Pandas Profiling in Python
The pandas_profiling library in Python include a method named as ProfileReport() which generate a basic report on the input DataFrame.
The report consist of the following:
- DataFrame overview,
- Each attribute on which DataFrame is defined,
- Correlations between attributes (Pearson Correlation and Spearman Correlation), and
- A sample of DataFrame.
Syntax :
pandas_profiling.ProfileReport(df, **kwargs)
| Arguments | Type | Description |
|---|---|---|
| df | DataFrame | Data to be analyzed |
| bins | int | Number of bins in histogram. The default is 10. |
| check_correlation | boolean | Whether or not to check correlation. It’s `True` by default. |
| correlation_threshold | float | Threshold to determine if the variable pair is correlated. The default is 0.9. |
| correlation_overrides | list | Variable names not to be rejected because they are correlated. There is no variable in the list (`None`) by default. |
| check_recoded | boolean | Whether or not to check recoded correlation (memory heavy feature). Since it’s an expensive computation it can be activated for small datasets. `check_correlation` must be true to disable this check. It’s `False` by default. |
| pool_size | int | Number of workers in thread pool. The default is equal to the number of CPU. |
Example:
Python3
# importing packagesimport pandas as pdimport pandas_profiling as pp # dictionary of datadct = {'ID': {0: 23, 1: 43, 2: 12, 3: 13, 4: 67, 5: 89, 6: 90, 7: 56, 8: 34}, 'Name': {0: 'Ram', 1: 'Deep', 2: 'Yash', 3: 'Aman', 4: 'Arjun', 5: 'Aditya', 6: 'Divya', 7: 'Chalsea', 8: 'Akash' }, 'Marks': {0: 89, 1: 97, 2: 45, 3: 78, 4: 56, 5: 76, 6: 100, 7: 87, 8: 81}, 'Grade': {0: 'B', 1: 'A', 2: 'F', 3: 'C', 4: 'E', 5: 'C', 6: 'A', 7: 'B', 8: 'B'} } # forming dataframe and printingdata = pd.DataFrame(dct)print(data) # forming ProfileReport and save# as output.html fileprofile = pp.ProfileReport(data)profile.to_file("output.html") |
Output:
DataFrame
The html file named as output.html is as follows :
