PANDAS 中的Datafram属性

import pandas as pd      
import numpy as np      
      
dict= {'2018':[85,73,80,64], '2019':[60,80,58,96], '2020':[90,60,74,87] }      
      
df1=pd.DataFrame(dict,index=['English','Math','Science','French'])      
print("Using 'Empty' on Dataframe1:",df1.empty)    
print('DataFrame is not Empty')      
print('\n')      
print('Finding NaN values... ','\n',df1.isna())      
print('NOT FOUND!!')    
print('\n')    
      
df2=pd.DataFrame(index=['English','Math','Science','French'])      
print("Using 'Empty' on Dataframe2:")    
print(df2.empty,'(DataFrame is Empty)')      
print('\n')      
print('Finding NaN values...',df2.isna())    
print('FOUND!!')   

输出

COUNT

import pandas as pd      
      
dict= {'2018':[85,73,80,64], '2019':[60,80,58,96], '2020':[90,64,74,87] }      
      
df=pd.DataFrame(dict,index=['English','Math','Science','French'])      
print(df)      
print('\n')    
print("When we use 'count()':")    
print(df.count())      
print('\n')    
print("When we use 'count(axis='index')':")    
print(df.count(axis='index'))      
print('\n')    
print("When we use 'count(1)':")    
print(df.count(1))      
print('\n')    
print("When we use 'count(axis='columns')':")    
print(df.count(axis='columns'))   

T

该属性用于转置DataFrame；即，行变成列，列变成行。

句法

<DataFrameObject>. <T>

import pandas as pd      
      
dict= {'2018':[85,73,80,64], '2019':[60,80,58,96], '2020':[90,64,74,87] }      
      
df=pd.DataFrame(dict,index=['English','Math','Science','French'])      
print(df)      
print('\n')      
df1=df.T    
print("After Transpose:")    
print(df1)   

输出

概括

在本文中，我们了解了 10 个数据框属性、它们的用途和实现。展望未来，您将成为 Pandas 的专家。苦练！

在我的下一篇文章中，我们将学习“如何访问 DataFrames 中的数据”。

非常欢迎与本文相关的反馈或查询。

谢谢阅读。