Pandas

Spreadsheet superpowers in code: load, clean, reshape and summarise real tables. 22 modules of 10 minutes each, with questions that check your answers in the browser.

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Foundations

• 1. Series and DataFrame basicsFree
  Meet pandas' two building blocks, the Series (one labelled column) and the DataFrame (a whole table).
• 2. Creating DataFrames and reading CSVsFree
  Build DataFrames from Python data, load CSV files with read_csv, and handle the separators, headers and number formats real exports throw at you.
• 3. Inspecting data (head, info, describe, dtypes)Free
  The five-minute health check you run on every new dataset: its size, columns, types, missing values, ranges and categories.
• 4. Selecting columns and rowsFree
  Pick the columns you need, slice rows by position, and understand when you get a Series back versus a DataFrame.
• 5. Filtering with boolean masksFree
  Keep only the rows that match a condition, combine conditions with & | ~, and use isin, between and query for readable filters.
• 6. loc vs ilocFree
  Select rows and columns precisely by label with .loc or by position with .iloc, and update values safely without the SettingWithCopyWarning.

Intermediate

• 7. Adding, renaming, and dropping columnsFree
  Shape a table for your analysis: calculated columns, conditional columns with np.where, assign() for chaining, rename, drop and reorder.
• 8. Handling missing data (NaN, fillna, dropna)Free
  Find missing values, understand how they affect calculations, and decide deliberately whether to fill, drop or flag them.
• 9. Sorting and rankingPro
  Order rows by one or more columns, find the top and bottom N quickly, and rank values with the tie rules your report needs.
• 10. Changing data types and parsing datesPro
  Convert text to numbers and dates safely with astype, to_numeric and to_datetime, then use the .dt accessor to pull out months, weekdays and durations.
• 11. String methods on columns (.str)Pro
  Clean, search, split and extract text across a whole column at once with the .str accessor, including regex.
• 12. apply, map, and vectorized operationsPro
  Transform columns with map and apply when you need custom logic, and know why vectorized operations should be your first choice.
• 13. Saving results and reading other sourcesPro
  Write clean CSV, Excel and JSON outputs, and load data from Excel sheets, JSON APIs and SQL databases into DataFrames.

Advanced

• 14. groupby and aggregationPro
  Split data into groups, calculate summaries per group, and combine them back: the split-apply-combine pattern behind every report.
• 15. Pivot tables and crosstabPro
  Build Excel-style pivot tables in code: rows by one field, columns by another, values aggregated, with totals and percentages.
• 16. Merging and joining DataFramesPro
  Combine tables on shared keys with merge: inner, left, right and outer joins, checking for duplicates and unmatched rows like a professional.
• 17. Concatenating and reshaping (melt, pivot)Pro
  Stack tables from several files with concat, and switch between wide and long shapes with melt and pivot.
• 18. MultiIndex basicsPro
  Work with hierarchical row and column labels from multi-key groupbys and pivots: selecting with loc and xs, swapping levels, and flattening back.
• 19. Time series: resampling and rolling windowsPro
  Turn timestamped data into daily, weekly and monthly series, smooth noise with rolling averages, and measure change with shift, diff and pct_change.

Expert

• 20. Method chaining and the pipe patternPro
  Write a whole analysis as one readable top-to-bottom chain of steps, with assign, query, pipe and named functions, instead of a pile of temporary variables.
• 21. Performance: avoiding loops, categoricals, memoryPro
  Make pandas code fast and lean on real-sized data: vectorise instead of looping, use categoricals and smaller dtypes, and measure before you optimise.
• 22. Writing clean, reusable data-cleaning pipelinesPro
  Turn one-off cleaning code into a tested pipeline of small functions with validation, so next month's messy file is cleaned the same way in seconds.

Track project

• Pandas Project: Toko Kita's half-year reviewPro
  Three graded tasks on the shop's real exports. Build the monthly revenue table for management, a one-row-per-customer summary for marketing, and a restock list from a messy warehouse count.

Pro modules unlock with one payment of Rp 149.000, yours to keep.