Python Pandas从列表中创建多个数据帧 [英] Python Pandas Create Multiple dataframes from list

问题描述

将此作为快速起点;

http://pandas.pydata.org/pandas-docs/stable/reshaping.html

In [1]: df
Out[1]: 
         date variable     value
0  2000-01-03        A  0.469112
1  2000-01-04        A -0.282863
2  2000-01-05        A -1.509059
3  2000-01-03        B -1.135632
4  2000-01-04        B  1.212112
5  2000-01-05        B -0.173215
6  2000-01-03        C  0.119209
7  2000-01-04        C -1.044236
8  2000-01-05        C -0.861849
9  2000-01-03        D -2.104569
10 2000-01-04        D -0.494929
11 2000-01-05        D  1.071804

然后隔离'A'给出：

In [2]: df[df['variable'] == 'A']
Out[2]: 
        date variable     value
0 2000-01-03        A  0.469112
1 2000-01-04        A -0.282863
2 2000-01-05        A -1.509059

现在创建新的数据帧be：

Now creating new dataframe would be:

dfA = df[df['variable'] == 'A'] 

让我们说B将是：

dfB = df[df['variable'] == 'B'] 

所以，隔离数据帧分为dfA，dfB，dfC ......

So, Isolating the dataframes into dfA, dfB, dfC......

dfList  = list(set(df['variable']))
dfNames = ["df" + row for row in dfList]  

for i, row in enumerate(dfList):
    dfName = dfNames[i]
    dfNew = df[df['variable'] == row]
    dfNames[i] = dfNew      

它运行......但是当尝试 dfA 我得到输出dfA未定义

It runs... But when try dfA I get output "dfA" is not defined

推荐答案

要按字面意思回答你的问题， globals（）['dfA'] = dfNew 将在全局命名空间中定义 dfA
：

To answer your question literally, globals()['dfA'] = dfNew would define dfA in the global namespace:

for i, row in enumerate(dfList):
    dfName = dfNames[i]
    dfNew = df[df['variable'] == row]
    globals()[dfName] = dfNew   

但是，从来没有好的定义动态命名变量的原因。

However, there is never a good reason to define dynamically-named variables.

• 如果直到运行时才知道名称 - 也就是名称是真正
  动态 - 然后您不能在代码中使用这些名称，因为您的代码在运行时之前要编写
  。那么如果您不能在代码中引用它，那么创建一个名为
  dfA 的变量有什么意义呢？

• If the names are not known until runtime -- that is, if the names are truly dynamic -- then you you can't use the names in your code since your code has to be written before runtime. So what's the point of creating a variable named dfA if you can't refer to it in your code?

另一方面，如果您事先知道您将拥有一个名为 dfA 的变量
，那么您的代码不是非常有活力。您有静态变量名称。
使用循环的唯一原因是减少锅炉板代码。但是，即使在这种情况下，
也有更好的选择。
解决方案是使用dict（见下文）或列出 ¹ 。

If, on the other hand, you know before hand that you will have a variable named dfA, then your code isn't really dynamic. You have static variable names. The only reason to use the loop is to cut down on boiler-plate code. However, even in this case, there is a better alternative. The solution is to use a dict (see below) or list¹.

添加动态命名变量pollutes全局命名空间。

Adding dynamically-named variables pollutes the global namespace.

它没有很好地概括。如果您有100个动态命名的变量，那么您将如何访问
？ 如何循环播放？

It does not generalize well. If you had 100 dynamically named variables, how would you access them? How would you loop over them?

To 管理动态命名的变量，你需要将它们的
名称列表保存为字符串：例如 ['dfA'，'dfB'，'dfC'，...] 然后通过访问新的
铸造的全局变量globals（） dict：例如全局（）[ DFA] 。那个
很尴尬。

To "manage" dynamically named variables you would need to keep a list of their names as strings: e.g. ['dfA', 'dfB', 'dfC',...] and then accessed the newly minted global variables via the globals() dict: e.g. globals()['dfA']. That is awkward.

所以程序员通过痛苦的经验得出的结论是
动态 - 名称变量介于尴尬和无用之间，并且
更加令人愉快，强大，实用，可以在键盘中存储键/值对。变量的
名称成为dict中的键，变量
的值变为与键关联的值。所以，你没有一个简单的名字 dfA
你将有一个dict dfs ，你可以访问 dfA DataFrame通过
dfs ['dfA'] ：

So the conclusion programmers reach through bitter experience is that dynamically-named variables are somewhere between awkward and useless and it is much more pleasant, powerful, practical to store key/value pairs in a dict. The name of the variable becomes a key in the dict, and the value of the variable becomes the value associated with the key. So, instead of having a bare name dfA you would have a dict dfs and you would access the dfA DataFrame via dfs['dfA']:

dfs = dict()
for i, row in enumerate(dfList):
    dfName = dfNames[i]
    dfNew = df[df['variable'] == row]
    dfs[dfName] = dfNew   

或者，如李建勋秀，

dfs = {k: g for k, g in df.groupby('variable')}

这就是为什么Jon Clements和Jianxun Li通过显示定义动态命名变量的
替代方案来回答你的问题。这是因为我们所有
都认为这是一个糟糕的主意。

This is why Jon Clements and Jianxun Li answered your question by showing alternatives to defining dynamically-named variables. It's because we all believe it is a terrible idea.

使用Jianxun Li的解决方案，循环遍历< a href =https://docs.python.org/3/library/stdtypes.html#dict.items =nofollow noreferrer> dict的键/值对，然后你可以使用：

Using Jianxun Li's solution, to loop over a dict's key/value pairs you could then use:

dfs = {k: g for k, g in df.groupby('variable')}
for key, df in dfs.items():
    ...

或使用Jon Clements的解决方案，要遍历群组，您可以使用：

or using Jon Clements' solution, to iterate through groups you could use:

grouped = df.groupby('variable')
for key, df in grouped:
    ...

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¹ 如果名称已编号或已订购，您可以使用列表而不是字典。

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¹If the names are numbered or ordered you could use a list instead of a dict.

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