{"id":1027181,"date":"2024-12-31T10:48:51","date_gmt":"2024-12-31T02:48:51","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1027181.html"},"modified":"2024-12-31T10:48:53","modified_gmt":"2024-12-31T02:48:53","slug":"python%e5%86%b3%e7%ad%96%e6%a0%91%e5%a6%82%e4%bd%95%e6%8f%90%e5%8f%96txt%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1027181.html","title":{"rendered":"python\u51b3\u7b56\u6811\u5982\u4f55\u63d0\u53d6txt\u6570\u636e"},"content":{"rendered":"

\"python\u51b3\u7b56\u6811\u5982\u4f55\u63d0\u53d6txt\u6570\u636e\"<\/p>\n

Python\u51b3\u7b56\u6811\u5982\u4f55\u63d0\u53d6txt\u6570\u636e<\/strong> <\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u4f7f\u7528\u51b3\u7b56\u6811\u7b97\u6cd5\u5904\u7406txt\u6570\u636e<\/strong>\u7684\u6b65\u9aa4\u4e3b\u8981\u5305\u62ec\uff1a\u8bfb\u53d6txt\u6587\u4ef6\u6570\u636e\u3001\u5904\u7406\u6570\u636e\u3001\u6784\u5efa\u51b3\u7b56\u6811\u6a21\u578b\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u9884\u6d4b\u4e0e\u8bc4\u4f30\u3002\u4e3a\u4e86\u8be6\u7ec6\u63cf\u8ff0\u5176\u4e2d\u4e00\u4e2a\u91cd\u8981\u6b65\u9aa4\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5904\u7406txt\u6587\u4ef6\u6570\u636e\u3002\u5904\u7406txt\u6587\u4ef6\u6570\u636e\u662f\u81f3\u5173\u91cd\u8981\u7684\u4e00\u6b65\uff0c\u56e0\u4e3a\u51b3\u7b56\u6811\u7b97\u6cd5\u9700\u8981\u7ed3\u6784\u5316\u7684\u6570\u636e\u8f93\u5165\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u8bfb\u53d6txt\u6587\u4ef6\u6570\u636e<\/h3>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684open()<\/code>\u51fd\u6570\u6216\u8005\u4f7f\u7528Pandas\u5e93\u6765\u8bfb\u53d6txt\u6587\u4ef6\u7684\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e24\u79cd\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n

1. \u4f7f\u7528open()\u51fd\u6570\u8bfb\u53d6txt\u6587\u4ef6<\/h4>\n<\/p>\n

# \u6253\u5f00\u5e76\u8bfb\u53d6txt\u6587\u4ef6<\/p>\n
with open('data.txt', 'r') as file:<\/p>\n
    data = file.readlines()<\/p>\n
\u6253\u5370\u8bfb\u53d6\u7684\u6570\u636e<\/strong><\/h2>\n
for line in data:<\/p>\n
    print(line.strip())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528Pandas\u8bfb\u53d6txt\u6587\u4ef6<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6txt\u6587\u4ef6\u5e76\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n
data = pd.read_csv('data.txt', delimiter='\\t')  # \u5047\u8bbe\u6570\u636e\u4ee5\u5236\u8868\u7b26\u5206\u9694<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5904\u7406\u6570\u636e<\/h3>\n<\/p>\n
\u5904\u7406\u6570\u636e\u662f\u5c06\u4ecetxt\u6587\u4ef6\u4e2d\u8bfb\u53d6\u7684\u6570\u636e\u8f6c\u6362\u4e3a\u9002\u5408\u51b3\u7b56\u6811\u6a21\u578b\u8f93\u5165\u7684\u683c\u5f0f\u3002\u8fd9\u901a\u5e38\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u7279\u5f81\u9009\u62e9\u548c\u7279\u5f81\u5de5\u7a0b\u3002<\/p>\n<\/p>\n
1. \u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u6570\u636e\u6e05\u6d17\u6b65\u9aa4\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u503c\u548c\u5f02\u5e38\u503c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
# \u5904\u7406\u7f3a\u5931\u503c<\/p>\n
data.fillna(data.mean(), inplace=True)  # \u7528\u5747\u503c\u586b\u5145\u7f3a\u5931\u503c<\/p>\n
\u5220\u9664\u91cd\u590d\u503c<\/strong><\/h2>\n
data.drop_duplicates(inplace=True)<\/p>\n
\u5904\u7406\u5f02\u5e38\u503c\uff08\u4f8b\u5982\uff0cZ-Score\u65b9\u6cd5\uff09<\/strong><\/h2>\n
from scipy import stats<\/p>\n
data = data[(np.abs(stats.zscore(data)) < 3).all(axis=1)]<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u7279\u5f81\u9009\u62e9<\/h4>\n<\/p>\n
\u7279\u5f81\u9009\u62e9\u662f\u9009\u62e9\u5bf9\u6a21\u578b\u8bad\u7ec3\u6709\u7528\u7684\u7279\u5f81\u3002\u53ef\u4ee5\u4f7f\u7528\u76f8\u5173\u6027\u5206\u6790\u6216\u7279\u5f81\u91cd\u8981\u6027\u6765\u9009\u62e9\u7279\u5f81\uff1a<\/p>\n<\/p>\n
# \u4f7f\u7528\u76f8\u5173\u6027\u5206\u6790\u9009\u62e9\u7279\u5f81<\/p>\n
correlation_matrix = data.corr()<\/p>\n
print(correlation_matrix)<\/p>\n
\u4f7f\u7528\u7279\u5f81\u91cd\u8981\u6027\u9009\u62e9\u7279\u5f81\uff08\u4f8b\u5982\uff0c\u57fa\u4e8e\u968f\u673a\u68ee\u6797\uff09<\/strong><\/h2>\n
from sklearn.ensemble import RandomForestClassifier<\/p>\n
X = data.drop('target', axis=1)<\/p>\n
y = data['target']<\/p>\n
model = RandomForestClassifier()<\/p>\n
model.fit(X, y)<\/p>\n
importances = model.feature_importances_<\/p>\n
\u6253\u5370\u7279\u5f81\u91cd\u8981\u6027<\/strong><\/h2>\n
feature_importance = pd.DataFrame({'feature': X.columns, 'importance': importances})<\/p>\n
print(feature_importance.sort_values(by='importance', ascending=False))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6784\u5efa\u51b3\u7b56\u6811\u6a21\u578b<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u5b8c\u6570\u636e\u4e4b\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Scikit-learn\u5e93\u6765\u6784\u5efa\u51b3\u7b56\u6811\u6a21\u578b\u3002\u4ee5\u4e0b\u662f\u6784\u5efa\u548c\u8bad\u7ec3\u51b3\u7b56\u6811\u6a21\u578b\u7684\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
from sklearn.tree import DecisionTreeClassifier<\/p>\n
\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/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
\u6784\u5efa\u51b3\u7b56\u6811\u6a21\u578b<\/strong><\/h2>\n
decision_tree = DecisionTreeClassifier()<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
decision_tree.fit(X_train, y_train)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6a21\u578b\u9884\u6d4b\u4e0e\u8bc4\u4f30<\/h3>\n<\/p>\n
\u5728\u6a21\u578b\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u9700\u8981\u4f7f\u7528\u6d4b\u8bd5\u96c6\u8fdb\u884c\u9884\u6d4b\uff0c\u5e76\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002\u4ee5\u4e0b\u662f\u9884\u6d4b\u548c\u8bc4\u4f30\u7684\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix<\/p>\n
\u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n
y_pred = decision_tree.predict(X_test)<\/p>\n
\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
print(f"Accuracy: {accuracy}")<\/p>\n
print("Classification Report:")<\/p>\n
print(classification_report(y_test, y_pred))<\/p>\n
print("Confusion Matrix:")<\/p>\n
print(confusion_matrix(y_test, y_pred))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4ecetxt\u6587\u4ef6\u4e2d\u63d0\u53d6\u6570\u636e\uff0c\u5e76\u4f7f\u7528\u51b3\u7b56\u6811\u7b97\u6cd5\u8fdb\u884c\u5efa\u6a21\u548c\u9884\u6d4b\u3002\u5173\u952e\u6b65\u9aa4\u5305\u62ec\u8bfb\u53d6txt\u6587\u4ef6\u6570\u636e\u3001\u5904\u7406\u6570\u636e\u3001\u6784\u5efa\u51b3\u7b56\u6811\u6a21\u578b\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u9884\u6d4b\u4e0e\u8bc4\u4f30\u3002\u8fd9\u4e9b\u6b65\u9aa4\u786e\u4fdd\u4e86\u6211\u4eec\u53ef\u4ee5\u6709\u6548\u5730\u4ece\u975e\u7ed3\u6784\u5316\u6570\u636e\u4e2d\u63d0\u53d6\u4fe1\u606f\uff0c\u5e76\u5e94\u7528\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u8fdb\u884c\u5206\u6790\u548c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8bfb\u53d6txt\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u4ee5\u4fbf\u8fdb\u884c\u51b3\u7b56\u6811\u5206\u6790\uff1f<\/strong>
\u8981\u8bfb\u53d6txt\u6587\u4ef6\u4e2d\u7684\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u5185\u7f6e\u7684open()<\/code>\u51fd\u6570\u6216pandas<\/code>\u5e93\u3002\u901a\u8fc7pandas<\/code>\u7684read_csv()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5bfc\u5165txt\u6587\u4ef6\u6570\u636e\uff0c\u7279\u522b\u662f\u5f53\u6570\u636e\u4ee5\u7279\u5b9a\u5206\u9694\u7b26\uff08\u5982\u9017\u53f7\u6216\u5236\u8868\u7b26\uff09\u683c\u5f0f\u5316\u65f6\u3002\u786e\u4fdd\u5728\u8bfb\u53d6\u6570\u636e\u540e\u5bf9\u5176\u8fdb\u884c\u6e05\u6d17\u548c\u5904\u7406\uff0c\u4ee5\u4fbf\u80fd\u591f\u6709\u6548\u5730\u7528\u4e8e\u51b3\u7b56\u6811\u6a21\u578b\u3002<\/p>\n
\u5728\u5904\u7406txt\u6570\u636e\u65f6\uff0c\u5982\u4f55\u786e\u4fdd\u6570\u636e\u7684\u8d28\u91cf\u548c\u5b8c\u6574\u6027\uff1f<\/strong>
\u6570\u636e\u8d28\u91cf\u548c\u5b8c\u6574\u6027\u662f\u51b3\u7b56\u6811\u5efa\u6a21\u6210\u529f\u7684\u5173\u952e\u3002\u53ef\u4ee5\u901a\u8fc7\u68c0\u67e5\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u4ee5\u53ca\u6570\u636e\u7c7b\u578b\u7684\u6b63\u786e\u6027\u6765\u786e\u4fdd\u6570\u636e\u7684\u8d28\u91cf\u3002\u4f7f\u7528pandas<\/code>\u7684isnull()<\/code>\u548cdescribe()<\/code>\u7b49\u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u548c\u5904\u7406\u8fd9\u4e9b\u95ee\u9898\u3002\u6570\u636e\u9884\u5904\u7406\u9636\u6bb5\u662f\u4e0d\u53ef\u5ffd\u89c6\u7684\uff0c\u9002\u5f53\u7684\u6e05\u7406\u548c\u8f6c\u6362\u5c06\u4e3a\u540e\u7eed\u6a21\u578b\u8bad\u7ec3\u6253\u4e0b\u575a\u5b9e\u57fa\u7840\u3002<\/p>\n
\u5982\u4f55\u5c06txt\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u8f6c\u6362\u4e3a\u51b3\u7b56\u6811\u6240\u9700\u7684\u7279\u5f81\u548c\u6807\u7b7e\u683c\u5f0f\uff1f<\/strong>
\u5728\u51b3\u7b56\u6811\u6a21\u578b\u4e2d\uff0c\u7279\u5f81\u548c\u6807\u7b7e\u7684\u6b63\u786e\u8bbe\u7f6e\u81f3\u5173\u91cd\u8981\u3002\u901a\u5e38\uff0c\u7279\u5f81\u662f\u6570\u636e\u96c6\u4e2d\u63cf\u8ff0\u5bf9\u8c61\u7684\u5c5e\u6027\uff0c\u800c\u6807\u7b7e\u662f\u76ee\u6807\u53d8\u91cf\u3002\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5c06txt\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u52a0\u8f7d\u4e3aDataFrame\uff0c\u4e4b\u540e\u901a\u8fc7\u9009\u62e9\u7279\u5b9a\u7684\u5217\u6765\u5206\u79bb\u7279\u5f81\u548c\u6807\u7b7e\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528DataFrame.iloc<\/code>\u65b9\u6cd5\u63d0\u53d6\u7279\u5f81\u5217\u548c\u6807\u7b7e\u5217\uff0c\u786e\u4fdd\u5b83\u4eec\u5728\u8bad\u7ec3\u6a21\u578b\u65f6\u6b63\u786e\u5bf9\u5e94\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u51b3\u7b56\u6811\u5982\u4f55\u63d0\u53d6txt\u6570\u636e \u5728Python\u4e2d\uff0c\u4f7f\u7528\u51b3\u7b56\u6811\u7b97\u6cd5\u5904\u7406txt\u6570\u636e\u7684\u6b65\u9aa4\u4e3b\u8981\u5305\u62ec\uff1a\u8bfb\u53d6txt […]","protected":false},"author":3,"featured_media":1027188,"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\/1027181"}],"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=1027181"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1027181\/revisions"}],"predecessor-version":[{"id":1027191,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1027181\/revisions\/1027191"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1027188"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1027181"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1027181"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1027181"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}