How To Extract Non NA Values In A List Or Dict From A Pandas Dataframe

I have a df like this, df, AAA BBB CCC 0 4 10 100 1 5 20 50 2 6 30 -30 3 7 40 -50 df_mask = pd.DataFrame({'AAA' : [True] * 4, 'BBB' : [False] * 4,'CCC' : [

Solution 1:

Let's use agg here:

v = df.where(df_mask).agg(lambda x: x.dropna().to_dict())

On older versions, apply does the same thing (albeit a bit slower).

v = df.where(df_mask).apply(lambda x: x.dropna().to_dict())

And now, filter out rows with empty dictionaries for the final step:

res = v[v.str.len() > 0].to_dict()

print(res)
{'AAA': {0: 4.0, 1: 5.0, 2: 6.0, 3: 7.0}, 'CCC': {0: 100.0, 2: -30.0}}

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Another apply-free option is a dict-comprehension:

v = df.where(df_mask)  
res = {k : v[k].dropna().to_dict() for k in df} 

print(res)
{'AAA': {0: 4, 1: 5, 2: 6, 3: 7}, 'BBB': {}, 'CCC': {0: 100.0, 2: -30.0}}

Note that this (slightly) simpler solution retains keys with empty values.

Solution 2:

You can iterate df's columns and apply dropna Serieswise

{col: df[col].dropna().values for col in df}

Which yields

{'AAA': array([4, 5, 6, 7]),
 'BBB': array([], dtype=float64),
 'CCC': array([ 100.,  -30.])}

You can filter out empty arrays such as 'BBB' with

{key: val for key, val in ddict.items() if val}