semvec/pypi-packages huggingface dataset:
mahitha36-pypi 0.1.0
A Python package for data analysis and visualization using Pandas, NumPy, Matplotlib, and Seaborn.
Requires Python: >=3.6

# Mahitha36_pypi 📊

Mahitha36_pypi 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 Mahitha36_pypi


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/Mahitha36/Mahitha36_pypi.git

In your Python project:




from Mahitha36_pypi 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 Mahitha36_pypi 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 Mahitha36_pypi import missing_report

missing_report(df)


3. correlation_matrix(df)
Plots a heatmap of correlation coefficients for all numeric features in the DataFrame.


from Mahitha36_pypi import correlation_matrix

correlation_matrix(df)



4. outlier_summary(df)
Returns a count of outliers per numeric column using the IQR method.

from Mahitha36_pypi import outlier_summary

outlier_summary(df)


5. plot_distributions(df)
Displays histograms of all numeric columns in the DataFrame.


from Mahitha36_pypi import plot_distributions

plot_distributions(df)


6. value_counts_all(df)
Returns a dictionary with value counts for all object (categorical) columns.


from Mahitha36_pypi import value_counts_all

value_counts_all(df)



🧑‍💻 Developer
Mahitha Nadakuditi
Artificial Intelligence & Data Science | ML & 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
cqlib-qschool 0.1.0
Cqlib qschool
Requires Python: >=3.10
Keywords: cqlib, cqlib adapter, quantum circuit, quantum computing, quantum programming language, quantum, sdk

# 天衍量子计算校验平台

## 安装

目前支持 Python 3.10+ 版本 ，推荐使用 pip 安装:

```bash
pip install cqlib-qschool
```

## 示例

登录 [天衍量子计算校验平台](https://qschool.zdxlz.com/lab/dashboard) ，获取链接密钥。并替换下面的 `<LOGIN-KEY>`。

```python
from cqlib import Circuit
from cqlib.mapping import transpile_qcis
from qschool import QSchoolPlatform

circuit = Circuit(2)
circuit.h(0)
circuit.cx(0, 1)
circuit.measure_all()

pf = QSchoolPlatform(login_key="<LOGIN-KEY>", machine_name='tianyan504')
new_circuit, _, _, _ = transpile_qcis(circuit.qcis, pf)

query_id = pf.submit_experiment(new_circuit.qcis, num_shots=1000)
data = pf.query_experiment(query_id, readout_calibration=True)
print(data[0])
```

## License

This project is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for details.