Introduction to Pandas

Python Pandas:

Pandas is a software library written for the Python programming language for data manipulation and analysis.

Pandas was developed by Wes McKinney in 2008.

What is Pandas?

Pandas is an open source, BSD-licensed python library.

The name is derived from the term “panel data”, an econometrics term for multidimensional structured data sets.

It is fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

Note:

Panda library uses most of the functionalities of Numpy. I suggest the beginners to go through my previous articles (Numpy) before proceeding.

Why Pandas?

1. Fast and efficient data frame objects.

2. Tools for loading data into in-memory data objects from different file formats are possible.

3. Easier to handle missing data.

4. Simpler in reshaping and pivoting the dataset.

5. Slicing, indexing and sub-setting of large dataset.

6. Columns from data structure can be deleted or inserted.

7. High performance joining and merging of data.

Installation of Pandas:

If you have python and pip installed on your system, then installation of pandas gets easier.

Install it using this command;

C:\User\YourName> pip install pandas

If this command fails, then use python distribution that already has pandas (by default) installed like anaconda, spyder etc.

Importing Pandas:

Once pandas is installed; importing it in your application gets even more easier, by using keyword “ import ”.

Now pandas is imported and ready to use.

Alias:

Pandas is usually imported under the pd alias

“alias: Alternate name used to refer something”

Alias is created using keyword ‘as’.

Now pandas package can be referred to as pd instead of pandas.

Version:

To check the version of pandas, the version string is stored under __version__ attribute.

Pandas will be specifically useful for the people who work with data cleansing and analysis.

With this we have come to the end of this article.

Happy coding…😊😊😊