Python Pandas：如何拆分数据帧列中排序的字典 [英] Python Pandas: How to split a sorted dictionary in a column of a dataframe

问题描述

我有一个这样的dataFrame：

I have a dataFrame like this:

id  asn      orgs
0   3320    {'Deutsche Telekom AG': 2288}
1   47886   {'Joyent': 16, 'Equinix (Netherlands) B.V.': 7}
2   47601   {'fusion services': 1024, 'GCE Global Maritime':16859}  
3   33438   {'Highwinds Network Group': 893}

我想排序组织列，其实际上是字典，然后提取得到两个不同列中具有最高值的对（k，v）。像这样：

I would like to sort the 'orgs' column which is actually a dictionary and then extract get the pair(k,v) with the highest values in two different columns. Like this:

id  asn      org                      value
0   3320    'Deutsche Telekom AG'     2288
1   47886   'Joyent'                  16
2   47601   'GCE Global Maritime'     16859 
3   33438   'Highwinds Network Group' 893

目前我正在运行这段代码，但没有正确排序，然后我不知道如何提取最高价值的对。

Currently I am running this code but it does not properly sort, and then I am not sure how to extract the pair with highest value.

df.orgs.apply(lambda x : sorted(x.items(),key=operator.itemgetter(1),reverse=True))

这给了我一个这样的列表：

which gave me a list like this:

id  asn      orgs
0   3320    [('Deutsche Telekom AG', 2288)]
1   47886   [('Joyent', 16),( 'Equinix (Netherlands) B.V.', 7)]
2   47601   [('GCE Global Maritime',16859),('fusion services', 1024)]   
3   33438   [('Highwinds Network Group', 893)]

现在我该怎么办关键和最高的价值分成两个单独的列？任何人都可以帮助？

Now how can I put the key and the value of the highest into two seperate columns? Can anybody help?

推荐答案

另一种方法定义一个刚刚调用 min 在dict上并返回一个系列，所以你可以分配到多个列（从 @ Alex Martelli的答案）：

Another approach define a function that just calls min on the dict and return a Series so you can assign to multiple columns (function body taken from @Alex Martelli's answer):

In [17]:

def func(x):
    k = min(x, key=x.get)
    return pd.Series([k, x[k]])
df[['orgs', 'value']] = df['orgs'].apply(func)
df

Out[17]:
     asn  id                        orgs  value
0   3320   0         Deutsche Telekom AG   2288
1  47886   1  Equinix (Netherlands) B.V.      7
2  47601   2             fusion services   1024
3  33438   3     Highwinds Network Group    893

编辑

如果您的数据有空的dicss，那么你可以测试 len ：

If your data has empty dicss, then you can just test the len:

In [34]:

df = pd.DataFrame({'id':[0,1,2,3,4],
                   'asn':[3320,47886,47601,33438,56],
                   'orgs':[{'Deutsche Telekom AG': 2288},
                           {'Joyent': 16, 'Equinix (Netherlands) B.V.': 7},
                           {'fusion services': 1024, 'GCE Global Maritime':16859},
                           {'Highwinds Network Group': 893},{}]})
df
Out[34]:
     asn  id                                               orgs
0   3320   0                      {'Deutsche Telekom AG': 2288}
1  47886   1    {'Equinix (Netherlands) B.V.': 7, 'Joyent': 16}
2  47601   2  {'GCE Global Maritime': 16859, 'fusion service...
3  33438   3                   {'Highwinds Network Group': 893}
4     56   4                                                 {}
In [36]:

def func(x):
    if len(x) > 0:
        k = min(x, key=x.get)
        return pd.Series([k, x[k]])
    return pd.Series([np.NaN, np.NaN])

df[['orgs', 'value']] = df['orgs'].apply(func)
df

Out[36]:
     asn  id                        orgs  value
0   3320   0         Deutsche Telekom AG   2288
1  47886   1  Equinix (Netherlands) B.V.      7
2  47601   2             fusion services   1024
3  33438   3     Highwinds Network Group    893
4     56   4                         NaN    NaN

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