Working with Tabular Data at Scale
Unglamorous techniques, each worth a large constant factor.
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Copy-on-Write and Chained Assignment
Learn why writing through a filtered DataFrame changes nothing, what copy-on-write guarantees in pandas 3, and how to modify a table in one step.
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DataFrame Merge and Concatenation
Learn when to stack tables with concat and when to join them with merge, why duplicate keys multiply rows, and how to check a join before trusting it.
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Split-Apply-Combine
Learn how groupby splits rows, applies a function, and combines the results, why text columns raise, and how missing keys vanish from a summary.
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DataFrame Index
Learn what a DataFrame index is, how loc and iloc differ, why a loc slice includes its endpoint, and when to reset the index after filtering.
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Pandas Foundations
A spreadsheet of support tickets, five questions, one table. Filter it, group it, join it to a lookup table, and see where pandas quietly changes the answer.
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Boolean Masking
Learn how a boolean mask filters rows in pandas, why conditions need parentheses and element-wise operators, and how missing values drop out silently.
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Python for Data Engineers
Build installable Python data jobs with explicit configuration, input validation, tests, and a deliberate policy for rejected records and reruns.