Build practical Pandas fluency through 100 numbered exercises in a single Jupyter Notebook. The first 50 establish everyday Series and DataFrame skills, including creation, selection, arithmetic, missing values, strings, and CSV or Excel files. The second half moves into time-based indexes, MultiIndex data, grouping, pivot tables, categorical values, cleaning, preprocessing, and plots.
Each numbered exercise includes working code, and most are followed by a blank cell where you can reproduce or adapt the operation. The examples use compact data created inside the notebook, allowing you to concentrate on individual Pandas techniques before combining them in larger analysis work.
What You Will Learn
- Create, index, slice, modify, and calculate with Pandas
Series - Construct and inspect
DataFrameobjects from NumPy arrays and dictionaries - Select, sort, copy, aggregate, and conditionally query tabular data
- Fill or remove missing values and apply vectorized string transformations
- Read and write CSV and Excel data from a notebook workflow
- Work with dates, time zones, MultiIndex objects, grouping, and pivot tables
- Clean irregular columns, remove duplicates, categorize values, and normalize numerical data
- Produce line, scatter, and bar charts directly from Pandas objects
Who This Course Is For
This course is for Python learners who want extensive Pandas practice and for data learners who need to reinforce common table-manipulation patterns. Beginners can follow the first half in sequence, while learners with prior Pandas exposure can use the later exercises to review specific advanced topics.
Prerequisites: Basic Python knowledge is recommended, including lists, dictionaries, functions, and simple NumPy arrays. Prior Pandas experience is helpful but not required because working examples are provided.
Learning environment: A browser-based Jupyter Notebook on Ubuntu 22.04 contains all 100 exercises and supports CSV, Excel, and inline plotting work. The necessary practice data is created within the notebook, so no external dataset or local installation is required.
Frequently Asked Questions
Are solutions included?
Yes. Every numbered exercise has working code, and most have an adjacent blank cell for you to repeat the task. For meaningful practice, predict or write the operation before relying on the supplied implementation.
Are my answers checked automatically?
No. The lab instructions state that notebook operations cannot be automatically validated. You should run each cell, inspect the resulting object or chart, and compare it with the demonstrated behavior.
Do I need to complete all 100 exercises in order?
The first 50 offer a useful progression through the fundamentals. Exercises 51–100 cover more advanced themes, several of which are marked optional, so experienced learners can select topics such as time series, pivot tables, cleaning, or visualization.
Does the course use a large real-world dataset or build one complete project?
No. It uses small, self-contained examples to isolate individual Pandas operations. The goal is breadth and repetition rather than an end-to-end analysis of one external dataset.
