CODEWITHSUNDEEP
pythonpandas
HHH
HHH

Reputation: 6485

How to find empty datatime in Pandas

I have a Pandas dataframe which has a column date_closed of type datetime64[ns]. When I find the unique values there are some NaT there. How can I only filter the rows whose date_closed in not NaT

df['date_closes'].unique()

btw, what does NaT means? is it Null? When I value_counts() , the NaT's does not appear! Why?

df['date_closed'].value_counts()

Upvotes: 2

Views: 1227

Answers (2)

piRSquared
piRSquared

Reputation: 294508

Th null value NaT means not a time just like NaN means not a number.

Use dropna

df.dropna(subset=[“date_closed”])

Upvotes: 0

Mihai Alexandru-Ionut
Mihai Alexandru-Ionut

Reputation: 48417

For datetime64[ns] types, NaT represents missing values. This is a pseudo-native sentinel value that can be represented by numpy in a singular dtype (datetime64[ns]). pandas objects provide intercompatibility between NaT and NaN.

You can read more about handling missing data here : https://pandas.pydata.org/pandas-docs/stable/missing_data.html

For filtering, you can use notnull method.

df['date_closed'] = df[df.date_closed.notnull()]

Upvotes: 2

Related Questions