{"id":1112599,"date":"2025-01-08T17:39:58","date_gmt":"2025-01-08T09:39:58","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1112599.html"},"modified":"2025-01-08T17:40:01","modified_gmt":"2025-01-08T09:40:01","slug":"%e7%94%a8python%e5%a6%82%e4%bd%95%e8%bf%9b%e8%a1%8c%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1112599.html","title":{"rendered":"\u7528python\u5982\u4f55\u8fdb\u884c\u6570\u636e\u5206\u6790"},"content":{"rendered":"

\"\u7528python\u5982\u4f55\u8fdb\u884c\u6570\u636e\u5206\u6790\"<\/p>\n

\u5728Python\u4e2d\u8fdb\u884c\u6570\u636e\u5206\u6790\u7684\u4e3b\u8981\u6b65\u9aa4\u5305\u62ec\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u63a2\u7d22\u6027\u5206\u6790\uff08EDA\uff09\u3001\u6570\u636e\u5efa\u6a21\u548c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/strong> \u5176\u4e2d\uff0c\u6570\u636e\u6e05\u6d17\u662f\u786e\u4fdd\u6570\u636e\u8d28\u91cf\u7684\u5173\u952e\u6b65\u9aa4\uff0c\u901a\u5e38\u9700\u8981\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u548c\u91cd\u590d\u6570\u636e\u7b49\u95ee\u9898\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u6bcf\u4e2a\u6b65\u9aa4\u5e76\u63d0\u4f9b\u4ee3\u7801\u793a\u4f8b\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u6536\u96c6<\/h3>\n<\/p>\n

\u6570\u636e\u6536\u96c6\u662f\u6570\u636e\u5206\u6790\u7684\u7b2c\u4e00\u6b65\uff0c\u53ef\u4ee5\u4ece\u5404\u79cd\u6765\u6e90\u83b7\u53d6\u6570\u636e\uff0c\u5982CSV\u6587\u4ef6\u3001\u6570\u636e\u5e93\u3001API\u7b49\u3002Python\u6709\u8bb8\u591a\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u65b9\u4fbf\u5730\u6536\u96c6\u6570\u636e\uff0c\u5982Pandas\u3001Requests\u3001BeautifulSoup\u7b49\u3002<\/p>\n<\/p>\n

1.1 \u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n

CSV\u6587\u4ef6\u662f\u6700\u5e38\u89c1\u7684\u6570\u636e\u5b58\u50a8\u683c\u5f0f\u4e4b\u4e00\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u8bfb\u53d6CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
data = pd.read_csv('data.csv')<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u4ece\u6570\u636e\u5e93\u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n
Python\u53ef\u4ee5\u901a\u8fc7\u5404\u79cd\u5e93\uff08\u5982SQLAlchemy\u3001Psycopg2\uff09\u8fde\u63a5\u5230\u6570\u636e\u5e93\u5e76\u8bfb\u53d6\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u4f7f\u7528SQLAlchemy\u4ecePostgreSQL\u6570\u636e\u5e93\u8bfb\u53d6\u6570\u636e\u7684\u793a\u4f8b\u3002<\/p>\n<\/p>\n
from sqlalchemy import create_engine<\/p>\n
\u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce<\/strong><\/h2>\n
engine = create_engine('postgresql:\/\/username:password@localhost:5432\/mydatabase')<\/p>\n
\u4ece\u6570\u636e\u5e93\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
data = pd.read_sql('SELECT * FROM mytable', engine)<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u6e05\u6d17<\/h3>\n<\/p>\n
\u6570\u636e\u6e05\u6d17\u662f\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\u6700\u7e41\u7410\u4f46\u975e\u5e38\u91cd\u8981\u7684\u4e00\u6b65\u3002\u6570\u636e\u6e05\u6d17\u7684\u4e3b\u8981\u4efb\u52a1\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u5904\u7406\u91cd\u590d\u6570\u636e\u3001\u5904\u7406\u5f02\u5e38\u503c\u7b49\u3002<\/p>\n<\/p>\n
2.1 \u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u7f3a\u5931\u503c\u662f\u6307\u6570\u636e\u96c6\u4e2d\u67d0\u4e9b\u6570\u636e\u70b9\u7f3a\u5931\u7684\u60c5\u51b5\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u68c0\u6d4b\u548c\u5904\u7406\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
# \u68c0\u6d4b\u7f3a\u5931\u503c<\/p>\n
print(data.isnull().sum())<\/p>\n
\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/strong><\/h2>\n
data = data.dropna()<\/p>\n
\u7528\u7279\u5b9a\u503c\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
data = data.fillna(0)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2 \u5904\u7406\u91cd\u590d\u6570\u636e<\/h4>\n<\/p>\n
\u91cd\u590d\u6570\u636e\u662f\u6307\u6570\u636e\u96c6\u4e2d\u5b58\u5728\u7684\u591a\u4f59\u8bb0\u5f55\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u68c0\u6d4b\u548c\u5220\u9664\u91cd\u590d\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u68c0\u6d4b\u91cd\u590d\u6570\u636e<\/p>\n
print(data.duplicated().sum())<\/p>\n
\u5220\u9664\u91cd\u590d\u6570\u636e<\/strong><\/h2>\n
data = data.drop_duplicates()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.3 \u5904\u7406\u5f02\u5e38\u503c<\/h4>\n<\/p>\n
\u5f02\u5e38\u503c\u662f\u6307\u6570\u636e\u96c6\u4e2d\u4e0e\u5176\u4ed6\u6570\u636e\u70b9\u660e\u663e\u4e0d\u540c\u7684\u503c\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u7edf\u8ba1\u65b9\u6cd5\u6216\u56fe\u5f62\u5316\u65b9\u6cd5\u6765\u68c0\u6d4b\u548c\u5904\u7406\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u4f7f\u7528Z-score\u65b9\u6cd5\u68c0\u6d4b\u5f02\u5e38\u503c<\/strong><\/h2>\n
data['z_score'] = (data['value'] - data['value'].mean()) \/ data['value'].std()<\/p>\n
data = data[np.abs(data['z_score']) < 3]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u63a2\u7d22\u6027\u5206\u6790\uff08EDA\uff09<\/h3>\n<\/p>\n
\u6570\u636e\u63a2\u7d22\u6027\u5206\u6790\uff08EDA\uff09\u662f\u901a\u8fc7\u7edf\u8ba1\u56fe\u8868\u548c\u6570\u636e\u603b\u7ed3\u6765\u7406\u89e3\u6570\u636e\u7684\u5206\u5e03\u3001\u5173\u7cfb\u548c\u8d8b\u52bf\u3002Pandas\u3001Matplotlib\u548cSeaborn\u662f\u5e38\u7528\u7684EDA\u5de5\u5177\u3002<\/p>\n<\/p>\n
3.1 \u6570\u636e\u603b\u7ed3<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u751f\u6210\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u4fe1\u606f\uff0c\u5982\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u6807\u51c6\u5dee\u7b49\u3002<\/p>\n<\/p>\n
# \u751f\u6210\u6570\u636e\u603b\u7ed3<\/p>\n
print(data.describe())<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316\u662f\u901a\u8fc7\u56fe\u8868\u6765\u5c55\u793a\u6570\u636e\u7684\u5206\u5e03\u548c\u5173\u7cfb\u3002Matplotlib\u548cSeaborn\u662f\u4e24\u4e2a\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
\u7ed8\u5236\u76f4\u65b9\u56fe<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
sns.histplot(data['value'], bins=30, kde=True)<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
sns.scatterplot(x='value1', y='value2', data=data)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6570\u636e\u5efa\u6a21<\/h3>\n<\/p>\n
\u6570\u636e\u5efa\u6a21\u662f\u6570\u636e\u5206\u6790\u7684\u6838\u5fc3\u6b65\u9aa4\uff0c\u901a\u8fc7\u5efa\u7acb\u6570\u5b66\u6a21\u578b\u6765\u63cf\u8ff0\u6570\u636e\u7684\u7279\u5f81\u548c\u5173\u7cfb\u3002\u5e38\u7528\u7684\u6570\u636e\u5efa\u6a21\u65b9\u6cd5\u5305\u62ec\u56de\u5f52\u5206\u6790\u3001\u5206\u7c7b\u5206\u6790\u3001\u805a\u7c7b\u5206\u6790\u7b49\u3002<\/p>\n<\/p>\n
4.1 \u56de\u5f52\u5206\u6790<\/h4>\n<\/p>\n
\u56de\u5f52\u5206\u6790\u662f\u7528\u4e8e\u9884\u6d4b\u6570\u503c\u578b\u76ee\u6807\u53d8\u91cf\u7684\u65b9\u6cd5\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Scikit-Learn\u5e93\u6765\u8fdb\u884c\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
from sklearn.metrics import mean_squared_error<\/p>\n
\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
X = data[['value1', 'value2']]<\/p>\n
y = data['target']<\/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\u5e76\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
mse = mean_squared_error(y_test, y_pred)<\/p>\n
print(f'Mean Squared Error: {mse}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2 \u5206\u7c7b\u5206\u6790<\/h4>\n<\/p>\n
\u5206\u7c7b\u5206\u6790\u662f\u7528\u4e8e\u9884\u6d4b\u7c7b\u522b\u578b\u76ee\u6807\u53d8\u91cf\u7684\u65b9\u6cd5\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Scikit-Learn\u5e93\u6765\u8fdb\u884c\u5206\u7c7b\u5206\u6790\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import train_test_split<\/p>\n
from sklearn.ensemble import RandomForestClassifier<\/p>\n
from sklearn.metrics import accuracy_score<\/p>\n
\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
X = data[['value1', 'value2']]<\/p>\n
y = data['target']<\/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\u968f\u673a\u68ee\u6797\u5206\u7c7b\u6a21\u578b<\/strong><\/h2>\n
model = RandomForestClassifier()<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b\u5e76\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
print(f'Accuracy: {accuracy}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u7684\u6700\u540e\u4e00\u6b65\uff0c\u901a\u8fc7\u56fe\u8868\u6765\u5c55\u793a\u6570\u636e\u5206\u6790\u7684\u7ed3\u679c\u3002Matplotlib\u548cSeaborn\u662f\u4e24\u4e2a\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u3002<\/p>\n<\/p>\n
5.1 \u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
\u6298\u7ebf\u56fe\u7528\u4e8e\u5c55\u793a\u6570\u636e\u968f\u65f6\u95f4\u53d8\u5316\u7684\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(data['date'], data['value'])<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Value')<\/p>\n
plt.title('Time Series Plot')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.2 \u70ed\u529b\u56fe<\/h4>\n<\/p>\n
\u70ed\u529b\u56fe\u7528\u4e8e\u5c55\u793a\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u3002<\/p>\n<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
sns.heatmap(data.corr(), annot=True, cmap='coolwarm')<\/p>\n
plt.title('Correlation Heatmap')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4ee5\u4e0a\u5c31\u662f\u5728Python\u4e2d\u8fdb\u884c\u6570\u636e\u5206\u6790\u7684\u57fa\u672c\u6b65\u9aa4\u548c\u65b9\u6cd5\u3002\u901a\u8fc7\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4ece\u6570\u636e\u4e2d\u63d0\u53d6\u6709\u4ef7\u503c\u7684\u4fe1\u606f\uff0c\u8f85\u52a9\u51b3\u7b56\u548c\u4f18\u5316\u4e1a\u52a1\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5f00\u59cb\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u5206\u6790\uff1f<\/strong>
\u8981\u5f00\u59cb\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u5206\u6790\uff0c\u60a8\u9700\u8981\u5b89\u88c5Python\u53ca\u5176\u5e38\u7528\u5e93\uff0c\u5982Pandas\u3001NumPy\u548cMatplotlib\u3002\u53ef\u4ee5\u901a\u8fc7Anaconda\u6216pip\u8f7b\u677e\u5b89\u88c5\u8fd9\u4e9b\u5e93\u3002\u5b66\u4e60\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5e93\u8fdb\u884c\u6570\u636e\u8bfb\u53d6\u3001\u6e05\u6d17\u3001\u5904\u7406\u548c\u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u7684\u57fa\u7840\u3002\u5728\u7ebf\u6559\u7a0b\u548c\u8bfe\u7a0b\u53ef\u4ee5\u5e2e\u52a9\u60a8\u5feb\u901f\u4e0a\u624b\u3002<\/p>\n
Python\u4e2d\u7684\u54ea\u4e9b\u5e93\u662f\u8fdb\u884c\u6570\u636e\u5206\u6790\u7684\u5fc5\u5907\u5de5\u5177\uff1f<\/strong>
\u8fdb\u884c\u6570\u636e\u5206\u6790\u65f6\uff0cPandas\u662f\u5904\u7406\u6570\u636e\u8868\u683c\u7684\u6838\u5fc3\u5e93\uff0cNumPy\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0cMatplotlib\u548cSeaborn\u5219\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u3002\u6b64\u5916\uff0cScikit-learn\u662f\u8fdb\u884c\u673a\u5668\u5b66\u4e60<\/a>\u7684\u5f3a\u5927\u5de5\u5177\uff0cStatsmodels\u5219\u9002\u7528\u4e8e\u7edf\u8ba1\u5206\u6790\u3002\u638c\u63e1\u8fd9\u4e9b\u5e93\u5c06\u6781\u5927\u63d0\u5347\u60a8\u7684\u6570\u636e\u5206\u6790\u80fd\u529b\u3002<\/p>\n
\u5982\u4f55\u5904\u7406\u7f3a\u5931\u6570\u636e\u548c\u5f02\u5e38\u503c\uff1f<\/strong>
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