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[英]Group a pandas DataFrame with MultiIndex columns for axis=0
[英]Functional approach to group DataFrame columns into MultiIndex
是否有更簡單的功能方法將列分組到 MultiIndex 中?
# Setup
l = [...]
l2,l3,l4 = do_things(l, [2,3,4])
d = {2:l2, 3:l3, 4:l4}
# Or,
l = l2 = l3 = l4 = list(range(20))
我的方法存在的問題:
# Cons:
# * Complicated
# * Requires multiple iterations over the dictionary to occur
# in the same order. This is guaranteed as the dictionary is
# unchanged but I'm not happy with the implicit dependency.
df = pd.DataFrame\
( zip(*d.values())
, index=l
, columns=pd.MultiIndex.from_product([["group"], d.keys()])
).rename_axis("x").reset_index().reset_index()
# Cons:
# * Complicated
# * Multiple assignments
df = pd.DataFrame(d, index=l).rename_axis("x")
df.columns = pd.MultiIndex.from_product([["group"],df.columns])
df = df.reset_index().reset_index()
我正在尋找類似的東西:
df =\
( pd.DataFrame(d, index=l)
. rename_axis("x")
. group_columns("group")
. reset_index().reset_index()
)
結果:
index x group
2 3 4
0 0 2 0 0 0
1 1 2 0 0 0
2 2 2 0 0 0
3 3 2 0 0 0
4 4 1 0 0 0
5 5 2 0 0 0
6 6 1 0 0 0
7 7 2 0 0 0
8 8 4 0 1 1
9 9 4 0 1 1
10 10 4 0 1 1
11 11 0 0 1 1
12 12 1 0 1 1
13 13 1 0 1 1
14 14 3 1 2 2
15 15 1 1 2 2
16 16 1 1 2 3
17 17 1 1 2 3
18 18 4 1 2 3
19 19 3 1 2 3
20 20 4 1 2 3
21 21 4 1 2 3
22 22 4 1 2 3
23 23 4 1 2 3
重新格式化字典並將其傳遞給 DataFrame 構造函數可能是最簡單的:
# Sample Data
size = 5
lst = np.arange(size) + 10
d = {2: lst, 3: lst + size, 4: lst + (size * 2)}
df = pd.DataFrame(
# Add group level by changing keys to tuples
{('group', k): v for k, v in d.items()},
index=lst
)
Output:
group
2 3 4
10 10 15 20
11 11 16 21
12 12 17 22
13 13 18 23
14 14 19 24
請注意,元組被自動解釋為 MultiIndex
這可以跟隨任何所需的操作鏈:
df = pd.DataFrame(
{('group', k): v for k, v in d.items()},
index=lst
).rename_axis('x').reset_index().reset_index()
df
:
index x group
2 3 4
0 0 10 10 15 20
1 1 11 11 16 21
2 2 12 12 17 22
3 3 13 13 18 23
4 4 14 14 19 24
也可以結合步驟,直接生成完整的DataFrame:
df = pd.DataFrame({
('index', ''): pd.RangeIndex(len(lst)),
('x', ''): lst,
**{('group', k): v for k, v in d.items()}
})
df
:
index x group
2 3 4
0 0 10 10 15 20
1 1 11 11 16 21
2 2 12 12 17 22
3 3 13 13 18 23
4 4 14 14 19 24
自然地,可以使用字典理解和 pandas 操作的任意組合。
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