Introduction to Pandas Module in Python | Data Science and Big data | Codeing School

pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

Introduction to Pandas Module in Python

pandas is a NumFOCUS sponsored project. This will help ensure the success of the development of pandas as a world-class open-source project and makes it possible to donate to the project.

What is Pandas?

PANDAS (Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections) happens when strep triggers a misled invulnerable reaction results in aggravation on a youngster's cerebrum. Thus, the tyke rapidly starts to show extraordinary manifestations, for example, OCD, uneasiness, tics, identity changes, the decrease in math and penmanship capacities, tactile sensitivities, prohibitive eating, and the sky is the limit from there.

PANDAS Network evaluates that PANDAS/PANS influences upwards of 1 out of 200 kids.

Current Version of Pandas:

v0.23.4 Final (August 3, 2018)

This is a minor bug-fix release in the 0.23.x series and includes some regression fixes, bug fixes, and performance improvements. We recommend that all users upgrade to this version.

Installing Pandas:

Step 1. Open your WindowsPower Shell.

Step 2. Type pip install pandas

Step 3. Your Pandas module will install on your computer.

*To use the Pandas module in your Python Project, type this syntax à import pandas

For more information read the official Pandas documentation. Click Here.

What problem do pandas solve?

Python has for quite some time been incredible for information munging and planning, however less so for information investigation and displaying. pandas help fill this hole, empowering you to do your whole information investigation work process in Python without switching to a more space particular dialect like R.

Joined with the magnificent IPython toolbox and different libraries, nature for doing information investigation in Python exceeds expectations in execution, profitability, and the capacity to work together.

pandas do not execute huge displaying usefulness outside of direct and board relapse; for this, look to statsmodels and scikit-learn. More work is as yet expected to make Python a top of the line factual displaying condition, yet we are well on our way toward that objective.

Library Highlights:

• A fast and efficient DataFrame object for data manipulation with integrated indexing.

• Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format.

• Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form.

• Flexible reshaping and pivoting of data sets.

• Intelligent label-based slicing, fancy indexing, and subsetting of large data sets.

• Columns can be inserted and deleted from data structures for size mutability.

• Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets.

• High-performance merging and joining of data sets.

• Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure.

• Time series-functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data.

• Highly optimized for performance, with critical code paths written in Cython or C.

• Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.