Convert single column in dataframe to rows | Pandas Facebook Ads

Hello Stackoverflowers!

I have a DataFrame, obtained from Facebook Marketing API, and I'd like to unnest a column into several rows.

This is the a sample of the data I obtained through the API:

ad_name      video_play_curve_actions
ad_1         [{'action_type': 'video_view', 'value': [100, 40, 16, 10, 7, 5, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}]
ad_2         [{'action_type': 'video_view', 'value': [100, 51, 22, 13, 9, 7, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}]

What I am looking for, is an outcome that looks like illustrated below

I'd need a for loop for this because the solution needs to work for many 100 rows.

I've added example data and desired output to this sheet: https://docs.google.com/spreadsheets/d/1jjbtJlfBNZV_wyyAoPY_scyn_jCNFD04XO1-JsztKAg/edit?usp=sharing

Really hope someone here can help me out.

Thanks in advance

Edit:

Thank you so much. Seems like there are multiple ways to fix it, but all the solutions included: pandas.explode: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.explode.html

Will definitely use it going forward.

Happy Wednesday

You're looking for pandas.explode: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.explode.html, plus some necessary preprocessing for your video_play_curve_actions column.

import pandas as pd
import argparse

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--filepath')
    args = parser.parse_args()

    df = pd.read_csv(args.filepath)
    df['video_play_curve_actions'] = df['video_play_curve_actions'].apply(lambda x: eval(x)[0]['value'])
    df_exploded = df.explode('video_play_curve_actions').rename(columns={'video_play_curve_actions': 'value'})
    print(df_exploded.head())

output:

  ad_name value
0    ad_1   100
0    ad_1    40
0    ad_1    16
0    ad_1    10
0    ad_1     7

Note that I used eval here to process the values in video_play_curve_actions which isn't always considered best practice. If the input contained double quotes " instead of single quotes ' we could have used json.loads instead.