Introduction to Pandas

Pandas is a python-based package that includes fast, flexible and expressive data structures, which are designed to work with both relational or labeled data. It is a fundamental high-level building block for doing practical, real-world data analysis in Python.

NOTE: Before you learn Pandas, we recommend you go through our Python tutorial.

Prior to Pandas, it was majorly Python that was used for data merging and preparation; though it could not contribute to data analysis much. Hence Pandas was developed to solve this problem.

What exactly is Pandas?

• Pandas is a library for Python, which has taken the data analysis world by storm.

• If you want to get started with Machine Learning, Data analysis, or any data-intensive work, then Pandas should be the first thing you should learn.

• It works well with the other libraries in Python like Numpy and MatPlotLib, which are also essential in the field of data science.

• Reading Excel sheets full of data becomes super easy with pandas.

Why use Pandas?

Recently data science has experienced a boom. This is mainly because of the increasing availability of data to analyze.

But traditional languages like C and Java take up a lot of time and effort, hence yield very less efficiency. Therefore we use a programming language like Python along with libraries like pandas which comes pre-loaded with functions which are specially defined for data science use cases.

Some features of Pandas:

• The availability of native data structures like Series and DataFrames, to deal with homogeneous and heterogeneous data.

• Features to easily edit data structures that make it easy to input, add, delete data. This makes data manipulation extremely flexible.

• Adding multiple labels to data is allowed.

• Data along multiple axes can hierarchically indexed.

• Missing or irregular data can be detected and fixed and the data can be automatically aligned

• The data can be easily pivoted or reshaped according to your desire.

• Data can be grouped according to parameters and functions can be applied to these groups.

• Hierarchical labeling of axes.

• Flexible reshaping and pivoting of datasets.

• Intuitive merging and joining data sets.

These are just some of the few features of the Pandas library.

Pandas is well suited for:

• Ordered and unordered (not necessarily fixed-frequency)time-series data.

• Tabular data with heterogeneously-typed columns, as in SQL(structured query language) table or Excel spreadsheet

• Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels.

• Any other form of observational/statistical data sets . Note that the data actually need not be labeled at all to be placed into the pandas data structure.

Conclusion:

This tutorial summed up the entire Pandas library up for you and cleared your basic understanding of the library so that you can easily jump into the tutorials which are to follow this article. Hopefully, you will learn a lot from this course and be able to implement Pandas in your real-world project.

In the next tutorial we will learn how to install Pandas library on your computer/laptop to start coding.