{"id":1018368,"date":"2024-12-27T12:41:40","date_gmt":"2024-12-27T04:41:40","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1018368.html"},"modified":"2024-12-27T12:41:43","modified_gmt":"2024-12-27T04:41:43","slug":"python-%e5%a6%82%e4%bd%95%e5%a4%84%e7%90%86%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1018368.html","title":{"rendered":"python \u5982\u4f55\u5904\u7406\u6570\u636e"},"content":{"rendered":"

\"python<\/p>\n

\u5728Python\u4e2d\u5904\u7406\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0\uff0c\u4e3b\u8981\u4f9d\u8d56\u4e8ePython\u4e30\u5bcc\u7684\u5e93\u548c\u7075\u6d3b\u7684\u7f16\u7a0b\u80fd\u529b\u3002\u6570\u636e\u7684\u8bfb\u53d6\u4e0e\u5199\u5165\u3001\u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406\u3001\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316\u3001\u673a\u5668\u5b66\u4e60<\/a>\u4e0e\u9884\u6d4b<\/strong>\u662fPython\u5904\u7406\u6570\u636e\u7684\u51e0\u4e2a\u91cd\u8981\u73af\u8282\u3002\u6570\u636e\u7684\u8bfb\u53d6\u4e0e\u5199\u5165\u662f\u6570\u636e\u5904\u7406\u7684\u57fa\u7840\uff0c\u901a\u5e38\u4f7f\u7528pandas\u5e93\u6765\u8bfb\u53d6CSV\u3001Excel\u7b49\u683c\u5f0f\u7684\u6570\u636e\uff1b\u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406\u5305\u62ec\u7f3a\u5931\u503c\u5904\u7406\u3001\u6570\u636e\u8f6c\u6362\u7b49\uff1b\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316\u53ef\u4ee5\u901a\u8fc7pandas\u3001matplotlib\u3001seaborn\u7b49\u5e93\u8fdb\u884c\uff1b\u6700\u540e\uff0c\u673a\u5668\u5b66\u4e60\u4e0e\u9884\u6d4b\u5219\u5229\u7528scikit-learn\u7b49\u5e93\u5b9e\u73b0\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u7684\u8bfb\u53d6\u4e0e\u5199\u5165<\/h3>\n<\/p>\n

Python\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u8bfb\u53d6\u4e0e\u5199\u5165\u5e93\u662fpandas\uff0c\u5b83\u80fd\u591f\u5904\u7406\u591a\u79cd\u6570\u636e\u683c\u5f0f\u5982CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u3002\u901a\u8fc7pandas\u7684read_csv()<\/code>\u3001read_excel()<\/code>\u7b49\u51fd\u6570\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u6570\u636e\u5bfc\u5165Python\u73af\u5883\u4e2d\u8fdb\u884c\u5904\u7406\u3002<\/p>\n<\/p>\n

1.1 \u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n

CSV\uff08Comma Separated Values\uff09\u662f\u6700\u5e38\u89c1\u7684\u6570\u636e\u5b58\u50a8\u683c\u5f0f\u4e4b\u4e00\uff0cpandas\u63d0\u4f9b\u4e86read_csv()<\/code>\u51fd\u6570\u7528\u4e8e\u8bfb\u53d6CSV\u6587\u4ef6\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528read_csv()<\/code>\u51fd\u6570\u7684\u57fa\u672c\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
data = pd.read_csv('data.csv')<\/p>\n
\u67e5\u770b\u524d\u4e94\u884c\u6570\u636e<\/strong><\/h2>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be5\u65b9\u6cd5\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u53c2\u6570\u6765\u5904\u7406\u4e0d\u540c\u683c\u5f0f\u7684CSV\u6587\u4ef6\uff0c\u4f8b\u5982\u6307\u5b9a\u5206\u9694\u7b26\u3001\u5904\u7406\u7f3a\u5931\u503c\u7b49\u3002<\/p>\n<\/p>\n
1.2 \u8bfb\u53d6Excel\u6587\u4ef6<\/h4>\n<\/p>\n
Excel\u6587\u4ef6\u901a\u5e38\u7528\u4e8e\u5b58\u50a8\u7ed3\u6784\u5316\u7684\u6570\u636e\uff0cpandas\u63d0\u4f9b\u4e86read_excel()<\/code>\u51fd\u6570\u7528\u4e8e\u8bfb\u53d6Excel\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
# \u8bfb\u53d6Excel\u6587\u4ef6<\/p>\n
data = pd.read_excel('data.xlsx', sheet_name='Sheet1')<\/p>\n
\u67e5\u770b\u524d\u4e94\u884c\u6570\u636e<\/strong><\/h2>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8bfb\u53d6Excel\u6587\u4ef6\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9asheet_name<\/code>\u53c2\u6570\u6765\u9009\u62e9\u9700\u8981\u8bfb\u53d6\u7684\u5de5\u4f5c\u8868\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406<\/h3>\n<\/p>\n
\u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406\u662f\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\u5fc5\u4e0d\u53ef\u5c11\u7684\u6b65\u9aa4\uff0c\u5b83\u4e3b\u8981\u5305\u62ec\u7f3a\u5931\u503c\u5904\u7406\u3001\u6570\u636e\u8f6c\u6362\u3001\u5f02\u5e38\u503c\u5904\u7406\u7b49\u3002<\/p>\n<\/p>\n
2.1 \u7f3a\u5931\u503c\u5904\u7406<\/h4>\n<\/p>\n
\u7f3a\u5931\u503c\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e38\u89c1\u95ee\u9898\uff0cpandas\u63d0\u4f9b\u4e86\u4e00\u4e9b\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\uff0c\u5982dropna()<\/code>\u548cfillna()<\/code>\u3002<\/p>\n<\/p>\n
# \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c<\/p>\n
clean_data = data.dropna()<\/p>\n
\u7528\u6307\u5b9a\u503c\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
filled_data = data.fillna(value=0)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u5e38\uff0c\u7f3a\u5931\u503c\u53ef\u4ee5\u901a\u8fc7\u5220\u9664\u3001\u586b\u5145\u7b49\u65b9\u5f0f\u5904\u7406\uff0c\u5177\u4f53\u9009\u62e9\u54ea\u79cd\u65b9\u5f0f\u9700\u8981\u6839\u636e\u6570\u636e\u7684\u7279\u6027\u548c\u5206\u6790\u7684\u9700\u6c42\u6765\u51b3\u5b9a\u3002<\/p>\n<\/p>\n
2.2 \u6570\u636e\u8f6c\u6362<\/h4>\n<\/p>\n
\u6570\u636e\u8f6c\u6362\u5305\u62ec\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u3001\u6807\u51c6\u5316\u3001\u5f52\u4e00\u5316\u7b49\u6b65\u9aa4\uff0c\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u5206\u6790\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/p>\n
data['column_name'] = data['column_name'].astype('int')<\/p>\n
\u6570\u636e\u6807\u51c6\u5316<\/strong><\/h2>\n
from sklearn.preprocessing import StandardScaler<\/p>\n
scaler = StandardScaler()<\/p>\n
standardized_data = scaler.fit_transform(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6570\u636e\u8f6c\u6362\u53ef\u4ee5\u6539\u5584\u6570\u636e\u7684\u5206\u5e03\u7279\u6027\uff0c\u4f7f\u5f97\u540e\u7eed\u7684\u5206\u6790\u66f4\u4e3a\u51c6\u786e\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316\u662f\u6570\u636e\u79d1\u5b66\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u90e8\u5206\uff0c\u5b83\u5e2e\u52a9\u6211\u4eec\u4ece\u6570\u636e\u4e2d\u63d0\u53d6\u4fe1\u606f\u5e76\u8fdb\u884c\u89e3\u91ca\u3002<\/p>\n<\/p>\n
3.1 \u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
\u6570\u636e\u5206\u6790\u53ef\u4ee5\u901a\u8fc7pandas\u8fdb\u884c\u5feb\u901f\u7684\u7edf\u8ba1\u5206\u6790\uff0c\u5982\u63cf\u8ff0\u6027\u7edf\u8ba1\u3001\u76f8\u5173\u6027\u5206\u6790\u7b49\u3002<\/p>\n<\/p>\n
# \u63cf\u8ff0\u6027\u7edf\u8ba1<\/p>\n
print(data.describe())<\/p>\n
\u76f8\u5173\u6027\u5206\u6790<\/strong><\/h2>\n
print(data.corr())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8fd9\u4e9b\u5206\u6790\u53ef\u4ee5\u521d\u6b65\u4e86\u89e3\u6570\u636e\u7684\u5206\u5e03\u7279\u6027\u548c\u53d8\u91cf\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n<\/p>\n
3.2 \u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316\u53ef\u4ee5\u901a\u8fc7matplotlib\u548cseaborn\u7b49\u5e93\u6765\u5b9e\u73b0\uff0c\u5e2e\u52a9\u6211\u4eec\u76f4\u89c2\u5730\u7406\u89e3\u6570\u636e\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
sns.scatterplot(x='column_x', y='column_y', data=data)<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u70ed\u529b\u56fe<\/strong><\/h2>\n
sns.heatmap(data.corr(), annot=True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u53ef\u89c6\u5316\uff0c\u590d\u6742\u7684\u6570\u636e\u5173\u7cfb\u53ef\u4ee5\u901a\u8fc7\u56fe\u5f62\u5316\u7684\u65b9\u5f0f\u5f97\u4ee5\u5c55\u73b0\u3002<\/p>\n<\/p>\n
\u56db\u3001\u673a\u5668\u5b66\u4e60\u4e0e\u9884\u6d4b<\/h3>\n<\/p>\n
\u5728\u6570\u636e\u5904\u7406\u7684\u6700\u540e\u9636\u6bb5\uff0c\u901a\u5e38\u4f1a\u6d89\u53ca\u5230\u673a\u5668\u5b66\u4e60\u4e0e\u9884\u6d4b\uff0cscikit-learn\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u5e93\u3002<\/p>\n<\/p>\n
4.1 \u6570\u636e\u5206\u5272<\/h4>\n<\/p>\n
\u5728\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u4e4b\u524d\uff0c\u901a\u5e38\u9700\u8981\u5c06\u6570\u636e\u96c6\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u4ee5\u4fbf\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
X = data.drop('target', axis=1)<\/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
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7train_test_split()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u6570\u636e\u96c6\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u3002<\/p>\n<\/p>\n
4.2 \u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30<\/h4>\n<\/p>\n
\u5728\u8bad\u7ec3\u6a21\u578b\u65f6\uff0c\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u7b97\u6cd5\uff0c\u5982\u7ebf\u6027\u56de\u5f52\u3001\u51b3\u7b56\u6811\u3001\u968f\u673a\u68ee\u6797\u7b49\u3002\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u4f7f\u7528\u6d4b\u8bd5\u96c6\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/p>\n<\/p>\n
from sklearn.ensemble import RandomForestClassifier<\/p>\n
from sklearn.metrics import accuracy_score<\/p>\n
\u6a21\u578b\u8bad\u7ec3<\/strong><\/h2>\n
model = RandomForestClassifier()<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u6a21\u578b\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
\u6a21\u578b\u8bc4\u4f30<\/strong><\/h2>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
print(f"Accuracy: {accuracy}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u548c\u53c2\u6570\uff0c\u80fd\u591f\u63d0\u9ad8\u9884\u6d4b\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5b9e\u8df5\u6848\u4f8b<\/h3>\n<\/p>\n
\u4e0b\u9762\u6211\u4eec\u901a\u8fc7\u4e00\u4e2a\u7b80\u5355\u7684\u6848\u4f8b\u6765\u5c55\u793a\u5982\u4f55\u5728Python\u4e2d\u5904\u7406\u6570\u636e\uff0c\u6848\u4f8b\u4e2d\u5c06\u6db5\u76d6\u6570\u636e\u8bfb\u53d6\u3001\u6e05\u6d17\u3001\u5206\u6790\u3001\u53ef\u89c6\u5316\u548c\u673a\u5668\u5b66\u4e60\u3002<\/p>\n<\/p>\n
5.1 \u6848\u4f8b\u80cc\u666f<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5173\u4e8e\u623f\u4ef7\u7684\u6570\u636e\u96c6\uff0c\u6211\u4eec\u7684\u76ee\u6807\u662f\u901a\u8fc7\u591a\u4e2a\u5f71\u54cd\u56e0\u7d20\u6765\u9884\u6d4b\u623f\u4ef7\u3002<\/p>\n<\/p>\n
5.2 \u6570\u636e\u8bfb\u53d6\u4e0e\u9884\u5904\u7406<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u8bfb\u53d6\u6570\u636e\uff0c\u5e76\u8fdb\u884c\u5fc5\u8981\u7684\u6e05\u6d17\u4e0e\u9884\u5904\u7406\u3002<\/p>\n<\/p>\n
# \u8bfb\u53d6\u6570\u636e<\/p>\n
data = pd.read_csv('housing.csv')<\/p>\n
\u67e5\u770b\u6570\u636e\u57fa\u672c\u4fe1\u606f<\/strong><\/h2>\n
print(data.info())<\/p>\n
\u5904\u7406\u7f3a\u5931\u503c<\/strong><\/h2>\n
data = data.fillna(data.mean())<\/p>\n
\u6570\u636e\u8f6c\u6362<\/strong><\/h2>\n
data['ocean_proximity'] = data['ocean_proximity'].astype('category').cat.codes<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e00\u6b65\uff0c\u6211\u4eec\u586b\u5145\u4e86\u7f3a\u5931\u503c\uff0c\u5e76\u5c06\u7c7b\u522b\u53d8\u91cf\u8f6c\u6362\u4e3a\u6570\u503c\u578b\u3002<\/p>\n<\/p>\n
5.3 \u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
\u901a\u8fc7\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u7279\u6027\u3002<\/p>\n<\/p>\n
# \u63cf\u8ff0\u6027\u7edf\u8ba1<\/p>\n
print(data.describe())<\/p>\n
\u53ef\u89c6\u5316\u623f\u4ef7\u5206\u5e03<\/strong><\/h2>\n
sns.histplot(data['median_house_value'], bins=30)<\/p>\n
plt.show()<\/p>\n
\u76f8\u5173\u6027\u70ed\u529b\u56fe<\/strong><\/h2>\n
sns.heatmap(data.corr(), annot=True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e9b\u6b65\u9aa4\u5e2e\u52a9\u6211\u4eec\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u6a21\u5f0f\u548c\u5173\u7cfb\u3002<\/p>\n<\/p>\n
5.4 \u6a21\u578b\u8bad\u7ec3\u4e0e\u9884\u6d4b<\/h4>\n<\/p>\n
\u6700\u540e\uff0c\u6211\u4eec\u9009\u62e9\u4e00\u4e2a\u5408\u9002\u7684\u6a21\u578b\u6765\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
# \u7279\u5f81\u4e0e\u6807\u7b7e<\/p>\n
X = data.drop('median_house_value', axis=1)<\/p>\n
y = data['median_house_value']<\/p>\n
\u6570\u636e\u5206\u5272<\/strong><\/h2>\n
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
\u6a21\u578b\u8bad\u7ec3<\/strong><\/h2>\n
from sklearn.ensemble import RandomForestRegressor<\/p>\n
model = RandomForestRegressor()<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u6a21\u578b\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
\u6a21\u578b\u8bc4\u4f30<\/strong><\/h2>\n
from sklearn.metrics import mean_squared_error<\/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
\u901a\u8fc7\u8fd9\u4e00\u6b65\uff0c\u6211\u4eec\u5b8c\u6210\u4e86\u4ece\u6570\u636e\u8bfb\u53d6\u5230\u6700\u7ec8\u9884\u6d4b\u7684\u5b8c\u6574\u6d41\u7a0b\u3002<\/p>\n<\/p>\n
\u603b\u7ed3\u6765\u8bf4\uff0cPython\u5728\u6570\u636e\u5904\u7406\u65b9\u9762\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5de5\u5177\u548c\u7075\u6d3b\u7684\u65b9\u6cd5\uff0c\u4ece\u6570\u636e\u7684\u8bfb\u53d6\u3001\u6e05\u6d17\u3001\u5206\u6790\u5230\u673a\u5668\u5b66\u4e60\u7684\u5e94\u7528\uff0c\u65e0\u4e0d\u5c55\u73b0\u51fa\u5176\u5728\u6570\u636e\u79d1\u5b66\u9886\u57df\u7684\u5e7f\u6cdb\u5e94\u7528\u3002\u901a\u8fc7\u4e0d\u65ad\u7684\u5b9e\u8df5\u548c\u63a2\u7d22\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u5229\u7528Python\u6765\u89e3\u51b3\u590d\u6742\u7684\u6570\u636e\u95ee\u9898\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5bfc\u5165\u6570\u636e\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u5e93\u6765\u5bfc\u5165\u6570\u636e\uff0c\u4f8b\u5982Pandas\u3001NumPy\u548cCSV\u6a21\u5757\u3002Pandas\u662f\u5904\u7406\u6570\u636e\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528pd.read_csv()<\/code>\u51fd\u6570\u8f7b\u677e\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u800c\u4f7f\u7528pd.read_excel()<\/code>\u5219\u53ef\u4ee5\u8bfb\u53d6Excel\u6587\u4ef6\u3002\u5bf9\u4e8e\u5927\u578b\u6570\u636e\u96c6\uff0c\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528Dask\u5e93\uff0c\u5b83\u652f\u6301\u5904\u7406\u8d85\u51fa\u5185\u5b58\u9650\u5236\u7684\u6570\u636e\u96c6\u3002<\/p>\n
\u5982\u4f55\u6e05\u6d17\u548c\u9884\u5904\u7406\u6570\u636e\uff1f<\/strong>
\u6570\u636e\u6e05\u6d17\u548c\u9884\u5904\u7406\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528Pandas\u4e2d\u7684dropna()<\/code>\u65b9\u6cd5\u5220\u9664\u7f3a\u5931\u503c\uff0c\u6216\u8005\u4f7f\u7528fillna()<\/code>\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\u3002\u6b64\u5916\uff0c\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528astype()<\/code>\u6765\u8f6c\u6362\u6570\u636e\u7c7b\u578b\uff0c\u786e\u4fdd\u6570\u636e\u683c\u5f0f\u4e00\u81f4\u3002\u5b57\u7b26\u4e32\u5904\u7406\u65b9\u9762\uff0cPandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\uff0c\u6bd4\u5982str.replace()<\/code>\u548cstr.lower()<\/code>\uff0c\u53ef\u4ee5\u5e2e\u52a9\u60a8\u8fdb\u884c\u6570\u636e\u6e05\u7406\u3002<\/p>\n
\u5982\u4f55\u5728Python\u4e2d\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff1f<\/strong>
\u6570\u636e\u53ef\u89c6\u5316\u662f\u7406\u89e3\u6570\u636e\u7684\u91cd\u8981\u65b9\u5f0f\u3002\u5728Python\u4e2d\uff0cMatplotlib\u548cSeaborn\u662f\u5e38\u7528\u7684\u53ef\u89c6\u5316\u5e93\u3002\u4f7f\u7528Matplotlib\uff0c\u60a8\u53ef\u4ee5\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5305\u62ec\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u548c\u6563\u70b9\u56fe\u3002Seaborn\u5219\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u7edf\u8ba1\u56fe\u8868\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7sns.barplot()<\/code>\u6216sns.boxplot()<\/code>\u8f7b\u677e\u751f\u6210\u76f8\u5173\u56fe\u5f62\u3002\u6b64\u5916\uff0cPlotly\u4e5f\u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u529f\u80fd\uff0c\u975e\u5e38\u9002\u5408\u5c55\u793a\u590d\u6742\u6570\u636e\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u5904\u7406\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0\uff0c\u4e3b\u8981\u4f9d\u8d56\u4e8ePython\u4e30\u5bcc\u7684\u5e93\u548c\u7075\u6d3b\u7684\u7f16\u7a0b\u80fd\u529b\u3002\u6570\u636e\u7684\u8bfb\u53d6\u4e0e\u5199\u5165 […]","protected":false},"author":3,"featured_media":1018375,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1018368"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1018368"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1018368\/revisions"}],"predecessor-version":[{"id":1018376,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1018368\/revisions\/1018376"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1018375"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1018368"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1018368"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1018368"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}