Pandas - 重新索引以便我可以保留值
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Pandas - reindex so I can keep values
提问by jwillis0720
Long story short
长话短说
I have a nested dictionary. When I turn it into a dataframe.
我有一个嵌套字典。当我把它变成一个数据框时。
import pandas
pdf = pandas.DataFrame(nested_dict)
95 96 97 98 99 100 101 102 103 104 105 \
A 70019 102 4243 3083 3540 6311 4851 5938 4140 4659 3100
C 0 185 427 433 1190 910 3898 3869 2861 2149 3065
D 8 9 23463 1237 2574 4174 3640 4747 3557 4582 5934
E 141 89 5034 1576 2303 3416 2377 1252 1204 1703 718
F 7 12 1937 2246 1687 1154 1317 3473 1881 2221 3060
G 343 1550 13497 10659 12343 8213 9251 7341 6354 9058 9022
H 1 1978 1829 1394 1945 1003 1382 1489 4182 932 556
I 5 772 1361 3914 3255 3242 2808 3765 3284 2127 3120
K 3 10353 540 2364 1196 882 3439 2107 803 743 621
L 6 14 1599 11759 4571 4821 3450 5071 4364 1891 3677
M 1 6 158 211 524 2738 686 443 612 509 1721
N 6 186 299 2971 791 1440 2028 1163 1689 4296 1535
P 54 31 726 6208 7160 5494 6184 4282 3587 3727 3821
Q 10 87 1228 2233 1016 1801 1768 1693 3414 515 563
R 7 53939 3030 8904 6712 6134 5127 3223 4764 3768 6429
S 76 5213 3676 7480 9831 7666 5410 8185 7508 11237 8298
T 4369 1253 3087 2487 6559 4572 6863 3184 7352 6068 4756
V 732 5 7595 4331 5216 5444 5187 6013 4245 4545 4761
W 0 6 103 1225 598 888 601 713 1298 1323 908
Y 12 9 1968 1085 2787 5489 5529 7840 8691 9745 10136
Eventually I want to melt down this data frame to look like the following.
最终我想把这个数据框分解成如下所示。
residue residue_num count
A 95 70019
A 96 102
A 97 4243
....
The residue column is being marked as the index so I don't know how to make it an arbitrary index like 0,1,2,3 and call "A C D E F.." another name.
残差列被标记为索引，所以我不知道如何使它成为像 0,1,2,3 这样的任意索引并调用“ACDE F..”另一个名称。
EDITAnswered myself as per suggestion
编辑根据建议回答我自己
回答by jwillis0720
import pandas
pdf = pandas.DataFrame(the_matrix)
pdf = pdf.reset_index()
pdf.rename(columns={'index':'aa'},inplace=True)
pandas.melt(pdf,id_vars='aa',var_name="position",value_name="counts")
aa position counts
0 A 95 70019
1 C 95 0
2 D 95 8
3 E 95 141
4 F 95 7
5 G 95 343
6 H 95 1
7 I 95 5
8 K 95 3
回答by tozCSS
Your pdf looks like a pivot table. Let's assume we have a dataframe with three columns. We can pivot it with a single function like this:
您的 pdf 看起来像一个数据透视表。假设我们有一个包含三列的数据框。我们可以使用这样的单个函数来旋转它：
pivoted = df.pivot(index='col1',columns='col2',values='col3')
Unpivoting it back without losing the index requires a reset_indexdance:
在不丢失索引的情况下反转它需要一个reset_index舞蹈：
pivoted.reset_index().melt(id_vars=pivoted.index.name)
To get the exact original df:
要获得确切的原始 df：
pivoted.reset_index().melt(id_vars=pivoted.index.name, var_name='col2', value_name='col3')
PS. To my surprise, melt does not get a kwarg like keep_index=True. Enhancement suggestion is still open: https://github.com/pandas-dev/pandas/issues/17440
附注。令我惊讶的是，melt 并没有像keep_index=True. 增强建议仍然开放：https: //github.com/pandas-dev/pandas/issues/17440
