我需要重塑一个csv数据透视表。一小段摘录如下：
country location confirmedcases_10-02-2020 deaths_10-02-2020 confirmedcases_11-02-2020 deaths_11-02-2020
0 Australia New South Wales 4.0 0.0 4 0.0
1 Australia Victoria 4.0 0.0 4 0.0
2 Australia Queensland 5.0 0.0 5 0.0
3 Australia South Australia 2.0 0.0 2 0.0
4 Cambodia Sihanoukville 1.0 0.0 1 0.0
5 Canada Ontario 3.0 0.0 3 0.0
6 Canada British Columbia 4.0 0.0 4 0.0
7 China Hubei 31728.0 974.0 33366 1068.0
8 China Zhejiang 1177.0 0.0 1131 0.0
9 China Guangdong 1177.0 1.0 1219 1.0
10 China Henan 1105.0 7.0 1135 8.0
11 China Hunan 912.0 1.0 946 2.0
12 China Anhui 860.0 4.0 889 4.0
13 China Jiangxi 804.0 1.0 844 1.0
14 China Chongqing 486.0 2.0 505 3.0
15 China Sichuan 417.0 1.0 436 1.0
16 China Shandong 486.0 1.0 497 1.0
17 China Jiangsu 515.0 0.0 543 0.0
18 China Shanghai 302.0 1.0 311 1.0
19 China Beijing 342.0 3.0 352 3.0
有没有ready to use熊猫工具来实现它？
变成这样：
country location date confirmedcases deaths
0 Australia New South Wales 2020-02-10 4.0 0.0
1 Australia Victoria 2020-02-10 4.0 0.0
2 Australia Queensland 2020-02-10 5.0 0.0
3 Australia South Australia 2020-02-10 2.0 0.0
4 Cambodia Sihanoukville 2020-02-10 1.0 0.0
5 Canada Ontario 2020-02-10 3.0 0.0
6 Canada British Columbia 2020-02-10 4.0 0.0
7 China Hubei 2020-02-10 31728.0 974.0
8 China Zhejiang 2020-02-10 1177.0 0.0
9 China Guangdong 2020-02-10 1177.0 1.0
10 China Henan 2020-02-10 1105.0 7.0
11 China Hunan 2020-02-10 912.0 1.0
12 China Anhui 2020-02-10 860.0 4.0
13 China Jiangxi 2020-02-10 804.0 1.0
14 China Chongqing 2020-02-10 486.0 2.0
15 China Sichuan 2020-02-10 417.0 1.0
16 China Shandong 2020-02-10 486.0 1.0
17 China Jiangsu 2020-02-10 515.0 0.0
18 China Shanghai 2020-02-10 302.0 1.0
19 China Beijing 2020-02-10 342.0 3.0
20 Australia New South Wales 2020-02-11 4.0 0.0
21 Australia Victoria 2020-02-11 4.0 0.0
22 Australia Queensland 2020-02-11 5.0 0.0
23 Australia South Australia 2020-02-11 2.0 0.0
24 Cambodia Sihanoukville 2020-02-11 1.0 0.0
25 Canada Ontario 2020-02-11 3.0 0.0
26 Canada British Columbia 2020-02-11 4.0 0.0
27 China Hubei 2020-02-11 33366.0 1068.0
28 China Zhejiang 2020-02-11 1131.0 0.0
29 China Guangdong 2020-02-11 1219.0 1.0
30 China Henan 2020-02-11 1135.0 8.0
31 China Hunan 2020-02-11 946.0 2.0
32 China Anhui 2020-02-11 889.0 4.0
33 China Jiangxi 2020-02-11 844.0 1.0
34 China Chongqing 2020-02-11 505.0 3.0
35 China Sichuan 2020-02-11 436.0 1.0
36 China Shandong 2020-02-11 497.0 1.0
37 China Jiangsu 2020-02-11 543.0 0.0
38 China Shanghai 2020-02-11 311.0 1.0
39 China Beijing 2020-02-11 352.0 3.0
答案 0 :(得分：2)
使用pd.wide_to_long：
print (pd.wide_to_long(df,stubnames=["confirmedcases","deaths"],
i=["country","location"],j="date",sep="_",
suffix=r'\d{2}-\d{2}-\d{4}').reset_index())
country location date confirmedcases deaths
0 Australia New South Wales 10-02-2020 4.0 0.0
1 Australia New South Wales 11-02-2020 4.0 0.0
2 Australia Victoria 10-02-2020 4.0 0.0
3 Australia Victoria 11-02-2020 4.0 0.0
4 Australia Queensland 10-02-2020 5.0 0.0
5 Australia Queensland 11-02-2020 5.0 0.0
6 Australia South Australia 10-02-2020 2.0 0.0
7 Australia South Australia 11-02-2020 2.0 0.0
8 Cambodia Sihanoukville 10-02-2020 1.0 0.0
9 Cambodia Sihanoukville 11-02-2020 1.0 0.0
10 Canada Ontario 10-02-2020 3.0 0.0
11 Canada Ontario 11-02-2020 3.0 0.0
12 Canada British Columbia 10-02-2020 4.0 0.0
13 Canada British Columbia 11-02-2020 4.0 0.0
14 China Hubei 10-02-2020 31728.0 974.0
15 China Hubei 11-02-2020 33366.0 1068.0
16 China Zhejiang 10-02-2020 1177.0 0.0
17 China Zhejiang 11-02-2020 1131.0 0.0
18 China Guangdong 10-02-2020 1177.0 1.0
19 China Guangdong 11-02-2020 1219.0 1.0
20 China Henan 10-02-2020 1105.0 7.0
21 China Henan 11-02-2020 1135.0 8.0
22 China Hunan 10-02-2020 912.0 1.0
23 China Hunan 11-02-2020 946.0 2.0
24 China Anhui 10-02-2020 860.0 4.0
25 China Anhui 11-02-2020 889.0 4.0
26 China Jiangxi 10-02-2020 804.0 1.0
27 China Jiangxi 11-02-2020 844.0 1.0
28 China Chongqing 10-02-2020 486.0 2.0
29 China Chongqing 11-02-2020 505.0 3.0
30 China Sichuan 10-02-2020 417.0 1.0
31 China Sichuan 11-02-2020 436.0 1.0
32 China Shandong 10-02-2020 486.0 1.0
33 China Shandong 11-02-2020 497.0 1.0
34 China Jiangsu 10-02-2020 515.0 0.0
35 China Jiangsu 11-02-2020 543.0 0.0
36 China Shanghai 10-02-2020 302.0 1.0
37 China Shanghai 11-02-2020 311.0 1.0
38 China Beijing 10-02-2020 342.0 3.0
39 China Beijing 11-02-2020 352.0 3.0
答案 1 :(得分：0)
如果只有一个特征，则使用{dataframe} .reshape（（-1,1）），如果只有一个特征，则使用{dataframe} .reshape（（1，-1））
答案 2 :(得分：0)
是的，您可以通过reshaping the dataframe来实现。
必须将列熔化才能将其作为值：
df = df.melt(['country', 'location'],
[ p for p in df.columns if p not in ['country', 'location'] ],
'key',
'value')
#> country location key value
#> 0 Australia New South Wales confirmedcases_10-02-2020 4
#> 1 Australia Victoria confirmedcases_10-02-2020 4
#> 2 Australia Queensland confirmedcases_10-02-2020 5
#> 3 Australia South Australia confirmedcases_10-02-2020 2
#> 4 Cambodia Sihanoukville confirmedcases_10-02-2020 1
#> .. ... ... ... ...
#> 75 China Sichuan deaths_11-02-2020 1
#> 76 China Shandong deaths_11-02-2020 1
#> 77 China Jiangsu deaths_11-02-2020 0
#> 78 China Shanghai deaths_11-02-2020 1
#> 79 China Beijing deaths_11-02-2020 3
此后，您需要将key列中的值分开：
key_split_series = df.key.str.split("_", expand=True)
df["key"] = key_split_series[0]
df["date"] = key_split_series[1]
#> country location key value date
#> 0 Australia New South Wales confirmedcases 4 10-02-2020
#> 1 Australia Victoria confirmedcases 4 10-02-2020
#> 2 Australia Queensland confirmedcases 5 10-02-2020
#> 3 Australia South Australia confirmedcases 2 10-02-2020
#> 4 Cambodia Sihanoukville confirmedcases 1 10-02-2020
#> .. ... ... ... ... ...
#> 75 China Sichuan deaths 1 11-02-2020
#> 76 China Shandong deaths 1 11-02-2020
#> 77 China Jiangsu deaths 0 11-02-2020
#> 78 China Shanghai deaths 1 11-02-2020
#> 79 China Beijing deaths 3 11-02-2020
最后，您只需要旋转表以将confirmedcases和deaths重新作为列：
df = df.set_index(["country", "location", "date", "key"])["value"].unstack().reset_index()
#> key country location date confirmedcases deaths
#> 0 Australia New South Wales 10-02-2020 4 0
#> 1 Australia New South Wales 11-02-2020 4 0
#> 2 Australia Queensland 10-02-2020 5 0
#> 3 Australia Queensland 11-02-2020 5 0
#> 4 Australia South Australia 10-02-2020 2 0
#> .. ... ... ... ... ...
#> 35 China Shanghai 11-02-2020 311 1
#> 36 China Sichuan 10-02-2020 417 1
#> 37 China Sichuan 11-02-2020 436 1
#> 38 China Zhejiang 10-02-2020 1177 0
#> 39 China Zhejiang 11-02-2020 1131 0