First there is a large amount of data, which is divided into 3 units:
类型 第一个值 第二值
For example, there are 100 pieces of such data now. How to deal with it?
Step one
My idea is to construct 1:n:1 through a dictionary.
However, the current situation is. If the type is key, what should be done if there are the same type? Because the dictionary key is unique.
The second step is that I want to get the second value through the first value. How can I get the second value from the first value without knowing the first value?
Thanks! ~
The expression may not be clear. Let me give an example of 1:n:1
For example, there are two pieces of data like this
类型 第一个值 第二个值
（1） zhangsan 2017-01-01 是的我来了
（2） zhangsan 2017-05-01 我要走了Then I want to compose something like this:
'zhangsan'：{{'2017-01-01': '是的我来了'},{'2017-05-01':'我要走了'}}This is the result I want.
That is to say. 1: n: 1 means Type: first value: second value
I don’t know if there is a feasible solution
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The first one: If the first value is unique under the same type, then you can try the following structure:
The above means that creating a default value
字典的字典mapping, 然后用类型和第一个值分别作为索引的key, 我个人觉得, 在类型和第一个值的共同约束下, 找到的第二个值should be unique. If it is not unique, then you need to consider whether to overwrite it or exist in the form of a listSecond:
If you use the data structure I mentioned above, then without knowing the first value, you can only traverse mapping[value_type], and then determine whether the value is what you want
What does 1:n:1 mean? Mapping relationship? But this is not important. I have a simple and crude way to write each piece of data as a 3-tuple (type, val1, val2), and then save each piece of data into an array [];
Under construction When making an array, make 3 dicts, typeDict = {type: [arrIdx]}, val1Dict={val1:[arrIdx]}, val2Dict={val2:[arrIdx]}
When you want to use type to find data, just use typeDict from Find all record locations corresponding to type.
Similarly, val1 and val2 are the same.
When you want to find typeA, val1=n, you only need to intersect the result sets found from typeDict, val1Dict.
If there is a large amount of data, it seems that it would be more efficient to use a database such as mysql or pandas that specializes in data processing. Pandas also has a dedicated to_dict function.