3. Pie Charts_Box Plots_Scatter Plot_Bubble Plots.ipynb

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    "# Pie Charts, Box Plots, Scatter Plots, and Bubble Plots\n",
    "\n",
    "Estimated time needed: **30** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "-   Explore Matplotlib library further\n",
    "-   Create pie charts, box plots, scatter plots and bubble charts\n"
   ]
  },
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    "## Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "\n",
    "1.  [Exploring Datasets with _p_andas](#0)<br>\n",
    "2.  [Downloading and Prepping Data](#2)<br>\n",
    "3.  [Visualizing Data using Matplotlib](#4) <br>\n",
    "4.  [Pie Charts](#6) <br>\n",
    "5.  [Box Plots](#8) <br>\n",
    "6.  [Scatter Plots](#10) <br>\n",
    "7.  [Bubble Plots](#12) <br> \n",
    "    </div>\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Exploring Datasets with _pandas_ and Matplotlib<a id=\"0\"></a>\n",
    "\n",
    "Toolkits: The course heavily relies on [_pandas_](http://pandas.pydata.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&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-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) and [**Numpy**](http://www.numpy.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&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-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&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-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
    "\n",
    "Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) from United Nation's website.\n",
    "\n",
    "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data.\n"
   ]
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   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Import primary modules.\n"
   ]
  },
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   "source": [
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library"
   ]
  },
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   "source": [
    "Let's download and import our primary Canadian Immigration dataset using _pandas_ `read_excel()` method. Normally, before we can do that, we would need to download a module which _pandas_ requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
    "\n",
    "```\n",
    "!conda install -c anaconda xlrd --yes\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "source": [
    "Download the dataset and read it into a _pandas_ dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "new_sheet": false,
    "run_control": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded and read into a dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_excel('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx',\n",
    "                       sheet_name='Canada by Citizenship',\n",
    "                       skiprows=range(20),\n",
    "                       skipfooter=2\n",
    "                      )\n",
    "\n",
    "print('Data downloaded and read into a dataframe!')"
   ]
  },
  {
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   },
   "source": [
    "Let's take a look at the first five items in our dataset.\n"
   ]
  },
  {
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   "execution_count": 4,
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       "      <td>Immigrants</td>\n",
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       "      <td>903</td>\n",
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       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
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       "      <td>3623</td>\n",
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       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
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       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...     0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "Let's find out how many entries there are in our dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 43)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the dimensions of the dataframe\n",
    "df_can.shape"
   ]
  },
  {
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   "source": [
    "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to _Introduction to Matplotlib and Line Plots_ and _Area Plots, Histograms, and Bar Plots_ for a detailed description of this preprocessing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Afghanistan</th>\n",
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       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "      <td>58639</td>\n",
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       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
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       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "      <td>69439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Continent           Region             DevName  1980  1981  \\\n",
       "Country                                                                     \n",
       "Afghanistan         Asia    Southern Asia  Developing regions    16    39   \n",
       "Albania           Europe  Southern Europe   Developed regions     1     0   \n",
       "Algeria           Africa  Northern Africa  Developing regions    80    67   \n",
       "American Samoa   Oceania        Polynesia  Developing regions     0     1   \n",
       "Andorra           Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "                1982  1983  1984  1985  1986  ...  2005  2006  2007  2008  \\\n",
       "Country                                       ...                           \n",
       "Afghanistan       39    47    71   340   496  ...  3436  3009  2652  2111   \n",
       "Albania            0     0     0     0     1  ...  1223   856   702   560   \n",
       "Algeria           71    69    63    44    69  ...  3626  4807  3623  4005   \n",
       "American Samoa     0     0     0     0     0  ...     0     1     0     0   \n",
       "Andorra            0     0     0     0     2  ...     0     1     1     0   \n",
       "\n",
       "                2009  2010  2011  2012  2013  Total  \n",
       "Country                                              \n",
       "Afghanistan     1746  1758  2203  2635  2004  58639  \n",
       "Albania          716   561   539   620   603  15699  \n",
       "Algeria         5393  4752  4325  3774  4331  69439  \n",
       "American Samoa     0     0     0     0     0      6  \n",
       "Andorra            0     0     0     1     1     15  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 필요없는 컬럼 삭제하기\n",
    "df_can.drop(columns=['Type', 'Coverage', 'AREA', 'REG', 'DEV'], inplace=True)\n",
    "\n",
    "# 컬럼 이름 재설정\n",
    "df_can.rename({'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, axis=1, inplace=True)\n",
    "\n",
    "# 나라이름을 index로 설정\n",
    "df_can.set_index('Country', inplace=True)\n",
    "\n",
    "# Total 컬럼 추가\n",
    "df_can['Total'] = df_can.sum(axis=1)\n",
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "False\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
       "       '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
       "       '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
       "       '2011', '2012', '2013', 'Total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 컬럼 label의 type을 string으로 바꿈\n",
    "for column in df_can.columns :\n",
    "    print(isinstance(column, int))    # 숫자 컬럼 label: int\n",
    "    \n",
    "# 또다른 확인 방법\n",
    "df_can.columns                        # str에는 ''가 있고, int는 없다\n",
    "\n",
    "df_can.columns = map(str, df_can.columns)\n",
    "df_can.columns                        # 모두 str으로 변환됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1980',\n",
       " '1981',\n",
       " '1982',\n",
       " '1983',\n",
       " '1984',\n",
       " '1985',\n",
       " '1986',\n",
       " '1987',\n",
       " '1988',\n",
       " '1989',\n",
       " '1990',\n",
       " '1991',\n",
       " '1992',\n",
       " '1993',\n",
       " '1994',\n",
       " '1995',\n",
       " '1996',\n",
       " '1997',\n",
       " '1998',\n",
       " '1999',\n",
       " '2000',\n",
       " '2001',\n",
       " '2002',\n",
       " '2003',\n",
       " '2004',\n",
       " '2005',\n",
       " '2006',\n",
       " '2007',\n",
       " '2008',\n",
       " '2009',\n",
       " '2010',\n",
       " '2011',\n",
       " '2012',\n",
       " '2013']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# years 리스트 만들기\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Visualizing Data using Matplotlib<a id=\"4\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Import `Matplotlib`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Matplotlib version:', '3.1.3')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.style.available\n",
    "mpl.style.use('ggplot')\n",
    "\n",
    "'Matplotlib version:', mpl.__version__      # version check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Pie Charts <a id=\"6\"></a>\n",
    "\n",
    "A `pie chart` is a circualr graphic that displays numeric proportions by dividing a circle (or pie) into proportional slices. You are most likely already familiar with pie charts as it is widely used in business and media. We can create pie charts in Matplotlib by passing in the `kind=pie` keyword.\n",
    "\n",
    "Let's use a pie chart to explore the proportion (percentage) of new immigrants grouped by continents for the entire time period from 1980 to 2013. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Gather data. \n",
    "\n",
    "We will use _pandas_ `groupby` method to summarize the immigration data by `Continent`. The general process of `groupby` involves the following steps:\n",
    "\n",
    "1.  **Split:** Splitting the data into groups based on some criteria.\n",
    "2.  **Apply:** Applying a function to each group independently:\n",
    "       .sum()\n",
    "       .count()\n",
    "       .mean() \n",
    "       .std() \n",
    "       .aggregate()\n",
    "       .apply()\n",
    "       .etc..\n",
    "3.  **Combine:** Combining the results into a data structure.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig4SplitApplyCombine.png\" height=400 align=\"center\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.groupby.generic.DataFrameGroupBy"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# groupby 결과물은 groupby object\n",
    "type(df_can.groupby('Continent', axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>3951</td>\n",
       "      <td>4363</td>\n",
       "      <td>3819</td>\n",
       "      <td>2671</td>\n",
       "      <td>2639</td>\n",
       "      <td>2650</td>\n",
       "      <td>3782</td>\n",
       "      <td>7494</td>\n",
       "      <td>7552</td>\n",
       "      <td>9894</td>\n",
       "      <td>...</td>\n",
       "      <td>27523</td>\n",
       "      <td>29188</td>\n",
       "      <td>28284</td>\n",
       "      <td>29890</td>\n",
       "      <td>34534</td>\n",
       "      <td>40892</td>\n",
       "      <td>35441</td>\n",
       "      <td>38083</td>\n",
       "      <td>38543</td>\n",
       "      <td>618948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>31025</td>\n",
       "      <td>34314</td>\n",
       "      <td>30214</td>\n",
       "      <td>24696</td>\n",
       "      <td>27274</td>\n",
       "      <td>23850</td>\n",
       "      <td>28739</td>\n",
       "      <td>43203</td>\n",
       "      <td>47454</td>\n",
       "      <td>60256</td>\n",
       "      <td>...</td>\n",
       "      <td>159253</td>\n",
       "      <td>149054</td>\n",
       "      <td>133459</td>\n",
       "      <td>139894</td>\n",
       "      <td>141434</td>\n",
       "      <td>163845</td>\n",
       "      <td>146894</td>\n",
       "      <td>152218</td>\n",
       "      <td>155075</td>\n",
       "      <td>3317794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>39760</td>\n",
       "      <td>44802</td>\n",
       "      <td>42720</td>\n",
       "      <td>24638</td>\n",
       "      <td>22287</td>\n",
       "      <td>20844</td>\n",
       "      <td>24370</td>\n",
       "      <td>46698</td>\n",
       "      <td>54726</td>\n",
       "      <td>60893</td>\n",
       "      <td>...</td>\n",
       "      <td>35955</td>\n",
       "      <td>33053</td>\n",
       "      <td>33495</td>\n",
       "      <td>34692</td>\n",
       "      <td>35078</td>\n",
       "      <td>33425</td>\n",
       "      <td>26778</td>\n",
       "      <td>29177</td>\n",
       "      <td>28691</td>\n",
       "      <td>1410947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and the Caribbean</th>\n",
       "      <td>13081</td>\n",
       "      <td>15215</td>\n",
       "      <td>16769</td>\n",
       "      <td>15427</td>\n",
       "      <td>13678</td>\n",
       "      <td>15171</td>\n",
       "      <td>21179</td>\n",
       "      <td>28471</td>\n",
       "      <td>21924</td>\n",
       "      <td>25060</td>\n",
       "      <td>...</td>\n",
       "      <td>24747</td>\n",
       "      <td>24676</td>\n",
       "      <td>26011</td>\n",
       "      <td>26547</td>\n",
       "      <td>26867</td>\n",
       "      <td>28818</td>\n",
       "      <td>27856</td>\n",
       "      <td>27173</td>\n",
       "      <td>24950</td>\n",
       "      <td>765148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern America</th>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>7705</td>\n",
       "      <td>6469</td>\n",
       "      <td>6790</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7677</td>\n",
       "      <td>7892</td>\n",
       "      <td>8503</td>\n",
       "      <td>241142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>1942</td>\n",
       "      <td>1839</td>\n",
       "      <td>1675</td>\n",
       "      <td>1018</td>\n",
       "      <td>878</td>\n",
       "      <td>920</td>\n",
       "      <td>904</td>\n",
       "      <td>1200</td>\n",
       "      <td>1181</td>\n",
       "      <td>1539</td>\n",
       "      <td>...</td>\n",
       "      <td>1585</td>\n",
       "      <td>1473</td>\n",
       "      <td>1693</td>\n",
       "      <td>1834</td>\n",
       "      <td>1860</td>\n",
       "      <td>1834</td>\n",
       "      <td>1548</td>\n",
       "      <td>1679</td>\n",
       "      <td>1775</td>\n",
       "      <td>55174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  1980   1981   1982   1983   1984   1985  \\\n",
       "Continent                                                                   \n",
       "Africa                            3951   4363   3819   2671   2639   2650   \n",
       "Asia                             31025  34314  30214  24696  27274  23850   \n",
       "Europe                           39760  44802  42720  24638  22287  20844   \n",
       "Latin America and the Caribbean  13081  15215  16769  15427  13678  15171   \n",
       "Northern America                  9378  10030   9074   7100   6661   6543   \n",
       "Oceania                           1942   1839   1675   1018    878    920   \n",
       "\n",
       "                                  1986   1987   1988   1989  ...    2005  \\\n",
       "Continent                                                    ...           \n",
       "Africa                            3782   7494   7552   9894  ...   27523   \n",
       "Asia                             28739  43203  47454  60256  ...  159253   \n",
       "Europe                           24370  46698  54726  60893  ...   35955   \n",
       "Latin America and the Caribbean  21179  28471  21924  25060  ...   24747   \n",
       "Northern America                  7074   7705   6469   6790  ...    8394   \n",
       "Oceania                            904   1200   1181   1539  ...    1585   \n",
       "\n",
       "                                   2006    2007    2008    2009    2010  \\\n",
       "Continent                                                                 \n",
       "Africa                            29188   28284   29890   34534   40892   \n",
       "Asia                             149054  133459  139894  141434  163845   \n",
       "Europe                            33053   33495   34692   35078   33425   \n",
       "Latin America and the Caribbean   24676   26011   26547   26867   28818   \n",
       "Northern America                   9613    9463   10190    8995    8142   \n",
       "Oceania                            1473    1693    1834    1860    1834   \n",
       "\n",
       "                                   2011    2012    2013    Total  \n",
       "Continent                                                         \n",
       "Africa                            35441   38083   38543   618948  \n",
       "Asia                             146894  152218  155075  3317794  \n",
       "Europe                            26778   29177   28691  1410947  \n",
       "Latin America and the Caribbean   27856   27173   24950   765148  \n",
       "Northern America                   7677    7892    8503   241142  \n",
       "Oceania                            1548    1679    1775    55174  \n",
       "\n",
       "[6 rows x 35 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# groupby continents\n",
    "df_cont = df_can.groupby('Continent', axis=0).sum()\n",
    "df_cont"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot the data. We will pass in `kind = 'pie'` keyword, along with the following additional parameters:\n",
    "\n",
    "-   `autopct` -  is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be `fmt%pct`.\n",
    "-   `startangle` - rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.\n",
    "-   `shadow` - Draws a shadow beneath the pie (to give a 3D feel).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.234568\n",
      "1.2\n",
      "1.2\n",
      "1.2%\n"
     ]
    }
   ],
   "source": [
    "# string formatting for autopct\n",
    "float = 1.23456789\n",
    "\n",
    "print('%f' %(float))         # 소수점 6자리까지 출력\n",
    "print('%.1f' %(float))       # 소수점 첫째자리까지만 출력 (둘째자리에서 반올림) \n",
    "print('%1.1f' %(float))      # 1 자리확보 --> 사실상 출력에 차이는 없음\n",
    "print('%1.1f%%' %(float))    # %%: 뒤에 % 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cont['Total'].plot(kind='pie', figsize=(6, 6), autopct='%1.1f%%', startangle=90, shadow=True)\n",
    "\n",
    "plt.title('Pie chart')\n",
    "plt.axis('equal')        # 원처럼 보이도록 한다\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The above visual is not very clear, the numbers and text overlap in some instances. Let's make a few modifications to improve the visuals:\n",
    "\n",
    "-   Remove the text labels on the pie chart by passing in `legend` and add it as a seperate legend using `plt.legend()`.\n",
    "-   Push out the percentages to sit just outside the pie chart by passing in `pctdistance` parameter.\n",
    "-   Pass in a custom set of colors for continents by passing in `colors` parameter.\n",
    "-   **Explode** the pie chart to emphasize the lowest three continents (Africa/North America/Oceania) by pasing in `explode` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['crimson', 'darksalmon', 'dodgerblue', 'dimgrey', 'forestgreen', 'linen']  # 대륙 알파벳 순서대로 지정됨\n",
    "explode = [0.2, 0, 0, 0, 0.2, 0.2]           # 가장 적은 3대륙을 explode. 반지름=1 기준으로 해당 길이만큼 멀어짐\n",
    "\n",
    "df_cont['Total'].plot(kind='pie', figsize=(15, 8), autopct='%1.1f%%', startangle=90, shadow=True,\n",
    "                      labels=None,           # label 없애고 legend 설정\n",
    "                      pctdistance=1.12,      # percentage를 파이 밖에 적히도록 함. 반지름=1에 대한 비율\n",
    "                      colors=colors,\n",
    "                      explode=explode)\n",
    " \n",
    "# scale the title up by 12% to match pctdistance    \n",
    "plt.title('Immigration Pie Chart', y=1.12)\n",
    "\n",
    "plt.axis('equal')\n",
    "\n",
    "# Legend 설정\n",
    "plt.legend(labels=df_cont.index, loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question:** Using a pie chart, explore the proportion (percentage) of new immigrants grouped by continents in the year 2013.\n",
    "\n",
    "**Note**: You might need to play with the explore values in order to fix any overlapping slice values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cont['2013'].plot(kind='pie', figsize=(15, 8), autopct='%.2f%%', startangle=100, shadow=True,\n",
    "                     labels=None, pctdistance=1.13, \n",
    "                     colors=['royalblue', 'springgreen', 'mintcream', 'sienna', 'rebeccapurple', 'tan'],\n",
    "                     explode=[0, 0, 0, 0.1, 0.2, 0.3])\n",
    "\n",
    "plt.title('Immigration Pie Chart in 2013', y=1.11)\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.legend(labels=df_cont.index, loc='center left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "    df_continents['2013'].plot(kind='pie',\n",
    "                                figsize=(15, 6),\n",
    "                                autopct='%1.1f%%', \n",
    "                                startangle=90,    \n",
    "                                shadow=True,       \n",
    "                                labels=None,                 # turn off labels on pie chart\n",
    "                                pctdistance=1.12,            # the ratio between the pie center and start of text label\n",
    "                                explode=explode_list         # 'explode' lowest 3 continents\n",
    "                                )\n",
    "\n",
    "    # scale the title up by 12% to match pctdistance\n",
    "    plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
    "    plt.axis('equal') \n",
    "\n",
    "    # add legend\n",
    "    plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "    # show plot\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Box Plots <a id=\"8\"></a>\n",
    "\n",
    "A `box plot` is a way of statistically representing the _distribution_ of the data through five main dimensions: \n",
    "\n",
    "-   **Minimun:** Smallest number in the dataset excluding the outliers.\n",
    "-   **First quartile:** Middle number between the `minimum` and the `median`.\n",
    "-   **Second quartile (Median):** Middle number of the (sorted) dataset.\n",
    "-   **Third quartile:** Middle number between `median` and `maximum`.\n",
    "-   **Maximum:** Highest number in the dataset excluding the outliers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/boxplot_complete.png\" width=440, align=\"center\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "To make a `box plot`, we can use `kind=box` in `plot` method invoked on a _pandas_ series or dataframe.\n",
    "\n",
    "Let's plot the box plot for the Japanese immigrants between 1980 - 2013.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Even though we are extracting the data for just one country, we will obtain it as a dataframe. This will help us with calling the `dataframe.describe()` method to view the percentiles.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Japan\n",
       "1980       701\n",
       "1981       756\n",
       "1982       598\n",
       "1983       309\n",
       "1984       246"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jp = df_can.loc[['Japan'], years].transpose()       # dataframe으로 보기 위해 [[]]\n",
    "df_jp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot by passing in `kind='box'`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_jp.plot(kind='box', figsize=(6, 6))\n",
    "\n",
    "plt.title('Box plot of Japan')\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('# of Immigrants')\n",
    "\n",
    "import matplotlib.patches as mpatches\n",
    "colormap = plt.cm.coolwarm\n",
    "\n",
    "plt.legend(handles=[mpatches.Patch(color=colormap(1.0), label='Japan')], loc='lower right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can immediately make a few key observations from the plot above:\n",
    "\n",
    "1.  The minimum number of immigrants is around 200 (min), maximum number is around 1300 (max), and  median number of immigrants is around 900 (median).\n",
    "2.  25% of the years for period 1980 - 2013 had an annual immigrant count of ~500 or fewer (First quartile).\n",
    "3.  75% of the years for period 1980 - 2013 had an annual immigrant count of ~1100 or fewer (Third quartile).\n",
    "\n",
    "We can view the actual numbers by calling the `describe()` method on the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1284.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country        Japan\n",
       "count      34.000000\n",
       "mean      814.911765\n",
       "std       337.219771\n",
       "min       198.000000\n",
       "25%       529.000000\n",
       "50%       902.000000\n",
       "75%      1079.000000\n",
       "max      1284.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jp.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "One of the key benefits of box plots is comparing the distribution of multiple datasets. In one of the previous labs, we observed that China and India had very similar immigration trends. Let's analyize these two countries further using box plots.\n",
    "\n",
    "**Question:** Compare the distribution of the number of new immigrants from India and China for the period 1980 - 2013.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset for China and India and call the dataframe **df_CI**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>5123</td>\n",
       "      <td>8880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>6682</td>\n",
       "      <td>8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3308</td>\n",
       "      <td>8147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1863</td>\n",
       "      <td>7338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1527</td>\n",
       "      <td>5704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  China  India\n",
       "1980      5123   8880\n",
       "1981      6682   8670\n",
       "1982      3308   8147\n",
       "1983      1863   7338\n",
       "1984      1527   5704"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_CI = df_can.loc[['China', 'India'], years].T\n",
    "df_CI.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "    df_CI.head()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's view the percentages associated with both countries using the `describe()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19410.647059</td>\n",
       "      <td>20350.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13568.230790</td>\n",
       "      <td>10007.342579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1527.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5512.750000</td>\n",
       "      <td>10637.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19945.000000</td>\n",
       "      <td>20235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31568.500000</td>\n",
       "      <td>28699.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42584.000000</td>\n",
       "      <td>36210.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country         China         India\n",
       "count       34.000000     34.000000\n",
       "mean     19410.647059  20350.117647\n",
       "std      13568.230790  10007.342579\n",
       "min       1527.000000   4211.000000\n",
       "25%       5512.750000  10637.750000\n",
       "50%      19945.000000  20235.000000\n",
       "75%      31568.500000  28699.500000\n",
       "max      42584.000000  36210.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_CI.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI.describe()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_CI.plot(kind='box', figsize=(8, 6))\n",
    "\n",
    "plt.title('Immigration Comparison of China and India')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI.plot(kind='box', figsize=(10, 7))\n",
    "\n",
    "    plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "    plt.ylabel('Number of Immigrants')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can observe that, while both countries have around the same median immigrant population (~20,000),  China's immigrant population range is more spread out than India's. The maximum population from India for any year (36,210) is around 15% lower than the maximum population from China (42,584).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Horizontal Box Plot\n",
    "If you prefer to create horizontal box plots, you can pass the `vert` parameter in the **plot** function and assign it to _False_. You can also specify a different color in case you are not a big fan of the default red color.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Horizontal box plot\n",
    "df_CI.plot(kind='box', vert=False, figsize=(8, 6), color='steelblue')\n",
    "\n",
    "plt.title('Horizontal Box Plot')\n",
    "plt.xlabel('# of Immigrants')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Subplots ###\n",
    "\n",
    "Often times we might want to plot multiple plots within the same figure. For example, we might want to perform a side by side comparison of the box plot with the line plot of China and India's immigration.\n",
    "\n",
    "To visualize multiple plots together, we can create a **`figure`** (overall canvas) and divide it into **`subplots`**, each containing a plot. With **subplots**, we usually work with the **artist layer** instead of the **scripting layer**. \n",
    "\n",
    "Typical syntax is : <br>\n",
    "\n",
    "```python\n",
    "    fig = plt.figure() # create figure\n",
    "    ax = fig.add_subplot(nrows, ncols, plot_number) # create subplots\n",
    "```\n",
    "\n",
    "Where\n",
    "\n",
    "-   `nrows` and `ncols` are used to notionally split the figure into (`nrows` * `ncols`) sub-axes,  \n",
    "-   `plot_number` is used to identify the particular subplot that this function is to create within the notional grid. `plot_number` starts at 1, increments across rows first and has a maximum of `nrows` * `ncols` as shown below.\n",
    "\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig5Subplots_V2.png\" width=500 align=\"center\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can then specify which subplot to place each plot by passing in the `ax` paramemter in `plot()` method as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()      # figure 생성\n",
    "\n",
    "ax1 = fig.add_subplot(121)      # 전체 figure 공간을 1행, 2열로 나누고, 첫번째 칸 차지\n",
    "ax2 = fig.add_subplot(1, 2, 2)     \n",
    "\n",
    "# Subplot 1: Box plot\n",
    "df_CI.plot(kind='box', vert=False, figsize=(10, 6), color='cadetblue', ax=ax1)   # subplot 1에 추가\n",
    "ax1.set_title('Box Plot')\n",
    "# ax1.set_xlabel('# of Immigrants')\n",
    "# ax1.set_ylabel('Countries')\n",
    "ax1.set(xlabel='# of Immigrants', ylabel='Countries')\n",
    "\n",
    "# Subplot 2: Line plot\n",
    "df_CI.plot(kind='line', figsize=(15, 8), ax=ax2)    # subplot 2에 추가\n",
    "ax2.set_title('Line Plot')\n",
    "ax2.set_xlabel('Years')\n",
    "ax2.set_ylabel('# of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "** * Tip regarding subplot convention **\n",
    "\n",
    "In the case when `nrows`, `ncols`, and `plot_number` are all less than 10, a convenience exists such that the a 3 digit number can be given instead, where the hundreds represent `nrows`, the tens represent `ncols` and the units represent `plot_number`. For instance,\n",
    "\n",
    "```python\n",
    "   subplot(211) == subplot(2, 1, 1) \n",
    "```\n",
    "\n",
    "produces a subaxes in a figure which represents the top plot (i.e. the first) in a 2 rows by 1 column notional grid (no grid actually exists, but conceptually this is how the returned subplot has been positioned).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's try something a little more advanced. \n",
    "\n",
    "Previously we identified the top 15 countries based on total immigration from 1980 - 2013.\n",
    "\n",
    "**Question:** Create a box plot to visualize the distribution of the top 15 countries (based on total immigration) grouped by the _decades_ `1980s`, `1990s`, and `2000s`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Get the top 15 countries based on Total immigrant population. Name the dataframe **df_top15**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>8880</td>\n",
       "      <td>8670</td>\n",
       "      <td>8147</td>\n",
       "      <td>7338</td>\n",
       "      <td>5704</td>\n",
       "      <td>4211</td>\n",
       "      <td>7150</td>\n",
       "      <td>...</td>\n",
       "      <td>36210</td>\n",
       "      <td>33848</td>\n",
       "      <td>28742</td>\n",
       "      <td>28261</td>\n",
       "      <td>29456</td>\n",
       "      <td>34235</td>\n",
       "      <td>27509</td>\n",
       "      <td>30933</td>\n",
       "      <td>33087</td>\n",
       "      <td>691904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>5123</td>\n",
       "      <td>6682</td>\n",
       "      <td>3308</td>\n",
       "      <td>1863</td>\n",
       "      <td>1527</td>\n",
       "      <td>1816</td>\n",
       "      <td>1960</td>\n",
       "      <td>...</td>\n",
       "      <td>42584</td>\n",
       "      <td>33518</td>\n",
       "      <td>27642</td>\n",
       "      <td>30037</td>\n",
       "      <td>29622</td>\n",
       "      <td>30391</td>\n",
       "      <td>28502</td>\n",
       "      <td>33024</td>\n",
       "      <td>34129</td>\n",
       "      <td>659962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>22045</td>\n",
       "      <td>24796</td>\n",
       "      <td>20620</td>\n",
       "      <td>10015</td>\n",
       "      <td>10170</td>\n",
       "      <td>9564</td>\n",
       "      <td>9470</td>\n",
       "      <td>...</td>\n",
       "      <td>7258</td>\n",
       "      <td>7140</td>\n",
       "      <td>8216</td>\n",
       "      <td>8979</td>\n",
       "      <td>8876</td>\n",
       "      <td>8724</td>\n",
       "      <td>6204</td>\n",
       "      <td>6195</td>\n",
       "      <td>5827</td>\n",
       "      <td>551500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>6051</td>\n",
       "      <td>5921</td>\n",
       "      <td>5249</td>\n",
       "      <td>4562</td>\n",
       "      <td>3801</td>\n",
       "      <td>3150</td>\n",
       "      <td>4166</td>\n",
       "      <td>...</td>\n",
       "      <td>18139</td>\n",
       "      <td>18400</td>\n",
       "      <td>19837</td>\n",
       "      <td>24887</td>\n",
       "      <td>28573</td>\n",
       "      <td>38617</td>\n",
       "      <td>36765</td>\n",
       "      <td>34315</td>\n",
       "      <td>29544</td>\n",
       "      <td>511391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>978</td>\n",
       "      <td>972</td>\n",
       "      <td>1201</td>\n",
       "      <td>900</td>\n",
       "      <td>668</td>\n",
       "      <td>514</td>\n",
       "      <td>691</td>\n",
       "      <td>...</td>\n",
       "      <td>14314</td>\n",
       "      <td>13127</td>\n",
       "      <td>10124</td>\n",
       "      <td>8994</td>\n",
       "      <td>7217</td>\n",
       "      <td>6811</td>\n",
       "      <td>7468</td>\n",
       "      <td>11227</td>\n",
       "      <td>12603</td>\n",
       "      <td>241600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>Northern America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7676</td>\n",
       "      <td>7891</td>\n",
       "      <td>8501</td>\n",
       "      <td>241122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1172</td>\n",
       "      <td>1429</td>\n",
       "      <td>1822</td>\n",
       "      <td>1592</td>\n",
       "      <td>1977</td>\n",
       "      <td>1648</td>\n",
       "      <td>1794</td>\n",
       "      <td>...</td>\n",
       "      <td>5837</td>\n",
       "      <td>7480</td>\n",
       "      <td>6974</td>\n",
       "      <td>6475</td>\n",
       "      <td>6580</td>\n",
       "      <td>7477</td>\n",
       "      <td>7479</td>\n",
       "      <td>7534</td>\n",
       "      <td>11291</td>\n",
       "      <td>175923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>185</td>\n",
       "      <td>371</td>\n",
       "      <td>290</td>\n",
       "      <td>197</td>\n",
       "      <td>1086</td>\n",
       "      <td>845</td>\n",
       "      <td>1838</td>\n",
       "      <td>...</td>\n",
       "      <td>4930</td>\n",
       "      <td>4714</td>\n",
       "      <td>4123</td>\n",
       "      <td>4756</td>\n",
       "      <td>4547</td>\n",
       "      <td>4422</td>\n",
       "      <td>3309</td>\n",
       "      <td>3338</td>\n",
       "      <td>2394</td>\n",
       "      <td>148358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Korea</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1011</td>\n",
       "      <td>1456</td>\n",
       "      <td>1572</td>\n",
       "      <td>1081</td>\n",
       "      <td>847</td>\n",
       "      <td>962</td>\n",
       "      <td>1208</td>\n",
       "      <td>...</td>\n",
       "      <td>5832</td>\n",
       "      <td>6215</td>\n",
       "      <td>5920</td>\n",
       "      <td>7294</td>\n",
       "      <td>5874</td>\n",
       "      <td>5537</td>\n",
       "      <td>4588</td>\n",
       "      <td>5316</td>\n",
       "      <td>4509</td>\n",
       "      <td>142581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>863</td>\n",
       "      <td>2930</td>\n",
       "      <td>5881</td>\n",
       "      <td>4546</td>\n",
       "      <td>3588</td>\n",
       "      <td>2819</td>\n",
       "      <td>4808</td>\n",
       "      <td>...</td>\n",
       "      <td>1405</td>\n",
       "      <td>1263</td>\n",
       "      <td>1235</td>\n",
       "      <td>1267</td>\n",
       "      <td>1013</td>\n",
       "      <td>795</td>\n",
       "      <td>720</td>\n",
       "      <td>779</td>\n",
       "      <td>852</td>\n",
       "      <td>139241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1409</td>\n",
       "      <td>1119</td>\n",
       "      <td>1159</td>\n",
       "      <td>789</td>\n",
       "      <td>1253</td>\n",
       "      <td>1683</td>\n",
       "      <td>2576</td>\n",
       "      <td>...</td>\n",
       "      <td>3709</td>\n",
       "      <td>3802</td>\n",
       "      <td>3467</td>\n",
       "      <td>3566</td>\n",
       "      <td>3077</td>\n",
       "      <td>3432</td>\n",
       "      <td>3072</td>\n",
       "      <td>1614</td>\n",
       "      <td>2172</td>\n",
       "      <td>115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1729</td>\n",
       "      <td>2027</td>\n",
       "      <td>2219</td>\n",
       "      <td>1490</td>\n",
       "      <td>1169</td>\n",
       "      <td>1177</td>\n",
       "      <td>1298</td>\n",
       "      <td>...</td>\n",
       "      <td>4429</td>\n",
       "      <td>4002</td>\n",
       "      <td>4290</td>\n",
       "      <td>4532</td>\n",
       "      <td>5051</td>\n",
       "      <td>4646</td>\n",
       "      <td>4080</td>\n",
       "      <td>6280</td>\n",
       "      <td>5623</td>\n",
       "      <td>109091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>Latin America and the Caribbean</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>3198</td>\n",
       "      <td>2634</td>\n",
       "      <td>2661</td>\n",
       "      <td>2455</td>\n",
       "      <td>2508</td>\n",
       "      <td>2938</td>\n",
       "      <td>4649</td>\n",
       "      <td>...</td>\n",
       "      <td>1945</td>\n",
       "      <td>1722</td>\n",
       "      <td>2141</td>\n",
       "      <td>2334</td>\n",
       "      <td>2456</td>\n",
       "      <td>2321</td>\n",
       "      <td>2059</td>\n",
       "      <td>2182</td>\n",
       "      <td>2479</td>\n",
       "      <td>106431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1191</td>\n",
       "      <td>1829</td>\n",
       "      <td>2162</td>\n",
       "      <td>3404</td>\n",
       "      <td>7583</td>\n",
       "      <td>5907</td>\n",
       "      <td>2741</td>\n",
       "      <td>...</td>\n",
       "      <td>1852</td>\n",
       "      <td>3153</td>\n",
       "      <td>2574</td>\n",
       "      <td>1784</td>\n",
       "      <td>2171</td>\n",
       "      <td>1942</td>\n",
       "      <td>1723</td>\n",
       "      <td>1731</td>\n",
       "      <td>2112</td>\n",
       "      <td>97146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>375</td>\n",
       "      <td>438</td>\n",
       "      <td>583</td>\n",
       "      <td>543</td>\n",
       "      <td>524</td>\n",
       "      <td>604</td>\n",
       "      <td>656</td>\n",
       "      <td>...</td>\n",
       "      <td>5048</td>\n",
       "      <td>4468</td>\n",
       "      <td>3834</td>\n",
       "      <td>2837</td>\n",
       "      <td>2076</td>\n",
       "      <td>1922</td>\n",
       "      <td>1776</td>\n",
       "      <td>1588</td>\n",
       "      <td>1512</td>\n",
       "      <td>93585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                          Continent  \\\n",
       "Country                                                                               \n",
       "India                                                                          Asia   \n",
       "China                                                                          Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...                           Europe   \n",
       "Philippines                                                                    Asia   \n",
       "Pakistan                                                                       Asia   \n",
       "United States of America                                           Northern America   \n",
       "Iran (Islamic Republic of)                                                     Asia   \n",
       "Sri Lanka                                                                      Asia   \n",
       "Republic of Korea                                                              Asia   \n",
       "Poland                                                                       Europe   \n",
       "Lebanon                                                                        Asia   \n",
       "France                                                                       Europe   \n",
       "Jamaica                                             Latin America and the Caribbean   \n",
       "Viet Nam                                                                       Asia   \n",
       "Romania                                                                      Europe   \n",
       "\n",
       "                                                                Region  \\\n",
       "Country                                                                  \n",
       "India                                                    Southern Asia   \n",
       "China                                                     Eastern Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...     Northern Europe   \n",
       "Philippines                                         South-Eastern Asia   \n",
       "Pakistan                                                 Southern Asia   \n",
       "United States of America                              Northern America   \n",
       "Iran (Islamic Republic of)                               Southern Asia   \n",
       "Sri Lanka                                                Southern Asia   \n",
       "Republic of Korea                                         Eastern Asia   \n",
       "Poland                                                  Eastern Europe   \n",
       "Lebanon                                                   Western Asia   \n",
       "France                                                  Western Europe   \n",
       "Jamaica                                                      Caribbean   \n",
       "Viet Nam                                            South-Eastern Asia   \n",
       "Romania                                                 Eastern Europe   \n",
       "\n",
       "                                                               DevName   1980  \\\n",
       "Country                                                                         \n",
       "India                                               Developing regions   8880   \n",
       "China                                               Developing regions   5123   \n",
       "United Kingdom of Great Britain and Northern Ir...   Developed regions  22045   \n",
       "Philippines                                         Developing regions   6051   \n",
       "Pakistan                                            Developing regions    978   \n",
       "United States of America                             Developed regions   9378   \n",
       "Iran (Islamic Republic of)                          Developing regions   1172   \n",
       "Sri Lanka                                           Developing regions    185   \n",
       "Republic of Korea                                   Developing regions   1011   \n",
       "Poland                                               Developed regions    863   \n",
       "Lebanon                                             Developing regions   1409   \n",
       "France                                               Developed regions   1729   \n",
       "Jamaica                                             Developing regions   3198   \n",
       "Viet Nam                                            Developing regions   1191   \n",
       "Romania                                              Developed regions    375   \n",
       "\n",
       "                                                     1981   1982   1983  \\\n",
       "Country                                                                   \n",
       "India                                                8670   8147   7338   \n",
       "China                                                6682   3308   1863   \n",
       "United Kingdom of Great Britain and Northern Ir...  24796  20620  10015   \n",
       "Philippines                                          5921   5249   4562   \n",
       "Pakistan                                              972   1201    900   \n",
       "United States of America                            10030   9074   7100   \n",
       "Iran (Islamic Republic of)                           1429   1822   1592   \n",
       "Sri Lanka                                             371    290    197   \n",
       "Republic of Korea                                    1456   1572   1081   \n",
       "Poland                                               2930   5881   4546   \n",
       "Lebanon                                              1119   1159    789   \n",
       "France                                               2027   2219   1490   \n",
       "Jamaica                                              2634   2661   2455   \n",
       "Viet Nam                                             1829   2162   3404   \n",
       "Romania                                               438    583    543   \n",
       "\n",
       "                                                     1984  1985  1986  ...  \\\n",
       "Country                                                                ...   \n",
       "India                                                5704  4211  7150  ...   \n",
       "China                                                1527  1816  1960  ...   \n",
       "United Kingdom of Great Britain and Northern Ir...  10170  9564  9470  ...   \n",
       "Philippines                                          3801  3150  4166  ...   \n",
       "Pakistan                                              668   514   691  ...   \n",
       "United States of America                             6661  6543  7074  ...   \n",
       "Iran (Islamic Republic of)                           1977  1648  1794  ...   \n",
       "Sri Lanka                                            1086   845  1838  ...   \n",
       "Republic of Korea                                     847   962  1208  ...   \n",
       "Poland                                               3588  2819  4808  ...   \n",
       "Lebanon                                              1253  1683  2576  ...   \n",
       "France                                               1169  1177  1298  ...   \n",
       "Jamaica                                              2508  2938  4649  ...   \n",
       "Viet Nam                                             7583  5907  2741  ...   \n",
       "Romania                                               524   604   656  ...   \n",
       "\n",
       "                                                     2005   2006   2007  \\\n",
       "Country                                                                   \n",
       "India                                               36210  33848  28742   \n",
       "China                                               42584  33518  27642   \n",
       "United Kingdom of Great Britain and Northern Ir...   7258   7140   8216   \n",
       "Philippines                                         18139  18400  19837   \n",
       "Pakistan                                            14314  13127  10124   \n",
       "United States of America                             8394   9613   9463   \n",
       "Iran (Islamic Republic of)                           5837   7480   6974   \n",
       "Sri Lanka                                            4930   4714   4123   \n",
       "Republic of Korea                                    5832   6215   5920   \n",
       "Poland                                               1405   1263   1235   \n",
       "Lebanon                                              3709   3802   3467   \n",
       "France                                               4429   4002   4290   \n",
       "Jamaica                                              1945   1722   2141   \n",
       "Viet Nam                                             1852   3153   2574   \n",
       "Romania                                              5048   4468   3834   \n",
       "\n",
       "                                                     2008   2009   2010  \\\n",
       "Country                                                                   \n",
       "India                                               28261  29456  34235   \n",
       "China                                               30037  29622  30391   \n",
       "United Kingdom of Great Britain and Northern Ir...   8979   8876   8724   \n",
       "Philippines                                         24887  28573  38617   \n",
       "Pakistan                                             8994   7217   6811   \n",
       "United States of America                            10190   8995   8142   \n",
       "Iran (Islamic Republic of)                           6475   6580   7477   \n",
       "Sri Lanka                                            4756   4547   4422   \n",
       "Republic of Korea                                    7294   5874   5537   \n",
       "Poland                                               1267   1013    795   \n",
       "Lebanon                                              3566   3077   3432   \n",
       "France                                               4532   5051   4646   \n",
       "Jamaica                                              2334   2456   2321   \n",
       "Viet Nam                                             1784   2171   1942   \n",
       "Romania                                              2837   2076   1922   \n",
       "\n",
       "                                                     2011   2012   2013  \\\n",
       "Country                                                                   \n",
       "India                                               27509  30933  33087   \n",
       "China                                               28502  33024  34129   \n",
       "United Kingdom of Great Britain and Northern Ir...   6204   6195   5827   \n",
       "Philippines                                         36765  34315  29544   \n",
       "Pakistan                                             7468  11227  12603   \n",
       "United States of America                             7676   7891   8501   \n",
       "Iran (Islamic Republic of)                           7479   7534  11291   \n",
       "Sri Lanka                                            3309   3338   2394   \n",
       "Republic of Korea                                    4588   5316   4509   \n",
       "Poland                                                720    779    852   \n",
       "Lebanon                                              3072   1614   2172   \n",
       "France                                               4080   6280   5623   \n",
       "Jamaica                                              2059   2182   2479   \n",
       "Viet Nam                                             1723   1731   2112   \n",
       "Romania                                              1776   1588   1512   \n",
       "\n",
       "                                                     Total  \n",
       "Country                                                     \n",
       "India                                               691904  \n",
       "China                                               659962  \n",
       "United Kingdom of Great Britain and Northern Ir...  551500  \n",
       "Philippines                                         511391  \n",
       "Pakistan                                            241600  \n",
       "United States of America                            241122  \n",
       "Iran (Islamic Republic of)                          175923  \n",
       "Sri Lanka                                           148358  \n",
       "Republic of Korea                                   142581  \n",
       "Poland                                              139241  \n",
       "Lebanon                                             115359  \n",
       "France                                              109091  \n",
       "Jamaica                                             106431  \n",
       "Viet Nam                                             97146  \n",
       "Romania                                              93585  \n",
       "\n",
       "[15 rows x 38 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top15 = df_can.sort_values('Total', ascending=False, axis=0).head(15)\n",
    "df_top15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "    df_top15\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Step 2: Create a new dataframe which contains the aggregate for each decade. One way to do that:\n",
    "\n",
    "1.  Create a list of all years in decades 80's, 90's, and 00's.\n",
    "2.  Slice the original dataframe df_top15 and sum across all years for each country to create a series for each decade .\n",
    "3.  Merge the three series into a new data frame. Call your dataframe **new_df**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\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>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>179171</td>\n",
       "      <td>261966</td>\n",
       "      <td>83413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>60764</td>\n",
       "      <td>138482</td>\n",
       "      <td>172904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>10591</td>\n",
       "      <td>65302</td>\n",
       "      <td>127598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>76824</td>\n",
       "      <td>56915</td>\n",
       "      <td>75173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <td>21477</td>\n",
       "      <td>54871</td>\n",
       "      <td>65794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>14796</td>\n",
       "      <td>70421</td>\n",
       "      <td>49678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Korea</th>\n",
       "      <td>16259</td>\n",
       "      <td>38189</td>\n",
       "      <td>68183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>57602</td>\n",
       "      <td>64864</td>\n",
       "      <td>13629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>24918</td>\n",
       "      <td>49245</td>\n",
       "      <td>30906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>17137</td>\n",
       "      <td>30028</td>\n",
       "      <td>41297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>34328</td>\n",
       "      <td>40329</td>\n",
       "      <td>22733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>30638</td>\n",
       "      <td>37726</td>\n",
       "      <td>21274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>7613</td>\n",
       "      <td>33659</td>\n",
       "      <td>45515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     1980s   1990s   2000s\n",
       "Country                                                                   \n",
       "India                                                82154  180395  303591\n",
       "China                                                32003  161528  340385\n",
       "United Kingdom of Great Britain and Northern Ir...  179171  261966   83413\n",
       "Philippines                                          60764  138482  172904\n",
       "Pakistan                                             10591   65302  127598\n",
       "United States of America                             76824   56915   75173\n",
       "Iran (Islamic Republic of)                           21477   54871   65794\n",
       "Sri Lanka                                            14796   70421   49678\n",
       "Republic of Korea                                    16259   38189   68183\n",
       "Poland                                               57602   64864   13629\n",
       "Lebanon                                              24918   49245   30906\n",
       "France                                               17137   30028   41297\n",
       "Jamaica                                              34328   40329   22733\n",
       "Viet Nam                                             30638   37726   21274\n",
       "Romania                                               7613   33659   45515"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. list of all years of each decade\n",
    "yr_80 = list(map(str, range(1980, 1990)))\n",
    "yr_90 = list(map(str, range(1990, 2000)))\n",
    "yr_00 = list(map(str, range(2000, 2010)))\n",
    "\n",
    "#2. Slice df_can and sum\n",
    "df_80 = df_top15.loc[:, yr_80].sum(axis=1)\n",
    "df_90 = df_top15.loc[:, yr_90].sum(axis=1)\n",
    "df_00 = df_top15.loc[:, yr_00].sum(axis=1)\n",
    "\n",
    "#3. Merge the three into new_df\n",
    "new_df = pd.DataFrame({'1980s':df_80, '1990s':df_90, '2000s':df_00})\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    \n",
    "    # create a list of all years in decades 80's, 90's, and 00's\n",
    "    years_80s = list(map(str, range(1980, 1990))) \n",
    "    years_90s = list(map(str, range(1990, 2000))) \n",
    "    years_00s = list(map(str, range(2000, 2010))) \n",
    "\n",
    "    # slice the original dataframe df_top15 to create a series for each decade\n",
    "    df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "    df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "    df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "\n",
    "    # merge the three series into a new data frame\n",
    "    new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "\n",
    "    # display dataframe\n",
    "    new_df.head()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "Let's learn more about the statistics associated with the dataframe using the `describe()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
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   "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>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44418.333333</td>\n",
       "      <td>85594.666667</td>\n",
       "      <td>97471.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>44190.676455</td>\n",
       "      <td>68237.560246</td>\n",
       "      <td>100583.204205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7613.000000</td>\n",
       "      <td>30028.000000</td>\n",
       "      <td>13629.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16698.000000</td>\n",
       "      <td>39259.000000</td>\n",
       "      <td>36101.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30638.000000</td>\n",
       "      <td>56915.000000</td>\n",
       "      <td>65794.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59183.000000</td>\n",
       "      <td>104451.500000</td>\n",
       "      <td>105505.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179171.000000</td>\n",
       "      <td>261966.000000</td>\n",
       "      <td>340385.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1980s          1990s          2000s\n",
       "count      15.000000      15.000000      15.000000\n",
       "mean    44418.333333   85594.666667   97471.533333\n",
       "std     44190.676455   68237.560246  100583.204205\n",
       "min      7613.000000   30028.000000   13629.000000\n",
       "25%     16698.000000   39259.000000   36101.500000\n",
       "50%     30638.000000   56915.000000   65794.000000\n",
       "75%     59183.000000  104451.500000  105505.500000\n",
       "max    179171.000000  261966.000000  340385.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df.describe()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Step 3: Plot the box plots.\n"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df.plot(kind='box', figsize=(10,6), color='seagreen')\n",
    "\n",
    "plt.title('Immigration Sum of Top 15 Countries in Each Decade')\n",
    "plt.xlabel('Period')\n",
    "plt.ylabel('# of Immigrants')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df.plot(kind='box', figsize=(10, 6))\n",
    "\n",
    "    plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Note how the box plot differs from the summary table created. The box plot scans the data and identifies the outliers. **In order to be an outlier**, the data value must be:<br>\n",
    "\n",
    "-   larger than Q3 by at least 1.5 times the interquartile range (IQR), or,\n",
    "-   smaller than Q1 by at least 1.5 times the IQR.\n",
    "\n",
    "Let's look at decade 2000s as an example: <br>\n",
    "\n",
    "-   Q1 (25%) = 36,101.5 <br>\n",
    "-   Q3 (75%) = 105,505.5 <br>\n",
    "-   IQR = Q3 - Q1 = 69,404 <br>\n",
    "\n",
    "Using the definition of outlier, any value that is greater than Q3 by 1.5 times IQR will be flagged as outlier.\n",
    "\n",
    "Outlier > 105,505.5 + (1.5 * 69,404) <br>\n",
    "Outlier > 209,611.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "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>Country</th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country  1980s   1990s   2000s\n",
       "0   India  82154  180395  303591\n",
       "1   China  32003  161528  340385"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check how many entries fall above the outlier threshold \n",
    "new_df1 = new_df.reset_index()             # 왜 한거지???????????\n",
    "new_df1[new_df1['2000s'] > 209611.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df=new_df.reset_index()\n",
    "    new_df[new_df['2000s']> 209611.5]\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- The correct answer is:\n",
    "new_df[new_df['2000s']> 209611.5]\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "China and India are both considered as outliers since their population for the decade exceeds 209,611.5. \n",
    "\n",
    "The box plot is an advanced visualizaiton tool, and there are many options and customizations that exceed the scope of this lab. Please refer to [Matplotlib documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.boxplot?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&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-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) on box plots for more information.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Scatter Plots <a id=\"10\"></a>\n",
    "\n",
    "A `scatter plot` (2D) is a useful method of comparing variables against each other. `Scatter` plots look similar to `line plots` in that they both map independent and dependent variables on a 2D graph. While the datapoints are connected together by a line in a line plot, they are not connected in a scatter plot. The data in a scatter plot is considered to express a trend. With further analysis using tools like regression, we can mathematically calculate this relationship and use it to predict trends outside the dataset.\n",
    "\n",
    "Let's start by exploring the following:\n",
    "\n",
    "Using a `scatter plot`, let's visualize the trend of total immigrantion to Canada (all countries combined) for the years 1980 - 2013.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Since we are expecting to use the relationship betewen `years` and `total population`, we will convert `years` to `int` type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>69978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>86048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>134771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>139306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>164432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>188054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>207509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>221687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>222049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>193665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>187712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>200085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>192885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>160727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>179818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>216712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>242643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>223111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>217297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>232083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>257457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>247057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>232405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>243047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>248768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>276956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>246194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>256222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>257537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sum\n",
       "1980   99137\n",
       "1981  110563\n",
       "1982  104271\n",
       "1983   75550\n",
       "1984   73417\n",
       "1985   69978\n",
       "1986   86048\n",
       "1987  134771\n",
       "1988  139306\n",
       "1989  164432\n",
       "1990  188054\n",
       "1991  207509\n",
       "1992  221687\n",
       "1993  222049\n",
       "1994  193665\n",
       "1995  187712\n",
       "1996  200085\n",
       "1997  192885\n",
       "1998  160727\n",
       "1999  179818\n",
       "2000  216712\n",
       "2001  242643\n",
       "2002  223111\n",
       "2003  217297\n",
       "2004  232083\n",
       "2005  257457\n",
       "2006  247057\n",
       "2007  232405\n",
       "2008  243047\n",
       "2009  248768\n",
       "2010  276956\n",
       "2011  246194\n",
       "2012  256222\n",
       "2013  257537"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 연마다 총합 구하기\n",
    "df_can[years].sum(axis=0)     # type: series\n",
    "df_tot = pd.DataFrame(df_can[years].sum(axis=0), columns=['Sum'])\n",
    "df_tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990,\n",
       "            1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,\n",
       "            2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,\n",
       "            2013],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 이후 regression을 위해서 years를 int으로 바꾸기\n",
    "df_tot.index            # type: str\n",
    "\n",
    "df_tot.index = df_tot.index.map(int)\n",
    "df_tot.index            # type: int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1985</td>\n",
       "      <td>69978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1986</td>\n",
       "      <td>86048</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1987</td>\n",
       "      <td>134771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1988</td>\n",
       "      <td>139306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1989</td>\n",
       "      <td>164432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1990</td>\n",
       "      <td>188054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1991</td>\n",
       "      <td>207509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1992</td>\n",
       "      <td>221687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1993</td>\n",
       "      <td>222049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1994</td>\n",
       "      <td>193665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1995</td>\n",
       "      <td>187712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1996</td>\n",
       "      <td>200085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1997</td>\n",
       "      <td>192885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1998</td>\n",
       "      <td>160727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1999</td>\n",
       "      <td>179818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2000</td>\n",
       "      <td>216712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2001</td>\n",
       "      <td>242643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2002</td>\n",
       "      <td>223111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2003</td>\n",
       "      <td>217297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2004</td>\n",
       "      <td>232083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2005</td>\n",
       "      <td>257457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2006</td>\n",
       "      <td>247057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2007</td>\n",
       "      <td>232405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2008</td>\n",
       "      <td>243047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2009</td>\n",
       "      <td>248768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2010</td>\n",
       "      <td>276956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2011</td>\n",
       "      <td>246194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2012</td>\n",
       "      <td>256222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>257537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index     Sum\n",
       "0    1980   99137\n",
       "1    1981  110563\n",
       "2    1982  104271\n",
       "3    1983   75550\n",
       "4    1984   73417\n",
       "5    1985   69978\n",
       "6    1986   86048\n",
       "7    1987  134771\n",
       "8    1988  139306\n",
       "9    1989  164432\n",
       "10   1990  188054\n",
       "11   1991  207509\n",
       "12   1992  221687\n",
       "13   1993  222049\n",
       "14   1994  193665\n",
       "15   1995  187712\n",
       "16   1996  200085\n",
       "17   1997  192885\n",
       "18   1998  160727\n",
       "19   1999  179818\n",
       "20   2000  216712\n",
       "21   2001  242643\n",
       "22   2002  223111\n",
       "23   2003  217297\n",
       "24   2004  232083\n",
       "25   2005  257457\n",
       "26   2006  247057\n",
       "27   2007  232405\n",
       "28   2008  243047\n",
       "29   2009  248768\n",
       "30   2010  276956\n",
       "31   2011  246194\n",
       "32   2012  256222\n",
       "33   2013  257537"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index를 컬럼으로 보내기 --> scatter plot에서 x, y 지정할 때 컬럼이어야 하기 때문!!\n",
    "df_tot.reset_index(inplace=True)\n",
    "df_tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Year</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year   Total\n",
       "0  1980   99137\n",
       "1  1981  110563\n",
       "2  1982  104271\n",
       "3  1983   75550\n",
       "4  1984   73417"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename columns\n",
    "df_tot.columns=['Year', 'Total']\n",
    "df_tot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot the data. In `Matplotlib`, we can create a `scatter` plot set by passing in `kind='scatter'` as plot argument. We will also need to pass in `x` and `y` keywords to specify the columns that go on the x- and the y-axis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3769bbrlFycnJ2rBhg5YsWaIOHTrUzIkCQWYztfmFFAAA/DRnzhzdd999KigoqPFL50BjxaVbAEBQ/OlPf9KgQYPkdDr11Vdf6bHHHtNtt91GyANqECN6AICguOWWW7Ry5Url5+crPj5eN910k6ZMmXLOS7cA/EfQAwAAsChuxgAAALAogh4AAIBFEfQAAAAsirtuK7F///5a3X9sbKwlZrevS/QscPQscPQscPQscPQsMPSraq1bt670NUb0AAAALIqgBwAAYFEEPQAAAIsi6AEAAFgUQQ8AAMCiCHoAAAAWRdADAACwKIIeAACARRH0AAAALIqgBwAAYFEEPQAAUC/Z99jlHuZWk65N5B7mln2vPdglNTgEPQAAUC85JzgVtj5MtkybwtaHyTneGeySGhyCHgAAqJfsefYql3FuBD0AAFAveV3eKpdxbgQ9AABQL3nSPSrpUSKTaFTSo0SedE+wS2pwHMEuAAAA4Gy88V7lLs5VbGyscnNyg11Og8SIHgAAgEUR9AAAACyKoAcAAGBRBD0AAACLIugBAABYFEEPAADAogh6AAAAFkXQAwAAsCiCHgAAgEUR9AAAACyKoAcAAGBRBD0AAACLIugBAABYFEEPAADAogh6AAAAFkXQAwAAsCiCHgAAgEUR9AAAACyKoAcAAGBRBD0AAACLIugBAABYFEEPAADAogh6AADUEfseu9zD3GrStYncw9yy77UHuyTUklOfdYu+LYL6WRP0AACoI84JToWtD5Mt06aw9WFyjncGuyTUklOftSPLEdTP2hGUowIA0AjZ8+xVLqP+sO+xyznBKXueXV6XV550j7zxXv/fX08+a0b0AACoI16Xt8pl1B/nOyJXXz5rgh4AAHXEk+5RSY8SmUSjkh4l8qR7gl0SKnG+I3KnPuuyhLKgftZcugUAoI54473KXZyr2NhY5ebkBrscVMHr8sqR5aiwHND7//9nHWyM6AEAAJymvozInS9G9AAAAE5TX0bkzhcjegAAABZF0AMAALCoOrl0m5OTo5kzZ8rj8chms2nQoEG6/vrr9cEHH+iTTz5RVFSUJOnWW2/VZZddJklatGiRli9frpCQEN15551KTk6WJG3atEnz589XeXm5rr76at14442SpOzsbM2YMUNFRUVKSEjQhAkT5HA4dOLECaWnp2v37t1q3ry5Jk6cqBYtWtTFaQMAAARVnQQ9u92uESNGqEOHDjp+/LgmTZqk7t27S5KGDBmiYcOGVdh+3759+uKLL/SXv/xF+fn5evrpp/Xiiy9KkubNm6fHH39cbrdbjz76qFJSUtS2bVu99dZbGjJkiK644grNmTNHy5cv1+DBg7V8+XI1a9ZML730klavXq23335bDzzwQF2cNgAAQFDVyaXbmJgYdejQQZLUtGlTtWnTRnl5eZVuv3btWvXp00dNmjRRixYt1KpVK2VmZiozM1OtWrVSy5Yt5XA41KdPH61du1bGGGVkZKh3796SpAEDBmjt2rWSpHXr1mnAgAGSpN69e2vLli0yxtTuCQMAANQDdX7XbXZ2trKyspSYmKht27Zp6dKlWrVqlTp06KA77rhDkZGRysvLU6dOnXzvcblcvmDodrt9691ut3bu3KnCwkJFRETIbrefsX1eXp7vPXa7XRERESosLPRdLj5l2bJlWrZsmSRp2rRpio2Nrb0mSHI4HLV+DKuhZ4GjZ4GjZ4GjZ4FrVD3LkhyjHLLl2GRijcpeK5MSAttFo+pXDavToFdcXKzp06dr1KhRioiI0ODBg3XTTTdJkt5//3298cYbGjduXKUjbmdbb7PZqjymv+8ZNGiQBg0a5FvOycmpcr/nKzY2ttaPYTX0LHD0LHD0LHD0LHCNqWfu29wKWX/yAqIt0yZzmwl42pLG1K/qaN26daWv1dldt2VlZZo+fbquvPJKXX755ZIkp9OpkJAQhYSE6Oqrr9auXbsknRypy8394YcgLy9PLpfrjPW5ubmKiYlR8+bNdezYMXm93grbn74vr9erY8eOKTIysk7OGQCAmmLfY5d7mFst+raQe5hb9r2BPZIrWM73UWI4P3US9Iwxmj17ttq0aaOhQ4f61ufn5/v+/vXXX6tdu3aSpJSUFH3xxRc6ceKEsrOzdeDAASUmJqpjx446cOCAsrOzVVZWpi+++EIpKSmy2Wzq2rWr1qxZI0lauXKlUlJSJEk9evTQypUrJUlr1qxR165dzzkKCABAfeOc4FTY+jA5shwKWx8m53hnsEvyy+mPDgv0UWI4P3Vy6Xb79u1atWqV4uPj9cgjj0g6OZXK6tWr9d1338lmsykuLk5jxoyRJLVr104//elP9eCDDyokJER33XWXQkJOZtLRo0dr6tSpKi8v18CBA33h8LbbbtOMGTP03nvvKSEhQVdddZUk6aqrrlJ6eromTJigyMhITZw4sS5OGQCAGtVQR8Y86R45xztlz7PL6/LW6aPE7Hvsck6oeGxvfOMKmjbDLahntX///lrdP983CBw9Cxw9Cxw9Cxw9C1x1euYe5lbY+jDfckmPEks8ossf1f0Zayw9qxff0QMAANXnSfeopEeJyhLKVNKjpE5HxhqqhjoKWpPqfHoVAAAQOG+815KjUbXJ6/LKkeWosNzYMKIHAACq1FDv+GUUlBE9AEAQ8CX5huXUHb+S5MhyyDne2SBGFxkFZUQPABAEDXWqkMaK77o1XAQ9AECdIzg0LMyF13AR9AAAdY7g0LDwXbeGi+/oAQDqXDAn0UXg+K5bw0XQAwDUOYIDUDe4dAsAAGBRBD0AAACLIugBAABYFEEPAADAogh6AAAAFkXQAwAAsCiCHgAAgEUR9AAAACyKCZMBALA4+x67nBMqPonEG89j5xoDRvQAAPCTfY9d7mFutejbQu5hbtn32oNdkl+cE5wKWx8mR5ZDYevD5BzvDHZJqCMEPQAA/NRQA5M9z17lMqyLoAcAgJ8aamDyurxVLsO6CHoAAPipoQYmT7pHJT1KVJZQppIeJfKke4JdEuoIN2MAAOAnT7pHzvEVb2poCLzxXuUuzg12GQgCgh4AAH4iMKGh4dItAACARRH0AAAALIqgBwAAYFEEPQAAAIsi6AEAAFgUQQ8AAMCiCHoAAAAWxTx6AIAGxb7HLueEipMWe+MbxhMqgLrGiB6AesO+xy73MLda9G0h9zC37HsbxnNEUbecE5wKWx8mR5ZDYevD5BzvDHZJQL1F0ANQo84nrPELHP6w59mrXAbwA4IegBp1PmGNX+Dwh9flrXIZwA8IegBq1PmENX6Bwx+edI9KepSoLKFMJT1K5En3BLskoN7iZgwANcrr8sqR5aiw7C9PukfO8RW/ZA+czhvvVe7i3GCXATQIBD0ANep8whq/wAGgZhH0ANQowhoA1B8EPQBAo8I8fGhMuBkDAFAtp6bSadK1SYOa95BpfNCYEPQAANVyKjDZMm0NKjAxjQ8aE4IeAKBaGmpgYhofNCYEPQBAtTTUwMQ8fGhMuBkDAFAtp6bSCS0IVWl0aYMJTNwZjsaEoAcAqJZTgSk2Nla5OQQnoD7i0i0AAIBFEfQAAAAsiqAHAABgUQQ9AAAAiyLoAQAAWBRBDwAAwKIIegAAABbFPHoAKrDvscs5wSl7nl1el1eedI+88Q3jiQcAgIr8GtHbtm2bDh8+LEkqKCjQnDlz9Oqrr+rIkSO1WhyAunfqQfWOLEeDelA9AOBMfgW9OXPmyBgjSXrjjTdUWFio48ePa86cObVaHIC611AfVA8AOJNfl25zc3PVokULlZeXa9OmTXrppZfUpEkT3XvvvbVdH4A65nV55chyVFgGADRMfgW98PBwHTlyRN9//71at26tiIgIlZWVqaysrLbrA1DHTj2o/sff0QMANEx+Bb1rrrlGjz32mEpLS3XbbbdJknbs2KHWrVvXanEA6t6pB9UDABo+v4LeTTfdpJ49e8put6tt27aSpKioKI0ZM6ZWiwMAAED1+XUzRmpqqi688EJfyJOktm3bavHixbVWGAAAAM6PX0Fv06ZNAa0HAABA8FV56XbhwoWSpLKyMt/fT8nOzpbb7fbrIDk5OZo5c6Y8Ho9sNpsGDRqk66+/XkVFRUpNTdXhw4cVFxenBx54QJGRkTLGaP78+dq4caPCwsI0btw4dejQQZK0cuVKXy3Dhw/XgAEDJEm7d+/WzJkzVVpaqksvvVR33nmnbDZbpccAAACwuipH9Pbu3au9e/eqvLzc9/e9e/fq+++/V3h4uCZOnOjXQex2u0aMGKHU1FRNnTpVS5cu1b59+/Thhx+qW7duSktLU7du3fThhx9KkjZu3KiDBw8qLS1NY8aM0auvvipJKioq0oIFC/Tss8/q2Wef1YIFC1RUVCRJmjt3ru655x6lpaXp4MGDvtHGyo4BAABgdVWO6J0KckuWLNF1111X7YPExMQoJiZGktS0aVO1adNGeXl5Wrt2rZ588klJUv/+/fXkk0/q9ttv17p169SvXz/ZbDZ17txZR48eVX5+vjIyMtS9e3ffiFz37t21adMmde3aVcePH1fnzp0lSf369dPatWt16aWXVnoMAKgpPDYOQH3l11231113nUpKSnTw4EEVFxdXeK1Lly4BHTA7O1tZWVlKTExUQUGBLwDGxMT4HqmWl5en2NhY33vcbrfy8vKUl5dX4XKxy+U66/pT20uq9BgAUFNOPTZOkhxZDjnHO5miBkC94FfQ+/zzzzV37lxJUlhYmG+9zWbTK6+84vfBiouLNX36dI0aNUoRERGVbnfqcWs/ZrPZzrqtzWY76/aBWrZsmZYtWyZJmjZtWoWgWRscDketH8Nq6Fng6FngqtOzJgVNKiyHFoQ2qr7zcxY4ehYY+lV9fgW9t99+W/fdd5969epV7QOVlZVp+vTpuvLKK3X55ZdLkqKjo5Wfn6+YmBjl5+crKipK0skRuZycHN97c3NzFRMTI5fLpa1bt/rW5+XlKSkpSW63W7m5uRW2d7lcVR7jdIMGDdKgQYN8yz8+fm2IjY2t9WNYDT0LHD0LXHV65o52K0w//CO4NLpUuTmNZ0SPn7PA0bPA0K+qVfUAC7+mVzlx4oRSUlKqXYAxRrNnz1abNm00dOhQ3/qUlBR9+umnkqRPP/1UPXv29K1ftWqVjDHasWOHIiIiFBMTo+TkZG3evFlFRUUqKirS5s2blZycrJiYGDVt2lQ7duyQMUarVq3y1VvZMQCgpnjSPSrpUaKyhDKV9CjhsXEA6g2b8eO656JFi2Sz2XTjjTdW6yDbtm3TE088ofj4eN8l2FtvvVWdOnVSamqqcnJyFBsbqwcffNA3vcq8efO0efNmhYaGaty4cerYsaMkafny5Vq0aJGkk9OrDBw4UJK0a9cuzZo1S6WlpUpOTtbo0aNls9lUWFh41mOcy/79+6t1rv7iXyeBo2eBo2eBo2eBo2eBo2eBoV9Vq2pEz6+gd//99+vw4cMKDw8/49Lniy++eP4V1kMEvfqHngWOngWOngWOngWOngWGflWtqqDn13f0Ro8eXWPFAAAAoG74FfSSk5Nruw4AAADUML+CniTt27dP27ZtU2FhYYXpTIYPH14rhQFAoJi4GAAq8ivorVixQvPmzVNSUpIyMjLUtWtXZWRk6LLLLqvt+gDAbw114mICKoDa4tf0Kh9++KEmTZqkyZMnKzQ0VJMnT9YDDzygZs2a1XZ9AOA3e569yuX66lRAdWQ5FLY+TM7xzmCXBMAi/Ap6Ho9HF198saQfnkTRo0cPff3117VaHAAEwuvyVrlcXzXUgAqg/vMr6LlcLt9tza1atdLGjRu1a9cu2e38zwhA/dFQJy5uqAEVQP3n13f0hgwZoj179ig2NlbDhw/X9OnT5fV6NWLEiNquDwD85o33Nojv5J3Ok+6Rc3zF7+gBQE04Z9Azxuiyyy6T03nyOyMpKSn661//qtLSUjVv3rzWCwQAq2uoARVA/efXpdv777+/wnJYWBghDwAAoJ47Z9Cz2Wy68MILlZ2dXRf1AKgB9j12uYe51aRrE7mHuWXfy/dpz4WeAbAiv76jd8kll2jq1Km6+uqr5Xa7ZbPZfK/17du31ooDUD0/nk8uTGENZj65YKJnAKzIr6C3ed9QpXwAAB4aSURBVPNmRUVFae3atRXW22w2gh5QDzFdR+DoGQAr8ivoTZ06tbbrAFCDvC6vHFmOCsuoGj0DYEV+3YwBoGE5NZ+cSTQNaj65YKJnAKzIrxG9W2+99azrmzRpIrfbrV69emn48OEKCwur0eIAVM+p6TpiY2OVm8P3zPxBzwBYkV9B74477tCaNWv085//XG63Wzk5Ofroo4/Uo0cPtWjRQgsXLlRhYaHGjBlT2/UCAADAT35dul2yZIkeeeQRXXbZZbrwwgvVo0cPPfjgg1q6dKl69eqlhx9+WOvWravtWgEAABAAv4Le0aNH5fWe9ixGr1dHjx6VdPJZuCUlJTVfHQAAAKrNr0u3V1xxhaZOnaqhQ4fK7XYrNzdXH3/8sW9qlS1btqhVq1a1WigAAAAC41fQGzVqlJYsWaJ///vfysvLU0xMjPr166drr71WktS5c2dNmTKlVgsFAABAYPwKeiEhIRoyZIiGDBly1tfDw8NrtCgAAACcv0qD3pdffqmf/vSnkqTPP/+80h3wZAwAaJjse+xyTnDKnmeX1+WVJ90jbzwTRQNWUmnQ+/e//+0LekuWLDnrNjwCDQAarh8/39eR5eD5voAFVRr0nnjiCd/feQQaAFgPz/cFrM+v7+idcuLEiTOmUYmMjKzRggAAdYPn+wLW51fQ27p1q+bOnav9+/ef8dr7779f40UBAGqfJ90j5/iK39EDYC1+Bb1Zs2Zp6NCh6tOnj0JDQ2u7JgBAHTj1fF8A1uVX0Dt+/LgGDx6skBC/HqQBAACAesCv5Hbdddfp//7v/2q7FgAAANQgv0b0+vXrp6eeekqLFi1SVFRUhddSU1NrpTAAAACcH7+C3vTp05WYmKjevXvzHT0AAIAGwq+gd+DAAT333HN8Rw8AAKAB8Su5XXbZZdq2bVtt1wIAAIAa5NeIXkhIiJ577jl169ZN0dHRFV675557aqUwAAAAnB+/gl5sbKyuu+662q4FAAAANcivoPeb3/ymtusAAABADfP7Wbf5+fnau3eviouLK6y//PLLa7woAAAAnD+/gt5HH32kd999V61atTpjehWCHgAAQP3kV9BbtGiRpk6dqvbt29dyOQAAAKgpfk2vEhERodatW9d2LQAAAKhBfgW9kSNHas6cOdq7d68KCgoq/AEAAED95NelW7vdrm+++UafffbZGa+9//77NV4UAAAAzp9fQW/OnDkaPny4+vTpw7NuAQAAGgi/gl5paakGDx7Ms26BOmTfY5dzglP2PLu8Lq886R55473BLgsA0ID4ldyGDBmijz76qLZrAfAjzglOha0PkyPLobD1YXKOdwa7JABAA+PXiN7KlSt1+PBhLVq0SFFRURVee/HFF2ulMKCxs+fZq1wGAOBc/Ap6o0ePru06AJzG6/LKkeWosAwAQCD8CnrJycm1XQeA03jSPXKOr/gdPQAAAlFl0Pv888/PuYO+ffvWWDEAfuCN9yp3cW6wy0A9x007AKpSZdBbsmRJlW+22WwEPQAIolM37UiSI8sh53gn/0AA4FNl0Js6dWpd1QEAqAZu2gFQFSbGA4AG7PSbdLhpB8CPEfQAoAHzpHtU0qNEZQllKulRwk07ACrw665bAED9xE07AKrCiB4AAIBFVTqil5eX59cOXC5XjRUDAACAmlNp0Bs7dqxfO3j//fdrrBgAAADUnEqD3ltvvVWXdQAAAKCGVRr0mjRpUpd1AAAAoIb5dddteXm5li9frq1bt6qwsFDGGN9rjz/+eK0VBwAAgOrz667bN998U//4xz8UHx+vbdu26eKLL9ahQ4eUmJhY2/UBAACgmvwKel9++aUee+wx3XjjjQoJCdGNN96o3//+99q+fXtt1wcAAIBq8uvSbXFxsVq0aCFJCg0NVWlpqdq1a6fdu3f7dZBZs2Zpw4YNio6O1vTp0yVJH3zwgT755BNFRUVJkm699VZddtllkqRFixZp+fLlCgkJ0Z133qnk5GRJ0qZNmzR//nyVl5fr6quv1o033ihJys7O1owZM1RUVKSEhARNmDBBDodDJ06cUHp6unbv3q3mzZtr4sSJvvMAAACwOr9G9Nq0aeMLdR06dNDChQu1ePFiOZ1Ovw4yYMAATZ48+Yz1Q4YM0QsvvKAXXnjBF/L27dunL774Qn/5y1/02GOPad68eSovL1d5ebnmzZunyZMnKzU1VatXr9a+ffsknbxDeMiQIUpLS1OzZs20fPlySdLy5cvVrFkzvfTSSxoyZIjefvttv+oFAACwAr+C3h133OG7AWPEiBHKyMjQZ599pt/+9rd+HSQpKUmRkZF+bbt27Vr16dNHTZo0UYsWLdSqVStlZmYqMzNTrVq1UsuWLeVwONSnTx+tXbtWxhhlZGSod+/ekk6GyrVr10qS1q1bpwEDBkiSevfurS1btlS4kQQAAMDK/Lp026ZNG19Qa9u2rZ5++mlJ0tGjR8/r4EuXLtWqVavUoUMH3XHHHYqMjFReXp46derk28blcvme0uF2u33r3W63du7cqcLCQkVERMhut5+xfV5enu89drtdERERKiws9F0u/rFly5Zp2bJlkqRp06YpNjb2vM7tXBwOR60fw2roWeDoWeDoWeDoWeDoWWDoV/X5FfTuu+8+vf7662esHz9+vObPn1+tAw8ePFg33XSTpJNP13jjjTc0bty4SkfczrbeZrNVeYxA3jNo0CANGjTIt5yTk1Plvs9XbGxsrR/DauhZ4OhZ4OhZ4OhZ4OhZYOhX1Vq3bl3pa35duj1bYCouLlZIiF9vPyun06mQkBCFhITo6quv1q5duySdHKnLzc31bZeXlyeXy3XG+tzcXMXExKh58+Y6duyYvF5vhe1P35fX69WxY8f8voQMAADQ0FU5onf//ffLZrOptLRUv/vd7yq8VlBQoJSUlGofOD8/XzExMZKkr7/+Wu3atZMkpaSkKC0tTUOHDlV+fr4OHDigxMREGWN04MABZWdny+Vy6YsvvvDV17VrV61Zs0ZXXHGFVq5c6aurR48eWrlypTp37qw1a9aoa9eu5xwFBAAAsAqbqeLuhE2bNkmS/vznP+vhhx/+4U02m6Kjo9W+fXu/DjJjxgzfUzWio6P161//WhkZGfruu+9ks9kUFxenMWPG+ILfwoULtWLFCoWEhGjUqFG69NJLJUkbNmzQ66+/rvLycg0cOFDDhw+XJB06dOiM6VWaNGmi0tJSpaenKysrS5GRkZo4caJatmzpV8379+/3a7vqYhg6cPQscPQscPQscPQscPQsMPSralVduq0y6J1SVFTU6C55EvTqH3oWOHoWOHoWOHoWOHoWGPpVtaqCnl83Y0RERGjRokVatWqV7ztw/fr107Bhw3x3uwIAAKB+8SvovfPOO8rIyNBtt93mS9WLFi1SUVGRRowYUds1AgAAoBr8CnqrV6/W888/75t/rn379urUqZP+8Ic/EPQAAADqKb/mRykvLz9jKhW73a7y8vJaKQoAAADnz68RvZ49e+qFF17QzTffrNjYWB0+fFgLFixQr169ars+AAAAVJNfQe+OO+7QBx98oLS0NHk8HsXExKhPnz66+eaba7s+AAAAVFOVQe/zzz9X3759FRoaqttvv1233357XdUFAACA81Tld/Tmzp1bV3UAAACghlU5oufHXMoAKmHfY5dzglP2PLu8Lq886R55473BLgsA0IhUGfTKy8u1ZcuWKndw8cUX12hBgFU4JzgVtj5MkuTIcsg53qncxblBrgoA0JhUGfROnDih2bNnVzqyZ7PZlJ6eXiuFAQ2dPc9e5TIAALWtyqAXHh5OkAOqyevyypHlqLAMAEBd8mvCZACB86R7VNKjRGUJZSrpUSJPuifYJQEAGhluxgBqiTfey3fyAABBVWXQe+ONN+qqDqBe4s5ZAEBDxqVboAqn7px1ZDkUtj5MzvHOYJcEAIDfCHpAFbhzFgDQkBH0gCqcfqcsd84CABoSgh5QBe6cBQA0ZFXejAE0dtw5CwBoyBjRAwAAsCiCHgAAgEUR9AAAACyKoAcAAGBRBD0AAACLIugBAABYFEEPAADAogh6AAAAFkXQAwAAsCiCHgAAgEUR9AAAACyKoAcAAGBRBD0AAACLIugBAABYFEEPAADAogh6AAAAFkXQAwAAsCiCHgAAgEUR9AAAACyKoAcAAGBRBD0AAACLIugBAABYFEEPAADAogh6AAAAFkXQAwAAsCiCHgAAgEUR9AAAACyKoAcAAGBRBD0AAACLcgS7AATGvscu5wSn7Hl2eV1eedI98sZ7g10WAACohxjRa2CcE5wKWx8mR5ZDYevD5BzvDHZJAACgniLoNTD2PHuVywAAAKcQ9BoYr8tb5TIAAMApBL0GxpPuUUmPEpUllKmkR4k86Z5glwQAAOopbsZoYLzxXuUuzg12GQAAoAFgRA8AAMCiCHoAAAAWRdADAACwKL6j14gw2TIAAI0LI3qNCJMtAwDQuBD0GhEmWwYAoHEh6DUiTLYMAEDjQtBrRJhsGQCAxqVObsaYNWuWNmzYoOjoaE2fPl2SVFRUpNTUVB0+fFhxcXF64IEHFBkZKWOM5s+fr40bNyosLEzjxo1Thw4dJEkrV67UwoULJUnDhw/XgAEDJEm7d+/WzJkzVVpaqksvvVR33nmnbDZbpcdorJhsGQCAxqVORvQGDBigyZMnV1j34Ycfqlu3bkpLS1O3bt304YcfSpI2btyogwcPKi0tTWPGjNGrr74q6WQwXLBggZ599lk9++yzWrBggYqKiiRJc+fO1T333KO0tDQdPHhQmzZtqvIYAAAAjUGdBL2kpKQzRtLWrl2r/v37S5L69++vtWvXSpLWrVunfv36yWazqXPnzjp69Kjy8/O1adMmde/eXZGRkYqMjFT37t21adMm5efn6/jx4+rcubNsNpv69evn21dlxwgm+x673MPcatK1idzD3LLv5YYIAABQO4I2j15BQYFiYmIkSTExMTpy5IgkKS8vT7Gxsb7t3G638vLylJeXJ7fb7VvvcrnOuv7U9lUd42yWLVumZcuWSZKmTZtWoYaa5PilQyHrT+brMIUp7oE4lX1aVivHshqHw1Frn4tV0bPA0bPA0bPA0bPA0K/qq3cTJhtjzlhns9nOuq3NZjvr9tUxaNAgDRo0yLeck5NTI/s9XYtDLRTyo4HU8kPltXYsq4mNjaVXAaJngaNngaNngaNngaFfVWvdunWlrwXtrtvo6Gjl5+dLkvLz8xUVFSXp5Ijcjz/M3NxcxcTEyOVyKTf3hxsJ8vLyFBMTI7fbXWF9bm6uXC5XlccIJqY4AQAAdSVoQS8lJUWffvqpJOnTTz9Vz549fetXrVolY4x27NihiIgIxcTEKDk5WZs3b1ZRUZGKioq0efNmJScnKyYmRk2bNtWOHTtkjNGqVauUkpJS5TGC6dQUJybRMMUJAACoVTZTU9c+qzBjxgxt3bpVhYWFio6O1q9//Wv17NlTqampysnJUWxsrB588EHf9Crz5s3T5s2bFRoaqnHjxqljx46SpOXLl2vRokWSTk6vMnDgQEnSrl27NGvWLJWWlio5OVmjR4+WzWZTYWHhWY/hj/3799dOM/4/hqEDR88CR88CR88CR88CR88CQ7+qVtWl2zoJeg0RQa/+oWeBo2eBo2eBo2eBo2eBoV9Vq5ff0QMAAEDtIugBAABYFEEPAADAogh6AAAAFkXQAwAAsCiCHgAAgEUR9AAAACyKoAcAAGBRBD0AAACLIugBAABYFEEPAADAogh6AAAAFuUIdgFAbbPvscs5wSl7nl1el1eedI+88d5glwUAQK1jRA+W55zgVNj6MDmyHApbHybneGewSwIAoE4Q9GB59jx7lcsAAFgVQQ+W53V5q1wGAMCqCHqwPE+6RyU9SlSWUKaSHiXypHuCXRIAAHWCmzFged54r3IX5wa7DAAA6hwjegAAABZF0AMAALAogh4AAIBFEfQAAAAsiqAHAABgUQQ9AAAAiyLoAQAAWBRBDwAAwKIIegAAABbFkzHgF/seu5wTnLLn2eV1eeVJ98gbzzNjAQCozxjRg1+cE5wKWx8mR5ZDYevD5BzvDHZJAADgHAh68Is9z17lMgAAqH8IevCL1+Wtcvlc7Hvscg9zq0XfFnIPc8u+l6AIAEBtI+jBL550j0p6lKgsoUwlPUrkSfcE9H4u/QIAUPe4GQN+8cZ7lbs4t9rv59IvAAB1jxE91InzvfQLAAACR9BDnTjfS78AACBwXLpFnTifS7+n5vBrUtBE7mg3c/gBAOAnRvRQ7526kcOWaeNGDgAAAkDQQ73HjRwAAFQPQQ/1HjdyAABQPQQ91HunbuQwiYYbOQAACAA3Y6DeO3UjR2xsrHJzqj+XHwAAjQ0jegAAABZF0AMAALAogh4AAIBFEfQAAAAsiqAHAABgUQQ9AAAAiyLoAQAAWBRBDwAAwKIIegAAABZF0AMAALAogh4AAIBF2YwxJthFAAAAoOYxohckkyZNCnYJDQ49Cxw9Cxw9Cxw9Cxw9Cwz9qj6CHgAAgEUR9AAAACzK/uSTTz4Z7CIaqw4dOgS7hAaHngWOngWOngWOngWOngWGflUPN2MAAABYFJduAQAALMoR7AKsZNasWdqwYYOio6M1ffp0SdJ3332nuXPnqri4WHFxcbr//vsVERGhsrIyzZ49W1lZWSovL1e/fv30i1/8QpK0adMmzZ8/X+Xl5br66qt14403BvO0alVN9ey+++5TeHi4QkJCZLfbNW3atGCeVq0JtF9z5szRrl27FBISolGjRqlr166SpN27d2vmzJkqLS3VpZdeqjvvvFM2my2Yp1ZraqpnTz75pPLz8xUaGipJevzxxxUdHR2086pNOTk5mjlzpjwej2w2mwYNGqTrr79eRUVFSk1N1eHDhxUXF6cHHnhAkZGRMsZo/vz52rhxo8LCwjRu3DjfZbaVK1dq4cKFkqThw4drwIABQTyz2lOTPbv55psVHx8vSYqNjdUf/vCHYJ5arQm0Z//97381a9YsZWVl6ZZbbtGwYcN8+2pMvzcDZlBjMjIyzK5du8yDDz7oWzdp0iSTkZFhjDHmk08+Me+++64xxpjPPvvMpKamGmOMKS4uNuPGjTOHDh0yXq/XjB8/3hw8eNCcOHHCPPzww+b777+v+5OpIzXRM2OMGTdunCkoKKjj6uteIP1asmSJmTlzpjHGGI/HY37/+98br9fre8/27dtNeXm5mTp1qtmwYUMdn0ndqameTZkyxWRmZtZx9cGRl5dndu3aZYwx5tixY+b+++8333//vXnzzTfNokWLjDHGLFq0yLz55pvGGGPWr19vpk6dasrLy8327dvNo48+aowxprCw0Nx3332msLCwwt+tqKZ6Zowxt99+e92fQBAE2jOPx2N27txp3nnnHfO///u/vv00tt+bgeLSbQ1KSkpSZGRkhXX79+/XT37yE0lS9+7d9dVXX/leKy4ultfrVWlpqRwOhyIiIpSZmalWrVqpZcuWcjgc6tOnj9auXVun51GXaqJnjUkg/dq3b58uvvhiSVJ0dLSaNWum3bt3Kz8/X8ePH1fnzp1ls9nUr18/fsbO0bPGJiYmxje61LRpU7Vp00Z5eXlau3at+vfvL0nq37+/7+dm3bp16tevn2w2mzp37qyjR48qPz9fmzZtUvfu3RUZGanIyEh1795dmzZtCtp51aaa6lljEmjPoqOjlZiYKLvdXmE/je33ZqAIerWsXbt2WrdunSRpzZo1ys3NlST17t1b4eHhGjNmjMaNG6cbbrhBkZGRysvLk9vt9r3f7XYrLy8vKLUHS6A9O2Xq1Kn6wx/+oGXLlgWl7mCprF/t27fXunXr5PV6lZ2drd27dysnJ4efMQXes1NmzZqlRx55RAsWLJBpJPexZWdnKysrS4mJiSooKFBMTIykk7+kjxw5IknKy8tTbGys7z2nfqZO/1lzuVyN4mftfHomSSdOnNCkSZP02GOP6euvv677EwgCf3pWGf6fVjW+o1fLxo4dq/nz52vBggVKSUmRw3Gy5ZmZmQoJCdErr7yio0eP6oknnlC3bt3O+svDqt+dqkygPWvZsqWefvppuVwuFRQU6JlnnlHr1q2VlJQU5DOpG5X1a+DAgdq3b58mTZqkuLg4denSRXa7vdEElKoE2jNJuv/+++VyuXT8+HFNnz5dq1at8o06WFVxcbGmT5+uUaNGVTl6Hsj/t6z+/7Oa6NmsWbPkcrl06NAhPfXUU4qPj1erVq1qreZg87dnleH3ZtUIerWsTZs2evzxxyWdvFy0YcMGSdLnn3+u5ORkORwORUdHq0uXLtq1a5diY2N9owuSlJub6/uXTWMRaM9atmwpl8sl6eTQfs+ePZWZmdlogl5l/bLb7Ro1apRvu8cff1wXXHCBmjVrdsbP2Kn+NRaB9kySr0dNmzZV3759lZmZaemgV1ZWpunTp+vKK6/U5ZdfLunkf1/5+fmKiYlRfn6+oqKiJJ0cQfnxyOep/2+5XC5t3brVtz4vL8/S/13WRM+kH37WWrZsqaSkJH333XeWDXqB9Kwybre70f/erAqXbmtZQUGBJKm8vFwLFy7UNddcI+nknVRbtmyRMUbFxcXauXOn2rRpo44dO+rAgQPKzs5WWVmZvvjiC6WkpATzFOpcoD0rLi7W8ePHJZ38l+E333zju2OtMaisXyUlJSouLpYkffPNN7Lb7Wrbtq1iYmLUtGlT7dixQ8YYrVq1ip+xc/TM6/X6Lh+VlZVp/fr1ateuXXCKrwPGGM2ePVtt2rTR0KFDfetTUlL06aefSpI+/fRT9ezZ07d+1apVMsZox44dioiIUExMjJKTk7V582YVFRWpqKhImzdvVnJyclDOqbbVVM+Kiop04sQJSdKRI0e0fft2tW3btu5PqA4E2rPK8HuzakyYXINmzJihrVu3qrCwUNHR0fr1r3+t4uJiLV26VJLUq1cv/eY3v5HNZlNxcbFmzZqlffv2yRijgQMH+m4V37Bhg15//XWVl5dr4MCBGj58eDBPq1bVRM8OHTqkP//5z5Ikr9ervn37WrZngfQrOztbU6dOVUhIiFwul+69917FxcVJknbt2qVZs2aptLRUycnJGj16tGUvddREz4qLizVlyhR5vV6Vl5erW7duGjlypEJCrPlv5W3btumJJ55QfHy87+fi1ltvVadOnZSamqqcnBzFxsbqwQcf9E0VMm/ePG3evFmhoaEaN26cOnbsKElavny5Fi1aJOnk9CoDBw4M2nnVpprq2fbt2zVnzhyFhISovLxcQ4YM0VVXXRXks6sdgfbM4/Fo0qRJOn78uGw2m8LDw/WXv/xFERERjer3ZqAIegAAABZlzX+OAgAAgKAHAABgVQQ9AAAAiyLoAQAAWBRBDwAAwKIIegAAABZF0AOAAKWlpWnWrFkV1m3dulWjR49udA+mB1C/EfQAIEB33nmnNm7cqG+++UaSVFpaqldeeUV33HFHjT56qby8vMb2BaBx4lm3ABCg5s2ba/To0XrllVc0ffp0LVy4UC1bttSAAQNUXl6uDz/8UCtWrNCxY8fUrVs33X333YqMjFR5eblSU1O1bds2nThxQu3bt9fdd9/te8RVWlqaIiIidOjQIW3btk2TJk1S165dg3y2ABoynowBANU0ffp0lZWVafv27frTn/6k2NhYLV68WGvXrtUDDzygyMhIzZs3T2VlZZowYYLKy8u1atUqXX755bLb7XrzzTe1c+dOTZs2TdLJoLdx40Y9+uijSkxMlNfrVZMmTYJ8lgAaMi7dAkA13XXXXdqyZYtuuukmxcbGSpKWLVumW2+9VS6XS6GhofrVr36lL7/8UuXl5QoJCdGAAQPUtGlT32u7d+9WcXGxb589e/ZU586dFRISQsgDcN64dAsA1eR0OhUVFeW79CpJOTk5ev75530PaZckm82mI0eOKCoqSu+8847WrFmjwsJC3zaFhYUKDw+XJF9gBICaQNADgBrkdrt1//33q1OnTme8tmLFCm3cuFFPPPGE4uLiVFhYqLvvvlt8gwZAbeHSLQDUoGuuuUbvvvuucnJyJEkFBQVat26dJOn48eNyOBxq3ry5SkpK9N577wWzVACNACN6AFCDhg4dKkl66qmn5PF4FB0drSuuuEIpKSkaOHCgvvnmG91zzz1q3ry5fvWrX2nZsmVBrhiAlXHXLQAAgEVx6RYAAMCiCHoAAAAWRdADAACwKIIeAACARRH0AAAALIqgBwAAYFEEPQAAAIsi6AEAAFgUQQ8AAMCi/h/iMVmDiukb5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', figsize=(10, 6), x='Year', y='Total', color='magenta')     # 어느 것이 x, y가 되어도 상관없음\n",
    "\n",
    "plt.title('Scatter Plot of Total Immigration')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Total Immigrants')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice how the scatter plot does not connect the datapoints together. We can clearly observe an upward trend in the data: as the years go by, the total number of immigrants increases. We can mathematically analyze this upward trend using a regression line (line of best fit). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "So let's try to plot a **linear line of best fit**, and use it to  predict the number of immigrants in 2015.\n",
    "\n",
    "Step 1: Get the equation of line of best fit. We will use **Numpy**'s `polyfit()` method by passing in the following:\n",
    "\n",
    "-   `x`: x-coordinates of the data. \n",
    "-   `y`: y-coordinates of the data. \n",
    "-   `deg`: Degree of fitting polynomial. 1 = linear, 2 = quadratic (이차의), and so on.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.56709228e+03, -1.09261952e+07])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scatter plot과는 별개로 Regression line 생성\n",
    "x = df_tot['Year']      # x축 설정\n",
    "y = df_tot['Total']     # y축 설정\n",
    "\n",
    "fit = np.polyfit(x, y, deg=1)   # 1차식 linear\n",
    "fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The output is an array with the polynomial coefficients, highest powers first. Since we are plotting a linear regression `y= a*x + b`, our output has 2 elements `[5.56709228e+03, -1.09261952e+07]` with the the slope in position 0 (fit[0]) and intercept in position 1 (fit[1]). \n",
    "\n",
    "Step 2: Plot the regression line on the `scatter plot`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot과 regression line을 같이 그리기\n",
    "# Scatter plot\n",
    "df_tot.plot(kind='scatter', figsize=(10, 6), x='Year', y='Total', color='sandybrown')\n",
    "\n",
    "plt.title('Scatter Plot & Regression Line')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Total Immigrants')\n",
    "\n",
    "# Regression line 추가 - plot(x, y, format)\n",
    "plt.plot(x, fit[0]*x + fit[1], '--', color='darkblue')       # x = df_tot['Year']\n",
    "\n",
    "# Annotation - annotate('넣을 말', xy=(위, 치))\n",
    "plt.annotate('y={0:.1f}x + {1:.1f}'.format(fit[0], fit[1]), xy=(2000, 200000), fontsize=16)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Total Number of Immigrants = 5567 * Year + -10926195'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the line of the best fit\n",
    "'Total Number of Immigrants = {0:.0f} * Year + {1:.0f}'.format(fit[0], fit[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Using the equation of line of best fit, we can estimate the number of immigrants in 2015:\n",
    "\n",
    "```python\n",
    "No. Immigrants = 5567 * Year - 10926195\n",
    "No. Immigrants = 5567 * 2015 - 10926195\n",
    "No. Immigrants = 291,310\n",
    "```\n",
    "\n",
    "When compared to the actuals from Citizenship and Immigration Canada's (CIC) [2016 Annual Report](http://www.cic.gc.ca/english/resources/publications/annual-report-2016/index.asp?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&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-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ), we see that Canada accepted 271,845 immigrants in 2015. Our estimated value of 291,310 is within 7% of the actual number, which is pretty good considering our original data came from United Nations (and might differ slightly from CIC data).\n",
    "\n",
    "As a side note, we can observe that immigration took a dip around 1993 - 1997. Further analysis into the topic revealed that in 1993 Canada introcuded Bill C-86 which introduced revisions to the refugee determination system, mostly restrictive. Further amendments to the Immigration Regulations cancelled the sponsorship required for \"assisted relatives\" and reduced the points awarded to them, making it more difficult for family members (other than nuclear family) to immigrate to Canada. These restrictive measures had a direct impact on the immigration numbers for the next several years.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "**Question**: Create a scatter plot of the total immigration from Denmark, Norway, and Sweden to Canada from 1980 to 2013?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
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    }
   },
   "source": [
    "Step 1: Get the data:\n",
    "\n",
    "1.  Create a dataframe the consists of the numbers associated with Denmark, Norway, and Sweden only. Name it **df_dns**.\n",
    "2.  Sum the immigration numbers across all three countries for each year and turn the result into a dataframe. Name this new dataframe **df_total**.\n",
    "3.  Reset the index in place.\n",
    "4.  Rename the columns to **year** and **total**.\n",
    "5.  Display the resulting dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Year</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Total\n",
       "0  1980    669\n",
       "1  1981    678\n",
       "2  1982    627\n",
       "3  1983    333\n",
       "4  1984    252"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract Denmark, Norway, and Sweden\n",
    "df_dns = df_can.loc[['Denmark', 'Norway','Sweden'], years]\n",
    "\n",
    "# Calculate sum of each year\n",
    "sum = df_dns.sum(axis=0)\n",
    "# Make dataframe from series\n",
    "df_total = pd.DataFrame({'Total':sum})\n",
    "\n",
    "# Reset index\n",
    "df_total.reset_index(inplace=True)\n",
    "\n",
    "# Rename\n",
    "df_total.columns = ['Year', 'Total']\n",
    "df_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year     int32\n",
       "Total    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Regression line을 위해 dtype 확인 --> int로 변경\n",
    "df_total.dtypes\n",
    "\n",
    "df_total['Year'] = df_total['Year'].astype('int')\n",
    "df_total.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # create df_countries dataframe\n",
    "    df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "\n",
    "    # create df_total by summing across three countries for each year\n",
    "    df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "\n",
    "    # reset index in place\n",
    "    df_total.reset_index(inplace=True)\n",
    "\n",
    "    # rename columns\n",
    "    df_total.columns = ['year', 'total']\n",
    "\n",
    "    # change column year from string to int to create scatter plot\n",
    "    df_total['year'] = df_total['year'].astype(int)\n",
    "\n",
    "    # show resulting dataframe\n",
    "    df_total.head()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the scatter plot by plotting the total versus year in **df_total**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "button": false,
    "jupyter": {
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    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot\n",
    "df_total.plot(kind='scatter', figsize=(10, 6), x='Year', y='Total', color='darkolivegreen')\n",
    "\n",
    "plt.title('Sum of Immigrants of Denmark, Norway, and Sweden')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # generate scatter plot\n",
    "    df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "    # add title and label to axes\n",
    "    plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "    plt.xlabel('Year')\n",
    "    plt.ylabel('Number of Immigrants')\n",
    "\n",
    "    # show plot\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Bubble Plots <a id=\"12\"></a>\n",
    "\n",
    "A `bubble plot` is a variation of the `scatter plot` that displays three dimensions of data (x, y, z). The datapoints are replaced with bubbles, and **the size of the bubble is determined by the third variable 'z', also known as the weight**. In `maplotlib`, we can pass in an array or scalar to the keyword `s` to `plot()`, that contains the weight of each point.\n",
    "\n",
    "**Let's start by analyzing the effect of Argentina's great depression**.\n",
    "\n",
    "Argentina suffered a great depression from 1998 - 2002, which caused widespread unemployment, riots, the fall of the government, and a default on the country's foreign debt. In terms of income, over 50% of Argentines were poor, and seven out of ten Argentine children were poor at the depth of the crisis in 2002. \n",
    "\n",
    "Let's analyze the effect of this crisis, and compare Argentina's immigration to that of it's neighbour Brazil. Let's do that using a `bubble plot` of immigration from Brazil and Argentina for the years 1980 - 2013. We will set the weights for the bubble as the _normalized_ value of the population for each year.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the data for Brazil and Argentina. Like in the previous example, we will convert the `Years` to type int and bring it in the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": 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>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Brazil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>368</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>426</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>626</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>241</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>237</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1985</td>\n",
       "      <td>196</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1986</td>\n",
       "      <td>213</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1987</td>\n",
       "      <td>519</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1988</td>\n",
       "      <td>374</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1989</td>\n",
       "      <td>538</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1990</td>\n",
       "      <td>678</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1991</td>\n",
       "      <td>763</td>\n",
       "      <td>877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1992</td>\n",
       "      <td>1023</td>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1993</td>\n",
       "      <td>1022</td>\n",
       "      <td>866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1994</td>\n",
       "      <td>366</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1995</td>\n",
       "      <td>380</td>\n",
       "      <td>572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1996</td>\n",
       "      <td>396</td>\n",
       "      <td>586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1997</td>\n",
       "      <td>391</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1998</td>\n",
       "      <td>349</td>\n",
       "      <td>531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1999</td>\n",
       "      <td>318</td>\n",
       "      <td>626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2000</td>\n",
       "      <td>427</td>\n",
       "      <td>845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2001</td>\n",
       "      <td>635</td>\n",
       "      <td>847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2002</td>\n",
       "      <td>865</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2003</td>\n",
       "      <td>1745</td>\n",
       "      <td>839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2004</td>\n",
       "      <td>1591</td>\n",
       "      <td>917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2005</td>\n",
       "      <td>1153</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2006</td>\n",
       "      <td>847</td>\n",
       "      <td>1181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2007</td>\n",
       "      <td>620</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2008</td>\n",
       "      <td>540</td>\n",
       "      <td>2138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2009</td>\n",
       "      <td>467</td>\n",
       "      <td>2509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2010</td>\n",
       "      <td>459</td>\n",
       "      <td>2598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2011</td>\n",
       "      <td>278</td>\n",
       "      <td>1508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2012</td>\n",
       "      <td>263</td>\n",
       "      <td>1642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>282</td>\n",
       "      <td>1714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  Argentina  Brazil\n",
       "0        1980        368     211\n",
       "1        1981        426     220\n",
       "2        1982        626     192\n",
       "3        1983        241     139\n",
       "4        1984        237     145\n",
       "5        1985        196     130\n",
       "6        1986        213     205\n",
       "7        1987        519     244\n",
       "8        1988        374     394\n",
       "9        1989        538     650\n",
       "10       1990        678     650\n",
       "11       1991        763     877\n",
       "12       1992       1023    1066\n",
       "13       1993       1022     866\n",
       "14       1994        366     566\n",
       "15       1995        380     572\n",
       "16       1996        396     586\n",
       "17       1997        391     591\n",
       "18       1998        349     531\n",
       "19       1999        318     626\n",
       "20       2000        427     845\n",
       "21       2001        635     847\n",
       "22       2002        865     745\n",
       "23       2003       1745     839\n",
       "24       2004       1591     917\n",
       "25       2005       1153     969\n",
       "26       2006        847    1181\n",
       "27       2007        620    1746\n",
       "28       2008        540    2138\n",
       "29       2009        467    2509\n",
       "30       2010        459    2598\n",
       "31       2011        278    1508\n",
       "32       2012        263    1642\n",
       "33       2013        282    1714"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract Argentina and Brazil\n",
    "df_ab = df_can.loc[['Argentina', 'Brazil'], years]\n",
    "\n",
    "# Change 'Years' type \n",
    "df_ab.columns                 # type: str\n",
    "df_ab.columns = df_ab.columns.map(int)\n",
    "df_ab.columns                 # type: int\n",
    "\n",
    "df_ab = df_ab.T      # tranpose: scatter plot 만들기 위해서\n",
    "\n",
    "# Index label - 이후에 index를 reset하게되면 column의 이름이 됨\n",
    "df_ab.index.name = 'Year'\n",
    "\n",
    "# Reset index\n",
    "df_ab.reset_index(inplace=True)\n",
    "df_ab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Create the normalized weights. \n",
    "\n",
    "There are several methods of normalizations in statistics, each with its own use. In this case, we will use [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&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-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) to bring all values into the range [0,1]. The general formula is:\n",
    "\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig3FeatureScaling.png\" align=\"center\">\n",
    "\n",
    "where _`X`_ is an original value, _`X'`_ is the normalized value. The formula sets the max value in the dataset to 1, and sets the min value to 0. The rest of the datapoints are scaled to a value between 0-1 accordingly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.032820\n",
       "1    0.036467\n",
       "2    0.025122\n",
       "3    0.003647\n",
       "4    0.006078\n",
       "Name: Brazil, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize Argentina data --> 이후 weight로 사용\n",
    "norm_a = (df_ab['Argentina'] - df_ab['Argentina'].min()) / (df_ab['Argentina'].max() - df_ab['Argentina'].min())\n",
    "norm_a.head()     # series\n",
    "\n",
    "# Normalize Brazil data\n",
    "norm_b = (df_ab['Brazil'] - df_ab['Brazil'].min()) / (df_ab['Brazil'].max() - df_ab['Brazil'].min())\n",
    "norm_b.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the data. \n",
    "\n",
    "-   To plot two different scatter plots in one plot, we can include the axes of one plot into the other by passing it via the `ax` parameter. \n",
    "-   We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore we will:\n",
    "    -   multiply weights by 2000 to scale it up on the graph, and,\n",
    "    -   add 10 to compensate for the min value (which has a 0 weight and therefore scale with x2000).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Argentina plotting\n",
    "ax_a = df_ab.plot(kind='scatter',           # Bubble plot = Scatter plot + s\n",
    "                  figsize = (9, 5),\n",
    "                  x='Year',\n",
    "                  y='Argentina',\n",
    "                  color='darkturquoise',\n",
    "                  alpha=0.7,                # 투명도\n",
    "                  xlim=(1975, 2020),\n",
    "                  s=norm_a * 2000 + 10)       # weight\n",
    "\n",
    "# Brazil plotting\n",
    "ax_b = df_ab.plot(kind='scatter',\n",
    "                  x='Year',\n",
    "                  y='Brazil',\n",
    "                  ax=ax_a,                  # Argentina와 축을 공유하기 때문에 figsize 지정하지 않아도 됨\n",
    "                  color='darkred',\n",
    "                  alpha=0.5,\n",
    "                  s= norm_b * 2000 + 10)\n",
    "\n",
    "ax_a.set_title('Comparison of Argentina to Brazil mainly in 1998-2002')\n",
    "ax_a.set_ylabel('Number of Immigrants')\n",
    "ax_a.legend(['Argentina', 'Brazil'], loc='upper left', fontsize=14,      # label은 plotting 순서대로 인식됨\n",
    "            markerfirst=False, title='Country color', shadow=True, facecolor='palegoldenrod', edgecolor='palegreen') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The size of the bubble corresponds to the magnitude of immigrating population for that year, compared to the 1980 - 2013 data. The larger the bubble, the more immigrants in that year.\n",
    "\n",
    "From the plot above, we can see a corresponding increase in immigration from Argentina during the 1998 - 2002 great depression. We can also observe a similar spike around 1985 to 1993. In fact, Argentina had suffered a great depression from 1974 - 1990, just before the onset of 1998 - 2002 great depression. \n",
    "\n",
    "On a similar note, Brazil suffered the _Samba Effect_ where the Brazilian real (currency) dropped nearly 35% in 1999. There was a fear of a South American financial crisis as many South American countries were heavily dependent on industrial exports from Brazil. The Brazilian government subsequently adopted an austerity program, and the economy slowly recovered over the years, culminating in a surge in 2010. The immigration data reflect these events.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question**: Previously in this lab, we created box plots to compare immigration from China and India to Canada. Create bubble plots of immigration from China and India to visualize any differences with time from 1980 to 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1: Make dataframe for China and India."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>5123</td>\n",
       "      <td>8880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>6682</td>\n",
       "      <td>8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>3308</td>\n",
       "      <td>8147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>1863</td>\n",
       "      <td>7338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>1527</td>\n",
       "      <td>5704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  China  India\n",
       "0        1980   5123   8880\n",
       "1        1981   6682   8670\n",
       "2        1982   3308   8147\n",
       "3        1983   1863   7338\n",
       "4        1984   1527   5704"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ci = df_can.loc[['China', 'India'], years].transpose()\n",
    "\n",
    "# Index type --> int\n",
    "df_ci.index = map(int, df_ci.index)\n",
    "\n",
    "# Index label rename\n",
    "df_ci.index.name = 'Year'\n",
    "\n",
    "# Index reset\n",
    "df_ci.reset_index(inplace=True)\n",
    "\n",
    "df_ci.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Normalize the data pertaining to China and India.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "button": false,
    "collapsed": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.145911\n",
       "1     0.139348\n",
       "2     0.123004\n",
       "3     0.097722\n",
       "4     0.046658\n",
       "5     0.000000\n",
       "6     0.091847\n",
       "7     0.186818\n",
       "8     0.228476\n",
       "9     0.191631\n",
       "10    0.244695\n",
       "11    0.297603\n",
       "12    0.295697\n",
       "13    0.540173\n",
       "14    0.450295\n",
       "15    0.446201\n",
       "16    0.614019\n",
       "17    0.564299\n",
       "18    0.407200\n",
       "19    0.461358\n",
       "20    0.761305\n",
       "21    0.844151\n",
       "22    0.864965\n",
       "23    0.717022\n",
       "24    0.750773\n",
       "25    1.000000\n",
       "26    0.926185\n",
       "27    0.766618\n",
       "28    0.751586\n",
       "29    0.788931\n",
       "30    0.938279\n",
       "31    0.728085\n",
       "32    0.835089\n",
       "33    0.902403\n",
       "Name: India, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nomalization of population, to get weight\n",
    "norm_c = (df_ci['China'] - df_ci['China'].min()) / (df_ci['China'].max() - df_ci['China'].min())\n",
    "norm_c        # China\n",
    "\n",
    "norm_i = (df_ci['India'] - df_ci['India'].min()) / (df_ci['India'].max() - df_ci['India'].min())\n",
    "norm_i        # India"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # normalize China data\n",
    "    norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "    # normalize India data\n",
    "    norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Generate the bubble plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "button": false,
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# China plotting\n",
    "ax_c = df_ci.plot(kind='scatter', figsize=(15, 8), x='Year', y='China', color='mediumaquamarine', alpha=0.7,\n",
    "                  s = norm_c * 3000 + 20)\n",
    "\n",
    "ax_i = df_ci.plot(kind='scatter', x='Year', y='India', ax=ax_c, color='mediumslateblue', alpha=0.6,\n",
    "                  s = norm_i * 3000 +20)\n",
    "\n",
    "# 일부러 plt도 되는지 test\n",
    "plt.title('Comparison between China and India')\n",
    "plt.ylabel('Immigrants')\n",
    "plt.legend(['China', 'India'], loc='upper left', fontsize=14,             # 모든 argument가 ax.legend와 동일\n",
    "            markerfirst=False, title='Country color', title_fontsize=18, ncol=2, labelspacing=1.1, markerscale=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # China\n",
    "    ax0 = df_can_t.plot(kind='scatter',\n",
    "                        x='Year',\n",
    "                        y='China',\n",
    "                        figsize=(14, 8),\n",
    "                        alpha=0.5,                  # transparency\n",
    "                        color='green',\n",
    "                        s=norm_china * 2000 + 10,  # pass in weights \n",
    "                        xlim=(1975, 2015)\n",
    "                       )\n",
    "\n",
    "    # India\n",
    "    ax1 = df_can_t.plot(kind='scatter',\n",
    "                        x='Year',\n",
    "                        y='India',\n",
    "                        alpha=0.5,\n",
    "                        color=\"blue\",\n",
    "                        s=norm_india * 2000 + 10,\n",
    "                        ax = ax0\n",
    "                       )\n",
    "\n",
    "    ax0.set_ylabel('Number of Immigrants')\n",
    "    ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "    ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/aklson/\" target=\"_blank\">Alex Aklson</a>\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
    "[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&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-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
    "[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&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-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By   | Change Description                 |\n",
    "| ----------------- | ------- | ------------ | ---------------------------------- |\n",
    "| 2021-01-20        | 2.5     | LakshmiHolla | Changed TOC markdown section       |\n",
    "| 2021-01-05        | 2.4     | LakshmiHolla | Changed markdown for outliers      |\n",
    "| 2020-11-12        | 2.3     | LakshmiHolla | Added example code for outliers    |\n",
    "| 2020-11-03        | 2.2     | LakshmiHolla | Changed URL of excel file          |\n",
    "| 2020-09-29        | 2.1     | LakshmiHolla | Made fix to a boxplot label        |\n",
    "| 2020-08-27        | 2.0     | Lavanya      | Moved lab to course repo in GitLab |\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
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