Pandas DataFrames Explained —Create, Modify and Understand Missing Data in Python Dr Colleen Spiegel
In this lesson, we dive hands-on into Pandas DataFrames — the most important data structure in Python data science. If you have ever worked with a spreadsheet, a DataFrame will feel immediately familiar. But unlike a spreadsheet, a DataFrame gives you the full power of Python to manipulate, analyze, and transform your data programmatically.
What we cover in this video:
-What a DataFrame is — a labeled two-dimensional data structure similar to a spreadsheet table
-Creating a DataFrame from a Python dictionary containing mixed data types — lists, NumPy arrays, and tuples
-Adding custom row labels using the index argument
-Accessing a specific column by name
-Adding a new column to an existing DataFrame
-Inserting a Pandas Series into a DataFrame — including how rows are matched by index and how unmatched rows are automatically filled with NaN (not a number)
That last point — NaN handling — is especially important in real data science work. Real datasets are almost never complete. Understanding how Pandas handles missing values from the very beginning will save you significant confusion and debugging time on real projects.
🗂️ This video is part of a foundational Python for Data Science series, originally recorded for an online data science school and now freely available here.