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
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#!/usr/bin/env python# -*- coding:utf-8 -*-# author:zhengzhengliuimport numpy as npimport pandas as pdfrom 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
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#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
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import numpy as npimport pandas as pdfrom 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.nanprint(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
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import numpy as npimport pandas as pdfrom 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
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import numpy as npimport pandas as pdfrom 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
希望本文所述对大家python程序设计有所帮助。
原文链接：https://blog.csdn.net/loveliuzz/article/details/78498094
