eda_report.bivariate

class eda_report.bivariate.Dataset(data: Iterable)

Analyze two-dimensional datasets to obtain descriptive statistics and correlation information.

Input data is stored as a pandas.DataFrame in order to leverage pandas’ built-in statistical methods.

Parameters:

data (Iterable) – The data to analyze.

Example

>>> Dataset(iris_data)
                  Summary Statistics for Numeric features (4)
                  -------------------------------------------
                count     avg  stddev  min  25%   50%  75%  max  skewness  kurtosis
  sepal_length    150  5.8433  0.8281  4.3  5.1  5.80  6.4  7.9    0.3149   -0.5521
  sepal_width     150  3.0573  0.4359  2.0  2.8  3.00  3.3  4.4    0.3190    0.2282
  petal_length    150  3.7580  1.7653  1.0  1.6  4.35  5.1  6.9   -0.2749   -1.4021
  petal_width     150  1.1993  0.7622  0.1  0.3  1.30  1.8  2.5   -0.1030   -1.3406

                Summary Statistics for Categorical features (1)
                -----------------------------------------------
                    count unique     top freq relative freq
            species   150      3  setosa   50        33.33%


                        Pearson's Correlation (Top 20)
                        ------------------------------
      petal_length & petal_width -> very strong positive correlation (0.96)
     sepal_length & petal_length -> very strong positive correlation (0.87)
      sepal_length & petal_width -> very strong positive correlation (0.82)
      sepal_width & petal_length -> moderate negative correlation (-0.43)
       sepal_width & petal_width -> weak negative correlation (-0.37)
      sepal_length & sepal_width -> very weak negative correlation (-0.12)