Note. The choice of using NaN internally to denote missing data was largely for simplicity and performance reasons. Starting from pandas 1.0, some optional data types start experimenting with a native NA scalar using a mask-based approach. See here for more.
One has to be mindful that in Python (and NumPy), the nan's don't compare equal, but None's do. Note that pandas/NumPy uses the fact that np.nan != np.nan , and ...
16.03.2018 · NaTType is a private class, in a private module, so you are reaching into the implementation. It is a singleton, though it actually doesn't enforce this pattern. We have exactly one NaT and that is defined (internally), then referenced at the top level of the pandas namespace.. so is comparison work.. I am going to close this, but if you wanted to submit a …
Pandas DataFrame Replace NaT with None. Make the dtype object. dfTest2 = pd.DataFrame (dict (InvoiceDate=pd.to_datetime ( ['2017-06-01', pd.NaT]))) dfTest2.InvoiceDate.astype (object).where (dfTest2.InvoiceDate.notnull (), None) 0 2017-06-01 00:00:00 1 None Name: InvoiceDate, dtype: object. The simplest solution I found that worked for me is...
This answer is not useful. Show activity on this post. Make the column type as str first. dfTest2.InvoiceDate = dfTest2.InvoiceDate.astype (str) then compare it directly with "NaT" and replace with None. dfTest2.InvoiceDate = dfTest2.InvoiceDate.apply (lambda x : …
29.10.2019 · pandas.DataFrame treats numpy.nan and None similarly. Both numpy.nan and None can be detected using pandas.isnull() . Then, to eliminate the missing value, we may choose to fill in different data ...