Menu
I deal with a lot of data analysis in pandas and use pandas.datetime everyday. Lately I get the warning:
"FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead."
I've used Python datetime package before when pandas datetime wouldn't work with I wanted to do, and I understand it is a library that is more related to "date", but does anyone know the idea behind this shift to datetime package?
747
DateTime¶ ↑ A subclass of Date that easily handles date, hour, minute, second, and offset. DateTime class is considered deprecated.
Pandas has a built-in function called to_datetime()that converts date and time in string format to a DateTime object. As you can see, the 'date' column in the DataFrame is currently of a string-type object. Thus, to_datetime() converts the column to a series of the appropriate datetime64 dtype.
pandas contains extensive capabilities and features for working with time series data for all domains. Using the NumPy datetime64 and timedelta64 dtypes, pandas has consolidated a large number of features from other Python libraries like scikits.
Note that if the date is not a pandas datetime date, you need to first covert it using pd.to_datetime () before you can use the dt.date attribute. Let’s look at some examples of using the above syntax. 1. Remove time from a pandas date Let’s first look at how to time from a pandas datetime object. For this, apply the .date () function.
The pandas to_datetime () method converts a date/time value stored in a DataFrame column into a DateTime object. Having date/time values as DateTime objects makes manipulating them much easier.
The pandas library provides a DateTime object with nanosecond precision called Timestamp to work with date and time values. The Timestamp object derives from the NumPy’s datetime64 data type, making it more accurate and significantly faster than Python’s DateTime object.
We can define time-series data as a collection of data points obtained at different time intervals and ordered chronologically. Pandas library basically was developed for analyzing financial time series data and providing a comprehensive framework for working with times, dates, and time-series data.
Summary: datetime (from the Python Standard Libary) was never really a part of Pandas or its API, which the developers are now making a concerted effort to warn about. You can access the datetime class directly:
from datetime import datetime
Detail: Older versions of Pandas (example: 0.20) contained:
from datetime import datetime
in pandas/__init__.py
This meant that, if you really wanted to, you could reference and use pd.datetime, which is really just the standard library's datetime class.
Per Pandas issue #30610 and issue #30296, pandas.datetime is really just standard datetime.datetime. It looks like deprecating them (which, interesting, is done via overriding __getattr__ at the module level*), is done to discourage "roundabout usage" where other 3rd party libraries/modules are brought into the top-level Pandas namespace.
That is, the Pandas library doesn't consider datetime (or numpy) being available in that namespace to be a part of its API, so you shouldn't depend on it being there in the first place.
*Specifically, this looks like:
# pandas/__init__.py
def __getattr__(name):
import warnings
elif name == "datetime":
warnings.warn(...)
from datetime import datetime as dt
return dt
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
