{"id":986,"date":"2024-09-21T22:48:34","date_gmt":"2024-09-21T14:48:34","guid":{"rendered":"https:\/\/blog.xuxiny.top\/?p=986"},"modified":"2024-10-01T12:08:04","modified_gmt":"2024-10-01T04:08:04","slug":"%e3%80%90pandas%e5%ae%9e%e6%88%98%e3%80%91%e7%bb%9f%e8%ae%a1%e6%9c%8d%e5%8a%a1%e5%99%a8cpu-gpu%e6%b8%a9%e5%ba%a6%e6%83%85%e5%86%b5","status":"publish","type":"post","link":"https:\/\/blog.xuxiny.top\/index.php\/2024\/09\/21\/986\/","title":{"rendered":"\u3010Pandas\u5b9e\u6218\u3011\u7edf\u8ba1\u670d\u52a1\u5668CPU\/GPU\u6e29\u5ea6\u60c5\u51b5"},"content":{"rendered":"\n

\u5728\u6574\u4e2a\u9ad8\u4e2d\u9636\u6bb5\u7684\u4fe1\u606f\u6280\u672f\u5b66\u4e60\u4e2d\uff0c\u6211\u5df2\u7ecf\u505a\u4e86\u4e0d\u77e5\u9053\u591a\u5c11Pandas\u7684\u9898\u76ee\u4e86\uff0c\u4e0d\u8fc7\u6211\u53d1\u73b0\u6211\u4f3c\u4e4e\u4ece\u6765\u6ca1\u7528\u8fc7Pandas\uff0c\u6b63\u597d\u6700\u8fd1\u6709\u8fd9\u4e2a\u9700\u6c42\uff0c\u6240\u4ee5\u5c31\u8d81\u8fd9\u4e2a\u673a\u4f1a\u5b9e\u6218\u4e86\u4e00\u6b21<\/p>\n\n\n\n

\u8d77\u56e0\u662f\u88ab\u6211\u62ff\u6765\u505a\u5bb6\u5ead\u670d\u52a1\u5668\u7684Linux\u5f00\u53d1\u677f\u7ecf\u5e38\u81ea\u52a8\u5173\u673a\uff0c\u67e5\u4e86\u65e5\u5fd7\u4e5f\u770b\u4e0d\u51fa\u4ec0\u4e48\u5f02\u5e38\uff0c\u6240\u4ee5\u6211\u8ba9ChatGPT<\/span>\u5199\u4e86\u4e2aPython\u811a\u672c\u8bb0\u5f55CPU\/GPU\u7684\u6e29\u5ea6\u60c5\u51b5\uff0c\u540c\u65f6\u4e5f\u8bb0\u5f55\u4e86CPU\u5360\u7528\u7387\u3002<\/p>\n\n\n\n

import csv\nimport psutil\nimport schedule\nimport time\nfrom datetime import datetime\n\n# Function to read temperature\ndef read_temp(file_path):\n    try:\n        with open(file_path, 'r') as file:\n            temp = round(int(file.read()) \/ 1000.0, 2)  # Convert millidegrees Celsius to Celsius\n            return temp\n    except FileNotFoundError:\n        return None\n\n# Function to log CPU, GPU temperatures and CPU load\ndef log_temperatures_and_load():\n    # Read CPU and GPU temperatures\n    cpu_temp = read_temp(\"\/sys\/class\/thermal\/thermal_zone0\/temp\")\n    gpu_temp = read_temp(\"\/sys\/class\/thermal\/thermal_zone1\/temp\")\n\n    # Get CPU load (average over 1, 5, and 15 minutes)\n    cpu_load = psutil.getloadavg()\n\n    # Get the current timestamp\n    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n\n    # File path to save the data\n    csv_file = 'temp&load.csv'\n\n    # Write the data to a CSV file\n    with open(csv_file, mode='a', newline='') as file:\n        writer = csv.writer(file)\n\n        # Write the header if the file is empty\n        if file.tell() == 0:\n            writer.writerow(['Timestamp', 'CPU Temperature (\u00b0C)', 'GPU Temperature (\u00b0C)',\n                             'CPU Load (1 min)'])\n\n        # Write the temperature and CPU load data\n        writer.writerow([timestamp,\n                         cpu_temp if cpu_temp is not None else 'N\/A',\n                         gpu_temp if gpu_temp is not None else 'N\/A',\n                         round(cpu_load[0],2)])\n\n    print(f\"Data written to {csv_file} at {timestamp}\")\n\nschedule.every().hour.at(\":00\").do(log_temperatures_and_load)\n\nprint(\"Script started\")\n\nwhile True:\n    schedule.run_pending()\n    time.sleep(60)<\/code><\/pre>\n\n\n\n
\u8fd0\u884c\u51e0\u5929\u540e\u6211\u4eec\u62ff\u5230\u4e86\u4e00\u4e2acsv\u6587\u4ef6\uff1a<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u6253\u5f00\u4e00\u770b\u6700\u540e\u4e00\u6b21\u8bb0\u5f55\u662f2024\/9\/18 2:00\uff0c\u4eca\u5929\u662f9\u670821\u65e5\uff0c\u8bf4\u660e\u57289\u670818\u65e5\u51cc\u6668\u5f00\u53d1\u677f\u5c31\u5df2\u7ecf\u81ea\u52a8\u5173\u673a\u4e86\u3002\u622a\u53d6\u4e86\u4e00\u90e8\u5206\u6570\u636e\u5982\u4e0b\uff1a<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u7eaf\u6570\u5b57\u7684\u6570\u636e\u8ba9\u4eba\u770b\u8d77\u6765\u5f88\u5934\u5927\uff0c\u6240\u4ee5\u6211\u4eec\u4f7f\u7528Pandas\u5904\u7406\u6570\u636e\uff0c\u4e3a\u4e86\u65b9\u4fbf\u76f4\u89c2\u5730\u83b7\u53d6\u7ed3\u679c\uff0c\u6211\u9009\u62e9\u5728Jupter Notebook\u4e2d\u7f16\u5199\u8fd0\u884c\u4ee3\u7801\uff0c\u5f97\u76ca\u4e8ecell\u7684\u8bbe\u8ba1\uff0c\u4ee3\u7801\u53ef\u4ee5\u5206\u6bb5\u968f\u65f6\u8fd0\u884c\uff0c\u6570\u636e\u5206\u6790\u5f88\u65b9\u4fbf<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u9996\u5148\u5728\u7b2c\u4e00\u4e2acell\u4e2d\uff0c\u8be5\u6bb5\u4ee3\u7801\u5bfc\u5165\u4e86Pandas\u4ee5\u53ca\u7528\u4e8e\u753b\u56fe\u7684matplotlib<\/p>\n\n\n\n
import pandas as pd\nimport matplotlib.pyplot as plt\ndf = pd.read_csv(\"temp&load.csv\")<\/code><\/pre>\n\n\n\n
\u6b64\u65f6\u6211\u4eec\u8fd0\u884c\u4e0b\u4e00\u4e2acell\u4e2d\u7684<\/p>\n\n\n\n
print(df)<\/code><\/pre>\n\n\n\n
\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u53ef\u4ee5\u770b\u5230csv\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u5df2\u7ecf\u88ab\u6210\u529f\u5bfc\u5165\u8fdb\u4e86Pandas\u4e2d\u3002<\/p>\n\n\n\n
\u67e5\u770b\u6b64DataFrame\u7684columns\uff1a<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u73b0\u5728\u6211\u4eec\u5c1d\u8bd5\u7ed8\u5236CPU\u6e29\u5ea6\u66f2\u7ebf\u56fe\uff1a<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u5728\u8be5\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4ee5\u65f6\u95f4\u4e3aX\u8f74\uff0cCPU Temperature \u4e3aY\u8f74\uff0c\u753b\u51fa\u4e86\u8fd9\u4e2a\u6298\u7ebf\u56fe\uff0c\u53ef\u4ee5\u5f88\u76f4\u89c2\u7684\u770b\u5230\uff0c\u5728\u6e29\u5ea6\u964d\u5230\u6700\u4f4e\u540e\uff0c\u6700\u540e\u7684\u6e29\u5ea6\u76f4\u98d970\u4ee5\u4e0a\uff0c\u4f30\u8ba1\u5f00\u53d1\u677f\u81ea\u52a8\u5173\u673a\u7684\u539f\u56e0\u5c31\u662f\u6e29\u5ea6\u8fc7\u9ad8\u3002<\/p>\n\n\n\n
\u7528\u540c\u6837\u7684\u65b9\u6cd5\u753b\u51faGPU\u6e29\u5ea6\u6298\u7ebf\u56fe\uff1a<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u540c\u6837\u53ef\u4ee5\u770b\u5230\u6700\u540e\u7684\u6e29\u5ea6\u5f88\u9ad8\u3002<\/p>\n\n\n\n
CPU\u5360\u7528\u60c5\u51b5\u5012\u662f\u4e00\u76f4\u4fdd\u6301\u5728\u8f83\u4f4e\u6c34\u5e73\uff0c\u6bd5\u7adf\u8fd9\u5757\u677f\u5b50\u4e0a\u6211\u4e5f\u6ca1\u8dd1\u4ec0\u4e48\u5360\u7528CPU\u7684\u7a0b\u5e8f\uff0c\u53ea\u6709\u51e0\u4e2adocker\u5e94\u7528\u548c\u8fd9\u4e2a\u8bb0\u5f55\u6570\u636e\u7684\u811a\u672c\u3002<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u63a5\u4e0b\u6765\u6211\u4eec\u8bd5\u4e00\u4e0b\u7edf\u8ba1\u6bcf\u5929\u7684\u5e73\u5747\u6e29\u5ea6<\/p>\n\n\n\n
day = []\nfor i in df.index:\n    day.append(df.at[i, \"Timestamp\"][7:9])\ndf[\"Day\"] = day\ndf.drop(\"Timestamp\", axis = 1, inplace = True)\ndf1 = df.groupby(\"Day\", as_index = False).mean()<\/code><\/pre>\n\n\n\n
\u7531\u4e8e\u539f\u59cb\u7684Timestamp\u662f\u5b8c\u6574\u7684\u65f6\u95f4\u5b57\u7b26\u4e32\uff0c\u6240\u4ee5\u9700\u8981\u5148\u8f6c\u6362\u6210\u5929<\/p>\n\n\n\n
df1 = df.groupby(\"Day\", as_index = False).mean()<\/code><\/pre>\n\n\n\n
\u8fd9\u884c\u4ee3\u7801\u5b9e\u73b0\u4e86\u6309\u5929\u5206\u7ec4\u540e\u7edf\u8ba1\u5e73\u5747\u503c<\/p>\n\n\n\n
\u8fd9\u4e2adrop()\u65b9\u6cd5\u6211\u4e00\u76f4\u8bb0\u4e0d\u592a\u6e05\u695a\uff0c\u6240\u4ee5\u6211\u95ee\u4e86\u4e0bAI\uff1a<\/p>\n\n\n\n
\n
\n
\n
In the df.drop()<\/code> method in pandas, the default value of the axis<\/code> parameter is 0<\/code>.<\/p>\n\n\n\n
When axis=0<\/code>, it indicates that the operation should be performed along rows, i.e., rows will be dropped based on the labels provided.<\/p>\n\n\n\n
When axis=1<\/code>, it indicates that the operation should be performed along columns, i.e., columns will be dropped based on the labels provided.<\/p>\n\n\n\n
If the axis<\/code> parameter is not explicitly specified, the default behavior is to drop rows (axis=0<\/code>).<\/p>\n\n\n\n
\u8fd9\u91cc\u6ce8\u610f\u5982\u679c\u6ca1\u6709inplace = True\uff0cPandas\u4e0d\u4f1a\u5bf9\u539f\u59cb\u6570\u636e\u8fdb\u884c\u4fee\u6539\uff0c\u8981\u8fdb\u884c\u8d4b\u503c\u3002<\/p>\n<\/blockquote>\n<\/blockquote>\n<\/blockquote>\n\n\n\n
df.drop(\"Timestamp\", axis = 1, inplace = True)<\/code><\/pre>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u53ef\u4ee5\u770b\u51fa\u6bcf\u5929\u7684\u5e73\u5747\u6e29\u5ea6\u90fd\u5728\u5347\u9ad8\u3002<\/p>\n\n\n\n
\u5173\u4e8ePandas\u7684\u5b9e\u6218\u5230\u8fd9\u91cc\u5c31\u5df2\u7ecf\u7ed3\u675f\u4e86\uff0c\u5f00\u53d1\u677f\u81ea\u52a8\u5173\u673a\u7684\u539f\u56e0\u57fa\u672c\u786e\u5b9a\u662f\u6e29\u5ea6\u8fc7\u9ad8\u5bfc\u81f4\u7684\uff0c\u4e0d\u8fc7\u76ee\u524d\u6765\u8bf4\u5e76\u6ca1\u6709\u5f88\u597d\u7684\u89e3\u51b3\u65b9\u6848\uff0c\u6bd5\u7adf\u6211\u90fd\u4e0a\u4e86\u6563\u70ed\u98ce\u6247\u4e86\uff0c\u6e29\u5ea6\u8fd8\u662f\u4f1a\u5728\u957f\u65f6\u95f4\u8fd0\u884c\u540e\u53d8\u5f97\u5f88\u9ad8\u3002<\/p>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
\u5982\u4f55\u95ee\u9898\u89e3\u51b3\uff0c\u4f30\u8ba1\u4f1a\u518d\u5199\u4e00\u7bc7blog\u8bb0\u5f55\u7684\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u5728\u6574\u4e2a\u9ad8\u4e2d\u9636\u6bb5\u7684\u4fe1\u606f\u6280\u672f\u5b66\u4e60\u4e2d\uff0c\u6211\u5df2\u7ecf\u505a\u4e86\u4e0d\u77e5\u9053\u591a\u5c11Pandas\u7684\u9898\u76ee\u4e86\uff0c\u4e0d\u8fc7\u6211\u53d1\u73b0\u6211\u4f3c\u4e4e\u4ece\u6765\u6ca1\u7528\u8fc7Panda […]<\/p>\n","protected":false},"author":1,"featured_media":1001,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10],"tags":[68,54,69],"class_list":["post-986","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tec","tag-pandas","tag-54","tag-69"],"views":1356,"_links":{"self":[{"href":"https:\/\/blog.xuxiny.top\/index.php\/wp-json\/wp\/v2\/posts\/986","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.xuxiny.top\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.xuxiny.top\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.xuxiny.top\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.xuxiny.top\/index.php\/wp-json\/wp\/v2\/comments?post=986"}],"version-history":[{"count":3,"href":"https:\/\/blog.xuxiny.top\/index.php\/wp-json\/wp\/v2\/posts\/986\/revisions"}],"predecessor-version":[{"id":1000,"href":"https:\/\/blog.xuxiny.top\/index.php\/wp-json\/wp\/v2\/posts\/986\/revisions\/1000"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.xuxiny.top\/index.php\/wp-json\/wp\/v2\/media\/1001"}],"wp:attachment":[{"href":"https:\/\/blog.xuxiny.top\/index.php\/wp-json\/wp\/v2\/media?parent=986"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.xuxiny.top\/index.php\/wp-json\/wp\/v2\/categories?post=986"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.xuxiny.top\/index.php\/wp-json\/wp\/v2\/tags?post=986"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}