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Introduction
Grouping
Summary
17. Summary

## Instruction

It's time to wrap things up. Here's what we learned:

1. Some basic statistical functions you can use in pandas are:

• describe() – shows multiple basic statistics,
• count() – counts the number of non-empty values,
• sum() – calculates the total sum,
• min() / max() – finds the minimum/maximum value, respectively,
• std() – calculates the standard deviation,
• mean() – calculates the mean value,
• quantile(...) – calculates specified quantiles:
quantile([0.25,0.5,0.75])
will calculate 1st, 2nd and 3rd quartiles
2. To group rows by a single column, use
dataFrame.groupby('column')
3. To group rows by multiple columns, use
dataFrame.groupby(['column1','column2'...])
4. The size() function shows the number of rows in each group.
5. You can access columns in grouped rows using square brackets: grouped['column'].
6. You can use statistical functions and sorting with grouped rows. For example:
grouped['aces'].mean().sort_values(ascending=False)

Alright, how about a short quiz now?

## Exercise

Click to continue.