Convert datetime Column to Index of pandas DataFrame in Python (3 Examples)

In this Python programming tutorial, you’ll learn how to use date and time columns as index of a pandas DataFrame.

Preparing the Examples

Before we start, we first have to load the pandas library, as you can see below:

import pandas as pd                                                        # Import pandas library in Python
data_x = pd.DataFrame({'date':['02-07-2016',                               # Generate example data
                               '09-05-2022',
                               '04-08-2023',
                               '03-04-2024'],
                       'time':['18:46:00',
                               '09:54:00',
                               '17:13:00',
                               '11:39:00'],
                       'x1':[1, 7, 5, 4],
                       'x2':['a', 'd', 'f', 'y']})
print(data_x)                                                              # Print example DataFrame
#          date      time  x1 x2
# 0  02-07-2016  18:46:00   1  a
# 1  09-05-2022  09:54:00   7  d
# 2  04-08-2023  17:13:00   5  f
# 3  03-04-2024  11:39:00   4  y

Example 1: Convert DatetimeIndex from Divided Date & Time Column by Applying the format Parameter

To set DatetimeIndex from separated date and time columns, we use the format parameter followed by the set_index function:

data_x1 = data_x.copy()                                                    # Duplicate data
data_x1['datetime'] = pd.to_datetime(data_x1['date'] +                     # Generate datetime column by applying the format parameter
                                     data_x1['time'],
                                     format = '%m-%d-%Y%H:%M:%S')
data_x1 = data_x1.set_index('datetime')                                    # Using the set_index function
print(data_x1)                                                             # Show DataFrame with changed index
#                            date      time  x1 x2
# datetime                                        
# 2016-02-07 18:46:00  02-07-2016  18:46:00   1  a
# 2022-09-05 09:54:00  09-05-2022  09:54:00   7  d
# 2023-04-08 17:13:00  04-08-2023  17:13:00   5  f
# 2024-03-04 11:39:00  03-04-2024  11:39:00   4  y
del data_x1['time']                                                        # Delete column time
del data_x1['date']                                                        # Delete column date
print(data_x1)                                                             # Show adapted DataFrame
#                      x1 x2
# datetime                  
# 2016-02-07 18:46:00   1  a
# 2022-09-05 09:54:00   7  d
# 2023-04-08 17:13:00   5  f
# 2024-03-04 11:39:00   4  y

Example 2: Convert DatetimeIndex from Divided Date & Time Column by Applying the + Operator

Another possibility to create a datetime column and set it as DatetimeIndex is by using the + operator combined with the to_datetime function followed by the set_index function:

data_x2 = data_x.copy()                                                    # Duplicate data
data_x2['datetime'] = pd.to_datetime(data_x2['date'] +                     # Generate datetime column by applying the + operator
                                     ' ' +
                                     data_x2['time'])
data_x2 = data_x2.set_index('datetime')                                    # Using the + Operator
print(data_x2)                                                             # Show DataFrame with changed index
#                            date      time  x1 x2
# datetime                                        
# 2016-02-07 18:46:00  02-07-2016  18:46:00   1  a
# 2022-09-05 09:54:00  09-05-2022  09:54:00   7  d
# 2023-04-08 17:13:00  04-08-2023  17:13:00   5  f
# 2024-03-04 11:39:00  03-04-2024  11:39:00   4  y
del data_x2['time']                                                        # Delete column time
del data_x2['date']                                                        # Delete column date
print(data_x2)                                                             # Show Adapted DataFrame
#                      x1 x2
# datetime                  
# 2016-02-07 18:46:00   1  a
# 2022-09-05 09:54:00   7  d
# 2023-04-08 17:13:00   5  f
# 2024-03-04 11:39:00   4  y

Example 3: Convert DatetimeIndex from Already Existing datetime Column

In case, we already have an existing datetime column, we simply apply the set_index function to get our DatetimeIndex:

data_x3 = pd.DataFrame({'datetime':pd.to_datetime(['09-04-2016 21:12:00',  # Creating example data
                                                   '08-07-2017 23:08:00',
                                                   '06-04-2018 11:32:00',
                                                   '05-01-2019 12:53:00']),
                        'x1':[1, 7, 5, 4],
                        'x2':['a', 'd', 'f', 'y']})
print(data_x3)                                                             # Show example DataFrame
#              datetime  x1 x2
# 0 2016-09-04 21:12:00   1  a
# 1 2017-08-07 23:08:00   7  d
# 2 2018-06-04 11:32:00   5  f
# 3 2019-05-01 12:53:00   4  y
data_x3 = data_x3.set_index('datetime')                                    # Using set_index function
print(data_x3)                                                             # Show new DataFrame with changed index
#                      x1 x2
# datetime                  
# 2016-09-04 21:12:00   1  a
# 2017-08-07 23:08:00   7  d
# 2018-06-04 11:32:00   5  f
# 2019-05-01 12:53:00   4  y

Further Resources & Related Tutorials

Here, you may find some further resources on topics such as importing data, data objects, and counting:

 

Matthias Bäuerlen Python Programmer

Note: This article was created in collaboration with Matthias Bäuerlen. Matthias is a programmer who helps to create tutorials on the Python programming language. You might find more info about Matthias and his other articles on his profile page.

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