assignment03-pie.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Assignment 3**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pie chart of email domains from the csv file using a url."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Import libraries \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Load the data \n",
    "df = pd.read_csv('https://drive.google.com/uc?id=1AWPf-pJodJKeHsARQK_RHiNsE8fjPCVK&export=download')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Index</th>\n",
       "      <th>User Id</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Email</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Date of birth</th>\n",
       "      <th>Job Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8717bbf45cCDbEe</td>\n",
       "      <td>Shelia</td>\n",
       "      <td>Mahoney</td>\n",
       "      <td>Male</td>\n",
       "      <td>pwarner@example.org</td>\n",
       "      <td>857.139.8239</td>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>Probation officer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3d5AD30A4cD38ed</td>\n",
       "      <td>Jo</td>\n",
       "      <td>Rivers</td>\n",
       "      <td>Female</td>\n",
       "      <td>fergusonkatherine@example.net</td>\n",
       "      <td>+1-950-759-8687</td>\n",
       "      <td>1931-07-26</td>\n",
       "      <td>Dancer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>810Ce0F276Badec</td>\n",
       "      <td>Sheryl</td>\n",
       "      <td>Lowery</td>\n",
       "      <td>Female</td>\n",
       "      <td>fhoward@example.org</td>\n",
       "      <td>(599)782-0605</td>\n",
       "      <td>2013-11-25</td>\n",
       "      <td>Copy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>BF2a889C00f0cE1</td>\n",
       "      <td>Whitney</td>\n",
       "      <td>Hooper</td>\n",
       "      <td>Male</td>\n",
       "      <td>zjohnston@example.com</td>\n",
       "      <td>+1-939-130-6258</td>\n",
       "      <td>2012-11-17</td>\n",
       "      <td>Counselling psychologist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9afFEafAe1CBBB9</td>\n",
       "      <td>Lindsey</td>\n",
       "      <td>Rice</td>\n",
       "      <td>Female</td>\n",
       "      <td>elin@example.net</td>\n",
       "      <td>(390)417-1635x3010</td>\n",
       "      <td>1923-04-15</td>\n",
       "      <td>Biomedical engineer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index          User Id First Name Last Name     Sex  \\\n",
       "0      1  8717bbf45cCDbEe     Shelia   Mahoney    Male   \n",
       "1      2  3d5AD30A4cD38ed         Jo    Rivers  Female   \n",
       "2      3  810Ce0F276Badec     Sheryl    Lowery  Female   \n",
       "3      4  BF2a889C00f0cE1    Whitney    Hooper    Male   \n",
       "4      5  9afFEafAe1CBBB9    Lindsey      Rice  Female   \n",
       "\n",
       "                           Email               Phone Date of birth  \\\n",
       "0            pwarner@example.org        857.139.8239    2014-01-27   \n",
       "1  fergusonkatherine@example.net     +1-950-759-8687    1931-07-26   \n",
       "2            fhoward@example.org       (599)782-0605    2013-11-25   \n",
       "3          zjohnston@example.com     +1-939-130-6258    2012-11-17   \n",
       "4               elin@example.net  (390)417-1635x3010    1923-04-15   \n",
       "\n",
       "                  Job Title  \n",
       "0         Probation officer  \n",
       "1                    Dancer  \n",
       "2                      Copy  \n",
       "3  Counselling psychologist  \n",
       "4       Biomedical engineer  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first 5 rows of the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>User Id</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Email</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Date of birth</th>\n",
       "      <th>Job Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>996</td>\n",
       "      <td>fedF4c7Fd9e7cFa</td>\n",
       "      <td>Kurt</td>\n",
       "      <td>Bryant</td>\n",
       "      <td>Female</td>\n",
       "      <td>lyonsdaisy@example.net</td>\n",
       "      <td>021.775.2933</td>\n",
       "      <td>1959-01-05</td>\n",
       "      <td>Personnel officer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>ECddaFEDdEc4FAB</td>\n",
       "      <td>Donna</td>\n",
       "      <td>Barry</td>\n",
       "      <td>Female</td>\n",
       "      <td>dariusbryan@example.com</td>\n",
       "      <td>001-149-710-7799x721</td>\n",
       "      <td>2001-10-06</td>\n",
       "      <td>Education administrator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>2adde51d8B8979E</td>\n",
       "      <td>Cathy</td>\n",
       "      <td>Mckinney</td>\n",
       "      <td>Female</td>\n",
       "      <td>georgechan@example.org</td>\n",
       "      <td>+1-750-774-4128x33265</td>\n",
       "      <td>1918-05-13</td>\n",
       "      <td>Commercial/residential surveyor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>Fb2FE369D1E171A</td>\n",
       "      <td>Jermaine</td>\n",
       "      <td>Phelps</td>\n",
       "      <td>Male</td>\n",
       "      <td>wanda04@example.net</td>\n",
       "      <td>(915)292-2254</td>\n",
       "      <td>1971-08-31</td>\n",
       "      <td>Ambulance person</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>8b756f6231DDC6e</td>\n",
       "      <td>Lee</td>\n",
       "      <td>Tran</td>\n",
       "      <td>Female</td>\n",
       "      <td>deannablack@example.org</td>\n",
       "      <td>079.752.5424x67259</td>\n",
       "      <td>1947-01-24</td>\n",
       "      <td>Nurse, learning disability</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Index          User Id First Name Last Name     Sex  \\\n",
       "995    996  fedF4c7Fd9e7cFa       Kurt    Bryant  Female   \n",
       "996    997  ECddaFEDdEc4FAB      Donna     Barry  Female   \n",
       "997    998  2adde51d8B8979E      Cathy  Mckinney  Female   \n",
       "998    999  Fb2FE369D1E171A   Jermaine    Phelps    Male   \n",
       "999   1000  8b756f6231DDC6e        Lee      Tran  Female   \n",
       "\n",
       "                       Email                  Phone Date of birth  \\\n",
       "995   lyonsdaisy@example.net           021.775.2933    1959-01-05   \n",
       "996  dariusbryan@example.com   001-149-710-7799x721    2001-10-06   \n",
       "997   georgechan@example.org  +1-750-774-4128x33265    1918-05-13   \n",
       "998      wanda04@example.net          (915)292-2254    1971-08-31   \n",
       "999  deannablack@example.org     079.752.5424x67259    1947-01-24   \n",
       "\n",
       "                           Job Title  \n",
       "995                Personnel officer  \n",
       "996          Education administrator  \n",
       "997  Commercial/residential surveyor  \n",
       "998                 Ambulance person  \n",
       "999       Nurse, learning disability  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the last 5 rows of the data\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                pwarner@example.org\n",
       "1      fergusonkatherine@example.net\n",
       "2                fhoward@example.org\n",
       "3              zjohnston@example.com\n",
       "4                   elin@example.net\n",
       "                   ...              \n",
       "995           lyonsdaisy@example.net\n",
       "996          dariusbryan@example.com\n",
       "997           georgechan@example.org\n",
       "998              wanda04@example.net\n",
       "999          deannablack@example.org\n",
       "Name: Email, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display email column\n",
    "df.Email"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Index          1000 non-null   int64 \n",
      " 1   User Id        1000 non-null   object\n",
      " 2   First Name     1000 non-null   object\n",
      " 3   Last Name      1000 non-null   object\n",
      " 4   Sex            1000 non-null   object\n",
      " 5   Email          1000 non-null   object\n",
      " 6   Phone          1000 non-null   object\n",
      " 7   Date of birth  1000 non-null   object\n",
      " 8   Job Title      1000 non-null   object\n",
      "dtypes: int64(1), object(8)\n",
      "memory usage: 70.4+ KB\n"
     ]
    }
   ],
   "source": [
    "info = df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>User Id</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Email</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Date of birth</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Email_Domain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8717bbf45cCDbEe</td>\n",
       "      <td>Shelia</td>\n",
       "      <td>Mahoney</td>\n",
       "      <td>Male</td>\n",
       "      <td>pwarner@example.org</td>\n",
       "      <td>857.139.8239</td>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>Probation officer</td>\n",
       "      <td>example.org</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3d5AD30A4cD38ed</td>\n",
       "      <td>Jo</td>\n",
       "      <td>Rivers</td>\n",
       "      <td>Female</td>\n",
       "      <td>fergusonkatherine@example.net</td>\n",
       "      <td>+1-950-759-8687</td>\n",
       "      <td>1931-07-26</td>\n",
       "      <td>Dancer</td>\n",
       "      <td>example.net</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>810Ce0F276Badec</td>\n",
       "      <td>Sheryl</td>\n",
       "      <td>Lowery</td>\n",
       "      <td>Female</td>\n",
       "      <td>fhoward@example.org</td>\n",
       "      <td>(599)782-0605</td>\n",
       "      <td>2013-11-25</td>\n",
       "      <td>Copy</td>\n",
       "      <td>example.org</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>BF2a889C00f0cE1</td>\n",
       "      <td>Whitney</td>\n",
       "      <td>Hooper</td>\n",
       "      <td>Male</td>\n",
       "      <td>zjohnston@example.com</td>\n",
       "      <td>+1-939-130-6258</td>\n",
       "      <td>2012-11-17</td>\n",
       "      <td>Counselling psychologist</td>\n",
       "      <td>example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9afFEafAe1CBBB9</td>\n",
       "      <td>Lindsey</td>\n",
       "      <td>Rice</td>\n",
       "      <td>Female</td>\n",
       "      <td>elin@example.net</td>\n",
       "      <td>(390)417-1635x3010</td>\n",
       "      <td>1923-04-15</td>\n",
       "      <td>Biomedical engineer</td>\n",
       "      <td>example.net</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index          User Id First Name Last Name     Sex  \\\n",
       "0      1  8717bbf45cCDbEe     Shelia   Mahoney    Male   \n",
       "1      2  3d5AD30A4cD38ed         Jo    Rivers  Female   \n",
       "2      3  810Ce0F276Badec     Sheryl    Lowery  Female   \n",
       "3      4  BF2a889C00f0cE1    Whitney    Hooper    Male   \n",
       "4      5  9afFEafAe1CBBB9    Lindsey      Rice  Female   \n",
       "\n",
       "                           Email               Phone Date of birth  \\\n",
       "0            pwarner@example.org        857.139.8239    2014-01-27   \n",
       "1  fergusonkatherine@example.net     +1-950-759-8687    1931-07-26   \n",
       "2            fhoward@example.org       (599)782-0605    2013-11-25   \n",
       "3          zjohnston@example.com     +1-939-130-6258    2012-11-17   \n",
       "4               elin@example.net  (390)417-1635x3010    1923-04-15   \n",
       "\n",
       "                  Job Title Email_Domain  \n",
       "0         Probation officer  example.org  \n",
       "1                    Dancer  example.net  \n",
       "2                      Copy  example.org  \n",
       "3  Counselling psychologist  example.com  \n",
       "4       Biomedical engineer  example.net  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# regex pattern to get the domain of the email address and store it in a new column called Email_Domain. \n",
    "\n",
    "df['Email_Domain'] = df['Email'].str.extract(r'@(.+)$') # Extracting the domain from the email address. (.+) is a regex pattern that matches one or more of any character. $ is a special character that matches the end of a string.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#df['domain'] = df.Email.str.split('@').str[1]   \n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Email_Domain\n",
       "example.org    341\n",
       "example.com    339\n",
       "example.net    320\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show amount of domains\n",
    "df['Email_Domain'].nunique() # nunique() is used to get the number of unique values in the column.\n",
    "\n",
    "# show top 3 domains\n",
    "df['Email_Domain'].value_counts().head(3)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "email_colors = ['#FF66B2', '#66B3FF', '#99FF99']   \n",
    "\n",
    "# plot pie chart of email domains\n",
    "domain_counts = df['Email_Domain'].value_counts()\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.pie(domain_counts, labels=domain_counts.index, autopct='%1.1f%%', colors=email_colors, shadow=True)\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.title('Email Domains', size=20, color='blue', loc='center', pad=30, fontweight='bold')\n",
    "\n",
    "plt.legend(title='Email Domains', title_fontsize='large', loc='upper right', fontsize='medium', shadow=True)\n",
    "plt.savefig('email_domains.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### **End**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}