将不包含任何值的字典列表转换为多个 Dataframe 列

Question

I have a Dataframe with 10k rows of different user input text responses. And for each of the responses, several other columns in the Dataframe contain list of dictionaries as below:

top row of column[0]:

{'action': 'CLICK', 'docid': 'url_1', 'position': 1 }

top row of column[1]:

{'action': 'NoAction', 'docid': 'url_2', 'position': 2 }`

I need to convert this into multiple Dataframe columns and concatenate back to the original file.

I tried to do the following on column[0], it works fine:

top1_list = df[df.columns[0]].tolist()
df_result1 = pd.DataFrame.from_dict(top1_list)
df_result1.columns = ['action_1', 'docid_1', 'position_1']
data1 = pd.concat([df_result1, df], axis=1)

When I tried the same approach on column[1], since for some user input responses, the column[1] is empty. I got an error of 'NoneType' object has no attribute 'keys' . But if I remove the none value when I convert column[1] to list, then I lost the original index position and can't concatenate back to the original file correctly.

So what would be the solution to convert list of dictionaries (that contain none values) into multiple Dataframe columns while keep them at the right index position?

Thank you!

Answer 1

solve the question using

df['result2'].apply(pd.Series)

Thanks.