eda-k 0.1.0

Automated, local exploratory data analysis: stats, charts, correlations, outliers, a chat assistant, and self-contained HTML reports.

eda-k

Automated, local exploratory data analysis — as a Python library you can import, with an optional Streamlit UI on top.

Runs 100% locally. Your data never leaves your machine, no API key needed.

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Install

pip install -e .

Want everything in one shot (library + Streamlit app + OLS trendlines)?

pip install -e ".[app,trend]"

Or pick extras individually:

Need the bundled Streamlit app too?

pip install -e ".[app]"

Need OLS trendlines on scatter plots (charts.pairwise_scatter_with_trendline)?

pip install -e ".[trend]"

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Use it as a library

import eda_k

result = eda_k.analyze("data.csv")   # path, file-like, or DataFrame all work

print(result)                  # <EDAResult 'data.csv' rows=150 cols=5 ...>
print(result.summary())        # quick text overview
print(result.ask("which columns have missing values?"))

result.to_html("report.html")     # self-contained HTML report (charts inline)
result.to_csv_zip("tables.zip")   # every summary table as CSVs in one ZIP

result.df is the loaded pandas.DataFrame, and result.results is the raw dict of every table (overview, missing_summary, numeric_summary, outliers, categorical_summary, correlation, top_correlations, dtype_table) if you want to work with the data directly.

Lower-level access

The original modules are available as submodules, unchanged, for full control:

from eda_k import eda_engine, charts, chat_assistant, report_builder

df = eda_engine.load_file(open("data.csv", "rb"), "data.csv")
results = eda_engine.run_full_eda(df)
fig = charts.histogram(df, "some_column")

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Use the Streamlit UI

pip install -e ".[app]"
streamlit run apps/streamlit_app.py

Opens a browser tab with upload, tabs (Overview / Missing / Numeric / Outliers / Categorical / Correlation / Chat / Download), and one-click export of the HTML report or a ZIP of CSVs — same as before, just now built on top of the installed eda_k package instead of loose scripts.

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Project layout

eda-k/
├── pyproject.toml
├── README.md
├── requirements.txt          # convenience: pip install -r requirements.txt == pip install -e ".[app]"
├── src/
│   └── eda_k/
│       ├── __init__.py        # public API: analyze(), EDAResult
│       ├── eda_engine.py       # core analysis (pandas/numpy/scipy, no UI)
│       ├── charts.py           # Plotly chart builders
│       ├── chat_assistant.py   # local rule-based Q&A
│       └── report_builder.py   # self-contained HTML report builder
└── apps/
    └── streamlit_app.py        # optional UI, imports from the installed package

Supported file types

CSV, TSV, TXT (auto-delimiter-detect), XLSX, XLS, JSON, Parquet.

Notes / known limits

• Very large files (millions of rows) will be slower to chart; consider sampling first if you hit performance issues.
• The "likely datetime column" detector is a heuristic on a small sample — always double check it against the Overview before trusting it blindly.
• Normality test (Shapiro-Wilk) auto-samples to 5,000 rows for large columns for speed.