通过构造函数创建MultiIndexed数据框 [英] Create MultiIndexed dataframe through constructor
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问题描述
给出两个数组:
x
[('010_628', '2543677'), ('010_228', '2543677'), ('015_634', '2543677')]
y
array([['me', 10228955],
['me', 10228955],
['me', 10228955]], dtype=object)
当前,此代码为我提供了一个具有元组的平面索引的数据框:
Currently, this code gets me a dataframe with a flat index of tuples:
df = pd.DataFrame(x, index=y, columns=['pm_code', 'sec_pm'])
df
pm_code sec_pm
(me, 10228955) 010_628 2543677
(me, 10228955) 010_228 2543677
(me, 10228955) 015_634 2543677
我该如何创建一个像这样的MultiIndex
数据框?
How can I instead create a MultiIndex
dataframe that looks like this?
pm_code sec_pm
state site_no
me 10228955 010_628 2543677
010_228 2543677
015_634 2543677
我尝试使用pd.MultiIndex.from_tuples
,但无法正确执行此操作.感谢您的帮助.
I've tried using pd.MultiIndex.from_tuples
but I'm not able to get this right. Thanks for the help.
附录:性能比较
Appendix: Performance Comparisons
# unutbu #1
%timeit pd.DataFrame(x, index=pd.MultiIndex.from_arrays(y.T), columns=['pm_code', 'sec_pm'])
1000 loops, best of 3: 1.25 ms per loop
# unutbu #2
%timeit pd.DataFrame(x, index=pd.MultiIndex.from_tuples(y.tolist()), columns=['pm_code', 'sec_pm'])
1000 loops, best of 3: 1.47 ms per loop
# piRSquared
%timeit pd.DataFrame(x, index=y.T.tolist(), columns=['pm_code', 'sec_pm'])
1000 loops, best of 3: 1.41 ms per loop
# Andrew L
%timeit pd.DataFrame(x, index=[y[:,0], y[:,1]], columns=['pm_code', 'sec_pm'])
1000 loops, best of 3: 1.29 ms per loop
大
x2 = np.repeat(x, 10000, 0)
y2 = np.repeat(x, 10000, 0)
# unutbu #1
%timeit pd.DataFrame(x2, index=pd.MultiIndex.from_arrays(y2.T), columns=['pm_code', 'sec_pm'])
100 loops, best of 3: 17.3 ms per loop
# unutbu #2
%timeit pd.DataFrame(x2, index=pd.MultiIndex.from_tuples(y2.tolist()), columns=['pm_code', 'sec_pm'])
10 loops, best of 3: 30.5 ms per loop
# piRSquared
%timeit pd.DataFrame(x2, index=y2.T.tolist(), columns=['pm_code', 'sec_pm'])
10 loops, best of 3: 37.2 ms per loop
# Andrew L
%timeit pd.DataFrame(x2, index=[y2[:,0], y2[:,1]], columns=['pm_code', 'sec_pm'])
100 loops, best of 3: 22 ms per loop
来自此问题的数据.
Data from this question.
推荐答案
您可以使用pd.MultiIndex.from_arrays(y.T)
:
In [53]: pd.DataFrame(x, index=pd.MultiIndex.from_arrays(y.T), columns=['pm_code', 'sec_pm'])
Out[53]:
pm_code sec_pm
me 10228955 010_628 2543677
10228955 010_228 2543677
10228955 015_634 2543677
或pd.MultiIndex.from_tuples(y.tolist())
:
In [54]: pd.DataFrame(x, index=pd.MultiIndex.from_tuples(y.tolist()), columns=['pm_code', 'sec_pm'])
Out[54]:
pm_code sec_pm
me 10228955 010_628 2543677
10228955 010_228 2543677
10228955 015_634 2543677
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