Stage 1 · Lesson 3 of 7
Loading and inspecting a DataFrame
A is a table in memory: named columns, each one a single type, with rows that line up across them. Almost every analysis starts with pd.read_csv or a similar reader, then three checks: shape for the number of rows and columns, info() for types and non-missing counts, and head() for a first look at real values.
Tell the reader which columns are dates with parse_dates, and check that numbers really came in as numbers. Learn the difference between a DataFrame and a Series, which is a single column, and how the index labels rows.
Common trapskipping the type check: one stray "N/A" string in a price column makes pandas read the whole column as text, and every later calculation breaks or quietly gives the wrong answer.
A question to check yourself, an AI practice session and 1 cheat sheet entry come with the full roadmap. Unlock
Resources for this stage
Start with· follow one of these
- CoursePaidGrokking Python Fundamentals
Design Gurus · Beginner · $50
Solid Python basics first: data types, collections, functions and files, the ground pandas stands on
- CourseFreeKaggle Learn
Kaggle · Beginner
Free: the Python and Pandas courses, short lessons with exercises that run in the browser