Remove Time from Date in Pandas

In this tutorial, we will look at how to remove the time component from a date in pandas with the help of some examples.

How to remove time from date in Pandas?

If you’re working with a pandas datetime type date then you can simply call the .date() function or if you’re working with a pandas series you can use the .dt.date attribute to remove the time from the dates. This is similar to getting just the year or month from a pandas date. The following is the syntax:

# remove time from a pandas timestamp object
sample_date.date()
# remove time from a pandas series of dates
df['Date'].dt.date

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.

import pandas as pd

sample_date = pd.Timestamp("2020-04-16 08:30:00")
# display the date
print(sample_date)
# remove the time
print(sample_date.date())

Output:

2020-04-16 08:30:00
2020-04-16

You can see that we only get the date without its time component. Also note that the type of the returned date is datetime.date while the original date was a pandas timestamp object.

print(type(sample_date))
print(type(sample_date.date()))

Output:

<class 'pandas._libs.tslibs.timestamps.Timestamp'>
<class 'datetime.date'>

2. Remove time from dates in a pandas column

To remove time from dates in a pandas series, you can use the .dt.date attribute on the series. Here’s an example –

# create a dataframe
df = pd.DataFrame({
    'Date': ["2020-04-16 08:30:00", "2020-07-02 12:37:24", "2021-05-11 18:00:21"]
})
# display the dataframe
print(df)

Output:

                  Date
0  2020-04-16 08:30:00
1  2020-07-02 12:37:24
2  2021-05-11 18:00:21

Before we proceed to remove the time, we first need to convert the column to pandas datetime.

# convert to datetime
df['Date'] = pd.to_datetime(df['Date'])

Now, let’s add a new column to the dataframe which contains the date without the time part.

# remove time from Date and store it in a new column
df['Date_New'] = df['Date'].dt.date
# display the dataframe
print(df)

Output:

                 Date    Date_New
0 2020-04-16 08:30:00  2020-04-16
1 2020-07-02 12:37:24  2020-07-02
2 2021-05-11 18:00:21  2021-05-11

You can see that resulting dates don’t have time with them. Note that the new column doesn’t have a datetime datatype.

# check the column datatypes
df.dtypes

Output:

Date        datetime64[ns]
Date_New            object
dtype: object

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5