How to extract date-related information from a timeseries effectively in Python?

I am working with time-series data in Python and I want to find out some information from these dates, such as the day of the month, the week number, and the day of the year. Are there methods that allow me to extract such information from complete dates which are in the format yyyy-mm-dd or any other format? If there are such Python methods, please provide them with an example code. You can use the following part of my time-series data as an example to use in the methods:

time_series = pd.Series(["2022-01-10", "2022-02-15", "2022-03-23",
                         "2022-04-09", "2022-05-19","2022-06-30"])

@mubashir_rizvi You can use datetime library to extract date-related information, This is how it’s done:

  • The datetime library is used to extract information from a Pandas series with dates as strings. A custom function extracts the day of the month, week number, and day of the year.
  • The datetime.strptime() method inside the function is used to convert the date string into a datetime object.
  • This function is applied to the original date series using apply() and join(), and sort_index() is used to sort the columns alphabetically.


  • One advantage of this code is that it is simple and easy to understand. It uses standard Python libraries and doesn’t require any external dependencies.
  • It also leverages the power of Pandas for data manipulation and provides a convenient way to extract date-related information from a series of dates.

Hello @mubashir_rizvi , You can also consider my approach to achieving this task. You can get this by using Pandas dt accessor.

You can convert a series into a series of DateTime objects using the pd.to_datetime() function and a new data frame is created from this series with a column called date. The dt accessor extracts the day of the month, week number, and day of the year from the DateTime objects in the date column.
Then you can combine date properties into a new data frame called results and concatenated with the original time-series data frame using pd.concat().

I hope the above example helps you!

You can use the time module to convert date strings into a time structure, and extract specific components of the date. Use the apply() method with a lambda function to apply time.strptime() to each date string and extract the information using an attribute. Combine the resulting series for each component into a new dataframe using pd.DataFrame() . Let’s consider this example:


  • The advantage of this approach is that it does not require any external libraries, making it a lightweight and efficient method for extracting date-related information.
  • However, it is important to note that this method assumes that the date strings in the time series are formatted consistently and in the format as Year-Month-Day.