将类别字符串拆分为特定的Dataframe列[重复]

Question

我有以下类别的< code>Dataframe列:

    data = {'People': ['John','Mary','Andy','April'], 
             'Class': ['Math, Science','English, Math, Science','Math, Science','Science, English, Math']}
    
    df = pd.DataFrame(data, columns = ['People', 'Class'])

如何创建新列并将数据帧转换为：

> | People | Math | Science | English |
> ------------------------------------- 
> | John   | Math | Science |         | 
> | Mary   | Math | Science | English | 
> | Andy   | Math | Science |         |
> | April  | Math | Science | English |

Answer 1

这是一个解决方案，

# Strip-out white spaces before `,\s+`, use dummies to create categorical variable

df = df.set_index('People')

dummies = (
    df.Class.str.replace(',\s+', ",", regex=True)
        .str.get_dummies(sep=",")
)

   English  Math  Science
0        0     1        1
1        1     1        1
2        0     1        1
3        1     1        1

# Create a "hash map" to substitute categorical data
replace_ = {i : j for i, j in enumerate(dummies.columns, 1)}

# multiply keys with & replace to fill in the column values.
dummies.mul(list(replace_.keys())).replace(replace_)

        English  Math  Science
People                        
John          0  Math  Science
Mary    English  Math  Science
Andy          0  Math  Science
April   English  Math  Science

Answer 2

以下代码可能对您有所帮助

columns = set([x for lst in df['Class'] for x in lst.replace(" ", "").split(",") ])
for col in columns:
  df[col] = ""*len(df)

for i, val in enumerate(df["Class"]):
  cl = val.replace(" ", "").split(",")
  print(cl)
  for value in cl:
    df.loc[i][value] = value
df.drop('Class', axis=1, inplace=True)

输出:

    People  Science English Math
0   John    Science         Math
1   Mary    Science English Math
2   Andy    Science         Math
3   April   Science English Math

Answer 3

• 使用.get_dummies获取 Class 列的 1 和 0 表
• 使用 np.where 将 1 替换为列名，将 0 替换为空字符串。
• 断续器Class.str.get_dummies（'， '）.apply（lambda x： np.其中（x == 1， x.name， ''）） 创建一个单独的数据帧，我们使用 .join 将其合并回 df。
• .删除不需要的“类”列。

import pandas as pd
import numpy as np

updated = df.join(df.Class.str.get_dummies(', ').apply(lambda x: np.where(x == 1, x.name, ''))).drop(columns=['Class'])

# display(updated)
  People  English  Math  Science
0   John           Math  Science
1   Mary  English  Math  Science
2   Andy           Math  Science
3  April  English  Math  Science