{"id":1166935,"date":"2025-01-15T15:37:51","date_gmt":"2025-01-15T07:37:51","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1166935.html"},"modified":"2025-01-15T15:37:54","modified_gmt":"2025-01-15T07:37:54","slug":"python-%e5%a6%82%e4%bd%95%e5%a4%84%e7%90%86%e7%a6%bb%e6%95%a3%e5%80%bc","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1166935.html","title":{"rendered":"python \u5982\u4f55\u5904\u7406\u79bb\u6563\u503c"},"content":{"rendered":"

\"python<\/p>\n

\u5728\u5904\u7406\u79bb\u6563\u503c\u65f6\uff0cPython \u63d0\u4f9b\u4e86\u591a\u79cd\u5de5\u5177\u548c\u5e93\uff0c\u5305\u62ec\u4f7f\u7528 Pandas \u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3001Scikit-Learn \u8fdb\u884c\u9884\u5904\u7406\u3001\u4ee5\u53ca\u4f7f\u7528OneHotEncoder\u8fdb\u884c\u7f16\u7801<\/strong>\u3002\u5176\u4e2d\uff0c\u4f7f\u7528 Pandas \u8fdb\u884c\u6570\u636e\u6e05\u6d17\u662f\u6700\u5e38\u89c1\u7684\u65b9\u5f0f\u3002Pandas \u63d0\u4f9b\u4e86\u8bb8\u591a\u51fd\u6570\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u548c\u5206\u7c7b\u53d8\u91cf\u7b49\u3002\u4f8b\u5982\uff0c\u4f7f\u7528 pd.get_dummies()<\/code> \u51fd\u6570\u53ef\u4ee5\u5c06\u5206\u7c7b\u53d8\u91cf\u8f6c\u5316\u4e3a\u54d1\u53d8\u91cf<\/strong>\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528 Pandas \u5904\u7406\u79bb\u6563\u503c\uff0c\u5e76\u63a2\u8ba8\u5176\u4ed6\u65b9\u6cd5\u5982 Scikit-Learn \u548c OneHotEncoder \u7684\u4f7f\u7528\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528 Pandas \u8fdb\u884c\u6570\u636e\u6e05\u6d17<\/p>\n<\/p>\n

Pandas \u662f Python \u4e2d\u7528\u4e8e\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u7684\u5f3a\u5927\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u548c\u51fd\u6570\u6765\u5904\u7406\u79bb\u6563\u503c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n

1\u3001\u5904\u7406\u7f3a\u5931\u503c<\/h3>\n<\/p>\n

\u7f3a\u5931\u503c\u662f\u6570\u636e\u96c6\u4e2d\u5e38\u89c1\u7684\u95ee\u9898\uff0cPandas \u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\u3002\u53ef\u4ee5\u4f7f\u7528 .isna()<\/code> \u548c .fillna()<\/code> \u65b9\u6cd5\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u7f3a\u5931\u503c\u7684 DataFrame<\/strong><\/h2>\n
data = {'A': [1, 2, None, 4],<\/p>\n
        'B': [None, 2, 3, 4]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u68c0\u67e5\u7f3a\u5931\u503c<\/strong><\/h2>\n
print(df.isna())<\/p>\n
\u7528\u7279\u5b9a\u503c\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
df_filled = df.fillna(0)<\/p>\n
print(df_filled)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5904\u7406\u5f02\u5e38\u503c<\/h3>\n<\/p>\n
\u5f02\u5e38\u503c\u53ef\u4ee5\u901a\u8fc7\u63cf\u8ff0\u6027\u7edf\u8ba1\u548c\u53ef\u89c6\u5316\u65b9\u6cd5\u8bc6\u522b\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u7bb1\u7ebf\u56fe\u548c z-score\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u5f02\u5e38\u503c\u7684 DataFrame<\/strong><\/h2>\n
data = {'A': [1, 2, 3, 4, 100]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4f7f\u7528\u7bb1\u7ebf\u56fe\u8bc6\u522b\u5f02\u5e38\u503c<\/strong><\/h2>\n
sns.boxplot(x=df['A'])<\/p>\n
plt.show()<\/p>\n
\u4f7f\u7528 z-score \u8bc6\u522b\u5f02\u5e38\u503c<\/strong><\/h2>\n
from scipy import stats<\/p>\n
z_scores = np.abs(stats.zscore(df['A']))<\/p>\n
df_cleaned = df[(z_scores < 3)]<\/p>\n
print(df_cleaned)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5904\u7406\u5206\u7c7b\u53d8\u91cf<\/h3>\n<\/p>\n
\u5c06\u5206\u7c7b\u53d8\u91cf\u8f6c\u6362\u4e3a\u6570\u503c\u7c7b\u578b\u662f\u5e38\u89c1\u7684\u9700\u6c42\uff0c\u53ef\u4ee5\u4f7f\u7528 pd.get_dummies()<\/code> \u51fd\u6570\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u4e2a\u5305\u542b\u5206\u7c7b\u53d8\u91cf\u7684 DataFrame<\/p>\n
data = {'Color': ['Red', 'Blue', 'Green', 'Red']}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u5c06\u5206\u7c7b\u53d8\u91cf\u8f6c\u6362\u4e3a\u54d1\u53d8\u91cf<\/strong><\/h2>\n
df_dummies = pd.get_dummies(df)<\/p>\n
print(df_dummies)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528 Scikit-Learn \u8fdb\u884c\u9884\u5904\u7406<\/p>\n<\/p>\n
Scikit-Learn \u662f\u4e00\u4e2a\u7528\u4e8e\u673a\u5668\u5b66\u4e60<\/a>\u7684 Python \u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u9884\u5904\u7406\u5de5\u5177\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
1\u3001\u6807\u51c6\u5316\u6570\u636e<\/h3>\n<\/p>\n
\u6807\u51c6\u5316\u662f\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u5747\u503c\u4e3a\u96f6\u3001\u65b9\u5dee\u4e3a\u4e00\u7684\u5f62\u5f0f\u3002\u53ef\u4ee5\u4f7f\u7528 StandardScaler<\/code>\u3002<\/p>\n<\/p>\n
from sklearn.preprocessing import StandardScaler<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u6570\u503c\u6570\u636e\u7684 DataFrame<\/strong><\/h2>\n
data = {'A': [1, 2, 3, 4, 5]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u6807\u51c6\u5316\u6570\u636e<\/strong><\/h2>\n
scaler = StandardScaler()<\/p>\n
df_scaled = scaler.fit_transform(df)<\/p>\n
print(df_scaled)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5f52\u4e00\u5316\u6570\u636e<\/h3>\n<\/p>\n
\u5f52\u4e00\u5316\u662f\u5c06\u6570\u636e\u7f29\u653e\u5230\u4e00\u4e2a\u7279\u5b9a\u8303\u56f4\u5185\uff0c\u901a\u5e38\u662f [0, 1]\u3002\u53ef\u4ee5\u4f7f\u7528 MinMaxScaler<\/code>\u3002<\/p>\n<\/p>\n
from sklearn.preprocessing import MinMaxScaler<\/p>\n
\u5f52\u4e00\u5316\u6570\u636e<\/strong><\/h2>\n
scaler = MinMaxScaler()<\/p>\n
df_normalized = scaler.fit_transform(df)<\/p>\n
print(df_normalized)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u7f16\u7801\u5206\u7c7b\u53d8\u91cf<\/h3>\n<\/p>\n
Scikit-Learn \u63d0\u4f9b\u4e86\u591a\u79cd\u7f16\u7801\u65b9\u6cd5\uff0c\u5305\u62ec OneHotEncoder<\/code> \u548c LabelEncoder<\/code>\u3002<\/p>\n<\/p>\n
from sklearn.preprocessing import OneHotEncoder<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u5206\u7c7b\u53d8\u91cf\u7684 DataFrame<\/strong><\/h2>\n
data = {'Color': ['Red', 'Blue', 'Green', 'Red']}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4f7f\u7528 OneHotEncoder \u8fdb\u884c\u7f16\u7801<\/strong><\/h2>\n
encoder = OneHotEncoder(sparse=False)<\/p>\n
df_encoded = encoder.fit_transform(df)<\/p>\n
print(df_encoded)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528 OneHotEncoder \u8fdb\u884c\u7f16\u7801<\/p>\n<\/p>\n
OneHotEncoder \u662f\u5904\u7406\u5206\u7c7b\u53d8\u91cf\u7684\u4e00\u79cd\u5e38\u7528\u65b9\u6cd5\uff0c\u5c06\u6bcf\u4e2a\u7c7b\u522b\u8f6c\u6362\u4e3a\u4e00\u4e2a\u4e8c\u8fdb\u5236\u5411\u91cf\u3002<\/p>\n<\/p>\n
1\u3001\u57fa\u672c\u7528\u6cd5<\/h3>\n<\/p>\n
# \u4f7f\u7528 OneHotEncoder \u8fdb\u884c\u7f16\u7801<\/p>\n
encoder = OneHotEncoder(sparse=False)<\/p>\n
df_encoded = encoder.fit_transform(df)<\/p>\n
print(df_encoded)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5904\u7406\u591a\u5217\u5206\u7c7b\u53d8\u91cf<\/h3>\n<\/p>\n
\u53ef\u4ee5\u5904\u7406\u5305\u542b\u591a\u4e2a\u5206\u7c7b\u53d8\u91cf\u7684 DataFrame\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u5206\u7c7b\u53d8\u91cf\u7684 DataFrame<\/p>\n
data = {'Color': ['Red', 'Blue', 'Green', 'Red'],<\/p>\n
        'Size': ['S', 'M', 'L', 'XL']}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4f7f\u7528 OneHotEncoder \u8fdb\u884c\u7f16\u7801<\/strong><\/h2>\n
df_encoded = encoder.fit_transform(df)<\/p>\n
print(df_encoded)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5904\u7406\u7a00\u758f\u77e9\u9635<\/h3>\n<\/p>\n
\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0cOneHotEncoder \u4f1a\u751f\u6210\u7a00\u758f\u77e9\u9635\uff0c\u53ef\u4ee5\u901a\u8fc7 sparse<\/code> \u53c2\u6570\u63a7\u5236\u8f93\u51fa\u683c\u5f0f\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u7a00\u758f\u77e9\u9635<\/p>\n
encoder_sparse = OneHotEncoder(sparse=True)<\/p>\n
df_encoded_sparse = encoder_sparse.fit_transform(df)<\/p>\n
print(df_encoded_sparse)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528\u5176\u4ed6\u65b9\u6cd5\u5904\u7406\u79bb\u6563\u503c<\/p>\n<\/p>\n
\u9664\u4e86 Pandas \u548c Scikit-Learn \u4e4b\u5916\uff0c\u8fd8\u6709\u5176\u4ed6\u4e00\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u5904\u7406\u79bb\u6563\u503c\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528\u81ea\u5b9a\u4e49\u51fd\u6570<\/h3>\n<\/p>\n
\u53ef\u4ee5\u7f16\u5199\u81ea\u5b9a\u4e49\u51fd\u6570\u6765\u5904\u7406\u7279\u5b9a\u7684\u79bb\u6563\u503c\u60c5\u51b5\u3002<\/p>\n<\/p>\n
def custom_transform(value):<\/p>\n
    if value == 'Red':<\/p>\n
        return 1<\/p>\n
    elif value == 'Blue':<\/p>\n
        return 2<\/p>\n
    else:<\/p>\n
        return 0<\/p>\n
df['Color_transformed'] = df['Color'].apply(custom_transform)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528 Feature-engine<\/h3>\n<\/p>\n
Feature-engine \u662f\u4e00\u4e2a\u4e13\u95e8\u7528\u4e8e\u7279\u5f81\u5de5\u7a0b\u7684\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u5904\u7406\u79bb\u6563\u503c\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
from feature_engine.encoding import OneHotEncoder as fe_OneHotEncoder<\/p>\n
\u4f7f\u7528 Feature-engine \u8fdb\u884c\u7f16\u7801<\/strong><\/h2>\n
encoder = fe_OneHotEncoder()<\/p>\n
df_encoded = encoder.fit_transform(df)<\/p>\n
print(df_encoded)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528 Category Encoders<\/h3>\n<\/p>\n
Category Encoders \u662f\u4e00\u4e2a\u5904\u7406\u5206\u7c7b\u53d8\u91cf\u7684\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u7f16\u7801\u65b9\u6cd5\uff0c\u5982\u76ee\u6807\u7f16\u7801\u3001\u9891\u7387\u7f16\u7801\u7b49\u3002<\/p>\n<\/p>\n
from category_encoders import TargetEncoder<\/p>\n
\u4f7f\u7528\u76ee\u6807\u7f16\u7801<\/strong><\/h2>\n
encoder = TargetEncoder()<\/p>\n
df_encoded = encoder.fit_transform(df['Color'], df['Size'])<\/p>\n
print(df_encoded)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u603b\u7ed3<\/p>\n<\/p>\n
\u5904\u7406\u79bb\u6563\u503c\u662f\u6570\u636e\u9884\u5904\u7406\u4e2d\u7684\u91cd\u8981\u73af\u8282\uff0cPython \u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u548c\u5e93\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002\u4f7f\u7528 Pandas \u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u548c\u9884\u5904\u7406\uff0cScikit-Learn \u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u9884\u5904\u7406\u5de5\u5177\uff0cOneHotEncoder \u662f\u5904\u7406\u5206\u7c7b\u53d8\u91cf\u7684\u5e38\u7528\u65b9\u6cd5\u3002\u6b64\u5916\uff0c\u8fd8\u6709\u5176\u4ed6\u4e00\u4e9b\u5e93\u548c\u65b9\u6cd5\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u4f7f\u7528\u3002\u901a\u8fc7\u5408\u7406\u5730\u5904\u7406\u79bb\u6563\u503c\uff0c\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5224\u65ad\u6570\u636e\u96c6\u4e2d\u54ea\u4e9b\u503c\u662f\u79bb\u6563\u503c\uff1f<\/strong>
\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u79bb\u6563\u503c\u901a\u5e38\u6307\u7684\u662f\u90a3\u4e9b\u53d6\u503c\u6709\u9650\u7684\u53d8\u91cf\uff0c\u5982\u7c7b\u522b\u53d8\u91cf\u6216\u6574\u6570\u503c\u3002\u53ef\u4ee5\u901a\u8fc7\u89c2\u5bdf\u6570\u636e\u7684\u5206\u5e03\u3001\u4f7f\u7528\u63cf\u8ff0\u6027\u7edf\u8ba1\uff08\u5982\u8ba1\u6570\u4e0d\u540c\u503c\u7684\u6570\u91cf\uff09\u4ee5\u53ca\u7ed8\u5236\u76f4\u65b9\u56fe\u6216\u6761\u5f62\u56fe\u6765\u5224\u65ad\u6570\u636e\u96c6\u4e2d\u54ea\u4e9b\u503c\u662f\u79bb\u6563\u503c\u3002<\/p>\n
\u5904\u7406\u79bb\u6563\u503c\u7684\u5e38\u89c1\u65b9\u6cd5\u6709\u54ea\u4e9b\uff1f<\/strong>
\u5904\u7406\u79bb\u6563\u503c\u7684\u65b9\u6cd5\u4e3b\u8981\u5305\u62ec\u7f16\u7801\u3001\u5206\u7ec4\u548c\u805a\u5408\u3002\u5bf9\u4e8e\u7c7b\u522b\u578b\u79bb\u6563\u503c\uff0c\u53ef\u4ee5\u4f7f\u7528\u72ec\u70ed\u7f16\u7801\uff08One-Hot Encoding\uff09\u6216\u6807\u7b7e\u7f16\u7801\uff08Label Encoding\uff09\u5c06\u5176\u8f6c\u5316\u4e3a\u6570\u503c\u578b\u6570\u636e\u3002\u5bf9\u4e8e\u6570\u503c\u578b\u79bb\u6563\u503c\uff0c\u53ef\u80fd\u9700\u8981\u901a\u8fc7\u5206\u7ec4\u5c06\u5176\u8f6c\u5316\u4e3a\u533a\u95f4\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u8fdb\u884c\u5206\u6790\u3002<\/p>\n
\u5982\u4f55\u8bc4\u4f30\u5904\u7406\u79bb\u6563\u503c\u540e\u7684\u6a21\u578b\u6027\u80fd\uff1f<\/strong>
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