Time_series_example_for_Economic_freedom.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Yl4pzRo9WdJs",
    "outputId": "b8e6668a-4934-44f8-b69c-2520d58c87e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "NpZwYMxKXV48"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import plotly "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "id": "Sg-5gv0wU2dV",
    "outputId": "79ecc5f9-4211-409a-e5a6-b272b6f9dfc9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'\\nLife expectancy:\\nhttps://www.kaggle.com/kumarajarshi/life-expectancy-who\\n\\nEconomic freedom \\nhttps://www.kaggle.com/gsutters/economic-freedom \\n\\nCountry to continent\\nhttps://www.kaggle.com/andradaolteanu/country-mapping-iso-continent-region \\n'"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download Life expectancy based on economic freedom with time line\n",
    "\n",
    "'''\n",
    "Life expectancy:\n",
    "https://www.kaggle.com/kumarajarshi/life-expectancy-who\n",
    "\n",
    "Economic freedom \n",
    "https://www.kaggle.com/gsutters/economic-freedom \n",
    "\n",
    "Country to continent\n",
    "https://www.kaggle.com/andradaolteanu/country-mapping-iso-continent-region \n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "eh9gmBGtXshy"
   },
   "outputs": [],
   "source": [
    "# File location \n",
    "\n",
    "economic_freedom = 'drive/My Drive/NYU/Programming_for_DS/Data/efw_cc.csv'\n",
    "life_expectancy  = 'drive/My Drive/NYU/Programming_for_DS/Data/Life Expectancy Data.csv'\n",
    "continents       = 'drive/My Drive/NYU/Programming_for_DS/Data/continents2.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "HrlSJVWKXFPh"
   },
   "outputs": [],
   "source": [
    "df_ef = pd.read_csv(economic_freedom)\n",
    "df_le = pd.read_csv(life_expectancy)\n",
    "conti = pd.read_csv(continents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eMPcwICFOWxK",
    "outputId": "a9342027-f36b-46db-ea6d-37f024d3b52c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013\n",
      " 2014 2015]\n"
     ]
    }
   ],
   "source": [
    "#Common years between two datasets\n",
    "\n",
    "ef_date = df_ef.year.unique()\n",
    "df_date = df_le.Year.unique()\n",
    "common_years = np.intersect1d(ef_date, df_date)\n",
    "\n",
    "print(common_years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241
    },
    "id": "zUcuXdJ-yoSr",
    "outputId": "89674e8e-286c-47ab-ddf5-526619b1c0a7"
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>ISO_code</th>\n",
       "      <th>countries</th>\n",
       "      <th>ECONOMIC FREEDOM</th>\n",
       "      <th>rank</th>\n",
       "      <th>quartile</th>\n",
       "      <th>1a_government_consumption</th>\n",
       "      <th>1b_transfers</th>\n",
       "      <th>1c_gov_enterprises</th>\n",
       "      <th>1d_top_marg_tax_rate</th>\n",
       "      <th>1_size_government</th>\n",
       "      <th>2a_judicial_independence</th>\n",
       "      <th>2b_impartial_courts</th>\n",
       "      <th>2c_protection_property_rights</th>\n",
       "      <th>2d_military_interference</th>\n",
       "      <th>2e_integrity_legal_system</th>\n",
       "      <th>2f_legal_enforcement_contracts</th>\n",
       "      <th>2g_restrictions_sale_real_property</th>\n",
       "      <th>2h_reliability_police</th>\n",
       "      <th>2i_business_costs_crime</th>\n",
       "      <th>2j_gender_adjustment</th>\n",
       "      <th>2_property_rights</th>\n",
       "      <th>3a_money_growth</th>\n",
       "      <th>3b_std_inflation</th>\n",
       "      <th>3c_inflation</th>\n",
       "      <th>3d_freedom_own_foreign_currency</th>\n",
       "      <th>3_sound_money</th>\n",
       "      <th>4a_tariffs</th>\n",
       "      <th>4b_regulatory_trade_barriers</th>\n",
       "      <th>4c_black_market</th>\n",
       "      <th>4d_control_movement_capital_ppl</th>\n",
       "      <th>4_trade</th>\n",
       "      <th>5a_credit_market_reg</th>\n",
       "      <th>5b_labor_market_reg</th>\n",
       "      <th>5c_business_reg</th>\n",
       "      <th>5_regulation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>7.54</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.232353</td>\n",
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       "      <td>8.0</td>\n",
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       "      <td>6.933500</td>\n",
       "      <td>6.215401</td>\n",
       "      <td>0.948718</td>\n",
       "      <td>5.071814</td>\n",
       "      <td>8.986454</td>\n",
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       "      <td>9.74360</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.553657</td>\n",
       "      <td>8.963556</td>\n",
       "      <td>7.489905</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>6.406138</td>\n",
       "      <td>8.214900</td>\n",
       "      <td>7.098562</td>\n",
       "      <td>6.916278</td>\n",
       "      <td>6.705863</td>\n",
       "      <td>6.906901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>4.99</td>\n",
       "      <td>159.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.150000</td>\n",
       "      <td>7.817129</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
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       "      <td>6.955962</td>\n",
       "      <td>8.339152</td>\n",
       "      <td>8.72046</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.253894</td>\n",
       "      <td>6.872533</td>\n",
       "      <td>2.481294</td>\n",
       "      <td>5.56391</td>\n",
       "      <td>1.590362</td>\n",
       "      <td>4.127025</td>\n",
       "      <td>5.100509</td>\n",
       "      <td>5.029513</td>\n",
       "      <td>5.676956</td>\n",
       "      <td>5.268992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>5.17</td>\n",
       "      <td>155.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>8.886739</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6.496685</td>\n",
       "      <td>1.843129</td>\n",
       "      <td>1.974566</td>\n",
       "      <td>2.512364</td>\n",
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       "      <td>4.166667</td>\n",
       "      <td>2.302200</td>\n",
       "      <td>5.455882</td>\n",
       "      <td>3.016104</td>\n",
       "      <td>4.291197</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>2.963635</td>\n",
       "      <td>9.385679</td>\n",
       "      <td>4.986742</td>\n",
       "      <td>3.05400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.606605</td>\n",
       "      <td>6.989244</td>\n",
       "      <td>2.024949</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>2.044823</td>\n",
       "      <td>5.264754</td>\n",
       "      <td>7.064905</td>\n",
       "      <td>4.560325</td>\n",
       "      <td>4.930271</td>\n",
       "      <td>5.518500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>4.84</td>\n",
       "      <td>160.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.335294</td>\n",
       "      <td>6.048930</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.346056</td>\n",
       "      <td>3.689690</td>\n",
       "      <td>2.930563</td>\n",
       "      <td>4.255995</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>3.632872</td>\n",
       "      <td>6.857195</td>\n",
       "      <td>3.385786</td>\n",
       "      <td>4.133832</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>3.904030</td>\n",
       "      <td>5.233264</td>\n",
       "      <td>5.224079</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.614336</td>\n",
       "      <td>6.421600</td>\n",
       "      <td>4.811105</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.697482</td>\n",
       "      <td>3.982547</td>\n",
       "      <td>5.419820</td>\n",
       "      <td>5.151405</td>\n",
       "      <td>5.535831</td>\n",
       "      <td>5.369019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>ARM</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>7.57</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.264706</td>\n",
       "      <td>7.748532</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.003310</td>\n",
       "      <td>3.867379</td>\n",
       "      <td>4.197569</td>\n",
       "      <td>5.664317</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.196693</td>\n",
       "      <td>9.801963</td>\n",
       "      <td>5.714028</td>\n",
       "      <td>7.007654</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.809215</td>\n",
       "      <td>9.078264</td>\n",
       "      <td>9.263496</td>\n",
       "      <td>9.74600</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.521940</td>\n",
       "      <td>8.547556</td>\n",
       "      <td>7.194410</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>6.830998</td>\n",
       "      <td>8.143241</td>\n",
       "      <td>9.102046</td>\n",
       "      <td>6.234630</td>\n",
       "      <td>6.797530</td>\n",
       "      <td>7.378069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year ISO_code  countries  ...  5b_labor_market_reg  5c_business_reg  5_regulation\n",
       "0  2016      ALB    Albania  ...             6.916278         6.705863      6.906901\n",
       "1  2016      DZA    Algeria  ...             5.029513         5.676956      5.268992\n",
       "2  2016      AGO     Angola  ...             4.560325         4.930271      5.518500\n",
       "3  2016      ARG  Argentina  ...             5.151405         5.535831      5.369019\n",
       "4  2016      ARM    Armenia  ...             6.234630         6.797530      7.378069\n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Economic Freedom\n",
    "\n",
    "df_ef.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "id": "CiXTz4XOpP3n",
    "outputId": "f1dc1d69-ed07-4111-a744-3d7c838b9c4f"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
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       "      <th>Total expenditure</th>\n",
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       "      <th>thinness  1-19 years</th>\n",
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       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
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       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
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       "      <td>83</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>17.2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62.0</td>\n",
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       "      <td>18.6</td>\n",
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       "      <td>58.0</td>\n",
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       "      <td>17.5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64.0</td>\n",
       "      <td>430</td>\n",
       "      <td>18.1</td>\n",
       "      <td>89</td>\n",
       "      <td>62.0</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>17.6</td>\n",
       "      <td>93</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>17.2</td>\n",
       "      <td>97</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year  ... Income composition of resources  Schooling\n",
       "0  Afghanistan  2015  ...                           0.479       10.1\n",
       "1  Afghanistan  2014  ...                           0.476       10.0\n",
       "2  Afghanistan  2013  ...                           0.470        9.9\n",
       "3  Afghanistan  2012  ...                           0.463        9.8\n",
       "4  Afghanistan  2011  ...                           0.454        9.5\n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Life Expectancy\n",
    "\n",
    "df_le.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "-UxTlLDZR8aP"
   },
   "outputs": [],
   "source": [
    "#Renaming columns to make year and county column name consistant b/w df's\n",
    "\n",
    "df_ef['Year']      = df_ef['year']\n",
    "df_ef['Country']   = df_ef['countries']\n",
    "df_le['Life expectancy'] = df_le['Life expectancy ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "-2nrtSoVOXaM"
   },
   "outputs": [],
   "source": [
    "# Merging dataframe\n",
    "\n",
    "merged_df = df_ef.merge(df_le, on=['Year', 'Country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "zjHgqLhCOYKL"
   },
   "outputs": [],
   "source": [
    "# Interested in Life expectancy and ECONOMIC FREEDOM\n",
    "\n",
    "interested_cols = ['Year', 'ISO_code','Country','ECONOMIC FREEDOM', 'Adult Mortality', 'infant deaths', 'Life expectancy', 'Population', 'Status']\n",
    "clean_df = merged_df[interested_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9ZDQ-FO0OYmj",
    "outputId": "03d337db-6d19-4694-ec92-c2fa829b70d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133\n",
      "133\n"
     ]
    }
   ],
   "source": [
    "# Check if : (all the merged rows)  and (where count is ==16 rows) are same\n",
    "\n",
    "print(clean_df.groupby(by='Country').count()['Year'].shape[0])\n",
    "print(clean_df.groupby(by='Country').count() [ clean_df.groupby(by='Country').count()['Year']==16].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0PgRAz-Gr7HC",
    "outputId": "5a8fa098-192b-43dc-cc40-cf23335f6589"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005,\n",
       "       2004, 2003, 2002, 2001, 2000])"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking Unique Years in clean_df\n",
    "\n",
    "clean_df.Year.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "3WWHK0NVOY9-",
    "outputId": "3da14a65-6dd2-4996-ed69-01e1b28eb823"
   },
   "outputs": [
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\"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},\n",
       "                        {\"responsive\": true}\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                \n",
       "            </script>\n",
       "        </div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting time series \n",
    "\n",
    "clean_df = clean_df.dropna()\n",
    "data_slider = []\n",
    "steps = []\n",
    "\n",
    "# Popluate data_slider\n",
    "for year in clean_df.Year.unique():\n",
    "    if year !=2000:\n",
    "      df1   = clean_df[clean_df['Year']== year]\n",
    "      \n",
    "      temp_Df = df1.copy()\n",
    "      temp_Df.dropna()\n",
    "\n",
    "      # Column z: Higher value means more correlation \n",
    "      temp_Df['z'] = df1['ECONOMIC FREEDOM'].corr(df1['Life expectancy'])\n",
    "\n",
    "\n",
    "      data_one_year = dict(\n",
    "                          type='choropleth',\n",
    "                          locations = temp_Df['ISO_code'],\n",
    "                          z=temp_Df['z'],\n",
    "                          locationmode='ISO-3',\n",
    "                          colorscale = 'Sunsetdark'\n",
    "                          )\n",
    "\n",
    "      data_slider.append(data_one_year) \n",
    "\n",
    "for i in range(len(data_slider)):\n",
    "    step = dict(method='restyle',\n",
    "                args=['visible', [False] * len(data_slider)],\n",
    "                #Label to be displayed for each step (year)\n",
    "                label='Year {}'.format(i + 2001)) \n",
    "    step['args'][1][i] = True\n",
    "    steps.append(step)\n",
    "\n",
    "sliders = [dict(active=0, pad={\"t\": 1}, steps=steps)]  \n",
    "layout = dict(geo=dict(scope='world'), sliders=sliders)\n",
    "fig = dict(data=data_slider, layout=layout) \n",
    "\n",
    "plotly.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6siqwEo2gBXU",
    "outputId": "99967d90-f64f-4127-994b-ae85d093f803"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting bubbly\n",
      "  Downloading https://files.pythonhosted.org/packages/d1/ba/78a1262f6e96c92ac5b290c5654bc4cf6b8016d0d97c604576899f571cf0/bubbly-1.0.2.tar.gz\n",
      "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from bubbly) (4.4.1)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from bubbly) (1.1.4)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from plotly->bubbly) (1.15.0)\n",
      "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->bubbly) (1.3.3)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->bubbly) (2018.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->bubbly) (2.8.1)\n",
      "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.6/dist-packages (from pandas->bubbly) (1.18.5)\n",
      "Building wheels for collected packages: bubbly\n",
      "  Building wheel for bubbly (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for bubbly: filename=bubbly-1.0.2-cp36-none-any.whl size=5429 sha256=a5b89a310f974bd805909e579f8eb7a29248b29b3b03f503330c5fde9f34d113\n",
      "  Stored in directory: /root/.cache/pip/wheels/cf/3c/75/8c09dbf7cd6b320b69a6bc679fd111abd1139866c722b1476b\n",
      "Successfully built bubbly\n",
      "Installing collected packages: bubbly\n",
      "Successfully installed bubbly-1.0.2\n"
     ]
    }
   ],
   "source": [
    "\n",
    "!pip install bubbly\n",
    "from bubbly.bubbly import bubbleplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "czks8ivFhdwS"
   },
   "outputs": [],
   "source": [
    "clean_df = clean_df.sort_values(by=['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 752
    },
    "id": "JKDoaVxkhzM1",
    "outputId": "79e99b05-b1cf-414c-c558-c86c3600e54e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/plotly/offline/offline.py:159: UserWarning:\n",
      "\n",
      "\n",
      "Unrecognized config options supplied: ['scrollzoom']\n",
      "\n"
     ]
    },
    {
     "data": {
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      ]
     },
     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "figure = bubbleplot(dataset=clean_df, x_column='ECONOMIC FREEDOM', y_column='Life expectancy', \n",
    "                    bubble_column='Country', time_column='Year', size_column='Population', color_column='Status', \n",
    "                    x_title=\"Economic Freddom\", y_title=\"Life Expectancy\",\n",
    "                    x_logscale=True, scale_bubble=3, height=650)\n",
    "plotly.offline.iplot(figure, config={'scrollzoom': True})"
   ]
  },
  {
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   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "eSZy0w12lyCc",
    "outputId": "6d528422-1dea-4211-d300-d0266a7bd65e"
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   "outputs": [
    {
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       "<p>249 rows × 2 columns</p>\n",
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      "text/plain": [
       "               Country   region\n",
       "0          Afghanistan     Asia\n",
       "1        Åland Islands   Europe\n",
       "2              Albania   Europe\n",
       "3              Algeria   Africa\n",
       "4       American Samoa  Oceania\n",
       "..                 ...      ...\n",
       "244  Wallis and Futuna  Oceania\n",
       "245     Western Sahara   Africa\n",
       "246              Yemen     Asia\n",
       "247             Zambia   Africa\n",
       "248           Zimbabwe   Africa\n",
       "\n",
       "[249 rows x 2 columns]"
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     },
     "execution_count": 18,
     "metadata": {
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   ],
   "source": [
    "conti['Country'] = conti['name']\n",
    "conti = conti[['Country', 'region']]\n",
    "conti"
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  },
  {
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   "execution_count": 19,
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    "outputId": "5ba42d7f-583b-40ad-f2ae-bbb25ad31213"
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   "outputs": [
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       "      <td>2976877.0</td>\n",
       "      <td>Developing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>7.53</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>77.8</td>\n",
       "      <td>28873.0</td>\n",
       "      <td>Developing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1712 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year ISO_code      Country  ...  Life expectancy   Population      Status\n",
       "2127  2000      ZWE     Zimbabwe  ...             46.0   12222251.0  Developing\n",
       "2032  2000      SLV  El Salvador  ...             69.0    5867626.0  Developing\n",
       "2033  2000      EST      Estonia  ...             78.0    1396985.0  Developing\n",
       "2035  2000      FJI         Fiji  ...             67.7     811223.0  Developing\n",
       "2036  2000      FIN      Finland  ...             77.5     517629.0  Developing\n",
       "...    ...      ...          ...  ...              ...          ...         ...\n",
       "94    2015      NGA      Nigeria  ...             54.5  181181744.0  Developing\n",
       "95    2015      NOR       Norway  ...             81.8     518867.0   Developed\n",
       "97    2015      PAK     Pakistan  ...             66.4   18938513.0  Developing\n",
       "83    2015      MNG     Mongolia  ...             68.8    2976877.0  Developing\n",
       "0     2015      ALB      Albania  ...             77.8      28873.0  Developing\n",
       "\n",
       "[1712 rows x 9 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {
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     },
     "output_type": "execute_result"
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   ],
   "source": [
    "clean_df"
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   "execution_count": 20,
   "metadata": {
    "colab": {
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    },
    "id": "Y27zFYIDl9uo",
    "outputId": "6a3c3488-f866-4748-ee95-405bf1f7957b"
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    {
     "data": {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>ISO_code</th>\n",
       "      <th>Country</th>\n",
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       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>4.51</td>\n",
       "      <td>665.0</td>\n",
       "      <td>24</td>\n",
       "      <td>46.0</td>\n",
       "      <td>12222251.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>3.87</td>\n",
       "      <td>686.0</td>\n",
       "      <td>25</td>\n",
       "      <td>45.3</td>\n",
       "      <td>12366165.0</td>\n",
       "      <td>Developing</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>3.74</td>\n",
       "      <td>73.0</td>\n",
       "      <td>25</td>\n",
       "      <td>44.8</td>\n",
       "      <td>125525.0</td>\n",
       "      <td>Developing</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>3.82</td>\n",
       "      <td>715.0</td>\n",
       "      <td>26</td>\n",
       "      <td>44.5</td>\n",
       "      <td>12633897.0</td>\n",
       "      <td>Developing</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>3.25</td>\n",
       "      <td>723.0</td>\n",
       "      <td>27</td>\n",
       "      <td>44.3</td>\n",
       "      <td>12777511.0</td>\n",
       "      <td>Developing</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>2013</td>\n",
       "      <td>SYC</td>\n",
       "      <td>Seychelles</td>\n",
       "      <td>7.24</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>72.9</td>\n",
       "      <td>89949.0</td>\n",
       "      <td>Developing</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>2014</td>\n",
       "      <td>SYC</td>\n",
       "      <td>Seychelles</td>\n",
       "      <td>7.46</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>91359.0</td>\n",
       "      <td>Developing</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>2015</td>\n",
       "      <td>SYC</td>\n",
       "      <td>Seychelles</td>\n",
       "      <td>7.33</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.2</td>\n",
       "      <td>93419.0</td>\n",
       "      <td>Developing</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>2014</td>\n",
       "      <td>LBR</td>\n",
       "      <td>Liberia</td>\n",
       "      <td>7.18</td>\n",
       "      <td>329.0</td>\n",
       "      <td>8</td>\n",
       "      <td>58.1</td>\n",
       "      <td>439737.0</td>\n",
       "      <td>Developing</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>2015</td>\n",
       "      <td>LBR</td>\n",
       "      <td>Liberia</td>\n",
       "      <td>6.87</td>\n",
       "      <td>259.0</td>\n",
       "      <td>8</td>\n",
       "      <td>61.4</td>\n",
       "      <td>4499621.0</td>\n",
       "      <td>Developing</td>\n",
       "      <td>Africa</td>\n",
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       "  </tbody>\n",
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       "<p>1679 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year ISO_code     Country  ...  Population      Status  region\n",
       "0     2000      ZWE    Zimbabwe  ...  12222251.0  Developing  Africa\n",
       "1     2001      ZWE    Zimbabwe  ...  12366165.0  Developing  Africa\n",
       "2     2002      ZWE    Zimbabwe  ...    125525.0  Developing  Africa\n",
       "3     2003      ZWE    Zimbabwe  ...  12633897.0  Developing  Africa\n",
       "4     2004      ZWE    Zimbabwe  ...  12777511.0  Developing  Africa\n",
       "...    ...      ...         ...  ...         ...         ...     ...\n",
       "1674  2013      SYC  Seychelles  ...     89949.0  Developing  Africa\n",
       "1675  2014      SYC  Seychelles  ...     91359.0  Developing  Africa\n",
       "1676  2015      SYC  Seychelles  ...     93419.0  Developing  Africa\n",
       "1677  2014      LBR     Liberia  ...    439737.0  Developing  Africa\n",
       "1678  2015      LBR     Liberia  ...   4499621.0  Developing  Africa\n",
       "\n",
       "[1679 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {
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   ],
   "source": [
    "df_with_continenets = clean_df.merge(conti, on=['Country'])\n",
    "df_with_continenets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
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    "id": "X8v6NTB6qH4g",
    "outputId": "1c70521e-ab55-49c0-cf36-d70fcdfd948a"
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    {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
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       "      <td>54.326923</td>\n",
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       "      <th>4</th>\n",
       "      <td>2000</td>\n",
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       "      <td>7.420000</td>\n",
       "      <td>149.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>73.600000</td>\n",
       "      <td>4.151880e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2015</td>\n",
       "      <td>Africa</td>\n",
       "      <td>6.241429</td>\n",
       "      <td>229.285714</td>\n",
       "      <td>42.314286</td>\n",
       "      <td>62.691429</td>\n",
       "      <td>1.260439e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2015</td>\n",
       "      <td>Americas</td>\n",
       "      <td>6.951429</td>\n",
       "      <td>131.238095</td>\n",
       "      <td>6.857143</td>\n",
       "      <td>74.923810</td>\n",
       "      <td>1.038025e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2015</td>\n",
       "      <td>Asia</td>\n",
       "      <td>6.929615</td>\n",
       "      <td>138.076923</td>\n",
       "      <td>69.384615</td>\n",
       "      <td>72.923077</td>\n",
       "      <td>1.811709e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2015</td>\n",
       "      <td>Europe</td>\n",
       "      <td>7.430968</td>\n",
       "      <td>86.225806</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>79.787097</td>\n",
       "      <td>9.095802e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2015</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>7.410000</td>\n",
       "      <td>123.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>76.350000</td>\n",
       "      <td>1.234074e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year    region  ...  Life expectancy    Population\n",
       "0   2000    Africa  ...        54.326923  7.372275e+06\n",
       "1   2000  Americas  ...        72.445000  1.347437e+07\n",
       "2   2000      Asia  ...        69.925000  2.470027e+07\n",
       "3   2000    Europe  ...        76.289655  5.858037e+06\n",
       "4   2000   Oceania  ...        73.600000  4.151880e+05\n",
       "..   ...       ...  ...              ...           ...\n",
       "75  2015    Africa  ...        62.691429  1.260439e+07\n",
       "76  2015  Americas  ...        74.923810  1.038025e+07\n",
       "77  2015      Asia  ...        72.923077  1.811709e+07\n",
       "78  2015    Europe  ...        79.787097  9.095802e+06\n",
       "79  2015   Oceania  ...        76.350000  1.234074e+07\n",
       "\n",
       "[80 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {
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   "source": [
    "df_with_continenets = df_with_continenets.groupby(['Year', 'region']).mean().reset_index()\n",
    "df_with_continenets"
   ]
  },
  {
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    "id": "RzDwReJVjCqH",
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     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "figure = bubbleplot(dataset=df_with_continenets, x_column='ECONOMIC FREEDOM', y_column='Life expectancy', \n",
    "                    bubble_column='region', time_column='Year', size_column='Population', color_column='region', \n",
    "                    x_title=\"Economic Freddom\", y_title=\"Life Expectancy\", \n",
    "                    x_logscale=True, scale_bubble=3, height=650)\n",
    "plotly.offline.iplot(figure, config={'scrollzoom': True})"
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   "execution_count": 22,
   "metadata": {
    "id": "1e_VMOW1xl56"
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