I'm going a little further this previous question about mapping dictionary values to dataframes. I have a simple dataframe df like:
我将进一步讨论将字典值映射到数据帧的上一个问题。我有一个简单的数据帧df,如:
U,id
111,01
112,02
112,03
113,04
113,05
113,06
114,07
and I want to map on a new column the following nested dictionary:
我想在新列上映射以下嵌套字典:
d = {112: {'en': 1, 'es': 2}, 113: {'zh': 1, 'ja': 1, 'es': 2}, 114: {'es': 1}, 111: {'ar': 2, 'en': 1}}
taking into account only the most frequent L values, i.e. 112:'es', 113:'es', 114:'es', 111:'ar'.
只考虑最常见的L值,即112:'es',113:'es',114:'es',111:'ar'。
On a simple dictionary case, I can use df['C'] = df['U'].map(d). How can I do the same taking only the previous highest values? The resulting dataframe would appear as:
在一个简单的字典案例中,我可以使用df ['C'] = df ['U']。map(d)。我怎样才能只采用以前的最高值?生成的数据框将显示为:
U,id,C
111,01,ar
112,02,es
112,03,es
113,04,es
113,05,es
113,06,es
114,07,es
1 个解决方案
#1
I'd flatten the dict to create a new dict and then you can call map as before:
我将dict压平以创建一个新的dict然后你可以像以前一样调用map:
In [44]:
max_d={}
for k,v in d.items():
max_d[k] = max(v, key=v.get)
max_d
Out[44]:
{111: 'ar', 112: 'es', 113: 'es', 114: 'es'}
In [45]:
df['C'] = df['U'].map(max_d)
df
Out[45]:
U id C
0 111 1 ar
1 112 2 es
2 112 3 es
3 113 4 es
4 113 5 es
5 113 6 es
6 114 7 es
#1
I'd flatten the dict to create a new dict and then you can call map as before:
我将dict压平以创建一个新的dict然后你可以像以前一样调用map:
In [44]:
max_d={}
for k,v in d.items():
max_d[k] = max(v, key=v.get)
max_d
Out[44]:
{111: 'ar', 112: 'es', 113: 'es', 114: 'es'}
In [45]:
df['C'] = df['U'].map(max_d)
df
Out[45]:
U id C
0 111 1 ar
1 112 2 es
2 112 3 es
3 113 4 es
4 113 5 es
5 113 6 es
6 114 7 es