Python：如何替換大量字符串

Question

我正在嘗試將大量字符串（僅三個字符串示例，但實際上我有數千個字符串）替換為在“replaceWord”上定義的其他字符串。

• “replaceWord”沒有規律性。

然而，我寫的代碼並沒有像我預期的那樣工作。

運行腳本后，output如下：

     before     after
0  test1234  test1234
1  test1234  test1234
2  test1234      1349
3  test1234  test1234
4  test1234  test1234

我需要 output 如下；

  before    after
1 test1234  1349
2 test9012  te1210st
3 test5678  8579
4 april     I was born August
5 mcdonalds i like checkin

腳本

import os.path, time, re
import pandas as pd
import csv


body01_before="test1234"
body02_before="test9012"
body03_before="test5678"
body04_before="i like mcdonalds"
body05_before="I was born april"

replaceWord = [
                ["test9012","te1210st"],
                ["test5678","8579"],
                ["test1234","1349"],
                ["april","August"],
                ["mcdonalds","chicken"],

]

cols = ['before','after']
df = pd.DataFrame(index=[], columns=cols)

for word in replaceWord:
    
    body01_after = re.sub(word[0], word[1], body01_before)
    body02_after = re.sub(word[0], word[1], body02_before)
    body03_after = re.sub(word[0], word[1], body03_before)
    body04_after = re.sub(word[0], word[1], body04_before)
    body05_after = re.sub(word[0], word[1], body05_before)

    df=df.append({'before':body01_before,'after':body01_after}, ignore_index=True)
    
#df.head()
print(df)

df.to_csv('test_replace.csv')

Answer 1

使用正則表達式將非數字(\D+)捕獲為第一組，將數字(\d+)捕獲為第二組。 通過從第二組\2然后第一組\1開始替換文本

df['after'] = df['before'].str.replace(r'(\D+)(\d+)', r'\2\1', regex = True)

df
     before     after
1  test1234  1234test
2  test9012  9012test
3  test5678  5678test

編輯

似乎您沒有數據集。 你有變量：

body01_before="test1234"
body02_before="test9012"
body03_before="test5678"
body04_before="i like mcdonalds"
body05_before="I was born april"

replaceWord = [
                ["test9012","te1210st"],
                ["test5678","8579"],
                ["test1234","1349"],
                ["april","August"],
                ["mcdonalds","chicken"],

]

# Gather the variables in a list
vars = re.findall('body0\\d[^,]+', ','.join(globals().keys()))
df = pd.DataFrame(vars, columns = ['before_1'])
# Obtain the values of the variable
df['before'] = df['before_1'].apply(lambda x:eval(x))

# replacement function
repl = lambda x: x[0] if (rp:=dict(replaceWord).get(x[0])) is None else rp

# Do the replacement
df['after'] = df['before'].str.replace('(\\w+)',repl, regex= True)

df
        before_1            before              after
0  body01_before          test1234               1349
1  body02_before          test9012           te1210st
2  body03_before          test5678               8579
3  body04_before  i like mcdonalds     i like chicken
4  body05_before  I was born april  I was born August

Answer 2

這符合您的目的嗎？

words = ["test9012", "test5678", "test1234"]
updated = []

for word in words:
    for i, char in enumerate(word):
        if 47 < ord(char) < 58: # the character codes for digits 1-9
            updated.append(f"{word[i:]}{word[:i]}")
            break

print(updated)

代碼打印： ['9012test', '5678test', '1234test']

Answer 3

據我了解，您有一個字符串列表和一個映射字典，其形式為： {oldString1: newString1, oldString2: newString2, ...}您想要用來替換原始字符串列表。 我能想到的最快（也許是最 Pythonic）的方法是將映射字典簡單地保存為 Python dict 。 例如：

mapping = {
   "test9012":"9012test",
   "test5678","5678test",
   "test1234","1234test",
}

如果您的字符串列表存儲為 Python 列表，您可以使用以下代碼獲取替換列表：

new_list = [mapping.get(key=old_string, default=old_string) for old_string in old_list]

注意：我們將mapping.get()與default=old_string一起使用，以便 function 返回old_string ，以防它不在映射字典中。

如果您的字符串列表存儲在 Pandas 系列（或 Pandas DataFrame 的列）中，您可以快速將字符串替換為：

new_list = old_list.map(mapping, na_action='ignore')

注意：我們設置na_action='ignore'以便 function 返回old_string ，以防它不在映射字典中。

Answer 4

您可以使用正則表達式來匹配模式。

import os.path, time, re
import pandas as pd
import csv

words = ["test9012", "test5678", "test1234"]

for word in words:
  textOnlyMatch = re.match("(([a-z]|[A-Z])*)", word)
  textOnly = textOnlyMatch.group(0) // take the entire match group
  numberPart = word.split(textOnly)[1] // take string of number only
  result = numberPart + textOnly
  df = df.append({'before':word,'after':result}, ignore_index=True)

#df.head()
print(df)

df.to_csv('test_replace.csv')

因此，通過使用正則表達式匹配，您可以僅分隔字母和僅數字部分。