Introduction to Pandas

In the previous chapter, we dove into NumPy and its ndarray object, which provides efficient storage and manipulation of dense typed arrays in Python. In this chapter, we’ll introduce Pandas (Python for data analysis). Pandas is one of the most widely used Python libraries in data scienceData science and analytics. Pandas is an open-source Python library for data analysis. It gives Python the ability to work with spreadsheet-like data for fast data loading, manipulating, aligning, merging, etc. It provides numerous functions and methods that expedite the data analysis and preprocessing steps. It contains data structures and data manipulation tools designed to make data cleaning and analysis fast and easy. Pandas is often used in conjunction with numerical computing tools like NumPy and SciPy, analytical libraries like statmodels and scikit-learn, and data visualization libraries like matplotlib. While Pandas adopts many coding idioms from NumPy, the biggest difference is that Pandas is designed for working with tabular or heterogeneous data. NumPy by contrast is best suited for working with homogeneous numerical array data. Python with Pandas is used in a wide rangeRange of fields, such as academic research, retail, finance, economics, statistics, analytics, bioinformatics and medical drug discovery research, and many other fields. Pandas is the name of a Python module, which encompasses the capabilities of NumPy, SciPy, and Matplotlib. The word Pandas is an acronym which is derived from “Python and data analysis” and “panel data”. Pandas is defined as an open-source library that provides high-performance data processing capabilities in Python.