#Testing your data with asserts
#Here, you'll practice writing assert statements using the Ebola dataset from previous chapters to programmatically check for missing values and to confirm that all values are positive. The dataset has been pre-loaded into a DataFrame called ebola.

#In the video, you saw Dan use the .all() method together with the .notnull() DataFrame method to check for missing values in a column. The .all() method returns True if all values are True. When used on a DataFrame, it returns a Series of Booleans - one for each column in the DataFrame. So if you are using it on a DataFrame, like in this exercise, you need to chain another .all() method so that you return only one True or False value. When using these within an assert statement, nothing will be returned if the assert statement is true: This is how you can confirm that the data you are checking are valid.

#Note: You can use pd.notnull(df) as an alternative to df.notnull().





# Assert that there are no missing values
assert pd.notnull(ebola).all().all()

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