Summary  
This chapter covers how to use pandas’ groupby method to group rows by a specified column and apply aggregation functions (such as sum, mean, median, count, min, and max) to each group.  

General domain of usage  
Analyzing flight delay data in airline operations.

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.

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.

Note

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

DayOfWeek	Time
3	804.993130
4	804.452984
5	702.888362

.groupby() Method