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Phir Bhi Dil Hai Hindustani Full Movie Download 720405



 


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31 0 woh hindustani movie 72040561 0 woh hindustani movie 72040571 0 A: One approach uses pandas groupby operations, cumsum, convert to binary, and use str.replace() to remove the leading zeros: import pandas as pd df = pd.DataFrame({"File_ID": ['72040531', '72040561', '72040571'], "Movie_ID": ['72040531', '72040561', '72040571']}) df['flag'] = pd.factorize(df['File_ID'])[0].astype(int) df['binary'] = (df['flag'] == 0).astype(int) # Removes leading zeros: df['Binary'] = df['binary'].str.replace('0', '') # Applies flag/binary transformation df['value'] = pd.cumsum(df['binary']).astype(int) df File_ID Movie_ID flag binary value 0 72040531 72040531 1 1 2 1 72040561 72040561 1 1 1 2 72040571 72040571 1 1 3 For more information on pandas column types, check out the docs: For your second question: The above code is just a way to create a binary flag column. If you want to apply the flag column to only a subset of columns, use map: def flag_func(row): return pd.factorize(row['File_ID'])[0].astype(int) df['flag'] = df.apply(flag_func, axis=1) df['value'] = pd.cumsum(df['flag']).

 

 


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Phir Bhi Dil Hai Hindustani Full Movie Download 720405

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