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#data (12)

Cheat sheets (12)

awk / gawk — Text Processing

Pattern-action language for structured text. Field splitting, built-in variables, arithmetic, string functions, arrays, BEGIN/END blocks, and practical data-processing recipes.
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jq — JSON Processor

Slice, filter, map, and transform JSON data from the command line. Covers all essential filters, built-in functions, select, map, reduce, streaming, jq 1.7/1.8 additions, and real-world API response processing.
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qsv — CSV Toolkit

Comprehensive reference for qsv: count, headers, stats, moarstats, select, search, sort, dedup, frequency, join, sqlp, luau, apply, schema, validate, sample, split, MCP server, and more — with examples and outputs.
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datetime — Dates, Times, Timezones

Work with dates, times, and timezones in Python using the stdlib datetime module and zoneinfo. Covers aware vs naive datetimes, ISO-8601 parsing, strftime/strptime, timedelta arithmetic, and DST handling.
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json — Stdlib JSON Encoder/Decoder

Encode and decode JSON in Python with the stdlib json module. Covers dumps/loads, indent/sort_keys/separators, custom default= and JSONEncoder, object_hook decoding, JSONL streaming, and orjson/ujson/msgspec comparison.
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dagster — Modern Data Orchestration

Build, schedule, and observe data pipelines as software-defined assets with Dagster. Covers assets, jobs, schedules, sensors, resources, partitions, and the Dagster UI.
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modin — Drop-in pandas at Scale

Speed up pandas workloads across all CPU cores with a one-line import swap. Covers Ray and Dask backends, config tuning, pandas interop, and when modin wins vs polars.
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polars — Fast DataFrames

High-performance DataFrames with a lazy expression API. Covers read/write, select, filter, group_by, joins, LazyFrame, datetime, string operations, and pandas interop.
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jupyter — Interactive Notebooks

Run interactive Python notebooks with Jupyter. Covers JupyterLab setup, cell types, keyboard shortcuts, magic commands, nbconvert export, and common pitfalls.
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numpy — Numerical Arrays

Create and manipulate N-dimensional arrays with NumPy. Covers array creation, broadcasting, vectorized math, indexing, and matrix operations.
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pandas — DataFrames

Load, filter, transform, and aggregate tabular data with pandas. Covers DataFrame creation, read_csv, groupby, merge, and the SettingWithCopy pitfall.
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sort, uniq & wc — Counting & Ordering

Sort lines (numerically, by field, human-readable sizes), deduplicate with uniq, count lines/words/bytes with wc, and number lines with nl. With real-world pipeline recipes.
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