Exercises - Week 1 to 6 of Statistics for Data Science with Python/2. Visualizing_Data.ipynb

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   "source": [
    "<center>\n",
    "    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Data Visualization**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **30** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab, you will learn how to visualize and interpret data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-   Import Libraries\n",
    "-   Lab Exercises\n",
    "    -   Identifying duplicates\n",
    "    -   Plotting Scatterplots\n",
    "    -   Plotting Boxplots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* * *\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All Libraries required for this lab are listed below. The libraries pre-installed on Skills Network Labs are commented. If you run this notebook in a different environment, e.g. your desktop, you may need to uncomment and install certain libraries.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install pandas\n",
    "# !pip install numpy\n",
    "# !pip install matplotlib\n",
    "# !pip install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the libraries we need for the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the csv file from the url using the request library\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>77</td>\n",
       "      <td>123</td>\n",
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       "      <td>0.933396</td>\n",
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       "      <td>upper</td>\n",
       "      <td>yes</td>\n",
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       "      <td>0.111563</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>34</td>\n",
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       "      <td>upper</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>45</td>\n",
       "      <td>86</td>\n",
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       "      <td>0.643014</td>\n",
       "      <td>4.3</td>\n",
       "      <td>upper</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>22</td>\n",
       "      <td>29</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>51</td>\n",
       "      <td>female</td>\n",
       "      <td>more</td>\n",
       "      <td>0.391822</td>\n",
       "      <td>3.0</td>\n",
       "      <td>upper</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
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       "<p>463 rows × 19 columns</p>\n",
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       "    minority  age  gender credits    beauty  eval division native tenure  \\\n",
       "0        yes   36  female    more  0.289916   4.3    upper    yes    yes   \n",
       "1        yes   36  female    more  0.289916   3.7    upper    yes    yes   \n",
       "2        yes   36  female    more  0.289916   3.6    upper    yes    yes   \n",
       "3        yes   36  female    more  0.289916   4.4    upper    yes    yes   \n",
       "4         no   59    male    more -0.737732   4.5    upper    yes    yes   \n",
       "..       ...  ...     ...     ...       ...   ...      ...    ...    ...   \n",
       "458       no   37    male    more  0.933396   3.5    upper    yes    yes   \n",
       "459       no   58  female    more  0.111563   3.5    upper    yes    yes   \n",
       "460       no   42    male    more -0.900580   4.0    upper    yes    yes   \n",
       "461       no   39    male    more  0.643014   4.3    upper    yes    yes   \n",
       "462       no   51  female    more  0.391822   3.0    upper    yes    yes   \n",
       "\n",
       "     students  allstudents  prof  PrimaryLast  vismin  female  single_credit  \\\n",
       "0          24           43     1            0       1       1              0   \n",
       "1          86          125     1            0       1       1              0   \n",
       "2          76          125     1            0       1       1              0   \n",
       "3          77          123     1            1       1       1              0   \n",
       "4          17           20     2            0       0       0              0   \n",
       "..        ...          ...   ...          ...     ...     ...            ...   \n",
       "458        15           17    40            1       0       0              0   \n",
       "459        26           34    47            1       0       1              0   \n",
       "460        45           86    61            1       0       0              0   \n",
       "461        22           29    62            1       0       0              0   \n",
       "462        47           67    69            1       0       1              0   \n",
       "\n",
       "     upper_division  English_speaker  tenured_prof  \n",
       "0                 1                1             1  \n",
       "1                 1                1             1  \n",
       "2                 1                1             1  \n",
       "3                 1                1             1  \n",
       "4                 1                1             1  \n",
       "..              ...              ...           ...  \n",
       "458               1                1             1  \n",
       "459               1                1             1  \n",
       "460               1                1             1  \n",
       "461               1                1             1  \n",
       "462               1                1             1  \n",
       "\n",
       "[463 rows x 19 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_url = 'teachingratings.csv'\n",
    "ratings_df = pd.read_csv(ratings_url)\n",
    "ratings_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lab Exercises\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify all duplicate cases using prof. Using all observations, find the average and standard deviation for age. Repeat the analysis by first filtering the data set to include one observation for each instructor with a total number of observations restricted to 94.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify all duplicate cases using prof variable - find the unique values of the prof variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36,\n",
       "       37, 38, 39, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55,\n",
       "       56, 57, 58, 59, 60, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 74, 75,\n",
       "       76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,\n",
       "       93, 94, 22, 30, 40, 47, 61, 62, 69], dtype=int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df.prof.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print out the number of unique values in the prof variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df.prof.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using all observations, Find the average and standard deviation for age\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.365010799136066"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.802742037864821"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df['age'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat the analysis by first filtering the data set to include one observation for each instructor with a total number of observations restricted to 94.\n",
    "\n",
    "> first we drop duplicates using prof as a subset and assign it a new dataframe name called no_duplicates_ratings_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <th>female</th>\n",
       "      <th>single_credit</th>\n",
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       "      <td>yes</td>\n",
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       "      <td>59</td>\n",
       "      <td>male</td>\n",
       "      <td>more</td>\n",
       "      <td>-0.737732</td>\n",
       "      <td>4.5</td>\n",
       "      <td>upper</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>no</td>\n",
       "      <td>51</td>\n",
       "      <td>male</td>\n",
       "      <td>more</td>\n",
       "      <td>-0.571984</td>\n",
       "      <td>3.7</td>\n",
       "      <td>upper</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>no</td>\n",
       "      <td>40</td>\n",
       "      <td>female</td>\n",
       "      <td>more</td>\n",
       "      <td>-0.677963</td>\n",
       "      <td>4.3</td>\n",
       "      <td>upper</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>40</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>no</td>\n",
       "      <td>31</td>\n",
       "      <td>female</td>\n",
       "      <td>more</td>\n",
       "      <td>1.509794</td>\n",
       "      <td>4.4</td>\n",
       "      <td>upper</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>42</td>\n",
       "      <td>48</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   minority  age  gender credits    beauty  eval division native tenure  \\\n",
       "0       yes   36  female    more  0.289916   4.3    upper    yes    yes   \n",
       "4        no   59    male    more -0.737732   4.5    upper    yes    yes   \n",
       "7        no   51    male    more -0.571984   3.7    upper    yes    yes   \n",
       "9        no   40  female    more -0.677963   4.3    upper    yes    yes   \n",
       "17       no   31  female    more  1.509794   4.4    upper    yes    yes   \n",
       "\n",
       "    students  allstudents  prof  PrimaryLast  vismin  female  single_credit  \\\n",
       "0         24           43     1            0       1       1              0   \n",
       "4         17           20     2            0       0       0              0   \n",
       "7         55           55     3            0       0       0              0   \n",
       "9         40           46     4            0       0       1              0   \n",
       "17        42           48     5            0       0       1              0   \n",
       "\n",
       "    upper_division  English_speaker  tenured_prof  \n",
       "0                1                1             1  \n",
       "4                1                1             1  \n",
       "7                1                1             1  \n",
       "9                1                1             1  \n",
       "17               1                1             1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_duplicates_ratings_df = ratings_df.drop_duplicates(subset =['prof'])\n",
    "no_duplicates_ratings_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Use the new dataset to get the mean of age\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.5531914893617"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_duplicates_ratings_df['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.25651329515495"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_duplicates_ratings_df['age'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using a bar chart, demonstrate if instructors teaching lower-division courses receive higher average teaching evaluations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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>minority</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>credits</th>\n",
       "      <th>beauty</th>\n",
       "      <th>eval</th>\n",
       "      <th>division</th>\n",
       "      <th>native</th>\n",
       "      <th>tenure</th>\n",
       "      <th>students</th>\n",
       "      <th>allstudents</th>\n",
       "      <th>prof</th>\n",
       "      <th>PrimaryLast</th>\n",
       "      <th>vismin</th>\n",
       "      <th>female</th>\n",
       "      <th>single_credit</th>\n",
       "      <th>upper_division</th>\n",
       "      <th>English_speaker</th>\n",
       "      <th>tenured_prof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>yes</td>\n",
       "      <td>36</td>\n",
       "      <td>female</td>\n",
       "      <td>more</td>\n",
       "      <td>0.289916</td>\n",
       "      <td>4.3</td>\n",
       "      <td>upper</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>24</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yes</td>\n",
       "      <td>36</td>\n",
       "      <td>female</td>\n",
       "      <td>more</td>\n",
       "      <td>0.289916</td>\n",
       "      <td>3.7</td>\n",
       "      <td>upper</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>86</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yes</td>\n",
       "      <td>36</td>\n",
       "      <td>female</td>\n",
       "      <td>more</td>\n",
       "      <td>0.289916</td>\n",
       "      <td>3.6</td>\n",
       "      <td>upper</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>76</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>yes</td>\n",
       "      <td>36</td>\n",
       "      <td>female</td>\n",
       "      <td>more</td>\n",
       "      <td>0.289916</td>\n",
       "      <td>4.4</td>\n",
       "      <td>upper</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>77</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>no</td>\n",
       "      <td>59</td>\n",
       "      <td>male</td>\n",
       "      <td>more</td>\n",
       "      <td>-0.737732</td>\n",
       "      <td>4.5</td>\n",
       "      <td>upper</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  minority  age  gender credits    beauty  eval division native tenure  \\\n",
       "0      yes   36  female    more  0.289916   4.3    upper    yes    yes   \n",
       "1      yes   36  female    more  0.289916   3.7    upper    yes    yes   \n",
       "2      yes   36  female    more  0.289916   3.6    upper    yes    yes   \n",
       "3      yes   36  female    more  0.289916   4.4    upper    yes    yes   \n",
       "4       no   59    male    more -0.737732   4.5    upper    yes    yes   \n",
       "\n",
       "   students  allstudents  prof  PrimaryLast  vismin  female  single_credit  \\\n",
       "0        24           43     1            0       1       1              0   \n",
       "1        86          125     1            0       1       1              0   \n",
       "2        76          125     1            0       1       1              0   \n",
       "3        77          123     1            1       1       1              0   \n",
       "4        17           20     2            0       0       0              0   \n",
       "\n",
       "   upper_division  English_speaker  tenured_prof  \n",
       "0               1                1             1  \n",
       "1               1                1             1  \n",
       "2               1                1             1  \n",
       "3               1                1             1  \n",
       "4               1                1             1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the average teaching evaluation in both groups of upper and lower-division\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "division_eval = ratings_df.groupby('division')[['eval']].mean().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the barplot using the seaborn library\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.barplot(x=\"division\", y=\"eval\", data=division_eval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using gender-differentiated scatter plots, plot the relationship between age and teaching evaluation scores.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a scatterplot with the seaborn library\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(x='age', y='eval', hue='gender',\n",
    "                     data=ratings_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a box plot for beauty scores differentiated by credits.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x='credits', y='beauty', data=ratings_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the number of courses taught by gender?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x296893b1460>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='gender', kind='count', data=ratings_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a group histogram of taught by gender and tenure\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will add the hue argument = Tenure\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x296893ea0d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 416.6x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='gender', hue = 'tenure', kind='count', data=ratings_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add division as another factor to the above histogram\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add another argument named row and use the division variable as the row\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x296893d8f70>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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akgbd0y4qKlJtba169+7d6ANv27bN/vR5r169tH79evu2+Ph45eTkNCi0zWZzo2uSpMDAwCZppzXJz893dwloApz7Dce57x6t+Vx1KLRPnDihJ554QmfOnJHValWXLl20YcMG+fv739FBrz6BvnLlSknSkSNHVFhYaJ+j22azycOjYbfbAwIC5O3tfUf1oHFa8y8QWjfOfbiaQzeNly5dqpkzZ+rw4cPKz8/XnDlz9OKLL97xQY8cOaLvf//76tChg6QrIb18+XJduHBBNTU12r59O0+OAwBwHYdC+9///rcmTpxoX46Ojtb58+fv+KAnT55U9+7d7cv9+/fXrFmzNGXKFI0fP14DBgxQeHj4HbcPAEBL5NA16Lq6OpWVlalz586SpHPnzjXqoOPGjdO4cePqrYuLi1NcXFyj2gUAoCVzKLQfffRRPfLIIxo7dqxMJpPeffddTZs2zdm1AQCAazh0eTwkJESSVFNTo2PHjtk/Sw0AAFzHoZ52UlKS4uLilJCQIIvFoj/+8Y9KSUnRxo0bnV0fAAD4/znU0z5//rwSEhIkSd7e3kpMTFRpaalTCwMAAPU5FNp1dXUqKSmxL589e1Y2m81pRQEAgBs5dHk8MTFREyZM0EMPPSSTyaSDBw82ehhTAADQMA6F9qRJkxQQEKBDhw6pbdu2euyxx9SvXz9n1wYAAK7h8Fih/fv3V//+/Z1ZCwAAuA3mvgQAwCAIbQAADILQBgDAIAhtAAAMgtAGAMAgCG0AAAyC0AYAwCAIbQAADILQBgDAIBweEa0pxcfH69y5c/LwuHL4pUuX6tKlS1qxYoUsFovGjh2r+fPnu6M0AACaLZeHts1mU2Fhod5//317aFdVVWnMmDHasmWLevToodmzZ+vDDz9USEiIq8sDAKDZcnlof/3115KkGTNmqKysTLGxserXr5969+6tXr16SZIiIiKUm5tLaAMAcA2X39MuLy9XUFCQ1q9frzfeeEPbtm1TcXGxfH197fv4+fnVm78bAAC4oac9dOhQDR061L48adIkrV27VoGBgfZ1NptNJpOpQe2azeYmqe/aOuCY/Px8d5eAJsC533Cc++7Rms9Vl4f2J598opqaGgUFBUm6EtA9e/ZUaWmpfZ/S0lL5+fk1qN2AgAB5e3s3aa1wTGv+BULrxrkPV3P55fGLFy9q1apVslgsqqio0Ntvv61nn31Wx48f14kTJ1RXV6ddu3YpODjY1aUBANCsubynPWLECBUUFGjChAmyWq2aOnWqhg4dqpUrV+rJJ5+UxWJRSEiIxowZ4+rSAABo1tzyOe1nnnlGzzzzTL11QUFBeuedd9xRDgAAhsCIaAAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoY1GsdbWuLsEQ+H7BaAx3DJhCFqONh6eyl81091lGEbgot+7uwQABkZPGwAAgyC0AQAwCEIbAACDILQBADAItzyItm7dOu3evVuSFBISokWLFik5OVn5+flq3769JGnevHkKDQ11R3kAADRLLg/tgwcP6qOPPtLbb78tk8mkmTNn6r333pPZbNbWrVvl5+fn6pIAADAEl18e9/X1VVJSkry8vOTp6Sl/f38VFxeruLhYKSkpioiI0Nq1a2W1Wl1dGgAAzZrLQ7tv37669957JUmFhYXavXu3HnroIQ0fPlzLly9XZmamPvnkE2VlZbm6NAAAmjW3Da5y9OhRzZ49W4sWLdIPf/hDrV+/3r4tPj5eOTk5io2Ndbg9s9ncJHUFBgY2STvAreTn57u7hJvi3G+45vqzbOla87nqltDOz8/XU089pZSUFI0fP15HjhxRYWGhRo8eLUmy2Wzy8GhYaQEBAfL29nZGuUCTas3/4bQ0/Czhai6/PH769GnNnTtX6enpGj9+vKQrIb18+XJduHBBNTU12r59O0+OAwBwHZf3tDdt2iSLxaKVK1fa102ePFmzZs3SlClTVFtbq7CwMIWHh7u6NAAAmjWXh3ZqaqpSU1Nvui0uLs7F1QDAnbHW1qiNh6e7yzAMvl9Ng1m+AOAOMMNdwzDDXdNgGFMAAAyC0AYAwCAIbQAADILQBgDAIAhtAAAMgtAGAMAgCG0AAAyC0AYAwCAIbQAADILQBgDAIAhtAAAMgtAGAMAgCG0AAAyC0AYAwCAIbQAADILQBgDAIJpVaO/cuVPjxo1TWFiYMjIy3F0OAADNioe7C7iqpKREq1evVnZ2try8vDR58mQNGzZMffr0cXdpAAA0C82mp33w4EENHz5cnTt3VocOHTR69Gjl5ua6uywAAJqNZtPTPnPmjHx9fe3Lfn5++vTTT7/1dTabTZJUXV3dZLXc1cGzydpq6SwWi9Suk7vLMAyLxeLuEm6Lc99xnPsN09TnvpeXl0wmU5O2aQTNJrStVmu9H4DNZnPoB1JTUyNJ+vLLL5uslscj/JusrZbObDZLDzzq7jIMw2w2u7uE2+LcdxznfsM09bkfEBAgb2/vJm3TCJpNaHfv3l2ffPKJfbm0tFR+fn7f+rqOHTuqX79+8vT0bJV/dQFAa+Tl5eXuEtyi2YT2/fffr9/+9rc6d+6c2rdvr7179+pXv/rVt76uTZs26tSJS1QAgJav2YR2t27dNH/+fCUkJKimpkaTJk3S4MGD3V0WAADNhsl29UkuAADQrDWbj3wBAIDbI7QBAC3ejBkzdO7cOXeX0WiENgCgxTtw4IC7S2gSzeZBNAAAnCE5OVmSNG3aNL322mtavny5Tp8+rZqaGo0fP16/+MUvdOrUKSUmJiokJEQFBQUqLy/XwoULFRoaqt/+9rc6f/68lixZIkn1luPj4/Wd73xHX3/9taZMmaIJEyZo2bJl+vLLL1VTU6OgoCAtWrRIHh5NE7f0tAEALdqKFSskSX/4wx+UnJys6OhoZWdnKysrSwcPHtS7774rSTp58qQefPBBZWVlacGCBVq+fLlD7d9111169913FR8fr+XLl2vQoEHKzs5WTk6Ozp8/r82bNzfZe6GnDQBoFS5fvqzDhw/rwoULWrNmjSSpsrJSX3zxhQYPHixPT0+FhIRIkgYOHKiysjKH2r3vvvvsX3/wwQf67LPPlJWVJUmqqqpq0vdAaAMAWgWTySSbzaZt27apffv2kqRz587J29tb58+fl6enp9q0aWPf9/rXXXV1+OyrOnToYP/aarVqzZo18ve/MiRweXl5k47WyeVxAECL17ZtW3l4eOjee++1X64uLy/XlClTtH///tu+tkuXLvrXv/4lm82miooKvf/++7fc98EHH9Qbb7whm82m6upqzZkzR1u3bm2y90FoAwBavDFjxig+Pl5Lly5VQUGBIiIiFBMTo/DwcP385z+/7Wt//vOf6+6771ZYWJh+8Ytf6Gc/+9kt9128eLEqKysVERGhiIgI9evXTzNnzmyy98GIaAAAGAQ9bQAADILQBgDAIAhtAAAMgtAGAMAgCG3AAbm5uYqPj5ckrVmzRjk5ObfdPzIyUuXl5bfcvn//fr300ktNWqMj4uPjlZub6/LjAmgaDK4CNNDTTz/9rfvs2LHjtttHjRqlUaNGNVVJAFoJQhu4hTVr1mjnzp3q3LmzevfubV+flJSkvn37ysfHR++//75ee+01SdKxY8eUmJioDz74QAMHDlReXp7q6ur0y1/+UufPn5ckhYSE6JlnnlF2drb27NmjDRs26JtvvtELL7ygoqIi2Ww2TZgwQTNnzrztBAbX+uqrr7RgwYIb6k9ISFB0dPQt39++ffu0bt06Wa1WdezYUcnJyerVq5dGjhypAwcOqEOHDlqyZIm+/vpr++AQYWFh+p//+R/5+Pho6dKlN510IS4uTv7+/ioqKtKWLVvk5+fX6J8F8G2qa+rk5dnWMO3eKUIbuIl9+/Zp7969ysnJUbt27TR37twb9hk/frzS09NVWloqX19fZWdnKyoqSm3b/ucXPDMzU/fcc49ef/11VVZWavHixbp48WK9dp577jmNGjVK06dP18WLFxUXF6cePXpoyJAh9gkMnn/+ee3Zs0fLly+/IbT79OnzrT376x07dkxpaWnatm2bevXqpby8PD3xxBPKzc3Vj3/8Y3388ccaMWKEPv74Y1VUVOjSpUs6ffq0PDw85O/vr4SEBCUmJmrkyJGyWCx6/PHH9b3vfU+DBw/WN998o1deeaXeeMyAs3l5ttXURRlN3u6bq+KavM3GILSBm8jLy1NoaKh8fHwkSdHR0dqyZUu9fXx8fBQaGqp33nlHiYmJ2rlzpzIy6v+n8dBDD2nWrFk6ffq07r//fi1YsECdOnWyb6+srNQ//vEPvf7665KkTp06KSoqSn/96181ZMgQhyYwuJOe9qFDhzR8+HD16tVLkhQUFKS7775bZrNZoaGh+utf/6rvfe976tatm/r166fDhw/ryJEjCgsLU2Vl5W0nXbg6VCTQWixcuFA//elPFRsbK+nKsyPPPfecfvOb36isrEzt2rXT888/r4EDB2rnzp36/e9/r7Zt2+qee+7Ryy+/LG9vb4ePRWgDt3DtYIHX9p6vFRsbq+eff17+/v7y9/e3h+BVgwcP1v79+5WXl6dDhw4pJiZGGzdutG+3Wq26flBCq9Wq2tpaSbrlBAbXupOettVqvaE9m82m2tpahYaGKi4uTt///vf1wAMP6K677tJHH32kzz77TC+++KK95ltNuuDl5dVkcwcDRhAdHa3f/va3io2NVVFRkc6dO6cVK1ZoyZIlGjhwoL766ivNnTtXe/bs0W9+8xtlZmaqa9eu+u///m99/fXXGjBggMPH4ulx4CaCg4OVm5ur8vJyWa3WW4bi1R7l+vXrFRMTc8P29PR0vfrqq3r44Ye1ePFi9enTR0ePHrVv9/Hx0ZAhQ+w99IsXLyonJ0f333+/E97VfwQFBemjjz7SyZMnJV25snD69GkNGTJE3bt3V5cuXbRt2zY98MADevDBB7V3716VlZWpf//+8vHxuaNJF4CWatiwYTpz5oxOnTqlnJwcjR07VmazWcnJyYqMjNSCBQtUWVmp8+fPa8SIEZoyZYpWrVql0aNHNyiwJUIbuKmQkBBFR0crOjpaMTEx9S5pXy8mJkYnT57Uww8/fMO2adOm6YsvvlB4eLiio6N1zz33aPz48fX2SU9PV15eniIiIjRp0iSFhYUpKiqqyd/Ttfr06aO0tDTNmzdP4eHheuWVV/Taa6/Z32doaKjOnTungQMHqlevXmrXrl2995eent7gSReAlspkMmnChAn685//rN27d2vixIny8vLSjh077P/eeustde7cWampqVq7dq2+853vaOHChQ2+SsaEIQCAFsGdD6KdPn1aU6dOVZ8+fbRx40ZFRUVp2rRpioyM1IEDB7RkyRLl5uZq/Pjx2rJli7p166Z169apoqJCSUlJDtfDjScAABqpR48e6tGjhyZOnChJevnll/XCCy/o97//vTw9PbV69Wp5enrqqaee0owZM+Tt7a2uXbtq5cqVDToOPW0AgOG583PaNptNZ86cUXx8vHbt2iUvL68mr+Mq7mkDAAzPWQOgONLunj17FBkZqWeffdapgS21gJ62zWZTdXW1vLy8bvmRGAAAWgLD97Srq6tlNptVXV3t7lIAAHAqw4c2AACtBaENAIBBENoAABgEoQ0AgEE4NbQrKioUHh6uU6dOSZKSk5MVFhamyMhIRUZG6r333pMkff7554qKitLo0aO1ePFi+2QJAAA4wlpbY6h275TTRkQrKChQamqqCgsL7evMZrO2bt0qPz+/evsuXLhQL730ku69916lpKQoMzNTU6dOdVZpAIAWpo2Hp/JXzWzydgMX/b7J22wMp/W0MzMzlZaWZg/oy5cvq7i4WCkpKYqIiNDatWtltVpVVFSkqqoq+2xJUVFRys3NdVZZAAA0uY8//lgzZszQE088odGjR+upp55SdXW1/vSnPyk8PFwRERFKSkrSpUuXGnUcp/W0ly1bVm/57NmzGj58uNLS0tSpUyfNnj1bWVlZ6tu3r3x9fe37+fr6qqSkpMHHM5vNja4ZAND8BQYGuruEm/rnP/+p3bt3y8/PT7GxseuQPeQAABWSSURBVNq+fbv+93//V5mZmerSpYtefPFFrVu3Tr/85S/v+BgumzCkV69eWr9+vX05Pj5eOTk58vf3rzeSmc1mu6ORzQICAuTt7d0ktQIA0FB9+/ZV9+7dJUn+/v66cOGCRowYoS5dukiSHnnkESUnJzfqGC57evzIkSPas2ePfdlms8nDw0Pdu3dXaWmpff3Zs2dvuOcNAEBzd23H0WQy6a677qq33WazNfpBa5eFts1m0/Lly3XhwgXV1NRo+/btCg0NVc+ePeXt7a38/HxJ0o4dOxQcHOyqsgAAcJq//OUvKisrk3TlWa9hw4Y1qj2XXR7v37+/Zs2apSlTpqi2tlZhYWEKDw+XJKWnpys1NVUVFRUaNGiQEhISXFUWAKAFsNbWOOVJb2ttjdp4eN7Ra318fDR79mzFx8erpqZGgwYN0osvvtioegw/y5fFYpHZbOaeNgCgxWNENAAADILQBgDAIAhtAAAMgtAGAMAgCG0AAAyC0AYAwCAIbQAADILQBgDAIAhtAAAMgtAGAMAgCG0AAAyC0AYAwCAIbQAADILQBgDAIAhtAAAMgtAGAMAgCG0AAAzCqaFdUVGh8PBwnTp1SpK0fft2hYeHKyIiQsnJyaqurpYkrVu3TiNGjFBkZKQiIyOVkZHhzLIAADAkD2c1XFBQoNTUVBUWFkqSjh8/rk2bNik7O1sdO3ZUUlKS3nzzTSUmJspsNuvXv/61hg4d6qxyAAAwPKf1tDMzM5WWliY/Pz9JkpeXl9LS0uTj4yOTyaR+/fqpuLhYkmQ2m7VhwwZFRERo6dKlslgszioLAADDclpPe9myZfWWe/bsqZ49e0qSzp07p4yMDK1YsUKXLl3SgAEDtHDhQvXu3VtJSUl69dVXNX/+/AYdz2w2N1ntAIDmKzAw0N0luI3TQvtWSkpKNHPmTEVHR2vYsGGSpI0bN9q3z5gxQykpKQ0O7YCAAHl7ezdprQAANCcufXr82LFjmjx5siZOnKi5c+dKkoqLi5WVlWXfx2azycPD5X9LAADQ7LkstCsqKvTYY4/p6aef1owZM+zr27Vrp5dfflknT56UzWZTRkaGQkNDXVUWAACG4bIubVZWls6ePavNmzdr8+bNkqSRI0fq6aef1tKlSzVnzhzV1NToJz/5iaZPn+6qsgAAMAyTzWazubuIxrBYLDKbzdzTBgC0eIyIBgCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABiEU0O7oqJC4eHhOnXqlCTp4MGDioiIUFhYmFavXm3f7/PPP1dUVJRGjx6txYsXq7a21pllAQBgSE4L7YKCAk2ZMkWFhYWSpKqqKqWkpOjVV1/Vu+++K7PZrA8//FCStHDhQi1ZskR79uyRzWZTZmams8oCAMCwnBbamZmZSktLk5+fnyTp008/Ve/evdWrVy95eHgoIiJCubm5KioqUlVVle69915JUlRUlHJzc51VFgAAhuXhrIaXLVtWb/nMmTPy9fW1L/v5+amkpOSG9b6+viopKXFWWQAAGJbTQvt6VqtVJpPJvmyz2WQymW65vqHMZnOT1AkAaN4CAwPdXYLbuCy0u3fvrtLSUvtyaWmp/Pz8blh/9uxZ+yX1hggICJC3t3eT1AoAQHPkso98DRkyRMePH9eJEydUV1enXbt2KTg4WD179pS3t7fy8/MlSTt27FBwcLCrygIAwDBc1tP29vbWypUr9eSTT8pisSgkJERjxoyRJKWnpys1NVUVFRUaNGiQEhISXFUWAACGYbLZbDZ3F9EYFotFZrOZy+MAgBaPEdEAADAIQhsAAIMgtAEAMAhCGwAAg3AotG82QtlXX33V5MUAAIBbu21ol5WVqaysTI8//rguXLhgXz579qzmzZvnqhoBAIC+5XPaCxYs0IEDByRJw4YN+8+LPDw0evRo51YGAADquW1ob9q0SZKUnJysFStWuKQgAABwcw4PrlJUVKQLFy7o2t0HDRrktMIcxeAqAIDWwqFhTNeuXatNmzapa9eu9nUmk0n79+93WmEAAKA+h0I7JydHe/fuVbdu3ZxdDwAAuAWHPvLVo0cPAhsAADdzqKcdFBSkVatWadSoUWrXrp19fXO4pw0AQGvhUGhnZ2dLknJzc+3ruKcNAIBrORTaf/nLX5xdBwAA+BYOhfbmzZtvun769OlNWgwAALg1h0L7yy+/tH9dXV2tw4cPKygoyGlFAQCAGzkU2tePhlZSUqLFixff0QHfeustbd261b586tQpRUZG6vLly8rPz1f79u0lSfPmzVNoaOgdHQMAgJbIodC+Xrdu3VRUVHRHB4yJiVFMTIwk6ejRo5o7d67mzZunadOmaevWrfLz87ujdgEAaOkafE/bZrPJbDbXGx3tTr3wwguaP3++2rdvr+LiYqWkpKikpEShoaGaN2+e2rRhum8AAK5q8D1t6cpgK4sWLWrUgQ8ePKiqqiqNHTtWJ0+e1PDhw5WWlqZOnTpp9uzZysrKUmxsbKOOAQBAS+LwhCHSlUlDamtr1bt370Yf+KmnnlJYWJjCw8Nv2Pbee+8pJydH69ev/9Z2rk4YAgBoHQIDA91dgts41NM+ceKEnnjiCZ05c0ZWq1VdunTRhg0b5O/vf0cHvfoE+sqVKyVJR44cUWFhoX2ObpvNJg+Pht1uZ5YvAEBL59BN46VLl2rmzJk6fPiw8vPzNWfOHL344ot3fNAjR47o+9//vjp06CDpSkgvX75cFy5cUE1NjbZv386T4wAAXMeh0P73v/+tiRMn2pejo6N1/vz5Oz7oyZMn1b17d/ty//79NWvWLE2ZMkXjx4/XgAEDbnrZHACA1syha9B1dXUqKytT586dJUnnzp1r1EHHjRuncePG1VsXFxenuLi4RrULAEBL5lBoP/roo3rkkUc0duxYmUwmvfvuu5o2bZqzawMAANdw6PJ4SEiIJKmmpkbHjh2zf5YaAAC4jkMf+UpMTNTIkSOVkJAgi8WiP/7xjzpw4IA2btzoihpv6+pHvnh6HADQ0jnU0z5//rwSEhIkSd7e3kpMTFRpaalTCwMAAPU5FNp1dXUqKSmxL589e1YNGJMFgAFU19S5uwRD4fsFd3DoQbTExERNmDBBDz30kEwmkw4ePNjoYUwBNC9enm01dVGGu8swjDdX8WkXuJ5DoT1p0iQFBATo0KFDatu2rR577DH169fP2bUBAIBrODxWaP/+/dW/f39n1gIAAG6DuS8BADAIQhsAAIMgtAEAMAhCGwAAgyC0AQAwCEIbAACDILQBADAIQhsAAIMgtAEAMAiHR0RrSvHx8Tp37pw8PK4cfunSpbp06ZJWrFghi8WisWPHav78+e4oDQCAZsvloW2z2VRYWKj333/fHtpVVVUaM2aMtmzZoh49emj27Nn68MMPFRIS4uryAABotlwe2l9//bUkacaMGSorK1NsbKz69eun3r17q1evXpKkiIgI5ebmEtoAAFzD5fe0y8vLFRQUpPXr1+uNN97Qtm3bVFxcLF9fX/s+fn5+9ebvBgAAbuhpDx06VEOHDrUvT5o0SWvXrlVgYKB9nc1mk8lkalC7ZrO5yWoEWqNrfwfhmPz8fHeX0Cq15nPV5aH9ySefqKamRkFBQZKuBHTPnj1VWlpq36e0tFR+fn4NajcgIEDe3t5NWisA3E5rDg+4h8svj1+8eFGrVq2SxWJRRUWF3n77bT377LM6fvy4Tpw4obq6Ou3atUvBwcGuLg0AgGbN5T3tESNGqKCgQBMmTJDVatXUqVM1dOhQrVy5Uk8++aQsFotCQkI0ZswYV5cGAECzZrLZbDZ3F9EYFotFZrOZy+NAE5i6KMPdJRjGm6vi3F0CWiFGRAMAwCAI7etU19S5uwRD4fsFAK7jlmFMmzMvz7ZcImwALhECgOvQ0wYAwCAIbQAADILQBgDAIAhtAAAMgtAGAMAgCG0AAAyC0AYAwCAIbQAADILQBgDAIAhtAAAMgtAGAMAgCG0AuAPW2hp3l2AofL+aBhOGAMAdaOPhqfxVM91dhmEELvq9u0toEehpAwBgEG7paa9bt067d++WJIWEhGjRokVKTk5Wfn6+2rdvL0maN2+eQkND3VEeAADNkstD++DBg/roo4/09ttvy2QyaebMmXrvvfdkNpu1detW+fn5ubokAAAMweWXx319fZWUlCQvLy95enrK399fxcXFKi4uVkpKiiIiIrR27VpZrVZXlwYAQLPm8p5237597V8XFhZq9+7dysjI0N///nelpaWpU6dOmj17trKyshQbG+twu2azuUnqCwwMbJJ2WpP8/Hx3l4AmwLkPZ2uq/yta87nqtqfHjx49qtmzZ2vRokX64Q9/qPXr19u3xcfHKycnp0GhHRAQIG9vb2eUim/Rmn+BADiO/ysazy1Pj+fn5ysxMVELFizQxIkTdeTIEe3Zs8e+3WazycODT6MBAHAtl4f26dOnNXfuXKWnp2v8+PGSroT08uXLdeHCBdXU1Gj79u08OQ4AwHVc3p3dtGmTLBaLVq5caV83efJkzZo1S1OmTFFtba3CwsIUHh7u6tIAAGjWXB7aqampSk1Nvem2uLg4F1cDAIBxMCIaAAAGQWgDAGAQhDYAAAZBaAMAYBCENhqFOXIbhu8XgMZgBBM0CnMKNwxzCgNoDHraAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGAShDQCAQRDaAAAYBKENAIBBENoAABgEoQ0AgEEQ2gAAGESzCu2dO3dq3LhxCgsLU0ZGhrvLAQCgWWk2E4aUlJRo9erVys7OlpeXlyZPnqxhw4apT58+7i4NAIBmodmE9sGDBzV8+HB17txZkjR69Gjl5uZq3rx5t32dzWaTJFVXVzdZLXd18Gyytlo6i8Uitevk7jIMw2KxuLuE2+LcdxznfsM09bnv5eUlk8nUpG0agcl2NfXcbMOGDaqsrNT8+fMlSW+99ZY+/fRT/epXv7rt6y5evKgvv/zSFSUCAJqJgIAAeXt7u7sMl2s2PW2r1VrvryabzebQX1EdO3ZUv3795Onp2Sr/6gKA1sjLy8vdJbhFswnt7t2765NPPrEvl5aWys/P71tf16ZNG3XqxCUqAEDL12yeHr///vuVl5enc+fO6fLly9q7d6+Cg4PdXRYAAM1Gs+lpd+vWTfPnz1dCQoJqamo0adIkDR482N1lAQDQbDSbB9EAAMDtNZvL4wAA4PYIbQAADILQBgDAIAhtAAAMgtCGkpOTNWrUKO3atavJ205KSlJ2dnaTtws0NyNHjtSpU6fcXQZauGbzkS+4z9tvv61PP/201Y4wBABGQWi3cr/4xS9ks9kUExOj6dOn6w9/+IOsVqsGDRqktLQ0eXt764EHHtCoUaP06aef6rvf/a6io6O1ZcsWffPNN1q5cqV+9rOf6e9//7tWr16tqqoqlZeXKzk5WQ8//HC9Y+Xk5Ny0faC5+Pjjj/Xaa6/J09NTp06d0siRI9WhQwft27dPkvS73/1Oubm52rFjhy5fvixPT0+98sor+uEPf2hvo66uTqtWrdLf//531dXVKSoqSomJiW56R2hpuDzeyr322muSpPT0dGVmZmrbtm3asWOHunbtqk2bNkmSzp49q+DgYOXk5MhisWjfvn1688039eSTT+oPf/iDJGnr1q166aWX9Pbbb+ull17SmjVr6h3n6NGjt2wfaE4KCgr04osv6k9/+pMyMjJ09913Kzs7Wz/60Y/05z//Wfv27dOWLVu0a9cu/dd//ZcyMjLqvT4zM1PSlStYWVlZ2r9/f70hmoHGoKcNSVd6GCdOnFBsbKwkqaamRgMHDrRvvzqkbM+ePRUYGChJ+n//7/+pvLxckvTyyy/r/fffV25urgoKCnTp0qUGtQ80F/369VOPHj0kSV26dFFQUJCk/5zvr7zyiv785z+rsLBQf/vb3zRgwIB6r8/Ly9Pnn3+uQ4cOSZIqKyt15MgR3Xfffa59I2iRCG1IunJJb+zYsUpNTZUkXbp0SXV1dfbt197vbtu27Q2vnzp1qoYNG6Zhw4YpKChIzz33XIPaB5oLT8/6c4pfe76fPn1ajzzyiB599FEFBwfru9/9rj7//PN6+9fV1WnhwoUKCwuTJJ07d04dO3Z0fuFoFbg8DknSsGHD9N577+nf//63bDabXnjhBful729TVlamwsJCPf300woODtb+/ftvCOTGtA80F5999pl69+6txMRE/fjHP9a+fftuONeHDx+uzMxM1dTU6NKlS5o6dar+7//+z00Vo6Whpw1JUv/+/TVv3jxNmzZNVqtVAwYM0KxZsxx6befOnTVp0iSNHz9eHh4eGj58uKqqqlRZWdkk7QPNxYMPPqgvvvhC48aNk81m009/+lMdPXq03j6TJ0/WiRMnNHHiRNXW1ioqKkrDhg1zU8VoaZgwBAAAg+DyOAAABkFoAwBgEIQ2AAAGQWgDAGAQhDYAAAZBaAMtSG5uruLj491dBgAnIbQBADAIBlcB3OB3v/udsrKy1LFjR913333av3+/cnNzlZ6ersOHD6uurk4DBw5UamqqfHx8NHLkSE2cOFF5eXk6ffq0IiMj9cwzz0iS1qxZo507d6pz587q3bu3/RjV1dW3bW/w4ME6cuSInn32WYWGhrrrWwGgAehpAy72t7/9TdnZ2crKylJ2drZ9cpXf/e53atu2rbKzs/XOO+/Iz89P6enp9tdVVlbqzTff1LZt2/T666/r5MmT2rdvn/bu3aucnBxt27ZNFRUV9v2/rb2+fftq9+7dBDZgIPS0ARf78MMPNWbMGN11112SpLi4OB06dEgffPCBLl68qIMHD0q6MhNa165d7a8bNWqUJKlbt27q2rWrLly4oLy8PIWGhsrHx0eS7HOdS/rW9ph1CjAeQhtwMQ8PD107evDVWaSsVqtSUlIUEhIi6cpMaBaLxb6ft7e3/WuTyWRv42ZtOdJehw4dmvJtAXABLo8DLhYSEqK9e/fq4sWLkqSsrCxJVyajyMjIUHV1taxWq55//nn9+te/vm1bwcHBys3NVXl5uaxWq3bs2GHfdiftAWjeCG3AxYKCghQbG6tHHnlEUVFRunjxotq3b68nnnhCPXv21MSJE+2zSCUlJd22rZCQEEVHRys6OloxMTHq1KmTfdudtAegeWOWL8DFPvvsM/3zn/9UQkKCJGnz5s0qKCjQb37zGzdXBqC5I7QBF6uoqFBKSoq+/vprmUwm9ejRQ7/61a/UrVs3d5cGoJkjtAEAMAjuaQMAYBCENgAABkFoAwBgEIQ2AAAGQWgDAGAQhDYAAAbx/wHeLoKJZS8YRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 488.6x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='gender', hue = 'tenure', row = 'division',\n",
    "            kind='count', data=ratings_df,\n",
    "            height = 3, aspect = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Does age affect teaching evaluation scores?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29689517f10>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 505.975x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"age\", y=\"eval\", hue=\"gender\",\n",
    "            row=\"tenure\",\n",
    "            data=ratings_df, height = 3, aspect = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a distribution plot of teaching evaluation scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(ratings_df['eval'], kde = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a distribution plot of teaching evaluation score with gender as a factor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## use the distplot function from the seaborn library\n",
    "sns.distplot(ratings_df[ratings_df['gender'] == 'female']['eval'], color='green', kde=False) \n",
    "sns.distplot(ratings_df[ratings_df['gender'] == 'male']['eval'], color=\"orange\", kde=False) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a box plot - age of the instructor by gender\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=\"gender\", y=\"age\", data=ratings_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare age along with tenure and gender\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=\"tenure\", y=\"age\", hue=\"gender\",\n",
    "                 data=ratings_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice Questions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1: Create a distribution plot of beauty scores with Native English speaker as a factor\n",
    "\n",
    "-   Make the color of the native English speakers plot - orange and non - native English speakers - blue\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0dUidSYEHDpbHCmi+jkHJcgeR70zzEP3f683SkAtSa3PAz7/S9JnJSh7qR4ZLr62vzB2WpaQhcSFlucxSu5JsnVJn6Mez4uL8W2M//t2F7TXaOtRhWX3mCvbnok8B/lwGqkfefp6viyDnCnqNI/napIsdlnCx07p0m6+nXerMyx16pTirt3v98Pzzz6ulpUUrV66UJG3fvl0HDx7UU089ddXn/e9//9ORI0eCmRIABryJEydq2LBhXe4L+oy8s7NTcXEfn8lYltXldl9SUlI0ceJEJSYm+jUeAHCxYz0ej1JSUno8FnSRjx49WgcOHPDebmxslN1u9/m8QYMG9fjbBADg2+DBg3u9P+h3rXzuc5/TG2+8odOnT6u1tVWvvfaapk+fHnRAAEBwgj4jHzVqlFauXKnFixfL4/Fo4cKFuv3228OZDQDgh6B/2QkAiA18shMADEeRA4DhKHIAMBxFDgCGi9ki37hxo372s5/1+tjJkyf12c9+VnPnztXcuXO1ZMmSCKfr6Wp529ra9Nhjjyk/P1/z58/Xe++9F+F0H6urq9P999+vvLw8PfLII2pu7rkdQKysr69N2Q4fPqzCwkLl5ubqe9/7ntrb26OQsitfmTdt2qSZM2d61zYWNps7f/68Zs+erffff7/HY7G4xlfLG4vru2nTJjkcDjkcDq1fv77H42FZYyvGNDU1WY8//rh1++23W88991yvY6qrq60nnngiwsl650/eX/3qV968+/fvt774xS9GMmIXS5cutSorKy3LsqxNmzZZ69ev7zEmFtb3gw8+sGbOnGmdOXPGam5utgoKCqyjR492GeNwOKx//OMflmVZ1uOPP25t27YtGlG9/Mm8bNky6+9//3uUEvb0z3/+05o9e7b16U9/2jpx4kSPx2NtjX3ljbX1/etf/2p9+ctfttxut9XW1mYtXrzYeu2117qMCccax9wZ+Z///GfdfPPN+sY3vtHnmLfffltHjhzR3LlztXjxYr3zzjsRTNiVP3n37NmjOXPmSJLuvPNOnT59WnV1dZGK6OXxePTWW28pNzdXklRYWKjq6uoe42Jhfa/clC05Odm7KdtlJ0+e1IULFzR58mRJfb+WSPKVWZKcTqeef/55FRQU6Ic//GFAmyb1h9/97ndas2ZNr5/KjsU1vlpeKfbWNy0tTcXFxUpKSlJiYqJuueWWLv/th2uNY67I582bp6VLlyo+Pr7PMTabTXPmzNHOnTu1ZMkSLV++PKDdFMPJn7ynTp1SWlqa93ZaWpo++OCDSMTr4syZMxo6dKgSEhK8ORoaGnqMi4X17b5mdru9S9be1rS31xJJvjI3Nzfrtttu02OPPaadO3eqqalJZWVl0Yjq9cwzz2jq1N63TY7FNb5a3lhc3wkTJnhL+vjx49q1a5eys7O9j4drjYP+ZGeodu3apR//+Mdd7hs3bpx++9vf+nxuUVGR98/Z2dn6yU9+ov/85z+69dZbwx3TK5S8VrcNxSzL0qBB/ft3aG95MzIyemxU1tvGZdFY3+58bcoW7KZt/clXppSUFP3yl7/03n7wwQdVUlLi3UE01sTiGl9NLK/v0aNHtWzZMq1atUo333yz9/5wrXHUijw/P1/5+flBPXfr1q2aPXu2RowYIenii798ltlfQsk7atQonTp1SjfddJMk6cMPP/Rrg7FQ9JbX4/HorrvuUkdHh+Lj4/vc6Cwa69udr03ZRo8ercbGRu/tSKypL74y19XVqaamRgsXLpQUnXUNRCyu8dXE6vrW1tZqxYoVKikpkcPh6PJYuNY45i6t+OOtt97S73//e0nS/v371dnZqXHjxkU5Vd+ys7NVXl4uSTpw4IBsNpvGjBkT8RyJiYmaOnWqqqqqJEkvvfRSrxudxcL6+tqU7ROf+IRsNptqa2slSeXl5VHftM1X5sGDB+vZZ5/ViRMnZFmWtm3bpvvuuy+Kia8uFtf4amJxfevr67V8+XJt2LChR4lLYVzjYH8b29+ee+65Lu8CeeGFF6yNGzdalnXx3QEPPPCA5XA4rMLCQuvw4cPRiul1tbwXLlywVq1aZc2aNcuaN2+e5XQ6oxXTev/9962vfe1rVn5+vvXggw9aZ8+e7ZE3Vta3oqLCcjgcVk5OjrV582bLsizroYcesg4ePGhZlmUdPnzYWrBggZWbm2t9+9vfttxud1RyXslX5urqau/jxcXFMZHZsixr5syZ3neBxPoaW1bfeWNtfZ966ilr8uTJ1pw5c7z/vPDCC2FfYzbNAgDDGXlpBQDwMYocAAxHkQOA4ShyADAcRQ4AhqPIcc148803NXv27H6f58SJE10+/QpEG0UOBKiurk7Hjh2LdgzAiyLHNaWlpUUrVqzQ3Llz9fWvf13Hjh1TW1ubfvSjH2n+/PmaM2eOiouLdf78eUnS66+/rkWLFqmwsFAzZszQxo0bJfU8u798u6OjQ6tXr9Z///tfLVmyRD//+c/1ne98xzvuwIEDmjdvXmRfNAY8ihzXlPr6ej3wwAMqLy/X7NmztWrVKm3evFnx8fHasWOHKioqZLfbtWHDBlmWpV//+tdau3atduzYoRdffFGbN2/W6dOn+zx+fHy8nn76ad10003asmWLvvSlL2nPnj06e/aspIvbrC5atChSLxeQFMVNs4D+8MlPflJ33HGHJGn+/Pl68skn5fF41NraqpqaGkkXNw9LTU1VXFycfvGLX2jPnj2qrKzUe++9J8uy1Nra6vd8qampmjFjhsrLyzVv3jzt27dPa9as6ZfXBvSFIsc1pfv2wJe3BC0pKfHuA93c3Cy3262WlhbNnz9f9957r6ZOnaoFCxboT3/6k3cr0St3r/B4PH3Oef/99+vJJ59UQkKCcnJylJKS0g+vDOgbl1ZwTXnnnXd0+PBhSdKLL76oKVOmaPr06dq2bZva2trU2dmpJ554Qj/96U/lcrl0/vx5fetb39IXvvAFvfnmm94xI0eOVF1dnT766CNZlqVXXnnFO0d8fHyXYr/jjjs0aNAgbdmyhcsqiArOyHFNGTdunDZt2qQTJ04oNTVVa9euVWpqqtatW6f58+ero6NDt912m4qLi5WcnKwZM2YoPz9fSUlJmjhxosaPHy+Xy6XPf/7zWrRokRYsWKC0tDTNmDFDb7/9tiRp/PjxstlsWrhwobZv3664uDgVFhaqqqoqol++AVzG7odAiNrb2/Xoo49qzpw5mjVrVrTjYADi0goQgnfffVdZWVkaMWKE8vLyoh0HAxRn5ABgOM7IAcBwFDkAGI4iBwDDUeQAYDiKHAAMR5EDgOH+H2R5nMeHrNtzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## insert code\n",
    "sns.distplot(ratings_df[ratings_df['native'] == 'yes']['beauty'], color=\"orange\", kde=False) \n",
    "sns.distplot(ratings_df[ratings_df['native'] == 'no']['beauty'], color=\"blue\", kde=False) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- The answer is below:\n",
    "sns.distplot(ratings_df[ratings_df['native'] == 'yes']['beauty'], color=\"orange\", kde=False) \n",
    "sns.distplot(ratings_df[ratings_df['native'] == 'no']['beauty'], color=\"blue\", kde=False) \n",
    "plt.show()\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2: Create a Horizontal box plot of the age of the instructors by visible minority\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## insert code\n",
    "ax = sns.boxplot(x=\"age\", y=\"minority\", data=ratings_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click **here** for a hint.\n",
    "\n",
    "<!-- The hint is below:\n",
    "Remember that the positions of the argument determine whether it will be vertical or horizontal\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- The answer is below:\n",
    "ax = sns.boxplot(x=\"age\", y=\"minority\", data=ratings_df)\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3: Create a group histogram of tenure by minority and add the gender factor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29689781cd0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 488.6x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## insert code\n",
    "sns.catplot(x='tenure', hue = 'minority', row = 'gender',\n",
    "            kind='count', data=ratings_df,\n",
    "            height = 3, aspect = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- The answer is below:\n",
    "sns.catplot(x='tenure', hue = 'minority', row = 'gender',\n",
    "            kind='count', data=ratings_df,\n",
    "            height = 3, aspect = 2)\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4: Create a boxplot of the age variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bqgkAgElYFoAzZ85o165dGhkZ0cjIiI4cOaL7779fPT09CgaDikajampqUl5enlUTAACTsOwlIK/Xq46ODm3cuFGpqakqLCzU+vXrtXjxYlVUVCgSicjr9crn81k1ARY6evSoWlpa7J4xLXR3d0uSqqqqbF4yPaxbt04FBQV2z0ACLH0PoKKiQhUVFePO83g8CgQCVh4WuKYWL15s9wTgH7H8TWD8OxUUFPAoD5jh+CoIADAUAQAAQxEAADAUAQAAQxEAADAUAQAAQxEAADDUjPkcQCwWkySNjIzYvAQAZo7f7zN/vw/9oxkTgNHRUUlSV1eXzUsAYOYZHR3V7Nmzx53niF0pC9PQ2NiYBgcHlZaWlvRXSAOAqWKxmEZHRzVv3jylpIx/1X/GBAAAMLV4ExgADEUAAMBQBAAADEUAAMBQ/wujQ9IAOycPVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## you only have to specify the y-variable\n",
    "ax = sns.boxplot(y=\"age\", data=ratings_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- The answer is below:\n",
    "## you only habve to specify the y-variable\n",
    "ax = sns.boxplot(y=\"age\", data=ratings_df)\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Aije Egwaikhide](https://www.linkedin.com/in/aije-egwaikhide?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ST0151EN-SkillsNetwork-20531532&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) is a Data Scientist at IBM who holds a degree in Economics and Statistics from the University of Manitoba and a Post-grad in Business Analytics from St. Lawrence College, Kingston. She is a current employee of IBM where she started as a Junior Data Scientist at the Global Business Services (GBS) in 2018. Her main role was making meaning out of data for their Oil and Gas clients through basic statistics and advanced Machine Learning algorithms. The highlight of her time in GBS was creating a customized end-to-end Machine learning and Statistics solution on optimizing operations in the Oil and Gas wells. She moved to the Cognitive Systems Group as a Senior Data Scientist where she will be providing the team with actionable insights using Data Science techniques and further improve processes through building machine learning solutions. She recently joined the IBM Developer Skills Network group where she brings her real-world experience to the courses she creates.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By      | Change Description                     |\n",
    "| ----------------- | ------- | --------------- | -------------------------------------- |\n",
    "| 2020-08-14        | 0.1     | Aije Egwaikhide | Created the initial version of the lab |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ST0151EN-SkillsNetwork-20531532&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ST0151EN-SkillsNetwork-20531532&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ST0151EN-SkillsNetwork-20531532&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ST0151EN-SkillsNetwork-20531532&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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