## Overview

With a measures table created, the first obvious step is to compute summary statistics across it, which can be compared to the measures tables from other providers or games, etc. and inform further analyses. The gamba library's statistics module contains methods for running these tests, plus some general descriptive methods.

The most basic statistics function is the `descriptive_table`

function, which returns a dataframe holding mean, median, standard deviation, and inter-quartile ranges of each of the measures in the table.

With basic descriptive statistics computed, the next logical step is to compute a number of deeper statistical tests regarding the normality of each of the measure's distributions (`ks_test`

), and the correlations between the (potentially non-normally distributed) measures (`spearmans_r`

).

The statistics module also contains methods for computing statistics between groups as indicated by a label column in the measures table.

The `statistics`

module has some utility methods which may not be directly useful for an analysis but can be used to do simple tasks like join measures tables.

```
import scipy.stats
def standardise_measures_table(measures_table):
"Standardises all measures columns in a measures table by applying the scipy.stats.zscore function to each column. This is useful for column-wise comparisons and some clustering methods."
colnames = list(measures_table.columns)[1:]
standardised_table = pd.DataFrame()
standardised_table["player_id"] = measures_table["player_id"].values
for col in colnames:
standardised_table[col] = scipy.stats.zscore(measures_table[col].values)
return standardised_table
```