dfreplace不適用于pandas列中的分隔符

我有一個df

technologies= {
    'Courses':["Spark,ABCD","PySpark","Hadoop","Python","Pandas"],
    'Fee' :[22000,25000,23000,24000,26000],
    'Duration':['30days','50days','30days', None,np.nan],
    'Discount':[1000,2300,1000,1200,2500]
          }
df = pd.DataFrame(technologies)
print(df)

我試圖用 dict 值替換列值

dict = {"Spark" : 'S', "PySpark" : 'P', "Hadoop": 'H', "Python" : 'P', "Pandas": 'P'}
df2=df.replace({"Courses": dict})
print(df2)

但是帶有分隔符的行即使存在值也不會被替換將其作為輸出

      Courses    Fee Duration  Discount
0  Spark,ABCD  22000   30days      1000
1           P  25000   50days      2300
2           H  23000   30days      1000
3           P  24000     None      1200
4           P  26000      NaN      2500

但輸出應該是

      Courses    Fee Duration  Discount
0      S,ABCD 22000   30days      1000
1           P  25000   50days      2300
2           H  23000   30days      1000
3           P  24000     None      1200
4           P  26000      NaN      2500

uj5u.com熱心網友回復：

可能值得了解 regex 引數的作業原理，以便您將來可以利用它。盡管如此，還是可以拆分,和分解，以便每行有一個單詞。然后，您可以替換原始索引并將其分組，然后重新連接到逗號分隔的字串。

import pandas as pd
technologies= {
    'Courses':["Spark,ABCD","PySpark","Hadoop","Python","Pandas"],
    'Fee' :[22000,25000,23000,24000,26000],
    'Duration':['30days','50days','30days', None,np.nan],
    'Discount':[1000,2300,1000,1200,2500]
          }
df = pd.DataFrame(technologies)

d = {"Spark" : 'S', "PySpark" : 'P', "Hadoop": 'H', "Python" : 'P', "Pandas": 'P'}

df.Courses = (df.Courses.str.split(',').explode().replace(d)
                        .groupby(level=0).agg(','.join))

輸出

  Courses    Fee Duration  Discount
0  S,ABCD  22000   30days      1000
1       P  25000   50days      2300
2       H  23000   30days      1000
3       P  24000     None      1200
4       P  26000      NaN      2500

uj5u.com熱心網友回復：

方法一：確保所有復合詞都在單詞之前。在字典PySpark中是之前Spark

d = {"PySpark" : 'P', "Spark" : 'S', "Hadoop": 'H', "Python" : 'P', "Pandas": 'P'}
df2 = df.replace({"Courses": d}, regex  = True)
print(df2)

  Courses    Fee Duration  Discount
0  S,ABCD  22000   30days      1000
1       P  25000   50days      2300
2       H  23000   30days      1000
3       P  24000     None      1200
4       P  26000      NaN      2500

方法2：將單詞放在邊界中：

new_dict = pd.DataFrame(d.items(), columns = ['keys', 'values'])
new_dict['keys'] = '\\b'   new_dict['keys']   '\\b'
new_dict = new_dict.set_index('keys').to_dict()['values']
df3 = df.replace({"Courses": new_dict}, regex  = True)
df3

 Courses    Fee Duration  Discount
    0  S,ABCD  22000   30days      1000
    1       P  25000   50days      2300
    2       H  23000   30days      1000
    3       P  24000     None      1200
    4       P  26000      NaN      2500

uj5u.com熱心網友回復：

這是一種專注于您要更改的列 ( Courses) 的方法：

dct = {"Spark" : 'S', "PySpark" : 'P', "Hadoop": 'H', "Python" : 'P', "Pandas": 'P'}
df.Courses = df.Courses.transform(
    lambda x: x.str.split(',')).transform(
    lambda x: [dct[y] if y in dct else y for y in x]).str.join(',')

解釋：

• 用于transform將列中的每個 csv 字串值替換為串列
• 再次使用transform，這次是使用字典替換值串列中的每個專案dct
• 用于Series.str.join將每個值的串列轉換回 csv 字串。

完整的測驗代碼：

import pandas as pd
import numpy as np
technologies= {
    'Courses':["Spark,ABCD","PySpark","Hadoop","Python","Pandas"],
    'Fee' :[22000,25000,23000,24000,26000],
    'Duration':['30days','50days','30days', None,np.nan],
    'Discount':[1000,2300,1000,1200,2500]
          }
df = pd.DataFrame(technologies)
print(df)

dct = {"Spark" : 'S', "PySpark" : 'P', "Hadoop": 'H', "Python" : 'P', "Pandas": 'P'}
df.Courses = df.Courses.transform(
    lambda x: x.str.split(',')).transform(
    lambda x: [dct[y] if y in dct else y for y in x]).str.join(',')
print(df)

輸入：

      Courses    Fee Duration  Discount
0  Spark,ABCD  22000   30days      1000
1     PySpark  25000   50days      2300
2      Hadoop  23000   30days      1000
3      Python  24000     None      1200
4      Pandas  26000      NaN      2500

輸出：

  Courses    Fee Duration  Discount
0  S,ABCD  22000   30days      1000
1       P  25000   50days      2300
2       H  23000   30days      1000
3       P  24000     None      1200
4       P  26000      NaN      2500

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