i'm in this situation, my df is like that
A B
0 0.0 2.0
1 3.0 4.0
2 NaN 1.0
3 2.0 NaN
4 NaN 1.0
5 4.8 NaN
6 NaN 1.0
and i want to apply this line of code:
df['A'] = df['B'].fillna(df['A'])
and I expect a workflow and final output like that:
A B
0 2.0 2.0
1 4.0 4.0
2 1.0 1.0
3 NaN NaN
4 1.0 1.0
5 NaN NaN
6 1.0 1.0
A B
0 2.0 2.0
1 4.0 4.0
2 1.0 1.0
3 2.0 NaN
4 1.0 1.0
5 4.8 NaN
6 1.0 1.0
but I receive this error:
TypeError: Unsupported type Series
probably because each time there is an NA it tries to fill it with the whole series and not with the single element with the same index of the B column.
I receive the same error with a syntax like that:
df['C'] = df['B'].fillna(df['A'])
so the problem seems not to be the fact that I'm first changing the values of A with the ones of B and then trying to fill the "B" NA with the values of a column that is technically the same as B
I'm in a databricks environment and I'm working with koalas data frames but they work as the pandas ones. can you help me?
Another option
Suppose the following dataset
import pandas as pd
import numpy as np
df = pd.DataFrame(data={'State':[1,2,3,4,5,6, 7, 8, 9, 10],
'Sno Center': ["Guntur", "Nellore", "Visakhapatnam", "Biswanath", "Doom-Dooma", "Guntur", "Labac-Silchar", "Numaligarh", "Sibsagar", "Munger-Jamalpu"],
'Mar-21': [121, 118.8, 131.6, 123.7, 127.8, 125.9, 114.2, 114.2, 117.7, 117.7],
'Apr-21': [121.1, 118.3, 131.5, np.NaN, 128.2, 128.2, 115.4, 115.1, np.NaN, 118.3]})
df
State Sno Center Mar-21 Apr-21
0 1 Guntur 121.0 121.1
1 2 Nellore 118.8 118.3
2 3 Visakhapatnam 131.6 131.5
3 4 Biswanath 123.7 NaN
4 5 Doom-Dooma 127.8 128.2
5 6 Guntur 125.9 128.2
6 7 Labac-Silchar 114.2 115.4
7 8 Numaligarh 114.2 115.1
8 9 Sibsagar 117.7 NaN
9 10 Munger-Jamalpu 117.7 118.3
Then
df.loc[(df["Mar-21"].notnull()) & (df["Apr-21"].isna()), "Apr-21"] = df["Mar-21"]
df
State Sno Center Mar-21 Apr-21
0 1 Guntur 121.0 121.1
1 2 Nellore 118.8 118.3
2 3 Visakhapatnam 131.6 131.5
3 4 Biswanath 123.7 123.7
4 5 Doom-Dooma 127.8 128.2
5 6 Guntur 125.9 128.2
6 7 Labac-Silchar 114.2 115.4
7 8 Numaligarh 114.2 115.1
8 9 Sibsagar 117.7 117.7
9 10 Munger-Jamalpu 117.7 118.3