How to Extract Hour and Minute in Pandas   

In this tutorial, we're going to extract hours and minutes in Pandas. We will extract information from time stored as string or datetime.

Here are several ways to extract hours and minutes in Pandas:

(1) Use accessor on datetime column

df['date'].dt.hour

(2) Apply lambda and datetime

df['date'].apply(lambda x: datetime.strptime(x, '%d-%m-%Y %H:%M:%S %p').hour)

Setup

Let's work with the following DataFrame which has time information stored as a string:

import pandas as pd
dict = {'date': {0: '28-01-2022  5:25:00 PM',
  1: '27-02-2022  6:25:00 PM',
  2: '30-03-2022  7:25:00 PM',
  3: '29-04-2022  8:25:00 PM',
  4: '31-05-2022  9:25:00 PM'},
 'date_short': {0: 'Jan-2022', 1: 'Feb-2022', 2: 'Mar-2022', 3: 'Apr-2022', 4: 'May-2022'}}

df = pd.DataFrame(dict)

data:

	date	date_short
0	28-01-2022 5:25:00 PM	Jan-2022
1	27-02-2022 6:25:00 PM	Feb-2022
2	30-03-2022 7:25:00 PM	Mar-2022
3	29-04-2022 8:25:00 PM	Apr-2022
4	31-05-2022 9:25:00 PM	May-2022

We can't extract hour and minutes reliably just by using string operations. So in the next steps we will see how to extract time info in a reliable way.

Step 1: Convert string to time

First we will convert the string column to datetime. To do so in Pandas we can use method to_datetime(df['date']):

pd.to_datetime(df['date'])

result:

0   2022-01-28 17:25:00
1   2022-02-27 18:25:00
2   2022-03-30 19:25:00
3   2022-04-29 20:25:00
4   2022-05-31 21:25:00
Name: date, dtype: datetime64[ns]

Step 2: Extract hours from datetime

To extract hours from datetime or timestamp in Pandas we can use accessors: .dt.hour. So working with datetime column we get:

df['date'].dt.hour

Hours represented in 24 hour format:

0    17
1    18
2    19
3    20
4    21
Name: date, dtype: int64

Because we had initial information stored as PM

The image below shows the extract hours:

Step 3: Extract minutes from datetime

Extracting minutes in Pandas from datetime is available by accessor - .dt.minute:

df['date'].dt.minute

We get minutes from the datetime:

0    25
1    25
2    25
3    25
4    25
Name: date, dtype: int64

Step 4: Extract seconds from datetime

There is one last option - for seconds - .dt.second:

df['date'].dt.second

We extracted seconds for all rows:

0    0
1    0
2    0
3    0
4    0
Name: date, dtype: int64

Step 5: Extract hour in Pandas with lambda

We can use Python's datetime to extract hours directly from Pandas columns.

To extract hour or minutes we can:

• apply lambda
• select date and time format:

from datetime import datetime
df['date'].apply(lambda x: datetime.strptime(x, '%d-%m-%Y %H:%M:%S %p').hour)

Extract hours and minutes:

0    5
1    6
2    7
3    8
4    9
Name: date, dtype: int64

Refer to: Infer date format from string to find how to extract time format from string in Pandas

If you need to find more about datetime errors in Pandas check: How to Fix Pandas to_datetime: Wrong Date and Errors

Conclusion

In this article, we saw several ways to extract time information in Pandas. We saw how to extract hour and minute from datetime and timestamp in Pandas.