CODEWITHSUNDEEP
pythonpandas
metersk
metersk

Reputation: 12539

How to perform aggregate options on one groupby column, giving two column outputs

I am performing a bunch of aggregate stats on a groupby data frame. For one column in particular, ios_id, I would like a count and a distinct count. I'm not sure how o output this to two seaparate columns with different names. As of right now, the distinct count just overwrites the count.

How do I output both the distinct count and the count for the ios_id column to two separate columns?

df_new = df.groupby('video_id').agg({"ios_id": np.count_nonzero,
                                     "ios_id": pd.Series.nunique,
                                     "feed_position": np.average,
                                     "time_watched": np.sum,
                                     "video_length": np.sum}).sort('ios_id', ascending=False)

Upvotes: 2

Views: 297

Answers (1)

Alexander
Alexander

Reputation: 109666

Something like this should work. Note the nested dictionary structure for iOS_id.

df_new = df.groupby('video_id').agg({"ios_id": {"count": "count",
                                                "distinct": "unique"},
                                     "feed_position": np.average,
                                     "time_watched": np.sum,
                                     "video_length": np.sum})

For more details, please refer to Naming returned columns in Pandas aggregate function:

Upvotes: 1

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