I am happy to see you in this section. Here, we will group our data to find information about different groups of rows. Examine the data set on delays (you can scroll this table horizontally):


Grouping data is beneficial, and now we will dive deeper into it. Imagine you want to calculate the number of delays for each flight number. Look at the code example and then at the explanation:

import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/plane', index_col = 0)
data_flights = data[['Flight', 'Delay']].groupby('Flight').sum()
print(data_flights.head())

**Explanation:**

```python
data[['Flight', 'Delay']].groupby('Flight').sum()
```

- `data[['Flight', 'Delay']]` - These are the columns you will work on, including the columns you will group;
- `groupby('Flight')` - The `'Flight'` column is the argument for the `.groupby()` function. This means that rows with the same value in the `'Flight'` column will be grouped together;
- `.sum()` - This function operates on rows within each group created by `.groupby()`. In this case, it sums the values in the `'Delay'` column for rows that belong to the same `'Flight'` group. 

>Note
>
>Since the `'Delay'` column contains only `0` (no delay occurred) or `1` (a delay occurred) as its possible values, the sum of the rows represents the number of delays for each flight.


In fact, `.sum()` is one of many aggregation functions you can use. You will become familiar with all of them as you proceed.

This course contains a lot of useful functions for a future data analyst. You will learn different ways of extracting data and even set conditions on it. After it, you will be familiar with the methods of grouping data. Also, you will learn how to preprocess data. Each section has its data set so that the course will be gripping.

This section will teach you how to output specific columns by their titles or indices. Also, you will get acquainted with the ways you can select rows  by indices.

Here, you will learn how to extract data that has specific conditions. Also, you will learn how to combine them and even create your own.

In this section, you will expand your knowledge on setting different data conditions. You will learn to check if your data is in a defined list of values or between two values. You will also learn how to find the largest and smallest values.

This section is one of the most fascinating of the course. Here, you will learn how to group data in different ways. It will help you work as a data analyst to find out information on specific data groups.

This section is one of the most significant for a data analyst because if the data contains missing data values in the incorrect format, it will be impossible to work with. Thus, you will learn how to deal with such inappropriate values here. 

DayOfWeek	Time
3	804.993130
4	804.452984
5	702.888362

Advanced Techniques in pandas

Getting Familiar With the .groupby() Method