[英]How to build a MultiIndex Pandas DataFrame from a nested dictionary with lists
I have the following dictionary. 我有以下字典。
d= {'key1': {'sub-key1': ['a','b','c','d','e']},
'key2': {'sub-key2': ['1','2','3','5','8','9','10']}}
With the help of this post, I managed to successfully convert this dictionary to a DataFrame. 在这篇文章的帮助下,我成功地将这个字典转换为DataFrame。
df = pd.DataFrame.from_dict({(i,j): d[i][j]
for i in d.keys()
for j in d[i].keys()},
orient='index')
However, my DataFrame takes the following form: 但是,我的DataFrame采用以下形式:
0 1 2 3 4 5 6
(key1, sub-key1) a b c d e None None
(key2, sub-key2) 1 2 3 5 8 9 10
I can work with tuples, as index values, however I think it's better to work with a multilevel DataFrame. 我可以使用元组作为索引值,但我认为使用多级DataFrame更好。 Post such as this one have helped me to create it in two steps, however I am struggling to do it in one step (ie from the initial creation), as the list within the dictionary as well as the tuples afterwards are adding a level of complication.
像这样的帖子帮助我分两步创建它,但是我很难一步完成(即从最初的创建),因为字典中的列表以及之后的元组添加了一个级别并发症。
I think you are close, for MultiIndex
is possible used MultiIndex.from_tuples
method: 我认为你很接近,因为
MultiIndex
可能使用MultiIndex.from_tuples
方法:
d = {(i,j): d[i][j]
for i in d.keys()
for j in d[i].keys()}
mux = pd.MultiIndex.from_tuples(d.keys())
df = pd.DataFrame(list(d.values()), index=mux)
print (df)
0 1 2 3 4 5 6
key1 sub-key1 a b c d e None None
key2 sub-key2 1 2 3 5 8 9 10
Thanks, Zero for another solution: 谢谢, Zero为另一个解决方案:
df = pd.DataFrame.from_dict({(i,j): d[i][j]
for i in d.keys()
for j in d[i].keys()},
orient='index')
df.index = pd.MultiIndex.from_tuples(df.index)
print (df)
0 1 2 3 4 5 6
key1 sub-key1 a b c d e None None
key2 sub-key2 1 2 3 5 8 9 10
I will using stack
for two level dict
.... 我将使用
stack
为两级dict
....
df=pd.DataFrame(d)
df.T.stack().apply(pd.Series)
Out[230]:
0 1 2 3 4 5 6
key1 sub-key1 a b c d e NaN NaN
key2 sub-key2 1 2 3 5 8 9 10
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.