![]() ![]() Post that, you will be working with CSV, Excel, TXT, JSON files, and API responses.įinally, you will be working with DataFrames (indexing, slicing, adding, and deleting).īy 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. After that, you will explore important Jupyter Notebook commands. You will be then working with Pandas, iPython, Jupyter Notebook. This course starts with covering the fundamentals of data analysis. This course covers fundamentals of data analysis with Python, predominantly using Pandas library. Fields with the widespread use of Pandas include data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, and many areas of engineering. 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. This also includes the Apple and Android app with the same ability for downloading course content for offline use. All existing 15,000+ courses that were available on, are now found on LinkedIn Learning. Pandas is an open-source library providing you with high-performance, easy-to-use data structures and data analysis tools for Python. So from a content and course perspective, they are exactly the same (with some new additions, see below).
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