Course map
Pandas, Clearly contains 30 modules, numbered 00–29, with four guided lessons in each module. Use the sequence below as a path through the ideas. You can revisit any module after running its setup.
Choose a route
| Route | Start | What to concentrate on |
|---|---|---|
| New to programming | 00, then 01–08 in order | Values, names, lists, dictionaries, tables, and the meaning of each bracket. |
| Coming from spreadsheets | 00–02 as needed, then 03–08 | Labeled columns, indexes, row filtering, whole-column calculations, and reproducible steps. |
| Comfortable with Python | 03 onward | pandas types, label alignment, missing values, grouping, and joins. |
| Returning pandas user | 09–24, then 25–29 | Data-quality decisions, subtle behavior, debugging, and application to unfamiliar data. |
The routes change your entry point, not the standard for understanding. Explain the result and attempt the change task before deciding that a topic is already familiar.
Part 1: Python and table foundations
| Module | Topic | You should be able to… |
|---|---|---|
| 00 | A short Python bridge | Read assignments, lists, a small function, import aliases, and an error message. |
| 01 | Start with a dictionary | Identify keys and values; retrieve, add, change, and remove a pair. |
| 02 | From one student to many | Read a dictionary of lists and a list of records; check their structure. |
| 03 | Meet the pandas Series | Distinguish values from labels and explain label-aligned arithmetic. |
| 04 | Build and inspect a DataFrame | Construct a table and inspect its shape, columns, types, and previews. |
| 05 | Select exactly what you need | Choose rows, columns, and values using labels or positions deliberately. |
| 06 | Filter with conditions | Build Boolean masks and combine rules with AND, OR, membership, and ranges. |
| 07 | Sorting and index decisions | Order records, define tie behavior, and set, reset, or reindex labels. |
| 08 | Create and update columns | Calculate fields, rename and remove columns, and update the intended object. |
Checkpoint: Given a small new table, select a requested set of records, calculate one new field, and explain why the selection is correct.
Part 2: Prepare data carefully
| Module | Topic | You should be able to… |
|---|---|---|
| 09 | Data types and safe conversion | Convert numbers, distinguish parsing failures from missing input, and use nullable types. |
| 10 | Missing values and honest cleaning | Detect missing values and justify retaining, excluding, or filling them. |
| 11 | Duplicates and data quality | Separate exact duplicates from repeated keys and check meaningful constraints. |
| 12 | Clean and search text | Normalize labels, search literal or patterned text, split fields, and extract components. |
| 13 | Dates, times, and durations | Parse explicit date formats, select intervals, and calculate elapsed time. |
| 14 | Map, apply, and vectorize | Map labels, read a function applied to values or rows, and use direct column operations. |
Checkpoint: Explain each change you make to a messy table. Show what remains unknown and how many records are affected. Module 10's group-based filling example previews an idea developed in Module 16; return to it after that module if needed.
Part 3: Summarize, combine, and reshape
| Module | Topic | You should be able to… |
|---|---|---|
| 15 | Statistics you can explain | Interpret center, spread, counts, quantiles, and correlation with appropriate limits. |
| 16 | Group, summarize, and compare | Distinguish aggregation, transformation, and group filtering; account for missing keys. |
| 17 | Merge related tables | Choose a join, validate keys, and report unmatched records and unexpected row expansion. |
| 18 | Concatenate and align | Combine row batches or labeled columns and control source priority. |
| 19 | Reshape tables | Move between long and wide forms and distinguish rearrangement from aggregation. |
Checkpoint: Produce a grouped result from two tables and show the keys, row counts, match status, and denominator that make the result credible.
Part 4: Files, time, and visual interpretation
| Module | Topic | You should be able to… |
|---|---|---|
| 20 | Read and write files | Read and export CSV, JSON, Excel, and a small SQLite query result. |
| 21 | Work with time series | Expose missing dates, resample, use rolling windows, and compare periods. |
| 22 | Explore richer structures | Read a MultiIndex, stack or unstack, explode lists, and encode categories. |
| 23 | Work efficiently and reproducibly | Inspect memory, restrict imports, combine chunk totals, and take a repeatable sample. |
| 24 | Plot and interpret | Choose a bar chart, histogram, scatter plot, or time plot and label its meaning. |
Checkpoint: Read a file, state the unit of one row, produce a useful plot, and verify a saved report by loading it again.
Part 5: Apply the skills
| Module | Topic | You should be able to… |
|---|---|---|
| 25 | Debugging clinic | Repair selection, filter, update, and merge mistakes and explain their causes. |
| 26 | End-to-end classroom project | Clean fictional records, retain an audit trail, summarize, merge, visualize, and export. |
| 27 | Import CSV and text with confidence | Handle delimiters, comments, text encoding, IDs, and missing-value tokens deliberately. |
| 28 | Work across Excel and JSON formats | Select worksheets and work with record JSON, JSON Lines, and nested records. |
| 29 | Analyze a real public dataset | Audit a source, account for missingness, report denominators, and present a reproducible analysis. |
Modules 27–29 use the documented public-data fixtures, including Palmer Penguins and its derived formats. A separate import exercise is explicitly fictional. The independent assessment asks you to combine skills with fewer prompts than a guided lesson.
A practical order for study
Complete the foundations before trying to memorize many methods. Revisit missing values and indexes as you learn joins and grouping; they affect almost every analysis. After a guided project, try the independent assessment without opening the solution notebook. Use the reference guide when you forget syntax, and explain the decisions that syntax implements.
Scope
This course gives a broad practical foundation in pandas. Specialist areas such as distributed computing, production database administration, advanced statistical inference, and the full pandas extension API need additional study. The official pandas user guide and the reference guide provide useful next steps.