Results for “pearson-correlation”

Understand and query the NPSP data model, including namespace prefixes, GAU allocations, recurring donations, relationships, and affiliations.

Monitor bonding curves, graduation progress, and trade analytics on Pump.fun

Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

Screen US equities for parabolic exhaustion patterns, generate conditional pre-market short plans, and monitor intraday trigger signals using 5-min bars.

Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

Guides configuring connection pooling for Postgres to prevent connection exhaustion under load.

Builds statistical plots with the seaborn Python library and pandas DataFrame integration, on attractive matplotlib-based defaults. Use for quick exploration of distributions, relationships, and categorical comparisons — box plots, violin plots, swarm/strip plots, KDE/histograms, pair plots, joint plots, regression plots, correlation heatmaps, and faceted small multiples (relplot/displot/catplot/lmplot). For interactive/hover/zoom charts defer to alterlab-plotly; for exact journal/manuscript styling (column widths, point fonts, CMYK, vector export) defer to alterlab-scientific-viz; for low-level custom matplotlib figures defer to alterlab-matplotlib (seaborn integrates with it for fine-tuning). Part of the AlterLab Academic Skills suite.

Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

Your won/lost/churn pattern analyst. Connect a CRM plus a product-analytics tool, then turn retrospective data into forward-looking action. Three modes. (1) WON, win pattern recognition that feeds your ICP and lookalike search. (2) LOST, loss pattern plus competitive intel that feeds a messaging refresh. (3) CHURN, churn theme extraction plus predictive scoring (which active accounts look like recent churners?). Trigger on "why are we winning?", "why are we losing?", "closed-lost autopsy", "churn patterns", "competitive intel rollup", "who do we lose to most?", "show me lookalike candidates to {winning customer}", "predictive churn", "which active accounts look like churners?", or any portfolio-level pattern recognition.

Creates executable Polars dataframe contracts using Pandera's Polars backend for runtime validation of schemas, columns, and checks.