EDAeasy 1.1.1

Functions and tools for making Exploratory Data Analysis easy!

EDAeasy 😀

The package for quick exploratory data analysis

Instalation

pip install EDAeasy

Usage

The dataframe_summary function have relative simple summary of the columns of your dataframe for quick look at tabular data

Generate a summary DataFrame of the input DataFrame 'dataframe'.

Parameters
----------
dataframe : pandas.DataFrame
    The input DataFrame for which the summary needs to be generated.

Returns
-------
pandas.DataFrame
    A DataFrame containing summary information for each column in 'df':
    - Type: Data type of the column.
    - Min: Minimum value in the column.
    - Max: Maximum value in the column.
    - Nan %: Percentage of NaN values in the column.
    - # Unique Values: Total number of unique values in the column.
    - Unique values: List of unique values in the column.

Example
-------
>>> data = {
        'age': ['[40-50)', '[60-70)', '[70-80)'],
        'time_in_hospital': [8, 3, 5],
        'n_lab_procedures': [72, 34, 45],
        ...
    }
>>> dataframe = pd.DataFrame(data)
>>> result = dataframe_summary(df)
>>> print(result)
           Type       Min        Max  Nan %  # Unique Values                                  Unique values
Variables                                                                                                              
age       object   [40-50)    [90-100)    0.0        3      ['[70-80)', '[50-60)', '[60-70)', '[40-50)', '[80-90)', ...
time_in_hospital  int64    1           14    0.0        3        [8, 3, 5]
n_lab_procedures  int64    1          113    0.0        3        [72, 34, 45]
...

Note
----
The function uses vectorized operations to improve performance and memory usage.