Shawn Xiao Hu, Eugene You, Gilbert Akuja, Tien Nguyen
The fast_eda package simplifies data exploration by providing key functions for quick insights. The describe_function() summarizes numeric columns with statistics like mean and median. The distribution_plotting_function() visualizes both categorical and numeric columns using bar charts or histograms. The counts_function() identifies missing values in each column. The correlation_matrix() generates a correlation matrix between the features in the data frame. FastEDA helps users efficiently explore datasets and identify key patterns with minimal effort.
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describe(): Summarizes numeric columns in a DataFrame by calculating basic statistics such as mean, median, standard deviation, and range. It provides a quick overview of central tendencies and data spread. -
distribution_plotting(): Visualizes the distribution of both categorical and numeric columns by generating bar charts for categorical data and histograms for numeric data. This function helps in understanding the frequency distribution and patterns in the dataset. -
counts(): Counts the number of missing values in each column of the DataFrame, providing a clear overview of data completeness. This function is useful for identifying columns that may require cleaning or imputation. -
correlation_matrix(): Generates a correlation matrix to show the relationships between numeric features in the DataFrame. This function helps identify potential linear dependencies between variables, which can inform further analysis or feature selection.
The fast_eda package fits into the broader Python ecosystem as a lightweight tool designed to simplify the initial stages of exploratory data analysis (EDA). While there are other Python packages that offer similar functionality, such as ydata-profiling, fast_eda differentiates itself by focusing on providing quick and efficient summary statistics, visualizations, and missing data counts in a minimalistic and easy-to-use format. FastEDA aims for speed and simplicity, making it ideal for users who need a quick, lightweight solution without overwhelming complexity.
$ pip install fast_eda- TODO
Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
fast_eda was created by Tien Nguyen, Eugene You, Gilbert Akuja, Shawn Xiao Hu. It is licensed under the terms of the MIT license.
fast_eda was created with cookiecutter and the py-pkgs-cookiecutter template.
