Python 数据排序和列表迭代和列表推导应用

1.In-place sorting 原地排序

data=[6,4,5,2,3,1]
print ('before sort', data)
data.sort()
print ('after sort BIF:', data)

=========== RESTART: C:/Users/eric/Documents/Python/kelly/sort.py ===========
before sort [6, 4, 5, 2, 3, 1]
after sort BIF: [1, 2, 3, 4, 5, 6]

2. copied sorting 复制排序

test=[6,4,5,2,3,1]
print ('before sorted', test)
test2=sorted(test)
print ('after sorted BIF, test', test)
print ('after sorted BIF, test2',test2)

=========== RESTART: C:/Users/eric/Documents/Python/kelly/sort.py ===========
before sorted [6, 4, 5, 2, 3, 1]
after sorted BIF, test [6, 4, 5, 2, 3, 1]
after sorted BIF, test2 [1, 2, 3, 4, 5, 6]

3. use senitize func 列表迭代处理各个选手的列表数据，将清理过的值追加到适当新列表

def sanitize(time_string):
    if '-' in time_string:
        splitter = '-'
    elif ':' in time_string:
        splitter = ':'
    else:
        return (time_string)
    (mins, secs)=time_string.split(splitter)
    return(mins + '.' + secs)

with open ('james.txt') as jas: data = jas.readline()
james=data.strip().split(',')

with open('julie.txt') as jue:  data=jue.readline()
julie=data.strip().split(',')

with open('mikey.txt') as miy:  data=miy.readline()
mikey=data.strip().split(',')

with open('sarah.txt') as sah:  data=sah.readline()
sarah=data.strip().split(',')

print ('before sort and clean data' ,james,julie,mikey,sarah)

clean_james=[]
clean_julie=[]
clean_mikey=[]
clean_sarah=[]

for each_t in james:
    clean_james.append(sanitize(each_t))
for each_t in julie:
    clean_julie.append(sanitize(each_t))
for each_t in mikey:
    clean_mikey.append(sanitize(each_t))
for each_t in sarah:
    clean_sarah.append(sanitize(each_t))

print('after clean and sorted james is :',sorted(clean_james))
print('after clean and sorted julie is :',sorted(clean_julie))
print('after clean and sorted mikey is :',sorted(clean_mikey))
print('after clean and sorted sarah is :',sorted(clean_sarah))

=========== RESTART: C:UsersericDocumentsPythonkellykelly.py ===========
before sort and clean data ['2-34', '3:21', '2.34', '2.45', '3.01', '2:01', '2:01', '3:10', '2-22'] ['2.59', '2.11', '2:11', '2:23', '3-10', '2-23', '3:10', '3.21', '3-21'] ['2:22', '3.01', '3:01', '3.02', '3:02', '3.02', '3:22', '2.49', '2:38'] ['2:58', '2.58', '2:39', '2-25', '2-55', '2:54', '2.18', '2:55', '2:55']
after clean and sorted james is : ['2.01', '2.01', '2.22', '2.34', '2.34', '2.45', '3.01', '3.10', '3.21']
after clean and sorted julie is : ['2.11', '2.11', '2.23', '2.23', '2.59', '3.10', '3.10', '3.21', '3.21']
after clean and sorted mikey is : ['2.22', '2.38', '2.49', '3.01', '3.01', '3.02', '3.02', '3.02', '3.22']
after clean and sorted sarah is : ['2.18', '2.25', '2.39', '2.54', '2.55', '2.55', '2.55', '2.58', '2.58']

4.list comprehension 运用 “列表推导”减少代码，达到同样效果

def sanitize(time_string):
    if '-' in time_string:
        splitter = '-'
    elif ':' in time_string:
        splitter = ':'
    else:
        return (time_string)
    (mins, secs)=time_string.split(splitter)
    return(mins + '.' + secs)

with open ('james.txt') as jas: data = jas.readline()
james=data.strip().split(',')
with open('julie.txt') as jue:  data=jue.readline()
julie=data.strip().split(',')
with open('mikey.txt') as miy:  data=miy.readline()
mikey=data.strip().split(',')
with open('sarah.txt') as sah:  data=sah.readline()
sarah=data.strip().split(',')

print ('before sort and clean data' ,james,julie,mikey,sarah)

clean_james=[sanitize(each_t) for each_t in james]
clean_julie=[sanitize(each_t) for each_t in julie]
clean_mikey=[sanitize(each_t) for each_t in mikey]
clean_sarah=[sanitize(each_t) for each_t in sarah]

print('after clean and sorted james is :',sorted(clean_james))
print('after clean and sorted julie is :',sorted(clean_julie))
print('after clean and sorted mikey is :',sorted(clean_mikey))
print('after clean and sorted sarah is :',sorted(clean_sarah))

>>> 
=========== RESTART: C:UsersericDocumentsPythonkellykelly.py ===========
before sort and clean data ['2-34', '3:21', '2.34', '2.45', '3.01', '2:01', '2:01', '3:10', '2-22'] ['2.59', '2.11', '2:11', '2:23', '3-10', '2-23', '3:10', '3.21', '3-21'] ['2:22', '3.01', '3:01', '3.02', '3:02', '3.02', '3:22', '2.49', '2:38'] ['2:58', '2.58', '2:39', '2-25', '2-55', '2:54', '2.18', '2:55', '2:55']
after clean and sorted james is : ['2.01', '2.01', '2.22', '2.34', '2.34', '2.45', '3.01', '3.10', '3.21']
after clean and sorted julie is : ['2.11', '2.11', '2.23', '2.23', '2.59', '3.10', '3.10', '3.21', '3.21']
after clean and sorted mikey is : ['2.22', '2.38', '2.49', '3.01', '3.01', '3.02', '3.02', '3.02', '3.22']
after clean and sorted sarah is : ['2.18', '2.25', '2.39', '2.54', '2.55', '2.55', '2.55', '2.58', '2.58']