GitHub - TirendazAcademy/PANDAS-TUTORIAL: Jupyter Notebooks and Data Sets for Pandas Library

Welcome to the Python Pandas tutorial! In this tutorial, you will learn how to work with the Pandas library, a powerful and easy-to-use data analysis toolkit for Python. Whether you're a beginner or an experienced data analyst, this tutorial will provide you with a comprehensive introduction to the Pandas library and its features. Through clear explanations and practical examples, you'll learn how to manipulate, visualize, and analyze data using Pandas. Feel free to download and experiment with the code as you follow along. Let's get started!

Pandas is a Python library used for working with datasets. It is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool. Pandas has data structures for data analysis. The most commonly used data structures are Series and DataFrame. Series is one-dimensional. It consists of one column. DataFrame is two dimensional. It consists of rows and columns.

To install Pandas, use pip install pandas

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Name		Name	Last commit message	Last commit date
Latest commit History 40 Commits
.ipynb_checkpoints		.ipynb_checkpoints
DataSets		DataSets
Images		Images
01-What-is-Pandas.ipynb		01-What-is-Pandas.ipynb
02-Series Data Structure.ipynb		02-Series Data Structure.ipynb
03-Working with Series.ipynb		03-Working with Series.ipynb
04-DataFrame.ipynb		04-DataFrame.ipynb
05-Indexing-Selecting-Filtering.ipynb		05-Indexing-Selecting-Filtering.ipynb
06-Important Methods.ipynb		06-Important Methods.ipynb
07-Arithmetic Operations.ipynb		07-Arithmetic Operations.ipynb
08-Sorting and Ranking.ipynb		08-Sorting and Ranking.ipynb
09-Summary Statistics.ipynb		09-Summary Statistics.ipynb
10-Reading and Writing Data.ipynb		10-Reading and Writing Data.ipynb
11-Missing Data.ipynb		11-Missing Data.ipynb
12-Data Transformation.ipynb		12-Data Transformation.ipynb
13-Hierarchical Indexing.ipynb		13-Hierarchical Indexing.ipynb
14-Combining and Merging Datasets.ipynb		14-Combining and Merging Datasets.ipynb
15-Reshaping and Pivoting.ipynb		15-Reshaping and Pivoting.ipynb
16-GroupBy.ipynb		16-GroupBy.ipynb
17-Working with GroupBy.ipynb		17-Working with GroupBy.ipynb
18-Pivot Tables.ipynb		18-Pivot Tables.ipynb
19-Categorical Data.ipynb		19-Categorical Data.ipynb
20-Working with Text Data.ipynb		20-Working with Text Data.ipynb
21-Practical Data Analysis with Pandas.ipynb		21-Practical Data Analysis with Pandas.ipynb
22-Multiple Selecting-Filtering.ipynb		22-Multiple Selecting-Filtering.ipynb
23-Time Series Basics with Pandas.ipynb		23-Time Series Basics with Pandas.ipynb
24-Working with Methods in Pandas - Part 1 .ipynb		24-Working with Methods in Pandas - Part 1 .ipynb
25-Working with Methods in Pandas - Part 2 .ipynb		25-Working with Methods in Pandas - Part 2 .ipynb
26-Important Methods for Time Series in Pandas.ipynb		26-Important Methods for Time Series in Pandas.ipynb
27-Data Visualization with Pandas - Part 1.ipynb		27-Data Visualization with Pandas - Part 1.ipynb
28-Data Visualization with Pandas - Part 2.ipynb		28-Data Visualization with Pandas - Part 2.ipynb
29-Pandas Tutorial.ipynb		29-Pandas Tutorial.ipynb
30-Data-Analsis-with Python.ipynb		30-Data-Analsis-with Python.ipynb
Google-play-store-eda-data-visualization.ipynb		Google-play-store-eda-data-visualization.ipynb
README.md		README.md
Web Scraping with Python.ipynb		Web Scraping with Python.ipynb

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