将不同长度的numpy数组保存到同一个csv文件中的最佳方法是什么？

Question

我正在使用1d numpy数组，首先做一些数学运算，然后将所有内容保存到一个csv文件中。数据集的长度通常不同，我无法将它们放在一起。这是我能想到的最好的方法，但一定有更优雅的方法。在

import numpy as np
import pandas as pd
import os


array1 = np.linspace(1,20,10)
array2 = np.linspace(12,230,10)
array3 = np.linspace(7,82,20)
array4 = np.linspace(6,55,20)

output1 = np.column_stack((array1.flatten(),array2.flatten())) #saving first array set to file 
np.savetxt("tempfile1.csv", output1, delimiter=',')
output2 = np.column_stack((array3.flatten(),array4.flatten())) # doing it again second array
np.savetxt("tempfile2.csv", output2, delimiter=',')
a = pd.read_csv('tempfile1.csv')                               # use pandas to read both files
b = pd.read_csv("tempfile2.csv")
merged = b.join(a, rsuffix='*')                                # merge with panda for single file
os.remove('tempfile1.csv')
os.remove("tempfile2.csv")                                     # delete temp files
merged.to_csv('savefile.csv', index=False)                     # save merged file

Answer 1

您只需使用concat并传递paramaxis=1，将数组追加为列：

In [49]:

array1 = np.linspace(1,20,10)
array2 = np.linspace(12,230,10)
array3 = np.linspace(7,82,20)
array4 = np.linspace(6,55,20)

pd.concat([pd.DataFrame(array1), pd.DataFrame(array2), pd.DataFrame(array3), pd.DataFrame(array4)], axis=1)
Out[49]:
            0           0          0          0
0    1.000000   12.000000   7.000000   6.000000
1    3.111111   36.222222  10.947368   8.578947
2    5.222222   60.444444  14.894737  11.157895
3    7.333333   84.666667  18.842105  13.736842
4    9.444444  108.888889  22.789474  16.315789
5   11.555556  133.111111  26.736842  18.894737
6   13.666667  157.333333  30.684211  21.473684
7   15.777778  181.555556  34.631579  24.052632
8   17.888889  205.777778  38.578947  26.631579
9   20.000000  230.000000  42.526316  29.210526
10        NaN         NaN  46.473684  31.789474
11        NaN         NaN  50.421053  34.368421
12        NaN         NaN  54.368421  36.947368
13        NaN         NaN  58.315789  39.526316
14        NaN         NaN  62.263158  42.105263
15        NaN         NaN  66.210526  44.684211
16        NaN         NaN  70.157895  47.263158
17        NaN         NaN  74.105263  49.842105
18        NaN         NaN  78.052632  52.421053
19        NaN         NaN  82.000000  55.000000

然后你可以像平常一样把这个写出来

^{pr2}$

Answer 2

使用numpy.savetxt可能会找到一个不错的解决方案，而且可能还有一个比您的更简单的pandas解决方案，但是在本例中，使用标准库csv和{}的解决方案非常简洁：

In [45]: import csv

In [46]: from itertools import izip_longest   # Use zip_longest in Python 3.

In [47]: rows = izip_longest(array3, array4, array1, array2, fillvalue='')

In [48]: with open("out.csv", "w") as f:
   ....:     csv.writer(f).writerows(rows)
   ....:     

In [49]: !cat out.csv
7.0,6.0,1.0,12.0
10.947368421052632,8.5789473684210531,3.1111111111111112,36.222222222222221
14.894736842105264,11.157894736842106,5.2222222222222223,60.444444444444443
18.842105263157894,13.736842105263158,7.3333333333333339,84.666666666666657
22.789473684210527,16.315789473684212,9.4444444444444446,108.88888888888889
26.736842105263158,18.894736842105264,11.555555555555555,133.11111111111111
30.684210526315788,21.473684210526315,13.666666666666668,157.33333333333331
34.631578947368425,24.05263157894737,15.777777777777779,181.55555555555554
38.578947368421055,26.631578947368421,17.888888888888889,205.77777777777777
42.526315789473685,29.210526315789473,20.0,230.0
46.473684210526315,31.789473684210527,,
50.421052631578945,34.368421052631575,,
54.368421052631575,36.94736842105263,,
58.315789473684205,39.526315789473685,,
62.263157894736842,42.10526315789474,,
66.21052631578948,44.684210526315788,,
70.15789473684211,47.263157894736842,,
74.10526315789474,49.842105263157897,,
78.05263157894737,52.421052631578945,,
82.0,55.0,,