王五       68.0\n",
       "老路      134.0\n",
       "Jack     99.0\n",
       "Name: Python, dtype: float32"
      ]
     },
     "execution_count": 3,
     "metadata": {
   },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 一维的\n",
    "s = pd.Series(data = [88,103,68,134,99],index = ['张三','李四','王五','老路','Jack'],\n",
    "              dtype=np.float32,name = 'Python')\n",
    "s"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
   
    "collapsed": true
   },
   "outputs": [
    {
   
     "data": {
   
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Python</th>\n",
       "      <th>En</th>\n",
       "      <th>数学</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>张三</th>\n",
       "      <td>104</td>\n",
       "      <td>31</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>李四</th>\n",
       "      <td>50</td>\n",
       "      <td>62</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>王五</th>\n",
       "      <td>79</td>\n",
       "      <td>51</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>老路</th>\n",
       "      <td>87</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jack</th>\n",
       "      <td>144</td>\n",
       "      <td>59</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Python  En   数学\n",
       "张三       104  31   72\n",
       "李四        50  62  108\n",
       "王五        79  51   98\n",
       "老路        87   5    2\n",
       "Jack     144  59   31"
      ]
     },
     "execution_count": 9,
     "metadata": {
   },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data = np.random.randint(0,150,size=(5,3)),\n",
    "             index = ['张三','李四','王五','老路','Jack'],\n",
    "             columns=['Python','En','数学'])\n",
    "df"
   ]
  },
  {
   
   "cell_type": "markdown",
   "metadata": {
   },
   "source": [
    "### 查看数据"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
   },
   "outputs": [
    {
   
     "data": {
   
      "text/plain": [
       "68.0"
      ]
     },
     "execution_count": 11,
     "metadata": {
   },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['王五']"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
   
    "collapsed": true
   },
   "outputs": [
    {
   
     "data": {
   
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Python</th>\n",
       "      <th>En</th>\n",
       "      <th>数学</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>张三</th>\n",
       "      <td>104</td>\n",
       "      <td>31</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>李四</th>\n",
       "      <td>50</td>\n",
       "      <td>62</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>王五</th>\n",
       "      <td>79</td>\n",
       "      <td>51</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Python  En   数学\n",
       "张三     104  31   72\n",
       "李四      50  62  108\n",
       "王五      79  51   98"
      ]
     },
     "execution_count": 13,
     "metadata": {
   },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
   
    "collapsed": true
   },
   "outputs": [
    {
   
     "data": {
   
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n"
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值