importSACCTinfo.ipynb 9.36 KB
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T20:57:10.405006Z",
     "start_time": "2020-03-16T20:56:55.837670Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T20:57:11.865980Z",
     "start_time": "2020-03-16T20:57:10.414986Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('userusage.txt',delimiter='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T20:57:11.932878Z",
     "start_time": "2020-03-16T20:57:11.905219Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Start</th>\n",
       "      <th>JobID</th>\n",
       "      <th>JobName</th>\n",
       "      <th>State</th>\n",
       "      <th>Partition</th>\n",
       "      <th>MaxRSS</th>\n",
       "      <th>ReqMem</th>\n",
       "      <th>ReqCPUS</th>\n",
       "      <th>NodeList</th>\n",
       "      <th>NNodes</th>\n",
       "      <th>Elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>user</td>\n",
       "      <td>2019-01-06T22:00:21</td>\n",
       "      <td>2040834</td>\n",
       "      <td>_interactive</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000Mc</td>\n",
       "      <td>1</td>\n",
       "      <td>c0088</td>\n",
       "      <td>1</td>\n",
       "      <td>16:04:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-06T22:00:21</td>\n",
       "      <td>2040834.batch</td>\n",
       "      <td>batch</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1394528K</td>\n",
       "      <td>10000Mc</td>\n",
       "      <td>1</td>\n",
       "      <td>c0088</td>\n",
       "      <td>1</td>\n",
       "      <td>16:04:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>user</td>\n",
       "      <td>2019-01-07T16:15:21</td>\n",
       "      <td>2043373</td>\n",
       "      <td>Pipe_trim_galore</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000Mc</td>\n",
       "      <td>1</td>\n",
       "      <td>c0038</td>\n",
       "      <td>1</td>\n",
       "      <td>00:18:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-07T16:15:21</td>\n",
       "      <td>2043373.batch</td>\n",
       "      <td>batch</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58592K</td>\n",
       "      <td>2000Mc</td>\n",
       "      <td>1</td>\n",
       "      <td>c0038</td>\n",
       "      <td>1</td>\n",
       "      <td>00:18:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>user</td>\n",
       "      <td>2019-01-07T16:15:21</td>\n",
       "      <td>2043374</td>\n",
       "      <td>Pipe_trim_galore</td>\n",
       "      <td>COMPLETED</td>\n",
       "      <td>medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000Mc</td>\n",
       "      <td>1</td>\n",
       "      <td>c0063</td>\n",
       "      <td>1</td>\n",
       "      <td>00:15:48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       User                Start          JobID           JobName      State  \\\n",
       "0  user  2019-01-06T22:00:21        2040834      _interactive  COMPLETED   \n",
       "1       NaN  2019-01-06T22:00:21  2040834.batch             batch  COMPLETED   \n",
       "2    user  2019-01-07T16:15:21        2043373  Pipe_trim_galore  COMPLETED   \n",
       "3       NaN  2019-01-07T16:15:21  2043373.batch             batch  COMPLETED   \n",
       "4    user  2019-01-07T16:15:21        2043374  Pipe_trim_galore  COMPLETED   \n",
       "\n",
       "  Partition    MaxRSS   ReqMem  ReqCPUS NodeList  NNodes   Elapsed  \n",
       "0    medium       NaN  10000Mc        1    c0088       1  16:04:23  \n",
       "1       NaN  1394528K  10000Mc        1    c0088       1  16:04:23  \n",
       "2    medium       NaN   2000Mc        1    c0038       1  00:18:41  \n",
       "3       NaN    58592K   2000Mc        1    c0038       1  00:18:41  \n",
       "4    medium       NaN   2000Mc        1    c0063       1  00:15:48  "
      ]
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     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T20:57:14.154962Z",
     "start_time": "2020-03-16T20:57:11.967976Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['medium', nan, 'medium', ..., 'medium', nan, nan], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['jid','step']] = df.JobID.str.split(\".\",expand=True) \n",
    "df.Partition.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2020-03-16T20:56:57.392Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wsmonroe/.conda/envs/wsmplayground/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/home/wsmonroe/.conda/envs/wsmplayground/lib/python3.6/site-packages/pandas/core/generic.py:7626: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n",
      "/home/wsmonroe/.conda/envs/wsmplayground/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2961: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "batchDF=df.dropna(subset=[\"MaxRSS\"])\n",
    "userDF=df.dropna(subset=[\"User\"])\n",
    "for jid in df.jid.unique():\n",
    "    userDF['MaxRSS'][userDF['jid'] == jid]=batchDF['MaxRSS'][batchDF['jid'] == jid]\n",
    "    \n",
    "    #print(userDF[userDF['jid'] == jid])\n",
    "    \n",
    "userDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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