Date Functionality and its Importance in Pandas

Introduction

Apart from texts and numbers, data is also a very important data type in datasets. The Pandas library has a different set of tools that help us in performing all the necessary tasks on date-time data. In financial data analysis, date functionality plays a very important role.

The datetime Module in Pandas

In Python language, date and time are not considered separate datatypes, but a single module named datetime is imported to work with both. It is a built-in module in python. It contains a number of classes to deal with dates, times, and time intervals. There are six main classes in the datetime module:-

• date: Its attributes are year, month, and day. It follows the current Gregorian calendar.
• time: Its attributes are hour, minute, second, microsecond, and tzinfo.
• datetime : It's a combination of date and time with the attributes year, month, day, hour, minute, second, microsecond, and tzinfo.
• timedelta: A duration that is the difference between two date, time, or datetime instances accurate to the resolution of a microsecond.
• tzinfo: Gives time zone information objects.
• timezone: Used to implement the tzinfo abstract base class.

Importance of Date Functionality

Dates and times are good sources of information. They have a big impact on financial and healthcare machine-learning models. Not just in these domains but everywhere around you, there exists some data which has a lot of valuable information stored in the form of date and time.

The Date Functionality

While working on TimeSeries data, we might come across date data, and that is exactly where data functionalities come into play. Two major use case of date functionality is generating sequences of dates and converting the date series to different frequencies. Pandas support multiple functionalities for the manipulation of time-series data. Pandas support functionalities like:

• Parsing time series information from various sources and formats
• Generate sequences of fixed-frequency dates and time spans
• Manipulating and converting date times with timezone information.
• Resampling or converting a time series to a particular frequency
• Performing date and time arithmetic with absolute or relative time increments.

Code Example 1:

Output:

Code Example 2:

Output:

Code Example 3:

Output:

Code Example 4:

Output:

Code Example 5:

Output:

Creating a Range of Dates

This function is used to create a series of dates with respect to the given parameter values.

Code Example 6:

Output:

Changing the Date Frequency

This function is used to change the frequency of the given dates to some other frequency and produce output according to it. For eg: Initially, we create a range of dates on the basis of days the frequency will be set to ('D'), in order to get the output range in terms of months, we change the frequency to months('M') and hence we get the desired output. We look into a similar example in the code given below.

Code Example 7:

Output:

b_date() Function

The function bdate_range() stands for business date ranges. It behaves very similarly to the date_range function. Unlike date_range(), it excludes Saturday and Sunday.

Code Example 8:

Output: