missing.py

# Filling in NaN values of a particular feature variable
avg_height = 67 # Maybe this is a good number
data["height"] = data["height"].fillna(avg_height)

# Filling in NaN values with a calculated one 
avg_height = data["height"].median() # This is probably more accurate
data["height"] = data["height"].fillna(avg_height)

# Dropping rows with missing values
# Here we check which rows of "height" aren't null
# and only keep those
data = data[pd.notnull(data['height'])]