熊猫在多索引列上融化

Question

我有一个以下格式的csv文件：

| a  | b  | 2018 | 2018 | 2019 | 2019 |
|    |    | jan  | feb  | jan  | feb  |
---------------------------------------
| a1 | b1 | 0    | 1    | 2    | 3    |
| a1 | b2 | 4    | 5    | 6    | 7    |
| a2 | b1 | 8    | 9    | 10   | 11   |
| a2 | b2 | 12   | 13   | 14   | 15   |

我想把它读成熊猫DF，然后融化成以下格式：

| a  | b  | year | month | value |
----------------------------------
| a1 | b1 | 2018 | jan   | 0     |
| a1 | b1 | 2018 | feb   | 1     |
| a1 | b1 | 2019 | jan   | 2     |
| a1 | b1 | 2019 | feb   | 3     |
| a1 | b2 | 2018 | jan   | 4     |
| a1 | b2 | 2018 | feb   | 5     |
| a1 | b2 | 2019 | jan   | 6     |
| a1 | b2 | 2019 | feb   | 7     |
| a2 | b1 | 2018 | jan   | 8     |
| a2 | b1 | 2018 | feb   | 9     |
| a2 | b1 | 2019 | jan   | 10    |
| a2 | b1 | 2019 | feb   | 11    |
| a2 | b2 | 2018 | jan   | 12    |
| a2 | b2 | 2018 | feb   | 13    |
| a2 | b2 | 2019 | jan   | 14    |
| a2 | b2 | 2019 | feb   | 15    |

如何实现这一点？

Answer 1

对于纯数据帧，这应该有效：

import pandas as pd

df = pd.DataFrame({
'a': ['a1', 'a1', 'a2', 'a2',],
'b': ['b1', 'b2', 'b2', 'b2',],
'2018 jan': [0, 4, 8, 12],
'2018 feb': [1, 5, 9, 13],
'2019 jan': [2, 6, 10, 14],
'2019 feb': [3, 7, 11, 15],    
})
df = df.melt(id_vars=['a', 'b'], var_name='date', value_name='value')
df['date'] = df['date'].str.split(' ')
df['year'] = df['date'].str[0]
df['month'] = df['date'].str[1]
df.drop(columns='date', inplace=True)

输出：

a   b  value  year month
0   a1  b1      0  2018   jan
1   a1  b2      4  2018   jan
2   a2  b2      8  2018   jan
3   a2  b2     12  2018   jan
4   a1  b1      1  2018   feb
5   a1  b2      5  2018   feb
6   a2  b2      9  2018   feb
7   a2  b2     13  2018   feb
8   a1  b1      2  2019   jan
9   a1  b2      6  2019   jan
10  a2  b2     10  2019   jan
11  a2  b2     14  2019   jan
12  a1  b1      3  2019   feb
13  a1  b2      7  2019   feb
14  a2  b2     11  2019   feb
15  a2  b2     15  2019   feb

如果如注释中所述，列中有一些多索引，则可以在此处将其转换为纯数据帧：

df = pd.read_csv('file.csv', header=[0,1])
df.columns = [' '.join(col).strip() for col in df.columns.values]
df.rename(columns={'a Unnamed: 0_level_1': 'a', 'b Unnamed: 1_level_1': 'b'}, inplace=True)

Answer 2

@KOB我的答案通常可以适合任何带有 2 行标题的 CSV 文件，其中部分列仅在第一行，部分列位于第一行和第二行。根据您的问题，此代码将按照要求正确放置所有标头。 读取 CSV 和创建的多索引数据帧时：

df_multiidx = pd.read_csv('two_levels_header_file.csv', header=[0,1])
id_vars = [idv for idv in df_multiidx.columns if 'Unnamed' in idv[1]]
value_vars = [valv for valv in df_multiidx.columns if 'Unnamed' not in valv[1]]
df_multiidx= df_multiidx.melt(id_vars=id_vars, value_vars=value_vars,var_name=['year','month'])
df_multiidx.rename(columns={col_ren:col_ren[0] for col_ren in id_vars})

输出：

a   b   year    month   value
0   a1  b1  2018    jan 0
1   a1  b2  2018    jan 4
2   a2  b1  2018    jan 8
3   a2  b2  2018    jan 12
4   a1  b1  2018    feb 1
5   a1  b2  2018    feb 5
6   a2  b1  2018    feb 9
7   a2  b2  2018    feb 13
8   a1  b1  2019    jan 2
9   a1  b2  2019    jan 6
10  a2  b1  2019    jan 10
11  a2  b2  2019    jan 14
12  a1  b1  2019    feb 3
13  a1  b2  2019    feb 7
14  a2  b1  2019    feb 11
15  a2  b2  2019    feb 15