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I have a dataframe
import pandas as pd
import numpy as np
df1=pd.DataFrame({'group':[1,1,2,2,2],
'value':[2,3,np.nan,5,4]})
df1
group value
0 1 2
1 1 3
2 2 NaN
3 2 5
4 2 4
I want to add a row after each group in which the value of value is NaN . The desire output is:
group value
0 1 2
1 1 3
2 1 NaN
3 2 NaN
4 2 5
5 2 4
6 2 NaN
In my real dataset I have a lot of groups and more columns besides value, I want all of them to be NaN in newly added row.
Thanks a lot for the help
548
Appending an empty row in pandas dataframe We will first create a DataFrame and then we will add an empty row by using the concat() method or append() method, inside this method we will pass an empty Series such that it does not hold any value.
You can group DataFrame rows into a list by using pandas. DataFrame. groupby() function on the column of interest, select the column you want as a list from group and then use Series. apply(list) to get the list for every group.
Use concat() to Add a Row at Top of DataFrame Use pd. concat([new_row,df. loc[:]]). reset_index(drop=True) to add the row to the first position of the DataFrame as Index starts from zero.
Definition and Usage The values property returns all values in the DataFrame. The return value is a 2-dimensional array with one array for each row.
concat with append
s = df1.groupby('group')
out = pd.concat([i.append({'value': np.nan}, ignore_index=True) for _, i in s])
out.group = out.group.ffill().astype(int)
apply with append[1]
df1.groupby('group').apply(
lambda d: d.append({'group': d.name}, ignore_index=True).astype({'group': int})
).reset_index(drop=True)
Both produce:
group value
0 1 2.0
1 1 3.0
2 1 NaN
3 2 NaN
4 2 5.0
5 2 4.0
6 2 NaN
[1] This solution brought to you by your local @piRSquared
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