def smallest_value(reader: TextIO) -> int:
"""Read and process reader and return the smallest value after the
time_series header.
>>> infile = StringIO('Example\\n1\\n2\\n3\\n')
>>> smallest_value(infile)
1
>>> infile = StringIO('Example\\n3\\n1\\n2\\n')
>>> smallest_value(infile)
1
"""
line = time_series.skip_header(reader).strip()
# Now line contains the first data value; this is also the smallest value
# found so far, because it is the only one we have seen.
smallest = int(line)
for line in reader:
value = int(line.strip())
# If we find a smaller value, remember it.
if value < smallest:
smallest = value
return smallest
def smallest_value(reader: TextIO) -> int:
"""Read and process reader and return the smallest value after the
time_series header.
>>> infile = StringIO('Example\\n1\\n2\\n3\\n')
>>> smallest_value(infile)
1
>>> infile = StringIO('Example\\n3\\n1\\n2\\n')
>>> smallest_value(infile)
1
"""
line = time_series.skip_header(reader).strip()
if line != '':
smallest = int(line)
for line in reader:
line = line.strip()
if line != '-':
value = int(line)
if value < smallest:
smallest = value
return smallest
def process_file(reader: TextIO) -> int:
line = time_series.skip_header(reader)
largest = find_largest(line)
for line in reader:
large = find_largest(line)
if large > largest:
largest = large
return largest
def smallest_value(reader):
"""(file open for reading) -> NoneType
Read and process reader and return the smallest value after the
time_series header.
"""
line = time_series.skip_header(reader).strip()
smallest = int(line)
for line in reader:
value = int(line.strip())
if value < smallest:
smallest = value
return smallest
def process_file(reader):
"""(file open for reading) -> int
Read and process reader, which must start with a time_series header.
Return the largest value after the header. There may be multiple pieces
of data on each line.
"""
line = time_series.skip_header(reader).strip()
largest = find_largest(line)
for line in reader:
large = find_largest(line)
if large > largest:
largest = large
return largest
def smallest_value_skip(reader):
""" (file open for reading) -> NoneType
Read and process reader, which must start with a time_series header.
Return the smallest value after the header. Skip missing values, which
are indicated with a hyphen.
"""
line = time_series.skip_header(reader).strip()
# Now line contains the first data value; this is also the smallest value
# found so far, because it is the only one we have seen.
smallest = int(line)
for line in reader:
line = line.strip()
if line != '-':
value = int(line)
smallest = min(smallest, value)
return smallest
def process_file(reader: TextIO) -> int:
"""time_series 헤더로 시작하는 reader를 읽고 처리한다. 헤더 이후에 나오는
가장 큰 값을 반환한다. 각 줄마다 데이터가 여러 개일 수 있다.
>>> infile = StringIO('Example\\n 20. 3.\\n 100. 17. 15.\\n')
>>> process_file(infile)
100
"""
line = time_series.skip_header(reader).strip()
# 지금까지 가장 큰 값은 첫 번째 줄에 있는 데이터 중 가장 큰 값이다.
largest = find_largest(line)
# 나머지 줄을 확인해서 더 큰 값이 있는지 찾는다.
for line in reader:
large = find_largest(line)
if large > largest:
largest = large
return largest
def process_file(reader):
""" (file open for reading) -> int
Read and process reader, which must start with a time_series header.
Return the largest value after the header. There may be multiple pieces
of data on each line.
"""
line = time_series.skip_header(reader).strip()
# The largest value so far is the largest on this first line of data.
largest = find_largest(line)
# Check the rest of the lines for larger values.
for line in reader:
large = find_largest(line)
if large > largest:
largest = large
return largest
def smallest_value_skip(reader):
""" (file open for reading) -> int
Read and process reader, which must start with a time_series header.
Return the smallest value after the header. Skip missing values, which
are indicated with a hyphen.
"""
line = time_series.skip_header(reader).strip()
# Now line contains the first data value; this is also the smallest value
# found so far, because it is the only one we have seen.
smallest = int(line)
for line in reader:
line = line.strip()
if line != '-':
value = int(line)
smallest = min(smallest, value)
return smallest
def process_file(reader: TextIO) -> int:
"""Read and process reader, which must start with a time_series header.
Return the largest value after the header. There may be multiple pieces
of data on each line.
>>> infile = StringIO('Example\\n 20. 3.\\n 100. 17. 15.\\n')
>>> process_file(infile)
100
"""
line = time_series.skip_header(reader).strip()
# The largest value so far is the largest on this first line of data.
largest = find_largest(line)
# Check the rest of the lines for larger values.
for line in reader:
large = find_largest(line)
if large > largest:
largest = large
return largest
def smallest_value_skip(reader: TextIO) -> int:
"""Read and process reader, which must start with a time_series header.
Return the smallest value after the header.Skip missing values, which are indicated with a hyphen.
>>> infile = StringIO('Example\\n1\\n-\\n3\\n')
>>> smallest_value(infile)
1
"""
line = time_series.skip_header(reader).strip()
#Now line contains the first data value; this is also the smallest value
#found so far, because it is the only one we have seen .
smallest = int(line)
for line in reader:
line = line.strip()
if line != '-':
value = int(line)
smallest = min(smallest, value)
return smallest
def smallest_value(reader):
"""(file open for reading) -> NoneType
Read and process reader and return the smallest value after the
time_series header.
"""
line = time_series.skip_header(reader).strip()
# Now line contains the first value which is also the smallest value
# found so far, because it is the only one it has read.
smallest = line
for line in reader:
value = int(line.strip())
# if we find the smallest value rember it
if value < smallest:
smallest = value
return smallest
def smallest_value(reader):
""" (file open for reading) -> NoneType
Read and process reader and return the smallest value after the
time_series header.
"""
line = time_series.skip_header(reader).strip()
# Now line contains the first data value; this is also the smallest value
# found so far, because it is the only one we have seen.
smallest = int(line)
for line in reader:
value = int(line.strip())
# If we find a smaller value, remember it.
if value < smallest:
smallest = value
return smallest
def smallest_value(reader):
""" (file open for reading) -> int
Read and process reader and return the smallest value after the
time_series header.
"""
line = time_series.skip_header(reader).strip()
# Now line contains the first data value; this is also the smallest value
# found so far, because it is the only one we have seen.
smallest = int(line)
for line in reader:
value = int(line.strip())
# If we find a smaller value, remember it.
if value < smallest:
smallest = value
return smallest
def smallest_value_skip(reader: TextIO) -> int:
"""time_series 헤더로 시작하는 reader를 읽어서 처리한다.
헤더 이후에 나오는 가장 작은 값을 반환한다.
하이픈으로 표시되는 누락 값은 건너뛴다.
>>> infile = StringIO('Example\\n1\\n-\\n3\\n')
>>> smallest_value_skip(infile)
1
"""
line = time_series.skip_header(reader).strip()
# 이제 line에 첫 번째 데이터 값이 들어 있고,
# 유일하게 찾은 값이므로 이 값이 현재 가장 작은 값이다.
smallest = int(line)
for line in reader:
line = line.strip()
if line != '-':
value = int(line)
smallest = min(smallest, value)
return smallest
def smallest_value(reader):
""" (file open for reading) -> NoneType
Read and process reader and return the smallest value after the
time_series header.
"""
line = time_series.skip_header(reader).strip()
# Now line contains the first data value; this is also the smallest value
# found so far, because it is the only one we have seen.
smallest = int(line)
for line in reader:
if line.isdigit() :
value = int(line.strip())
smallest = min(smallest, value)
return smallest
def smallest_value(reader): """ (file open for reading) -> NoneType Read and process reader and return the smallest value after the time_series header """ line = time_series.skip_header(reader).strip() # now line contains the first data value; this is also the smallest value # found so far if line == '': smallest =0 line = 0 smallest = int(line) for line in reader: line = line.strip() if line == "-": continue value = int(line) smallest = min(smallest,value) return smallest
def smallest_value(reader):
""" (file open for reading) -> NoneType
Read and process reader and return the smallest value after
the time_series header
"""
line = time_series.skip_header(reader).strip()
# now line contains the first data value; this is also the smallest value
# found so far
if line == '':
smallest = 0
line = 0
smallest = int(line)
for line in reader:
line = line.strip()
if line == "-":
continue
value = int(line)
smallest = min(smallest, value)
return smallest
print(smallest_value(input_file))
import read_smallest
import urllib
url = 'http://robjhyndman.com/tsdldata/ecology1/hopedale.dat'
webpage= urllib.urlopen(url)
webpage
for line in webpage:
line = line.strip()
line = line.decode('utf-8')
print(line)
import urllib.request
import urllib2.request
import urllib
urllib.urlretrieve(url, filename= "hopedale2.txt')
urllib.urlretrieve(url, filename= "hopedale2.txt")
time_series.skip_header('hopedale2.txt')
import time_series
time_series.skip_header('hopedale2.txt')
time_series.skip_header('hopedale1.txt')
time_series.skip_header('hopedale.txt')
time_series.process_file('hopedale.txt')
%clear
with open('hopedale2.txt', 'r') as inuput_file
with open('hopedale2.txt', 'r') as input_file
with open('hopedale.txt', 'r') as input_file
import os
os.curdir
os.chdir(".')
os.chdir(".")
with open('hopedale.txt', 'r') as input_file
%clear
