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conditions = [
    (temp['calc_basement'] == 0),
    (temp['calc_basement'] > 0) & (temp['calc_basement'] <= 300),
    (temp['calc_basement'] > 300) & (temp['calc_basement'] <= 600),
    (temp['calc_basement'] > 600) & (temp['calc_basement'] <= 1000),
    (temp['calc_basement'] > 1000) & (temp['calc_basement'] <= 5000),
    ]
    
    values = ['level_1', 'level_2', 'level_3', 'level_4','level_5']
    

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temp['has_basement'] = np.where(temp['calc_basement'] == 0, False, True)
temp.head()

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df['renovation'] = df['yr_renovated'].fillna(pd.Series(np.random.choice([999, 0], 
                                                      p=[744/17011, 1-(744/17011)], 
                                                      size=len(df))))
                                                     

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print("Value counts for waterfront: \n{}".format(df.waterfront.value_counts()))
print("\nTotal waterfront values: {}".format(len(df.waterfront)))

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df.waterfront.dropna(axis=0,inplace=True)

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# Create df with zip code as index and mean latitude value for each zip code
no_index_zip_lat = df.groupby('zipcode', as_index=True)['lat'].mean()

# Convert the data frame to a dict, the zip is the key and mean latitude is the value
zip_lat_dict = no_index_zip_lat.to_dict()

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# This combines the two operations: find digits in the string and find the two numbers that equal 10
def sum_finder(test):
    target = 10
    success = []

    result = [int(i) for i in test if i.isdigit()]

    for i in range(0, len(result)-1):

        num_sum = (result[i] + result[i+1])

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def split(text):
    return [char for char in text] 

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def char_test(sample):
    
    # Call function to see if there are two numbers that add to 10 in the text string
    success = sum_finder(sample)
    
    # Check to see if success (the return is empty or not)
    if not success:
        print("False")
        
    else:

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def bracket_matcher(sample):
    
    for char in sample:
        if char == ')':
            print('True')
        else:
            print('False')