The task is basically this:
I am given the following csv file with lots of duplicate email addresses
Display Name,First Name,Last Name,Phone Number,Email Address,Login Date,Registration Date John,John,Doe,99999999,firstname.lastname@example.org,4/20/2015 21:56,4/20/2015 21:56 John,John,DOE,99999999,email@example.com,3/31/2015 14:05,3/31/2015 14:05
I need to remove duplicates based on email address with the following conditions:
- The row with the latest login date must be selected.
- The oldest registration date among the rows must be used.
I used Python/pandas to do this.
How do I optimize the for loop in this pandas script using groupby? I tried hard but I'm still banging my head against it.
import pandas as pd df = pd.read_csv('pra.csv') # first sort the data by Login Date since we always need the latest Login date first # we're making a copy so as to keep the original data intact, while still being able to sort by datetime df['Login Date Copy'] = pd.to_datetime(df['Login Date']) df['Registration Date Copy'] = pd.to_datetime(df['Registration Date']) # this way latest login date appears first for each duplicate pair df = df.sort_values(by='Login Date Copy', ascending=False) output_df = pd.DataFrame() # get rows for each email address and replace registration date with the oldest one # this can probably be optimized using groupby for email in df['Email Address'].unique(): subdf = df.loc[df['Email Address'] == email] oldest_date = subdf['Registration Date Copy'].min() # get corresponding registration date for a particular registration date copy oldest_reg_date = df[df['Registration Date Copy'] == oldest_date]['Registration Date'].values subdf['Registration Date'] = oldest_reg_date output_df = output_df.append(subdf) # drop working columns output_df.drop(['Login Date Copy', 'Registration Date Copy'], axis=1, inplace=True) # finally, get only the first of the duplicates and output the result output_df = output_df.drop_duplicates(subset='Email Address', keep='first') output_df.to_csv('~/output.csv', index=False)