Dates and times can be hard to work with, especially when it comes to putting them in the right format. Pandas is a library for analyzing data. It has several ways to handle and format date and time data. Changing the date-time format is a common task. We’ll look at how to change the date-time format in Pandas in this article.

First, let’s make an example date-time dataframe:

import pandas as pd
import numpy as np

date_rng = pd.date_range(start='1/1/2020', end='1/10/2020', freq='H')
df = pd.DataFrame(date_rng, columns=['date'])
df['data'] = np.random.randint(0,100,size=(len(date_rng)))

This is how the sample ‘df‘ looks like:

                   date  data
0  2020-01-01 00:00:00    60
1  2020-01-01 01:00:00    94
2  2020-01-01 02:00:00    96
3  2020-01-01 03:00:00    18
4  2020-01-01 04:00:00    90

To change the date-time format, we use the pd.to_datetime method and pass in the format argument:

df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d %H:%M:%S')
df['date'] = df['date'].dt.strftime('%m/%d/%Y %H:%M:%S')

n this example, we are converting the date-time format from '%Y-%m-%d %H:%M:%S' to '%m/%d/%Y %H:%M:%S'. The dt.strftime method is used to format the date-time data as per the desired format.

The resulting dataframe df will look like this:

                 date  data
0  01/01/2020 00:00:00    60
1  01/01/2020 01:00:00    94
2  01/01/2020 02:00:00    96
3  01/01/2020 03:00:00    18
4  01/01/2020 04:00:00    90

Last but not least, modifying the date-time format in Pandas only requires a small amount of code. The two main methods for formatting date-time data in Pandas are pd.to_datetime and dt.strftime. With these techniques, you can quickly adapt the date-time format to your requirements, streamlining and speeding up the data analysis process.

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