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How to loop over grouped Pandas dataframe?

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Can you loop through pandas DataFrame?

DataFrame Looping (iteration) with a for statement. You can loop over a pandas dataframe, for each column row by row. Below pandas. Using a DataFrame as an example.

How do you iterate over rows in a data frame?

In case you still want/have to iterate over a DataFrame or Series, you can use iterrows() or itertuples() methods.

How do you loop a panda series?

iteritems() function iterates over the given series object. the function iterates over the tuples containing the index labels and corresponding value in the series. Example #1: Use Series. iteritems() function to iterate over all the elements in the given series object.


df.groupby('l_customer_id_i').agg(lambda x: ','.join(x)) does already return a dataframe, so you cannot loop over the groups anymore.

In general:

  • df.groupby(...) returns a GroupBy object (a DataFrameGroupBy or SeriesGroupBy), and with this, you can iterate through the groups (as explained in the docs here). You can do something like:

    grouped = df.groupby('A')
    
    for name, group in grouped:
        ...
    
  • When you apply a function on the groupby, in your example df.groupby(...).agg(...) (but this can also be transform, apply, mean, ...), you combine the result of applying the function to the different groups together in one dataframe (the apply and combine step of the 'split-apply-combine' paradigm of groupby). So the result of this will always be again a DataFrame (or a Series depending on the applied function).


Here is an example of iterating over a pd.DataFrame grouped by the column atable. For this sample, "create" statements for an SQL database are generated within the for loop:

import pandas as pd

df1 = pd.DataFrame({
    'atable':     ['Users', 'Users', 'Domains', 'Domains', 'Locks'],
    'column':     ['col_1', 'col_2', 'col_a', 'col_b', 'col'],
    'column_type':['varchar', 'varchar', 'int', 'varchar', 'varchar'],
    'is_null':    ['No', 'No', 'Yes', 'No', 'Yes'],
})

df1_grouped = df1.groupby('atable')

# iterate over each group
for group_name, df_group in df1_grouped:
    print('\nCREATE TABLE {}('.format(group_name))

    for row_index, row in df_group.iterrows():
        col = row['column']
        column_type = row['column_type']
        is_null = 'NOT NULL' if row['is_null'] == 'No' else ''
        print('\t{} {} {},'.format(col, column_type, is_null))

    print(");")

You can iterate over the index values if your dataframe has already been created.

df = df.groupby('l_customer_id_i').agg(lambda x: ','.join(x))
for name in df.index:
    print name
    print df.loc[name]