When doing data analysis **there is no better help than pandas library**. Actually pandas is one of the reasons python became extremely popular in the data science field. It leverages one of the pillars of the field, numpy (a library for working with matrices), adding not only indexes and columns, but a wide functionality too. You can almost do magic with your data with pandas!

One of the most **common uses with pandas is grouping data**. You can make a group with the function *groupby()* and then apply some common action to that group, like *mean()*, *count()*, *median()*, etc.

The function *groupby()* sounds like SQL’s GROUP BY, and while **it’s similar to its SQL cosin, it comes with extended powers**. Let’s see some basic use before showing the tricks!

Let’s suppose **we have a dataset with the results of an exam**. We have 6 students that spent almost 2 hours (120 minutes) solving the problems from the exam, that took part in 2 different rooms (labeled 1 and 2).

Now let’s start to do **basic grouping**.

The **most basic way to do grouping** is by a column (or ‘feature’, in data analysis’ slang). In the case [4] we group by *room*, then choose only the *time* feature, and get the mean of time spent in each room.

Notice that **the functions that are used with groups can also be used without grouping**, as it’s showed in case [5]. We are also showing here **how to use square brackets** to choose only some columns, in this case result and time, so later further operations are done only on them.

In the case [6] we first chose 2 columns, result and time, and then we group by result, looking for the maximum values.

Given these examples, we can get an idea of basic use… but let’s see now **my 3 favorite tricks when grouping**.

**TRICK 1: list grouping**

You can do **grouping with more than one column**, and the result will be a multi-index dataframe, nice!

Ok, ok, I hear you ‘this can be done with SQL too’. That’s right, but later you can use the multi-index for further exploration.

**TRICK 2: grouping by function**

You can **pass a function as parameter to pandas’ groupby()** to create groups. The function will get an index as parameter, so you can use pandas’ *loc* to locate the data. For instance, let’s suppose we want to group by the number of ‘e’ letter that each student name have.

So people with zero ‘e’s in his name spent 104.5 minutes as mean, while people with 2 ‘e’s finished the exam in just 94 minutes.

Isn’t it amazing? Of course this is a stupid example, but you can do things like, for instance, group ages by decades (like I did in a notebook on kaggle).

**TRICK 3: Group and rank**

pandas has** a function called rank() that gets the order/rank of a column**. For example it can sort

*time*column and show a

**1**for the quickest student, then

**2**for the second one, etc.

But **how could we get the rank per room?** We want to know which student was the quickest for room 1, and which one for room 2…

So Alex was the number 1 globally and also the quickest in room 1. But George was the number 1 in room 2. Isn’t that magic?

Funny enough, rank() return the order as floats, but you can change it’s type with .astype(int) later.

**I hope you liked these tricks!** And I hope I’ll find a better way to share code with context coloring but copy-paste-able… ideas?