To find the ``max`` of the total ``df`` use:


  ```
  df.max()
  ```

To find the max of the column, use:


  ```
  df['column_name'].max()

  # or

  df.column_name.max()
  ```


The same code is used to find the ``min``, ``mean``, and ``median``.

> Pay attention! If there are 2 elements in the middle of the array, the the median will be counted in the following way: (a+b)/2 where a and b are central elements.

This course will teach you the necessary skills to manage simple analytical tasks. You are going to learn Python and its libraries, such as pandas and NumPy. Besides, you will deal with real-life analytical tasks such as calculating vitally important marketing metrics.

Here, we will face challenges related to data types in python. In this section, we will learn the Python syntax necessary for each person who wants to work with this language. After the section, we will undoubtedly be able to cope with simple Python tasks.

This section will teach you simple Python functions. We will be able to work with if/else statements and loops. Such skills are necessary for everyone who wants to start programming and doing fascinating Python tasks.

This section is the most complicated than the previous one, but it is gripping. Here, we will manage the most commonly used pandas function to help we do essential operations with data. Also, we will learn the basics of the NumPy library.

This section allows you to become a real analyst. Let's go!

Introduction to Python for Data Analysis

Max, min, mean, median

Fill in the missing gaps to get the output in the correct order. Imagine that you have a column 'price' in the df DataFrame and its values are [1, 2, 5, 6, 10, 12, 27]