python之如何从不同的文件中获取数据并在 python 中组合成一个数组

我必须收集数据来证明我的假设，即用惯用手打字比用非惯用手打字更快。我写了下面的代码，给参与者一个随机词，然后他们必须复制它。该代码将计算键入每个单词所需的时间，然后将该数据保存到新文件中。将为每位接受测试的参与者创建一个新的 CSV 文件。

现在我需要编写另一个脚本来计算每个参与者每只手的平均值，然后创建一个包含平均值的数组，这样我就可以创建一个图表来证明我的假设是否正确。我将如何从不同的文件中获取数据并将其组合到一个数组中？

我的脚本:

import random 
import time 
 
name = raw_input('Enter name: ')  # get some name for the file 
outfile = file(name + '.csv', 'w')  # create a file for this user's data 
 
# load up a list of 1000 common words 
words = file('1-1000.txt').read().split() 
 
ntrials = 50 
 
answers = [] 
print """Type With Dominant Hand""" 
for i in range(ntrials): 
    word = random.choice(words) 
    tstart = time.time() 
    ans = raw_input('Please type ' + word + ': ') 
    tstop = time.time() 
    answers.append((word, ans, tstop - tstart)) 
    print >>outfile, 'Dominant', word, ans, tstop - tstart  # write the data to the file 
    if (i % 5 == 3): 
        go = raw_input('take a break, type y to continue: ') 
 
print """Type With Nondominant Hand"""       
for i in range(ntrials): 
    word = random.choice(words) 
    tstart = time.time() 
    ans = raw_input('Please type ' + word + ': ') 
    tstop = time.time() 
    answers.append((word, ans, tstop - tstart)) 
    print >>outfile, 'Nondominant', word, ans, tstop - tstart  # write the data to the file 
    if (i % 5 == 3): 
        go = raw_input('take a break, type y to continue: ') 
 
 outfile.close()  # close the file 

上述脚本的示例结果:

Dominant sit sit 1.81511306763 
Dominant again again 2.54711103439 
Dominant from from 1.53057098389 
Dominant general general 1.98939108849 
Dominant horse horse 1.93938016891 
Dominant of of 1.07597017288 
Dominant clock clock 1.6587600708 
Dominant save save 1.42030906677 
Nondominant story story 3.92807888985 
Nondominant of of 0.93910908699 
Nondominant test test 1.69210004807 
Nondominant low low 1.13296699524 
Nondominant hit hit 1.15252614021 
Nondominant you you 1.22019600868 
Nondominant river river 1.42011594772 
Nondominant middle middle 1.61595511436 

请您参考如下方法:

如果您不熟悉 numpy，这可能看起来像是另一种语言，但这里有一个利用它的解决方案(注意没有循环!)

为了测试，我创建了第二个用户数据文件，每个条目递增 1 秒。

import glob 
import numpy as np 
 
usecols = [0, 3] # Columns to extract from data file 
str2num = {'Dominant': 0, 'Nondominant': 1} # Conversion dictionary 
converters = {0: (lambda s: str2num[s])} # Strings -> numbers 
 
userfiles = glob.glob('*.csv') 
userdat = np.array([np.loadtxt(f, usecols=usecols, converters=converters) 
                    for f in userfiles]) 
 
# Create boolean arrays to filter desired results 
dom    = userdat[..., 0] == 0  
nondom = userdat[..., 0] == 1  
 
# Filter and reshape to keep 'per-user' layout 
usercnt, _, colcnt = userdat.shape 
domdat    = userdat[dom   ].reshape(usercnt, -1, colcnt) 
nondomdat = userdat[nondom].reshape(usercnt, -1, colcnt) 
 
domavgs    = np.average(domdat,    axis=1)[:, 1] 
nondomavgs = np.average(nondomdat, axis=1)[:, 1] 
 
print 'Dominant averages by user:    ', domavgs 
print 'Non-dominant averages by user:', nondomavgs 

输出:

Dominant averages by user:     [ 1.74707571  2.74707571] 
Non-dominant averages by user: [ 1.63763103  2.63763103] 

如果您要进行大量分析，我强烈建议您使用 numpy。