Beside NumPy and SciPy, pandas is one of the most common scientific computing libraries for Python. Its authors aim to make pandas the most powerful and flexible open source data analysis and manipulation tool available in any language, and in fact they are almost achieving that goal. Its powerful and efficient library is a perfect match for data scientists. Like other Python packages, Pandas can easily be installed via PyPI:
pip install pandasFirst introduced in version 1.5, Matplotlib supports the use of pandas DataFrame as the input in various plotting classes. Unlike the simpler examples in previous chapters, where Python lists were supplied as the source of data, Pandas DataFrame is a powerful two-dimensional labeled data structure that supports indexing, querying, grouping, merging, and some other common relational database operations. DataFrame is similar to spreadsheets in the sense that each row of the DataFrame contains different variables of an instance, while...