Data in pandas is often used to feed statistical analysis in SciPy, plotting functions from Matplotlib, and machine learning algorithms in Scikit-learn. It is one of the most commonly used libraries for data analysis in python

Pandas is a python library used in data manipulation ( create, delete, and update the data).

It is one of the most commonly used libraries for data analysis in python. Pandas offer data structures and operations for manipulating numerical and time-series data. Pandas is the go-to library when it comes to Python

[pandas] is derived from the term “panel data”, an econometrics term for data sets that include observations over multiple time periods for the same individuals. — Wikipedia

- Calculate statistics and answer questions about the data (Mean, Median, Mode)
- Check correlation between Two datasets
- See the distribution of values of a dataset
- Clean the data by removing missing values or filtering rows and columns by some criteria
- Visualize the data with help from Matplotlib. Plot bars, lines, histograms, bubbles, and more.
- Store the cleaned, transformed data back into a CSV, another file.

Python Pandas is defined as an open-source library that provides high-performance data manipulation in Python. This tutorial is designed for both beginners and professionals. This is the 5th part of Data Science series. Make sure you already read the previous blog. ### Key Features of Pandas * It has a fast and efficient DataFrame object with the default and customized indexing. * Used for reshaping and pivoting of the data sets. * Group by data for aggregations and transformations. * It is used for data alignment and integration of the missing data. * Provide the functionality of Time Series. * Process a variety of data sets in different formats like matrix data, tabular heterogeneous, time series. * Handle multiple operations of the data sets such as subsetting, slicing, filtering, groupBy, re-ordering, and re-shaping. * It integrates with the other libraries such as SciPy, and scikit-learn. * Provides fast performance, and If you want to speed it, even more, you can use the **Cython(**It is an optimizing static compiler for Python**)**. ### Benefits of Pandas The benefits of pandas over using other language are as follows: * **Data Representation:** It represents the data in a form that is suited for data analysis through its DataFrame and Series. * **Clear code:** The clear API of the Pandas allows you to focus on the core part of the code. So, it provides clear and concise code for the user. Python Pandas is defined as an open-source library that provides high-performance data manipulation in Python. This tutorial is designed for both beginners and professionals.

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.