bgl_anomaly_file1.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "## File 1\n",
    "### This python file contains code for loading the log data, parsing, cleaning, exploring, filtering and exporting it as a CSV file for further operations.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## LOADING AND PARSING THE DATA LOG FILE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pH2VTjTEBnmP"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "list1 = []\n",
    "f = open(\"BGL.log\",\"r\", encoding=\"utf-8\", errors='strict')\n",
    "f = f.readlines()\n",
    "for line in f:\n",
    "    try:\n",
    "        list1.append(line)\n",
    "    except ValueError:\n",
    "        print('Error in line :' + line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WOJTca0OEfBc"
   },
   "outputs": [],
   "source": [
    "for i in range(len(list1)):\n",
    "    list1[i] = list1[i].split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_P-MylJeEmc1"
   },
   "outputs": [],
   "source": [
    "\n",
    "for i in range(len(list1)):\n",
    "    list1[i][len(list1[i])-1] = list1[i][len(list1[i])-1].replace('\\n', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MT06b9HdEpHo"
   },
   "outputs": [],
   "source": [
    "\n",
    "for i in range(0, len(list1)):\n",
    "    list1[i][9:len(list1[i])] = [\" \".join(list1[i][9:len(list1[i])])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xa-PqZIwErUJ"
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(list1, columns = ['Dash', 'Id1', 'Date', 'Id2', 'Time', 'Id3', 'RAS', 'Kernel', 'INFO', 'Message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EXPLORING THE DATA"
   ]
  },
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     "execution_count": 6,
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   "source": [
    "df.shape"
   ]
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       "  Dash         Id1        Date                  Id2  \\\n",
       "0    -  1117838570  2005.06.03  R02-M1-N0-C:J12-U11   \n",
       "1    -  1117838570  2005.06.03  R02-M1-N0-C:J12-U11   \n",
       "2    -  1117838570  2005.06.03  R02-M1-N0-C:J12-U11   \n",
       "3    -  1117838570  2005.06.03  R02-M1-N0-C:J12-U11   \n",
       "4    -  1117838570  2005.06.03  R02-M1-N0-C:J12-U11   \n",
       "\n",
       "                         Time                  Id3  RAS  Kernel  INFO  \\\n",
       "0  2005-06-03-15.42.50.363779  R02-M1-N0-C:J12-U11  RAS  KERNEL  INFO   \n",
       "1  2005-06-03-15.42.50.527847  R02-M1-N0-C:J12-U11  RAS  KERNEL  INFO   \n",
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       "\n",
       "                                    Message  \n",
       "0  instruction cache parity error corrected  \n",
       "1  instruction cache parity error corrected  \n",
       "2  instruction cache parity error corrected  \n",
       "3  instruction cache parity error corrected  \n",
       "4  instruction cache parity error corrected  "
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     "execution_count": 7,
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# REMOVING COLUMNS THAT ARE NOT REQUIRED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mfhdGJtSG2wp"
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   "source": [
    "df.drop([\"Time\", \"Date\", \"Id2\", \"Id3\", \"RAS\", \"Kernel\", \"INFO\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MORE DATA WRANGLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gmFV3-JMHOZZ"
   },
   "outputs": [],
   "source": [
    "df.rename(columns={\"Dash\": \"Anomaly Status\"}, inplace=True)"
   ]
  },
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       "  Anomaly Status         Id1                                   Message\n",
       "0              -  1117838570  instruction cache parity error corrected\n",
       "1              -  1117838570  instruction cache parity error corrected"
      ]
     },
     "execution_count": 10,
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   "source": [
    "df.head(2)"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sRNfdsXjKd1C"
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   "outputs": [],
   "source": [
    "anomaly_list = df['Anomaly Status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_7kspQgVLL8V"
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   "source": [
    "for i in range(len(anomaly_list)):\n",
    "  if anomaly_list[i] == '-':\n",
    "    df['Anomaly Status'] = df['Anomaly Status'].str.replace(anomaly_list[i],'0')\n",
    "  else:\n",
    "    df['Anomaly Status'] = df['Anomaly Status'].str.replace(anomaly_list[i],'1')"
   ]
  },
  {
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   "execution_count": null,
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    "id": "8_L4U_JHLq8s",
    "outputId": "7a9a0b15-df69-4817-beb8-9ce1d52dc24e"
   },
   "outputs": [
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      "text/plain": [
       "  Anomaly Status         Id1                                   Message\n",
       "0              -  1117838570  instruction cache parity error corrected\n",
       "1              -  1117838570  instruction cache parity error corrected\n",
       "2              -  1117838570  instruction cache parity error corrected\n",
       "3              -  1117838570  instruction cache parity error corrected\n",
       "4              -  1117838570  instruction cache parity error corrected"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, we will create multiple datasets, basically filtering it to reduce the number of rows and overall data for convenience. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "D4Q-YulRsAtJ"
   },
   "outputs": [],
   "source": [
    "df1 = df[0:4000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RxeNCBGa0LlP"
   },
   "outputs": [],
   "source": [
    "df2 = df[4900:9500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "m34NJ8JssBCC"
   },
   "outputs": [],
   "source": [
    "df3 = df[22900:23600]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DZYSvMiqsBbD"
   },
   "outputs": [],
   "source": [
    "df4 = df[225000:230000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cyFvmaQwsBrT"
   },
   "outputs": [],
   "source": [
    "df5 = df[391000:397000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-qHT1NSosB7b"
   },
   "outputs": [],
   "source": [
    "df6 = df[530000:535000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xVin6U3RsCdK"
   },
   "outputs": [],
   "source": [
    "df7 = df[546000:550000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nlpcIOpPsCqD"
   },
   "outputs": [],
   "source": [
    "df8 = df[559000:563000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TAdStsMssDNo"
   },
   "outputs": [],
   "source": [
    "df9 = df[590000:595000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yt6rMb4KsDo1"
   },
   "outputs": [],
   "source": [
    "df10 = df[700000:703000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "stJ4z2Om1Su4"
   },
   "outputs": [],
   "source": [
    "df11 = df[710000:711000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gg7yZ6Xu1TOt"
   },
   "outputs": [],
   "source": [
    "df12 = df[850000:854000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iRaoBkzf1Tfo"
   },
   "outputs": [],
   "source": [
    "df13 = df[950000:956000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KSIHQCdu1T34"
   },
   "outputs": [],
   "source": [
    "df14 = df[1050000:1053000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NKgCjKOJ1UDt"
   },
   "outputs": [],
   "source": [
    "df15 = df[1250000:1254000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NXZZC5EL1UPR"
   },
   "outputs": [],
   "source": [
    "df16 = df[1490000:1492500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MvDdKMW41UeL"
   },
   "outputs": [],
   "source": [
    "df17 = df[2980000:2984000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-VeLDplJ1UxY"
   },
   "outputs": [],
   "source": [
    "df18 = df[2989000:2992000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wAnhO5Wd5vJ7"
   },
   "outputs": [],
   "source": [
    "df19 = df[2920000:2925000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "k-ec3BEe5wr3"
   },
   "outputs": [],
   "source": [
    "df20 = df[3020000:3026000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sBK6Lq335w-7"
   },
   "outputs": [],
   "source": [
    "df21 = df[3230000:3236500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Y-kEzRvQ5xMJ"
   },
   "outputs": [],
   "source": [
    "df22 = df[3330000:3333500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "h4Um3XIM5xVt"
   },
   "outputs": [],
   "source": [
    "df23 = df[3530000:3535000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "288OjLd15xgZ"
   },
   "outputs": [],
   "source": [
    "df24 = df[4130000:4135000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AzmOZDds5xvj"
   },
   "outputs": [],
   "source": [
    "df25 = df[4240000:4245000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WI9M5jG55x8d"
   },
   "outputs": [],
   "source": [
    "df26 = df[4340000:4345000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YIdISYxX8G7V"
   },
   "outputs": [],
   "source": [
    "df27 = df[4540000:4544000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6KSRfV7g8Ilg"
   },
   "outputs": [],
   "source": [
    "df28 = df[4640000:4645000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_bL0xHIk8I2X"
   },
   "outputs": [],
   "source": [
    "df29 = df[4670000:4679000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Iaz4WlKz8JNH"
   },
   "outputs": [],
   "source": [
    "df30 = df[4741963:4747963]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "V8iCpFv48Jiy"
   },
   "outputs": [],
   "source": [
    "df = pd.concat([df1, df2, df3, df4, df5, df6, df7, df8, df9, df10, df11, df12, df13, df14, df15, df16, df17, df18, df19, df20,\n",
    "                df21, df22, df23, df24, df25, df26, df27, df28, df29, df30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Qjd6nvHY9LjB",
    "outputId": "6230257a-9c5e-4ec7-bf1e-4954f91a50fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(133800, 3)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOW< EXPORTING IT AS CSV FILE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kHEfeRQ2-7O7"
   },
   "outputs": [],
   "source": [
    "df.to_csv(r'bgl.csv', index = False, header=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1yZqKC7yBSPS"
   },
   "outputs": [],
   "source": []
  }
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