Metadata-Version: 2.4
Name: kevin_statistics
Version: 0.0.4
Summary: For performing operations on weather data
Home-page: https://github.com/kevinwhitedhrh/stats_by_kevin
Author: Kevin Whited
Author-email: kevinwhitedhrh@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

## Installation

Install the module with:

```bash
pip install stats-by-kevin
```
## Dependencies

```bash
pip install pandas
```
# Weather Data App

## Description

This is a project for a university course to read in weather data

## Objectives

- Read in weather data from a CSV file
- Perform statistical functions from the stats_by_kevin module
- Demonstrate python knowledge

## Current Features

- Reads in weather data from a csv file
- Uses the `stats_by_kevin` package to calculate statistics for numeric columns:
	- Mean and average
	- Minimum, maximum, and range
	- Median and mode
	- Variance and standard deviation
	- Count and sum
	- Quartiles
	- A selected percentile
	- A complete summary of the available statistics

## Technologies Used

- Python 3.12.0
- Modules: pandas

## Files

| File | Description |
| --- | --- |
| `main.py` | Main Python program |
| `main.html` | HTML page with documentation |
| `Weather Training Data.csv` | Training data used by the project |


## Installation

1. Install Python from [python.org](https://www.python.org/).
2. Download or clone this project.
3. Install required libraries:

	```bash
	pip install pandas
	```
4. Install the `stats_by_kevin` package:

	```bash
	pip install stats-by-kevin
	```

## How to Run

```bash
python main.py
```

## Results

Prints information about the training data

```bash
  row ID Location  MinTemp  ...  Temp3pm  RainToday  RainTomorrow
0   Row0   Albury     13.4  ...     21.8         No             0
1   Row1   Albury      7.4  ...     24.3         No             0
2   Row2   Albury     17.5  ...     29.7         No             0
3   Row3   Albury     14.6  ...     28.9         No             0
4   Row4   Albury      7.7  ...     25.5         No             0

[5 rows x 23 columns]
Rows: 99516
```

The current program also calculates the 10th percentile for every numeric
column by calling the package:

```python
from stats_by_kevin import statistics as stats

stats.percentile(data, 10)
```

Other available functions can be called in the same way:

```python
stats.mean(data)
stats.max(data)
stats.min(data)
stats.range(data)
stats.median(data)
stats.mode(data)
stats.variance(data)
stats.standard_deviation(data)
stats.count(data)
stats.quartiles(data)
stats.sum(data)
stats.summary_statistics(data)
```
## Testing

Testing for this initial program was minimal and further testing will be done on future iterations

## Limitations and Future Improvements

- The statistics functions operate on numeric columns and print their results
	instead of returning them.
- Future iterations may add returned values, error handling, and machine learning
	operations on the weather data.

## Author

**Name:** Kevin Whited  
**Class:** CS3270
**Date:** 9/3/2026

## References

- [https://www.kaggle.com/datasets/arunavakrchakraborty/australia-weather-data](https://www.kaggle.com/datasets/arunavakrchakraborty/australia-weather-data)
