Python 类似的数据帧和系列
我有一个数据帧列表，所有数据帧都具有相同的列。有时，数据帧只有一行，因此是一个系列。当我尝试将此列表与有一个系列的Python 类似的数据帧和系列,python,pandas,concatenation,Python,Pandas,Concatenation,我有一个数据帧列表，所有数据帧都具有相同的列。有时，数据帧只有一行，因此是一个系列。当我尝试将此列表与有一个系列的pd.concat连接时，它会将我想要的列放入索引中。下面是一个简单的工作示例 In [1]: import pandas as pd
pd.concatIn [1]: import pandas as pd
In [2]: import numpy as np
In [3]: d = {'a':np.random.randn(100), 'b':np.random.randn(100)}
In [4]: df = pd.DataFrame(d)
In [5]: thing1 = df.iloc[:10, :]
In [6]: thing1
Out[6]:
a b
0 -0.505268 -1.109089
1 -1.792729 -0.580566
2 -0.478042 0.410095
3 -0.758376 0.558772
4 0.112519 0.556316
5 -1.015813 -0.568148
6 1.234858 -1.062879
7 -0.455796 -0.107942
8 1.231422 0.780694
9 -1.082461 -1.809412
In [7]: thing2 = df.iloc[10,:]
In [8]: thing2
Out[8]:
a -1.527836
b 0.653610
Name: 10, dtype: float64
In [9]: thing3 = df.iloc[11:, :]
In [10]: thing3
Out[10]:
a b
11 -1.247939 -0.694491
12 1.359737 0.625284
13 -0.491533 -0.230665
14 1.360465 0.472451
15 0.691532 -1.822708
16 0.938316 1.310101
17 0.485776 -0.313206
18 1.398189 -0.232446
19 -0.626278 0.714052
20 -1.292272 -1.299580
21 -1.521746 -1.615611
22 1.464332 2.839602
23 0.707370 -0.162056
24 -1.825903 0.000278
25 0.917284 -0.094716
26 -0.239839 0.132572
27 -0.463240 -0.805458
28 1.174125 0.131057
29 0.183503 0.328603
30 0.045839 -0.244965
31 0.449265 0.642082
32 2.381600 -0.417044
33 0.276217 -0.257426
34 0.755067 0.012898
35 0.130339 -0.094300
36 -1.643097 0.038982
37 0.895719 0.789494
38 0.701480 -0.668440
39 -0.201400 1.441928
40 -2.018043 -0.106764
.. ... ...
70 0.971799 0.298164
71 1.307070 -2.093075
72 -1.049177 2.183065
73 -0.469273 -0.739449
74 0.685838 2.579547
75 1.994485 0.783204
76 -0.414760 -0.285766
77 -1.005873 -0.783886
78 1.486588 -0.349575
79 1.417006 -0.676501
80 1.284611 -0.817505
81 -0.624406 -1.659931
82 -0.921061 0.424663
83 -0.645472 -0.769509
84 -1.217172 -0.943542
85 -0.184948 0.482977
86 -0.253972 -0.080682
87 -0.699122 0.368751
88 1.391163 0.042899
89 -0.075512 0.019728
90 0.449151 0.486462
91 -0.182553 0.876379
92 -0.209162 0.390093
93 0.789094 1.570251
94 -1.018724 -0.084603
95 1.109534 1.840739
96 0.774806 -0.380387
97 0.534344 1.165343
98 1.003597 -0.221899
99 -0.659863 -1.061590
[89 rows x 2 columns]
In [11]: pd.concat([thing1, thing2, thing3])
Out[11]:
a b 0
0 -0.505268 -1.109089 NaN
1 -1.792729 -0.580566 NaN
2 -0.478042 0.410095 NaN
3 -0.758376 0.558772 NaN
4 0.112519 0.556316 NaN
5 -1.015813 -0.568148 NaN
6 1.234858 -1.062879 NaN
7 -0.455796 -0.107942 NaN
8 1.231422 0.780694 NaN
9 -1.082461 -1.809412 NaN
a NaN NaN -1.527836
b NaN NaN 0.653610
11 -1.247939 -0.694491 NaN
12 1.359737 0.625284 NaN
13 -0.491533 -0.230665 NaN
14 1.360465 0.472451 NaN
15 0.691532 -1.822708 NaN
16 0.938316 1.310101 NaN
17 0.485776 -0.313206 NaN
18 1.398189 -0.232446 NaN
19 -0.626278 0.714052 NaN
20 -1.292272 -1.299580 NaN
21 -1.521746 -1.615611 NaN
22 1.464332 2.839602 NaN
23 0.707370 -0.162056 NaN
24 -1.825903 0.000278 NaN
25 0.917284 -0.094716 NaN
26 -0.239839 0.132572 NaN
27 -0.463240 -0.805458 NaN
28 1.174125 0.131057 NaN
.. ... ... ...
70 0.971799 0.298164 NaN
71 1.307070 -2.093075 NaN
72 -1.049177 2.183065 NaN
73 -0.469273 -0.739449 NaN
74 0.685838 2.579547 NaN
75 1.994485 0.783204 NaN
76 -0.414760 -0.285766 NaN
77 -1.005873 -0.783886 NaN
78 1.486588 -0.349575 NaN
79 1.417006 -0.676501 NaN
80 1.284611 -0.817505 NaN
81 -0.624406 -1.659931 NaN
82 -0.921061 0.424663 NaN
83 -0.645472 -0.769509 NaN
84 -1.217172 -0.943542 NaN
85 -0.184948 0.482977 NaN
86 -0.253972 -0.080682 NaN
87 -0.699122 0.368751 NaN
88 1.391163 0.042899 NaN
89 -0.075512 0.019728 NaN
90 0.449151 0.486462 NaN
91 -0.182553 0.876379 NaN
92 -0.209162 0.390093 NaN
93 0.789094 1.570251 NaN
94 -1.018724 -0.084603 NaN
95 1.109534 1.840739 NaN
96 0.774806 -0.380387 NaN
97 0.534344 1.165343 NaN
98 1.003597 -0.221899 NaN
99 -0.659863 -1.061590 NaN
[101 rows x 3 columns]
thing2 = pd.DataFrame(thing2).transpose()
pd.concat([thing1, thing2, thing3])
transpose（）Pandas系列此处的文档： 在您的情况下，
transpose（）Pandas系列此处的文档：