Pandas 1.3 >>>importpandasaspd>>>importnumpyasnp>>>df=pd.DataFrame([0.5,np.nan])>>>df.where(pd.notnull(df),None)000.51NaN Problem description Replacing NaN values with None (or any other Python object) should work as in previous Pandas versions. ...
When replacing values in a categorical series with NA, I see the error "boolean value of NA is ambiguous". Expected Behavior If we replace with NumPy's NaN value instead of pandas' NA, it works as expected. import numpy as np import pandas as pd phonetic = pd.DataFrame({ "x": ["al...
In [1]: import pandas as pd In [2]: df=pd.DataFrame([[1, 2, 8],[3, 4, 8], [5, 1, 8]], columns=['A', 'B', 'C']) In [3]: df Out[3]: A B C 0 1 2 8 1 3 4 8 2 5 1 8 In [4]: df.loc[:, ['A', 'B']].replace([1, 3, 2], [3, 6, 7], ...