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Quick, easy and customizable data analysis with pandas and seaborn

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# EDAwesome

This is a package for quick, easy and customizable data analysis with pandas and seaborn. We automate all the routine so you can focus on the data. Clear and cuztomizable workflow is proposed. Check [my talk](https://youtu.be/g6qOyCaiuHI) about EDA good practices and the concepts of this library.

## Installation

EDAwesome is generally compatible with standard Anaconda environment in therms of dependencies. So, you can just install it in you environment with pip:

```bash
pip install edawesome
```

You can also install the dependencies, using `requirements.txt`:

```bash
pip install -r requirements.txt
```

If you use Poetry, just include the depedencies in your `pyproject.toml`:

```toml
[tool.poetry.dependencies]
python = ">=3.8,<3.11"
seaborn = "^0.12.1"
kaggle = "^1.5.12"
ipython = "^8.5.0"
transitions = "^0.9.0"
patool = "^1.12"
pyspark = "^3.3.1"
pandas = "^1.5.2"
statsmodels = "^0.13.5"
scikit-learn = ">=1.2.0"
scipy = "~1.8.0"
```

## Usage

This package is designed to be used in Jupyter Notebook. You can use step-by-step workflow or just import the functions you need. Below is the example of the step-by-step workflow:

### Quick start

```python
from edawesome.eda import EDA

eda = EDA(
    data_dir_path='/home/dreamtim/Desktop/Coding/turing-ds/MachineLearning/tiryko-ML1.4/data',
    archives=['/home/dreamtim//Downloads/home-credit-default-risk.zip'],
    use_pyspark=True,
    pandas_mem_limit=1024**2,
    pyspark_mem_limit='4g'   
)
```

This will create the `EDA` object. Now you can load the data into your EDA:

```python
eda.load_data()
```

This will display the dataframes and their shapes. You can also use `eda.dataframes` to see the dataframes. Now you can go to the next step:

```python
eda.next()
eda.clean_check()
```

Let us say, that we don't want to do any cleaning in this case. So, we just go to the next step:

```python
eda.next()
eda.categorize()
```

Now you can compare some numerical column by category just in one line:

```python
eda.compare_distributions('application_train', 'ext_source_3', 'target')
```

### Real-world examples

Full notebook which was used for the examples above can be found in [one of my real ML projects](https://github.com/timofeiryko/turing-ml-capstone/blob/master/EDA.ipynb).

### Documentation

You can find the documentation [here](https://timofeiryko.github.io/edawesome).

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

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