Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "oo = pd.read_csv('D:/my_workspace/pythons/data/olympics.csv', encoding='latin')\n",
    "oo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> 2008 Olympics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ol_2008 = oo[oo.Year == 2008]\n",
    "ol_2008.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = ol_2008.groupby(['Country_Code', 'Medal']).size().unstack(['Medal'], fill_value=0)\n",
    "g = g.sort_values(['Gold', 'Silver', 'Bronze'], ascending=False)[['Gold', 'Silver', 'Bronze']]\n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = g.transpose()\n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20, 5))\n",
    "sns.heatmap(data=g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Custom Color Maps\n",
    "    <h3> Visualizing Gold Silver Bronze Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = oo.groupby(['Athlete', 'Medal']).size().unstack('Medal', fill_value=0)\n",
    "g_sorted = g.sort_values(['Gold', 'Silver', 'Bronze'], ascending=False)\n",
    "g_ordered = g_sorted[['Gold', 'Silver', 'Bronze']]\n",
    "g_ordered.head().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.color_palette()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.palplot(sns.color_palette())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gold: #FFD700\n",
    "Silver: #C0C0C0\n",
    "Bronze: #824a02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "gsb = ['#FFD700', '#C0C0C0', '#824a02']\n",
    "sns.palplot(sns.color_palette(gsb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "medal_colors = ListedColormap(sns.color_palette(gsb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_ordered.head().plot(kind='bar', colormap=medal_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Exercise\n",
    "    <h3>  In every Olympics, which US athlete has won the most total number of medals? Include the athlete's discipline in your solution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "usa = oo[oo.Country_Code == 'USA']\n",
    "usa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = usa.groupby(['Year', 'Athlete', 'Medal']).size().unstack(['Medal'], fill_value=0)\n",
    "f['Total'] = f['Gold'] + f['Silver'] + f['Bronze']\n",
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ty = [athletes.sort_values(by=['Total'], ascending=False)[:1] for year, athletes in f.groupby('Year')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top = pd.DataFrame()\n",
    "for i in ty:\n",
    "    top = top.append(i)\n",
    "top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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