目錄

• 1.篩選出目標值所在行 
• 單列篩選
• 多列篩選
• 2.刪除目標值所在得行
• 擴展補充案例:刪除列為指定值所在得行

1.篩選出目標值所在行 

單列篩選

# df[列名].isin([目標值])對當前列中存在目標值得行會返回True，不存在得返回Falsedf[df[列名].isin([目標值])]

練習案例 

import pandas as pd df_bom_data = pd.DataFrame([['A123',1200,5],                            ['B456',550,2],                            ['C437',500,10],                            ['D112',621,7],                            ['E211',755,11],                            ['F985',833,8]                            ],columns=['Material','Price','Quantity']) df_material_shortage_data = pd.DataFrame([['A123','2022/6/21',100],                                          ['B456','2022/6/22',120],                                          ['C437','2022/6/23',250]                                          ],columns=['Material','Schedule','LT'])# 篩選出df_bom_data表中只包含df_material_shortage_data表中Material得行記錄df_bom_data = df_bom_data[df_bom_data['Material'].isin(df_material_shortage_data['Material'])]

df_bom_data

df_material_shortage_data 

df_bom_data(處理后)

多列篩選

# 同時滿足用&連接，或得話用 | 連接df[df[列名].isin([目標值]) & df[列名].isin([目標值])]df[df[列名].isin([目標值]) | df[列名].isin([目標值])]

練習案例 

import pandas as pd df = pd.DataFrame([['L123','A',0],                   ['L456','A',1],                   ['L437','C',0],                   ['L112','B',1],                   ['L211','A',0],                   ['L985','B',1]                  ],columns=['Material','Level','Passing'])# 篩選出指定列都有目標值得行res1 = df[df['Level'].isin(['A','C']) & df['Passing'].isin([0])]# 篩選出至少有一列有目標值得行res2 = df[df['Level'].isin(['A','C']) | df['Passing'].isin([0])]

df

res1

res2 

2.刪除目標值所在得行

練習案例

import pandas as pdimport numpy as np df_bom_data = pd.DataFrame([['A123',1200,5],                            ['B456',np.nan,np.nan],                            ['C437',500,10]                            ],columns=['Material','Price','Quantity']) df_material_shortage_data = pd.DataFrame([['A123','2022/6/21',100],                                          ['B456','2022/6/22',120],                                          ['C437','2022/6/23',250]                                          ],columns=['Material','Schedule','LT']) # 篩選出df_bom_data中'Price'和'Quantity'兩列字段得值都為空(nans)得行df_isnull_bom_data = df_bom_data[pd.isnull(df_bom_data[df_bom_data.columns.tolist()[1:]]).all(axis=1)] # df_material_shortage_data表刪除all_isnull_df_bom_data表中得Materialdf_material_shortage_data = df_material_shortage_data[~df_material_shortage_data['Material'].isin(df_isnull_bom_data['Material'])]

df_bom_data

df_material_shortage_data

df_isnull_bom_data 

df_material_shortage_data(處理后)

擴展補充案例:刪除列為指定值所在得行

import pandas as pd df = pd.DataFrame([[0,1,2,3],                  [4,5,6,7],                  [8,9,10,11]                  ],columns=['A','B','C','D']) # 通過重新取值，數據篩選后重新賦值，達到刪除列為指定值得行數據# 刪除A列中值為0得那一行記錄df = df[df['A'] != 0]

df

df(處理后) 

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