英文:
Create a dictionary with two columns of a panda Dataframe depending on another column values
问题
我想创建一个字典，根据数据框中另一列的值来创建带有两列的Pandas数据框。
我有一个如下所示的数据框：
a b c
0 brand F 0
1 brand G 1
2 seat yes 1
3 seat no 0
我想创建类似于以下的字典：
brand = {'F':0,'G':1}
seat = {'no':0,'yes':1}
尝试这样做没有给我想要的结果。
dic = {}
for x, y in zip(b.values, c.values):
dic.setdefault(y, []).append(x)
谢谢！
英文:
I would like to create a dictionary with two columns of a panda Dataframe depending on another column values.
I have a dataframe as below:
a b c
0 brand F 0
1 brand G 1
2 seat yes 1
3 seat no 0
I would like to create dictionaries like:
brand = {'F':0,'G':1}
seat = {'no':0,'yes':1}
Trying this does not give me the results.
dic = {}
for x,y in zip(b.values, c.values):
dic.setdefault(y,[]).append(x)
Thank you!
答案1
得分: 2
你的方法的修改如下：
dic = {}
for a, b, c in zip(df['a'], df['b'], df['c']):
dic.setdefault(a, {})[b] = c
[pandas] 等效的方法如下：
dic = (df.set_index('b').groupby('a')['c']
.agg(lambda g: g.to_dict())
.to_dict()
)
dic 的输出：
{'brand': {'F': 0, 'G': 1},
'seat': {'yes': 1, 'no': 0}}
英文:
A modification of your approach would be:
dic = {}
for a, b, c in zip(df['a'], df['b'], df['c']):
dic.setdefault(a, {})[b] = c
The [tag:pandas] equivalent:
dic = (df.set_index('b').groupby('a')['c']
.agg(lambda g: g.to_dict())
.to_dict()
)
Output dic:
{'brand': {'F': 0, 'G': 1},
'seat': {'yes': 1, 'no': 0}}
答案2
得分: 1
你无法以编程方式将变量名称从列中的值分配给变量。一个简单的解决方案是使用字典，并将字典作为值。
import pandas as pd
df = pd.DataFrame({
'a': ['brand', 'brand', 'seat', 'seat'],
'b': ['F', 'G', 'yes', 'no'],
'c': [0, 1, 1, 0]
})
dic = {}
for k, g in df.groupby('a'):
dic[k] = dict(zip(*g[['b','c']].values.T))
dic
# 返回结果:
{'brand': {'F': 0, 'G': 1}, 'seat': {'yes': 1, 'no': 0}}
英文:
You won't be able to programmatically assign variables names from values in the columns. An easy solution is a dictionary with dictionaries as the values.
import pandas as pd
df = pd.DataFrame({
'a': ['brand', 'brand', 'seat', 'seat'],
'b': ['F', 'G', 'yes', 'no'],
'c': [0, 1, 1, 0]
})
dic = {}
for k, g in df.groupby('a'):
dic[k] = dict(zip(*g[['b','c']].values.T))
dic
# returns:
{'brand': {'F': 0, 'G': 1}, 'seat': {'yes': 1, 'no': 0}}
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