Feb 15, 2025 · When working with datetime data in pandas, you’ll inevitably come across NaT, which stands for "Not a Time". It’s similar to NaN (Not a Number), but instead of representing missing numerical...

Jun 2, 2014 · The value NaT means "Not A Time", the equivalent of nan for timestamp values. Can you tell the dtypes of your data frame? Try casting the columns to float values.

Jul 1, 2022 · In Python, we face different values in place of missing data, such as None, NaN, and NaT. We know they are missing values, but what’s the difference, and how should we handle them? NaN: NaN (N ot...

SO can anyone please suggest , how to deal with this NaT while converting to date() and while doing groupby and min(), how to exclude NaT for calculation. if for any customer_name only NaT will be there in DATE field, then on groupby and min(), I am okay with nan or Null values.

Pandas NaT behaves like a floating-point NaN, in that it's not equal to itself. Instead, you can use pandas.isnull: This also returns True for None and NaN. Technically, you could also check for Pandas NaT with x != x, following a common pattern used for floating-point NaN.

The goal of NA is provide a “missing” indicator that can be used consistently across data types (instead of np.nan, None or pd.NaT depending on the data type). For example, when having missing values in a Series with the nullable integer dtype, it will use NA :

pandas.NaT# pandas. NaT = NaT # (N)ot-(A)-(T)ime, the time equivalent of NaN. Examples >>>

Oct 15, 2024 · NaT is a crucial concept in Pandas for handling missing datetime values. By understanding and utilizing NaT, you can ensure that your data analysis accounts for missing date and time information, leading to more accurate and reliable results.

next. Development. On this page NaT

Jun 8, 2024 · pd.NaT, short for “Not a Time,” is a special value in Pandas that represents missing or undefined datetime values. It is similar to None or NaN, but specifically designed for datetime objects.