Metadata-Version: 2.4
Name: tensor2
Version: 1.0.2
Summary: Business Analytics lab experiments — import, run, and save ready-to-use Python scripts.
License: MIT
Project-URL: Homepage, https://pypi.org/project/tensor2/
Project-URL: Repository, https://github.com/yourname/tensor2
Keywords: data analytics,experiments,pandas,machine learning,time series,ARIMA,sentiment analysis,regression
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Education
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.5
Requires-Dist: numpy>=1.23
Provides-Extra: all
Requires-Dist: matplotlib>=3.6; extra == "all"
Requires-Dist: seaborn>=0.12; extra == "all"
Requires-Dist: openpyxl>=3.1; extra == "all"
Requires-Dist: statsmodels>=0.14; extra == "all"
Requires-Dist: scikit-learn>=1.2; extra == "all"
Requires-Dist: textblob>=0.17; extra == "all"
Requires-Dist: vaderSentiment>=3.3; extra == "all"
Requires-Dist: nltk>=3.8; extra == "all"
Requires-Dist: wordcloud>=1.9; extra == "all"
Requires-Dist: yfinance>=0.2; extra == "all"
Requires-Dist: pmdarima>=2.0; extra == "all"
Dynamic: license-file

# tensor2

> Business Analytics lab experiments — import, browse, and generate ready-to-run Python scripts.

## Installation

```bash
pip install tensor2

# Install ALL optional experiment dependencies at once:
pip install "tensor2[all]"
```

## Quick Start

```python
from tensor2 import index

# ① Interactive menu — pick an experiment number, get a saved .py file
index.main()

# ② Just print the list
index.list_experiments()

# ③ Get source code as a string
code = index.get(3)
print(code)

# ④ Save directly to a file
index.get(5, save_to='data_quality.py')
```

## Available Experiments

| # | Title | Key Libraries |
|---|-------|---------------|
| 1 | Data Importing and Exporting using Pandas | pandas, openpyxl |
| 2 | Data Cleaning and Preprocessing | pandas, numpy, matplotlib, seaborn |
| 3 | Data Integration and Reshaping (Data Wrangling) | pandas, numpy |
| 4 | Dealing with Time Series Data | pandas, numpy, matplotlib |
| 5 | Data Quality Assurance (DQA) | pandas, numpy, matplotlib, seaborn |
| 6 | Exploratory Data Analysis and Visualization | pandas, numpy, matplotlib, seaborn |
| 7 | Big Data Analysis — Apache Hive & Neo4j* | *(external tools)* |
| 8 | Forecasting using ARIMA Model | statsmodels, scikit-learn |
| 9 | Sentiment Analysis of Social Media Data | textblob, vaderSentiment, nltk |
| 10 | Regression Analysis on Stock Data | scikit-learn, yfinance |

\* Experiment 7 prints the Hive/Cypher commands you need to run in their respective shells.

## Dataset Replacement

Every experiment uses a **synthetic dataset by default** so you can run it immediately.
Look for the comment block at the top of each experiment:

```python
# Replace with your own file:
#   df = pd.read_csv('your_file.csv')
```

Simply swap in your real data source and the rest of the script runs unchanged.

## How `index.main()` Works

1. Prints a numbered list of all experiments.
2. You type the experiment number.
3. You choose a file name (or press Enter for the default).
4. A ready-to-run `.py` file is saved in your current directory.

## License

MIT
