Metadata-Version: 2.4
Name: quickeda-tool
Version: 0.1.0
Summary: Automated exploratory data analysis with a one-line API and a built-in Streamlit UI.
Author: Krina Patel
License: MIT
Project-URL: Homepage, https://github.com/krinapatel/quickeda-tool
Project-URL: Repository, https://github.com/krinapatel/quickeda-tool
Project-URL: Issues, https://github.com/krinapatel/quickeda-tool/issues
Keywords: eda,exploratory-data-analysis,streamlit,pandas,auto-eda,data-profiling
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: streamlit>=1.30.0
Requires-Dist: pandas>=2.0.0
Requires-Dist: numpy>=1.24.0
Requires-Dist: matplotlib>=3.7.0
Requires-Dist: seaborn>=0.12.0
Requires-Dist: plotly>=5.17.0
Requires-Dist: openpyxl>=3.1.0
Requires-Dist: xlrd>=2.0.1
Requires-Dist: scipy>=1.11.0
Requires-Dist: Jinja2>=3.1.0
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: build>=1.0; extra == "dev"
Requires-Dist: twine>=4.0; extra == "dev"
Dynamic: license-file

# QuickEDA

**Automated exploratory data analysis with a one-line API and a built-in Streamlit UI.**

QuickEDA wraps every common EDA task — profiling, missingness, duplicates, outliers, statistics, correlation, time-series trends, target analysis, plain-language insights, and an exportable HTML report — behind one class and one command.

---

## Install

```bash
pip install quickeda-tool
```

## Three ways to use it

### 1. Launch the full Streamlit UI from Python

```python
import quickeda
quickeda.launch()
```

That's it — your browser opens with the full UI: upload box, 8 analysis tabs, dynamic filters, interactive Plotly charts, and a one-click HTML report.

### 2. Pre-load a DataFrame into the UI

```python
import pandas as pd
import quickeda

df = pd.read_csv("sales.csv")
quickeda.launch(df, name="sales")     # UI opens with data already loaded
```

### 3. Run analysis programmatically (notebooks)

```python
from quickeda import AutoEDA
import pandas as pd

df = pd.read_csv("sales.csv")
eda = AutoEDA(df, name="sales")

eda.summary()              # plain-text summary in stdout
eda.show()                 # rich HTML view inline (Jupyter)
eda.to_html("report.html") # self-contained HTML report

# Filter and re-analyse
sub = eda.filter(region=["North", "South"], price=(100, 500))
sub.summary()

# Target-based EDA
result = eda.target("revenue")
print(result["correlations"])
print(result["insights"])
```

## Or just use the command line

```bash
quickeda                         # launch UI, blank
quickeda data.csv                # launch UI with file pre-loaded
quickeda data.xlsx --port 8502   # custom port
```

## What you get

| Tab               | What's inside                                                  |
|-------------------|----------------------------------------------------------------|
| Overview          | shape, memory, dtypes, preview, CSV download                   |
| Cleaning          | missing values, duplicates, IQR outliers (analysis only)       |
| Statistics        | mean / median / std / skew, top-N for categoricals             |
| Visualisations    | Plotly histogram, boxplot, scatter, bar chart, heatmap         |
| Time Series       | auto datetime detect, rolling avg, peaks/troughs, seasonality  |
| Target EDA        | correlations or grouped comparisons, ranked importance         |
| Insights          | plain-language bullets — missingness, skew, dominance, outliers|
| Report            | one-click self-contained HTML report                           |

## Public API

```python
from quickeda import (
    AutoEDA,                     # one-class facade
    launch,                      # open the Streamlit UI

    # Direct functions (use any subset)
    load_dataset,                # CSV / Excel / JSON loader
    detect_column_types,
    missing_value_summary,
    duplicate_summary,
    detect_outliers_iqr,
    dataset_overview,
    numeric_statistics,
    categorical_statistics,
    generate_insights,
    correlations_with_target,
    group_means,
    categorical_importance,
    generate_target_insights,
    detect_datetime_columns,
    prepare_series,
    time_series_plot,
    trend_insights,
    build_html_report,
)
```

## License

MIT
