Python_如何在numpy读取的每个文件的新列中写入数据？ [英] Python_How to write data in new columns for every file read by numpy?

问题描述

我有几个这样的结构的文本文件。列数相同但行数不同：

I have several text files with such a construction. Same number of columns but different rows:

1.txt
2013-08-29T15:11:18.55912   0.019494552 0.110042184 0.164076427 0.587849877
2013-08-29T15:11:18.65912   0.036270974 0.097213155 0.122628797 0.556928624
2013-08-29T15:11:18.75912   0.055350041 0.104121094 0.121641949 0.593113069
2013-08-29T15:11:18.85912   0.057159263 0.107410588 0.198122695 0.591797271
2013-08-29T15:11:18.95912   0.05288292  0.102476346 0.172958062 0.591139372
2013-08-29T15:11:19.05912   0.043507861 0.104121094 0.162102731 0.598376261
2013-08-29T15:11:19.15912   0.068343545 0.102805296 0.168517245 0.587849877
2013-08-29T15:11:19.25912   0.054527668 0.105765841 0.184306818 0.587191978
2013-08-29T15:11:19.35912   0.055678991 0.107739538 0.169997517 0.539165352
2013-08-29T15:11:19.45912   0.05321187  0.102476346 0.167530397 0.645744989

2.txt
2013-08-29T16:46:05.41730   0.048771052 0.10642374  0.180852849 0.430612023
2013-08-29T16:46:05.51730   0.046303932 0.112673779 0.166050124 0.518112585
2013-08-29T16:46:05.61730   0.059955334 0.149845068 0.164569851 0.511533595
2013-08-29T16:46:05.71730   0.042192064 0.107410588 0.115227435 0.476007051
2013-08-29T16:46:05.81730   0.037915721 0.115634324 0.177892304 0.519428383
2013-08-29T16:46:05.91730   0.043507861 0.120568566 0.187267364 0.483243939
2013-08-29T16:46:06.01730   0.042356538 0.10642374  0.143352612 0.522059978

此代码读取文件夹中的所有文本文件，进行一些数学运算，并且应将每个文本文件的结果写入单个csv的新列中。

This code reads all the text files in the folder, do some math and is supposed to write results of each text file in new columns in a single csv.

files_ = glob.glob('D:\Test files\New folder\*.txt')
averages_ = []
seg_len = 3
def cum_sum(lis):
    total = 0
    for x in lis:
        total += x[1]
        yield total
with open ('outfile.csv', 'wb') as outfile:
    writer = csv.writer(outfile)
    for i in files_:
        acols, f_column, average_original, fcol = [], [], [], []
        data = loadtxt(i , usecols = (1,2,3,4))
        for x in range(0, len(data[:,0]), seg_len):
            #some math on each column
            sample_means = [x] + [mean(data[x:x+seg_len,i]) for i in range(4)]
            #change types and save in a list
            float_means = ["%1f" % (x) for x in sample_means]
            #append previous two lines in lists
            average_original.append(sample_means)
            acols.append(float_means)

        fcol = list(cum_sum(average_original))
        #write fcol in a column next to acols
        acols = [row + [col] for row, col in zip(acols, fcol)]
        averages_.append(acols)

    for row in averages_:
        writer.writerows(row)

问：
为每个新文件写入新列的代码。我发现的最相关的帖子是 Python：我如何为每个读取的文件获取一个新列？，但 line.strip（）不适用于我。

Q: But I cannot get the code to write new columns for each new file. The most relevant post I found was Python : How do i get a new column for every file I read?, but line.strip() doesn't work for me. I appreciate any hints how to approach this please.

推荐答案

这对你有用吗？

import pandas as pd

df = pd.DataFrame()
mad = lambda x: x[0] + x.mean()

A = []

for f in ['1.txt', '2.txt']:
    tmp = pd.read_csv(f, header=None, delim_whitespace=True)
    tmp = tmp.ix[:,1:5]
    df = pd.concat([df, pd.rolling_apply(tmp, 3, mad)], axis=1)

df.to_csv('test.csv')

b $ b

在这种情况下， rolling_apply 函数在窗口为3的列上应用移动函数。

The rolling_apply function applies a moving function along columns with a window of 3 in this case.

对不起，如果这不是你想要的，但我认为它显示了熊猫有多么强大。

I'm sorry if this isn't quite what you want, but I think it shows how powerful pandas can be.

这篇关于Python_如何在numpy读取的每个文件的新列中写入数据？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！