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Db2 SPUFI — Interactive SQL, DSN commands, BIND, and catalog queries on z/OS

Run SQL through SPUFI, drive Db2 with DSN subsystem commands, BIND packages and plans, schedule DSNTEP2 in JCL, query the SYSIBM catalog, and generate DCLGEN.
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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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BeautifulSoup — HTML Parsing & Scraping

Parse, search, and mutate HTML/XML with BeautifulSoup 4. Covers parser choice (html.parser/lxml/html5lib), find/find_all/select, tree navigation, attribute access, and pairing with requests/httpx/playwright for end-to-end scraping.
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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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scikit-learn — Classical Machine Learning

Build classical ML pipelines with scikit-learn. Covers the estimator API, train_test_split, Pipeline, ColumnTransformer, cross-validation, metrics, and model persistence.
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streamlit — Data Apps in Pure Python

Build interactive web apps for data and ML in pure Python. Covers widgets, layout, session state, caching, multipage apps, and deployment patterns.
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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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DuckDB — Embedded Analytics Database

Run fast analytical SQL queries in-process with DuckDB. Covers Python API, CSV/Parquet ingestion, pandas interop, Arrow, window functions, and persistent databases.
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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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prefect — Workflow Orchestration

Build, schedule, and observe Python workflows with Prefect. Covers flows, tasks, retries, schedules, deployments, caching, concurrency, and Prefect Cloud.
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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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matplotlib — Plotting

Create publication-quality 2-D plots with matplotlib. Covers pyplot basics, subplots, savefig, common chart types, and the show-vs-save pitfall.
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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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Pillow — Image Processing

Open, resize, crop, convert, and save images with Pillow (PIL fork). Covers format conversion, filters, drawing, and EXIF handling.
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scipy — Scientific Computing

Statistical distributions, optimization, integration, signal processing, and linear algebra with SciPy. Builds on NumPy arrays.
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