instanteda 0.1.1

One-call exploratory data analysis with publication-ready outputs

instanteda

Instantly make paper-ready EDA plots and tables.

One function call. Any DataFrame. Publication-quality PNGs -- sized, typeset, and styled for direct inclusion in scientific manuscripts.

from instanteda import eda

results = eda(df, target="label", save_dir="figures")

Why instanteda

• You have a dataset and need to explore it before modeling
• You want figures you can drop straight into a LaTeX document or journal submission
• You don't want to spend an hour configuring matplotlib for every project

instanteda does the entire exploratory data analysis in a single call and writes every output to disk as a 600 DPI PNG.

Features

Publication-ready formatting

• 3.54 x 3.54 in (90 mm) single-column figures, 600 DPI
• DejaVu Sans, 12 pt
• Cividis colormap with hatch textures for grayscale and colorblind accessibility
• Three-line tables with titled headers, scientific notation for large numbers

Smart type detection

• Numeric columns with low unique-to-row ratio and integer values are automatically treated as categorical (catches 0/1 flags, ordinal labels, encoded classes)
• Date strings in mixed formats are auto-parsed to datetime
• Datetime columns get line plots, not bar charts

Target-aware analysis -- when a target column is provided, every feature is plotted against it using the right chart:

Target	Feature	Plot
Continuous	Continuous	Scatter
Continuous	Categorical	Box plot
Categorical	Continuous	Overlaid histograms
Categorical	Categorical	Grouped bar chart
Any	Datetime	Line over time
Datetime	Continuous	Line over time

Fast on large files -- heavy computation (null counts, duplicates, descriptive stats, correlations, group-by aggregations) runs in Polars. Small result tables are converted to pandas for rendering.

Jupyter support -- tables and plots display inline in notebooks. PNGs are always saved to disk regardless.

Install

pip install instanteda

From source:

git clone https://github.com/tutur787/instanteda.git
cd instanteda
pip install .

For development:

pip install -e ".[dev]"

Quickstart

from instanteda import eda
import pandas as pd

df = pd.read_csv("data.csv")

# Basic EDA
results = eda(df)

# With a target variable
results = eda(df, target="species")

# Custom output directory
results = eda(df, target="price", save_dir="figures")

Polars DataFrames and LazyFrames work too:

import polars as pl

df = pl.read_csv("data.csv")
results = eda(df, target="label")

Output

All PNGs are written to save_dir (default eda/):

eda/
  # Tables
  missing.png              Null counts per column
  duplicates.png           Duplicate row summary
  types.png                Column types, non-null counts, unique counts
  describe.png             Descriptive statistics (count, mean, std, quartiles)

  # Distributions
  cat_{column}.png         Bar chart per categorical column
  num_{column}.png         Histogram per numerical column
  dt_{column}.png          Record-count line plot per datetime column
  correlations.png         Correlation heatmap

  # Target analysis (when target is provided)
  target_summary.png                   Grouped stats or correlations
  target_{col}_scatter.png             Continuous x continuous
  target_{col}_box.png                 Continuous target x categorical feature
  target_{col}_hist.png                Categorical target x continuous feature
  target_{col}_bar.png                 Categorical x categorical
  target_{col}_by_{dt_col}_line.png    Value or counts over time

Project structure

instanteda/
├── __init__.py      Public API — re-exports eda()
├── constants.py     Shared constants (figure size, DPI, colormap, hatches)
├── utils.py         DataFrame conversion, style setup, column classification
├── tables.py        Table computation and PNG rendering
├── plots.py         Univariate distribution plots (categorical, numerical, datetime, correlations)
├── target.py        Target-aware plots and summary tables
└── eda.py           Main entry point — orchestrates tables, plots, and target analysis

API

eda(df, target=None, save_dir="eda")

Parameter	Type	Default	Description
df	pandas.DataFrame, polars.DataFrame, polars.LazyFrame	--	Input data
target	str or None	None	Target column for grouped analysis
save_dir	str	"eda"	Output directory (created if needed)

Returns a dict mapping result names to pandas.DataFrame (tables) or matplotlib.Figure (plots).

Requirements

• Python >= 3.9
• numpy >= 1.24
• pandas >= 2.0
• polars >= 1.0
• matplotlib >= 3.7

License

MIT