Topic
Turning raw data or a messy set of facts into a clear conclusion is where most analysis work gets stuck. This page collects prompts built for that exact step: breaking down a dataset or report into key patterns, comparing options against a set of criteria, and structuring findings into a conclusion someone can actually act on. They're useful for analysts, product managers, and anyone who needs to make sense of numbers or unstructured information quickly. Every prompt is free and reusable — plug in your own data or context using the marked {{variable}} fields and get a structured, decision-ready answer instead of a generic summary.
62 prompts · page 4 of 7
Examines how key metrics actually moved over a date range and granularity before anyone tries to forecast them. Covers overall trend direction, seasonality at weekly through annual scales, anomalies or change points, day-of-week patterns, and year-over-year growth rates, reporting the time window and magnitude behind every finding so it feeds directly into an anomaly report or forecast.
A general-purpose starting point for pulling business-relevant takeaways out of almost any dataset, given its context and key metrics. - Covers: key trends, anomalies or outliers, correlations between variables, and segment performance - Produces: actionable recommendations, risks or warnings, and next steps - Framed around business impact rather than raw statistics - Good default when it's unclear which specialized analysis prompt fits better
Scans a full interview transcript for everything one participant said about a single named topic, quoting directly wherever possible instead of paraphrasing, and flags any conflicting or nuanced responses on that topic. Useful for building a topic-by-topic view across multiple transcripts before formal coding begins, rather than reading full summaries each time.
Feed in a target variable, its type, a list of candidate predictor columns, and the dataset's row and column counts, and get back a rigorous drivers-of-X analysis instead of a hand-wavy guess. The prompt runs univariate correlation, mutual information, tree-based feature importance, and permutation importance side by side, explains why the four rankings disagree, flags features that are likely proxies for the same underlying driver, and closes with a plain-language summary naming the top three drivers and what they mean for the business.
Describe your DataFrame's columns and sample rows, list up to four charts you need, and specify a color palette, figure size, and where the charts will be viewed — slides, web, or print. The prompt returns complete, runnable matplotlib and seaborn code for all four charts at once, each with a descriptive title, labeled axes with units, a legend only when needed, and annotations calling out key data points, without unnecessary chartjunk.
Paste a raw interview transcript to get a first-pass inductive code list before any themes are decided. - Builds a granular code list rather than pre-collapsed themes - Each code gets a short 2-5 word label - Each code gets a one-sentence definition - Each code is paired with a direct illustrative quote from the transcript
Paste in the flat code list from an earlier coding pass and this prompt organizes it into 3-6 coherent themes for a qualitative results write-up. Each theme gets a descriptive name that makes an analytical claim rather than just labeling a topic, a list of the component codes that belong to it, and a 2-3 sentence description of what the theme actually argues, while pushing back on single-code themes unless there's a strong reason for one.
List the columns with missing data — name, type, approximate percent missing, and likely cause — and this prompt classifies each one as MCAR, MAR, or MNAR before recommending a fix. It weighs whether the missingness itself is informative, considers adding a was_missing flag, justifies the chosen imputation method against alternatives for every column, and outputs the Python code implementing the strategy column by column.
Paste raw dashboard numbers plus business context, the time period, stated goals, and the prior period's figures to get a narrative read ready for a metrics review meeting. - Performance summary against goals and the previous period - Positive trends and concerning trends called out separately - Progress toward each stated goal and the likely drivers behind changes - Recommended actions and a forecast for the next period