To perform grouping in `pandas`, apply the `.groupby()` method to dataframe, with the column that will determine the groups passed as the parameter.

> Note that grouping itself doesn't change the dataframe. Grouping always should come in pair with some aggregation.

For example, our dataframe has the `'morgh'` column, representing if the dwelling is mortgaged or not (1 - Yes, 2 - No, 0 - House is being rented). Let's count number of household within each of possible groups (by applying the `.size()` method).

# 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/data5.csv')

# Grouping and aggregating data
print(df.groupby('morgh').size())

As you can see, 248 people are currently renting a house, while among 771 owners 365 have mortgages.

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.