Pandas is an open-source library providing you with high-performance, easy-to-use data structures and data analysis tools for Python. Pandas provide a powerful and comprehensive toolset for working with data, including tools for reading and writing diverse files, data cleaning and wrangling, analysis and modeling, and visualization. Fields with the widespread use of Pandas include data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, and many areas of engineering.

This course covers fundamentals of data analysis with Python, predominantly using Pandas library. This course starts with covering the fundamentals of data analysis. You will be then working with Pandas, iPython, Jupyter Notebook. After that, you will explore important Jupyter Notebook commands. Post that, you will be working with CSV, Excel, TXT, JSON files, and API responses.

Finally, you will be working with DataFrames (indexing, slicing, adding, and deleting).

By the end of this course, you will have a good understanding of Pandas and will be ready to explore data analysis in-depth in the future.

All the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/pandas-resources

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Spis treści

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