pandas sklearn 中的多列单热编码和命名列
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One-hot-encoding multiple columns in sklearn and naming columns
提问by Gideon Blinick
I have the following code to one-hot-encode 2 columns I have.
我有以下代码可以对我拥有的 2 列进行单热编码。
# encode city labels using one-hot encoding scheme
city_ohe = OneHotEncoder(categories='auto')
city_feature_arr = city_ohe.fit_transform(df[['city']]).toarray()
city_feature_labels = city_ohe.categories_
city_features = pd.DataFrame(city_feature_arr, columns=city_feature_labels)
phone_ohe = OneHotEncoder(categories='auto')
phone_feature_arr = phone_ohe.fit_transform(df[['phone']]).toarray()
phone_feature_labels = phone_ohe.categories_
phone_features = pd.DataFrame(phone_feature_arr, columns=phone_feature_labels)
What I'm wondering is how I do this in 4 lines while getting properly named columns in the output. That is, I can create a properly one-hot-encoded array by include both columns names in fit_transformbut when I try and name the resulting dataframe's columns, it tells me that there is a mismatch between the shape of the indices:
我想知道的是如何在 4 行中执行此操作，同时在输出中正确命名列。也就是说，我可以通过包含两个列名称来创建一个正确的单热编码数组，fit_transform但是当我尝试命名结果数据框的列时，它告诉我索引的形状之间存在不匹配：
ValueError: Shape of passed values is (6, 50000), indices imply (3, 50000)
For background, both phone and city have 3 values.
对于背景，电话和城市都有 3 个值。
city phone
0 CityA iPhone
1 CityB Android
2 CityB iPhone
3 CityA iPhone
4 CityC Android
回答by MaximeKan
You you are almost there... Like you said you can add all the columns you want to encode in fit_transformdirectly.
你快到了......就像你说的那样，你可以直接添加所有要编码的列fit_transform。
ohe = OneHotEncoder(categories='auto')
feature_arr = ohe.fit_transform(df[['phone','city']]).toarray()
feature_labels = ohe.categories_
And then you just need to do the following:
然后你只需要执行以下操作：
feature_labels = np.array(feature_labels).ravel()
Which enables you to name your columns like you wanted:
这使您可以根据需要命名列：
features = pd.DataFrame(feature_arr, columns=feature_labels)
回答by panktijk
Why don't you take a look at pd.get_dummies? Here's how you can encode:
你为什么不看看pd.get_dummies？以下是您可以编码的方法：
df['city'] = df['city'].astype('category')
df['phone'] = df['phone'].astype('category')
df = pd.get_dummies(df)
