Create a list of single-entry dictionaries where each group by a given column contributes a value from a 2nd column for all but 1st row which is key

I have a pandas dataframe that looks like this:

header1 header2
First row1
Second row2
Third row1
Fourth row2
Fifth row1

I want to create a list of dictionaries where, for all rows with matching value in the header2 column (except the first such row), a dictionary is added to the list using the first row’s header1 column value as the lone dict key, and every other row’s header1 column value as the lone dict value.

Expected output:

[{"First":"Third},{"Second":"Fourth"}, {"First":"Fifth"}]

or even

{"First":"Third","Second":"Fourth"} (This output doesn’t handle multiple matches in header2)

Ideally the solution isn’t going to be computationally intensive as I am able to accomplish this with nested for loops already.

Edit based on something brought up in comments: In case of multiple values in the first column with matching header2, assume first occurrence will be the key and duplicate with the value. For example: [{"First":"Third},{"Second":"Fourth"}, {"First":"Fifth"}]. In other words, the header1 value in the first matching row will be repeating key, with one single-entry dict added to the result list for each subsequent matching row.

Thank you

>Solution :

Here’s a way to do what your question asks:

out = []
df.groupby('header2')['header1'].apply(lambda x: out.extend([{x.iloc[0]:x.iloc[i]} for i in range(1, len(x))]) if len(x) > 1 else None)
idxByHeader1 = df.reset_index(drop=False).set_index('header1')['index']
out = sorted(out, key=lambda x: idxByHeader1[list(x.values())[0]])


[{'First': 'Third'}, {'Second': 'Fourth'}, {'First': 'Fifth'}]

Leave a Reply