Data Analysis with Pandas

If NumPy gave structure to numbers, Pandas gives structure to data. Pandas (short for Panel Data) is the cornerstone of modern data analysis in Python. While NumPy works best with raw numerical arrays, Pandas is designed for structured data — tables, datasets, and labeled collections that resemble what we encounter every day in spreadsheets, databases, or research archives.

In a sense, Pandas is like an intelligent version of Excel, built on top of NumPy but far more flexible and efficient. It can load, clean, and analyze data from almost any source — from .csv files and Excel sheets to SQL databases and JSON APIs — all within Python.

By the end of this chapter, the following will be covered:

• How to use Pandas Series and DataFrames — the two core data structures.
• How to access, modify, and filter data.
• How to handle missing values.
• How to combine datasets and compute summary statistics.

These tools support practical investigations, such as comparing word frequencies across documents, joining catalogue records and summarizing measured observations.

Installing and Importing Pandas

If the Anaconda distribution is already being used, Pandas comes pre-installed. Otherwise, it can be installed manually from the command line using either conda or pip:

conda install pandas

or

python -m pip install pandas

Once installed, the library is imported in Python using:

import pandas as pd

The alias pd is the standard convention used in nearly all code examples and documentation.

Chapter exercisesWorked answers after an attempt