Pandas筛选DataFrame含有空值的数据行的实现_python_

import pandas as pd df = pd.DataFrame([['ABC','Good',1], ['FJZ',None,2], ['FOC','Good',None] ],columns=['Site','Remark','Quantity'])

# 语法 df[pd.isnull(df[col])] df[df[col].isnull()] 

# 获取Remark字段为None的行 df_isnull_remark = df[df['Remark'].isnull()] # 获取Quantity字段为None的行 df_isnull_quantity = df[df['Quantity'].isnull()]

# 语法 df[pd.notnull(df[col])] df[df[col].notnull()] 

# 获取Remark字段为非None的行 df_notnull_remark = df[df['Remark'].notnull()] # 获取Quantity字段为非None的行 df_notnull_quantity = df[df['Quantity'].notnull()]

# 语法 df[df[[cols]].isnull().all(axis=1)] df[pd.isnull(df[[cols]]).all(axis=1)]

df1 = pd.DataFrame([['ABC','Good',1], ['FJZ',None,2], ['FOC','Good',None], [None,None,None] ],columns=['Site','Remark','Quantity'])

# 获取df1所有列有空值的数据行 all_df_isnull = df1[df1[['Site','Remark','Quantity']].isnull().all(axis=1)]

# 语法 df[df[[cols]].notnull().all(axis=1)] df[pd.notnull(df[[cols]]).all(axis=1)]

# 获取df1所有列没有空值的数据行 all_df_notnull = df1[df1[['Site','Remark','Quantity']].notnull().all(axis=1)]

# 语法 df[df[[cols]].isnull().any(axis=1)] df[pd.isnull(df[[cols]]).any(axis=1)]

# 获取df1所有列中满足任意一列有空值的数据行 any_df_isnull = df1[df1[['Site','Remark','Quantity']].isnull().any(axis=1)] 

# 语法 df[df[[cols]].notnull().any(axis=1)] df[pd.notnull(df[[cols]]).any(axis=1)]

# 获取df1所有列中满足任意一列没有空值的数据行 any_df_notnull = df1[df1[['Site','Remark','Quantity']].notnull().any(axis=1)]

import pandas as pd import numpy as np df = pd.DataFrame({'site1': ['a', 'b', 'c', ''], 'site2': ['a', np.nan, '', 'd'], 'site3': ['a', 'b', 'c', 'd']})

df['contact_site'] = df['site1'] + df['site2'] + df['site3']

res1 = df[df['site2'].isnull()] res2 = df[df['site2'].isna()] res3 = df[df['site2']=='']