Python3.5 Pandas模块之DataFrame用法实例分析

更新时间：2020-06-30 12:18:01 作者：startmvc

本文实例讲述了Python3.5Pandas模块之DataFrame用法。分享给大家供大家参考，具体如下：1、DataF

本文实例讲述了Python3.5 Pandas模块之DataFrame用法。分享给大家供大家参考，具体如下：

1、DataFrame的创建

（1）通过二维数组方式创建


#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author:ZhengzhengLiu

import numpy as np
import pandas as pd
from pandas import Series,DataFrame

#1.DataFrame通过二维数组创建
print("======DataFrame直接通过二维数组创建======")
d1 = DataFrame([["a","b","c","d"],[1,2,3,4]])
print(d1)

print("======DataFrame借助array二维数组创建======")
arr = np.array([
 ["jack",78],
 ["lili",86],
 ["amy",97],
 ["tom",100]
])

d2 = DataFrame(arr,index=["01","02","03","04"],columns=["姓名","成绩"])
print(d2)
print("========打印行索引========")
print(d2.index)
print("========打印列索引========")
print(d2.columns)
print("========打印值========")
print(d2.values)

运行结果：

======DataFrame直接通过二维数组创建====== 0 1 2 3 0 a b c d 1 1 2 3 4 ======DataFrame借助array二维数组创建====== 姓名成绩 01 jack 78 02 lili 86 03 amy 97 04 tom 100 ========打印行索引======== Index(['01', '02', '03', '04'], dtype='object') ========打印列索引======== Index(['姓名', '成绩'], dtype='object') ========打印值======== [['jack' '78'] ['lili' '86'] ['amy' '97'] ['tom' '100']]

（2）通过字典方式创建


#2.DataFrame通过字典创建，键作为列索引，键值作为数据值，行索引值自动生成

data = {
 "apart":['1101',"1102","1103","1104"],
 "profit":[2000,4000,5000,3500],
 "month":8
}

d3 = DataFrame(data)
print(d3)
print("========行索引========")
print(d3.index)
print("========列索引========")
print(d3.columns)
print("========数据值========")
print(d3.values)

运行结果：

apart month profit 0 1101 8 2000 1 1102 8 4000 2 1103 8 5000 3 1104 8 3500 ========行索引======== RangeIndex(start=0, stop=4, step=1) ========列索引======== Index(['apart', 'month', 'profit'], dtype='object') ========数据值======== [['1101' 8 2000] ['1102' 8 4000] ['1103' 8 5000] ['1104' 8 3500]]

2、DataFrame数据获取


import numpy as np
import pandas as pd
from pandas import Series,DataFrame

#3.DataFrame获取数据
data = {
 "apart":['1101',"1102","1103","1104"],
 "profit":[2000,4000,5000,3500],
 "month":8
}

d3 = DataFrame(data)
print(d3)

print("======获取一列数据======")
print(d3["apart"])
print("======获取一行数据======")
print(d3.ix[1])

print("======修改数据值======")
d3["month"] = [7,8,9,10] #修改值
d3["year"] = [2001,2001,2003,2004] #新增列
d3.ix["4"] = np.NaN
print(d3)

运行结果：

apart month profit 0 1101 8 2000 1 1102 8 4000 2 1103 8 5000 3 1104 8 3500 ======获取一列数据====== 0 1101 1 1102 2 1103 3 1104 Name: apart, dtype: object ======获取一行数据====== apart 1102 month 8 profit 4000 Name: 1, dtype: object ======修改数据值====== apart month profit year 0 1101 7.0 2000.0 2001.0 1 1102 8.0 4000.0 2001.0 2 1103 9.0 5000.0 2003.0 3 1104 10.0 3500.0 2004.0 4 NaN NaN NaN NaN

3、pandas基本功能

（1）pandas数据文件读取


import numpy as np
import pandas as pd
from pandas import Series,DataFrame

#pandas基本操作
#1.数据文件读取

df = pd.read_csv("data.csv")
print(df)

运行结果：

name age source 0 gerry 18 98.5 1 tom 21 78.2 2 lili 24 98.5 3 john 20 89.2

（2）数据过滤获取


import numpy as np
import pandas as pd
from pandas import Series,DataFrame

#pandas基本操作
#1.数据文件读取

df = pd.read_csv("data.csv")
print(df)

#2.数据过滤获取

columns = ["姓名","年龄","成绩"]
df.columns = columns #更改列索引
print("=======更改列索引========")
print(df)

#获取几列的值
df1 = df[columns[1:]]
print("=======获取几列的值========")
print(df1)
print("=======获取几行的值========")
print(df.ix[1:3])

#删除含有NaN值的行
df2 = df1.dropna()
print("=======删除含有NaN值的行=======")
print(df2)

运行结果：

name age source 0 gerry 18 98.5 1 tom 21 NaN 2 lili 24 98.5 3 john 20 89.2 =======更改列索引======== 姓名年龄成绩 0 gerry 18 98.5 1 tom 21 NaN 2 lili 24 98.5 3 john 20 89.2 =======获取几列的值======== 年龄成绩 0 18 98.5 1 21 NaN 2 24 98.5 3 20 89.2 =======获取几行的值======== 姓名年龄成绩 1 tom 21 NaN 2 lili 24 98.5 3 john 20 89.2 =======删除含有NaN值的行======= 年龄成绩 0 18 98.5 2 24 98.5 3 20 89.2