{"id":1102177,"date":"2025-01-08T15:57:13","date_gmt":"2025-01-08T07:57:13","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1102177.html"},"modified":"2025-01-08T15:57:19","modified_gmt":"2025-01-08T07:57:19","slug":"python%e5%a6%82%e4%bd%95%e5%af%b9%e8%80%83%e8%af%95%e6%88%90%e7%bb%a9%e5%88%86%e6%9e%90-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1102177.html","title":{"rendered":"python\u5982\u4f55\u5bf9\u8003\u8bd5\u6210\u7ee9\u5206\u6790"},"content":{"rendered":"

\"python\u5982\u4f55\u5bf9\u8003\u8bd5\u6210\u7ee9\u5206\u6790\"<\/p>\n

\u4f7f\u7528Python\u5bf9\u8003\u8bd5\u6210\u7ee9\u8fdb\u884c\u5206\u6790\u7684\u6838\u5fc3\u65b9\u6cd5\u6709\uff1a\u6570\u636e\u9884\u5904\u7406\u3001\u7edf\u8ba1\u5206\u6790\u3001\u53ef\u89c6\u5316\u5206\u6790\u3001\u56de\u5f52\u5206\u6790\u3002<\/strong> \u5176\u4e2d\uff0c\u6570\u636e\u9884\u5904\u7406\u662f\u6700\u57fa\u7840\u4e5f\u662f\u6700\u91cd\u8981\u7684\u4e00\u6b65\uff0c\u56e0\u4e3a\u6570\u636e\u8d28\u91cf\u76f4\u63a5\u5f71\u54cd\u5206\u6790\u7ed3\u679c\u7684\u51c6\u786e\u6027\u3002\u6570\u636e\u9884\u5904\u7406\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u683c\u5f0f\u8f6c\u6362\u3001\u7f3a\u5931\u503c\u5904\u7406\u548c\u6570\u636e\u6807\u51c6\u5316\u7b49\u6b65\u9aa4\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u5bf9\u8003\u8bd5\u6210\u7ee9\u8fdb\u884c\u5206\u6790\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u9884\u5904\u7406<\/h3>\n<\/p>\n

\u6570\u636e\u9884\u5904\u7406\u662f\u5206\u6790\u8003\u8bd5\u6210\u7ee9\u7684\u7b2c\u4e00\u6b65\uff0c\u5b83\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u683c\u5f0f\u8f6c\u6362\u3001\u7f3a\u5931\u503c\u5904\u7406\u548c\u6570\u636e\u6807\u51c6\u5316\u7b49\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

1\u3001\u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n

\u6570\u636e\u6e05\u6d17\u662f\u6307\u8bc6\u522b\u548c\u7ea0\u6b63\u6570\u636e\u4e2d\u7684\u9519\u8bef\u6216\u4e0d\u4e00\u81f4\u3002\u4f8b\u5982\uff0c\u68c0\u67e5\u6570\u636e\u662f\u5426\u6709\u91cd\u590d\u8bb0\u5f55\uff0c\u662f\u5426\u5b58\u5728\u5f02\u5e38\u503c\u7b49\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u6765\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
data = pd.read_csv('exam_scores.csv')<\/p>\n
\u68c0\u67e5\u662f\u5426\u6709\u91cd\u590d\u8bb0\u5f55<\/strong><\/h2>\n
duplicates = data.duplicated()<\/p>\n
print("\u662f\u5426\u6709\u91cd\u590d\u8bb0\u5f55: ", duplicates.any())<\/p>\n
\u5220\u9664\u91cd\u590d\u8bb0\u5f55<\/strong><\/h2>\n
data = data.drop_duplicates()<\/p>\n
\u68c0\u67e5\u662f\u5426\u6709\u7f3a\u5931\u503c<\/strong><\/h2>\n
missing_values = data.isnull().sum()<\/p>\n
print("\u7f3a\u5931\u503c: \\n", missing_values)<\/p>\n
\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u8bb0\u5f55<\/strong><\/h2>\n
data = data.dropna()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u683c\u5f0f\u8f6c\u6362<\/h4>\n<\/p>\n
\u683c\u5f0f\u8f6c\u6362\u662f\u6307\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u5206\u6790\u6240\u9700\u7684\u683c\u5f0f\u3002\u4f8b\u5982\uff0c\u5c06\u65e5\u671f\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u5bf9\u8c61\uff0c\u5c06\u5206\u7c7b\u53d8\u91cf\u8f6c\u6362\u4e3a\u6570\u503c\u53d8\u91cf\u7b49\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u6765\u8fdb\u884c\u683c\u5f0f\u8f6c\u6362\u3002<\/p>\n<\/p>\n
# \u5c06\u65e5\u671f\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u5bf9\u8c61<\/p>\n
data['exam_date'] = pd.to_datetime(data['exam_date'])<\/p>\n
\u5c06\u5206\u7c7b\u53d8\u91cf\u8f6c\u6362\u4e3a\u6570\u503c\u53d8\u91cf<\/strong><\/h2>\n
data['gender'] = data['gender'].map({'male': 1, 'female': 0})<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u7edf\u8ba1\u5206\u6790<\/h3>\n<\/p>\n
\u7edf\u8ba1\u5206\u6790\u662f\u5bf9\u6570\u636e\u8fdb\u884c\u63cf\u8ff0\u6027\u7edf\u8ba1\u548c\u63a8\u65ad\u6027\u7edf\u8ba1\u3002\u4f8b\u5982\uff0c\u8ba1\u7b97\u5e73\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u6807\u51c6\u5dee\u7b49\u63cf\u8ff0\u6027\u7edf\u8ba1\u91cf\uff0c\u8fdb\u884c\u5047\u8bbe\u68c0\u9a8c\u3001\u76f8\u5173\u6027\u5206\u6790\u7b49\u63a8\u65ad\u6027\u7edf\u8ba1\u5206\u6790\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas\u548cscipy\u5e93\u6765\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n
1\u3001\u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790<\/h4>\n<\/p>\n
\u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790\u662f\u5bf9\u6570\u636e\u8fdb\u884c\u57fa\u672c\u7edf\u8ba1\u63cf\u8ff0\uff0c\u4f8b\u5982\u8ba1\u7b97\u5e73\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u6807\u51c6\u5dee\u7b49\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u6765\u8fdb\u884c\u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u5e73\u5747\u503c<\/p>\n
mean_score = data['score'].mean()<\/p>\n
print("\u5e73\u5747\u6210\u7ee9: ", mean_score)<\/p>\n
\u8ba1\u7b97\u4e2d\u4f4d\u6570<\/strong><\/h2>\n
median_score = data['score'].median()<\/p>\n
print("\u4e2d\u4f4d\u6210\u7ee9: ", median_score)<\/p>\n
\u8ba1\u7b97\u6807\u51c6\u5dee<\/strong><\/h2>\n
std_score = data['score'].std()<\/p>\n
print("\u6210\u7ee9\u6807\u51c6\u5dee: ", std_score)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u63a8\u65ad\u6027\u7edf\u8ba1\u5206\u6790<\/h4>\n<\/p>\n
\u63a8\u65ad\u6027\u7edf\u8ba1\u5206\u6790\u662f\u5bf9\u6570\u636e\u8fdb\u884c\u5047\u8bbe\u68c0\u9a8c\u3001\u76f8\u5173\u6027\u5206\u6790\u7b49\uff0c\u4ee5\u63a8\u65ad\u603b\u4f53\u7279\u5f81\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528scipy\u5e93\u6765\u8fdb\u884c\u63a8\u65ad\u6027\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n
from scipy import stats<\/p>\n
t\u68c0\u9a8c<\/strong><\/h2>\n
t_test = stats.ttest_1samp(data['score'], 50)<\/p>\n
print("t\u68c0\u9a8c\u7ed3\u679c: ", t_test)<\/p>\n
\u76f8\u5173\u6027\u5206\u6790<\/strong><\/h2>\n
correlation = data.corr()<\/p>\n
print("\u76f8\u5173\u6027\u5206\u6790: \\n", correlation)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u53ef\u89c6\u5316\u5206\u6790<\/h3>\n<\/p>\n
\u53ef\u89c6\u5316\u5206\u6790\u662f\u901a\u8fc7\u56fe\u5f62\u6765\u5c55\u793a\u6570\u636e\u7279\u5f81\uff0c\u4f8b\u5982\u7ed8\u5236\u76f4\u65b9\u56fe\u3001\u7bb1\u7ebf\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib\u548cseaborn\u5e93\u6765\u8fdb\u884c\u53ef\u89c6\u5316\u5206\u6790\u3002<\/p>\n<\/p>\n
1\u3001\u7ed8\u5236\u76f4\u65b9\u56fe<\/h4>\n<\/p>\n
\u76f4\u65b9\u56fe\u7528\u4e8e\u5c55\u793a\u6570\u636e\u5206\u5e03\u60c5\u51b5\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib\u5e93\u6765\u7ed8\u5236\u76f4\u65b9\u56fe\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u76f4\u65b9\u56fe<\/strong><\/h2>\n
plt.hist(data['score'], bins=10, edgecolor='k')<\/p>\n
plt.xlabel('\u6210\u7ee9')<\/p>\n
plt.ylabel('\u9891\u6570')<\/p>\n
plt.title('\u6210\u7ee9\u5206\u5e03\u76f4\u65b9\u56fe')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/h4>\n<\/p>\n
\u7bb1\u7ebf\u56fe\u7528\u4e8e\u5c55\u793a\u6570\u636e\u5206\u5e03\u7684\u4e94\u6570\u6982\u62ec\uff08\u6700\u5c0f\u503c\u3001\u4e0b\u56db\u5206\u4f4d\u6570\u3001\u4e2d\u4f4d\u6570\u3001\u4e0a\u56db\u5206\u4f4d\u6570\u3001\u6700\u5927\u503c\uff09\u548c\u5f02\u5e38\u503c\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528seaborn\u5e93\u6765\u7ed8\u5236\u7bb1\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
sns.boxplot(x=data['score'])<\/p>\n
plt.xlabel('\u6210\u7ee9')<\/p>\n
plt.title('\u6210\u7ee9\u7bb1\u7ebf\u56fe')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u7ed8\u5236\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
\u6563\u70b9\u56fe\u7528\u4e8e\u5c55\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib\u5e93\u6765\u7ed8\u5236\u6563\u70b9\u56fe\u3002<\/p>\n<\/p>\n
# \u7ed8\u5236\u6563\u70b9\u56fe<\/p>\n
plt.scatter(data['study_hours'], data['score'])<\/p>\n
plt.xlabel('\u5b66\u4e60\u65f6\u95f4')<\/p>\n
plt.ylabel('\u6210\u7ee9')<\/p>\n
plt.title('\u5b66\u4e60\u65f6\u95f4\u4e0e\u6210\u7ee9\u7684\u5173\u7cfb')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u56de\u5f52\u5206\u6790<\/h3>\n<\/p>\n
\u56de\u5f52\u5206\u6790\u662f\u5efa\u7acb\u81ea\u53d8\u91cf\u4e0e\u56e0\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u6a21\u578b\uff0c\u4f8b\u5982\u7ebf\u6027\u56de\u5f52\u3001\u903b\u8f91\u56de\u5f52\u7b49\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528scikit-learn\u5e93\u6765\u8fdb\u884c\u56de\u5f52\u5206\u6790\u3002<\/p>\n<\/p>\n
1\u3001\u7ebf\u6027\u56de\u5f52<\/h4>\n<\/p>\n
\u7ebf\u6027\u56de\u5f52\u7528\u4e8e\u5efa\u7acb\u81ea\u53d8\u91cf\u4e0e\u56e0\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u7cfb\u6a21\u578b\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528scikit-learn\u5e93\u6765\u8fdb\u884c\u7ebf\u6027\u56de\u5f52\u5206\u6790\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
X = data[['study_hours']]<\/p>\n
y = data['score']<\/p>\n
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
\u5efa\u7acb\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
model = LinearRegression()<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
from sklearn.metrics import mean_squared_error, r2_score<\/p>\n
mse = mean_squared_error(y_test, y_pred)<\/p>\n
r2 = r2_score(y_test, y_pred)<\/p>\n
print("\u5747\u65b9\u8bef\u5dee: ", mse)<\/p>\n
print("R\u5e73\u65b9: ", r2)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u903b\u8f91\u56de\u5f52<\/h4>\n<\/p>\n
\u903b\u8f91\u56de\u5f52\u7528\u4e8e\u5efa\u7acb\u81ea\u53d8\u91cf\u4e0e\u56e0\u53d8\u91cf\u4e4b\u95f4\u7684\u4e8c\u5206\u7c7b\u5173\u7cfb\u6a21\u578b\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528scikit-learn\u5e93\u6765\u8fdb\u884c\u903b\u8f91\u56de\u5f52\u5206\u6790\u3002<\/p>\n<\/p>\n
from sklearn.linear_model import LogisticRegression<\/p>\n
\u76ee\u6807\u53d8\u91cf\u4e8c\u5206\u7c7b<\/strong><\/h2>\n
data['pass'] = data['score'].apply(lambda x: 1 if x >= 60 else 0)<\/p>\n
\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
X = data[['study_hours']]<\/p>\n
y = data['pass']<\/p>\n
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
\u5efa\u7acb\u903b\u8f91\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
model = LogisticRegression()<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report<\/p>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
conf_matrix = confusion_matrix(y_test, y_pred)<\/p>\n
class_report = classification_report(y_test, y_pred)<\/p>\n
print("\u51c6\u786e\u7387: ", accuracy)<\/p>\n
print("\u6df7\u6dc6\u77e9\u9635: \\n", conf_matrix)<\/p>\n
print("\u5206\u7c7b\u62a5\u544a: \\n", class_report)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7ed3\u8bba<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u5bf9\u8003\u8bd5\u6210\u7ee9\u8fdb\u884c\u5168\u9762\u5206\u6790\u3002\u4ece\u6570\u636e\u9884\u5904\u7406\u5f00\u59cb\uff0c\u4fdd\u8bc1\u6570\u636e\u8d28\u91cf\uff0c\u7136\u540e\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u548c\u53ef\u89c6\u5316\u5206\u6790\uff0c\u53d1\u73b0\u6570\u636e\u7279\u5f81\u548c\u89c4\u5f8b\uff0c\u6700\u540e\u901a\u8fc7\u56de\u5f52\u5206\u6790\u5efa\u7acb\u53d8\u91cf\u5173\u7cfb\u6a21\u578b\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528Python\u8fdb\u884c\u8003\u8bd5\u6210\u7ee9\u5206\u6790\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u5bf9\u8003\u8bd5\u6210\u7ee9\u8fdb\u884c\u53ef\u89c6\u5316\u5206\u6790\uff1f<\/strong>
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