adding rows with Nan values to Pandas DataFrame

Question

I want to insert rows with Nan values after each row

index	values
0	44
1	50
2	51
3	66
4	23

DataFrame should look like this

index	values
0	44
1	Nan
2	50
3	Nan
4	51
5	Nan
6	66
7	Nan
8	23

Asked By: what's wrong CSE

||

Source

Answer 1

Use concat with DataFrame filled by NaNs and same indices and then use DataFrame.sort_index:

df = (pd.concat([df, pd.DataFrame(index=df.index)])
        .sort_index(kind='stable', ignore_index=True))
print (df)
   values
0    44.0
1     NaN
2    50.0
3     NaN
4    51.0
5     NaN
6    66.0
7     NaN
8    23.0
9     NaN

If need remove last missing value:

df = (pd.concat([df, pd.DataFrame(index=df.index)])
        .sort_index(kind='stable', ignore_index=True)
        .iloc[:-1])
print (df)
   values
0    44.0
1     NaN
2    50.0
3     NaN
4    51.0
5     NaN
6    66.0
7     NaN
8    23.0

Answered By: jezrael

Answer 2

One option:

(df.assign(index=df['index']*2)
   .set_index('index')
   .reindex(range(len(df)*2))
   .reset_index()
)

output:

   index  values
0      0    44.0
1      1     NaN
2      2    50.0
3      3     NaN
4      4    51.0
5      5     NaN
6      6    66.0
7      7     NaN
8      8    23.0
9      9     NaN

Answered By: mozway

Answer 3

import pandas as pd
import numpy as np

df=pd.read_csv("C:\Users\baf03\Downloads\water-physical-stock-account-quarterly-1995-2020-CSV.csv")

df.loc[0,"yq"]=np.nan

df

Answered By: Bilal Ahmed

adding rows with Nan values to Pandas DataFrame

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