Pandas Time Series

The Time series data is defined as an important source for information that provides a strategy that is used in various businesses. From a conventional finance industry to the education industry, it consist of a lot of details about the time.

Time series forecasting is the machine learning modeling that deals with the Time Series data for predicting future values through Time Series modeling.

The Pandas have extensive capabilities and features that work with the time series data for all the domains. By using the NumPy datetime64 and timedelta64 dtypes. The Pandas has consolidated different features from other python libraries like scikits.timeseries as well as created a tremendous amount of new functionality for manipulating the time series data.

For example, pandas support to parse the time-series information from various sources and formats.

Importing Packages and Data

Before starting, you have to import some packages that will make use of numpy, pandas, matplotlib, and seaborn.

You can attach the images to be plotted in the Jupyter Notebook, by adding %matplotlib inline to the code and can also switch to Seaborn defaults by using sns.set():

Date and time

The Pandas provide the number of functionalities for dates, times, deltas, and timespans. It is mainly used for data science applications.

Native dates and times:

We have two native date and time available that reside in datetime module. We can also perform lots of useful functionalities on date and time by using the dateutil function. You can also parse the dates from a variety of string formats:

Example1:

Output:

DatetimeIndex(['2013-05-04 00:00:00', '2013-05-04 00:00:01',
               '2013-05-04 00:00:02', '2013-05-04 00:00:03',
               '2013-05-04 00:00:04', '2013-05-04 00:00:05',
               '2013-05-04 00:00:06', '2013-05-04 00:00:07'],
              dtype='datetime64[ns]', freq='S')

Example1:

Output:

2018-12-12 06:25:18
<2 * DateOffsets>