Using For Loop to get count of rows for specific columns (Python/Pandas)

Question

I would like to re-work my code to use a For Loop to get row counts by specific columns using Python (there are 15 columns in total and I am looking for row counts for 4 specific ones at this time) -

This is the current input:

#get row count by affiliate, race, ethnicity and abortion type print('Column_1:', batch_df.groupby('Column_1').size().sum()) print('Column_2:', batch_df.groupby('Column_2').size().sum()) print('Column_3:',batch_df.groupby('Column_3').size().sum()) print('Column_4:',batch_df.groupby('Column_4').size().sum())

The output (which is correct) is below:

Column_1: 468676 Column_2: 465755 Column_3: 468400 Column_4: 468676

Is there a way to re-work the input so that it is a For Loop?

What have you tried so far? It's a simple for loop. If you don't know how to do this read about for loops. — Yevhen Kuzmovych
– Yevhen Kuzmovych, Commented Oct 6, 2021 at 16:32

AveragePythonEnjoyer · Accepted Answer · 2021-10-06 16:35:37Z

This should work if you want to specify the columns by name:

for col in ['Column_1', 'Column_2', 'Column_3', 'Column_4']: print('{}:'.format(col), batch_df.groupby(col).size().sum())

Esa Tuulari · Accepted Answer · 2021-10-06 17:16:36Z

No need to write all column names as df.columns returns column names and then you can loop them true:

for c in df.columns: print(c)

For example with dataframe

df = pd.DataFrame({ 'Column_1': ['1', '2', '3', '4'], 'Column_2' : ['11','12','13','14'], 'Column_3': ['101', '102','103', '104']})

will print

Column_1 Column_2 Column_3

Is it possible to please get a little more context on this? Do I need to define the data frame to specifically call out these columns? For example, per your example I would want it to print - Column_1: 4 (for the number or rows) and I also only want row counts for specific columns, not all 15

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