'''
Thinking about the question at hand

Since you are given life expectancy level data by country and year, you could ask 
questions about how much the average life expectancy changes over each year.

Before continuing, however, it's important to make sure that the following 
assumptions about the data are true:

'Life expectancy' is the first column (index 0) of the DataFrame.
The other columns contain either null or numeric values.
The numeric values are all greater than or equal to 0.
There is only one instance of each country.
You can write a function that you can apply over the entire DataFrame to verify 
some of these assumptions. Note that spending the time to write such a script will 
help you when working with other datasets as well.

INSTRUCTIONS
100 XP
Define a function called check_null_or_valid() that takes in one argument: row_data.
Inside the function, convert no_na to a numeric data type using pd.to_numeric().
Write an assert statement to make sure the first column (index 0) of 
the g1800s DataFrame is 'Life expectancy'.
Write an assert statement to test that all the values are valid for 
the g1800s DataFrame. Use the check_null_or_valid() function placed 
inside the .apply() method for this. Note that because you're applying it over the 
entire DataFrame, and not just one column, you'll have to chain the .all() method 
twice, and remember that you don't have to use () for functions placed 
inside .apply().
Write an assert statement to make sure that each country occurs only once in the 
data. Use the .value_counts() method on the 'Life expectancy' column for this. 
Specifically, index 0 of .value_counts() will contain the most frequently occuring 
value. If this is equal to 1 for the 'Life expectancy' column, then you can be 
certain that no country appears more than once in the data.
'''
