我有兩個資料框如下，
import pandas as pd
df = pd.DataFrame({'text':['I go to school','open the green door', 'go out and play'],
'pos':[['PRON','VERB','ADP','NOUN'],['VERB','DET','ADJ','NOUN'],['VERB','ADP','CCONJ','VERB']]})
df2 = pd.DataFrame({'verbs':['go','open','close','share','divide'],
'new_verbs':['went','opened','closed','shared','divided']})
如果在 df2.verbs 中找到動詞，我想用 df2.new_verbs 中的過去形式替換 df.text 中的動詞。到目前為止，我已經完成了以下作業，
df['text'] = df['text'].str.split()
new_df = df.apply(pd.Series.explode)
new_df = new_df.assign(new=lambda d: d['pos'].mask(d['pos'] == 'VERB', d['text']))
new_df.text[new_df.new.isin(df2.verbs)] = df2.new_verbs
但是當我列印出結果時，并非所有動詞都被正確替換。我想要的輸出是，
text pos new
0 I PRON PRON
0 went VERB go
0 to ADP ADP
0 school NOUN NOUN
1 opened VERB open
1 the DET DET
1 green ADJ ADJ
1 door NOUN NOUN
2 went VERB go
2 out ADP ADP
2 and CCONJ CCONJ
2 play VERB play
uj5u.com熱心網友回復：
您可以為此使用正則運算式：
import re
regex = '|'.join(map(re.escape, df2['verbs']))
s = df2.set_index('verbs')['new_verbs']
df['text'] = df['text'].str.replace(regex, lambda m: s.get(m.group(), m),
regex=True)
輸出（為清楚起見，此處為第 2列文本）：
text pos text2
0 I go to school [PRON, VERB, ADP, NOUN] I went to school
1 open the green door [VERB, DET, ADJ, NOUN] opened the green door
2 go out and play [VERB, ADP, CCONJ, VERB] went out and play
uj5u.com熱心網友回復：
對于較小的串列，您可以使用 pandasreplace和這樣的字典：
verbs_map = dict(zip(df2.verbs, df2.new_verbs))
new_df.text.replace(verbs_map)
基本上，dict(zip(df2.verbs, df2.new_verbs)創建一個新字典，將舊動詞映射到它們的新（過去時）動詞，例如{'go' : 'went' , 'close' : 'closed', ...}。
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標籤：Python python-3.x 数据框 代替
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