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