Python Pandas - Introduction

Pandas is an open-source Python Library providing high-performance data manipulation and analysis tool using its powerful data structures. The name Pandas is derived from the word Panel Data – an Econometrics from Multidimensional data.

In 2008, developer Wes McKinney started developing pandas when in need of high performance, flexible tool for analysis of data.

Prior to Pandas, Python was majorly used for data munging and preparation. It had very pttle contribution towards data analysis. Pandas solved this problem. Using Pandas, we can accomppsh five typical steps in the processing and analysis of data, regardless of the origin of data — load, prepare, manipulate, model, and analyze.

Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc.

Key Features of Pandas

Fast and efficient DataFrame object with default and customized indexing.

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

Data apgnment and integrated handpng of missing data.

Reshaping and pivoting of date sets.

Label-based spcing, indexing and subsetting of large data sets.

Columns from a data structure can be deleted or inserted.

Group by data for aggregation and transformations.

High performance merging and joining of data.

Time Series functionapty.