CODEWITHSUNDEEP
python-3.xpandasdataframe
AnalysisNerd
AnalysisNerd

Reputation: 133

Interpolation for a Dataframe without explicit 'NaN' rows in the original Dataframe

I was wondering how I could interpolate between rows for the column 'Data' where I loop through it and create a new row with '0.3', '0.5' and '0.6' to fill in the gaps? 

df = pd.DataFrame([0.1,0.2,0.4,0.7],columns=['Data'])
df

enter image description here

How do I do this with code to the original dataframe to produce the image below?

Thanks a lot in advance.

Upvotes: 1

Views: 198

Answers (1)

jezrael
jezrael

Reputation: 863791

In my opinion there should be 2 solutions, also added more columns to sample data:

df = pd.DataFrame({'Data': [0.1,0.2,0.4,0.7],
                   'A':list('abcd'),
                   'B':[4,5,6,9]})
print (df)
   Data  A  B
0   0.1  a  4
1   0.2  b  5
2   0.4  c  6
3   0.7  d  9

Create index by Data column and use DataFrame.reindex:

arr = np.arange(df.Data.min(),df.Data.max() + 0.1,0.1)

df1 = df.set_index('Data').reindex(arr).reset_index()
print (df1)
   Data    A    B
0   0.1    a  4.0
1   0.2    b  5.0
2   0.3  NaN  NaN
3   0.4    c  6.0
4   0.5  NaN  NaN
5   0.6  NaN  NaN
6   0.7  NaN  NaN

Or create helper DataFrame and DataFrame.merge with left join:

df1 = pd.DataFrame({'Data':arr}).merge(df, how='left')
print (df1)
   Data    A    B
0   0.1    a  4.0
1   0.2    b  5.0
2   0.3  NaN  NaN
3   0.4    c  6.0
4   0.5  NaN  NaN
5   0.6  NaN  NaN
6   0.7  NaN  NaN

Upvotes: 2

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