Pandas简单操作（学习总结）
Pandas 的主要数据结构是 Series （一维数据）与 DataFrame（二维数据），是一个提供高性能、易于使用的数据结构和数据分析工具。接下来查看Pandas的基本使用：
# 导入模块
import pandas as pd
import numpy as np
# 读取文件
stu = pd.read_excel('./stu_data.xlsx')
stu.head()https://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE2NTc0NDY3Ni0zODc3NzY2MjgucG5n
# 查看数据 （数据类型，是否有空值）
stu.info()https://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE2NTgxMzIxMy0yODQ1MDY0NzMucG5n
# 转换数据类型
stu['日期'] = stu['日期'].astype('str')
stu.info()https://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE2NTgzODQ1MC02MDI3OTYxODUucG5n
切片操作
# iloc or loc切片 (学号，身高，体重）
stu.iloc[:,]# 获取学号,身高,体重，所有行信息
stu.loc ]https://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE3MDAyMTg4My0xMDI0MTQ4MjcucG5n
查询操作
# sql查询语言 身高高于170 性别是女
stu.query('身高 > 170 and 性别 == "女"')
# pandas查询
stu[ (stu['身高'] > 170) & (stu['性别'] =="女") ]https://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE3MDEwNDUyNi05MTc3NTQ3NDQucG5n
# 通过索引号获取信息
stu.query('10')https://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE3MDEzNTk3OC0yMDU1MDM3MDIucG5n
排序操作
stu['身高'].sort_values() # 默认正序
stu['身高'].sort_values(ascending=False) # 默认正序https://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE3MDIwNzI2MS0xMDQ1ODIxODQxLnBuZw==
分组操作
# 按课程分组，查看分组里面的数据
stu = stu.groupby('课程')
stu.groupshttps://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE3MDI1NTM4MC0xODMwNzgwMDk4LnBuZw==
# 查看分组描述
stu.describe()https://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE3MDM1NDgxNy0xMDMzMTAzMTY2LnBuZw==
# 分组汇总
# stu.agg(['mean','std']) # 分组后每一列的均值和标准差
print(stu.身高.agg(max))https://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE3MDQyMjA5Ny0yMTMzMTUxMjczLnBuZw==
数值变量分段stu = pd.read_excel('./stu_data.xlsx')
stu['新体重'] = pd.cut(stu.体重,bins=,right=False)
stu.head()https://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE3MDUwNTA2OS0xMTY3ODg2NDE0LnBuZw==
时间拆分
# stu.日期
stu['年份'] = stu.日期.dt.year
stu['月份'] = stu.日期.dt.month
stu['天数'] = stu.日期.dt.day
stu.head()https://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE3MDU1MDk0MS0xNzI4MDc1MDE5LnBuZw==
表连接
# 创建新Series对象
stu1 = pd.Series(np.arange(12345678900,12345678952),name='手机号')
stu1https://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE3MDY1MTk3MS0xMjc3MDgwNDQucG5n
# 合并表<br>stu3 = pd.concat(,axis=1)
stu3.head()https://dis.qidao123.com/imgproxy/aHR0cHM6Ly9pbWcyMDIyLmNuYmxvZ3MuY29tL2Jsb2cvMjg3MzIyMi8yMDIyMDcvMjg3MzIyMi0yMDIyMDcyNjE3MDcyMzk0Mi0xNzA3MTEzODEucG5n
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