The univariate_analysis function in the hana_ml.algorithms.pal.stats module provides an overview of a dataset, calculating various statistical quantities for continuous and discrete columns, and allowing for customization of the analysis through parameters such as the significance level and trimmed percentage.
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Here is the executable code template for the `univariate_analysis` function:

```python
from hana_ml.algorithms.pal.stats import univariate_analysis

# Assuming that 'df' is your DataFrame
continuous, categorical = univariate_analysis(
    data=df,
    key=None,
    cols=None,
    categorical_variable=None,
    significance_level=0.05,
    trimmed_percentage=0.05
)

# To view the results
print(continuous.collect())
print(categorical.collect())
```

Please replace `'df'` with your actual DataFrame. You can also specify the optional parameters (`key`, `cols`, `categorical_variable`, `significance_level`, `trimmed_percentage`) according to your needs.