Day 1 - skill visualization..ipynb

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   "outputs": [],
   "source": [
    "#Import all used modules.\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\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>Name</th>\n",
       "      <th>Skill Set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aishat</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aishat</td>\n",
       "      <td>HTML</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aishat</td>\n",
       "      <td>CSS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aishat</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aishat</td>\n",
       "      <td>Javascript</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aishat</td>\n",
       "      <td>Java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Abdulbaqqi</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Abdulbaqqi</td>\n",
       "      <td>Graphics Design</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Abdulbaqqi</td>\n",
       "      <td>video animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Abdulbaqqi</td>\n",
       "      <td>video editor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bisola</td>\n",
       "      <td>AutoCAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bisola</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bisola</td>\n",
       "      <td>Tailoring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bisola</td>\n",
       "      <td>Data Analysis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bisola</td>\n",
       "      <td>Writing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bisola</td>\n",
       "      <td>Power point</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Bisola</td>\n",
       "      <td>Microsoft Word</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Bisola</td>\n",
       "      <td>Microsoft Excel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Oluwaferanmi</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Oluwaferanmi</td>\n",
       "      <td>HTML</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Oluwaferanmi</td>\n",
       "      <td>CSS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Oluwaferanmi</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Oluwaferanmi</td>\n",
       "      <td>Javascript</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Oluwaferanmi</td>\n",
       "      <td>Java</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Samuel</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Samuel</td>\n",
       "      <td>Machine learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Samuel</td>\n",
       "      <td>MATLAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Samuel</td>\n",
       "      <td>Microsoft Word</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Samuel</td>\n",
       "      <td>Microsoft Excel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Theophilus</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Theophilus</td>\n",
       "      <td>Machine learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Theophilus</td>\n",
       "      <td>Microsoft office</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name          Skill Set\n",
       "0         Aishat             Python\n",
       "1         Aishat               HTML\n",
       "2         Aishat                CSS\n",
       "3         Aishat                  C\n",
       "4         Aishat         Javascript\n",
       "5         Aishat               Java\n",
       "6     Abdulbaqqi             Python\n",
       "7     Abdulbaqqi    Graphics Design\n",
       "8     Abdulbaqqi    video animation\n",
       "9     Abdulbaqqi       video editor\n",
       "10        Bisola            AutoCAD\n",
       "11        Bisola             Python\n",
       "12        Bisola          Tailoring\n",
       "13        Bisola      Data Analysis\n",
       "14        Bisola            Writing\n",
       "15        Bisola        Power point\n",
       "16        Bisola     Microsoft Word\n",
       "17        Bisola    Microsoft Excel\n",
       "18  Oluwaferanmi             Python\n",
       "19  Oluwaferanmi               HTML\n",
       "20  Oluwaferanmi                CSS\n",
       "21  Oluwaferanmi                  C\n",
       "22  Oluwaferanmi         Javascript\n",
       "23  Oluwaferanmi               Java\n",
       "24        Samuel             Python\n",
       "25        Samuel   Machine learning\n",
       "26        Samuel             MATLAB\n",
       "27        Samuel     Microsoft Word\n",
       "28        Samuel    Microsoft Excel\n",
       "29    Theophilus             Python\n",
       "30    Theophilus   Machine learning\n",
       "31    Theophilus   Microsoft office"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Skills = pd.read_csv('Skill_Sets.csv')\n",
    "skills = Skills.drop(['Unnamed: 0'], axis=1)\n",
    "skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Name</th>\n",
       "      <th>Skill Set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abdulbaqqi</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aishat</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bisola</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Oluwaferanmi</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samuel</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Theophilus</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name  Skill Set\n",
       "0    Abdulbaqqi          4\n",
       "1        Aishat          6\n",
       "2        Bisola          8\n",
       "3  Oluwaferanmi          6\n",
       "4        Samuel          5\n",
       "5    Theophilus          3"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "skills_=skills.groupby('Name', as_index=False).count()\n",
    "skills_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Number of skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abdulbaqqi</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aishat</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bisola</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Oluwaferanmi</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samuel</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Theophilus</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name  Number of skills\n",
       "0    Abdulbaqqi                 4\n",
       "1        Aishat                 6\n",
       "2        Bisola                 8\n",
       "3  Oluwaferanmi                 6\n",
       "4        Samuel                 5\n",
       "5    Theophilus                 3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skills_.columns = ['Name','Number of skills']\n",
    "skills_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Changing the datatype in order to read their individual values.\n",
    "Number = [tuple(x) for x in skills_[['Name']].values]\n",
    "z = np.arange(len(Number))\n",
    "Freq = list(skills_['Number of skills'].values)\n",
    "\n",
    "#Plotting bar chart of Frequency against Skill Set.\n",
    "plt.bar(z, [int(x) for x in Freq], align='center', alpha=0.25, color='gold')\n",
    "plt.xticks(z, Number, rotation=90, fontsize=16)\n",
    "plt.yticks(fontsize=7)\n",
    "plt.xlabel('\\nName', color='b', fontsize=14)\n",
    "plt.ylabel('Number of skills\\n', color='b', fontsize=14)\n",
    "plt.title('\\n\\nNumber of Skills per person\\n\\n', fontsize=20, color='r')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#From the plot, it can be deduced that Bisola has the most skills with 8 skills,\n",
    "#Followed by Oluwaferanmi and Aishat with 6 skills each,\n",
    "#Abdulbaqqi with 4 skills,\n",
    "#and lastly Theophilus with 3 skills."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Skill Set</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CSS</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Analysis</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Graphics Design</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HTML</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Java</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Javascript</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MATLAB</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Machine learning</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Microsoft Excel</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Microsoft Word</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Microsoft office</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Power point</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Python</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tailoring</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Writing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>video animation</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>video editor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AutoCAD</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Python</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Skill Set  Name\n",
       "0                   C     2\n",
       "1                 CSS     2\n",
       "2       Data Analysis     1\n",
       "3     Graphics Design     1\n",
       "4                HTML     2\n",
       "5                Java     2\n",
       "6          Javascript     2\n",
       "7              MATLAB     1\n",
       "8    Machine learning     2\n",
       "9     Microsoft Excel     2\n",
       "10     Microsoft Word     2\n",
       "11   Microsoft office     1\n",
       "12        Power point     1\n",
       "13             Python     1\n",
       "14          Tailoring     1\n",
       "15            Writing     1\n",
       "16    video animation     1\n",
       "17       video editor     1\n",
       "18            AutoCAD     1\n",
       "19             Python     5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skill = skills.groupby('Skill Set', as_index=False).count()\n",
    "skill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Skill Set</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CSS</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Analysis</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Graphics Design</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HTML</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Java</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Javascript</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MATLAB</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Machine learning</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Microsoft Excel</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Microsoft Word</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Microsoft office</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Power point</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Python</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tailoring</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Writing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>video animation</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>video editor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AutoCAD</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Python</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Skill Set  Frequency\n",
       "0                   C          2\n",
       "1                 CSS          2\n",
       "2       Data Analysis          1\n",
       "3     Graphics Design          1\n",
       "4                HTML          2\n",
       "5                Java          2\n",
       "6          Javascript          2\n",
       "7              MATLAB          1\n",
       "8    Machine learning          2\n",
       "9     Microsoft Excel          2\n",
       "10     Microsoft Word          2\n",
       "11   Microsoft office          1\n",
       "12        Power point          1\n",
       "13             Python          1\n",
       "14          Tailoring          1\n",
       "15            Writing          1\n",
       "16    video animation          1\n",
       "17       video editor          1\n",
       "18            AutoCAD          1\n",
       "19             Python          5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Freq_ = skill[['Skill Set', 'Name']]\n",
    "Freq_.columns= ['Skill Set', 'Frequency']\n",
    "Freq_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Changing the datatype in order to read their individual values.\n",
    "Skill = [tuple(x) for x in Freq_[['Skill Set']].values]\n",
    "z = np.arange(len(Skill))\n",
    "Freq = list(Freq_['Frequency'].values)\n",
    "\n",
    "#Plotting bar chart of Frequency against Skill Set.\n",
    "plt.bar(z, [int(x) for x in Freq], align='center', alpha=0.5, color='b')\n",
    "plt.xticks(z, Skill, rotation=90, fontsize=12)\n",
    "plt.yticks(fontsize=7)\n",
    "plt.xlabel('\\nSkill', color='r')\n",
    "plt.ylabel('Frequency\\n', color='r')\n",
    "plt.title('Frequency of Skills\\n\\n', fontsize=14, color='r')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The plot shows that python is the most learned skill among the data sets with 5 people\n",
    "#C, CSS,HTML, Java, Javascript, Machine learning, Microsoft word and excel are next with 2 people each\n",
    "#And the remaining skills are unique to the individuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Name</th>\n",
       "      <th>Number of skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Abdulbaqqi</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Aishat</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Bisola</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Oluwaferanmi</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Samuel</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Theophilus</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          Name  Number of skills\n",
       "0           0    Abdulbaqqi                 4\n",
       "1           1        Aishat                 6\n",
       "2           2        Bisola                 8\n",
       "3           3  Oluwaferanmi                 6\n",
       "4           4        Samuel                 5\n",
       "5           5    Theophilus                 3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skills_.to_csv('Skill_analysis.csv', encoding='utf-8')\n",
    "pd.read_csv('Skill_analysis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Skill Set</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CSS</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Data Analysis</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Graphics Design</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>HTML</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Java</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Javascript</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>MATLAB</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Machine learning</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Microsoft Excel</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Microsoft Word</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Microsoft office</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Power point</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Python</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Tailoring</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Writing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>video animation</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>video editor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>AutoCAD</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Python</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0          Skill Set  Frequency\n",
       "0            0                  C          2\n",
       "1            1                CSS          2\n",
       "2            2      Data Analysis          1\n",
       "3            3    Graphics Design          1\n",
       "4            4               HTML          2\n",
       "5            5               Java          2\n",
       "6            6         Javascript          2\n",
       "7            7             MATLAB          1\n",
       "8            8   Machine learning          2\n",
       "9            9    Microsoft Excel          2\n",
       "10          10     Microsoft Word          2\n",
       "11          11   Microsoft office          1\n",
       "12          12        Power point          1\n",
       "13          13             Python          1\n",
       "14          14          Tailoring          1\n",
       "15          15            Writing          1\n",
       "16          16    video animation          1\n",
       "17          17       video editor          1\n",
       "18          18            AutoCAD          1\n",
       "19          19             Python          5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Freq_.to_csv('Skill_frequency.csv', encoding='utf-8')\n",
    "pd.read_csv('Skill_frequency.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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