Pandas is a BSD licensed, open source package of Python which is popular for data science. It has been built on the Numpy package. It offers powerful, flexible and expressive data structures that make the manipulation of the data and make the analysis easier. One of the data structures available is the DataFame. The Pandas DataFrame can be seen as a table. In this data structure, data is organized into rows and columns, which makes a two dimensional data structure. The size of this data structure is mutable and can be modified.If a dataset is to be explored in a CSV on a computer, Pandas will extract the data from that CSV into DataFrame in the form of a table.

advantages of using Pandas Framework


Pandas Framework serves numerous advantages. Some of them are explained below:

streamlined forms of data representation

It provides extremely streamlined forms of data representation which helps to understand and analyze data better.

Huge set of Commands

Huge set of commands is provided in Pandas library which is used to easily analyze the data.

Great customization options

There is a huge feature set in Pandas to apply on the data to customize edit and pivot it according to own requirements.

Development using Pandas Framework

Pandas enables Python to be more attractive and provides a practical scientific computing environment for both academic and industry practitioners. Through Pandas, the data can be cleaned, transformed and analyzed. In Pandas data structure, each axis has an index object in which labeling information about each tick is stored along that axis. The index object serves the following purposes:

Forming unions and intersections of index objects

Enabling intuitive selection to form new index objects

Performing lookup to select subsets of slices of an object

Providing fast data alignment routines for aligning the one object with another

A variety of utilities is offered by Pandas to perform input and output operations in a much unified manner. Apart from DataFrame structure of Pandas, there is another main data structure called series. It is similar to one dimensional array, and can store any type of data. In order to create Pandas series, Pandas package is first imported through the import command in Python. The series structure cannot be modified.Pandas can also fit use its data for statistical analysis in SciPy, plotting functions from Matplotlib, and machine learning algorithms in Scikit-learn. For data exploration, the data from Pandas can be used in Jupyter Notebook which gives the ability to excute the code in a particular cell as opposed to running the entire file.

PythonDevs - A Team Of Expert Pandas Developers

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