Metadata-Version: 2.1
Name: easy-df-profiling
Version: 0.1.2
Summary: A package that allows you to easily profile your dataframe, check for missing values, outliers, data types.
Home-page: UNKNOWN
Author: Sam Faraday
Author-email: <saurater@gmail.com>
License: MIT
Keywords: python,profiling,outliers,missing values
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
Requires-Dist: pandas

Data Frame Profiling - A package that allows to easily profile your dataframe, check for missing values, outliers, data types. <p> <ul><b>Import Lib</b><li>from  df_profiling  import  DF_Profiling </li></ul><ul> <b>Profile your Data:</b><li> DF_Profiling.profiling("my_file.csv")</li></ul><p><b> <ul> <b>Either using Google Colab or Saving it as csv file, use the filter options to easily check for:<li>Data Types</li> <li>Counts</li> <li>Missing Values Count</li> <li>Missing Values Percentage</li> <li> Min Value</li><li>Quartiles: 1st, 3rd</li> <li>Median</li> <li>Lower Bound Limits</li><li>Upper Bound Limits</li><li> Max Value</li><li> Unique Values</li><li> Spot Potential Outliers</li></ul><p><ul> <b>Save / Export your Analyses</b><p><li> DF_Profiling.profiling("my_file.csv").to_csv("my_profiling.csv")</li></ul>


