Pandas 根据其他列标准插入缺失值

Question

I have the current dataframe:

Date	Source	Type	Visits	Sales
01/01/2020	Source1	Type1	100	10
01/01/2020	Source2	Type1	150	5
02/01/2020	Source1	Type1	NaN	NaN
02/01/2020	Source2	Type1	125	15
03/01/2020	Source1	Type2	150	18
03/01/2020	Source2	Type2	NaN	NaN
04/01/2020	Source1	Type2	150	25
04/01/2020	Source2	Type2	120	05

What I'd like to do is a simple.interpolate() for the missing data however I need to group it by the Source and Type to keep the data as accurate as possible rather than basing it on the above and below rows which aren't relevant.

I've got to this stage:

df_fixed = df[['Source','Type','Visits','Sales']].loc[(df['Source'] == 'Source1') & (df['Type'] == 'Type1')].interpolate()

Which does the first step but can't get any further and feel like there's an easier way.

What would be the most elegant way to complete this?

Answer 1

One idea is change the NAN - of median or mean.

df['visits'].fillna(df['visits'].median(), inplace=True)
df.fillna(df.mean())

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Edit:

If You decide to use.interpolate() - so

need to group it by the Source and Type:

You can use groupBy() - method for group by Source and Type:

df.groupby(['Source', 'Type'])