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将Pandas DF多重索引转换为列

[英]Convert pandas df multi index to columns

 原文  2016-05-28 18:25:21  8   1    python/ pandas/ multi-index

问题描述

first post here, any assistance is much appreciated. 在这里的第一篇文章，我们将不胜感激。

After reading a csv file response from an api request using python urllib2, i am left with a multi index df. 使用python urllib2从api请求中读取csv文件响应后，我剩下了多索引df。 It contains 19 index' then only 2 'columns'. 它包含19个索引”，然后仅包含2个“列”。

How do i convert these 19 index' to additional columns please? 请问如何将这19个索引转换为其他列？

I tried resting the index, but with no luck. 我试着休息一下索引，但是没有运气。

from urllib2 import Request, urlopen, URLError import pandas as pd 

url = 'URL string here'

response = urlopen(request)
df = pd.read_csv(response)
df.reset_index().head()

to be clear, for example if index 1 contains letters a,b,c,d, i would simply like to change this index to a column tittled 'letter' with now every row containing one of these letters. 明确一点，例如，如果索引1包含字母a，b，c，d，我只想将此索引更改为标题为“字母”的列，现在每一行都包含这些字母之一。 When i do reset_index, it does indeed populate every row with a letter, however, the column itself is still an index.. 当我执行reset_index时，的确确实在每一行中都填充了一个字母，但是，列本身仍然是一个索引。

Edit.. .added more code this first part gets the df. 编辑..添加更多代码，这第一部分得到了df。 

from urllib2 import Request, urlopen, URLError
import pandas as pd

host = 'testapi.bmreports.com'
port = '443'
rep_name = 'DETSYSPRICES'
version = 'v1'
key = 'ldytgh1ylq0k92c'
sd = '2016-05-26'
sp = 20
criteria = (host,port,rep_name,version,key,sd,sp)
url = 'https://%s:%s/BMRS/%s/%s?APIKey=%s&SettlementDate=%s&SettlementPeriod=%d&ServiceType=CSV' % criteria
request = Request(url)
#print url

response = urlopen(request)
df = pd.read_csv(response)
df


HDR INDICATIVE SYSTEM PRICE STACK DATA
BID 20160526    20  1   1   NaN NaN F   T   F   F   NaN 266.0329    -230.211    -230.211    -149.786    -149.786    -48.05  266.0329    1.00000 -48.050 -12782.88
2   T_DIDCB6    109615  -1  F   F   F   F   NaN 26.0000 -1.950  -1.950  -1.950  -1.950  -1.95   26.0000 0.98947 -1.929  -50.17
3   T_COSO-1    119674  -1  T   F   F   F   NaN 25.9000 -0.279  0.000   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
OFFER   20160526    20  1   T_WBURB-2   25968   2   F   F   F   F   0   46.0000 16.163  16.163  0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
2   T_WBURB-2   25968   1   F   F   F   F   0   46.0000 3.037   3.037   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
3   T_CNQPS-4   45744   1   F   F   F   F   0   50.0000 0.975   0.000   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
4   T_DAMC-1    85044   1   F   F   F   F   0   64.5000 4.583   4.583   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
5   T_DAMC-1    85045   1   F   F   F   F   0   64.5000 0.083   0.083   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
6   T_DAMC-1    85046   1   F   F   F   F   0   64.5000 22.000  22.000  0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
7   T_BAGE-1    33725   1   T   F   F   F   0   70.0000 1.583   1.583   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
8   T_SUTB-1    68274   1   T   F   F   F   0   72.0000 4.000   4.000   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
9   T_HUMR-1    99956   2   F   F   F   F   0   82.4700 9.250   9.250   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
10  T_HUMR-1    99956   1   F   F   F   F   0   82.4700 0.250   0.000   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
11  T_HUMR-1    99955   2   F   F   F   F   0   82.4700 0.647   0.647   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
12  T_HUMR-1    99955   1   F   F   F   F   0   82.4700 0.033   0.000   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
13  T_HUMR-1    99956   3   F   F   F   F   0   95.9700 10.317  10.317  0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
14  T_HUMR-1    99955   3   F   F   F   F   0   95.9700 0.004   0.004   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
15  T_FOYE-1    92982   1   T   F   F   F   0   103.0000    1.258   1.258   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
16  T_FOYE-1    92983   1   T   F   F   F   0   103.0000    7.500   7.500   0.000   0.000   0.00    0.0000  0.00000 0.000   0.00
FTR 19  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

in this second part, i attempt the reset... 在第二部分中，我尝试重置... 

df.reset_index()
df.index  


MultiIndex(levels=[[u'BID', u'FTR', u'OFFER'], [19, 20160526], [20.0], [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0], [u'1', u'T_BAGE-1', u'T_CNQPS-4', u'T_COSO-1', u'T_DAMC-1', u'T_DIDCB6', u'T_FOYE-1', u'T_HUMR-1', u'T_SUTB-1', u'T_WBURB-2'], [25968.0, 33725.0, 45744.0, 68274.0, 85044.0, 85045.0, 85046.0, 92982.0, 92983.0, 99955.0, 99956.0, 109615.0, 119674.0], [-1.0, 1.0, 2.0, 3.0], [u'F', u'T'], [u'F', u'T'], [u'F'], [u'F'], [0.0], [25.9, 26.0, 46.0, 50.0, 64.5, 70.0, 72.0, 82.47, 95.97, 103.0, 266.0329], [-230.211, -1.95, -0.279, 0.004, 0.033, 0.083, 0.25, 0.647, 0.975, 1.258, 1.583, 3.037, 4.0, 4.583, 7.5, 9.25, 10.317, 16.163, 22.0], [-230.211, -1.95, 0.0, 0.004, 0.083, 0.647, 1.258, 1.583, 3.037, 4.0, 4.583, 7.5, 9.25, 10.317, 16.163, 22.0], [-149.786, -1.95, 0.0], [-149.786, -1.95, 0.0], [-48.05, -1.95, 0.0], [0.0, 26.0, 266.0329], [0.0, 0.98947, 1.0]],
           labels=[[0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], [0, 1, 2, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, -1], [0, 5, 3, 9, 9, 2, 4, 4, 4, 1, 8, 7, 7, 7, 7, 7, 7, 6, 6, -1], [-1, 11, 12, 0, 0, 2, 4, 5, 6, 1, 3, 10, 10, 9, 9, 10, 9, 7, 8, -1], [-1, 0, 0, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 3, 3, 1, 1, -1], [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, -1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], [-1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], [10, 1, 0, 2, 2, 3, 4, 4, 4, 5, 6, 7, 7, 7, 7, 8, 8, 9, 9, -1], [0, 1, 2, 17, 11, 8, 13, 5, 18, 10, 12, 15, 6, 7, 4, 16, 3, 9, 14, -1], [0, 1, 2, 14, 8, 2, 10, 4, 15, 7, 9, 12, 2, 5, 2, 13, 3, 6, 11, -1], [0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, -1], [0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, -1], [0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, -1], [2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], [2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1]])

1 个解决方案

解决方案1
 0 已采纳  2016-05-28 21:39:24

I think you can add parameter inplace to reset_index : 我想你可以添加参数inplace到reset_index ： 

df.reset_index(inplace=True)

Or assign it back: 或将其分配回去： 

df = df.reset_index()

Sample: 样品： 

import pandas as pd
import io

temp=u"""HDR;INDICATIVE;SYSTEM;PRICE;STACK;DATA
BID;20160526;20;1;1;NaN;NaN;F;T;F;F;NaN;266.0329;-230.211;-230.211;-149.786;-149.786;-48.05;266.0329;1.00000;-48.050;-12782.88
2;T_DIDCB6;109615;-1;F;F;F;F;NaN;26.0000;-1.950;-1.950;-1.950;-1.950;-1.95;26.0000;0.98947;-1.929;-50.17
3;T_COSO-1;119674;-1;T;F;F;F;NaN;25.9000;-0.279;0.000;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
OFFER;20160526;20;1;T_WBURB-2;25968;2;F;F;F;F;0;46.0000;16.163;16.163;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
2;T_WBURB-2;25968;1;F;F;F;F;0;46.0000;3.037;3.037;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
3;T_CNQPS-4;45744;1;F;F;F;F;0;50.0000;0.975;0.000;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
4;T_DAMC-1;85044;1;F;F;F;F;0;64.5000;4.583;4.583;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
5;T_DAMC-1;85045;1;F;F;F;F;0;64.5000;0.083;0.083;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
6;T_DAMC-1;85046;1;F;F;F;F;0;64.5000;22.000;22.000;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
7;T_BAGE-1;33725;1;T;F;F;F;0;70.0000;1.583;1.583;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
8;T_SUTB-1;68274;1;T;F;F;F;0;72.0000;4.000;4.000;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
9;T_HUMR-1;99956;2;F;F;F;F;0;82.4700;9.250;9.250;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
10;T_HUMR-1;99956;1;F;F;F;F;0;82.4700;0.250;0.000;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
11;T_HUMR-1;99955;2;F;F;F;F;0;82.4700;0.647;0.647;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
12;T_HUMR-1;99955;1;F;F;F;F;0;82.4700;0.033;0.000;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
13;T_HUMR-1;99956;3;F;F;F;F;0;95.9700;10.317;10.317;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
14;T_HUMR-1;99955;3;F;F;F;F;0;95.9700;0.004;0.004;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
15;T_FOYE-1;92982;1;T;F;F;F;0;103.0000;1.258;1.258;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
16;T_FOYE-1;92983;1;T;F;F;F;0;103.0000;7.500;7.500;0.000;0.000;0.00;0.0000;0.00000;0.000;0.00
FTR;19;NaN;NaN;NaN;NaN;NaN;NaN;NaN;NaN;NaN;NaN;NaN;NaN;NaN;NaN;NaN;NaN;NaN;NaN;NaN;NaN"""
#after testing replace io.StringIO(temp) to filename
df = pd.read_csv(io.StringIO(temp), sep=";", index_col=None, parse_dates=False)
#print (df)

print (df.index)
MultiIndex(levels=[['10', '11', '12', '13', '14', '15', '16', '2', '3', '4', '5', '6', '7', '8', '9', 'BID', 'FTR', 'OFFER'], ['19', '20160526', 'T_BAGE-1', 'T_CNQPS-4', 'T_COSO-1', 'T_DAMC-1', 'T_DIDCB6', 'T_FOYE-1', 'T_HUMR-1', 'T_SUTB-1', 'T_WBURB-2'], [20.0, 25968.0, 33725.0, 45744.0, 68274.0, 85044.0, 85045.0, 85046.0, 92982.0, 92983.0, 99955.0, 99956.0, 109615.0, 119674.0], [-1.0, 1.0, 2.0, 3.0], ['1', 'F', 'T', 'T_WBURB-2'], ['25968', 'F'], ['2', 'F'], ['F'], ['0', 'F', 'T'], ['103.0000', '25.9000', '26.0000', '46.0000', '50.0000', '64.5000', '70.0000', '72.0000', '82.4700', '95.9700', 'F'], ['-0.279', '-1.950', '0.004', '0.033', '0.083', '0.250', '0.647', '0.975', '1.258', '1.583', '10.317', '22.000', '3.037', '4.000', '4.583', '7.500', '9.250', 'F'], [-1.95, 0.0, 0.004, 0.083, 0.647, 1.258, 1.583, 3.037, 4.0, 4.583, 7.5, 9.25, 10.317, 22.0], [-1.95, 0.0, 46.0, 266.0329], [-230.211, -1.95, 0.0, 16.163], [-230.211, -1.95, 0.0, 16.163], [-149.786, 0.0, 26.0]],
           labels=[[15, 7, 8, 17, 7, 8, 9, 10, 11, 12, 13, 14, 0, 1, 2, 3, 4, 5, 6, 16], [1, 6, 4, 1, 10, 3, 5, 5, 5, 2, 9, 8, 8, 8, 8, 8, 8, 7, 7, 0], [0, 12, 13, 0, 1, 3, 5, 6, 7, 2, 4, 11, 11, 10, 10, 11, 10, 8, 9, -1], [1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 3, 3, 1, 1, -1], [0, 1, 2, 3, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, -1], [-1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], [2, -1, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], [10, 2, 1, 10, 3, 4, 5, 5, 5, 6, 7, 8, 8, 8, 8, 9, 9, 0, 0, -1], [17, 1, 0, 17, 12, 7, 14, 4, 11, 9, 13, 16, 5, 6, 3, 10, 2, 8, 15, -1], [-1, 0, 1, 1, 7, 1, 9, 3, 13, 6, 8, 11, 1, 4, 1, 12, 2, 5, 10, -1], [3, 0, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1], [0, 1, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, -1], [0, 1, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, -1], [0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1]])

df.reset_index(inplace=True)

print (df.index)
RangeIndex(start=0, stop=20, step=1)

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