Creating Date Ranges with Pandas

Creating date ranges with Pandas can significantly speed up your workflow when you need to iterate over a number of dates. For example, when we run queries on APIs or databases, we may need to generate a list of dates that can be iterated over. This can be a time-consuming task, but thankfully we can easily accomplish this with Pandas.

The Pandas Date Range Function

Let’s take a look at the Pandas date_range function available in Pandas. For the purposes of this tutorial, we’ll look at a reduced number of the parameters:

pd.date_range(
   start=None, 
   end=None, 
   periods=None, 
   freq=None
)

• The start and end parameters look for strings or datetime-like objects.
• The periods parameter identifies the number of periods to generate.
• The frequency parameter identifies the number of periods to generate.

Generating our First Date Range

Let’s say we want to create a list of the first of the month of every month in 2020. We could write the following code:

import pandas as pd
first2020 = pd.date_range(start='2020-01-01', end='2020-12-01', freq='MS')

We use the frequency of MS to signal that we want to return the start of the month. This would generate a list contains the following:

DatetimeIndex (['2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01',               '2020-09-01', '2020-10-01', '2020-11-01', '2020-12-01'],             
dtype='datetime64[ns]', freq='MS') 

Each item in this is a datetime object. If we want to generate this into a string, we could iterate over each item. For this, we can use a for loop, as shown below:

import pandas as pd
first2020 = pd.date_range(start='2020-01-01', end='2020-12-01', freq='MS')
list2020 = []
for i in first2020:
  list2020.append(i.strftime('%Y-%m-%d'))

This returns a list containing:

['2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01', '2020-09-01',
 '2020-10-01', '2020-11-01', '2020-12-01']

Similarly, you could rewrite the entire code as a list comprehension:

import pandas as pd
first2020 = pd.date_range(start='2020-01-01', end='2020-12-01', freq='MS')
list2020 = [i.strftime('%Y-%m-%d') for i in first2020]

Some Other Helpful Frequencies

We may want to use some other frequencies:

• B: business day frequency
• D: calendar day frequency
• W: weekly frequency
• M: month-end frequency

Conclusion: Creating Date Ranges with Pandas

In this post we learned how to create handy date ranges with Pandas using Python. It’s a handy and quick way to help create iterable objects for querying APIs and databases. You can learn more about the function by visiting the official documentation.

Additional Resources

• Pandas to_datetime: Convert a Pandas String Column to Date Time
• DateTime in Pandas and Python
• Pandas Datetime to Date Parts (Month, Year, etc.)
• Pandas: Add Days to a Date Column