Python Data Foundations: NumPy & Pandas Basics
Learn how NumPy arrays and pandas DataFrames work by predicting what real code returns on a fictional bakery's data: indexing, vectorised maths, filters, sorting, statistics, groupby and CSV files, with every answer checked as you go.
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About this course
In this beginner course you learn how NumPy and pandas really behave by predicting what real code returns. Each of the 20 labs shows a short Python snippet next to a fictional bakery's data, and you work out the result in a live sheet: element-wise maths and broadcasting, slices and the axis argument, boolean masks and np.where, floor division, Series and DataFrames, loc versus iloc, filters with & and isin, sorting and ranks, describe and its ddof trap, value_counts, groupby, NaN handling and reading CSV files with the right separator. Every answer is checked instantly, the first lesson is free and a 15-question exam leads to your certificate.
Lessons
- Lists vs NumPy Arrays: Element-wise MathsFree
- Creating Arrays: arange, linspace, zeros and shape Locked
- Indexing and Slicing 1-D Arrays Locked
- 2-D Arrays: Rows, Columns and the axis Argument Locked
- Vectorised Maths and Broadcasting Locked
- Boolean Masks and np.where Locked
- Aggregations: median, std, argmax and cumsum Locked
- dtypes, Floor Division and Modulo Locked
- pandas Series: Values with Labels Locked
- DataFrame Anatomy: shape, head, tail and NaN Locked
- Selecting Data with loc and iloc Locked
- Filtering Rows with Conditions Locked
- Sorting, Top N and rank() Locked
- New Columns with Vectorised Arithmetic Locked
- describe(): std, Quartiles and the IQR Locked
- value_counts, unique and nunique Locked
- groupby: Split, Apply, Combine Locked
- Missing Data: isna, dropna and fillna Locked
- Reading and Writing CSV Files Locked
- Final Project: The Weekly Store Report Locked