M3ExploratoryDataAnalysis-lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork928-2023-01-01\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Exploratory Data Analysis Lab**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **30** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this module you get to work with the cleaned dataset from the previous module.\n",
    "\n",
    "In this assignment you will perform the task of exploratory data analysis.\n",
    "You will find out the distribution of data, presence of outliers and also determine the correlation between different columns in the dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab you will perform the following:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-   Identify the distribution of data in the dataset.\n",
    "\n",
    "-   Identify outliers in the dataset.\n",
    "\n",
    "-   Remove outliers from the dataset.\n",
    "\n",
    "-   Identify correlation between features in the dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* * *\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hands on Lab\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the pandas module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn==0.11.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (0.11.0)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from seaborn==0.11.0) (1.21.6)\n",
      "Requirement already satisfied: scipy>=1.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from seaborn==0.11.0) (1.7.3)\n",
      "Requirement already satisfied: pandas>=0.23 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from seaborn==0.11.0) (1.3.5)\n",
      "Requirement already satisfied: matplotlib>=2.2 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from seaborn==0.11.0) (3.5.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.0) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.0) (4.38.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.0) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.0) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.0) (8.1.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.0) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn==0.11.0) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from pandas>=0.23->seaborn==0.11.0) (2023.3)\n",
      "Requirement already satisfied: typing-extensions in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn==0.11.0) (4.5.0)\n",
      "Requirement already satisfied: six>=1.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn==0.11.0) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "%pip install seaborn==0.11.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset into a dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DA0321EN-SkillsNetwork/LargeData/m2_survey_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine how the data is distributed\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column `ConvertedComp` contains Salary converted to annual USD salaries using the exchange rate on 2019-02-01.\n",
    "\n",
    "This assumes 12 working months and 50 working weeks.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution curve for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc6dc12da10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1050x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "sns.displot(df['ConvertedComp'], kind=\"kde\", height=7, aspect = 1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the histogram for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc6dbfcdd50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "sns.displot(df['ConvertedComp'],bins=50, height=7, aspect = 1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the median of the column `ConvertedComp`?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.058200e+04\n",
       "mean     1.315967e+05\n",
       "std      2.947865e+05\n",
       "min      0.000000e+00\n",
       "25%      2.686800e+04\n",
       "50%      5.774500e+04\n",
       "75%      1.000000e+05\n",
       "max      2.000000e+06\n",
       "Name: ConvertedComp, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['ConvertedComp'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many responders identified themselves only as a **Man**?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Man                                                            10480\n",
       "Woman                                                            731\n",
       "Non-binary, genderqueer, or gender non-conforming                 63\n",
       "Man;Non-binary, genderqueer, or gender non-conforming             26\n",
       "Woman;Non-binary, genderqueer, or gender non-conforming           14\n",
       "Woman;Man                                                          9\n",
       "Woman;Man;Non-binary, genderqueer, or gender non-conforming        2\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out the  median ConvertedComp of responders identified themselves only as a **Woman**?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6.790000e+02\n",
       "mean     1.011031e+05\n",
       "std      2.202510e+05\n",
       "min      0.000000e+00\n",
       "25%      2.654650e+04\n",
       "50%      5.770800e+04\n",
       "75%      9.500000e+04\n",
       "max      2.000000e+06\n",
       "Name: ConvertedComp, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['ConvertedComp'][(df.Gender == 'Woman')].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Give the five number summary for the column `Age`?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Double click here for hint**.\n",
    "\n",
    "<!--\n",
    "min,q1,median,q3,max of a column are its five number summary.\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    11111.000000\n",
       "mean        30.778895\n",
       "std          7.393686\n",
       "min         16.000000\n",
       "25%         25.000000\n",
       "50%         29.000000\n",
       "75%         35.000000\n",
       "max         99.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['Age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a histogram of the column `Age`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc6db9a0490>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "sns.displot(df['Age'], height=7, aspect = 1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outliers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding outliers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out if outliers exist in the column `ConvertedComp` using a box plot?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "sns.boxplot(data=df['ConvertedComp'], orient = 'h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out the Inter Quartile Range for the column `ConvertedComp`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1 26868.0\n",
      "Q3 100000.0\n",
      "IQR 73132.0\n"
     ]
    }
   ],
   "source": [
    "# your code goes here\n",
    "q1 = df['ConvertedComp'].quantile(0.25)\n",
    "q3 = df['ConvertedComp'].quantile(0.75)\n",
    "\n",
    "print('Q1', q1)\n",
    "print('Q3', q3)\n",
    "\n",
    "IQR = q3 - q1\n",
    "print('IQR', IQR)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out the upper and lower bounds.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upper bound: 209698.0\n",
      "Lower bound: -82830.0\n"
     ]
    }
   ],
   "source": [
    "# your code goes here\n",
    "up_bound = q3 + IQR*1.5\n",
    "print('Upper bound:', up_bound)\n",
    "\n",
    "lw_bound = q1 - IQR*1.5\n",
    "print('Lower bound:', lw_bound)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify how many outliers are there in the `ConvertedComp` column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "879"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "num_outliers = 0\n",
    "\n",
    "for conv_comp in df['ConvertedComp']:\n",
    "    if (conv_comp < lw_bound) or (conv_comp > up_bound):\n",
    "        num_outliers = num_outliers + 1\n",
    "\n",
    "num_outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new dataframe by removing the outliers from the `ConvertedComp` column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10519, 85)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "outl_index = df[(df.ConvertedComp < lw_bound) | (df.ConvertedComp > up_bound)].index\n",
    "\n",
    "df_outl = df.drop(outl_index)\n",
    "df_outl.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding correlation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the correlation between `Age` and all other numerical columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "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>Respondent</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>ConvertedComp</th>\n",
       "      <th>WorkWeekHrs</th>\n",
       "      <th>CodeRevHrs</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Respondent</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.013490</td>\n",
       "      <td>0.002181</td>\n",
       "      <td>-0.015314</td>\n",
       "      <td>0.004621</td>\n",
       "      <td>0.004041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompTotal</th>\n",
       "      <td>-0.013490</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001037</td>\n",
       "      <td>0.003510</td>\n",
       "      <td>0.007063</td>\n",
       "      <td>0.006970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConvertedComp</th>\n",
       "      <td>0.002181</td>\n",
       "      <td>0.001037</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021143</td>\n",
       "      <td>-0.033865</td>\n",
       "      <td>0.105386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkWeekHrs</th>\n",
       "      <td>-0.015314</td>\n",
       "      <td>0.003510</td>\n",
       "      <td>0.021143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026517</td>\n",
       "      <td>0.036518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CodeRevHrs</th>\n",
       "      <td>0.004621</td>\n",
       "      <td>0.007063</td>\n",
       "      <td>-0.033865</td>\n",
       "      <td>0.026517</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.004041</td>\n",
       "      <td>0.006970</td>\n",
       "      <td>0.105386</td>\n",
       "      <td>0.036518</td>\n",
       "      <td>-0.020469</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Respondent  CompTotal  ConvertedComp  WorkWeekHrs  CodeRevHrs  \\\n",
       "Respondent       1.000000  -0.013490       0.002181    -0.015314    0.004621   \n",
       "CompTotal       -0.013490   1.000000       0.001037     0.003510    0.007063   \n",
       "ConvertedComp    0.002181   0.001037       1.000000     0.021143   -0.033865   \n",
       "WorkWeekHrs     -0.015314   0.003510       0.021143     1.000000    0.026517   \n",
       "CodeRevHrs       0.004621   0.007063      -0.033865     0.026517    1.000000   \n",
       "Age              0.004041   0.006970       0.105386     0.036518   -0.020469   \n",
       "\n",
       "                    Age  \n",
       "Respondent     0.004041  \n",
       "CompTotal      0.006970  \n",
       "ConvertedComp  0.105386  \n",
       "WorkWeekHrs    0.036518  \n",
       "CodeRevHrs    -0.020469  \n",
       "Age            1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ramesh Sannareddy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Contributors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rav Ahuja\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
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    "| Date (YYYY-MM-DD) | Version | Changed By        | Change Description                 |\n",
    "| ----------------- | ------- | ----------------- | ---------------------------------- |\n",
    "| 2020-10-17        | 0.1     | Ramesh Sannareddy | Created initial version of the lab |\n"
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    " Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDA0321ENSkillsNetwork928-2023-01-01&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBM-DA0321EN-SkillsNetwork-21426264&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
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