# 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.
Raw data
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