Great! What is you need to apply certain functions to specific columns separately, like  find out median of one column, and mean of another one. Surely, you can  perform grouping twice. But this case is also covered by `pandas` and `.agg()` method. If you want to apply specific functions to certain columns, pass them as  a dictionary `{'column': 'function'}`. For instance, we can calculate  mean total income (`'totinch'`) and median number of people in a dwelling (`'hhsize'`) for  each number of bedrooms.

# 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')

# Minimal and maximal prices for each dwelling type
print(df.groupby('broomh').agg({'totinch': 'mean', 'hhsize': 'median'}))

> Note that if you pass dictionary as the `.agg()` method parameter, then there is no need to select columns after grouping.

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.

Advanced Aggregation [2/2]

Awesome!

Advanced Aggregation [2/2]