LABS/LAB 14. Customization.ipynb

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    "# Basic scatter plot, log scale\n",
    "plt.scatter(gdp_cap, life_exp)\n",
    "plt.xscale('log') \n",
    "\n",
    "# Strings\n",
    "xlab = 'GDP per Capita [in USD]'\n",
    "ylab = 'Life Expectancy [in years]'\n",
    "title = 'World Development in 2007'\n",
    "\n",
    "# Add axis labels\n",
    "plt.xlabel(xlab)\n",
    "plt.ylabel(ylab)\n",
    "\n",
    "\n",
    "# Add title\n",
    "plt.title(title)\n",
    "\n",
    "# After customizing, display the plot\n",
    "plt.show()"
   ]
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   "cell_type": "code",
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    "# Scatter plot\n",
    "plt.scatter(gdp_cap, life_exp)\n",
    "\n",
    "# Previous customizations\n",
    "plt.xscale('log') \n",
    "plt.xlabel('GDP per Capita [in USD]')\n",
    "plt.ylabel('Life Expectancy [in years]')\n",
    "plt.title('World Development in 2007')\n",
    "\n",
    "# Definition of tick_val and tick_lab\n",
    "tick_val = [1000,10000,100000]\n",
    "tick_lab = ['1k','10k','100k']\n",
    "\n",
    "# Adapt the ticks on the x-axis\n",
    "plt.xticks(tick_val, tick_lab)\n",
    "\n",
    "\n",
    "# After customizing, display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Import numpy as np\n",
    "import numpy as np\n",
    "\n",
    "# Store pop as a numpy array: np_pop\n",
    "np_pop = np.array(pop)\n",
    "\n",
    "# Double np_pop\n",
    "np_pop *= 2\n",
    "\n",
    "# Update: set s argument to np_pop\n",
    "plt.scatter(gdp_cap, life_exp, s = np_pop)\n",
    "\n",
    "# Previous customizations\n",
    "plt.xscale('log') \n",
    "plt.xlabel('GDP per Capita [in USD]')\n",
    "plt.ylabel('Life Expectancy [in years]')\n",
    "plt.title('World Development in 2007')\n",
    "plt.xticks([1000, 10000, 100000],['1k', '10k', '100k'])\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Specify c and alpha inside plt.scatter()\n",
    "plt.scatter(x = gdp_cap, y = life_exp, s = np.array(pop) * 2, c = col, alpha = 0.8)\n",
    "\n",
    "# Previous customizations\n",
    "plt.xscale('log') \n",
    "plt.xlabel('GDP per Capita [in USD]')\n",
    "plt.ylabel('Life Expectancy [in years]')\n",
    "plt.title('World Development in 2007')\n",
    "plt.xticks([1000,10000,100000], ['1k','10k','100k'])\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Scatter plot\n",
    "plt.scatter(x = gdp_cap, y = life_exp, s = np.array(pop) * 2, c = col, alpha = 0.8)\n",
    "\n",
    "# Previous customizations\n",
    "plt.xscale('log') \n",
    "plt.xlabel('GDP per Capita [in USD]')\n",
    "plt.ylabel('Life Expectancy [in years]')\n",
    "plt.title('World Development in 2007')\n",
    "plt.xticks([1000,10000,100000], ['1k','10k','100k'])\n",
    "\n",
    "# Additional customizations\n",
    "plt.text(1550, 71, 'India')\n",
    "plt.text(5700, 80, 'China')\n",
    "\n",
    "# Add grid() call\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpretation\n",
    "1. If you have a look at your colorful plot, it's clear that people live longer in countries with a higher GDP per capita. No high income countries have really short life expectancy, and no low income countries have very long life expectancy. Still, there is a huge difference in life expectancy between countries on the same income level. Most people live in middle income countries where difference in lifespan is huge between countries; depending on how income is distributed and how it is used. What can you say about the plot?\n",
    "    - The countries in blue, corresponding to Africa, have both low life expectancy and a low GDP per capita."
   ]
  }
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