如何使用 Sklearn.preprocessing 对包含列表的 pandas.DataFrame 列进行编码

Question

我有一个 pandas df ，其中一些列是其中包含数据的列表，我想对列表中的标签进行编码。

我收到此错误： ValueError: Expected 2D array, got 1D array instead:

from sklearn.preprocessing import OneHotEncoder
mins = pd.read_csv('recipes.csv')

enc = OneHotEncoder(handle_unknown='ignore')

X = mins['Ingredients']

'''
[[lettuce, tomatoes, ginger, vodka, tomatoes]
[lettuce, tomatoes, flour, vodka, tomatoes]
...
[flour, tomatoes, vodka, vodka, mustard]]
'''

enc.fit(X)

我希望得到一列包含正确编码信息的列表

[[lettuce, tomatoes, ginger, vodka, tomatoes]
[lettuce, tomatoes, flour, vodka, tomatoes]
...
[flour, tomatoes, vodka, vodka, mustard]

[[0, 1, 2, 3, 1]
[0, 1, 4, 3, 1]
...
[4, 1, 3, 3, 9]]

Answer 1

为了对 DataFrame 系列中的列表列表进行标签编码，我们首先使用唯一的文本标签训练编码器，然后将apply每个transform文本标签用于列表列表中经过训练的整数标签。这是一个例子：

In [2]: import pandas as pd

In [3]: from sklearn import preprocessing

In [4]: df = pd.DataFrame({"Day":["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"], "Veggies&Drinks":[["lettuce"
   ...: , "tomatoes", "ginger", "vodka", "tomatoes"], ["flour", "vodka", "mustard", "lettuce", "ginger"], ["mustard", "
   ...: tomatoes", "ginger", "vodka", "tomatoes"], ["ginger", "vodka", "lettuce", "tomatoes", "flour"], ["mustard", "le
   ...: ttuce", "ginger", "flour", "tomatoes"]]})

In [5]: df
Out[5]:
         Day                                Veggies&Drinks
0     Monday  [lettuce, tomatoes, ginger, vodka, tomatoes]
1    Tuesday      [flour, vodka, mustard, lettuce, ginger]
2  Wednesday  [mustard, tomatoes, ginger, vodka, tomatoes]
3   Thursday     [ginger, vodka, lettuce, tomatoes, flour]
4     Friday   [mustard, lettuce, ginger, flour, tomatoes]

In [9]: label_encoder = preprocessing.LabelEncoder()

In [19]: list_of_veggies_drinks = ["lettuce","tomatoes","ginger","vodka","flour","mustard"]

In [20]: label_encoder.fit(list_of_veggies_drinks)
Out[20]: LabelEncoder()

In [21]: integer_encoded = df["Veggies&Drinks"].apply(lambda x:label_encoder.transform(x))

In [22]: integer_encoded
Out[22]:
0    [2, 4, 1, 5, 4]
1    [0, 5, 3, 2, 1]
2    [3, 4, 1, 5, 4]
3    [1, 5, 2, 4, 0]
4    [3, 2, 1, 0, 4]
Name: Veggies&Drinks, dtype: object

In [23]: df["Encoded"] = integer_encoded

In [24]: df
Out[24]:
         Day                                Veggies&Drinks          Encoded
0     Monday  [lettuce, tomatoes, ginger, vodka, tomatoes]  [2, 4, 1, 5, 4]
1    Tuesday      [flour, vodka, mustard, lettuce, ginger]  [0, 5, 3, 2, 1]
2  Wednesday  [mustard, tomatoes, ginger, vodka, tomatoes]  [3, 4, 1, 5, 4]
3   Thursday     [ginger, vodka, lettuce, tomatoes, flour]  [1, 5, 2, 4, 0]
4     Friday   [mustard, lettuce, ginger, flour, tomatoes]  [3, 2, 1, 0, 4]