I am happy to see you in this section. Here, we will group our data to find out information on groups of rows. Examine the data set on delays:
||**Airline**|**Flight**|**AirportFrom**|**AirportTo**|**DayOfWeek**|**Time**|**Length**|**Delay**|
|---|---|---|---|---|---|---|---|---|
|0|CO|269|SFO|IAH|3|15|205|1|
|1|1|US|1558|PHX|CLT|3|15|222|
|2|AA|2400|LAX|DFW|3|20|165|1|
|3|AA|2466|SFO|DFW|3|20|195|1|
|4|AS|108|ANC|SEA|3|30|202|0|


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').count()
print(data_flights.head())

**Explanation:**

```python
data[['Flight', 'Delay']].groupby('Flight').count()
```
- `data[['Flight', 'Delay']]` - columns you will work on, including the columns you will group.
- `groupby('Flight')` - here, `'Flight'` is an argument of the function `.groupby()`. It is the name of the column by which you will group. So, in our case, if rows of the dataset have the same value in the column `'Flight'`,  they will relate to one group. Then, due to the function `.count()` that counts the rows, our function will calculate the number of rows of the column `'Delay'` that have the same value in the column `'Flight'`.
- `.count()` - a function that deals with rows having the same group. In this case, it counts the number of rows having the same value in the column `'Flight'`. This function is not the only one you can use. Now you will get familiar with all of them.

|Function title|Implementation|Syntax|
|-----|-----|-----|
|`.mean()`|Finds the mean value of all values in a column that relate to one group|`data.groupby('column_name').mean()`|
|`.median()`|Finds the median value of all values in a column that relate to one group|`data.groupby('column_name').median()`|
|`.sum()`|Finds the sum of all values in a column that relate to one group|`data.groupby('column_name').sum()`|
|`.count()`|Finds the amount of values in a column that relate to one group|`data.groupby('column_name').count()`|
|`.min()`|Finds the minimum value of all values in a column that relate to one group|`data.groupby('column_name').min()`|
|`.max()`|Finds the maximim value of all values in a column that relate to one group|`data.groupby('column_name').max()`|

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. 

Function title	Implementation	Syntax
`.mean()`	Finds the mean value of all values in a column that relate to one group	`data.groupby('column_name').mean()`
`.median()`	Finds the median value of all values in a column that relate to one group	`data.groupby('column_name').median()`
`.sum()`	Finds the sum of all values in a column that relate to one group	`data.groupby('column_name').sum()`
`.count()`	Finds the amount of values in a column that relate to one group	`data.groupby('column_name').count()`
`.min()`	Finds the minimum value of all values in a column that relate to one group	`data.groupby('column_name').min()`
`.max()`	Finds the maximim value of all values in a column that relate to one group	`data.groupby('column_name').max()`

DayOfWeek	Time
3	804.993130
4	804.452984
5	702.888362

Advanced Techniques in pandas

Get Familiar With the .groupby() Method

Fill in the gaps to find the mean value of the `'Time'` column depending on the `'DayOfWeek'` column.

	Airline	Flight	AirportFrom	AirportTo	DayOfWeek	Time	Length	Delay
0	CO	269	SFO	IAH	3	15	205	1
1	1	US	1558	PHX	CLT	3	15	222
2	AA	2400	LAX	DFW	3	20	165	1
3	AA	2466	SFO	DFW	3	20	195	1
4	AS	108	ANC	SEA	3	30	202	0