替換 pandas 系列中同一列中的字符串

Question

事情可能乍一看並不容易理解，但一步一步來......

這是我大約 50000 行 Dataframe df_answers_clean的開始

使用len(set(df_answers_clean['Race']))后，我得到了 98 個獨特的位置，這對於我未來的分類來說太多了。 我舉了一個例子——他們的 25 個職位列表如下：

['Native American, Pacific Islander, or Indigenous Australian; South Asian; White or of European descent', 'Hispanic or Latino/Latina; South Asian', 'East Asian; Hispanic or Latino/Latina',
 'East Asian', 'Black or of African descent; East Asian; South Asian; White or of European descent',
 'Black or of African descent; East Asian; Hispanic or Latino/Latina; Middle Eastern; Native American, Pacific Islander, or Indigenous Australian; South Asian; White or of European descent',
 'Hispanic or Latino/Latina; White or of European descent', 'White or of European descent; I prefer not to say', 'South Asian; White or of European descent', 'White or of European descent',
 'Hispanic or Latino/Latina', 'Black or of African descent; I don’t know; I prefer not to say',
 'Native American, Pacific Islander, or Indigenous Australian; White or of European descent; I don’t know', 'East Asian; White or of European descent; I don’t know', 'Native American, Pacific Islander, or Indigenous Australian', 'South Asian; White or of European descent; I don’t know',
 'Black or of African descent; Middle Eastern; White or of European descent; I don’t know',
 'Hispanic or Latino/Latina; Middle Eastern; White or of European descent',
 'Middle Eastern; White or of European descent',
 'Middle Eastern; South Asian']

我用多行代碼清理了這個爛攤子：

df_answers_clean['Race'] = df_answers_clean['Race'].str.replace('^Black or of African descent[\s\S]*', 'Black or of African descent')    
df_answers_clean['Race'] = df_answers_clean['Race'].str.replace('^East Asian[\s\S]*', 'East Asian')    
df_answers_clean['Race'] = df_answers_clean['Race'].str.replace('^Hispanic or Latino/Latina[\s\S]*', 'Hispanic or Latino/Latina')
df_answers_clean['Race'] = df_answers_clean['Race'].str.replace('^Middle Eastern[\s\S]*', 'Middle Eastern')
df_answers_clean['Race'] = df_answers_clean['Race'].str.replace('^Native American, Pacific Islander, or Indigenous Australian[\s\S]*', 'Native American, Pacific Islander, or Indigenous Australian')    
df_answers_clean['Race'] = df_answers_clean['Race'].str.replace('^South Asian[\s\S]*', 'South Asian')    
df_answers_clean['Race'] = df_answers_clean['Race'].str.replace('^White or of European descent[\s\S]*', 'White or of European descent')    
df_answers_clean['Race'] = df_answers_clean['Race'].str.replace('^I don’t know[\s\S]*', 'No data')    
df_answers_clean['Race'] = df_answers_clean['Race'].str.replace('^I prefer not to say[\s\S]*', 'No data')

結果是唯一的組，它現在才對后面的分類任務有用：

{'Black or of African descent',
 'East Asian',
 'Hispanic or Latino/Latina',
 'Middle Eastern',
 'Native American, Pacific Islander, or Indigenous Australian',
 'No data',
 'South Asian',
 'White or of European descent'}

正如我所說 - 它可以工作，但是許多重復的代碼行不實用/不實用。

我這樣做的另一個想法是列出我的最終結果（ race_names_change ）並將所有內容都放在for循環中：

race_names_change = ['Black or of African descent', 'East Asian', 'Hispanic or Latino/Latina', 'Middle Eastern', 'South Asian', 'Native American, Pacific Islander, or Indigenous Australian', 'White or of European descent']

for i in race_names_change:
    replace_string = str('^'+ i +'[\s\S]*')
    df_answers_clean['Race'].str.replace('replace_string', i, regex=True)

但不幸的是它不起作用 - 列表與開始時相同（98 個位置）。

也許循環代碼或任何其他方式（映射，應用）有問題？

謝謝你的建議。

Answer 1

如果您可以使用所需的正則表達式模式和相應的輸出創建字典，那么您可以簡單地使用pd.Series.replace()

d = {
    'pattern1':'output1',
    'pattern2':'output2'
    }

df_answers_clean['Race'].replace(d, regex=True)

注意， pd.Series.str.replace()不同於pd.Series.replace()

---

嘗試這個 -

race_names_change = ['Black or of African descent', 'East Asian', 'Hispanic or Latino/Latina', 'Middle Eastern', 'South Asian', 'Native American, Pacific Islander, or Indigenous Australian', 'White or of European descent']

d = {}
for i in race_names_change:
    replace_string = str('^'+ i +'[\s\S]*')
    replace_string
    d.update({replace_string:i})
    
df_answers_clean['Race'].replace(d, regex=True)