你是對的，我認(rèn)為你執(zhí)行了兩次相同的語句（1 后 1）。在 Python 交互式終端上執(zhí)行的以下語句闡明了這一點(diǎn)。
注意：如果你傳遞字典，map() 將替換 Series 中的所有值，NaN 如果它與字典的鍵不匹配（我認(rèn)為，你也做了同樣的事情，即執(zhí)行兩次語句）。檢查熊貓地圖（），應(yīng)用（）。
Pandas 文檔說明：當(dāng)arg是字典時(shí)，系列中不在字典中的值（作為鍵）將轉(zhuǎn)換為NaN。
>>> import pandas as pd
>>>
>>> d = {
... "label": ["ham", "ham", "spam", "ham", "ham"],
... "sms_messsage": [
... "Go until jurong point, crazy.. Available only ...",
... "Ok lar... Joking wif u oni...",
... "Free entry in 2 a wkly comp to win FA Cup fina...",
... "U dun say so early hor... U c already then say...",
... "Nah I don't think he goes to usf, he lives aro..."
... ]
... }
>>>
>>> df = pd.DataFrame(d)
>>> df
label sms_messsage
0 ham Go until jurong point, crazy.. Available only ...
1 ham Ok lar... Joking wif u oni...
2 spam Free entry in 2 a wkly comp to win FA Cup fina...
3 ham U dun say so early hor... U c already then say...
4 ham Nah I don't think he goes to usf, he lives aro...
>>>
>>> df['label'] = df.label.map({'ham':0, 'spam':1})
>>> df
label sms_messsage
0 0 Go until jurong point, crazy.. Available only ...
1 0 Ok lar... Joking wif u oni...
2 1 Free entry in 2 a wkly comp to win FA Cup fina...
3 0 U dun say so early hor... U c already then say...
4 0 Nah I don't think he goes to usf, he lives aro...
>>>
>>> df['label'] = df.label.map({'ham':0, 'spam':1})
>>> df
label sms_messsage
0 NaN Go until jurong point, crazy.. Available only ...
1 NaN Ok lar... Joking wif u oni...
2 NaN Free entry in 2 a wkly comp to win FA Cup fina...
3 NaN U dun say so early hor... U c already then say...
4 NaN Nah I don't think he goes to usf, he lives aro...
>>>
獲得相同結(jié)果的其他方法
>>> import pandas as pd
>>>
>>> d = {
... "label": ['spam', 'ham', 'ham', 'ham', 'spam'],
... "sms_message": ["M1", "M2", "M3", "M4", "M5"]
... }
>>>
>>> df = pd.DataFrame(d)
>>> df
label sms_message
0 spam M1
1 ham M2
2 ham M3
3 ham M4
4 spam M5
>>>
第一個(gè)方法-使用map()與dictionary參數(shù)
>>> new_values = {'spam': 1, 'ham': 0}
>>>
>>> df
label sms_message
0 spam M1
1 ham M2
2 ham M3
3 ham M4
4 spam M5
>>>
>>> df.label = df.label.map(new_values)
>>> df
label sms_message
0 1 M1
1 0 M2
2 0 M3
3 0 M4
4 1 M5
>>>
第二個(gè)方法-使用map()與function參數(shù)
>>> df.label = df.label.map(lambda v: 0 if v == 'ham' else 1)
>>> df
label sms_message
0 1 M1
1 0 M2
2 0 M3
3 0 M4
4 1 M5
>>>
第三個(gè)方法-使用apply()與function參數(shù)
>>> df.label = df.label.apply(lambda v: 0 if v == "ham" else 1)
>>>
>>> df
label sms_message
0 1 M1
1 0 M2
2 0 M3
3 0 M4
4 1 M5
>>>
謝謝你。
