来自具有多个值的数据帧字符串列的一种热编码答案

【问题标题】：One hot encoding from a dataframe string column with multiple values来自具有多个值的数据帧字符串列的一种热编码
【发布时间】：2021-02-09 14:04:41
【问题描述】：

我有一个由 1245 行组成的数据框“df1”，包含一列文本（对象类型）和主题（对象类型）。主题列包含与文本标签对应的不同数字。这是一个例子：

        text                                                topic
1207    June 2019: The French Facility for Global Envi...   3 12 7
1208    May 2019: Participants from multi-stakeholder ...   8
1209    2 July 2019: UN Member States have reached agr...   1 7
1210    30 June 2019: The G20 Leaders’ Summit and asso...   7 8 9 11 12 13 14 15 17

我想获得一个这样的热编码形式（也在列名中的数字前添加一个“S”）：

        text                                                S1  S2  S3 ..... S7  S8 S9 etc.
1207    June 2019: The French Facility for Global Envi...   0    0   1  ..... 1   0  0
1208    May 2019: Participants from multi-stakeholder ...   0    0   0 ...... 0   1  0
1209    2 July 2019: UN Member States have reached agr...   1    0   0  ..... 1   0  0
1210    30 June 2019: The G20 Leaders’ Summit and asso...   0    0   0  ......1   1  1

这里的“困难”是我的文本是多标签的，所以简单的 one-hot-encoding 的代码不适用于我的情况。有什么想法吗？

【问题讨论】：

您是否期待 Python 中的答案（问题上的标签）。您尝试过的代码是一个 R 库
确实，我只用python。我现在知道为什么这段代码不起作用了……

标签： python dataframe data-cleaning one-hot-encoding

【解决方案1】：

仅使用 pandas，您可以执行以下操作：

import pandas as pd


data = [['June 2019: The French Facility for Global Envi...', '3 12 7'],
       ['May 2019: Participants from multi-stakeholder ...','8'],
       ['2 July 2019: UN Member States have reached agr...','1 7'],
       ['30 June 2019: The G20 Leaders’ Summit and asso...','7 8 9 11 12 13 14 15 17']]
df = pd.DataFrame(data , columns=['text', 'topic'])

# creating list of strings where each value is one number out of topic column
unique_values = ' '.join(df['topic'].values.tolist()).split(' ')

# creating new column for each value in unique_values
for number in unique_values:
    df[f'S{number}'] = 0
    
# changing 0 to 1 for every Snumber column where topic contains number
for idx, row in df.iterrows():
    for number in row['topic'].split(' '):
        df.loc[idx, f'S{number}'] = 1
df.drop('topic', axis=1, inplace=True)

结果：


    text                                                S3  S12 S7  S8  S1  S9  S11 S13 S14 S15 S17
0   June 2019: The French Facility for Global Envi...   1   1   1   0   0   0   0   0   0   0   0
1   May 2019: Participants from multi-stakeholder ...   0   0   0   1   0   0   0   0   0   0   0
2   2 July 2019: UN Member States have reached agr...   0   0   1   0   1   0   0   0   0   0   0
3   30 June 2019: The G20 Leaders’ Summit and asso...   0   1   1   1   0   1   1   1   1   1   1

【讨论】：

【解决方案2】：

数据稍作修改（出于可读性原因...）：

from io import StringIO
import pandas as pd

s = """id,text,topic
1207,One,1 2 5
1208,Two,3
1209,Three,1 4
1210,Four,1 2 3"""

df = pd.read_csv(StringIO(s))
df.topic = df.topic.str.split(' ').apply(lambda x: [int(y) for y in x])

b = np.zeros((df.topic.size, max(max(x) for x in df.topic) + 1))
for i in df.index:
    b[i, df.topic[i]] = 1

idx = {'id': df.id, 'text': df.text}
idx.update({f'S{i}': b[:, i] for i in range(1, b.shape[1])})
idx
df = pd.DataFrame(idx)
print(df.set_index('id').to_markdown())

这给了你：

|   id | text   |   S1 |   S2 |   S3 |   S4 |   S5 |
|-----:|:-------|-----:|-----:|-----:|-----:|-----:|
| 1207 | One    |    1 |    1 |    0 |    0 |    1 |
| 1208 | Two    |    0 |    0 |    1 |    0 |    0 |
| 1209 | Three  |    1 |    0 |    0 |    1 |    0 |
| 1210 | Four   |    1 |    1 |    1 |    0 |    0 |

【讨论】：