{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkST0151ENSkillsNetwork20531532-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#install specific version of libraries used in lab\n",
    "#! mamba install pandas==1.3.3\n",
    "#! mamba install numpy=1.21.2\n",
    "#! mamba install scipy=1.7.1-y\n",
    "#!  mamba install seaborn=0.9.0-y\n",
    "#!  mamba install matplotlib=3.4.3-y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the libraries we need for the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the csv file from the url using the request library\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_url = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ST0151EN-SkillsNetwork/labs/teachingratings.csv'\n",
    "ratings_df = pd.read_csv(ratings_url)"
   ]
  },
  {
   "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": 3,
   "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])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df.prof.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 463 entries, 0 to 462\n",
      "Data columns (total 19 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   minority         463 non-null    object \n",
      " 1   age              463 non-null    int64  \n",
      " 2   gender           463 non-null    object \n",
      " 3   credits          463 non-null    object \n",
      " 4   beauty           463 non-null    float64\n",
      " 5   eval             463 non-null    float64\n",
      " 6   division         463 non-null    object \n",
      " 7   native           463 non-null    object \n",
      " 8   tenure           463 non-null    object \n",
      " 9   students         463 non-null    int64  \n",
      " 10  allstudents      463 non-null    int64  \n",
      " 11  prof             463 non-null    int64  \n",
      " 12  PrimaryLast      463 non-null    int64  \n",
      " 13  vismin           463 non-null    int64  \n",
      " 14  female           463 non-null    int64  \n",
      " 15  single_credit    463 non-null    int64  \n",
      " 16  upper_division   463 non-null    int64  \n",
      " 17  English_speaker  463 non-null    int64  \n",
      " 18  tenured_prof     463 non-null    int64  \n",
      "dtypes: float64(2), int64(11), object(6)\n",
      "memory usage: 68.9+ KB\n"
     ]
    }
   ],
   "source": [
    "ratings_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print out the number of unique values in the prof variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.365010799136066"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.802742037864821"
      ]
     },
     "execution_count": 8,
     "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": 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>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>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",
       "    <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": 9,
     "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": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.5531914893617"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_duplicates_ratings_df['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.25651329515495"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>credits</th>\n",
       "      <th>beauty</th>\n",
       "      <th>eval</th>\n",
       "      <th>division</th>\n",
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       "      <th>tenure</th>\n",
       "      <th>students</th>\n",
       "      <th>allstudents</th>\n",
       "      <th>prof</th>\n",
       "      <th>PrimaryLast</th>\n",
       "      <th>vismin</th>\n",
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       "      <th>single_credit</th>\n",
       "      <th>upper_division</th>\n",
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       "      <td>more</td>\n",
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       "      <td>3.7</td>\n",
       "      <td>upper</td>\n",
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       "      <th>2</th>\n",
       "      <td>yes</td>\n",
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       "      <td>0.289916</td>\n",
       "      <td>3.6</td>\n",
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       "      <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",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <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": 12,
     "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": 13,
   "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": 14,
   "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": [
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.barplot(x=\"division\", y=\"eval\", data=division_eval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the relationship between age and teaching evaluation scores.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a scatterplot with the scatterplot function in the seaborn library\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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(x='age', y='eval', data=ratings_df)"
   ]
  },
  {
   "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 scatterplot function in the seaborn library this time add the <code>hue</code> argument\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "We use the <code>boxplot()</code> function from the seaborn library\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "We use the <code>catplot()</code> function from the seaborn library\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f12e0a097d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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 <code>hue = Tenure</code> argument\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f12e0418650>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 418.725x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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 <code>row</code> and use the division variable as the row\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f12e05e0b90>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 490.725x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": [
    "### Create a scatterplot of age and evaluation scores, differentiated by gender and tenure\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the <code>relplot()</code> function for complex scatter plots\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f12e0922350>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 510.975x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "markdown",
   "metadata": {},
   "source": [
    "We use the <code>distplot()</code> function from the seaborn library, set <code>kde = false</code> because we don'e need the curve\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": {
      "needs_background": "light"
     },
     "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": 23,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "## 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": 24,
   "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": [
    "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": 25,
   "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": [
    "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": 27,
   "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": [
    "## 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": 28,
   "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": [
    "## insert code\n",
    "ax = sns.boxplot(x=\"minority\", y=\"age\", 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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f12e01b7fd0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 490.725x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 30,
   "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": [
    "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/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkST0151ENSkillsNetwork20531532-2022-01-01) 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/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkST0151ENSkillsNetwork20531532-2022-01-01).\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}