From 14fac4db453f77bd418fa98bd4370764d8b54b75 Mon Sep 17 00:00:00 2001 From: Efere Precious <125882820+justEfere@users.noreply.github.com> Date: Fri, 10 Jul 2026 17:49:19 +0100 Subject: [PATCH] Revise documentation for clarity and conciseness Updated the introduction and installation instructions for clarity and conciseness. Improved the description of functions in the documentation. --- docs/index.md | 46 +++++++++++++++++++++++++--------------------- 1 file changed, 25 insertions(+), 21 deletions(-) diff --git a/docs/index.md b/docs/index.md index c1877f9..7f73752 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,24 +1,28 @@ +

+ Percentify +

+ # Percentify -**A niche data science library for practitioners and learners alike** — drawing its main dependencies from pandas and numpy, and including everyday statistics. +*The 20% of data science operations behind 80% of daily work: each a single, readable function call.* -Following the **Pareto principle**, Percentify brings the 20% of operations that make up 80% of daily data work to the forefront, each as a single, readable function call. No more digging through six-line recipes and hard-to-remember import paths for the checks you run on every dataset. +Following the *Pareto principle*, Percentify brings the checks you run on every dataset to the forefront, one call at a time. No more digging through six-line recipes and hard-to-remember import paths. -Percentify **does not aim to compete** with pandas, scipy, statsmodels, or scikit-learn — it stands on their shoulders and works alongside them. Every function names the underlying library it draws from, so the moment you need the full, configurable version, you know exactly where to go. +Built on pandas and numpy, it pairs the everyday tools you reach for constantly with lesser-known ones worth knowing. Where a function wraps an existing library (pandas, scipy, statsmodels, scikit-learn), it names it, so you always know where to dig deeper. --- ## Install -```bash +bash pip install percentify -``` -Requires `numpy` and `pandas`. + +Requires numpy and pandas. ## A quick taste -```python +python import pandas as pd from percentify import missing @@ -29,16 +33,16 @@ df = pd.DataFrame({ }) missing(df) -``` -```text + +text column missing_pct 0 salary 50.0 1 age 25.0 2 city 0.0 -``` -One import, one line — a clean, sorted DataFrame you can read or feed straight into the next step. + +One import, one line. A clean, sorted DataFrame you can read or feed straight into the next step. [Read the full documentation →](documentation.md){ .md-button .md-button--primary } @@ -48,13 +52,13 @@ One import, one line — a clean, sorted DataFrame you can read or feed straight | Function | What it answers | |---|---| -| `change` | How much did a value grow — as numbers, columns, or a whole series? | -| `vif` | Which features are collinear? | -| `missing` | How much of each column is missing? | -| `cv` | How variable is each column, relative to its mean? | -| `outliers` | What percentage of each column are outliers? | -| `r_squared` | How well do predictions fit? | -| `pca_variance` | How much variance does each principal component explain? | -| `difference` | How far apart are two values or columns (regardless of direction)? | -| `split` | How does a total divide across weights or groups? | -| `display` | Format numbers or a column as clean "%" strings for reports. | +| change | How much did a value grow (as numbers, columns, or a whole series)? | +| vif | Which features are collinear? | +| missing | How much of each column is missing? | +| cv | How variable is each column, relative to its mean? | +| outliers | What percentage of each column are outliers? | +| r_squared | How well do predictions fit? | +| pca_variance | How much variance does each principal component explain? | +| difference | How far apart are two values or columns, regardless of direction? | +| split | How does a total divide across weights or groups? | +| display | Format numbers or a column as clean "%" strings for reports. |