python求txt⽂件内平均值_如何使⽤python计算⼏个.dat⽂件
的平均值？
python怎么读取dat文件这是⼀种相当时间和资源有效的⽅法，它读取值并并⾏计算所有⽂件的平均值，但每次只读取⼀⾏⽂件 – 但它会暂时读取整个第⼀个.dat⽂件进⼊内存以确定每个⽂件中将有多少⾏和每列数字.
你没有说你的“数字”是整数还是浮点数或什么，所以这将它们作为浮点读取(即使它们不存在也会起作⽤).⽆论如何，平均值被计算并输出为浮点数.
更新
我已经修改了我的原始答案，还根据您的评论计算了每⾏和每列中值的总体标准差(西格玛).它在计算它们的平均值之后⽴即执⾏此操作，因此不需要再次读取所有数据.此外，为了响应注释中的建议，添加了上下⽂管理器以确保关闭所有输⼊⽂件.
请注意，标准偏差仅打印并且不会写⼊输出⽂件，但对相同或单独的⽂件执⾏此操作应该很容易添加.
from contextlib import contextmanager
from itertools import izip
from glob import iglob
from math import sqrt
from sys import exit
@contextmanager
def multi_file_manager(files, mode='rt'):
files = [open(file, mode) for file in files]
yield files
for file in files:
file.close()
# generator function to read, convert, and yield each value from a text file
def read_values(file, datatype=float):
for line in file:
for value in (datatype(word) for word in line.split()):
yield value
# enumerate multiple egual length iterables simultaneously as (i, n0, n1, ...)
def multi_enumerate(*iterables, **kwds):
start = ('start', 0)
return ((n,)+t for n, t in enumerate(izip(*iterables), start))
DATA_FILE_PATTERN = 'data*.dat'
MIN_DATA_FILES = 2
with multi_file_manager(iglob(DATA_FILE_PATTERN)) as datfiles:
num_files = len(datfiles)
if num_files < MIN_DATA_FILES:
print('Less than {} .dat files were found to process, '
'terminating.'.format(MIN_DATA_FILES))
exit(1)
# determine number of rows and cols from first file
temp = [line.split() for line in datfiles[0]]
num_rows = len(temp)
num_cols = len(temp[0])
datfiles[0].seek(0) # rewind first file
del temp # no longer needed
print '{} .dat files found, each must have {} rows x {} cols\n'.format(
num_files, num_rows, num_cols)
means = []
std_devs = []
divisor = float(num_files-1) # Bessel's correction for sample standard dev generators = [read_values(file) for file in datfiles]
for _ in xrange(num_rows): # main processing loop
for _ in xrange(num_cols):
# create a sequence of next cell values from each file
values = tuple(next(g) for g in generators)
mean = float(sum(values)) / num_files
means.append(mean)
means_diff_sq = ((value-mean)**2 for value in values)
std_dev = sqrt(sum(means_diff_sq) / divisor)
std_devs.append(std_dev)
print 'Average and (standard deviation) of values:'
with open('', 'wt') as averages:
for i, mean, std_dev in multi_enumerate(means, std_devs):
print '{:.2f} ({:.2f})'.format(mean, std_dev),
averages.write('{:.2f}'.format(mean)) # note std dev not written
if i % num_cols != num_cols-1: # not last column?
averages.write(' ') # delimiter between values on line
else:
print # newline
averages.write('\n')

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