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Grokking Data Science

### 6. Grouping

Things start looking really interesting when we group rows with certain criteria and then aggregate their data. 

Say we want to group our dataset by director and see **how much revenue (sum) each director earned at the box-office and then also look at the average rating (mean) for each director**. We can do this by using the`groupby` operation on the column of interest, followed by the appropriate aggregate (*sum/mean*), like so:


# Let’s group our dataset by director and see how much revenue each director has
movies_df.groupby('Director').sum()

# Let’s group our dataset by director and see the average rating of each director
movies_df.groupby('Director')[['Rating']].mean()

As we can see, Pandas grouped all the <i>'Director'</i> rows by name into one. And since we used *sum()* for aggregation, it added together all the numerical columns. The values for each of the columns now represent the sum of values in that column for that director. 

For example, we can see that the director Aamir Khan has a very high average rating (8.5) but his revenue is much lower compared to many other directors (only 1.20M $). This can be attributed to the fact that we are looking at a dataset with international movies, and Hollywood directors/movies have understandably much higher revenues compared to movies from international directors.

In addition to `sum()` and `mean()` Pandas provides multiple other aggregation functions like `min()`and `max()`.

***Alt! Can you find a problem in the code when we apply aggregation to get the sum?***

This is not the correct approach for all the columns. We do not want to sum all the <i>'Year'</i> values, for instance. To make Pandas apply the aggregation on some of the columns only, we can specify the name of the columns we are interested in. For example, in the second example, we specifically passed the *'Rating'* column, so that the mean did not get applied to all the columns.

Grouping is an easy and extremely powerful data analysis method. We can use it to fold datasets and uncover insights from them, while aggregation  is one of the foundational tools of statistics. In fact, ***learning to use `groupby()` to its full potential can be one of the greatest uses of the Pandas library***.


# 6. Grouping

Things start looking really interesting when we group rows with certain criteria and then aggregate their data. 

Say we want to group our dataset by director and see **how much revenue (sum) each director earned at the box-office and then also look at the average rating (mean) for each director**. We can do this by using the`groupby` operation on the column of interest, followed by the appropriate aggregate (*sum/mean*), like so:


Python Fundamentals for Data Science

The Fundamentals of Statistics

Machine Learning 101

End-to-End Machine Learning Project

The Real Talk

Pandas DataFrame Operations - Grouping and Sorting

6. Grouping