Pandas Lambda Function with Nan Support

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I am trying to write a lambda function in Pandas that checks to see if Col1 is a Nan and if so, uses another column's data. I have having trouble getting code (below) to compile/execute correctly. 

import pandas as pd
import numpy as np
df=pd.DataFrame({ 'Col1' : [1,2,3,np.NaN], 'Col2': [7, 8, 9, 10]})  
df2=df.apply(lambda x: x['Col2'] if x['Col1'].isnull() else x['Col1'], axis=1)

Does anyone have any good idea on how to write a solution like this with a lambda function or have I exceeded the abilities of lambda? If not, do you have another solution? Thanks.

pandas nan python-3.x lambda python
4 Answers

Within pandas 0.24.2, I use

df.apply(lambda x: x['col_name'] if x[col1] is np.nan else expressions_another, axis=1)

because pd.isnull() doesn't work.

in my work,I found the following phenomenon,

No running results:

df['prop'] = df.apply(lambda x: (x['buynumpday'] / x['cnumpday']) if pd.isnull(x['cnumpday']) else np.nan, axis=1)

Results exist:

df['prop'] = df.apply(lambda x: (x['buynumpday'] / x['cnumpday']) if x['cnumpday'] is not np.nan else np.nan, axis=1)

So far, I still don't know the deeper reason, but I have these experiences, for object, use [is np.nan()] or pd.isna(). For a float, use np.isnan() or pd.isna().

user_98175850
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