from pandas import DataFrame, Series
import numpy


def avg_medal_count():
    '''
    Compute the average number of bronze medals earned by countries who
    earned at least one gold medal.

    Save this to a variable named avg_bronze_at_least_one_gold.

    HINT-1:
    You can retrieve all of the values of a Pandas column from a
    data frame, "df", as follows:
    df['column_name']

    HINT-2:
    The numpy.mean function can accept as an argument a single
    Pandas column.

    For example, numpy.mean(df["col_name"]) would return the
    mean of the values located in "col_name" of a dataframe df.
    '''


    countries = ['Russian Fed.', 'Norway', 'Canada', 'United States',
                 'Netherlands', 'Germany', 'Switzerland', 'Belarus',
                 'Austria', 'France', 'Poland', 'China', 'Korea',
                 'Sweden', 'Czech Republic', 'Slovenia', 'Japan',
                 'Finland', 'Great Britain', 'Ukraine', 'Slovakia',
                 'Italy', 'Latvia', 'Australia', 'Croatia', 'Kazakhstan']

    gold = [13, 11, 10, 9, 8, 8, 6, 5, 4, 4, 4, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]
    silver = [11, 5, 10, 7, 7, 6, 3, 0, 8, 4, 1, 4, 3, 7, 4, 2, 4, 3, 1, 0, 0, 2, 2, 2, 1, 0]
    bronze = [9, 10, 5, 12, 9, 5, 2, 1, 5, 7, 1, 2, 2, 6, 2, 4, 3, 1, 2, 1, 0, 6, 2, 1, 0, 1]

    olympic_medal_counts = {'country_name' : Series(countries), 'gold' : Series(gold), 'silver' : Series(silver),
                             'bronze' : Series(bronze)}

    olympic_medal_counts_df = DataFrame(olympic_medal_counts)
    #avg_bronze = olympic_medal_counts_df['bronze'].apply(numpy.mean)
    return   olympic_medal_counts_df
    #return avg_bronze_at_least_one_gold