├── Chapter2_Fundamentals_Of_Data_Observability └── Visualize_Observability_issues.ipynb ├── Chapter4_Data_Observability_Elements ├── Compute_YTD.ipynb ├── Orders_Predict.ipynb └── conf.ini ├── Chapter5_Defining_Rules_on_Indicators ├── Age.csv ├── Data_Observability_Rules.ipynb ├── Name_Surname.csv ├── Personal_data.csv ├── conf.ini └── kensu_offline_events.txt ├── Chapter6_Root_Cause_Analysis └── Detect_Anomalies.ipynb ├── LICENSE ├── README.md ├── data ├── anomalies.csv ├── feb │ └── orders.csv ├── jan │ └── orders.csv ├── mar │ └── orders.csv └── ytd │ └── ordersYTD.csv └── requirements.txt /Chapter2_Fundamentals_Of_Data_Observability/Visualize_Observability_issues.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "a1b617a1", 6 | "metadata": {}, 7 | "source": [ 8 | "# Visualize Data Issues\n", 9 | "\n", 10 | "This notebook provides a visualization of common data quality issues that may arise within an application, highlighting the importance of robust data validation and cleaning processes." 11 | ] 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "id": "bed0168f", 16 | "metadata": {}, 17 | "source": [ 18 | "This notebook is designed to illustrate how data quality issues can manifest in an application. By visualizing these issues, we aim to gain a better understanding of their nature and impact." 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "id": "99deb5e8", 24 | "metadata": {}, 25 | "source": [ 26 | "## Libraries Import\n", 27 | "\n", 28 | "This cell is responsible for importing the necessary libraries required for our example. Ensuring that all the required libraries are loaded is essential for the analyses that will follow." 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": null, 34 | "id": "69da1ad4", 35 | "metadata": {}, 36 | "outputs": [], 37 | "source": [ 38 | "import pandas as pd\n", 39 | "import matplotlib.pyplot as plt\n", 40 | "import sqlite3" 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "id": "fc92d471", 46 | "metadata": {}, 47 | "source": [ 48 | "## January Data Analysis\n", 49 | "\n", 50 | "In January, we encountered a timeliness issue with our data, which could also be interpreted as a completeness issue. Let's begin by loading the dataset to investigate this matter further.\n", 51 | "\n", 52 | "- **Timeliness Issue**: This refers to the scenario where data is not available when expected or required.\n", 53 | "- **Completeness Issue**: This pertains to instances where data is missing or incomplete." 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": null, 59 | "id": "7946217a", 60 | "metadata": {}, 61 | "outputs": [], 62 | "source": [ 63 | "df_jan = pd.read_csv(\"../data/jan/orders.csv\",parse_dates=['date'])\n", 64 | "df_jan.head()" 65 | ] 66 | }, 67 | { 68 | "cell_type": "markdown", 69 | "id": "61d9cb23", 70 | "metadata": {}, 71 | "source": [ 72 | "At first glance, the head of the dataset does not exhibit any peculiarities. Nevertheless, we have received reports of an issue concerning the number of pages visited. To delve deeper into this matter, we'll visualize the average number of pages visited on a daily basis." 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": null, 78 | "id": "d729cb44", 79 | "metadata": {}, 80 | "outputs": [], 81 | "source": [ 82 | "mean_duration = df_jan.groupby('date')['page_visited'].mean()\n", 83 | "\n", 84 | "plt.bar(x=mean_duration.index, height=mean_duration.values)\n", 85 | "plt.xticks(rotation=45, ha=\"right\")\n", 86 | "\n", 87 | "plt.xlabel('Date') \n", 88 | "plt.ylabel('Average Page Visits') \n", 89 | "\n", 90 | "plt.title('Daily Average Page Visits in January')\n", 91 | "plt.show()" 92 | ] 93 | }, 94 | { 95 | "cell_type": "markdown", 96 | "id": "4c540fb1", 97 | "metadata": {}, 98 | "source": [ 99 | "The visualization reveals a gap in the data, indicating missing averages for certain days. A closer examination of each date confirms this observation, with data absent from the 17th to the 23rd of January." 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": null, 105 | "id": "e7e7d92d", 106 | "metadata": {}, 107 | "outputs": [], 108 | "source": [ 109 | "df_jan['date'].unique()" 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "id": "cbbd1cee", 115 | "metadata": {}, 116 | "source": [ 117 | "The subsequent step involves appending the dataset to the SQL `datalake.orders` table to ensure that our data lake is up-to-date with the latest information." 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": null, 123 | "id": "efa0e138", 124 | "metadata": {}, 125 | "outputs": [], 126 | "source": [ 127 | "con=sqlite3.connect('datalake')\n", 128 | "df_jan.to_sql('orders',con,if_exists='append')" 129 | ] 130 | }, 131 | { 132 | "cell_type": "markdown", 133 | "id": "dbfb5f5c", 134 | "metadata": {}, 135 | "source": [ 136 | "## February Data Issues\n", 137 | "\n", 138 | "In February, we encountered a significant issue: writing the dataset to the `datalake.orders` table was infeasible due to a breakdown in the application." 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": null, 144 | "id": "acc8812f", 145 | "metadata": {}, 146 | "outputs": [], 147 | "source": [ 148 | "df_feb = pd.read_csv(\"../data/feb/orders.csv\",parse_dates=['date'])" 149 | ] 150 | }, 151 | { 152 | "cell_type": "markdown", 153 | "id": "700b97ff", 154 | "metadata": {}, 155 | "source": [ 156 | "An inspection of the error reveals a completeness issue, this time attributable to an incomplete schema. Specifically, the field `email_customer` was added. In fact, the field `email` was renamed to `email_customer` in this dataset, which likely caused the problem." 157 | ] 158 | }, 159 | { 160 | "cell_type": "code", 161 | "execution_count": null, 162 | "id": "3dd82dbb", 163 | "metadata": {}, 164 | "outputs": [], 165 | "source": [ 166 | "df_feb.to_sql('orders',con,if_exists='append')" 167 | ] 168 | }, 169 | { 170 | "cell_type": "markdown", 171 | "id": "c6789276", 172 | "metadata": {}, 173 | "source": [ 174 | "## March Data Anomalies\n", 175 | "\n", 176 | "March brought forth another completeness issue. Due to complications with the cookies provider, there was a noticeable increase in the number of `NaN` values for the `page_visited` column, rising from approximately 5% to 25%." 177 | ] 178 | }, 179 | { 180 | "cell_type": "code", 181 | "execution_count": null, 182 | "id": "90fef183", 183 | "metadata": {}, 184 | "outputs": [], 185 | "source": [ 186 | "df_mar = pd.read_csv(\"../data/mar/orders.csv\",parse_dates=['date'])" 187 | ] 188 | }, 189 | { 190 | "cell_type": "code", 191 | "execution_count": null, 192 | "id": "34a7e4dd", 193 | "metadata": {}, 194 | "outputs": [], 195 | "source": [ 196 | "import matplotlib.pyplot as plt\n", 197 | "\n", 198 | "na_jan = sum(df_jan[\"page_visited\"].isna())/df_jan.shape[1]\n", 199 | "na_feb = sum(df_feb[\"page_visited\"].isna())/df_feb.shape[1]\n", 200 | "na_mar = sum(df_mar[\"page_visited\"].isna())/df_mar.shape[1]\n", 201 | "\n", 202 | "plt.bar(x=['jan', 'feb', 'mar'], height=[na_jan, na_feb, na_mar])\n", 203 | "\n", 204 | "\n", 205 | "plt.xlabel('Month') \n", 206 | "plt.ylabel('Proportion of Missing Page Visits') \n", 207 | "\n", 208 | "\n", 209 | "plt.title('Proportion of Missing Page Visits per Month')\n", 210 | "\n", 211 | "plt.show()" 212 | ] 213 | }, 214 | { 215 | "cell_type": "markdown", 216 | "id": "d584b4e9", 217 | "metadata": {}, 218 | "source": [ 219 | "## Summary\n", 220 | "\n", 221 | "Throughout this notebook, we have explored various data issues. Some caused immediate disruptions in the application, while others were more insidious. Recognizing and addressing these issues is crucial for maintaining data integrity and ensuring smooth application functionality." 222 | ] 223 | } 224 | ], 225 | "metadata": { 226 | "kernelspec": { 227 | "display_name": "Python 3 (ipykernel)", 228 | "language": "python", 229 | "name": "python3" 230 | }, 231 | "language_info": { 232 | "codemirror_mode": { 233 | "name": "ipython", 234 | "version": 3 235 | }, 236 | "file_extension": ".py", 237 | "mimetype": "text/x-python", 238 | "name": "python", 239 | "nbconvert_exporter": "python", 240 | "pygments_lexer": "ipython3", 241 | "version": "3.8.12" 242 | } 243 | }, 244 | "nbformat": 4, 245 | "nbformat_minor": 5 246 | } 247 | -------------------------------------------------------------------------------- /Chapter4_Data_Observability_Elements/Compute_YTD.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "e8b4efde", 6 | "metadata": {}, 7 | "source": [ 8 | "# Year-to-Date (YTD) Order Data Processing\n", 9 | "\n", 10 | "This notebook focuses on processing order data to generate a consolidated Year-to-Date (YTD) dataset. The steps involved include data loading, transformations, for possibly further analysis or model training. Throughout the process, we use the Kensu Provider to monitor and track data lineage, ensuring transparency in data operations." 11 | ] 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "id": "78ffe5a6", 16 | "metadata": {}, 17 | "source": [ 18 | "## Kensu Provider Initialization\n", 19 | "\n", 20 | "Kensu provides a way to monitor and track data lineage, ensuring that data processes are transparent and traceable. Initializing Kensu is the first step in this process. Once initialized, it will automatically track operations performed using its integrated libraries." 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "id": "f58439c8", 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "# Initialize the KensuProvider to start tracking data operations\n", 31 | "from kensu.utils.kensu_provider import KensuProvider\n", 32 | "K = KensuProvider().initKensu()" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "id": "96ab34e4", 38 | "metadata": {}, 39 | "source": [ 40 | "## Data Loading\n", 41 | "\n", 42 | "The order data for January and February is loaded in this section. The `kensu-pandas` library, a tracked version of pandas, is used for this purpose. By using kensu-pandas, each data operation becomes traceable, ensuring transparency in data processing." 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": null, 48 | "id": "c4d2e2f5", 49 | "metadata": {}, 50 | "outputs": [], 51 | "source": [ 52 | "# Load order data for January and February\n", 53 | "import kensu.pandas as pd\n", 54 | "df_jan = pd.read_csv(\"../data/jan/orders.csv\",parse_dates=['date'])\n", 55 | "df_feb = pd.read_csv(\"../data/feb/orders.csv\",parse_dates=['date'])" 56 | ] 57 | }, 58 | { 59 | "cell_type": "markdown", 60 | "id": "a7c5a0e1", 61 | "metadata": {}, 62 | "source": [ 63 | "## Data Transformation\n", 64 | "\n", 65 | "Once the data is loaded, it may require some transformations to be in the desired format. Here, columns are renamed for consistency, and datasets for different months are concatenated to produce the Year-to-Date (YTD) data. This consolidated data provides a comprehensive view of the orders up to the current month." 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": null, 71 | "id": "e745bd4d", 72 | "metadata": {}, 73 | "outputs": [], 74 | "source": [ 75 | "# Rename columns for consistency and concatenate datasets to get YTD data\n", 76 | "df_feb = df_feb.rename({'email_customer':'email'},axis=1)\n", 77 | "data_YTD = pd.concat([df_jan,df_feb])\n", 78 | "data_YTD.to_csv(\"../data/ytd/ordersYTD.csv\",index=False)" 79 | ] 80 | } 81 | ], 82 | "metadata": { 83 | "kernelspec": { 84 | "display_name": "Python 3 (ipykernel)", 85 | "language": "python", 86 | "name": "python3" 87 | }, 88 | "language_info": { 89 | "codemirror_mode": { 90 | "name": "ipython", 91 | "version": 3 92 | }, 93 | "file_extension": ".py", 94 | "mimetype": "text/x-python", 95 | "name": "python", 96 | "nbconvert_exporter": "python", 97 | "pygments_lexer": "ipython3", 98 | "version": "3.8.12" 99 | } 100 | }, 101 | "nbformat": 4, 102 | "nbformat_minor": 5 103 | } 104 | -------------------------------------------------------------------------------- /Chapter4_Data_Observability_Elements/Orders_Predict.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "67dc1738", 6 | "metadata": {}, 7 | "source": [ 8 | "# Predicting Orders with Linear Regression" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "id": "a3977bde", 14 | "metadata": {}, 15 | "source": [ 16 | "## Setup and Initialization" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "id": "37bbdc4c", 22 | "metadata": {}, 23 | "source": [ 24 | "In this section, we initialize the KensuProvider for tracking data operations and import the required libraries." 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": null, 30 | "id": "b43d8f35", 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "# Setup and Initialization\n", 35 | "# Initialize the KensuProvider to start tracking data operations\n", 36 | "import urllib3\n", 37 | "urllib3.disable_warnings()\n", 38 | "\n", 39 | "import kensu.pickle as pickle\n", 40 | "from kensu.sklearn.model_selection import train_test_split\n", 41 | "import kensu.pandas as pd\n", 42 | "\n", 43 | "from kensu.utils.kensu_provider import KensuProvider\n", 44 | "KensuProvider().initKensu(allow_reinit=True)\n" 45 | ] 46 | }, 47 | { 48 | "cell_type": "markdown", 49 | "id": "257127fb", 50 | "metadata": {}, 51 | "source": [ 52 | "## Data Loading" 53 | ] 54 | }, 55 | { 56 | "cell_type": "markdown", 57 | "id": "811dc321", 58 | "metadata": {}, 59 | "source": [ 60 | "Next, we load the orders data from a CSV file and display the first few rows for a quick overview." 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": null, 66 | "id": "32ea5b0d", 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "# Data Loading\n", 71 | "data = pd.read_csv(\"../data/mar/orders.csv\")\n", 72 | "data.head() # Display the first few rows of the dataset for overview\n" 73 | ] 74 | }, 75 | { 76 | "cell_type": "markdown", 77 | "id": "ca972f52", 78 | "metadata": {}, 79 | "source": [ 80 | "## Data Preprocessing" 81 | ] 82 | }, 83 | { 84 | "cell_type": "markdown", 85 | "id": "f7e593d4", 86 | "metadata": {}, 87 | "source": [ 88 | "Here, we preprocess the data by separating it into features (`X`) and target (`y`). We then split these into training and test sets." 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": null, 94 | "id": "391fff7b", 95 | "metadata": {}, 96 | "outputs": [], 97 | "source": [ 98 | "# Data Preprocessing\n", 99 | "df = data[['total_qty', 'total_basket']]\n", 100 | "\n", 101 | "X = df.drop('total_basket', axis=1)\n", 102 | "y = df['total_basket']\n", 103 | "\n", 104 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n" 105 | ] 106 | }, 107 | { 108 | "cell_type": "markdown", 109 | "id": "40c6588d", 110 | "metadata": {}, 111 | "source": [ 112 | "## Model Training" 113 | ] 114 | }, 115 | { 116 | "cell_type": "markdown", 117 | "id": "e529fbb7", 118 | "metadata": {}, 119 | "source": [ 120 | "In this step, we train a linear regression model using the training data." 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": null, 126 | "id": "72e75367", 127 | "metadata": {}, 128 | "outputs": [], 129 | "source": [ 130 | "# Model Training\n", 131 | "from kensu.sklearn.linear_model import LinearRegression\n", 132 | "model = LinearRegression().fit(X_train, y_train)\n" 133 | ] 134 | }, 135 | { 136 | "cell_type": "markdown", 137 | "id": "c88640aa", 138 | "metadata": {}, 139 | "source": [ 140 | "## Model Saving" 141 | ] 142 | }, 143 | { 144 | "cell_type": "markdown", 145 | "id": "ccfa2532", 146 | "metadata": {}, 147 | "source": [ 148 | "Finally, we save the trained model to a pickle file for future use." 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": null, 154 | "id": "caa36202", 155 | "metadata": {}, 156 | "outputs": [], 157 | "source": [ 158 | "# Model Saving\n", 159 | "with open('model.pickle', 'wb') as f:\n", 160 | " pickle.dump(model, f)\n" 161 | ] 162 | } 163 | ], 164 | "metadata": { 165 | "kernelspec": { 166 | "display_name": "Python 3 (ipykernel)", 167 | "language": "python", 168 | "name": "python3" 169 | }, 170 | "language_info": { 171 | "codemirror_mode": { 172 | "name": "ipython", 173 | "version": 3 174 | }, 175 | "file_extension": ".py", 176 | "mimetype": "text/x-python", 177 | "name": "python", 178 | "nbconvert_exporter": "python", 179 | "pygments_lexer": "ipython3", 180 | "version": "3.8.12" 181 | } 182 | }, 183 | "nbformat": 4, 184 | "nbformat_minor": 5 185 | } 186 | -------------------------------------------------------------------------------- /Chapter4_Data_Observability_Elements/conf.ini: -------------------------------------------------------------------------------- 1 | [kensu] 2 | 3 | process_name=Compute_YTD 4 | project_name=Reporting 5 | 6 | report_to_file=True 7 | compute_stats=True 8 | environment=Staging 9 | logical_naming=File 10 | input_stats=True 11 | sklearn_support=True -------------------------------------------------------------------------------- /Chapter5_Defining_Rules_on_Indicators/Age.csv: -------------------------------------------------------------------------------- 1 | id,Age 2 | 1,49 3 | 2,81 4 | 3,17 5 | 4,25 6 | 5,62 7 | 6,60 8 | 7,73 9 | 8,23 10 | 9,31 11 | 10,69 12 | 21,21 13 | 22,85 14 | 23,74 15 | 34,35 16 | 35,98 17 | 36,22 18 | 37,95 19 | 67,63 20 | 80,26 21 | -------------------------------------------------------------------------------- /Chapter5_Defining_Rules_on_Indicators/Data_Observability_Rules.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "db4a4b9c", 6 | "metadata": {}, 7 | "source": [ 8 | "# Data Observability Rules\n", 9 | "\n", 10 | "In this notebook, we will see how rules can act as a pre or a post condition on a use case. \n", 11 | "With customer files, we will create a Personal_data dataset by joining the Name and the Age of customers following their ID. " 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "id": "ab3571fe", 17 | "metadata": {}, 18 | "source": [ 19 | "## Init Agent\n", 20 | "We first init the data observability library, this will allow to track in a log file the data transformation happening in this notebook. " 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "id": "d72462ae", 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "from kensu.utils.kensu_provider import KensuProvider\n", 31 | "K = KensuProvider().initKensu()\n", 32 | "import kensu.pandas as pd" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "id": "6fbfc5e1", 38 | "metadata": {}, 39 | "source": [ 40 | "## Reading data\n", 41 | "Let's read the datasets:\n", 42 | "- The first one, `Name`, contains the full name of the customer, and the ID of the customer\n", 43 | "- The second, `Age`, contains the age and the ID of the customer" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": null, 49 | "id": "fa27e81f", 50 | "metadata": {}, 51 | "outputs": [], 52 | "source": [ 53 | "Name=pd.read_csv('Name_Surname.csv')\n", 54 | "Age=pd.read_csv('Age.csv')" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": null, 60 | "id": "ba8c8e91", 61 | "metadata": {}, 62 | "outputs": [], 63 | "source": [ 64 | "Name" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": null, 70 | "id": "0753de6e", 71 | "metadata": {}, 72 | "outputs": [], 73 | "source": [ 74 | "Age" 75 | ] 76 | }, 77 | { 78 | "cell_type": "markdown", 79 | "id": "0080385a", 80 | "metadata": {}, 81 | "source": [ 82 | "## Pre-Condition\n", 83 | "\n", 84 | "In the following cell, we are checking that the two input files contains the `id` column. \n", 85 | "Without this column, we can't merge the datasets. \n", 86 | "In case `id` is not present, we will have a warning. This will help in case of issue in the merge, to detect or exclude the cause. " 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": null, 92 | "id": "23982f5a", 93 | "metadata": {}, 94 | "outputs": [], 95 | "source": [ 96 | "if 'id' not in Name.columns or 'id' not in Age.columns:\n", 97 | " logging.warning('Missing key in the input file(s)')\n", 98 | " " 99 | ] 100 | }, 101 | { 102 | "cell_type": "markdown", 103 | "id": "1d0f9b9a", 104 | "metadata": {}, 105 | "source": [ 106 | "## Post-Condition\n", 107 | "\n", 108 | "We can now merge the two dataset. " 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": null, 114 | "id": "25ff6ca4", 115 | "metadata": {}, 116 | "outputs": [], 117 | "source": [ 118 | "Personal_data = Name.merge(Age, on='id')" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": null, 124 | "id": "16fd118a", 125 | "metadata": {}, 126 | "outputs": [], 127 | "source": [ 128 | "Personal_data" 129 | ] 130 | }, 131 | { 132 | "cell_type": "markdown", 133 | "id": "60bb2207", 134 | "metadata": {}, 135 | "source": [ 136 | "This check will be performed at the time the `Personal_data.csv` file will be written. \n", 137 | "It will check that the number of rows of the output dataset has the same number of rows as the inputs, meaning that for all the customer ids in the `Name` dataset, we have a corresponding id in the `Age` dataset. " 138 | ] 139 | }, 140 | { 141 | "cell_type": "code", 142 | "execution_count": null, 143 | "id": "12874d83", 144 | "metadata": {}, 145 | "outputs": [], 146 | "source": [ 147 | "from kensu.utils.rule_engine import check_nrows_consistency\n", 148 | "check_nrows_consistency()" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": null, 154 | "id": "88abddef", 155 | "metadata": {}, 156 | "outputs": [], 157 | "source": [ 158 | "Personal_data.to_csv('Personal_data.csv')" 159 | ] 160 | }, 161 | { 162 | "cell_type": "markdown", 163 | "id": "5cb61ba1", 164 | "metadata": {}, 165 | "source": [ 166 | "The warning indicates that only 10 out of 19 customers have their id in the `Age` dataset, meaning that the output dataset is of poor quality. " 167 | ] 168 | } 169 | ], 170 | "metadata": { 171 | "kernelspec": { 172 | "display_name": "Python 3 (ipykernel)", 173 | "language": "python", 174 | "name": "python3" 175 | }, 176 | "language_info": { 177 | "codemirror_mode": { 178 | "name": "ipython", 179 | "version": 3 180 | }, 181 | "file_extension": ".py", 182 | "mimetype": "text/x-python", 183 | "name": "python", 184 | "nbconvert_exporter": "python", 185 | "pygments_lexer": "ipython3", 186 | "version": "3.8.12" 187 | } 188 | }, 189 | "nbformat": 4, 190 | "nbformat_minor": 5 191 | } 192 | -------------------------------------------------------------------------------- /Chapter5_Defining_Rules_on_Indicators/Name_Surname.csv: -------------------------------------------------------------------------------- 1 | id,Name 2 | 1,Michael Smith 3 | 2,Patricia Jones 4 | 3,Mary Jones 5 | 4,Linda Miller 6 | 5,William Davis 7 | 6,William Jones 8 | 7,Michael Johnson 9 | 8,Linda Jones 10 | 9,Jennifer Jones 11 | 10,James Williams 12 | 11,Linda Garcia 13 | 12,Robert Rodriguez 14 | 13,Jennifer Smith 15 | 14,Elizabeth Garcia 16 | 15,John Smith 17 | 16,Elizabeth Johnson 18 | 17,Patricia Williams 19 | 18,Linda Smith 20 | 19,William Williams 21 | 20,Patricia Martinez 22 | -------------------------------------------------------------------------------- /Chapter5_Defining_Rules_on_Indicators/Personal_data.csv: -------------------------------------------------------------------------------- 1 | ,id,Name,Age 2 | 0,1,Michael Smith,49 3 | 1,2,Patricia Jones,81 4 | 2,3,Mary Jones,17 5 | 3,4,Linda Miller,25 6 | 4,5,William Davis,62 7 | 5,6,William Jones,60 8 | 6,7,Michael Johnson,73 9 | 7,8,Linda Jones,23 10 | 8,9,Jennifer Jones,31 11 | 9,10,James Williams,69 12 | -------------------------------------------------------------------------------- /Chapter5_Defining_Rules_on_Indicators/conf.ini: -------------------------------------------------------------------------------- 1 | [kensu] 2 | 3 | process_name=Join 4 | project_names=Project 5 | 6 | report_to_file=True 7 | compute_stats=True 8 | environment=Staging 9 | logical_data_source_naming_strategy=File 10 | input_stats=True 11 | 12 | compute_delta=True -------------------------------------------------------------------------------- /Chapter5_Defining_Rules_on_Indicators/kensu_offline_events.txt: -------------------------------------------------------------------------------- 1 | {"action": "add_entity", "entity": "PHYSICAL_LOCATION", "generatedEntityGUID": "empty", "schemaVersion": "0.1", "jsonPayload": {"name": "Unknown", "lat": 0.12341234, "lon": 0.12341234, "pk": {"city": "Unknown", "country": "Unknown"}}, "context": {"clientId": "", "clientEventTimestamp": 1700603942830, "serverReceivedTimestamp": 1700603942830}} 2 | {"action": "add_entity", "entity": "BATCH_ENTITY_REPORT", "generatedEntityGUID": "empty", "schemaVersion": "0.1", "jsonPayload": {"processes": [{"timestamp": 1700603942830, "entity": {"pk": {"qualifiedName": "Join"}}}], "processRuns": [{"timestamp": 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7 | "source": [ 8 | "# Detect and display anomalies in the observed metrics\n", 9 | "\n", 10 | "This notebook is dedicated to analyzing time series data from observability metrics of an e-commerce platform\n", 11 | "\n", 12 | "We will conduct the following analyses for some of the columns:\n", 13 | "- Generate a time series plot of the data.\n", 14 | "- Decompose the time series into trend, seasonality, and residuals.\n", 15 | "- Perform a week-by-week correlation analysis between the price and quantity.\n" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": null, 21 | "id": "017e970b", 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "import pandas as pd" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "id": "83b3a449", 31 | "metadata": {}, 32 | "source": [ 33 | "## Data Loading and Preliminary Inspection\n", 34 | "\n", 35 | "First, we load our dataset and display the first rows. " 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": null, 41 | "id": "92978141", 42 | "metadata": {}, 43 | "outputs": [], 44 | "source": [ 45 | "import pandas as pd\n", 46 | "df=pd.read_csv('../data/anomalies.csv')\n", 47 | "df.head()" 48 | ] 49 | }, 50 | { 51 | "cell_type": "markdown", 52 | "id": "fb80fecc", 53 | "metadata": {}, 54 | "source": [ 55 | "## Anomaly Detection in `buyer.cookie.duration`\n", 56 | "\n", 57 | "Using statistical methods, we identify days where the average duration spent by buyers on the website (`buyer.cookie.duration`) deviates significantly from the norm. This can help us detect technical issues or exceptional user behavior.\n" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": null, 63 | "id": "50d4597a", 64 | "metadata": {}, 65 | "outputs": [], 66 | "source": [ 67 | "import matplotlib.pyplot as plt\n", 68 | "df['date'] = pd.to_datetime(df['date'])\n", 69 | "\n", 70 | "plt.figure(figsize=(10, 6))\n", 71 | "plt.plot(df['date'], df['buyer.cookie.duration'], marker='o', linestyle='-')\n", 72 | "\n", 73 | "plt.title('buyer.cookie.duration over time')\n", 74 | "plt.xlabel('Date')\n", 75 | "plt.ylabel('buyer.cookie.duration')\n", 76 | "\n", 77 | "# Show the plot\n", 78 | "plt.show()" 79 | ] 80 | }, 81 | { 82 | "cell_type": "markdown", 83 | "id": "85b13738", 84 | "metadata": {}, 85 | "source": [ 86 | "We clearly see a drop in the data in June. Let's use the MAD method to confirm it statistically. " 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": null, 92 | "id": "c08920a6", 93 | "metadata": {}, 94 | "outputs": [], 95 | "source": [ 96 | "# Instantiate the MAD model\n", 97 | "from pyod.models.mad import MAD\n", 98 | "mad = MAD()\n", 99 | "\n", 100 | "# Fit the model on the 'buyer.cookie.duration' column\n", 101 | "mad.fit(df[['buyer.cookie.duration']])\n", 102 | "\n", 103 | "# Get the anomaly scores\n", 104 | "scores = mad.decision_scores_\n", 105 | "\n", 106 | "# Plotting the scores to visualize anomalies\n", 107 | "plt.figure(figsize=(10, 6))\n", 108 | "plt.plot(df['date'], scores, marker='o')\n", 109 | "plt.title('Anomaly Scores from MAD for buyer.cookie.duration')\n", 110 | "plt.xlabel('Date')\n", 111 | "plt.ylabel('Anomaly Score')\n", 112 | "plt.show()" 113 | ] 114 | }, 115 | { 116 | "cell_type": "markdown", 117 | "id": "bacad8c6", 118 | "metadata": {}, 119 | "source": [ 120 | "## Correlation Analysis Methodology\n", 121 | "\n", 122 | "We focus on the relationship between `orders.price.mean` and `orders.quantity.mean`. We calculate the Pearson correlation coefficient for these variables on a weekly basis to observe how their relationship evolves over time." 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": null, 128 | "id": "fdc6af63", 129 | "metadata": {}, 130 | "outputs": [], 131 | "source": [ 132 | "\n", 133 | "from scipy.stats import pearsonr\n", 134 | "df_correlated = df.set_index('date')\n", 135 | "\n", 136 | "\n", 137 | "df_correlated = df_correlated.resample('W').apply(\n", 138 | " lambda x: pearsonr(x['orders.price.mean'], x['orders.quantity.mean'])[0] if len(x) > 1 else np.nan\n", 139 | ")\n", 140 | "\n", 141 | "plt.figure(figsize=(12, 6))\n", 142 | "plt.plot(df_correlated.index, df_correlated, marker='o', linestyle='-')\n", 143 | "plt.title('Weekly Correlation between Price and Quantity')\n", 144 | "plt.xlabel('Week')\n", 145 | "plt.ylabel('Pearson Correlation Coefficient')\n", 146 | "plt.axhline(0, color='gray', linestyle='--')\n", 147 | "plt.show()\n" 148 | ] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "id": "950a9809", 153 | "metadata": {}, 154 | "source": [ 155 | "## Time Series Decomposition\n", 156 | "\n", 157 | "We apply time series decomposition to the `nrows` column to separate it into trend, seasonal, and residual components. This allows us to analyze and understand underlying patterns and outliers in the context of our time series data. The coming cell shows the data overtime, while the next one focuses on the seasonal decomposition of the 6 first weeks of data, where we clearly see an outlier in the residuals. " 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": null, 163 | "id": "b79d95b7", 164 | "metadata": {}, 165 | "outputs": [], 166 | "source": [ 167 | "import matplotlib.pyplot as plt\n", 168 | "df['date'] = pd.to_datetime(df['date'])\n", 169 | "\n", 170 | "\n", 171 | "plt.figure(figsize=(10, 6))\n", 172 | "plt.plot(df['date'], df['nrows'], marker='o', linestyle='-')\n", 173 | "\n", 174 | "\n", 175 | "plt.title('nrows Over Time')\n", 176 | "plt.xlabel('Date')\n", 177 | "plt.ylabel('nrows')\n", 178 | "\n", 179 | "plt.show()\n" 180 | ] 181 | }, 182 | { 183 | "cell_type": "code", 184 | "execution_count": null, 185 | "id": "425eaa19", 186 | "metadata": {}, 187 | "outputs": [], 188 | "source": [ 189 | "\n", 190 | "six_weeks_data = df[df['date'] < (df['date'].min() + pd.Timedelta(weeks=6))]\n", 191 | "\n", 192 | "six_weeks_data['day_of_week'] = six_weeks_data['date'].dt.strftime('%a')\n", 193 | "plt.figure(figsize=(12, 6))\n", 194 | "plt.plot(six_weeks_data['date'], six_weeks_data['nrows'], marker='o', linestyle='-')\n", 195 | "\n", 196 | "plt.title('nrows Over the First 6 Weeks with Days of the Week')\n", 197 | "plt.xlabel('Date')\n", 198 | "plt.ylabel('nrows')\n", 199 | "\n", 200 | "for i in range(len(six_weeks_data)):\n", 201 | " plt.text(six_weeks_data['date'].iloc[i], \n", 202 | " six_weeks_data['nrows'].iloc[i] + 150, # Adjust vertical position for more space\n", 203 | " six_weeks_data['day_of_week'].iloc[i], \n", 204 | " fontsize=8, ha='right')\n", 205 | "\n", 206 | "plt.xticks(rotation=45)\n", 207 | "plt.tight_layout()\n", 208 | "\n", 209 | "plt.show()\n", 210 | "\n", 211 | "\n", 212 | "\n" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": null, 218 | "id": "43f08ab7", 219 | "metadata": {}, 220 | "outputs": [], 221 | "source": [ 222 | "import statsmodels.api as sm\n", 223 | "import matplotlib.pyplot as plt\n", 224 | "decomposition = sm.tsa.seasonal_decompose(six_weeks_data['nrows'], model='additive', period=7)\n", 225 | "fig = decomposition.plot()\n", 226 | "plt.show()\n" 227 | ] 228 | } 229 | ], 230 | "metadata": { 231 | "kernelspec": { 232 | "display_name": "Python 3 (ipykernel)", 233 | "language": "python", 234 | "name": "python3" 235 | }, 236 | "language_info": { 237 | "codemirror_mode": { 238 | "name": "ipython", 239 | "version": 3 240 | }, 241 | "file_extension": ".py", 242 | "mimetype": "text/x-python", 243 | "name": "python", 244 | "nbconvert_exporter": "python", 245 | "pygments_lexer": "ipython3", 246 | "version": "3.8.12" 247 | } 248 | }, 249 | "nbformat": 4, 250 | "nbformat_minor": 5 251 | } 252 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Packt 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Data-Observability-for-Data-Engineering 2 | Data Observability for Data Engineering, published by Packt Publishing 3 | -------------------------------------------------------------------------------- /data/anomalies.csv: -------------------------------------------------------------------------------- 1 | date,nrows,orders.quantity.mean,orders.price.mean,buyer.cookie.duration 2 | 2023-06-19,6374,8.528104691935328,422.9931543305348,12.98396400272424 3 | 2023-06-20,5017,5.800314416734446,375.6828569191906,14.041045998045004 4 | 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| 2021-03-09,bf11f904-0fc7-4b07-a6e4-356dae0b57e5,editzel3y@symantec.com,,341.06,86.02,1,2381 111 | 2021-03-09,4a6ba271-5162-46f1-87ab-e1879e820534,mwynett4n@ovh.net,14.0,713.11,166.08,1,2381 112 | 2021-03-09,a0c82578-9ae7-4f13-b043-7675f99deb05,dsteane1r@aol.com,11.0,296.58,178.23,1,2381 113 | 2021-03-09,37d330de-9ef4-46e1-ac6f-41afc070de78,psiderfin8r@stumbleupon.com,,504.06,46.77,1,2381 114 | 2021-03-09,a5d6a7f6-0f5c-4bfa-984d-936de79a799b,cmatuszakex@devhub.com,6.0,730.83,434.64,1,2381 115 | 2021-03-09,7ea17448-82a5-4157-83d2-57a37cb542bb,jbrosoli4i@gravatar.com,11.0,349.46,164.09,1,2381 116 | 2021-03-09,54530f55-4980-4349-82a8-0052da628989,tpicklessac@npr.org,,208.01,398.17,1,2381 117 | 2021-03-09,83a8c877-6a9c-455c-8b91-51e04cca6de3,eosbaldestonpi@vimeo.com,,490.82,29.47,1,2381 118 | 2021-03-09,68dac990-3ef9-45e4-8f80-127983fecebf,bgladdolphos@dot.gov,4.0,563.67,342.6,1,2381 119 | 2021-03-09,eed384f7-45b8-4227-97ca-ebae03abbf99,lconiam1f@1688.com,,302.45,658.79,1,4713 120 | 2021-03-09,d046d515-9e63-47b2-ab49-3451ba6ff7db,tivanenkopg@columbia.edu,,974.71,219.65,1,2381 121 | 2021-03-09,3cce8fd3-fe8c-407f-93e6-c367af37a991,mdewdenyad@cbslocal.com,,333.56,263.43,1,2381 122 | 2021-03-09,9e9ce69a-8f8c-41e2-b943-75e38167e3bc,ygriffittsb5@sciencedaily.com,,110.16,525.2,1,2381 123 | 2021-03-09,1076ab01-892a-4b2f-ace1-e36d0f760afc,kryley2m@github.com,,130.72,673.62,1,4713 124 | 2021-03-09,188ce283-fda6-4e6e-8b4e-7698e724602f,shamalo9@discovery.com,1.0,785.56,108.3,1,2381 125 | 2021-03-09,03dbabbc-6dd2-45ea-b001-a87e30f77c96,lpoag5x@ox.ac.uk,,714.68,181.91,1,2381 126 | 2021-03-10,b318cb2a-919b-4b7a-8e79-732a8717b2a6,cagerd3@hugedomains.com,13.0,874.72,327.64,1,2381 127 | 2021-03-10,a8786408-861b-49b0-9440-fc6c7699fd77,mcowle8c@meetup.com,8.0,805.33,508.51,1,2381 128 | 2021-03-10,7f3cc498-00ec-412d-b88f-bc70451bc8c4,esunnucks95@msu.edu,13.0,393.17,404.5,1,2381 129 | 2021-03-10,b9c44038-9400-423d-bc69-85630391ac7b,nebertze7@xrea.com,,984.93,123.1,1,2381 130 | 2021-03-10,614b4dc2-295e-4aa1-b1c2-c2d517532e90,emellmerjh@simplemachines.org,10.0,628.74,98.98,1,2381 131 | 2021-03-10,5bb90dec-1082-45f2-92b1-7b919191e15b,emilthorpe9f@youku.com,,935.9,106.02,0,2381 132 | 2021-03-10,f913a394-a99b-41bc-8c8e-4a316efc835a,dclevelandfl@google.com.hk,,653.43,618.45,1,4713 133 | 2021-03-10,8629b9ff-6cb9-4982-a8dd-ba465c543303,mmcairt2r@stanford.edu,17.0,110.27,237.22,1,2381 134 | 2021-03-10,39f71f66-aed1-49c8-916a-163c464673e8,ndennefk@cisco.com,,351.03,446.41,1,2381 135 | 2021-03-10,d96e6c98-c3cf-4279-a518-e5a598af515e,kseeksao@bloglines.com,3.0,64.12,444.84,1,2381 136 | 2021-03-10,d65518b1-9013-42ab-a247-241923f63e3c,jswiffinj3@instagram.com,7.0,808.19,263.85,1,2381 137 | 2021-03-10,c1924f3a-859a-4ad0-b32b-3343001cccfd,audalljw@woothemes.com,7.0,927.41,580.56,1,2381 138 | 2021-03-10,f86bd127-6a89-4161-8d97-2ea00d519e4f,jredley2m@time.com,,926.91,148.49,1,2381 139 | 2021-03-10,72107932-2bf5-4ac6-bb7b-da14511cc7f4,emaslennt@bigcartel.com,,788.2,538.57,1,2381 140 | 2021-03-10,0953970a-9a9d-4077-90fd-81ce5b1759ea,imoter4@usgs.gov,,66.7,759.99,1,4713 141 | 2021-03-10,876130f8-0840-485f-b5de-d5d421479a6e,pmorenai2@nydailynews.com,6.0,714.23,474.06,0,2381 142 | 2021-03-11,c92d3f36-4c3c-407e-ae0b-7adb9d7de61b,jhabbinqn@fotki.com,8.0,425.02,540.07,1,2381 143 | 2021-03-11,d4d7e74f-5c86-484a-b40e-e35f054f582e,mcastagnei3@opensource.org,5.0,926.22,516.33,1,2381 144 | 2021-03-11,9a5dbd7e-9005-40b9-970a-a26d7f149047,cvindenaj@tinyurl.com,,226.13,131.24,1,2381 145 | 2021-03-11,99783e82-3a80-49b9-8b3d-50365ab829a2,crobeled@scribd.com,,535.71,412.49,1,2381 146 | 2021-03-11,b1eaf71e-42cf-4806-b444-9db845c8f770,mwason8j@privacy.gov.au,,545.84,657.31,0,4713 147 | 2021-03-11,a98cf91a-fc0c-4a0b-be12-30d0c0c0ad29,glaskeypt@wordpress.com,,449.38,312.6,1,2381 148 | 2021-03-11,d2b59d43-73b9-4923-9a6b-146628fc190d,awaumsleyqt@techcrunch.com,8.0,846.54,295.01,0,2381 149 | 2021-03-11,d37d3dbc-7e33-4938-b3da-f86508d6a043,abarratd7@ehow.com,11.0,387.73,187.04,0,2381 150 | 2021-03-11,308735b8-21ff-4f3f-8232-dcf67f64a18f,bhadeke93@ustream.tv,,800.08,339.33,0,2381 151 | 2021-03-11,38376917-3639-4b94-a74b-3b75756f486c,dprandyoe@mail.ru,2.0,538.04,19.2,1,2381 152 | 2021-03-11,38f56729-b944-466a-8044-d3d146b6a989,asevinv@google.co.jp,7.0,513.24,469.12,1,2381 153 | 2021-03-12,fbeddbd9-018b-47f7-9ca7-09d070c7d860,jrother11@google.co.uk,6.0,374.76,32.84,1,2381 154 | 2021-03-12,e600a0da-e2e6-4fb9-8263-99e733e820b2,mbenthamkg@usda.gov,11.0,483.7,568.4,1,2381 155 | 2021-03-12,81bdeac6-4ebf-416a-b91f-4e9648c32fe1,mpelferbv@jigsy.com,,398.43,322.3,1,2381 156 | 2021-03-12,b17d49a0-61ad-41aa-b0ca-7bdced8e278e,adimmerln@1688.com,11.0,833.22,255.37,1,2381 157 | 2021-03-12,7fb12fd1-afc9-4b87-aee6-b642a4077b88,kjobke51@nasa.gov,2.0,532.15,352.95,1,2381 158 | 2021-03-12,33b2734f-be23-4b32-b4cb-90cf4bae440d,treinerd@canalblog.com,12.0,474.51,751.99,1,4713 159 | 2021-03-12,f48f43e3-ef31-495b-972d-97727004a9b6,owapplingtonn7@irs.gov,11.0,47.85,412.74,1,2381 160 | 2021-03-12,905a18f8-fed2-4bfd-8403-b61cf8c37907,vhamnetcv@hao123.com,4.0,332.77,480.32,1,2381 161 | 2021-03-12,fc318049-ac14-48fe-aefa-3d47ad37602e,gkaasev@slashdot.org,13.0,497.03,139.47,1,2381 162 | 2021-03-12,de9b2d9d-16c0-4120-b934-72ba405ef473,mjaze48@macromedia.com,5.0,907.49,31.45,1,2381 163 | 2021-03-12,fe0eec61-0cd0-4541-adba-6f694755a7a8,ksoldiqe@boston.com,,560.94,460.36,1,2381 164 | 2021-03-12,387e33ca-6cff-4701-b5e9-be5270447594,idownerjw@hexun.com,13.0,413.1,482.71,1,2381 165 | 2021-03-12,0111cf9c-8d44-452f-9dbf-68edcb41b76c,dcursey3p@infoseek.co.jp,3.0,787.76,237.18,1,2381 166 | 2021-03-12,93494d06-0cb5-4142-a113-a7abba801a78,smillgatejj@twitter.com,,588.8,188.75,0,2381 167 | 2021-03-12,789bc1e2-1891-454d-b62c-da0dcd4ebfb9,mcarnoghandh@sourceforge.net,8.0,322.93,589.9,1,2381 168 | 2021-03-13,6b7d5cf8-dab8-4bda-a225-927145f5dfbb,bfeechum35@moonfruit.com,6.0,676.07,525.94,1,2381 169 | 2021-03-13,abcce0e1-c98b-44ab-a1a4-717d1a037144,gshirrelld2@w3.org,1.0,793.27,98.78,0,2381 170 | 2021-03-13,98282839-11e5-4315-91e5-85c57d414136,pmacdonell4r@nasa.gov,10.0,914.44,263.62,1,2381 171 | 2021-03-13,29c99403-e6a6-4c71-8e42-cd198b9d04be,fmuzzollogs@mit.edu,,932.77,52.68,1,2381 172 | 2021-03-13,993f70af-1327-4e58-b6ac-c60075ae3181,vdunnang6@tinyurl.com,,211.24,730.28,1,4713 173 | 2021-03-13,1c2a787a-0243-4dad-86bc-e0023b240b16,fokanea6@mozilla.org,17.0,775.7,547.7,1,2381 174 | 2021-03-13,06985a8a-b1f5-4b14-809f-8c83c9299193,kpietsmac0@last.fm,11.0,784.58,444.63,1,2381 175 | 2021-03-13,d4fa9cf9-126f-4528-9fb3-4178661739c2,lnelesd@telegraph.co.uk,,197.99,184.24,1,2381 176 | 2021-03-13,99ee65ba-e250-4467-b849-4eab1ce4c187,ojoscelyneol@army.mil,14.0,299.83,247.23,1,2381 177 | 2021-03-13,19cc4b1f-90c1-43f4-8463-d778967787e9,vhawkswellr2@google.ca,14.0,287.75,528.82,1,2381 178 | 2021-03-13,112c2cf7-0c8f-4337-bf5a-821e2935a1de,csent6i@nytimes.com,,620.08,417.41,1,2381 179 | 2021-03-14,11aaa478-9acb-43f9-8a5b-2fd3b5b5f7b1,icoppinsew@histats.com,,765.65,368.64,1,2381 180 | 2021-03-14,6de54393-196e-4030-8d62-5721282cbfa0,jronchkafx@themeforest.net,5.0,324.8,699.41,1,4713 181 | 2021-03-14,aa4a586b-e689-47fd-9ce5-245466d14c95,kblaskettn3@woothemes.com,5.0,372.18,294.98,1,2381 182 | 2021-03-14,c8b40f3d-09f9-4b3b-b0f6-d4f606fb420a,ahowellskp@forbes.com,,41.26,517.18,1,2381 183 | 2021-03-14,74c8c9a7-8458-4d04-b08e-56ebefb79e34,mtrownsonpu@networksolutions.com,,672.08,584.34,1,2381 184 | 2021-03-14,2ec16531-b944-492b-b456-c3b08949dd6d,acohanri@state.gov,,722.87,747.34,1,4713 185 | 2021-03-14,9f496a25-24b0-4011-abb6-44107d2e7ac4,mgrayshanay@biglobe.ne.jp,,339.3,733.98,1,4713 186 | 2021-03-14,adea81d6-9739-4a8c-adf7-a6a6f67d5411,sblyth43@bbc.co.uk,4.0,644.15,775.03,1,4713 187 | 2021-03-14,7949a7d1-478a-4627-b153-6890fb66c3d7,aloaldayit@usgs.gov,,309.42,524.79,1,2381 188 | 2021-03-14,8abcbb87-1267-47f6-90f7-cea6ca39a90b,tcody8i@tinypic.com,11.0,996.33,582.96,1,2381 189 | 2021-03-15,69d8b9ef-7d97-46ba-9ffe-2b9f47a3dc91,nbutsonnj@timesonline.co.uk,5.0,262.85,697.63,1,4713 190 | 2021-03-15,284b6098-9db5-4f1e-b5d9-4e855d6938fe,tmckniely2p@deliciousdays.com,,317.89,727.79,1,4713 191 | 2021-03-15,05b711d7-47f1-4e6e-89ce-4b0f33ff4425,ebraven5n@cbsnews.com,6.0,978.59,550.44,1,2381 192 | 2021-03-15,4a540193-86d7-419d-b566-fd82294a2fc5,tpattyq0@infoseek.co.jp,12.0,78.7,529.16,1,2381 193 | 2021-03-15,42ba28ea-1e81-495b-8c7f-b4864b2744d7,pharsesi9@sphinn.com,,78.88,223.3,1,2381 194 | 2021-03-15,17081706-e4e2-430c-9733-6cfc1c6e7615,kpoundb2@ted.com,7.0,272.1,687.48,1,4713 195 | 2021-03-15,3afccc17-c777-48c9-ab52-aa432e83f54f,hblackburnqs@aboutads.info,15.0,766.88,683.0,1,4713 196 | 2021-03-15,af44eab0-b428-49ba-afb0-da77cf6c7765,rraybouldob@vinaora.com,3.0,546.47,767.79,0,4713 197 | 2021-03-15,6da362c4-91a1-459c-b207-fd46035fa8a5,aemberym3@weibo.com,,797.87,458.06,1,2381 198 | 2021-03-15,2eb82c20-a85e-4dfa-927d-769045b855fc,blupartogg@globo.com,12.0,426.85,116.79,1,2381 199 | 2021-03-15,c8ae8720-0fc2-4dda-b0f6-c69d29769025,ahellekerfr@instagram.com,9.0,127.54,576.54,1,2381 200 | 2021-03-16,fe4c4b66-66ae-4942-a588-1720b87cd87a,flyddiatthk@wufoo.com,,786.72,511.26,1,2381 201 | 2021-03-16,ae425eff-0992-45cd-9afb-d2e351c59298,lspataro3y@amazon.co.jp,10.0,675.54,193.53,1,2381 202 | 2021-03-16,ed9254d6-e67c-4747-8dc1-52d12baeb642,aesson2a@linkedin.com,,332.66,228.85,1,2381 203 | 2021-03-16,7a32d8e0-2547-48f3-8ced-d8198beecf22,hgreenleaf49@wikia.com,,386.11,198.88,1,2381 204 | 2021-03-16,ba89aa23-9d98-480b-85bd-7b721b90ab2f,djinda2g@nsw.gov.au,7.0,494.17,753.6,1,4713 205 | 2021-03-16,0b7235e8-51cf-4464-86da-657e6a756099,bbravec@washingtonpost.com,3.0,65.72,654.67,1,4713 206 | 2021-03-16,362c5326-0f7a-40e8-98e0-74e8fe0691ce,thandsn9@princeton.edu,,157.49,422.11,1,2381 207 | 2021-03-16,ee7349aa-cf1c-4d03-aa58-063569191847,bshinglermp@amazon.co.jp,12.0,849.22,445.95,1,2381 208 | 2021-03-16,1e2f993a-b714-446b-bc7f-738def0b6851,lscarlin1u@walmart.com,,38.17,44.52,1,2381 209 | 2021-03-16,7147c86c-2015-4d3f-9d31-574baee04a2b,memlinlm@smh.com.au,,235.77,443.28,1,2381 210 | 2021-03-16,a625232c-3b03-49fd-bc45-5a5ca3a7cb72,otutept@google.de,,78.14,538.76,1,2381 211 | 2021-03-16,20515dd5-328d-45bd-86ba-761731b670f2,rbissilln2@tinyurl.com,,979.11,157.21,1,2381 212 | 2021-03-16,afe8af26-1394-409f-962d-031ea3bd8392,tklimentovd4@wunderground.com,7.0,285.23,713.56,1,4713 213 | 2021-03-16,11d9f79e-7984-4808-af52-e57b806eb833,rgilbyq8@craigslist.org,2.0,318.19,507.1,1,2381 214 | 2021-03-17,6dc82286-bfc2-4e7e-8672-926435fc5551,ctraynor9n@aol.com,,55.59,374.7,1,2381 215 | 2021-03-17,b1ecb5e5-040e-48f0-ad65-d168ec69b401,cgittins50@hhs.gov,7.0,890.28,647.65,1,4713 216 | 2021-03-17,7bf3c973-39cb-405a-ab45-7412bf41e9fa,jclampettgu@utexas.edu,,798.2,542.85,1,2381 217 | 2021-03-17,7d161113-f04e-4220-b40d-a29ee939da56,bleaming92@livejournal.com,,971.77,344.99,0,2381 218 | 2021-03-17,dbf1cc47-2015-40b6-a029-91ea4f689c14,gshevlinci@deliciousdays.com,9.0,757.19,87.09,1,2381 219 | 2021-03-17,b7b43a45-21b8-4e5e-9247-be6cbb881fa0,abingleo5@weather.com,6.0,958.64,661.77,1,4713 220 | 2021-03-17,bbda9249-e3e4-47fe-bdbc-0dbc9c360c5d,korrahrp@google.ca,10.0,106.34,498.19,1,2381 221 | 2021-03-17,4ae7a335-08d8-4109-a9ee-590fca24c67c,mphilpotm@example.com,,733.49,582.54,1,2381 222 | 2021-03-17,129654a9-b367-465d-812b-be1ed8c80a90,pcarneycn@alibaba.com,,606.9,415.28,1,2381 223 | 2021-03-17,6ec4788c-9aec-4c51-a6e5-ca18129265c4,kblundelb3@nps.gov,9.0,715.79,651.88,1,4713 224 | 2021-03-17,f6422fd5-d8da-4d43-a3d0-7d1eff983459,wdoidgedt@java.com,,171.46,412.85,1,2381 225 | 2021-03-17,4c691c0e-5344-4daa-91c8-17180c738920,rmurriganspp@macromedia.com,5.0,656.59,211.9,1,2381 226 | 2021-03-17,dbab8012-4ed3-482e-afe5-6a5483b7d596,jironsa8@stanford.edu,4.0,975.41,150.71,1,2381 227 | 2021-03-17,a2273875-8d4b-4c2a-ba4b-221bc8951885,deitterfx@discuz.net,9.0,53.65,362.88,1,2381 228 | 2021-03-18,f603f9f8-a88c-4dfa-8bb0-a8928690813a,cmalleyfr@jugem.jp,,899.29,312.56,1,2381 229 | 2021-03-18,5957c31a-1385-45f2-8acc-34c0c9b87ce7,barguile4p@businesswire.com,5.0,862.41,657.99,1,4713 230 | 2021-03-18,7fd2a0ce-9bff-480a-ae6c-b90c3a5ccc80,bbustonba@vistaprint.com,6.0,886.15,58.0,0,2381 231 | 2021-03-18,0ff002de-33e0-4fa6-8db1-55905dea0fca,hseekinger@drupal.org,3.0,535.28,647.29,1,4713 232 | 2021-03-18,b4b64bbe-0789-4754-bc6a-cd0a715062c8,paddisoo@usatoday.com,,132.57,343.94,1,2381 233 | 2021-03-18,d248d1c2-b39d-4338-80b3-ec48f6c33914,bveltenmg@rambler.ru,,35.59,690.75,1,4713 234 | 2021-03-18,1bca9c45-59ab-4d3d-8ec5-035d8c802f0c,veslemont17@istockphoto.com,7.0,807.0,320.47,1,2381 235 | 2021-03-18,9e3ce4b3-e07e-4a97-a4a7-e987ffe6b6e0,araittjp@dot.gov,11.0,39.97,669.22,1,4713 236 | 2021-03-18,27bdf6af-a508-4a27-97ba-e6ca69113b7e,clamanbyh2@seesaa.net,,138.23,336.75,1,2381 237 | 2021-03-18,d668e607-fd2d-42be-833f-13602296c378,mdufourp9@google.ru,,391.05,168.46,1,2381 238 | 2021-03-18,2b3f3ac8-ee1e-46b8-88ec-85fb6bc53617,kruskin6j@bravesites.com,4.0,933.22,116.13,1,2381 239 | 2021-03-18,c9ab85eb-6fc6-4ff7-93fb-079b499c0f20,kivakhnovni@bigcartel.com,,860.05,231.23,1,2381 240 | 2021-03-18,e92ac364-d64b-436d-8c07-56d49a6be9e1,mlhommeedo@google.co.jp,8.0,828.42,16.72,1,2381 241 | 2021-03-19,d52f87d5-7604-4cfd-9e05-83b70301efe5,efilewoodn0@columbia.edu,,985.59,536.41,1,2381 242 | 2021-03-19,42680be8-4281-4479-a6f8-97342de2b33f,asmedmore7h@123-reg.co.uk,3.0,487.09,93.9,1,2381 243 | 2021-03-19,1f31d881-dfaa-48ed-82fd-5d9157b30251,bbellewpz@angelfire.com,,464.91,775.68,1,4713 244 | 2021-03-19,4b721191-1a42-4344-adb4-ed87d8fab11b,rbardsley3o@freewebs.com,,598.28,446.5,1,2381 245 | 2021-03-19,fa4dbf68-6bf5-4f91-a75e-5fe069e0d174,fbelli23@apple.com,,124.8,500.6,0,2381 246 | 2021-03-19,1e529db3-3510-4ec0-8002-c5de0b020cb0,imatczak8w@admin.ch,,556.31,676.64,1,4713 247 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2021-03-19,0ea63cd9-1407-4085-baef-30cf0a6dcfac,akertessf0@hubpages.com,7.0,337.63,781.82,1,4713 257 | 2021-03-19,6f7c723c-9fb0-48b6-a70e-a44c28c3db04,hhoffmann7@nps.gov,6.0,485.39,40.06,1,2381 258 | 2021-03-19,69d8a4fc-725c-47a3-8e17-dcca0728a809,jduckittnt@ca.gov,10.0,526.74,501.69,1,2381 259 | 2021-03-19,e7395191-e62e-406a-b96e-b9ea243f443e,lkibbeyf5@webs.com,13.0,500.85,122.92,1,2381 260 | 2021-03-19,3b47dcfd-4d33-42eb-9c31-bbb6f0226245,cbruckshawlh@mayoclinic.com,5.0,880.99,598.0,1,2381 261 | 2021-03-20,fe777c60-03f4-4331-98cb-5461043d8bf4,pworledgept@amazon.com,9.0,355.33,346.54,1,2381 262 | 2021-03-20,42ebedc9-d399-43dd-941f-3fb98048d246,tgrafton2a@dion.ne.jp,,740.08,154.29,0,2381 263 | 2021-03-20,77f2da91-ca0c-40ed-b90b-ff2a56e47e5f,hgregolpq@g.co,5.0,553.75,187.36,1,2381 264 | 2021-03-20,a2873d01-5db8-47be-8738-f8dcdeecdbd6,hchoker7@elegantthemes.com,6.0,454.39,134.99,1,2381 265 | 2021-03-20,6c935f70-cc8c-46ba-90be-27ee895cee9d,svernini@state.gov,8.0,731.53,407.59,1,2381 266 | 2021-03-20,92a15547-78fb-4f9a-8589-b449052694b5,edigleft@delicious.com,,95.09,34.93,1,2381 267 | 2021-03-20,94924436-633b-4a82-bcc7-6bb149679940,bkirvincj@mayoclinic.com,2.0,330.28,700.24,1,4713 268 | 2021-03-20,3a4ed187-a5ec-49d1-bec3-d99f40e8c4d2,bbengaljv@weebly.com,9.0,632.01,764.18,1,4713 269 | 2021-03-20,b93aa186-b2ca-4d61-977f-68844700d4d2,ghamly5k@bbc.co.uk,,320.32,469.21,1,2381 270 | 2021-03-20,63701e0a-801b-4407-ad1c-9f3a4b6cf730,nconfort11@photobucket.com,10.0,235.64,750.56,1,4713 271 | 2021-03-20,5e419e22-92a2-41ab-ab5c-230b68e58acf,schatwin3k@rediff.com,12.0,359.37,177.33,1,2381 272 | 2021-03-20,16585a8b-40b9-45ec-ab69-86062360fea3,imcgirrdl@is.gd,8.0,801.32,236.17,1,2381 273 | 2021-03-20,94424ee4-8cf2-4f38-97ca-f6320c5322b3,jmaybeyjn@yellowpages.com,,639.4,325.95,0,2381 274 | 2021-03-20,a9f50726-3f62-4a38-8174-5edcafd41e8f,nkopfpq@prnewswire.com,7.0,567.24,397.38,1,2381 275 | 2021-03-20,882c8a74-e95a-4bf6-810b-6e650ccacfba,slivezley16@hud.gov,,849.58,108.86,1,2381 276 | 2021-03-20,5b54549a-6838-49fd-9e5d-02e9b976f87d,rkuhlmeyr9@hp.com,13.0,751.64,734.11,1,4713 277 | 2021-03-21,ff91d375-5dd0-4b97-8762-4a662da8cedb,mservantqu@quantcast.com,,634.15,530.68,1,2381 278 | 2021-03-21,b2cf022b-ab98-419d-a917-219a2628799d,dpatterson8m@ask.com,,144.45,594.25,0,2381 279 | 2021-03-21,0c101e62-45ee-4b41-9a2e-caa414b86696,ewhitcombe3g@purevolume.com,9.0,408.38,374.33,0,2381 280 | 2021-03-21,249b3a30-a4e2-4e3d-9165-e96780327fba,aderoeckpd@upenn.edu,,910.05,396.11,1,2381 281 | 2021-03-21,f44ee8d0-5f54-4c07-ba95-5e4cc12d1be8,ngillingwater4j@instagram.com,9.0,966.48,462.26,1,2381 282 | 2021-03-21,8a41b034-d5e6-4f43-afe4-f2f27ef330ec,lleandern0@netscape.com,5.0,839.37,183.83,1,2381 283 | 2021-03-21,da9a152f-1104-4ea3-ae60-797ab53cf904,kfrosch46@washingtonpost.com,6.0,59.38,409.3,1,2381 284 | 2021-03-21,7e8cc83f-c7fb-4d62-ad3a-9bd97b294497,ebracchi1c@google.co.uk,2.0,421.49,90.73,1,2381 285 | 2021-03-21,3517dd51-5983-418a-8d07-9c04df1a9eb5,pcookeo@creativecommons.org,,502.11,661.42,1,4713 286 | 2021-03-21,186d4e03-687d-42bb-839d-f4a5f00ad9b5,ocamilleri8m@freewebs.com,5.0,773.11,505.17,1,2381 287 | 2021-03-21,ff173cc3-8dd0-4607-9c51-3aa18fe5abf9,keasterlingk8@bluehost.com,5.0,478.3,545.64,1,2381 288 | 2021-03-21,d1fd8dd3-6715-4cae-a0cf-5a5546622350,fpeterffyl3@discuz.net,1.0,409.06,674.54,1,4713 289 | 2021-03-21,b5b7cdae-1b7d-445c-a8bf-f2028caaba80,mwaliszekbc@hibu.com,4.0,644.51,261.1,1,2381 290 | 2021-03-21,4056c9c7-12fe-49a3-a62f-1d667c77e842,cstandeven64@samsung.com,,191.11,752.15,1,4713 291 | 2021-03-21,730a9447-372e-40ff-9550-86b6884ce54a,eblanchflowera@mapquest.com,2.0,770.73,273.57,1,2381 292 | 2021-03-21,1ce2cf5d-929e-4c06-b593-15aaefef85ca,kpoytres42@vkontakte.ru,5.0,336.29,267.34,1,2381 293 | 2021-03-22,6d810c1d-b1d3-4db3-89bf-37d4ea6f26e1,pnorreecv@simplemachines.org,,372.24,95.41,1,2381 294 | 2021-03-22,f381778b-6f33-46ae-855d-856f2a8877a2,goddyem@xinhuanet.com,,863.59,247.49,0,2381 295 | 2021-03-22,b878f76a-f659-41f4-baa1-41b2c2da1b80,cbearblockfx@e-recht24.de,,982.74,220.78,1,2381 296 | 2021-03-22,8998c49a-59e1-4a9e-8a5a-f25fe6611537,clawille5g@theatlantic.com,7.0,752.33,46.33,1,2381 297 | 2021-03-22,e993a6b7-f393-4cdd-9d40-0397908bf3c0,emarnsct@tmall.com,,125.02,369.54,1,2381 298 | 2021-03-22,ea0b2977-1454-4300-9a3f-5b687ca471c7,njankovsky4g@studiopress.com,7.0,215.57,196.02,1,2381 299 | 2021-03-22,322895d8-78fd-455f-aee8-cf7f18aa0375,sbriggjn@indiatimes.com,8.0,471.27,250.23,0,2381 300 | 2021-03-22,dc71049c-07e9-481e-9629-e1bf23440473,dmooneygz@stumbleupon.com,,719.09,136.41,1,2381 301 | 2021-03-22,9565166d-4d5f-4038-a01c-eeff01d206f9,elillecropkt@mayoclinic.com,,676.38,739.84,1,4713 302 | 2021-03-22,2f9b6ae1-ee94-4b27-be10-ab16210cba41,kklemencicr9@fda.gov,,95.11,286.27,1,2381 303 | 2021-03-22,a1a7311f-dc8f-40b7-9556-2f5340b2dcce,stunderd1@pagesperso-orange.fr,,398.59,396.68,1,2381 304 | 2021-03-23,c1cc89f7-bb63-4787-b200-d6978212d1a6,mmerwe2a@icio.us,,835.94,208.58,1,2381 305 | 2021-03-23,cd530c18-8d21-47f9-b35f-0a492b16f371,dwinsbury52@ameblo.jp,,794.04,485.7,1,2381 306 | 2021-03-23,e389e457-b5df-404d-9981-a961bf85eaf1,rsimmonsg9@people.com.cn,7.0,328.55,717.94,1,4713 307 | 2021-03-23,95ed15a7-cdd4-441f-b803-9e8322120061,cdoubravad8@posterous.com,,785.53,228.14,1,2381 308 | 2021-03-23,f9874984-fcdc-468b-8899-a021afc729d7,edourinh9@drupal.org,11.0,365.14,196.19,1,2381 309 | 2021-03-23,1278e456-0873-4ae5-94b0-25b2684b98f9,srance2q@businesswire.com,,915.93,286.07,1,2381 310 | 2021-03-23,bf642367-5a26-4135-8e53-efde7b911b9e,jmanssuerp@netlog.com,,772.99,93.09,1,2381 311 | 2021-03-23,ee264b0b-f54d-4f9c-bb54-05e08506222f,rstrathmanib@rakuten.co.jp,,342.37,520.86,1,2381 312 | 2021-03-23,0d962773-9c32-40f3-85b4-07fb3815cfa4,cabrahams29@nba.com,10.0,457.27,106.54,1,2381 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2021-03-24,6aeea979-3df2-460b-8136-bbe1fd649c72,dphilpottsmb@4shared.com,,312.73,365.73,1,2381 324 | 2021-03-24,634268b7-da88-468d-a4cf-6c9cdcdb42c8,jetchesqx@harvard.edu,,192.28,175.56,1,2381 325 | 2021-03-24,4f31b652-18b3-41c4-b191-ad5cade350f2,eregitzbo@ucsd.edu,,287.48,777.82,1,4713 326 | 2021-03-24,976d7a2e-02e3-464c-8c22-a17ec76af006,kgrinhamhk@lulu.com,,656.5,237.05,1,2381 327 | 2021-03-24,a3e44d19-bfeb-4b69-a45d-3c5ee4156752,shulettlg@edublogs.org,14.0,864.75,72.8,0,2381 328 | 2021-03-24,2788fb04-a83d-4aad-b30c-36145ac68ffe,kgreyes16@wordpress.com,5.0,680.81,344.81,1,2381 329 | 2021-03-24,0ddcb0b2-fd27-4efb-a227-4b519fb07c21,lfaustinp4@cbslocal.com,14.0,236.72,131.5,1,2381 330 | 2021-03-24,d8ed644b-6749-4a12-8fbb-2b9e218e5239,jpaolillo2u@prweb.com,4.0,705.01,608.16,1,4713 331 | 2021-03-24,1fa19cf5-9fb0-4bc1-9a46-55e4beb3afc4,klafontns@phpbb.com,,553.63,765.66,1,4713 332 | 2021-03-24,399b34d5-b3f2-49ca-9c2e-93a074e87b09,betoilekm@reverbnation.com,,754.26,172.65,1,2381 333 | 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2021-02-28,03035bc8-6be2-40fa-8ccc-9353955b1fcc,tvandenhofox@tinyurl.com,11.0,711.0,243.26,0 742 | 2021-02-28,e360b1a1-7948-4fab-8afc-e2c839b3575d,dcastric4@friendfeed.com,5.0,80.84,276.35,1 743 | 2021-02-28,73348e70-d427-4073-bb05-bc47ee92d51f,kskeats5u@discuz.net,7.0,178.99,480.86,0 744 | 2021-02-28,74489af7-0cf5-4d12-95eb-60a985586077,fgreenwellm@time.com,12.0,282.33,391.07,1 745 | 2021-02-28,f7ae7b7a-f499-4d3d-af77-7b63b6027a10,asydenhamt@dyndns.org,6.0,760.42,765.69,1 746 | 2021-02-28,16cf6dfc-f3e8-4f2b-8f40-fe9ee037b37b,lborlease8k@plala.or.jp,3.0,619.78,522.83,1 747 | 2021-02-28,1f17d0dc-deb7-4957-999f-01425ae1e146,carnsonqn@washingtonpost.com,11.0,762.39,241.17,1 748 | 2021-02-28,62626939-e0f9-4cc6-945b-d37a41428927,lwinningdw@businessweek.com,4.0,117.2,157.79,1 749 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pandas==1.3.5 2 | kensu==2.7.1 3 | matplotlib==3.5.0 4 | pyod==1.1.0 5 | scipy==1.7.3 6 | statsmodels==0.13.5 7 | scikit-learn==1.0.2 --------------------------------------------------------------------------------