def majors_analysis(majorsli):
''' (list) -> str
Find and return the mode of a list of majors
>>> majors_analysis(['CIS', 'CIS', 'EC', 'GSS', 'PBA', 'PS', 'SDSC'])
'CIS'
>>> majors_analysis(['CIS', 'CIS', 'CIS', 'CIS', 'EC', 'GSS', 'J', 'J', 'J', 'J', 'PBA', 'PS', 'SDSC', 'UNDL', 'UNDL', 'UNDL', 'UNDL'])
'CIS, J, UNDL'
>>> majors_analysis(['ACTG', 'ATCH', 'ATCH', 'BI', 'BI', 'BI', 'BI', 'CEP', 'CEP', 'CH', 'CH', 'CH', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'CIS', 'EC', 'EC', 'EC', 'EC', 'EC', 'EC', 'ERTH', 'GEOG', 'GEOG', 'GS', 'GSS', 'GSS', 'HPHY', 'J', 'MACS', 'MACS', 'MACS', 'MACS', 'MACS', 'MATH', 'MATH', 'MATH', 'MATH', 'MATH', 'MATH', 'MATH', 'MATH', 'MATH', 'MATH', 'MATH', 'MATH', 'MATH', 'MATH', 'MATH', 'MUS', 'MUS', 'PBA', 'PBA', 'PBA', 'PBA', 'PBA', 'PBA', 'PBA', 'PDS', 'PEN', 'PEN', 'PEN', 'PHYS', 'PHYS', 'PHYS', 'PHYS', 'PHYS', 'PS', 'PS', 'PS', 'PSY', 'PSY', 'SDSC', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL', 'UNDL'])
'CIS'
'''
mode_list = p62.mode(majorsli)
#Present mode as a string and not list:
if len(
mode_list
) > 1: #When the mode is more than one major add commas between majors
majors_mode = ''
for item in mode_list:
majors_mode += item
if item != mode_list[-1]:
majors_mode += ', '
else:
majors_mode = mode_list[0]
return majors_mode
def majors_analysis(majorsli):
'''
(list) -> tuple
Determines the most frequently occuring
major(s) (mode) in a list of majors, and
also the count of the number of distinct
majors in the list.
>>> majors_analysis(['CIS', 'CIS', 'EXPL', 'COLT', 'EXPL'])
(['CIS', 'EXPL'], 3)
>>> majors_analysis(['CIS', 'EXPL', 'COLT'])
(['CIS', 'EXPL', 'COLT'], 3)
>>> majors_analysis(['CIS', 'CIS', 'EXPL', 'COLT'])
(['CIS'], 3)
'''
majors_mode = p62.mode(majorsli)
countdict = p62.genFrequencyTable(majorsli)
majors_ct = len(countdict)
return majors_mode, majors_ct
def equake_analysis(magnitudes):
'''(list) -> tuple
Function equake_analysis takes a list of earthquakes and returns the mean,
median, and mode of the data in a tuple.
>>> equake_analysis([5.2, 5.1, 6.0, 5.9, 5.6, 5.7, 5.0, 5.0, 5.2, 5.1, 5.4])
(5.381818181818183, 5.2, [5.1, 5.2, 5.0])
'''
mean = p6.mean(magnitudes)
median = p6.median(magnitudes)
mode = p6.mode(magnitudes)
data = (mean, median, mode)
return data
def equake_analysis(magnitudes):
'''(list) -> tuple
Find and return the mean, median, and mode
of the earthquake magnitudes as a tuple
>>> equake_analysis([2.51, 2.52, 2.54, 2.7, 2.75, 2.77, 2.82, 2.96, 3.38])
(2.772222222222222, 2.75, [2.51, 2.52, 2.54, 2.7, 2.75, 2.77, 2.82, 2.96, 3.38])
>>> equake_analysis([5.0, 5.0, 5.1, 5.1, 5.2, 5.2, 5.2, 5.4, 5.6, 5.6, 5.7, 5.9, 6.0])
(5.384615384615385, 5.2, [5.2])
>>> equake_analysis([2.51, 2.52, 2.54, 2.6, 2.6, 2.61, 2.7, 2.75, 2.77, 2.8, 2.82, 2.89, 2.96, 3.03, 3.03, 3.3, 3.38, 3.55, 3.9])
(2.908421052631579, 2.8, [2.6, 3.03])
'''
mean = p62.mean(magnitudes)
median = p62.median(magnitudes)
mode = p62.mode(magnitudes)
mmm = (mean, median, mode)
return mmm
def equake_analysis(magnitudes):
'''
(list) -> tuple
Returns the mean, median, and mode
from a list of earthquake magnitudes.
>>> equake_analysis([2.99, 2.56, 2.83, 2.76])
(2.785, 2.795, [2.99, 2.56, 2.83, 2.76])
>>> equake_analysis([5.0, 5.2, 5.1, 5.4, 5.2, 5.6, 5.6])
(5.3, 5.2, [5.2, 5.6])
>>> equake_analysis([5.2, 5.1, 6.0, 5.9, 5.6, 5.7, 5.0, 5.0])
(5.437500000000001, 5.4, [5.0])
'''
magnitudes_mean = p62.mean(magnitudes)
magnitudes_median = p62.median(magnitudes)
magnitudes_mode = p62.mode(magnitudes)
return magnitudes_mean, magnitudes_median, magnitudes_mode