我有一个MultiIndex数据帧看起来像,这只是局部的. 2007年至2015年的年度范围与每年相同的地方.
Jan Feb Mar Apr May June July Aug Sept Oct \
Year Place
2007 Johore 1.26 1.07 1.21 1.27 1.33 1.28 1.67 1.88 1.89 1.86
Kedah 1.20 1.27 1.50 1.38 1.38 1.52 1.84 2.09 2.08 2.02
Kelantan 0.92 0.90 1.01 1.10 1.07 0.87 0.93 1.02 1.08 1.17
Malacca 1.62 1.45 1.64 1.52 1.50 1.40 1.75 1.80 2.03 2.14
N. Sembilan 0.98 0.94 1.11 1.07 1.10 1.16 1.46 1.58 1.61 1.71
Nov Dec
Year Place
2007 Johore 1.95 1.72
Kedah 1.79 1.39
Kelantan 1.29 0.97
Malacca 2.44 2.13
N. Sembilan 1.75 1.58
我想旋转数据并获得索引为月的单个索引数据帧(例如2007年1月,2007年2月),列是不同的位置.
我试过“彭亨”作为例子做了：
In [14]:
Pahang=df.xs('Pahang',level='Place')
In [15]:
Pahang.unstack().unstack().unstack()
Out[15]:
Year
2007 Jan 1.19
Feb 1.01
Mar 1.13
Apr 1.19
May 1.24
June 1.17
July 1.43
Aug 1.59
Sept 1.63
Oct 1.64
Nov 1.82
Dec 1.31
2008 Jan 1.57
Feb 1.36
Mar 1.56
...
2014 Oct 1.87
Nov 1.74
Dec 1.09
2015 Jan 0.93
Feb 1.02
Mar 1.28
Apr 1.51
May NaN
June NaN
July NaN
Aug NaN
Sept NaN
Oct NaN
Nov NaN
Dec NaN
Length: 108, dtype: float64
我按照自己的意愿获得了彭亨专栏.我想知道是否有办法以更快的方式遍历所有地方,而不是一次只做一个地方.
谢谢！
解决方法:
您可以对所有地方进行重塑,然后只选择其中一个.
import pandas as pd
import numpy as np
# your data
# ===================================
multi_index = pd.MultiIndex.from_product([np.arange(2007,2016,1), 'A B C D E'.split()], names=['Year', 'Place'])
df = pd.DataFrame( np.random.randn(45,12), columns='Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec'.split(), index=multi_index)
df
Jan Feb Mar ... Oct Nov Dec
Year Place ...
2007 A -0.1512 0.7274 -0.3218 ... 1.2547 -1.8408 1.2585
B 0.0856 -1.0458 -1.1428 ... 1.0194 1.1958 0.4905
C -1.2021 -0.6989 -0.0486 ... -0.8053 -0.4929 1.6475
D -1.9948 -0.3465 1.3036 ... -0.2490 0.6285 -0.0568
E 0.0928 -1.3905 0.7203 ... -0.1138 2.9552 -0.0272
2008 A -1.2595 1.3072 0.6121 ... -1.4275 0.8769 2.0671
B 0.3611 -0.4187 -2.9609 ... -1.2944 1.2752 -0.0947
C 1.6492 0.0340 -0.9743 ... 0.0550 1.4135 0.8862
D 0.9034 -0.2957 0.2152 ... 1.0947 -0.2405 0.0367
E 0.9566 1.1927 0.0852 ... 0.7396 0.8240 -1.6628
... ... ... ... ... ... ... ...
2014 A 0.7478 -0.8905 0.6238 ... -1.0907 -0.2919 0.3261
B 3.6764 -0.0601 1.2751 ... 0.3294 -1.3375 -1.5087
C 2.3460 -0.4181 0.0607 ... -0.8270 0.0536 -0.4353
D 0.9733 -0.6863 0.5278 ... -1.8206 0.4788 1.1438
E -0.3514 2.4570 -0.8567 ... 1.3434 -1.5634 -0.9984
2015 A 1.2849 -1.0657 -0.1173 ... -0.1733 0.0441 0.0922
B 0.5802 -0.5912 1.1193 ... -0.1296 -0.6374 -1.7727
C -0.5026 -1.3111 -0.5499 ... 0.7308 1.2570 0.8733
D -1.6482 -0.2213 0.3336 ... -1.3141 -2.0377 -1.1468
E -2.0796 -0.2808 -1.4079 ... -0.3052 0.7999 0.3516
[45 rows x 12 columns]
# processing
# ==================================
res = df.stack().unstack(level='Place')
Place A B C D E
Year
2007 Jan -0.1512 0.0856 -1.2021 -1.9948 0.0928
Feb 0.7274 -1.0458 -0.6989 -0.3465 -1.3905
Mar -0.3218 -1.1428 -0.0486 1.3036 0.7203
Apr -1.4641 2.0384 0.6518 0.8756 -1.4627
May -0.8896 -1.6627 0.6990 0.2008 0.7423
June -0.5339 -0.6629 0.1121 0.3618 1.3838
July -0.4851 0.6544 0.5251 0.3394 -0.7016
Aug -1.2445 0.9671 -1.0684 -0.4776 -0.2936
Sept 1.1330 -0.7543 1.6029 0.5543 0.3234
Oct 1.2547 1.0194 -0.8053 -0.2490 -0.1138
... ... ... ... ... ...
2015 Mar -0.1173 1.1193 -0.5499 0.3336 -1.4079
Apr -1.0528 0.2421 0.3419 -2.1137 -0.2836
May -1.0709 -0.1794 -0.2682 -0.3226 0.8654
June -1.4538 -0.7313 0.3177 -1.4008 1.1357
July -1.6210 -0.3815 -0.9876 0.1019 1.7450
Aug 0.5692 0.7679 1.1893 -0.9612 0.0903
Sept 0.2371 0.6740 0.9204 -0.2909 -0.8197
Oct -0.1733 -0.1296 0.7308 -1.3141 -0.3052
Nov 0.0441 -0.6374 1.2570 -2.0377 0.7999
Dec 0.0922 -1.7727 0.8733 -1.1468 0.3516
[108 rows x 5 columns]
# select one place
res['A']
Year
2007 Jan -0.1512
Feb 0.7274
Mar -0.3218
Apr -1.4641
May -0.8896
June -0.5339
July -0.4851
Aug -1.2445
Sept 1.1330
Oct 1.2547
...
2015 Mar -0.1173
Apr -1.0528
May -1.0709
June -1.4538
July -1.6210
Aug 0.5692
Sept 0.2371
Oct -0.1733
Nov 0.0441
Dec 0.0922
Name: A, dtype: float64
标签：multi-index,python,pandas 来源： https://codeday.me/bug/20190727/1557944.html
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