5. Testing data with asserts

Assert statements

• Can detect early warning and errors
• Ensures the code is running correctly

Testing data with asserts

# Assert that there are no missing values

assert pd.notnull(ebola).all().all() #assert ebola.notnull().all().all() #The first .all() method will return a True or False for each column, while the second .all() method will return a single True or False.

assert (ebola >= 0).all().all() # Assert that all values are >= 0

Testing data with asserts 2

assert pd.notnull(gapminder.country).all() # Assert that country does not contain any missing values

assert pd.notnull(gapminder.year).all() # Assert that year does not contain any missing values

gapminder = gapminder.dropna() # Drop the missing values

print(gapminder.shape) # Print the shape of gapminder

Checking of data types ith Assert

gapminder.year = pd.to_numeric(gapminder.year, errors='coerce') # Convert the year column to numeric

assert gapminder.country.dtypes == np.object # Test if country is of type object

assert gapminder.year.dtypes == np.int64 # Test if year is of type int64

assert gapminder.life_expectancy.dtypes == np.float64 # Test if life_expectancy is of type float64