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DataFrame check which row was changed in a list of DataFrames

I have a DataFrame with several records. I have to check which row is changed in the last 5 records.
DF_Prev = pd.DataFrame({‘id’: [1, 2, 3, 4, 5, 6, 7, 8], ‘col1’: [‘A’, ‘B’, ‘C’, ‘D’, ‘E’, ‘F’, ‘G’, ‘H’]})

DF_New = pd.DataFrame({‘id’: [1, 2, 3, 4, 5, 6, 7, 8], ‘col1’: [‘A’, ‘B’, ‘C’, ‘D’, ‘E’, ‘F’, ‘G’, ‘H’]})

df_list = [DF_Prev, DF_New]

I have a list of list of DataFrames. I don’t want to loop and use.loc to update the data. I have to check if the id changes in the last 5 rows and print the new row, if it’s changed. I cannot use apply because I cannot add columns into the list (that is why I do this check in the first place). How can I do that?

A:

You can create helper columns by subtracting shifted versions of previous df. Then, only use one line of code to get a df of changed id’s:
df_list = [DF_Prev, DF_New]

ids = [id for id, df in zip(df_list, df_list[1:])]

df_all = [df[[‘id’, ‘col1’]] for df in df_list]

df_changed = df_all[~df_all.shift(1).notnull()].groupby(‘id’)[‘id’].first().reset_index(drop=True)

# check for only changed rows
df_changed[df_changed.col1 == df_changed.col1.shift(-5)]

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