As you saw in the previous chapter, you received one number per group. But what will be the result after applying, for example, the `.mean()` method? Let's find out!

# 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').mean())

As you can see, you received means for all numerical columns. If you want to get aggregated statistics for only certain columns, make a selection right after applying the `.groupby()` function. For example, for each value of the `'morth'` column we want to know average values of `'valueh', 'grosrth', 'omphtotinch'` columns.

# 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')[['valueh', 'grosrth', 'omphtotinch']].mean())

This time output looks much better. You got means for only selected columns. Zeros as means appeared as the result of house being mortgaged or not.

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.

Data Manipulation using pandas

Grouping in pandas [2/2]