edawesome 0.1.5

Quick, easy and customizable data analysis with pandas and seaborn

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.

Installation

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

pip install edawesome

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

pip install -r requirements.txt

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

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:

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:

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:

eda.next()
eda.categorize()

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

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

Real-world example

Full notebook which was used for examples above can be found in one of my real ML projects.

There is also an example quickstart.ipynb notebook in this repo.

Documentation

You can find the documentation here.

Contributing

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