有没有一种方法可以从列表字典中创建虚拟变量的数据框？ [英] Is there a method for creating dataframe of dummy variables from a dictionary of lists?

问题描述

在熊猫中，我有一本字典，看起来像下面的字典：

In pandas I have a dictionary that looks like the one below:

{'Anemones & allies': ['Carnivore'],
'Ants, bees & wasps': ['Omnivore',  'Herbivore',  'Nectar',  'Insects', 'Parasite'],
'Beetles & bugs': ['Herbivore', 'Carnivore', 'Nectar', 'Insects'],
'Birds': ['Carnivore'],
'Fishes': ['Carnivore', 'Plankton or Particles']}

我想将其转换为一个DataFrame，您可以在其中看到什么动物可能会吃。因此它看起来类似于下图：

I want to convert it into a DataFrame in which you can see, what the animaltype could possibly eat. So it would look similar to the image below:

当试图生成这样的表时，我感觉到我在用错误的方式来做它，因为我需要很多代码行。所以我的问题是，是否有一个很好的函数将此字典映射到DataFrame，使其看起来像上表？

When trying to generate such a table I got the feeling that I was doing it in an incorrect way because I needed quite some lines of code. So my question is, is there a nice function to map this dictionary to a DataFrame so it looks something like the above table?

推荐答案

最简单的方法

使用 pd.str.get_dummies

dct = {
    'Anemones & allies': ['Carnivore'],
    'Ants, bees & wasps': ['Omnivore',  'Herbivore',  'Nectar',  'Insects', 'Parasite'],
    'Beetles & bugs': ['Herbivore', 'Carnivore', 'Nectar', 'Insects'],
    'Birds': ['Carnivore'],
    'Fishes': ['Carnivore', 'Plankton or Particles']
}

pd.Series(dct).str.join('|').str.get_dummies()

                    Carnivore  Herbivore  Insects  Nectar  Omnivore  Parasite  Plankton or Particles
Anemones & allies           1          0        0       0         0         0                      0
Ants, bees & wasps          0          1        1       1         1         1                      0
Beetles & bugs              1          1        1       1         0         0                      0
Birds                       1          0        0       0         0         0                      0
Fishes                      1          0        0       0         0         0                      1

---

更复杂

但可能推荐

---

More Complicated
But probably recommended

from sklearn.preprocessing import MultiLabelBinarizer

dct = {
    'Anemones & allies': ['Carnivore'],
    'Ants, bees & wasps': ['Omnivore',  'Herbivore',  'Nectar',  'Insects', 'Parasite'],
    'Beetles & bugs': ['Herbivore', 'Carnivore', 'Nectar', 'Insects'],
    'Birds': ['Carnivore'],
    'Fishes': ['Carnivore', 'Plankton or Particles']
}

s = pd.Series(dct)

mlb = MultiLabelBinarizer()

d = mlb.fit_transform(s)
c = mlb.classes_
pd.DataFrame(d, s.index, c)

                    Carnivore  Herbivore  Insects  Nectar  Omnivore  Parasite  Plankton or Particles
Anemones & allies           1          0        0       0         0         0                      0
Ants, bees & wasps          0          1        1       1         1         1                      0
Beetles & bugs              1          1        1       1         0         0                      0
Birds                       1          0        0       0         0         0                      0
Fishes                      1          0        0       0         0         0                      1

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