What if you pass an inappropriate value as the `country` argument, or a value that is not in the dictionary? Let's predict it with some conditional statements!

# Data
countries_dict = {'USA': (9629091, 331002651), 'Canada': (9984670, 37742154), 'Germany': (357114, 83783942)}
countries_dict["Brazil"] = (8515767, 212559417)
countries_dict["India"] = (3166391, 1380004385)

# Modify our function
def country_information_mod(d, name):
  if name not in d.keys():
    print("There is no information about", name)
  else:
    print("Country:", name)
    print("Area:", d[name][0], 'sq km')
    print("Population:", round(d[name][1]/1000000, 2), 'mln')
        
# Test our function
country_information_mod(countries_dict, "Ukraine")
country_information_mod(countries_dict, "USA")

In this section, you will be introduced to the very basics of Python and to your workspace, learn some simple math operations, and print your results.

In this section, you will learn how to store numbers, text, etc. in computer memory and then be able to call them by name. Also, you will learn little manipulation with text in Python.

In this section, you will learn the next data type - boolean - and how it can help in creating programs that behave differently depending on certain conditions. 

In this section, you will continue learning different data types in Python: This time you will learn how to store different characteristics inside one variable using lists, tuples, and more interesting type dictionaries.

In this section, you will learn how to write loops in Python - things often used to iterate over a list, tuple, dictionary, or even in a future dataframe...

In the final section of our introduction course, you will learn a bit about built-in functions and then write some functions by yourself!