pandas python，将字典存储在数据帧中
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python, storing dictionaries inside a dataframe
提问by Sylvansight
I've built a pandas dataframe which is storing a simple dictionary in each cell. For example:
我构建了一个 Pandas 数据框，它在每个单元格中存储了一个简单的字典。例如：
{'Sales':0,'Revenue':0}
I can retrieve a specific value from the dataframe via:
我可以通过以下方式从数据框中检索特定值：
df[columnA][index100]['Revenue']
But now I'd like to plot a graph of all the Revenue values from the dictionaries in columnA- what is the best way of achieving this?
但是现在我想从字典中绘制所有收入值的图表columnA- 实现这一目标的最佳方法是什么？
Would life be easier in the long run if I dropped the dictionaries and instead used two identically sized dataframes? (Am very new to pandas so not sure of best practice).
如果我放弃字典而是使用两个相同大小的数据帧，从长远来看，生活会更轻松吗？（我对Pandas很陌生，所以不确定最佳实践）。
采纳答案by BrenBarn
A simple way to get all the Revenue values from a column A is df[columnA].map(lambda v: v['Revenue']).
从 A 列获取所有收入值的简单方法是df[columnA].map(lambda v: v['Revenue'])。
Depending on what you're doing, life may indeed be easier if you tweak your structure a bit. For instance, you could use a hierarchical index with "Sales" and "Revenue" as the keys in one level.
根据你在做什么，如果你稍微调整一下你的结构，生活确实会更容易。例如，您可以使用以“销售额”和“收入”作为一级键的分层索引。
回答by Andy Hayden
For the majority of use cases you it's not a good idea to be storing dictionaries in DataFrame.
Another datastructure worth mentioning is a Panel.
对于大多数用例，在 DataFrame 中存储字典并不是一个好主意。
另一个值得一提的数据结构是Panel。
Suppose you have something a DataFrame of dictionaries (with fairly consistent keys):
假设您有一些字典的 DataFrame（具有相当一致的键）：
In [11]: df = pd.DataFrame([[{'a': 1, 'b': 2}, {'a': 3, 'b': 4}], [{'a': 5, 'b': 6}, {'a': 7, 'b': 8}]], columns=list('AB'))
In [12]: df
Out[12]:
A B
0 {'a': 1, 'b': 2} {'a': 3, 'b': 4}
1 {'a': 5, 'b': 6} {'a': 7, 'b': 8}
You can create a Panel (note there are more direct/preferable ways to construct this!):
您可以创建一个面板（请注意，有更直接/更可取的方法来构建它！）：
In [13]: wp = pd.Panel({'A': df['A'].apply(pd.Series), 'B': df['B'].apply(pd.Series)})
In [14]: wp
Out[14]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 2 (major_axis) x 2 (minor_axis)
Items axis: A to B
Major_axis axis: 0 to 1
Minor_axis axis: a to b
Sections of which can be accessed efficiently as DataFrames in a variety of ways, for example:
其中的部分可以通过多种方式作为 DataFrame 有效访问，例如：
In [15]: wp.A
Out[15]:
a b
0 1 2
1 5 6
In [16]: wp.minor_xs('a')
Out[16]:
A B
0 1 3
1 5 7
In [17]: wp.major_xs(0)
Out[17]:
A B
a 1 3
b 2 4
So you can do all the pandas DataFrame whizziness:
所以你可以做所有的pandas DataFrame whizziness：
In [18]: wp.A.plot() # easy!
Out[18]: <matplotlib.axes.AxesSubplot at 0x1048342d0>
There are also ("experimental") higher dimensional Panels.
