pandas 融化功能 [英] Pandas Melt Function
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问题描述
我有一个数据框:
df = pd.DataFrame([[2, 4, 7, 8, 1, 3, 2013], [9, 2, 4, 5, 5, 6, 2014]], columns=['Amy', 'Bob', 'Carl', 'Chris', 'Ben', 'Other', 'Year'])
Amy Bob Carl Chris Ben Other Year
0 2 4 7 8 1 3 2013
1 9 2 4 5 5 6 2014
还有字典:
d = {'A': ['Amy'], 'B': ['Bob', 'Ben'], 'C': ['Carl', 'Chris']}
我想重塑我的数据框,使其看起来像这样:
I would like to reshape my dataframe to look like this:
Group Name Year Value
0 A Amy 2013 2
1 A Amy 2014 9
2 B Bob 2013 4
3 B Bob 2014 2
4 B Ben 2013 1
5 B Ben 2014 5
6 C Carl 2013 7
7 C Carl 2014 4
8 C Chris 2013 8
9 C Chris 2014 5
10 Other 2013 3
11 Other 2014 6
请注意,Other
在Name
列中没有任何值,并且行的顺序无关紧要.我想我应该使用melt
函数,但是我遇到的示例不太清楚.
Note that Other
doesn't have any values in the Name
column and the order of the rows does not matter. I think I should be using the melt
function but the examples that I've come across aren't too clear.
推荐答案
melt
可以帮助您实现目标.
melt
gets you part way there.
In [29]: m = pd.melt(df, id_vars=['Year'], var_name='Name')
除了Group
之外,其他所有内容都包含在内.为此,我们还需要重新调整d
的形状.
This has everything except Group
. To get that, we need to reshape d
a bit as well.
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
移至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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