DataCamp.Course_052_Exploratory Data Analysis in Python.ipynb

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    "# **# **DataCamp.Course_052_Exploratory Data Analysis in Python**\n",
    "\n",
    "### **Course Description**\n",
    "\n",
    "So you’ve got some interesting data - where do you begin your analysis? This course will cover the process of exploring and analyzing data, from understanding what’s included in a dataset to incorporating exploration findings into a data science workflow.\n",
    "\n",
    "Using data on unemployment figures and plane ticket prices, you’ll leverage Python to summarize and validate data, calculate, identify and replace missing values, and clean both numerical and categorical values. Throughout the course, you’ll create beautiful Seaborn visualizations to understand variables and their relationships.\n",
    "\n",
    "For example, you’ll examine how alcohol use and student performance are related. Finally, the course will show how exploratory findings feed into data science workflows by creating new features, balancing categorical features, and generating hypotheses from findings.\n",
    "\n",
    "By the end of this course, you’ll have the confidence to perform your own exploratory data analysis (EDA) in Python.You’ll be able to explain your findings visually to others and suggest the next steps for gathering insights from your data! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   YEAR GoogleKnowlege_Occupation     Show   Group    Raw_Guest_List\n",
      "0  1999                     actor  1/11/99  Acting    Michael J. Fox\n",
      "1  1999                  Comedian  1/12/99  Comedy   Sandra Bernhard\n",
      "2  1999        television actress  1/13/99  Acting     Tracey Ullman\n",
      "3  1999              film actress  1/14/99  Acting  Gillian Anderson\n",
      "4  1999                     actor  1/18/99  Acting  David Alan Grier\n"
     ]
    }
   ],
   "source": [
    "# read_csv function\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "folder_name = 'datasets'\n",
    "dir = r'G:\\My Drive\\Data Science\\Datacamp_Notebook\\Datacamp_Notebook\\{}'.format(folder_name)\n",
    "\n",
    "def read_csv_fun(folder_name,file_name, path):\n",
    "    path = dir\n",
    "    os.chdir(path)\n",
    "    df = pd.read_csv('{}.csv'.format(file_name), sep=',', low_memory=False, on_bad_lines='skip')\n",
    "    return df\n",
    "\n",
    "df_show = read_csv_fun('datasets','daily_show_guests_cleaned', dir)\n",
    "print(df_show.head(5))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Getting to Know a Dataset (Module 01-052)**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Initial exploration**\n",
    "\n",
    "1. Exploratory Data Analysis\n",
    "The process of reviewing and cleaning data\n",
    "to...\n",
    "derive insights\n",
    "generate hypotheses\n",
    "\n",
    "2. A first look with .head()\n",
    "books = pd.read_csv(\"books.csv\")\n",
    "books.head()\n",
    "\n",
    "3. Gathering more .info()\n",
    "books.info()\n",
    "\n",
    "4. A closer look at categorical columns\n",
    "books.value_counts(\"genre\")\n",
    "\n",
    "5. .describe() numerical columns\n",
    "books.describe()\n",
    "\n",
    "6. Visualizing numerical data\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.histplot(data=books, x=\"rating\")\n",
    "plt.show()\n",
    "\n",
    "7. Adjusting bin width\n",
    "sns.histplot(data=books, x=\"rating\", binwidth=.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>author</th>\n",
       "      <th>rating</th>\n",
       "      <th>year</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10-Day Green Smoothie Cleanse</td>\n",
       "      <td>JJ Smith</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2016</td>\n",
       "      <td>Non Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11/22/63: A Novel</td>\n",
       "      <td>Stephen King</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2011</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12 Rules for Life: An Antidote to Chaos</td>\n",
       "      <td>Jordan B. Peterson</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2018</td>\n",
       "      <td>Non Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984 (Signet Classics)</td>\n",
       "      <td>George Orwell</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2017</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5,000 Awesome Facts (About Everything!) (Natio...</td>\n",
       "      <td>National Geographic Kids</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2019</td>\n",
       "      <td>Childrens</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                name  \\\n",
       "0                      10-Day Green Smoothie Cleanse   \n",
       "1                                  11/22/63: A Novel   \n",
       "2            12 Rules for Life: An Antidote to Chaos   \n",
       "3                             1984 (Signet Classics)   \n",
       "4  5,000 Awesome Facts (About Everything!) (Natio...   \n",
       "\n",
       "                     author  rating  year        genre  \n",
       "0                  JJ Smith     4.7  2016  Non Fiction  \n",
       "1              Stephen King     4.6  2011      Fiction  \n",
       "2        Jordan B. Peterson     4.7  2018  Non Fiction  \n",
       "3             George Orwell     4.7  2017      Fiction  \n",
       "4  National Geographic Kids     4.8  2019    Childrens  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "books = read_csv_fun('datasets','clean_books', dir)\n",
    "display(books.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 350 entries, 0 to 349\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   name    350 non-null    object \n",
      " 1   author  350 non-null    object \n",
      " 2   rating  350 non-null    float64\n",
      " 3   year    350 non-null    int64  \n",
      " 4   genre   350 non-null    object \n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 13.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "genre\n",
       "Non Fiction    179\n",
       "Fiction        131\n",
       "Childrens       40\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.608571</td>\n",
       "      <td>2013.508571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.226941</td>\n",
       "      <td>3.284711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.300000</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>2013.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.900000</td>\n",
       "      <td>2019.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           rating         year\n",
       "count  350.000000   350.000000\n",
       "mean     4.608571  2013.508571\n",
       "std      0.226941     3.284711\n",
       "min      3.300000  2009.000000\n",
       "25%      4.500000  2010.000000\n",
       "50%      4.600000  2013.000000\n",
       "75%      4.800000  2016.000000\n",
       "max      4.900000  2019.000000"
      ]
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "    books.info()\n",
    ")\n",
    "\n",
    "display(\n",
    "    books.value_counts(\"genre\")\n",
    ")\n",
    "\n",
    "display(\n",
    "    books.describe()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.histplot(data=books, x=\"rating\")\n",
    "plt.show()\n",
    "\n",
    "sns.histplot(data=books, x=\"rating\", binwidth=.1)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Functions for initial exploration**\n",
    "\n",
    "You are researching `unemployment` rates worldwide and have been given a new dataset to work with. The data has been saved and loaded for you as a pandas DataFrame called unemployment. You've never seen the data before, so your first task is to use a few pandas functions to learn about this new data.\n",
    "\n",
    "`pandas` has been imported for you as `pd`.\n",
    "\n",
    "STEP01\n",
    "Use a pandas function to print the first five rows of the unemployment DataFrame.\n",
    "STEP02\n",
    "Use a pandas function to print a summary of column non-missing values and data types from the unemployment DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>continent</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>11.35</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.34</td>\n",
       "      <td>11.19</td>\n",
       "      <td>11.14</td>\n",
       "      <td>11.13</td>\n",
       "      <td>11.16</td>\n",
       "      <td>11.18</td>\n",
       "      <td>11.15</td>\n",
       "      <td>11.22</td>\n",
       "      <td>11.71</td>\n",
       "      <td>13.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "      <td>9.43</td>\n",
       "      <td>7.36</td>\n",
       "      <td>7.35</td>\n",
       "      <td>7.37</td>\n",
       "      <td>7.37</td>\n",
       "      <td>7.39</td>\n",
       "      <td>7.41</td>\n",
       "      <td>7.41</td>\n",
       "      <td>7.42</td>\n",
       "      <td>7.42</td>\n",
       "      <td>8.33</td>\n",
       "      <td>8.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>14.09</td>\n",
       "      <td>13.48</td>\n",
       "      <td>13.38</td>\n",
       "      <td>15.87</td>\n",
       "      <td>18.05</td>\n",
       "      <td>17.19</td>\n",
       "      <td>15.42</td>\n",
       "      <td>13.62</td>\n",
       "      <td>12.30</td>\n",
       "      <td>11.47</td>\n",
       "      <td>13.33</td>\n",
       "      <td>11.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.18</td>\n",
       "      <td>2.04</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.35</td>\n",
       "      <td>2.23</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>South America</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.18</td>\n",
       "      <td>7.22</td>\n",
       "      <td>7.10</td>\n",
       "      <td>7.27</td>\n",
       "      <td>7.52</td>\n",
       "      <td>8.11</td>\n",
       "      <td>8.35</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.84</td>\n",
       "      <td>11.46</td>\n",
       "      <td>10.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_code          country_name      continent   2010   2011   2012  \\\n",
       "0          AFG           Afghanistan           Asia  11.35  11.05  11.34   \n",
       "1          AGO                Angola         Africa   9.43   7.36   7.35   \n",
       "2          ALB               Albania         Europe  14.09  13.48  13.38   \n",
       "3          ARE  United Arab Emirates           Asia   2.48   2.30   2.18   \n",
       "4          ARG             Argentina  South America   7.71   7.18   7.22   \n",
       "\n",
       "    2013   2014   2015   2016   2017   2018   2019   2020   2021  \n",
       "0  11.19  11.14  11.13  11.16  11.18  11.15  11.22  11.71  13.28  \n",
       "1   7.37   7.37   7.39   7.41   7.41   7.42   7.42   8.33   8.53  \n",
       "2  15.87  18.05  17.19  15.42  13.62  12.30  11.47  13.33  11.82  \n",
       "3   2.04   1.91   1.77   1.64   2.46   2.35   2.23   3.19   3.36  \n",
       "4   7.10   7.27   7.52   8.11   8.35   9.22   9.84  11.46  10.90  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unemployment = read_csv_fun('datasets','clean_unemployment', dir)\n",
    "display(unemployment.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  country_code          country_name      continent   2010   2011   2012  \\\n",
      "0          AFG           Afghanistan           Asia  11.35  11.05  11.34   \n",
      "1          AGO                Angola         Africa   9.43   7.36   7.35   \n",
      "2          ALB               Albania         Europe  14.09  13.48  13.38   \n",
      "3          ARE  United Arab Emirates           Asia   2.48   2.30   2.18   \n",
      "4          ARG             Argentina  South America   7.71   7.18   7.22   \n",
      "\n",
      "    2013   2014   2015   2016   2017   2018   2019   2020   2021  \n",
      "0  11.19  11.14  11.13  11.16  11.18  11.15  11.22  11.71  13.28  \n",
      "1   7.37   7.37   7.39   7.41   7.41   7.42   7.42   8.33   8.53  \n",
      "2  15.87  18.05  17.19  15.42  13.62  12.30  11.47  13.33  11.82  \n",
      "3   2.04   1.91   1.77   1.64   2.46   2.35   2.23   3.19   3.36  \n",
      "4   7.10   7.27   7.52   8.11   8.35   9.22   9.84  11.46  10.90  \n"
     ]
    }
   ],
   "source": [
    "# Print the first five rows of unemployment\n",
    "print(unemployment.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 182 entries, 0 to 181\n",
      "Data columns (total 15 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   country_code  182 non-null    object \n",
      " 1   country_name  182 non-null    object \n",
      " 2   continent     177 non-null    object \n",
      " 3   2010          182 non-null    float64\n",
      " 4   2011          182 non-null    float64\n",
      " 5   2012          182 non-null    float64\n",
      " 6   2013          182 non-null    float64\n",
      " 7   2014          182 non-null    float64\n",
      " 8   2015          182 non-null    float64\n",
      " 9   2016          182 non-null    float64\n",
      " 10  2017          182 non-null    float64\n",
      " 11  2018          182 non-null    float64\n",
      " 12  2019          182 non-null    float64\n",
      " 13  2020          182 non-null    float64\n",
      " 14  2021          182 non-null    float64\n",
      "dtypes: float64(12), object(3)\n",
      "memory usage: 21.5+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Print a summary of non-missing values and data types in the unemployment DataFrame\n",
    "print(unemployment.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             2010        2011        2012        2013        2014        2015  \\\n",
      "count  182.000000  182.000000  182.000000  182.000000  182.000000  182.000000   \n",
      "mean     8.409286    8.315440    8.317967    8.344780    8.179670    8.058901   \n",
      "std      6.248887    6.266795    6.367270    6.416041    6.284241    6.161170   \n",
      "min      0.450000    0.320000    0.480000    0.250000    0.200000    0.170000   \n",
      "25%      4.015000    3.775000    3.742500    3.692500    3.625000    3.662500   \n",
      "50%      6.965000    6.805000    6.690000    6.395000    6.450000    6.170000   \n",
      "75%     10.957500   11.045000   11.285000   11.310000   10.695000   10.215000   \n",
      "max     32.020000   31.380000   31.020000   29.000000   28.030000   27.690000   \n",
      "\n",
      "             2016        2017        2018        2019        2020        2021  \n",
      "count  182.000000  182.000000  182.000000  182.000000  182.000000  182.000000  \n",
      "mean     7.925879    7.668626    7.426429    7.243736    8.420934    8.390879  \n",
      "std      6.045439    5.902152    5.818915    5.696573    6.040915    6.067192  \n",
      "min      0.150000    0.140000    0.110000    0.100000    0.210000    0.260000  \n",
      "25%      3.800000    3.690000    3.625000    3.487500    4.285000    4.335000  \n",
      "50%      5.925000    5.650000    5.375000    5.240000    6.695000    6.425000  \n",
      "75%     10.245000   10.315000    9.257500    9.445000   11.155000   10.840000  \n",
      "max     26.540000   27.040000   26.910000   28.470000   29.220000   33.560000  \n"
     ]
    }
   ],
   "source": [
    "# Print summary statistics for numerical columns in unemployment\n",
    "print(unemployment.describe())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Counting categorical values**\n",
    "\n",
    "Recall from the previous exercise that the `unemployment` DataFrame contains 182 rows of country data including `country_code`, `country_name`, `continent`, and unemployment percentages from 2010 through 2021.\n",
    "\n",
    "You'd now like to explore the categorical data contained in `unemployment` to understand the data that it contains related to each continent.\n",
    "\n",
    "The `unemployment` DataFrame has been loaded for you along with `pandas` as `pd`.\n",
    "\n",
    "STEPS\n",
    "Use a pandas function to count the values associated with each continent in the unemployment DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Africa           53\n",
      "Asia             47\n",
      "Europe           39\n",
      "North America    18\n",
      "South America    12\n",
      "Oceania           8\n",
      "Name: continent, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count the values associated with each continent in unemployment\n",
    "print(unemployment[\"continent\"].value_counts())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Global unemployment in 2021**\n",
    "\n",
    "It's time to explore some of the numerical data in `unemployment`! What was typical unemployment in a given year? What was the minimum and maximum unemployment rate, and what did the distribution of the unemployment rates look like across the world? A histogram is a great way to get a sense of the answers to these questions.\n",
    "\n",
    "Your task in this exercise is to create a histogram showing the distribution of global unemployment rates in 2021.\n",
    "\n",
    "The `unemployment` DataFrame has been loaded for you along with `pandas` as `pd`.\n",
    "\n",
    "STEPS\n",
    "    Import the required visualization libraries.\n",
    "    Create a histogram of the distribution of 2021 unemployment percentages across all countries in unemployment; show a full percentage point in each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the required visualization libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a histogram of 2021 unemployment; show a full percent in each bin\n",
    "sns.histplot(data=unemployment, x=\"2021\", binwidth=1)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Data validation**\n",
    "\n",
    "1. xxx\n",
    "\n",
    "2. xxx\n",
    "\n",
    "3. xxx\n",
    "\n",
    "4. xxx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **xxxx**\n",
    "\n",
    "1. xxx\n",
    "\n",
    "2. xxx\n",
    "\n",
    "3. xxx\n",
    "\n",
    "4. xxx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Data Cleaning and Imputation (Module 02-052)**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **xxxx**\n",
    "\n",
    "1. xxx\n",
    "\n",
    "2. xxx\n",
    "\n",
    "3. xxx\n",
    "\n",
    "4. xxx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **xxxx**\n",
    "\n",
    "1. xxx\n",
    "\n",
    "2. xxx\n",
    "\n",
    "3. xxx\n",
    "\n",
    "4. xxx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **xxxx**\n",
    "\n",
    "1. xxx\n",
    "\n",
    "2. xxx\n",
    "\n",
    "3. xxx\n",
    "\n",
    "4. xxx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **xxxx**\n",
    "\n",
    "1. xxx\n",
    "\n",
    "2. xxx\n",
    "\n",
    "3. xxx\n",
    "\n",
    "4. xxx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Relationships in Data (Module 03-052)**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **xxxx**\n",
    "\n",
    "1. xxx\n",
    "\n",
    "2. xxx\n",
    "\n",
    "3. xxx\n",
    "\n",
    "4. xxx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **xxxx**\n",
    "\n",
    "1. xxx\n",
    "\n",
    "2. xxx\n",
    "\n",
    "3. xxx\n",
    "\n",
    "4. xxx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **xxxx**\n",
    "\n",
    "1. xxx\n",
    "\n",
    "2. xxx\n",
    "\n",
    "3. xxx\n",
    "\n",
    "4. xxx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Turning Exploratory Analysis into Action (Module 04-052)**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **xxxx**\n",
    "\n",
    "1. xxx\n",
    "\n",
    "2. xxx\n",
    "\n",
    "3. xxx\n",
    "\n",
    "4. xxx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **xxxx**\n",
    "\n",
    "1. xxx\n",
    "\n",
    "2. xxx\n",
    "\n",
    "3. xxx\n",
    "\n",
    "4. xxx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **xxxx**\n",
    "\n",
    "1. xxx\n",
    "\n",
    "2. xxx\n",
    "\n",
    "3. xxx\n",
    "\n",
    "4. xxx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **xxxx**\n",
    "\n",
    "1. xxx\n",
    "\n",
    "2. xxx\n",
    "\n",
    "3. xxx\n",
    "\n",
    "4. xxx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perrenque!\n"
     ]
    }
   ],
   "source": [
    "print('Perrenque!')"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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