pandas 使用分层列创建 DataFrame
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Creating DataFrame with Hierarchical Columns
提问by Alex Rothberg
What is the easiest way to create a DataFramewith hierarchical columns?
创建DataFrame具有分层列的最简单方法是什么？
I am currently creating a DataFrame from a dict of names -> Seriesusing:
我目前正在从名称字典创建一个 DataFrame ->Series使用：
df = pd.DataFrame(data=serieses)
df = pd.DataFrame(data=serieses)
I would like to use the same columns names but add an additional level of hierarchy on the columns. For the time being I want the additional level to have the same value for columns, let's say "Estimates".
我想使用相同的列名称，但在列上添加额外的层次结构。目前，我希望附加级别对列具有相同的值，让我们说“估计”。
I am trying the following but that does not seem to work:
我正在尝试以下操作，但这似乎不起作用：
pd.DataFrame(data=serieses,columns=pd.MultiIndex.from_tuples([(x, "Estimates") for x in serieses.keys()]))
pd.DataFrame(data=serieses,columns=pd.MultiIndex.from_tuples([(x, "Estimates") for x in serieses.keys()]))
All I get is a DataFrame with all NaNs.
我得到的只是一个包含所有 NaN 的 DataFrame。
For example, what I am looking for is roughly:
例如，我正在寻找的大致是：
l1 Estimates
l2 one two one two one two one two
r1 1 2 3 4 5 6 7 8
r2 1.1 2 3 4 5 6 71 8.2
where l1 and l2 are the labels for the MultiIndex
其中 l1 和 l2 是 MultiIndex 的标签
回答by Alex Rothberg
This appears to work:
这似乎有效：
import pandas as pd
data = {'a': [1,2,3,4], 'b': [10,20,30,40],'c': [100,200,300,400]}
df = pd.concat({"Estimates": pd.DataFrame(data)}, axis=1, names=["l1", "l2"])
l1 Estimates
l2 a b c
0 1 10 100
1 2 20 200
2 3 30 300
3 4 40 400
回答by DimG
I know the question is really old but for pandasversion 0.19.1one can use direct dict-initialization:
我知道这个问题真的很老，但对于pandas版本0.19.1一可以使用直接 dict-initialization：
d = {('a','b'):[1,2,3,4], ('a','c'):[5,6,7,8]}
df = pd.DataFrame(d, index=['r1','r2','r3','r4'])
df.columns.names = ('l1','l2')
print df
l1 a
l2 b c
r1 1 5
r2 2 6
r3 3 7
r4 4 8
回答by Rutger Kassies
Im not sure but i think the use of a dict as input for your DF anda MulitIndex dont play well together. Using an array as input instead makes it work.
我不确定，但我认为使用 dict 作为 DF 的输入和MulitIndex 不能很好地配合使用。改为使用数组作为输入使其工作。
I often prefer dicts as input though, one way is to set the columns after creating the df:
我通常更喜欢 dicts 作为输入，一种方法是在创建 df 后设置列：
import pandas as pd
data = {'a': [1,2,3,4], 'b': [10,20,30,40],'c': [100,200,300,400]}
df = pd.DataFrame(np.array(data.values()).T, index=['r1','r2','r3','r4'])
tups = zip(*[['Estimates']*len(data),data.keys()])
df.columns = pd.MultiIndex.from_tuples(tups, names=['l1','l2'])
l1 Estimates
l2 a c b
r1 1 10 100
r2 2 20 200
r3 3 30 300
r4 4 40 400
Or when using an array as input for the df:
或者当使用数组作为 df 的输入时：
data_arr = np.array([[1,2,3,4],[10,20,30,40],[100,200,300,400]])
tups = zip(*[['Estimates']*data_arr.shape[0],['a','b','c'])
df = pd.DataFrame(data_arr.T, index=['r1','r2','r3','r4'], columns=pd.MultiIndex.from_tuples(tups, names=['l1','l2']))
Which gives the same result.
这给出了相同的结果。
