Python 熊猫多索引数据帧中的旋转数据
我有一个多索引数据框，看起来，这只是部分。这一年从2007年到2015年，每年都有相同的地点Python 熊猫多索引数据帧中的旋转数据,python,pandas,multi-index,Python,Pandas,Multi Index,我有一个多索引数据框，看起来，这只是部分。这一年从2007年到2015年，每年都有相同的地点 Jan Feb Mar Apr May June July Aug Sept Oct \ Year Place 2007 Johore 1.26 1.07 1.21 1.27 1.33
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
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
谢谢 您应该使用
pandas.pivot您可以使用以下方法来交换索引级别，而不是使用
unstack如果您的多索引具有多个级别，并且需要执行多个交换，那么还有一种方法非常有用。我的想法大致与@HappyLeapSecond相同，但将添加这一点，因为它与@HappyLeapSecond不完全相同，而且更通用（适用于所有行，而不仅仅是特定行） 首先，我将使用稍微不同的示例数据集。还要注意的是，我在发布时没有使用多索引，因为单级索引更容易复制和粘贴到熊猫中
year place Jan Feb Mar Apr May June July Aug
0 2007 Johore 1.26 1.07 1.21 1.27 1.33 1.28 1.67 1.88
1 2007 Kedah 1.20 1.27 1.50 1.38 1.38 1.52 1.84 2.09
2 2007 Kelantan 0.92 0.90 1.01 1.10 1.07 0.87 0.93 1.02
3 2007 Malacca 1.62 1.45 1.64 1.52 1.50 1.40 1.75 1.80
4 2008 Johore 1.26 1.07 1.21 1.27 1.33 1.28 1.67 1.88
5 2008 Kedah 1.20 1.27 1.50 1.38 1.38 1.52 1.84 2.09
6 2008 Kelantan 0.92 0.90 1.01 1.10 1.07 0.87 0.93 1.02
7 2008 Malacca 1.62 1.45 1.64 1.52 1.50 1.40 1.75 1.80
df = df.reset_index(drop=True).set_index(['year','place'])
Jan Feb Mar Apr May June July Aug
year place
2007 Johore 1.26 1.07 1.21 1.27 1.33 1.28 1.67 1.88
Kedah 1.20 1.27 1.50 1.38 1.38 1.52 1.84 2.09
Kelantan 0.92 0.90 1.01 1.10 1.07 0.87 0.93 1.02
Malacca 1.62 1.45 1.64 1.52 1.50 1.40 1.75 1.80
2008 Johore 1.26 1.07 1.21 1.27 1.33 1.28 1.67 1.88
Kedah 1.20 1.27 1.50 1.38 1.38 1.52 1.84 2.09
Kelantan 0.92 0.90 1.01 1.10 1.07 0.87 0.93 1.02
Malacca 1.62 1.45 1.64 1.52 1.50 1.40 1.75 1.80
取消堆栈（level='year'）df.unstack(level='year').swaplevel(0,1,axis=1).T.sortlevel(0)
place Johore Kedah Kelantan Malacca
year
2007 Jan 1.26 1.20 0.92 1.62
Feb 1.07 1.27 0.90 1.45
Mar 1.21 1.50 1.01 1.64
Apr 1.27 1.38 1.10 1.52
May 1.33 1.38 1.07 1.50
June 1.28 1.52 0.87 1.40
July 1.67 1.84 0.93 1.75
Aug 1.88 2.09 1.02 1.80
2008 Jan 1.26 1.20 0.92 1.62
Feb 1.07 1.27 0.90 1.45
Mar 1.21 1.50 1.01 1.64
Apr 1.27 1.38 1.10 1.52
May 1.33 1.38 1.07 1.50
June 1.28 1.52 0.87 1.40
July 1.67 1.84 0.93 1.75
Aug 1.88 2.09 1.02 1.80
df.stack().unstack(level='place')
这是一种更好的方法，可以解决将年/月放在同一个索引上，而将位置放在另一个索引上的问题，但我将把这个答案暂时留在这里，以防看到其他方法和解释会有所帮助。您可以对所有
位置进行重塑，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
在上一篇文章之后，您在格式化文章方面做得很好。：-）为了让它变得更好，最好是包含一个可复制的示例。这可以是一个代码片段，用于模拟与真实数据集具有相同结构的一些人工数据，也可以通过df.to_csv（）
将csv
字符串上载到df.to_csv（）
，以便其他人能够以最小的工作量快速复制您的数据集。：-）这个答案在细节上有点含糊不清，但pivot或pivot_表将是解决这个问题的另一个好方法。
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