semvec/pypi-packages huggingface dataset:
ksprepro 0.2
A Python package for data analysis and visualization using Pandas, NumPy, Matplotlib, and Seaborn.
Requires Python: >=3.6

readme

# ksprepro 📊

ksprepro is a lightweight and easy-to-use Python library built to accelerate Exploratory Data Analysis (EDA) and preliminary dataset inspection for data science projects. It wraps essential Pandas, NumPy, Seaborn, and Matplotlib utilities to help you quickly understand the structure, quality, and relationships within your dataset.

---

## ✨ Features

- ✅ Quick DataFrame summary: shape, datatypes, missing values, and statistics  
- ✅ Visualize correlation matrix with heatmaps  
- ✅ Detect outliers using the IQR method  
- ✅ Plot numeric feature distributions  
- ✅ Report missing values as proportions  
- ✅ Get value counts for all categorical columns  

---

## 📦 Installation


bash
pip install ksprepro


Since this is a custom library, clone or copy the source code into your project directory or package it using setuptools if needed.

bash
# Clone this repository or include the file directly
git clone https://github.com/yourusername/ksprepro.git




In your Python project:




from ksprepro import df_summary, missing_report, correlation_matrix, outlier_summary, plot_distributions, value_counts_all




🧪 How to Use


1. df_summary(df)
Provides an overall summary of the DataFrame including shape, data types, missing values, and descriptive statistics.



import pandas as pd
from ksprepro import df_summary

df = pd.read_csv("your_data.csv")
df_summary(df)


2. missing_report(df)
Returns a Series with the proportion of missing values in each column (sorted descending).


from ksprepro import missing_report

missing_report(df)


3. correlation_matrix(df)
Plots a heatmap of correlation coefficients for all numeric features in the DataFrame.


from ksprepro import correlation_matrix

correlation_matrix(df)



4. outlier_summary(df)
Returns a count of outliers per numeric column using the IQR method.

from ksprepro import outlier_summary

outlier_summary(df)


5. plot_distributions(df)
Displays histograms of all numeric columns in the DataFrame.


from ksprepro import plot_distributions

plot_distributions(df)


6. value_counts_all(df)
Returns a dictionary with value counts for all object (categorical) columns.


from ksprepro import value_counts_all

value_counts_all(df)



🧑‍💻 Developer
Keerthisri Kuntam
Artificial Intelligence & Data Science | MERN & Web Dev Enthusiast

📜 License
This project is licensed under the MIT License. Feel free to use and modify!

🙌 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you'd like to change.

🧠 Future Features
Advanced outlier detection (Z-score, Isolation Forest)

Interactive plots using Plotly

Automatic report generation using pandas-profiling or sweetviz


Let me know if you'd like help setting this up as a proper Python package
Pgenlib 0.93.0
Python wrapper for pgenlib's basic reader and writer.
Requires Python: >=3.9

This provides a basic Python API for pgenlib; see [python_api.txt](python_api.txt) for details.


### Build instructions
PyPI:
```
pip install 'pip>=20.3'
pip install Pgenlib
```

GitHub:
```
pip install 'pip>=20.3'
pip install -e 'git+https://github.com/chrchang/plink-ng.git#egg=Pgenlib&subdirectory=2.0/Python'
```

Or install from a cloned copy:
```
# clone repo
git clone https://github.com/chrchang/plink-ng
# go to python folder
cd plink-ng/2.0/Python
# install the package
pip install -e .
```

You can test the package with `pytest`.

##### Example usage:
```
#write a 2 sample file
import numpy as np
import pgenlib as pg

with pg.PgenWriter("test.pgen".encode("utf-8"), 2, variant_ct=3, nonref_flags=False) as writer:
	writer.append_alleles(np.array([0,1,1,1],dtype=np.int32))
	writer.append_alleles(np.array([0,1,0,0],dtype=np.int32))
	writer.append_alleles(np.array([0,0,0,0],dtype=np.int32))

```

See [tests/test_pgenlib.py](tests/test_pgenlib.py) for more sophisticated examples.