From the course: Python Data Analysis
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pandas overview - Python Tutorial
From the course: Python Data Analysis
pandas overview
- [Instructor] Pandas has gained broad acceptance in the Python community as the leading data analysis library in the data science stack. It's especially useful for statistics, but also to prepare data for visualization and machine learning. While NumPy works with arrays, pandas deals with data tables, which are known in the library as data frames. The main difference is that array rows, columns and elements are identified by integers, while data frame rows and columns can be indexed by strings, dates, floating point numbers, and other python objects. In fact, any data frame column can be promoted to act as the index. That is just one of many table manipulations that are possible. In addition, pandas can read and write many common data formats. It provides a consistent way to handle missing data. It implements powerful database operations such as joins, and it can even make plots. Historically, pandas used NumPy to store data behind the scenes. But since version 2.0, pandas has���
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