CODEWITHSUNDEEP
pythonpandas
Xandros
Xandros

Reputation: 727

Combine Dataframe rows to fill in missing data

Suppose I have a dataframe with rows containing missing data, but a set of columns acting as a key:

import pandas as pd
import numpy as np
data = {"id": [1, 1, 2, 2, 3, 3, 4 ,4], "name": ["John", "John", "Paul", "Paul", "Ringo", "Ringo", "George", "George"], "height": [178, np.nan, 182, np.nan, 175, np.nan, 188, np.nan], "weight": [np.nan, np.NaN, np.nan, 72, np.nan, 68, np.nan, 70]}

df = pd.DataFrame.from_dict(data)
print(df)


id    name  height  weight
0   1    John   178.0     NaN
1   1    John     NaN     NaN
2   2    Paul   182.0     NaN
3   2    Paul     NaN    72.0
4   3   Ringo   175.0     NaN
5   3   Ringo     NaN    68.0
6   4  George   188.0     NaN
7   4  George     NaN    70.0

How would I go about "squashing" these rows with duplicate keys down to pick the non-nan value (if it exists)?

desired output:
id    name  height  weight
0   1    John   178.0     NaN
2   2    Paul   182.0     72.0
4   3   Ringo   175.0     68.0
6   4  George   188.0     70.0

The index doesn't matter, and there is always at most one row with Non-NaN data. I think I need to use groupby(['id', 'name']), but I'm not sure where to go from there.

Upvotes: 2

Views: 180

Answers (1)

jezrael
jezrael

Reputation: 862581

If there are always only one non NaNs values per groups is possible aggregate many ways:

df = df.groupby(['id', 'name'], as_index=False).first()

Or:

df = df.groupby(['id', 'name'], as_index=False).last()

Or:

df = df.groupby(['id', 'name'], as_index=False).mean()

Or:

df = df.groupby(['id', 'name'], as_index=False).sum(min_count=1)

Upvotes: 2

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