pandas
The pandas Python library provides data structures and methods for manipulating different types of data, such as numerical and temporal data. These operations are easy to use and highly optimized for performance.
Data formats, such as CSV and JSON, and databases can be used to create DataFrames. DataFrames are the internal representations of data and are very similar to tables but are more powerful since they allow you to efficiently apply operations such as multiplications, aggregations, and even joins. Importing and reading both files and in-memory data is abstracted into a user-friendly interface. When it comes to handling missing data, pandas provide built-in solutions to clean up and augment your data, meaning it fills in missing values with reasonable values.
Integrated indexing and label-based slicing in combination with fancy indexing (what we already saw with NumPy) make handling data simple. More complex techniques, such as reshaping, pivoting, and melting data...
