melt 带您到那里。
In [29]: m = pd.melt(df, id_vars=['Year'], var_name='Name')
除了一切都有Group。为了做到这一点，我们还需要重塑d一点。
In [30]: d2 = {}
In [31]: for k, v in d.items():
for item in v:
d2[item] = k
....:
In [32]: d2
Out[32]: {'Amy': 'A', 'Ben': 'B', 'Bob': 'B', 'Carl': 'C', 'Chris': 'C'}
In [34]: m['Group'] = m['Name'].map(d2)
In [35]: m
Out[35]:
Year Name value Group
0 2013 Amy 2 A
1 2014 Amy 9 A
2 2013 Bob 4 B
3 2014 Bob 2 B
4 2013 Carl 7 C
.. ... ... ... ...
7 2014 Chris 5 C
8 2013 Ben 1 B
9 2014 Ben 5 B
10 2013 Other 3 NaN
11 2014 Other 6 NaN
[12 rows x 4 columns]
并将“其他”从Name转移到Group
In [8]: mask = m['Name'] == 'Other'
In [9]: m.loc[mask, 'Name'] = ''
In [10]: m.loc[mask, 'Group'] = 'Other'
In [11]: m
Out[11]:
Year Name value Group
0 2013 Amy 2 A
1 2014 Amy 9 A
2 2013 Bob 4 B
3 2014 Bob 2 B
4 2013 Carl 7 C
.. ... ... ... ...
7 2014 Chris 5 C
8 2013 Ben 1 B
9 2014 Ben 5 B
10 2013 3 Other
11 2014 6 Other
[12 rows x 4 columns]
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