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Python Package that performs Exploratory Data Analysis

exploredata is built on Pandas and Numpy. It gives an overview of a csv data file.

  • It imports csv file and returns the summary of the data in the file using the Pandas Dataframe.info() function.
  • It contains method (null_cat and null_num) that returns the number of missing categorical and numerical features respectively
  • It also has method that returns the number of numerical and categorical features as well the number of features containing outliers
  • It also contains method (num_viz and cat_viz) that plot histogram and boxplot for each numerical variable as well as the count plot for each categorical variables.

Installing

pip install exploredata

Usage

  • import exploredata as ep #imports the exploredata package

  • new_variable = ep.EDA(file path) #calls the EDA module in the package. The module import your csv file and assign it as a pandas dataframe to the variable 'train'. It also returns the summarry of the data

  • new_variable.df #prints the pandas dataframe

  • new_variable.num_var() #returns a tuple containing the number and the list of numerical variables in the dataframe

  • train.null_var() #returns a tuple containing the number and the list of numerical variables containing null values

  • help(ep.EDA) #provide information on all methods defined in the EDA module

Metadata

Release files for exploredata 0.0.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

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Source distribution for exploredata 0.0.2
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Table of built distributions (wheels) for exploredata 0.0.2
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exploredata-0.0.2-py3-none-any.whl Python 3 none any Details

Total release size: 6.4 kB

Release files / exploredata-0.0.2.tar.gz

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