>>> from pandas import Series, DataFrame
>>> d = {'one': Series([1,2,3], index=['a','b','c']),
... 'two': Series([1,2,3,4], index=['a','b','c','d'])}
>>> df = DataFrame(d)
>>> df
one two
a 1.0 1
b 2.0 2
c 3.0 3
d NaN 4
>>> import numpy
>>> df.apply(numpy.mean)
one 2.0
two 2.5
dtype: float64
>>> df['one'].map(lambda x: x>= 1)
a True
b True
c True
d False
Name: one, dtype: bool
>>> df.applymap(lambda x: x>= 1)
one two
a True True
b True True
c True True
d False True
from pandas import DataFrame, Series
import numpy
def avg_bronze_medal_count():
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)
bronze_at_least_one_gold = olympic_medal_counts_df['bronze'][olympic_medal_counts_df['gold'] >= 1]
avg_bronze_at_least_one_gold = numpy.mean(bronze_at_least_one_gold)
print(avg_bronze_at_least_one_gold)
