log_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "13cfa6c6",
   "metadata": {},
   "source": [
    "# BPI Data Client Log Analysis\n",
    "Enabling logging in your program is equally as important as developing the program. Log files is an example of unstructured data, and analyzing your program log files can provide insight into the health and performance of your program.\n",
    "\n",
    "This analysis attempts to answer these questions: \n",
    "- How performant has the data streaming client been?\n",
    "- How many errors occured during the program's runtime?\n",
    "- The program is expected to run continuously every minute, has this been the case?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45839183",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in ./.venv/lib/python3.10/site-packages (2.0.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in ./.venv/lib/python3.10/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in ./.venv/lib/python3.10/site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: tzdata>=2022.1 in ./.venv/lib/python3.10/site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: numpy>=1.21.0 in ./.venv/lib/python3.10/site-packages (from pandas) (1.25.0)\n",
      "Requirement already satisfied: six>=1.5 in ./.venv/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Requirement already satisfied: matplotlib in ./.venv/lib/python3.10/site-packages (3.7.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in ./.venv/lib/python3.10/site-packages (from matplotlib) (1.1.0)\n",
      "Requirement already satisfied: cycler>=0.10 in ./.venv/lib/python3.10/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in ./.venv/lib/python3.10/site-packages (from matplotlib) (4.40.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in ./.venv/lib/python3.10/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: numpy>=1.20 in ./.venv/lib/python3.10/site-packages (from matplotlib) (1.25.0)\n",
      "Requirement already satisfied: packaging>=20.0 in ./.venv/lib/python3.10/site-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in ./.venv/lib/python3.10/site-packages (from matplotlib) (10.0.0)\n",
      "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in ./.venv/lib/python3.10/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in ./.venv/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in ./.venv/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "%pip install pandas\n",
    "%pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83651dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "30c943ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>log_type</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-06-23 17:02</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.897626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2023-06-23 17:03</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.422284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2023-06-23 17:04</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.112622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2023-06-23 17:05</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.171625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2023-06-23 17:06</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.638440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row         timestamp log_type  duration\n",
       "0    1  2023-06-23 17:02     INFO  0.897626\n",
       "1    2  2023-06-23 17:03     INFO  0.422284\n",
       "2    3  2023-06-23 17:04     INFO  0.112622\n",
       "3    4  2023-06-23 17:05     INFO  0.171625\n",
       "4    5  2023-06-23 17:06     INFO  0.638440"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the dataset from CSV\n",
    "data = pd.read_csv('processed_log.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ed95299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row            int64\n",
       "timestamp     object\n",
       "log_type      object\n",
       "duration     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the data type in pandas DataFrame\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "335669ce",
   "metadata": {},
   "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>row</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18731.000000</td>\n",
       "      <td>18720.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9366.000000</td>\n",
       "      <td>0.442380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5407.318282</td>\n",
       "      <td>0.595650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.039291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4683.500000</td>\n",
       "      <td>0.124821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9366.000000</td>\n",
       "      <td>0.320136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14048.500000</td>\n",
       "      <td>0.532238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18731.000000</td>\n",
       "      <td>9.806623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                row      duration\n",
       "count  18731.000000  18720.000000\n",
       "mean    9366.000000      0.442380\n",
       "std     5407.318282      0.595650\n",
       "min        1.000000      0.039291\n",
       "25%     4683.500000      0.124821\n",
       "50%     9366.000000      0.320136\n",
       "75%    14048.500000      0.532238\n",
       "max    18731.000000      9.806623"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data.describe(include='all')\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f06812b0",
   "metadata": {},
   "source": [
    "## ETL process duration analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d9bedd33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting the 'duration' column\n",
    "duration_values = data['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "344bbfed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18720.000000\n",
       "mean         0.442380\n",
       "std          0.595650\n",
       "min          0.039291\n",
       "25%          0.124821\n",
       "50%          0.320136\n",
       "75%          0.532238\n",
       "max          9.806623\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basic descriptive statistics on the duration field\n",
    "duration_values.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d577364",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a histogram\n",
    "counts, bins, _ = plt.hist(duration_values, bins=10, edgecolor='black')\n",
    "\n",
    "# Finding the bin with the highest frequency\n",
    "max_count = max(counts)\n",
    "max_index = list(counts).index(max_count)\n",
    "max_bin = bins[max_index]\n",
    "\n",
    "# Adding data label for the bin with the highest frequency\n",
    "plt.text(max_bin, max_count, str(int(max_count)), ha='center')\n",
    "\n",
    "plt.xlabel('Bins')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Duration Values')\n",
    "plt.xticks(bins)  # Set the x-axis ticks to the bin values\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "00ecd825",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.15 seconds\n"
     ]
    }
   ],
   "source": [
    "# the 95th percentile\n",
    "print(f\"{round(duration_values.quantile(0.95), 2)} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d66bb64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replace null values in duration_values with 0\n",
    "duration_values = duration_values.fillna(0)\n",
    "\n",
    "# Creating a boolean mask for all duration values within the 95th percentile\n",
    "mask = duration_values.loc[:] <= duration_values.quantile(0.95)\n",
    "box_df = duration_values.loc[mask]\n",
    "\n",
    "# Plotting a box and whisker plot\n",
    "plt.boxplot([box_df])\n",
    "plt.ylabel('Duration')\n",
    "plt.title('Box and Whisker Plot of Duration Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "70352d0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duration\n",
      "<75th Percentile     14048\n",
      ">=75th Percentile     4683\n",
      "Name: duration, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the quartiles\n",
    "q75 = duration_values.quantile(0.75)\n",
    "\n",
    "# Categorize the duration values into four groups\n",
    "category_labels = ['<75th Percentile', '>=75th Percentile']\n",
    "categories = pd.cut(duration_values, bins=[float('-inf'), q75, float('inf')], labels=category_labels)\n",
    "\n",
    "# Perform the count aggregation for each category\n",
    "duration_counts = categories.groupby(categories).size()\n",
    "\n",
    "# Print the duration counts for each category\n",
    "print(duration_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ba9c52a",
   "metadata": {},
   "source": [
    "### Findings\n",
    "The ETL process `typically takes 0.32 seconds` to complete, with `75% of events taking less than 0.53 seconds` to complete. \n",
    "\n",
    "The distribution of duration values is `right skewed`, which is also evident comparing the mean and median values, i.e. `median < mean`. The range of `0.039 to 1.016` represents where the bulk of the duration values lie, with a higher concentration in that interval. \n",
    "\n",
    "**This suggests that the majority of events or processes represented by the duration values in the log dataset tend to have shorter durations,** with fewer instances of longer durations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60091ac0",
   "metadata": {},
   "source": [
    "## Process Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "84752c88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         row         timestamp log_type  duration\n",
      "0          1  2023-06-23 17:02     INFO  0.897626\n",
      "18730  18731  2023-07-06 17:17     INFO  0.279002\n"
     ]
    }
   ],
   "source": [
    "result = pd.concat([data.head(1), data.tail(1)])\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11ae615e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log_type\n",
       "INFO    18720\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('log_type').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9db8f17",
   "metadata": {},
   "source": [
    "### Findings\n",
    "The analysis was conducted with log entries within `2023-06-23 17:02` and `2023-06-23 17:02` GMT, and no errors have been recorded during the program's runtime. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6042657",
   "metadata": {},
   "source": [
    "## Process Runtime Intervals\n",
    "The program is expected to run continuously every minute, has this been the case? We have to investigate if the timestamps in the dataframe are continuous. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "02fea55f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>log_type</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-06-23 17:02</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.897626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2023-06-23 17:03</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.422284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2023-06-23 17:04</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.112622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row         timestamp log_type  duration\n",
       "0    1  2023-06-23 17:02     INFO  0.897626\n",
       "1    2  2023-06-23 17:03     INFO  0.422284\n",
       "2    3  2023-06-23 17:04     INFO  0.112622"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = data\n",
    "new_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f5153abc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 rows dropped\n"
     ]
    }
   ],
   "source": [
    "# Sort the dataframe by timestamp\n",
    "new_df = new_df.sort_values(\"timestamp\")\n",
    "\n",
    "# Drop null values from dataframe\n",
    "new_df = new_df.dropna()\n",
    "print(f\"{data['row'].count() - new_df['row'].count()} rows dropped\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "73712c66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row                   int64\n",
       "timestamp    datetime64[ns]\n",
       "log_type             object\n",
       "duration            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert timestamp column to datetime type\n",
    "new_df[\"timestamp\"] = pd.to_datetime(new_df[\"timestamp\"])\n",
    "new_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e87df32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2023-06-23 17:02:00\n",
       "1   2023-06-23 17:03:00\n",
       "Name: timestamp, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['timestamp'].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1cad8c02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('0 days 00:01:00')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the expected time difference\n",
    "expected_diff = new_df[\"timestamp\"].diff().median()\n",
    "expected_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9d9caa6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The timestamps are not continuous.\n"
     ]
    }
   ],
   "source": [
    "# Check for continuity\n",
    "is_continuous = new_df[\"timestamp\"].diff()[1:] == expected_diff\n",
    "if is_continuous.all():\n",
    "    print(\"The timestamps are continuous.\")\n",
    "else:\n",
    "    print(\"The timestamps are not continuous.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0119c8f9",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>4066</th>\n",
       "      <td>4067</td>\n",
       "      <td>2023-06-26 12:49:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.533864</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5420</th>\n",
       "      <td>5421</td>\n",
       "      <td>2023-06-27 11:24:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.626278</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8670</th>\n",
       "      <td>8671</td>\n",
       "      <td>2023-06-29 17:30:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>1.274357</td>\n",
       "      <td>Non-continuous</td>\n",
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       "    <tr>\n",
       "      <th>8672</th>\n",
       "      <td>8673</td>\n",
       "      <td>2023-06-29 17:32:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.576249</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10816</th>\n",
       "      <td>10817</td>\n",
       "      <td>2023-07-01 05:14:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.980047</td>\n",
       "      <td>Non-continuous</td>\n",
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       "    <tr>\n",
       "      <th>10818</th>\n",
       "      <td>10819</td>\n",
       "      <td>2023-07-01 05:16:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.854864</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14091</th>\n",
       "      <td>14092</td>\n",
       "      <td>2023-07-03 11:51:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.429191</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16214</th>\n",
       "      <td>16215</td>\n",
       "      <td>2023-07-04 23:17:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.258268</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18619</th>\n",
       "      <td>18620</td>\n",
       "      <td>2023-07-06 15:26:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.323946</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         row           timestamp log_type  duration            flag\n",
       "4066    4067 2023-06-26 12:49:00     INFO  0.533864  Non-continuous\n",
       "5420    5421 2023-06-27 11:24:00     INFO  0.626278  Non-continuous\n",
       "8670    8671 2023-06-29 17:30:00     INFO  1.274357  Non-continuous\n",
       "8672    8673 2023-06-29 17:32:00     INFO  0.576249  Non-continuous\n",
       "10816  10817 2023-07-01 05:14:00     INFO  0.980047  Non-continuous\n",
       "10818  10819 2023-07-01 05:16:00     INFO  0.854864  Non-continuous\n",
       "14091  14092 2023-07-03 11:51:00     INFO  0.429191  Non-continuous\n",
       "16214  16215 2023-07-04 23:17:00     INFO  0.258268  Non-continuous\n",
       "18619  18620 2023-07-06 15:26:00     INFO  0.323946  Non-continuous"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify rows with non-continuous timestamps\n",
    "is_continuous = new_df[\"timestamp\"].diff() == expected_diff\n",
    "non_continuous_rows = new_df.loc[~is_continuous]\n",
    "non_continuous_rows = non_continuous_rows[non_continuous_rows.index != 0]\n",
    "non_continuous_rows.insert(4, 'flag', 'Non-continuous', True)\n",
    "non_continuous_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7d43e9b8",
   "metadata": {},
   "outputs": [
    {
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       "      <td>INFO</td>\n",
       "      <td>1.412562</td>\n",
       "      <td>Preceding</td>\n",
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       "      <th>5419</th>\n",
       "      <td>5420</td>\n",
       "      <td>2023-06-27 11:22:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.272424</td>\n",
       "      <td>Preceding</td>\n",
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       "    <tr>\n",
       "      <th>8669</th>\n",
       "      <td>8670</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Preceding</td>\n",
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       "      <th>8671</th>\n",
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       "      <td>NaT</td>\n",
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       "      <td>Preceding</td>\n",
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       "    <tr>\n",
       "      <th>10815</th>\n",
       "      <td>10816</td>\n",
       "      <td>NaT</td>\n",
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       "    <tr>\n",
       "      <th>10817</th>\n",
       "      <td>10818</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Preceding</td>\n",
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       "    <tr>\n",
       "      <th>14090</th>\n",
       "      <td>14091</td>\n",
       "      <td>2023-07-03 11:47:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.079156</td>\n",
       "      <td>Preceding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16213</th>\n",
       "      <td>16214</td>\n",
       "      <td>2023-07-04 23:13:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.241826</td>\n",
       "      <td>Preceding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18618</th>\n",
       "      <td>18619</td>\n",
       "      <td>2023-07-06 15:21:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.232752</td>\n",
       "      <td>Preceding</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         row           timestamp log_type  duration       flag\n",
       "4065    4066 2023-06-26 12:47:00     INFO  1.412562  Preceding\n",
       "5419    5420 2023-06-27 11:22:00     INFO  0.272424  Preceding\n",
       "8669    8670                 NaT      NaN       NaN  Preceding\n",
       "8671    8672                 NaT      NaN       NaN  Preceding\n",
       "10815  10816                 NaT      NaN       NaN  Preceding\n",
       "10817  10818                 NaT      NaN       NaN  Preceding\n",
       "14090  14091 2023-07-03 11:47:00     INFO  0.079156  Preceding\n",
       "16213  16214 2023-07-04 23:13:00     INFO  0.241826  Preceding\n",
       "18618  18619 2023-07-06 15:21:00     INFO  0.232752  Preceding"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Masking out the preceding non-continuous timestamp rows from the original dataframe\n",
    "preceding_indices = [(index - 1) for index in new_df.loc[~is_continuous].index if index != 0]\n",
    "preceding_rows = data.loc[preceding_indices]\n",
    "preceding_rows.insert(4, 'flag', 'Preceding', True)\n",
    "preceding_rows[\"timestamp\"] = pd.to_datetime(preceding_rows[\"timestamp\"])\n",
    "preceding_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "854614ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2023-06-26 12:49:00</td>\n",
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       "      <td>Non-continuous</td>\n",
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       "    <tr>\n",
       "      <th>5419</th>\n",
       "      <td>5420</td>\n",
       "      <td>2023-06-27 11:22:00</td>\n",
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       "      <td>Preceding</td>\n",
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       "      <th>5420</th>\n",
       "      <td>5421</td>\n",
       "      <td>2023-06-27 11:24:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.626278</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8669</th>\n",
       "      <td>8670</td>\n",
       "      <td>NaT</td>\n",
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       "      <td>Preceding</td>\n",
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       "    <tr>\n",
       "      <th>8670</th>\n",
       "      <td>8671</td>\n",
       "      <td>2023-06-29 17:30:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>1.274357</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8671</th>\n",
       "      <td>8672</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Preceding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8672</th>\n",
       "      <td>8673</td>\n",
       "      <td>2023-06-29 17:32:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.576249</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10815</th>\n",
       "      <td>10816</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Preceding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10816</th>\n",
       "      <td>10817</td>\n",
       "      <td>2023-07-01 05:14:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.980047</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10817</th>\n",
       "      <td>10818</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Preceding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10818</th>\n",
       "      <td>10819</td>\n",
       "      <td>2023-07-01 05:16:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.854864</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14090</th>\n",
       "      <td>14091</td>\n",
       "      <td>2023-07-03 11:47:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.079156</td>\n",
       "      <td>Preceding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14091</th>\n",
       "      <td>14092</td>\n",
       "      <td>2023-07-03 11:51:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.429191</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16213</th>\n",
       "      <td>16214</td>\n",
       "      <td>2023-07-04 23:13:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.241826</td>\n",
       "      <td>Preceding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16214</th>\n",
       "      <td>16215</td>\n",
       "      <td>2023-07-04 23:17:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.258268</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18618</th>\n",
       "      <td>18619</td>\n",
       "      <td>2023-07-06 15:21:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.232752</td>\n",
       "      <td>Preceding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18619</th>\n",
       "      <td>18620</td>\n",
       "      <td>2023-07-06 15:26:00</td>\n",
       "      <td>INFO</td>\n",
       "      <td>0.323946</td>\n",
       "      <td>Non-continuous</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         row           timestamp log_type  duration            flag\n",
       "4065    4066 2023-06-26 12:47:00     INFO  1.412562       Preceding\n",
       "4066    4067 2023-06-26 12:49:00     INFO  0.533864  Non-continuous\n",
       "5419    5420 2023-06-27 11:22:00     INFO  0.272424       Preceding\n",
       "5420    5421 2023-06-27 11:24:00     INFO  0.626278  Non-continuous\n",
       "8669    8670                 NaT      NaN       NaN       Preceding\n",
       "8670    8671 2023-06-29 17:30:00     INFO  1.274357  Non-continuous\n",
       "8671    8672                 NaT      NaN       NaN       Preceding\n",
       "8672    8673 2023-06-29 17:32:00     INFO  0.576249  Non-continuous\n",
       "10815  10816                 NaT      NaN       NaN       Preceding\n",
       "10816  10817 2023-07-01 05:14:00     INFO  0.980047  Non-continuous\n",
       "10817  10818                 NaT      NaN       NaN       Preceding\n",
       "10818  10819 2023-07-01 05:16:00     INFO  0.854864  Non-continuous\n",
       "14090  14091 2023-07-03 11:47:00     INFO  0.079156       Preceding\n",
       "14091  14092 2023-07-03 11:51:00     INFO  0.429191  Non-continuous\n",
       "16213  16214 2023-07-04 23:13:00     INFO  0.241826       Preceding\n",
       "16214  16215 2023-07-04 23:17:00     INFO  0.258268  Non-continuous\n",
       "18618  18619 2023-07-06 15:21:00     INFO  0.232752       Preceding\n",
       "18619  18620 2023-07-06 15:26:00     INFO  0.323946  Non-continuous"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.concat([non_continuous_rows, preceding_rows])\n",
    "result.sort_values(\"row\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c7d3d6",
   "metadata": {},
   "source": [
    "### Findings\n",
    "There are **only 9 instances of discontinuous timestamps out of 18,731 recorded events.** Although, it is clear that the timestamps are not continuous, but the degree of discontinuity is infinitesimal. \n",
    "\n",
    "It is also unclear if there was a logging error, as some preceding timestamps are `NaT` or were not captured or correctly parsed. This reduces the number of clear and recorded incidences to 5. \n",
    "\n",
    "So we can conclude that **the program runs continuously every minute for most of the time.**"
   ]
  }
 ],
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