英文:
filter a pandas df with multiple check of different column equal
问题
I understand your request. You want to filter the rows in a DataFrame based on the conditions that date1 and date3 are different or date2 and date4 are different. Here's how you can achieve this using pandas in Python:
import pandas as pd# Assuming you have your data in a DataFrame named 'df'# Convert date columns to datetime type if they are not alreadydf['date1'] = pd.to_datetime(df['date1'], format='%d-%m-%Y', errors='coerce')df['date3'] = pd.to_datetime(df['date3'], format='%d-%m-%Y', errors='coerce')df['date2'] = pd.to_datetime(df['date2'], format='%d-%m-%Y', errors='coerce')df['date4'] = pd.to_datetime(df['date4'], format='%d-%m-%Y', errors='coerce')# Filter rows based on your conditionsfiltered_df = df[(df['date1'] != df['date3']) | (df['date2'] != df['date4'])]# Print the filtered DataFrameprint(filtered_df)
This code first converts the date columns to datetime type and then filters the rows based on your specified conditions. The resulting filtered_df will contain only the rows where date1 and date3 are different or date2 and date4 are different, or both conditions are met.
英文:
,unique_system_identifier,call_sign,date1,date2,date3,date40,3929436,WQZL268,14-06-2023,,14-06-2023,1,3929436,WQZL268,,,,2,3929437,WQZL269,14-06-2023,,14-06-2023,3,3929437,WQZL269,,,,4,3929438,WQZL270,14-06-2023,,14-06-2023,5,3929438,WQZL270,,,,6,3929439,WQZL271,14-06-2023,,14-06-2023,7,3929439,WQZL271,,,,8,3929440,WQZL272,14-06-2023,,14-06-2023,9,3929440,WQZL272,,,,10,3929441,WQZL273,14-06-2023,,14-06-2023,11,3929441,WQZL273,,,,12,3929442,WQZL274,14-06-2023,,14-06-2023,13,3929442,WQZL274,,,,14,3929443,WQZL275,14-06-2023,,14-06-2023,
I have a df like above need to take only the values which are date1 & date3 are have different or date2 or date4 have different if both different also need
how to do with pandas,
the columns are coming as pandas objectnote as datetime/string
答案1
得分: 1
你可以先替换缺失的数值，然后进行不等比较，将两个掩码用 | 连接以进行按位“或”操作：
df1 = df.fillna('')df = df[df1['date1'].ne(df1['date3']) | df1['date2'].ne(df1['date4'])]print(df)
输出结果为：
Empty DataFrameColumns: [Unnamed: 0, unique_system_identifier, call_sign, date1, date2, date3, date4]Index: []
英文:
You can replace missing values first and then compare for not eqaul, chain both mask by | for bitwise OR:
df1 = df.fillna('')df = df[df1['date1'].ne(df1['date3']) | df1['date2'].ne(df1['date4'])]print (df)Empty DataFrameColumns: [Unnamed: 0, unique_system_identifier, call_sign, date1, date2, date3, date4]Index: []
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