![]() One of the best-known IDEs, P圜harm offers useful features like code completion and inspections, project navigation, and refactorings. All of these functionalities make Python extremely flexible and effective in data analysis. On top of that, pandas integrates the functionality of the NumPy and Matplotlib packages, providing a wide range of opportunities for data analytics and visualization. It is a great tool for flexibly representing real data with rows and columns. ![]() A DataFrame is a 2D table that supports various data formats, including JSON, CSV, SQL, and XLSX. The main data structures in pandas are the Series and DataFrames classes. Because of its easy syntax and fast operations, pandas makes working with tabular data in formats such as spreadsheets or databases very convenient. The pandas (short for panel data) library is an open-source, high-performance Python library for data manipulation and analysis, built on top of NumPy. ![]() GET STARTED Benefits of learning pandas for data analysis Even if you are already working on machine learning or data visualization, or using spreadsheet software for data analysis, this track will help you upgrade your skills and master a powerful data toolkit. In our new Pandas for Data Analysis track, you can learn how to use one of the most popular Python libraries for data analysis and visualization. If you are eager to use large amounts of data to reveal meaningful insights, this career path might be right for you. As the amount of digital data continues to grow, companies increasingly rely on data analysis to make business decisions, creating a strong demand for skilled data professionals.
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