{"id":1102943,"date":"2025-01-08T16:05:14","date_gmt":"2025-01-08T08:05:14","guid":{"rendered":""},"modified":"2025-01-08T16:05:19","modified_gmt":"2025-01-08T08:05:19","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e6%b1%87%e6%80%bb%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\/1102943.html","title":{"rendered":"\u5982\u4f55\u7528python\u6c47\u603b\u8fdb\u884c\u6570\u636e\u5206\u6790"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u6c47\u603b\u8fdb\u884c\u6570\u636e\u5206\u6790\"<\/p>\n

\u7528Python\u6c47\u603b\u8fdb\u884c\u6570\u636e\u5206\u6790\u65f6\uff0c\u5e38\u7528\u7684\u5e93\u5305\u62ecPandas\u3001Numpy\u3001Matplotlib\u3001Seaborn\u3001Scipy\u7b49\uff0c\u5bfc\u5165\u6570\u636e\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u53d8\u6362\u3001\u6570\u636e\u53ef\u89c6\u5316\u662f\u4e3b\u8981\u6b65\u9aa4\u3002<\/strong> \u5176\u4e2d\uff0cPandas<\/strong> \u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u529f\u80fd\uff0c\u9002\u7528\u4e8e\u6570\u636e\u6e05\u6d17\u548c\u6570\u636e\u64cd\u4f5c\uff1bNumpy<\/strong> \u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\uff1bMatplotlib<\/strong> \u548c Seaborn<\/strong> \u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff1bScipy<\/strong> \u63d0\u4f9b\u4e86\u7edf\u8ba1\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u5de5\u5177\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u6b65\u9aa4\u53ca\u5176\u5e94\u7528\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u5bfc\u5165<\/h3>\n<\/p>\n

\u6570\u636e\u5bfc\u5165\u662f\u8fdb\u884c\u6570\u636e\u5206\u6790\u7684\u7b2c\u4e00\u6b65\uff0cPython \u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u5f0f\u6765\u5bfc\u5165\u4e0d\u540c\u683c\u5f0f\u7684\u6570\u636e\u3002\u6700\u5e38\u89c1\u7684\u683c\u5f0f\u5305\u62ec CSV\u3001Excel\u3001SQL \u6570\u636e\u5e93\u7b49\u3002<\/p>\n<\/p>\n

1.1 \u5bfc\u5165CSV\u6587\u4ef6<\/h4>\n<\/p>\n

CSV \u6587\u4ef6\u662f\u5e38\u89c1\u7684\u6570\u636e\u5b58\u50a8\u683c\u5f0f\u4e4b\u4e00\u3002\u53ef\u4ee5\u4f7f\u7528 Pandas \u5e93\u7684 read_csv<\/code> \u51fd\u6570\u6765\u5bfc\u5165 CSV \u6587\u4ef6\uff1a<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
df = pd.read_csv('data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u5bfc\u5165Excel\u6587\u4ef6<\/h4>\n<\/p>\n
Excel \u6587\u4ef6\u4e5f\u662f\u5e38\u7528\u7684\u6570\u636e\u5b58\u50a8\u683c\u5f0f\u3002\u53ef\u4ee5\u4f7f\u7528 Pandas \u5e93\u7684 read_excel<\/code> \u51fd\u6570\u6765\u5bfc\u5165 Excel \u6587\u4ef6\uff1a<\/p>\n<\/p>\n
df = pd.read_excel('data.xlsx', sheet_name='Sheet1')<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3 \u5bfc\u5165SQL\u6570\u636e\u5e93<\/h4>\n<\/p>\n
\u5982\u679c\u6570\u636e\u5b58\u50a8\u5728 SQL \u6570\u636e\u5e93\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528 SQLAlchemy \u6216\u5176\u4ed6\u6570\u636e\u5e93\u8fde\u63a5\u5e93\u6765\u5bfc\u5165\u6570\u636e\uff1a<\/p>\n<\/p>\n
from sqlalchemy import create_engine<\/p>\n
engine = create_engine('sqlite:\/\/\/data.db')<\/p>\n
df = pd.read_sql('SELECT * FROM table_name', engine)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u6e05\u6d17<\/h3>\n<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u4e4b\u524d\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\uff0c\u4ee5\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u6570\u636e\u548c\u5f02\u5e38\u503c\u7b49\u95ee\u9898\u3002<\/p>\n<\/p>\n
2.1 \u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u7f3a\u5931\u503c\u662f\u6570\u636e\u5206\u6790\u4e2d\u5e38\u89c1\u7684\u95ee\u9898\uff0c\u53ef\u4ee5\u4f7f\u7528 Pandas \u5e93\u7684 dropna<\/code> \u548c fillna<\/code> \u51fd\u6570\u6765\u5904\u7406\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n
# \u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c<\/p>\n
df.dropna(inplace=True)<\/p>\n
\u7528\u7279\u5b9a\u503c\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
df.fillna(0, inplace=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2 \u5904\u7406\u91cd\u590d\u6570\u636e<\/h4>\n<\/p>\n
\u91cd\u590d\u6570\u636e\u4e5f\u4f1a\u5f71\u54cd\u6570\u636e\u5206\u6790\u7684\u7ed3\u679c\uff0c\u53ef\u4ee5\u4f7f\u7528 Pandas \u5e93\u7684 drop_duplicates<\/code> \u51fd\u6570\u6765\u5220\u9664\u91cd\u590d\u6570\u636e\uff1a<\/p>\n<\/p>\n
df.drop_duplicates(inplace=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.3 \u5904\u7406\u5f02\u5e38\u503c<\/h4>\n<\/p>\n
\u5f02\u5e38\u503c\u901a\u5e38\u662f\u6570\u636e\u4e2d\u7684\u9519\u8bef\u503c\uff0c\u53ef\u4ee5\u901a\u8fc7\u7edf\u8ba1\u65b9\u6cd5\u6765\u68c0\u6d4b\u548c\u5904\u7406\u5f02\u5e38\u503c\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97z-score<\/p>\n
from scipy import stats<\/p>\n
df = df[(np.abs(stats.zscore(df)) < 3).all(axis=1)]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u53d8\u6362<\/h3>\n<\/p>\n
\u6570\u636e\u53d8\u6362\u662f\u5c06\u539f\u59cb\u6570\u636e\u8f6c\u6362\u6210\u9002\u5408\u5206\u6790\u7684\u5f62\u5f0f\uff0c\u5305\u62ec\u6570\u636e\u6807\u51c6\u5316\u3001\u5f52\u4e00\u5316\u3001\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u7b49\u3002<\/p>\n<\/p>\n
3.1 \u6570\u636e\u6807\u51c6\u5316<\/h4>\n<\/p>\n
\u6570\u636e\u6807\u51c6\u5316\u662f\u5c06\u6570\u636e\u8f6c\u6362\u6210\u5747\u503c\u4e3a0\uff0c\u65b9\u5dee\u4e3a1\u7684\u6807\u51c6\u6b63\u6001\u5206\u5e03\uff1a<\/p>\n<\/p>\n
from sklearn.preprocessing import StandardScaler<\/p>\n
scaler = StandardScaler()<\/p>\n
df_scaled = scaler.fit_transform(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u6570\u636e\u5f52\u4e00\u5316<\/h4>\n<\/p>\n
\u6570\u636e\u5f52\u4e00\u5316\u662f\u5c06\u6570\u636e\u7f29\u653e\u5230\u7279\u5b9a\u7684\u533a\u95f4\uff08\u901a\u5e38\u662f0\u52301\uff09\uff1a<\/p>\n<\/p>\n
from sklearn.preprocessing import MinMaxScaler<\/p>\n
scaler = MinMaxScaler()<\/p>\n
df_normalized = scaler.fit_transform(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.3 \u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/h4>\n<\/p>\n
\u6709\u65f6\u9700\u8981\u5c06\u6570\u636e\u7c7b\u578b\u8fdb\u884c\u8f6c\u6362\uff0c\u4f8b\u5982\u5c06\u5b57\u7b26\u4e32\u7c7b\u578b\u8f6c\u6362\u4e3a\u6570\u503c\u7c7b\u578b\uff1a<\/p>\n<\/p>\n
df['column_name'] = pd.to_numeric(df['column_name'], errors='coerce')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6570\u636e\u5206\u6790<\/h3>\n<\/p>\n
\u6570\u636e\u5206\u6790\u662f\u5bf9\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3001\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\uff08EDA\uff09\u548c\u6a21\u578b\u6784\u5efa\u7b49\u3002<\/p>\n<\/p>\n
4.1 \u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790<\/h4>\n<\/p>\n
\u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790\u662f\u8ba1\u7b97\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u91cf\uff0c\u5982\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u6807\u51c6\u5dee\u7b49\uff1a<\/p>\n<\/p>\n
df.describe()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2 \u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\uff08EDA\uff09<\/h4>\n<\/p>\n
\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\u662f\u901a\u8fc7\u56fe\u8868\u548c\u7edf\u8ba1\u91cf\u6765\u521d\u6b65\u4e86\u89e3\u6570\u636e\u7684\u7279\u5f81\u548c\u6a21\u5f0f\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
\u7ed8\u5236\u5206\u5e03\u56fe<\/strong><\/h2>\n
sns.histplot(df['column_name'])<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
sns.scatterplot(x='column_x', y='column_y', data=df)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.3 \u76f8\u5173\u6027\u5206\u6790<\/h4>\n<\/p>\n
\u76f8\u5173\u6027\u5206\u6790\u662f\u8ba1\u7b97\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u53ef\u4ee5\u4f7f\u7528 Pandas \u5e93\u7684 corr<\/code> \u51fd\u6570\uff1a<\/p>\n<\/p>\n
df.corr()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316\u662f\u5c06\u6570\u636e\u901a\u8fc7\u56fe\u5f62\u7684\u65b9\u5f0f\u5c55\u793a\u51fa\u6765\uff0c\u5e2e\u52a9\u7406\u89e3\u6570\u636e\u7684\u6a21\u5f0f\u548c\u7279\u5f81\u3002<\/p>\n<\/p>\n
5.1 \u4f7f\u7528Matplotlib\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
Matplotlib \u662f\u4e00\u4e2a\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.plot(df['column_name'])<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\n
plt.bar(df['column_x'], df['column_y'])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.2 \u4f7f\u7528Seaborn\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
Seaborn \u662f\u57fa\u4e8e Matplotlib \u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u548c\u7f8e\u89c2\u7684\u56fe\u8868\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
sns.boxplot(x='column_name', data=df)<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u70ed\u529b\u56fe<\/strong><\/h2>\n
sns.heatmap(df.corr(), annot=True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u6570\u636e\u5efa\u6a21<\/h3>\n<\/p>\n
\u6570\u636e\u5efa\u6a21\u662f\u901a\u8fc7\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u6784\u5efa\u9884\u6d4b\u6a21\u578b\u548c\u5206\u7c7b\u6a21\u578b\u3002<\/p>\n<\/p>\n
6.1 \u6570\u636e\u5206\u5272<\/h4>\n<\/p>\n
\u5728\u8fdb\u884c\u6570\u636e\u5efa\u6a21\u4e4b\u524d\uff0c\u901a\u5e38\u9700\u8981\u5c06\u6570\u636e\u5206\u5272\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff1a<\/p>\n<\/p>\n
from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
X = df.drop('target', axis=1)<\/p>\n
y = df['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
<\/code><\/pre>\n<\/p>\n
6.2 \u9009\u62e9\u548c\u8bad\u7ec3\u6a21\u578b<\/h4>\n<\/p>\n
\u9009\u62e9\u5408\u9002\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u5e76\u8bad\u7ec3\u6a21\u578b\uff1a<\/p>\n<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
model = LinearRegression()<\/p>\n
model.fit(X_train, y_train)<\/p>\n
<\/code><\/pre>\n<\/p>\n
6.3 \u6a21\u578b\u8bc4\u4f30<\/h4>\n<\/p>\n
\u4f7f\u7528\u6d4b\u8bd5\u96c6\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\uff1a<\/p>\n<\/p>\n
from sklearn.metrics import mean_squared_error<\/p>\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
\u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 Python \u5bf9\u6570\u636e\u8fdb\u884c\u5168\u9762\u7684\u5206\u6790\u3002\u9996\u5148\u5bfc\u5165\u6570\u636e\uff0c\u7136\u540e\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u548c\u53d8\u6362\uff0c\u63a5\u7740\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\uff0c\u6700\u540e\u901a\u8fc7\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u8fdb\u884c\u6570\u636e\u5efa\u6a21\u548c\u8bc4\u4f30\u3002Python \u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5e93\u548c\u5de5\u5177\uff0c\u5e2e\u52a9\u6211\u4eec\u9ad8\u6548\u5730\u5b8c\u6210\u6570\u636e\u5206\u6790\u4efb\u52a1\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u9009\u62e9\u9002\u5408\u7684\u6570\u636e\u5206\u6790\u5e93\u6765\u8fdb\u884cPython\u6c47\u603b\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u6700\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\u5305\u62ecPandas\u3001NumPy\u548cMatplotlib\u3002Pandas\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u7279\u522b\u9002\u5408\u5904\u7406\u8868\u683c\u6570\u636e\u3002NumPy\u5219\u4e3b\u8981\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0c\u9002\u5408\u8fdb\u884c\u9ad8\u6548\u7684\u6570\u7ec4\u548c\u77e9\u9635\u64cd\u4f5c\u3002Matplotlib\u5219\u662f\u4e00\u4e2a\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u5e2e\u52a9\u4f60\u53ef\u89c6\u5316\u6570\u636e\u3002\u6839\u636e\u4f60\u7684\u9700\u6c42\uff0c\u53ef\u4ee5\u9009\u62e9\u5408\u9002\u7684\u5e93\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002<\/p>\n
\u5bf9\u4e8e\u521d\u5b66\u8005\uff0c\u5982\u4f55\u5feb\u901f\u5165\u95e8Python\u6570\u636e\u5206\u6790\u6c47\u603b\uff1f<\/strong>
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