我有一個字典，其中包含所有 df 列名的串列并將它們映射到函式名。
import pandas as pd
data= [["john","","","English","","","","","","","","",""]]
df= pd.DataFrame(data,columns=['firstName', 'lastName', 'state', 'Communication_Language__c', 'country', 'company', 'email', 'industry', 'System_Type__c', 'AccountType', 'customerSegment', 'Existing_Customer__c', 'GDPR_Email_Permission__c'])
filename= 'Template'
lang_trans= {"English":"ENG", "French":"FR"}
def lang (lang_trans,df):
df.replace(lang_trans, inplace=True)
df.str.upper()
return df
parsing_map{
"Communication_Language__c": lang}
我想用'english'的縮寫替換df中的資料為'ENG'，我試圖用
def lang (lang_trans,df):
df.replace(lang_trans, inplace=True)
df.str.upper()
return df
但它沒有將 df 中的“english”轉換為“ENG”
我將如何從字典中呼叫函式 lang 以將值更改df[Communication_language_c為“ENG”
期望的輸出
data= [["john","","","ENG","","","","","","","","",""]]
df= pd.DataFrame(data,columns=['firstName', 'lastName', 'state', 'Communication_Language__c
uj5u.com熱心網友回復：
嘗試這樣的事情：
import pandas as pd
data= [["john","","","English","","","","","","","","",""]]
df= pd.DataFrame(data,columns=['firstName', 'lastName', 'state', 'Communication_Language__c', 'country', 'company', 'email', 'industry', 'System_Type__c', 'AccountType', 'customerSegment', 'Existing_Customer__c', 'GDPR_Email_Permission__c'])
filename= 'Template'
lang_trans= {"English":"ENG", "French":"FR"}
def update_lang(df_temp, column, lang_dict):
for k, v in lang_dict.items():
df_temp[column] = df[column].replace(k,v)
return df_temp
df = update_lang(df, 'Communication_Language__c', lang_trans)
print(df)
uj5u.com熱心網友回復：
不需要創建函式。如果您只想將“English”轉換為“ENG”并將“French”轉換為“FR”，我也不明白您為什么要使用“lang.str.upper()”行，所以我把它拿出來了。這會得到你想要的輸出嗎？
import pandas as pd
data = [["john", "", "", "English", "", "", "", "", "", "", "", "", ""]]
df = pd.DataFrame(data,
columns=['firstName', 'lastName', 'state', 'Communication_Language__c', 'country', 'company', 'email',
'industry', 'System_Type__c', 'AccountType', 'customerSegment', 'Existing_Customer__c',
'GDPR_Email_Permission__c'])
filename = 'Template'
lang_trans = {"English": "ENG", "French": "FR"}
df['Communication_Language__c'].replace(lang_trans, inplace=True)
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