SQL & Python Practice by Topic

ROW_NUMBER, RANK, LAG, LEAD, running totals, moving averages

INNER, LEFT, CROSS, self-joins, and multi-table joins

GROUP BY, HAVING, COUNT, SUM, AVG, and conditional aggregation

Correlated subqueries, EXISTS, IN, and Common Table Expressions

SELECT, WHERE, ORDER BY, LIMIT, LIKE, BETWEEN, and NULL handling

Date functions, arithmetic, bucketing, and time-series analysis

COALESCE, NULLIF, CASE WHEN, casting, and deduplication

UNION, UNION ALL, INTERSECT, and EXCEPT

Multi-step business cases combining joins, CTEs, windows, and aggregation

groupby, agg, transform, named aggregation, and pivot tables

merge, join, concat, multi-key matching, and merge indicators

Boolean indexing, loc, iloc, query, isin, and conditional selection

rolling, expanding, shift, diff, rank, and cumulative operations

DataFrame creation, selection, sorting, renaming, and type conversion

to_datetime, the dt accessor, resample, periods, and timedeltas

pivot_table, melt, stack, unstack, and wide-to-long transforms

apply, map, lambdas, and custom row or column transformations

Missing values, duplicates, text cleanup, replacement, and type repair

End-to-end data-wrangling cases combining several pandas operations

Build model-ready flags, ratios, buckets, and time-based features

Descriptive statistics, quartiles, correlation, and anomaly detection

Experiment lift, significance, sample adequacy, ANOVA, and CUPED

Welch tests, chi-square analysis, p-values, and bootstrap intervals

Regression diagnostics, train/test evaluation, and classification metrics