Meet the Data

Retrieving data is the foundational step of any data science project. Whether we’re reading a file stored locally, connecting to an external API, or collecting information from the web, our analysis can be as strong as the data we bring. Accessing relevant, reliable data is where insight begins.

How to meet the data

Python gives us the tools to easily pull data from various sources. Libraries like pandas, requests, and Beautiful Soup make it simple to read files, make web requests, and parse content. Once we know how to gather data from different places, we open the door to deeper insights and more impactful analysis.

Let’s explore three common data sources and how to fetch data from each using Python.

1. Working with files

Files are one of the most common ways to store and exchange data. We often encounter two popular formats: CSV (Comma-Separated Values) and JSON (JavaScript Object Notation).

A simple way to read data from these files is to use pandas, a powerful Python library built for data manipulation and analysis. At the heart of pandas is the DataFrame, a two-dimensional, table-like structure with rows and columns, similar to a spreadsheet.

With pandas, we can easily load data into a DataFrame. Once the data is in this structure, it can be explored, modified, cleaned, and analyzed using pandas’ rich built-in functions.

Working with CSV files using pandas

CSV (Comma-Separated Values) files store tabular data in plain text, with each line representing a row and commas separating the values. Python’s pandas library makes reading, modifying, ...