Pandas篩選DataFrame含有空值的資料行的實現

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

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

# 獲取Remark欄位為None的行
df_isnull_remark = df[df['Remark'].isnull()]
# 獲取Quantity欄位為None的行
df_isnull_quantity = df[df['Quantity'].isnull()]

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

# 獲取Remark欄位為非None的行
df_notnull_remark = df[df['Remark'].notnull()]
# 獲取Quantity欄位為非None的行
df_notnull_quantity = df[df['Quantity'].notnull()]

# 語法
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'])

# 獲取df1所有列有空值的資料行 
all_df_isnull = df1[df1[['Site','Remark','Quantity']].isnull().all(axis=1)]

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

# 獲取df1所有列沒有空值的資料行 
all_df_notnull = df1[df1[['Site','Remark','Quantity']].notnull().all(axis=1)]

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

# 獲取df1所有列中滿足任意一列有空值的資料行 
any_df_isnull = df1[df1[['Site','Remark','Quantity']].isnull().any(axis=1)] 

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

# 獲取df1所有列中滿足任意一列沒有空值的資料行 
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']=='']