1007Project_skp327.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Data\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "econ_freedom = pd.read_csv(\"economic_freedom_index2019_data.csv\",encoding='latin-1', index_col = \"Country\")\n",
    "gender_development = pd.read_csv(\"gender_development.csv\",index_col = \"Country\")\n",
    "gender_inequality = pd.read_csv(\"gender_inequality.csv\",index_col = \"Country\")\n",
    "historical_index = pd.read_csv(\"historical_index.csv\",index_col = \"Country\")\n",
    "human_development = pd.read_csv(\"human_development.csv\",index_col = \"Country\")\n",
    "inequality = pd.read_csv(\"inequality_adjusted.csv\",index_col = \"Country\")\n",
    "poverty = pd.read_csv(\"multidimensional_poverty.csv\",index_col = \"Country\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reconcile country names \n",
    "\n",
    "def UpdateCountry(df, old, new):\n",
    "    df.loc[new] = historical_index.loc[old].values\n",
    "    df = df.drop(old)\n",
    "    return df\n",
    "\n",
    "historical_index = UpdateCountry(historical_index,\"Hong Kong, China (SAR)\",\"Hong Kong SAR\")\n",
    "historical_index = UpdateCountry(historical_index,\"Viet Nam\",\"Vietnam\")\n",
    "historical_index = UpdateCountry(historical_index,\"Congo\",\"Congo, Democratic Republic of the Congo\")\n",
    "historical_index = UpdateCountry(historical_index,\"Kyrgyzstan\",\"Kyrgyz Republic\")\n",
    "historical_index = UpdateCountry(historical_index,\"Lao People's Democratic Republic\",\"Lao P.D.R.\")\n",
    "historical_index = UpdateCountry(historical_index,\"Moldova (Republic of)\",\"Moldova\")\n",
    "historical_index = UpdateCountry(historical_index,\"Russian Federation\",\"Russia\")\n",
    "historical_index = UpdateCountry(historical_index,\"Slovakia\",\"Slovak Republic\")\n",
    "historical_index = UpdateCountry(historical_index,\"Tanzania (United Republic of)\",\"Tanzania\")\n",
    "historical_index = UpdateCountry(historical_index,\"Venezuela (Bolivarian Republic of)\",\"Venezuela\") \n",
    "historical_index = UpdateCountry(historical_index,\"Bolivia (Plurinational State of)\",\"Bolivia\") \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Column pre-processing\n",
    "\n",
    "#shortening column names to make them more managable\n",
    "historical_index = historical_index.rename(columns = {\"Human Development Index (1990)\":\"1990\",\"Human Development Index (2000)\":\"2000\",\"Human Development Index (2010)\":\"2010\",\n",
    "                            \"Human Development Index (2011)\":\"2011\",\"Human Development Index (2012)\":\"2012\",\"Human Development Index (2013)\":\"2013\",\"Human Development Index (2014)\":\"2014\"})\n",
    "#updating column data types\n",
    "for col in historical_index:\n",
    "    historical_index[col] = historical_index[col].replace(\"..\", 0)\n",
    "    historical_index[col] = historical_index[col].astype(float)                                                                                                   \n",
    "\n",
    "#cleaning up miscellaneous text \n",
    "econ_freedom[\"GDP (Billions, PPP)\"] = econ_freedom[\"GDP (Billions, PPP)\"].astype(str)\n",
    "econ_freedom[\"GDP (Billions, PPP)\"] = econ_freedom[\"GDP (Billions, PPP)\"].str.replace('$','').str.replace(',','')\n",
    "econ_freedom[\"GDP (Billions, PPP)\"] = econ_freedom[\"GDP (Billions, PPP)\"].str.replace(\"2015 est.\",\"\")\n",
    "econ_freedom[\"GDP (Billions, PPP)\"] = econ_freedom[\"GDP (Billions, PPP)\"].str.replace(\"CHF.\",\"\")\n",
    "econ_freedom[\"GDP (Billions, PPP)\"] = econ_freedom[\"GDP (Billions, PPP)\"].str.replace(\"(2014 )\",\"\")\n",
    "econ_freedom[\"GDP (Billions, PPP)\"] = econ_freedom[\"GDP (Billions, PPP)\"].str.replace(\"(\",\"\")\n",
    "econ_freedom[\"GDP (Billions, PPP)\"] = econ_freedom[\"GDP (Billions, PPP)\"].str.replace(\")\",\"\")\n",
    "econ_freedom[\"GDP (Billions, PPP)\"] =  econ_freedom[\"GDP (Billions, PPP)\"].astype(float)                                                                                                   \n",
    "econ_freedom[\"FDI Inflow (Millions)\"] = econ_freedom[\"FDI Inflow (Millions)\"].str.replace(\",\",\"\")\n",
    "econ_freedom[\"FDI Inflow (Millions)\"] =  econ_freedom[\"FDI Inflow (Millions)\"].astype(float)                                                                                                   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove rows with missing required data\n",
    "historical_index = historical_index[(historical_index[\"2014\"] != 0) & (historical_index[\"1990\"] != 0)]\n",
    "\n",
    "#calculate percent change\n",
    "historical_index[\"Percent_Change\"] = (historical_index[\"2014\"] - historical_index[\"1990\"])/historical_index[\"1990\"]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Join data sets\n",
    "econ_vs_hdi = pd.merge(econ_freedom, historical_index, how='inner', on = \"Country\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Group countries by HDI Ranking into 7 groups\n",
    "\n",
    "total_size = econ_vs_hdi[\"HDI Rank\"].count()\n",
    "split_size = math.floor(total_size/7)\n",
    "\n",
    "econ_vs_hdi_ordered = econ_vs_hdi.sort_values(\"HDI Rank\")\n",
    "\n",
    "econ_vs_hdi_1 = econ_vs_hdi_ordered.head(split_size)\n",
    "econ_vs_hdi_2 = econ_vs_hdi_ordered.tail(total_size-1*split_size).head(split_size)\n",
    "econ_vs_hdi_3 = econ_vs_hdi_ordered.tail(total_size-2*split_size).head(split_size)\n",
    "econ_vs_hdi_4 = econ_vs_hdi_ordered.tail(total_size-3*split_size).head(split_size)\n",
    "econ_vs_hdi_5 = econ_vs_hdi_ordered.tail(total_size-4*split_size).head(split_size)\n",
    "econ_vs_hdi_6 = econ_vs_hdi_ordered.tail(total_size-5*split_size).head(split_size)\n",
    "econ_vs_hdi_7 = econ_vs_hdi_ordered.tail(total_size-6*split_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot GDP Growth vs. HDI Growth\n",
    "ttl1 = \"HDI Group 1\"\n",
    "ttl2 = \"HDI Group 2\"\n",
    "ttl3 = \"HDI Group 3\"\n",
    "ttl4 = \"HDI Group 4\"\n",
    "ttl5 = \"HDI Group 5\"\n",
    "ttl6 = \"HDI Group 6\"\n",
    "ttl7 = \"HDI Group 7\"\n",
    "\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.scatter(econ_vs_hdi_1[\"HDI Rank\"],econ_vs_hdi_1[\"Percent_Change\"], color = 'orange',s = 10,label = ttl1)\n",
    "plt.scatter(econ_vs_hdi_2[\"HDI Rank\"],econ_vs_hdi_2[\"Percent_Change\"], color = 'purple',s = 10, label = ttl2)\n",
    "plt.scatter(econ_vs_hdi_3[\"HDI Rank\"],econ_vs_hdi_3[\"Percent_Change\"], color = 'green',s = 10,label = ttl3)\n",
    "plt.scatter(econ_vs_hdi_4[\"HDI Rank\"],econ_vs_hdi_4[\"Percent_Change\"], color = 'blue',s = 10,label = ttl4)\n",
    "plt.scatter(econ_vs_hdi_5[\"HDI Rank\"],econ_vs_hdi_5[\"Percent_Change\"], color = 'red',s = 10,label = ttl5)\n",
    "plt.scatter(econ_vs_hdi_6[\"HDI Rank\"],econ_vs_hdi_6[\"Percent_Change\"], color = 'brown',s = 10,label = ttl6)\n",
    "plt.scatter(econ_vs_hdi_7[\"HDI Rank\"],econ_vs_hdi_7[\"Percent_Change\"], color = 'black',s = 10,label = ttl7)\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.xlabel(\"HDI Rank\")\n",
    "plt.ylabel(\"HDI Growth Rate\")\n",
    "plt.title(\"HDI Growth by HDI Ranking\")\n",
    "\n",
    "\n",
    "x_titles = [1,2,3,4,5,6,7]\n",
    "\n",
    "means_hdi = [econ_vs_hdi_1[\"Percent_Change\"].mean(),\n",
    "             econ_vs_hdi_2[\"Percent_Change\"].mean(),\n",
    "             econ_vs_hdi_3[\"Percent_Change\"].mean(),\n",
    "             econ_vs_hdi_4[\"Percent_Change\"].mean(),\n",
    "             econ_vs_hdi_5[\"Percent_Change\"].mean(),\n",
    "             econ_vs_hdi_6[\"Percent_Change\"].mean(),\n",
    "             econ_vs_hdi_7[\"Percent_Change\"].mean()]\n",
    "\n",
    "std_hdi = [econ_vs_hdi_1[\"Percent_Change\"].std(),\n",
    "           econ_vs_hdi_2[\"Percent_Change\"].std(),\n",
    "           econ_vs_hdi_3[\"Percent_Change\"].std(),\n",
    "           econ_vs_hdi_4[\"Percent_Change\"].std(),\n",
    "           econ_vs_hdi_5[\"Percent_Change\"].std(),\n",
    "           econ_vs_hdi_6[\"Percent_Change\"].std(),\n",
    "           econ_vs_hdi_7[\"Percent_Change\"].std()]\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(x_titles,means_hdi,'bo',label = \"Average HDI Growth\")\n",
    "plt.plot(x_titles, std_hdi, 'go',label = \"Standard Deviation\")\n",
    "plt.xlabel(\"HDI Group\")\n",
    "plt.title(\"Average HDI Growth and Standard Deviation by HDI Group\")\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.savefig(\"figure1.png\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize=(30, 15))\n",
    "\n",
    "gdp_growth_columnn = \"5 Year GDP Growth Rate (%)\"\n",
    "hdi_growth_column = \"Percent_Change\"\n",
    "\n",
    "plt.subplot(2,4,1)\n",
    "plt.scatter(econ_vs_hdi_1[gdp_growth_columnn],econ_vs_hdi_1[hdi_growth_column], color = 'orange',s = 10,label = ttl1)\n",
    "plt.scatter(econ_vs_hdi_2[gdp_growth_columnn],econ_vs_hdi_2[hdi_growth_column], color = 'purple',s = 10, label = ttl2)\n",
    "plt.scatter(econ_vs_hdi_3[gdp_growth_columnn],econ_vs_hdi_3[hdi_growth_column], color = 'green',s = 10,label = ttl3)\n",
    "plt.scatter(econ_vs_hdi_4[gdp_growth_columnn],econ_vs_hdi_4[hdi_growth_column], color = 'blue',s = 10,label = ttl4)\n",
    "plt.scatter(econ_vs_hdi_5[gdp_growth_columnn],econ_vs_hdi_5[hdi_growth_column], color = 'red',s = 10,label = ttl5)\n",
    "plt.scatter(econ_vs_hdi_6[gdp_growth_columnn],econ_vs_hdi_6[hdi_growth_column], color = 'brown',s = 10,label = ttl6)\n",
    "plt.scatter(econ_vs_hdi_7[gdp_growth_columnn],econ_vs_hdi_7[hdi_growth_column], color = 'black',s = 10,label = ttl7)\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.xlabel(\"GDP Growth Rate\")\n",
    "plt.ylabel(\"HDI Growth Rate\")\n",
    "plt.title(\"GDP Growth vs. HDI Growth\")\n",
    "plt.axis((-20,15,-0.1,1.1))\n",
    "\n",
    "plt.subplot(2,4,2)\n",
    "plt.axis((-20,15,-0.1,1.1))\n",
    "plt.title(\"Group 1 - GDP Growth vs. HDI Growth\")\n",
    "plt.xlabel(\"GDP Growth Rate\")\n",
    "plt.ylabel(\"HDI Growth Rate\")\n",
    "plt.scatter(econ_vs_hdi_1[gdp_growth_columnn],econ_vs_hdi_1[hdi_growth_column], color = 'orange',s = 10,label = ttl1)\n",
    "\n",
    "plt.subplot(2,4,3)\n",
    "plt.axis((-20,15,-0.1,1.1))\n",
    "plt.title(\"Group 2 - GDP Growth vs. HDI Growth\")\n",
    "plt.xlabel(\"GDP Growth Rate\")\n",
    "plt.ylabel(\"HDI Growth Rate\")\n",
    "plt.scatter(econ_vs_hdi_2[gdp_growth_columnn],econ_vs_hdi_2[hdi_growth_column], color = 'purple',s = 10, label = ttl2)\n",
    "\n",
    "plt.subplot(2,4,4)\n",
    "plt.axis((-20,15,-0.1,1.1))\n",
    "plt.title(\"Group 3 - GDP Growth vs. HDI Growth\")\n",
    "plt.xlabel(\"GDP Growth Rate\")\n",
    "plt.ylabel(\"HDI Growth Rate\")\n",
    "plt.scatter(econ_vs_hdi_3[gdp_growth_columnn],econ_vs_hdi_3[hdi_growth_column], color = 'green',s = 10,label = ttl3)\n",
    "\n",
    "plt.subplot(2,4,5)\n",
    "plt.axis((-20,15,-0.1,1.1))\n",
    "plt.title(\"Group 4 - GDP Growth vs. HDI Growth\")\n",
    "plt.xlabel(\"GDP Growth Rate\")\n",
    "plt.ylabel(\"HDI Growth Rate\")\n",
    "plt.scatter(econ_vs_hdi_4[gdp_growth_columnn],econ_vs_hdi_4[hdi_growth_column], color = 'blue',s = 10,label = ttl4)\n",
    "\n",
    "plt.subplot(2,4,6)\n",
    "plt.axis((-20,15,-0.1,1.1))\n",
    "plt.title(\"Group 5 - GDP Growth vs. HDI Growth\")\n",
    "plt.xlabel(\"GDP Growth Rate\")\n",
    "plt.ylabel(\"HDI Growth Rate\")\n",
    "plt.scatter(econ_vs_hdi_5[gdp_growth_columnn],econ_vs_hdi_5[hdi_growth_column], color = 'red',s = 10,label = ttl5)\n",
    "\n",
    "plt.subplot(2,4,7)\n",
    "plt.axis((-20,15,-0.1,1.1))\n",
    "plt.title(\"Group 6 - GDP Growth vs. HDI Growth\")\n",
    "plt.xlabel(\"GDP Growth Rate\")\n",
    "plt.ylabel(\"HDI Growth Rate\")\n",
    "plt.scatter(econ_vs_hdi_6[gdp_growth_columnn],econ_vs_hdi_6[hdi_growth_column], color = 'brown',s = 10,label = ttl6)\n",
    "\n",
    "plt.subplot(2,4,8)\n",
    "plt.axis((-20,15,-0.1,1.1))\n",
    "plt.title(\"Group 7 - GDP Growth vs. HDI Growth\")\n",
    "plt.xlabel(\"GDP Growth Rate\")\n",
    "plt.ylabel(\"HDI Growth Rate\")\n",
    "plt.scatter(econ_vs_hdi_7[gdp_growth_columnn],econ_vs_hdi_7[hdi_growth_column], color = 'black',s = 10,label = ttl7)\n",
    "plt.savefig(\"figure2.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}