Is it possible to group by pairs of values? For instance, we can group by countries and then by their regions. Yes, it's also possible in `pandas`! To group by several columns, use the same `.groupby()` method passing **list** of columns that will be used to determine groups. How does such a grouping work? Look at the picture below.

As you can see, at first values were grouped by `'Group'` and then by `'Subgroup'` among each of groups. For instance, let's find out number of households for each pair of `'roomh', 'hhsize'` columns values (number of rooms and number of people in a dwelling, respectively).

# Importing the library
import pandas as pd

# Reading the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data4.csv')

# Grouping and aggregating data
print(df.groupby(['roomh', 'hhsize']).size())

The output is quite big, since number of possible combinations is quite large. For instance, you can see that there are 59 dwellings with 10 or more rooms with 4 people living in it.

This course covers intermediate topics on pandas, a must-have tool for each data analyst. During the course, you will learn how to prepare data for further interactions and how to group it using different techniques. You will learn the easiest data visualization and be acquainted with data joining.

The data received from different sources can be messy, and to use it in the future, you must ensure it is convenient. In the first section, you will learn what data preprocessing is and will deal with some logical inconsistencies.

Missing or NA values, outliers, and inconsistencies are other types of problematic data. Throughout the second section of the course, you will learn how to deal with such issues.

As a data analyst, you will need to draw compact conclusions based on large amounts of data. In order to achieve that, you need to understand the data grouping idea and how to apply it to examples.

Sometimes one built-in function is not enough to draw a complete conclusion, so you need to use something more complex. This section will teach you how to apply multiple functions while grouping. Also, you will learn to visualize data using the pandas library only and will be acquainted with the main plot types. As a data analyst, you will need to draw compact conclusions based on large amounts of data. In order to achieve that, you need to understand the data grouping idea and how to apply it to examples.

As mentioned before, sometimes you may need to work with data received from multiple sources. This section will teach you how to join two dataframes using different techniques.

Grouping by Several Columns

Awesome!

Grouping by Several Columns