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Exploratory Data Analysis

Project description


Exploratory Data Analysis


This project explortory was created to perform Exploratory Data Analysis on any structured dataset. Dataset can have categorical or numerical data types. This project takes pandas dataframe and gives summary statistics and individual plots having categorical count for catagorical variables and PDF's, CDF's with mean, median and mode for numerical variables. The both the results are stored in PDF and CSV file in your current directory/path.


Use the package manager pip to install exploratory

pip install exploratory


from exploratory import EDA
# df --> pandas dataframe
#Please input the DPI value, as DPI value increases runtime would increase. Defualt DPI value: 150

Example Run:

Exploratory Run

Expected Outputs:

  • CSV File, DataFrame Containing
Column Description
Variable Variable Name in the dataset provided
Cardinality Number of levels/classes in each variable
total_count Count of total records (non null)
unique_rate Cardinality / total_count, Unique Rate of 1 indicates a ID variable
percent_missing Percentage of missing values across each column
mean Average of column (Ignores Object/String variables)
std Standard deviation of column (Ignores Object/String variables)
min Minimum of column (Ignores Object/String variables)
25% 25th percentile value of column (Ignores Object/String variables)
median 50th percentile value of column (Ignores Object/String variables)
75% 75th percentile value of column (Ignores Object/String variables)
max Maximum of column (Ignores Object/String variables)
data_types Data type of column (Int / Float / Object etc)
range Max Value - Min Value (Ignores Object/String variables)
  • PDF with Statistical Summary and variable distribution graphs (categorical & continous)

Exported PDF


Pull requests are welcome. Please use this '' Please feel free to contact authors for any suggestions or issues, Ram, Abhilash

Project details

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exploratory-3.4.12.tar.gz (7.0 kB view hashes)

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