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Python pandas DataFrame 行列的常用操作及运算

1、DataFrame的常用操作

1)查看DataFrame的大小

import pandas as pddf = pd.DataFrame([[10,6,7,8],                   [1,9,12,14],                   [5,8,10,6]],                  columns = ['a','b','c','d'])print(df.shape)

2)查看DataFrame的列名(columns)

import pandas as pddf = pd.DataFrame([[10,6,7,8],                   [1,9,12,14],                   [5,8,10,6]],                  columns = ['a','b','c','d'])print(df.columns)

3)查看DataFrame的行名(index)

import pandas as pddf = pd.DataFrame([[10,6,7,8],                   [1,9,12,14],                   [5,8,10,6]],                  columns = ['a','b','c','d'])print(df.index)

4)查看每行的数据类型

import pandas as pddf = pd.DataFrame([[10,6,7,8],                   [1,9,12,14],                   [5,8,10,6]],                  columns = ['a','b','c','d'])print(df.dtypes)

5)查看行列的内容

import pandas as pddf = pd.DataFrame([[10,6,7,8],                   [1,9,12,14],                   [5,8,10,6]],                  columns = ['a','b','c','d'])print(df)print(df.iloc[-1]) #选取DataFrame最后一行,返回的是Seriesprint(df.iloc[-1:]) #选取DataFrame最后一行,返回的是DataFrameprint(df.loc[0:1,'b':'c']) #这种用于选取行索引列索引已知print(df.iat[1,1]) #选取第二行第二列,用于已知行、列位置的选取。print(df.head(2)) #查看前两行的内容print(df.tail(2)) #查看后两行的内容print(df.loc[[df.index[0],df.index[1]]]) #通过label查看print(df.loc[[df.index[-2],df.index[-1]]])

6)查看每一列数据的整体特征

import pandas as pddf = pd.DataFrame([[10,6,7,8],                   [1,9,12,14],                   [5,8,10,6]],                  columns = ['a','b','c','d'])print(df.describe())

7)对DataFrame进行倒置

import pandas as pddf = pd.DataFrame([[10,6,7,8],                   [1,9,12,14],                   [5,8,10,6]],                  columns = ['a','b','c','d'])print(df.T)

2、DataFrame的行列运算

1)DataFrame中两列数据相加减

import pandas as pddf = pd.DataFrame([[10,6,7,8],                   [1,9,12,14],                   [5,8,10,6]],                  columns = ['a','b','c','d'])df['d - a'] = df['d'] - df['a']df['d + a'] = df['d'] + df['a']print(df)

2)DataFrame中两行数据相加减

import pandas as pddf = pd.DataFrame([[10,6,7,8],                   [1,9,12,14],                   [5,8,10,6]],                  columns = ['a','b','c','d'])print(df[df.a==10].values-df[df.a==1].values)print(df[df.a==10].values+df[df.a==1].values)

相关文档:

Python pandas.DataFrame.iloc函数方法的使用

Python pandas.DataFrame.loc函数方法的使用

Python pandas dataframe iloc 和 loc 的用法及区别

Python pandas.DataFrame.at函数方法的使用

Python pandas.DataFrame.iat函数方法的使用

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