index.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Serialization Formats - Cumulative Lab\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Now that you have learned about CSV and JSON file formats individually, it's time to bring them together with a cumulative lab! Even as a junior data scientist, you can often produce novel, interesting analyses by combining multiple datasets that haven't been combined before.\n",
    "\n",
    "## Objectives\n",
    "\n",
    "You will be able to:\n",
    "\n",
    "* Practice reading serialized JSON and CSV data from files into Python objects\n",
    "* Practice extracting information from nested data structures\n",
    "* Practice cleaning data (filtering, normalizing locations, converting types)\n",
    "* Combine data from multiple sources into a single data structure\n",
    "* Interpret descriptive statistics and data visualizations to present your findings\n",
    "\n",
    "## Your Task: Analyze the Relationship between Population and World Cup Performance\n",
    "\n",
    "![Russia 2018 branded soccer ball and trophy](https://curriculum-content.s3.amazonaws.com/data-science/images/world_cup.jpg)\n",
    "\n",
    "<span>Photo by <a href=\"https://unsplash.com/@fznsr_?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText\">Fauzan Saari</a> on <a href=\"https://unsplash.com/s/photos/soccer-world-cup?utm_source=unsplash&amp;utm_medium=referral&amp;utm_content=creditCopyText\">Unsplash</a></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Business Understanding\n",
    "\n",
    "#### What is the relationship between the population of a country and their performance in the 2018 FIFA World Cup?\n",
    "\n",
    "Intuitively, we might assume that countries with larger populations would have better performance in international sports competitions. While this has been demonstrated to be [true for the Olympics](https://www.researchgate.net/publication/308513557_Medals_at_the_Olympic_Games_The_Relationship_Between_Won_Medals_Gross_Domestic_Product_Population_Size_and_the_Weight_of_Sportive_Practice), the results for the FIFA World Cup are more mixed:\n",
    "\n",
    "<p><a href=\"https://web.archive.org/web/20230524202759/https://upload.wikimedia.org/wikipedia/commons/3/3c/World_cup_countries_best_results.png\"><img src=\"https://upload.wikimedia.org/wikipedia/commons/3/3c/World_cup_countries_best_results.png\" alt=\"World cup countries best results and hosts.PNG\" height=\"563\" width=\"1280\"></a><br><a href=\"http://creativecommons.org/licenses/by-sa/3.0/\" title=\"Creative Commons Attribution-Share Alike 3.0\">CC BY-SA 3.0</a>, <a href=\"https://commons.wikimedia.org/w/index.php?curid=578740\">Link</a></p>\n",
    "\n",
    "In this analysis, we are going to look specifically at the sample of World Cup games in 2018 and the corresponding 2018 populations of the participating nations, to determine the relationship between population and World Cup performance for this year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Understanding\n",
    "\n",
    "The data sources for this analysis will be pulled from two separate files.\n",
    "\n",
    "#### `world_cup_2018.json`\n",
    "\n",
    "* **Source**: This dataset comes from [`football.db`](http://openfootball.github.io/), a \"free and open public domain football database & schema for use in any (programming) language\"\n",
    "* **Contents**: Data about all games in the 2018 World Cup, including date, location (city and stadium), teams, goals scored (and by whom), and tournament group\n",
    "* **Format**: Nested JSON data (dictionary containing a list of rounds, each of which contains a list of matches, each of which contains information about the teams involved and the points scored)\n",
    "\n",
    "#### `country_populations.csv`\n",
    "\n",
    "* **Source**: This dataset comes from a curated collection by [DataHub.io](https://datahub.io/core/population), originally sourced from the World Bank\n",
    "* **Contents**: Data about populations by country for all available years from 1960 to 2018\n",
    "* **Format**: CSV data, where each row contains a country name, a year, and a population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Requirements\n",
    "\n",
    "#### 1. List of Teams in 2018 World Cup\n",
    "\n",
    "Create an alphabetically-sorted list of teams who competed in the 2018 FIFA World Cup.\n",
    "\n",
    "#### 2. Associating Countries with 2018 World Cup Performance\n",
    "\n",
    "Create a data structure that connects a team name (country name) to its performance in the 2018 FIFA World Cup. We'll use the count of games won in the entire tournament (group stage as well as knockout stage) to represent the performance.\n",
    "\n",
    "This will help create visualizations to help the reader understand the distribution of games won and the performance of each team.\n",
    "\n",
    "#### 3. Associating Countries with 2018 Population\n",
    "\n",
    "Add to the existing data structure so that it also connects each country name to its 2018 population, and create visualizations comparable to those from step 2.\n",
    "\n",
    "#### 4. Analysis of Population vs. Performance\n",
    "\n",
    "Choose an appropriate statistical measure to analyze the relationship between population and performance, and create a visualization representing this relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for Understanding\n",
    "\n",
    "Before moving on to the next step, pause and think about the strategy for this analysis.\n",
    "\n",
    "Remember, our business question is:\n",
    "\n",
    "> What is the relationship between the population of a country and their performance in the 2018 FIFA World Cup?\n",
    "\n",
    "#### Unit of Analysis\n",
    "\n",
    "First, what is our **unit of analysis**, and what is the **unique identifier**? In other words, what will one record in our final data structure represent, and what attribute uniquely describes it?\n",
    "\n",
    ".\n",
    "\n",
    ".\n",
    "\n",
    ".\n",
    "\n",
    "*Answer:* \n",
    "\n",
    "> What is the relationship between the population of a **country** and their performance in the 2018 FIFA World Cup?\n",
    "\n",
    "*Our unit of analysis is a* ***country*** *and the unique identifier we'll use is the* ***country name***\n",
    "\n",
    "#### Features\n",
    "\n",
    "Next, what **features** are we analyzing? In other words, what attributes of each country are we interested in?\n",
    "\n",
    ".\n",
    "\n",
    ".\n",
    "\n",
    ".\n",
    "\n",
    "*Answer:* \n",
    "\n",
    "> What is the relationship between the **population** of a country and their **performance in the 2018 FIFA World Cup**?\n",
    "\n",
    "*Our features are* ***2018 population*** *and* ***count of wins in the 2018 World Cup***\n",
    "\n",
    "#### Dataset to Start With\n",
    "\n",
    "Finally, which dataset should we **start** with? In this case, any record with missing data is not useful to us, so we want to start with the smaller dataset.\n",
    "\n",
    ".\n",
    "\n",
    ".\n",
    "\n",
    ".\n",
    "\n",
    "*Answer: There are only 32 countries that compete in the World Cup each year, compared to hundreds of countries in the world, so we should start with the* ***2018 World Cup*** *dataset. Then we can join it with the relevant records from the country population dataset.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the Data\n",
    "\n",
    "Below we import the `json` and `csv` modules, which will be used for reading from `world_cup_2018.json` and `country_populations.csv`, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "import json\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we open the relevant files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "world_cup_file = open(\"data/world_cup_2018.json\", encoding=\"utf8\")\n",
    "population_file = open(\"data/country_populations.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hint:** if your code below is not working, (e.g. `ValueError: I/O operation on closed file.`, or you get an empty list or dictionary) try re-running the cell above to reopen the files, then re-run your code.\n",
    "\n",
    "### 2018 World Cup Data\n",
    "\n",
    "In the cell below, use the `json` module to load the data from `world_cup_file` into a dictionary called `world_cup_data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace None with appropriate code\n",
    "world_cup_data = json.load(world_cup_file)\n",
    "\n",
    "# Close the file now that we're done reading from it\n",
    "world_cup_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure the `assert` passes, ensuring that `world_cup_data` has the correct type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "# Check that the overall data structure is a dictionary\n",
    "assert type(world_cup_data) == dict\n",
    "\n",
    "# Check that the dictionary has 2 keys, 'name' and 'rounds'\n",
    "assert list(world_cup_data.keys()) == [\"name\", \"rounds\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Population Data\n",
    "\n",
    "Now use the `csv` module to load the data from `population_file` into a list of dictionaries called `population_data`\n",
    "\n",
    "(Recall that you can convert a `csv.DictReader` object into a list of dictionaries using the built-in `list()` function.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace None with appropriate code\n",
    "population_data = list(csv.DictReader(population_file))\n",
    "\n",
    "# Close the file now that we're done reading from it\n",
    "population_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure the `assert`s pass, ensuring that `population_data` has the correct type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "# Check that the overall data structure is a list\n",
    "assert type(population_data) == list\n",
    "\n",
    "# Check that the 0th element is a dictionary\n",
    "# (csv.DictReader interface differs slightly by Python version;\n",
    "# either a dict or an OrderedDict is fine here)\n",
    "from collections import OrderedDict\n",
    "\n",
    "assert type(population_data[0]) == dict or type(population_data[0]) == OrderedDict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. List of Teams in 2018 World Cup\n",
    "\n",
    "> Create an alphabetically-sorted list of teams who competed in the 2018 FIFA World Cup.\n",
    "\n",
    "This will take several steps, some of which have been completed for you.\n",
    "\n",
    "### Exploring the Structure of the World Cup Data JSON\n",
    "\n",
    "Let's start by exploring the structure of `world_cup_data`. Here is a pretty-printed preview of its contents:\n",
    "\n",
    "```\n",
    "{\n",
    "  \"name\": \"World Cup 2018\",\n",
    "  \"rounds\": [\n",
    "    {\n",
    "      \"name\": \"Matchday 1\",\n",
    "      \"matches\": [\n",
    "        {\n",
    "          \"num\": 1,\n",
    "          \"date\": \"2018-06-14\",\n",
    "          \"time\": \"18:00\",\n",
    "          \"team1\": { \"name\": \"Russia\",       \"code\": \"RUS\" },\n",
    "          \"team2\": { \"name\": \"Saudi Arabia\", \"code\": \"KSA\" },\n",
    "          \"score1\":  5,\n",
    "          \"score2\":  0,\n",
    "          \"score1i\": 2,\n",
    "          \"score2i\": 0,\n",
    "          \"goals1\": [\n",
    "            { \"name\": \"Gazinsky\",   \"minute\": 12,              \"score1\": 1, \"score2\": 0 },\n",
    "            { \"name\": \"Cheryshev\",  \"minute\": 43,              \"score1\": 2, \"score2\": 0 },\n",
    "            { \"name\": \"Dzyuba\",     \"minute\": 71,              \"score1\": 3, \"score2\": 0 },\n",
    "            { \"name\": \"Cheryshev\",  \"minute\": 90, \"offset\": 1, \"score1\": 4, \"score2\": 0 },\n",
    "            { \"name\": \"Golovin\",    \"minute\": 90, \"offset\": 4, \"score1\": 5, \"score2\": 0 }\n",
    "          ],\n",
    "          \"goals2\": [],\n",
    "          \"group\": \"Group A\",\n",
    "          \"stadium\": { \"key\": \"luzhniki\", \"name\": \"Luzhniki Stadium\" },\n",
    "          \"city\": \"Moscow\",\n",
    "          \"timezone\": \"UTC+3\"\n",
    "        }\n",
    "      ]\n",
    "    },\n",
    "    {\n",
    "      \"name\": \"Matchday 2\",\n",
    "      \"matches\": [\n",
    "        {\n",
    "          \"num\": 2,\n",
    "          \"date\": \"2018-06-15\",\n",
    "          \"time\": \"17:00\",\n",
    "          \"team1\": { \"name\": \"Egypt\",   \"code\": \"EGY\" },\n",
    "          \"team2\": { \"name\": \"Uruguay\", \"code\": \"URU\" },\n",
    "          \"score1\":  0,\n",
    "          \"score2\":  1,\n",
    "          \"score1i\": 0,\n",
    "          \"score2i\": 0,\n",
    "          \"goals1\": [],\n",
    "          \"goals2\": [\n",
    "            { \"name\": \"Giménez\",  \"minute\": 89,  \"score1\": 0, \"score2\": 1 }\n",
    "          ],\n",
    "          \"group\": \"Group A\",\n",
    "          \"stadium\": { \"key\": \"ekaterinburg\", \"name\": \"Ekaterinburg Arena\" },          \n",
    "          \"city\": \"Ekaterinburg\",\n",
    "          \"timezone\": \"UTC+5\"\n",
    "        },\n",
    "        ...\n",
    "      ],\n",
    "    },\n",
    "  ],  \n",
    "}\n",
    "```\n",
    "\n",
    "As noted previously, `world_cup_data` is a dictionary with two keys, 'name' and 'rounds'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['name', 'rounds'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "world_cup_data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value associated with the 'name' key is simply identifying the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'World Cup 2018'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "world_cup_data[\"name\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Rounds\n",
    "\n",
    "The value associated with the 'rounds' key is a list containing all of the actual information about the rounds and the matches within those rounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type(rounds): <class 'list'>\n",
      "len(rounds): 20\n",
      "type(rounds[3]) <class 'dict'>\n",
      "rounds[3]:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'name': 'Matchday 4',\n",
       " 'matches': [{'num': 9,\n",
       "   'date': '2018-06-17',\n",
       "   'time': '21:00',\n",
       "   'team1': {'name': 'Brazil', 'code': 'BRA'},\n",
       "   'team2': {'name': 'Switzerland', 'code': 'SUI'},\n",
       "   'score1': 1,\n",
       "   'score2': 1,\n",
       "   'score1i': 1,\n",
       "   'score2i': 0,\n",
       "   'goals1': [{'name': 'Coutinho', 'minute': 20, 'score1': 1, 'score2': 0}],\n",
       "   'goals2': [{'name': 'Zuber', 'minute': 50, 'score1': 1, 'score2': 1}],\n",
       "   'group': 'Group E',\n",
       "   'stadium': {'key': 'rostov', 'name': 'Rostov Arena'},\n",
       "   'city': 'Rostov-on-Don',\n",
       "   'timezone': 'UTC+3'},\n",
       "  {'num': 10,\n",
       "   'date': '2018-06-17',\n",
       "   'time': '16:00',\n",
       "   'team1': {'name': 'Costa Rica', 'code': 'CRC'},\n",
       "   'team2': {'name': 'Serbia', 'code': 'SRB'},\n",
       "   'score1': 0,\n",
       "   'score2': 1,\n",
       "   'score1i': 0,\n",
       "   'score2i': 0,\n",
       "   'goals1': [],\n",
       "   'goals2': [{'name': 'Kolarov', 'minute': 56, 'score1': 0, 'score2': 1}],\n",
       "   'group': 'Group E',\n",
       "   'stadium': {'key': 'samara', 'name': 'Samara Arena'},\n",
       "   'city': 'Samara',\n",
       "   'timezone': 'UTC+4'},\n",
       "  {'num': 11,\n",
       "   'date': '2018-06-17',\n",
       "   'time': '18:00',\n",
       "   'team1': {'name': 'Germany', 'code': 'GER'},\n",
       "   'team2': {'name': 'Mexico', 'code': 'MEX'},\n",
       "   'score1': 0,\n",
       "   'score2': 1,\n",
       "   'score1i': 0,\n",
       "   'score2i': 1,\n",
       "   'goals1': [],\n",
       "   'goals2': [{'name': 'Lozano', 'minute': 35, 'score1': 0, 'score2': 1}],\n",
       "   'group': 'Group F',\n",
       "   'stadium': {'key': 'luzhniki', 'name': 'Luzhniki Stadium'},\n",
       "   'city': 'Moscow',\n",
       "   'timezone': 'UTC+3'}]}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "rounds = world_cup_data[\"rounds\"]\n",
    "\n",
    "print(\"type(rounds):\", type(rounds))\n",
    "print(\"len(rounds):\", len(rounds))\n",
    "print(\"type(rounds[3])\", type(rounds[3]))\n",
    "print(\"rounds[3]:\")\n",
    "rounds[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Translating this output into English:\n",
    "\n",
    "Starting with the original `world_cup_data` dictionary, we used the key `\"rounds\"` to extract a list of rounds, which we assigned to the variable `rounds`.\n",
    "\n",
    "`rounds` is a list of dictionaries. Each dictionary inside of `rounds` contains a name (e.g. `\"Matchday 4\"`) as well as a list of matches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Matches\n",
    "\n",
    "Now we can go one level deeper and extract all of the matches in the tournament. Because the round is irrelevant for this analysis, we can loop over all rounds and combine all of their matches into a single list.\n",
    "\n",
    "**Hint:** This is a good use case for using the `.extend` list method rather than `.append`, since we want to combine several lists of dictionaries into a single list of dictionaries, not a list of lists of dictionaries. [Documentation here.](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'num': 1,\n",
       " 'date': '2018-06-14',\n",
       " 'time': '18:00',\n",
       " 'team1': {'name': 'Russia', 'code': 'RUS'},\n",
       " 'team2': {'name': 'Saudi Arabia', 'code': 'KSA'},\n",
       " 'score1': 5,\n",
       " 'score2': 0,\n",
       " 'score1i': 2,\n",
       " 'score2i': 0,\n",
       " 'goals1': [{'name': 'Gazinsky', 'minute': 12, 'score1': 1, 'score2': 0},\n",
       "  {'name': 'Cheryshev', 'minute': 43, 'score1': 2, 'score2': 0},\n",
       "  {'name': 'Dzyuba', 'minute': 71, 'score1': 3, 'score2': 0},\n",
       "  {'name': 'Cheryshev', 'minute': 90, 'offset': 1, 'score1': 4, 'score2': 0},\n",
       "  {'name': 'Golovin', 'minute': 90, 'offset': 4, 'score1': 5, 'score2': 0}],\n",
       " 'goals2': [],\n",
       " 'group': 'Group A',\n",
       " 'stadium': {'key': 'luzhniki', 'name': 'Luzhniki Stadium'},\n",
       " 'city': 'Moscow',\n",
       " 'timezone': 'UTC+3'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace None with appropriate code\n",
    "matches = []\n",
    "\n",
    "# \"round\" is a built-in function in Python so we use \"round_\" instead\n",
    "for round_ in rounds:\n",
    "    # Extract the list of matches for this round\n",
    "    round_matches = round_['matches']\n",
    "    # Add them to the overall list of matches\n",
    "    matches.extend(round_matches)\n",
    "\n",
    "matches[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure the `assert`s pass before moving on to the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "# There should be 64 matches. If the length is 20, that means\n",
    "# you have a list of lists instead of a list of dictionaries\n",
    "assert len(matches) == 64\n",
    "\n",
    "# Each match in the list should be a dictionary\n",
    "assert type(matches[0]) == dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Teams\n",
    "\n",
    "Each match has a `team1` and a `team2`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'Russia', 'code': 'RUS'}\n",
      "{'name': 'Saudi Arabia', 'code': 'KSA'}\n"
     ]
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "print(matches[0][\"team1\"])\n",
    "print(matches[0][\"team2\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a list of all unique team names by looping over every match in `matches` and adding the `\"name\"` values associated with both `team1` and `team2`. (Same as before when creating a list of matches, it doesn't matter right now whether a given team was \"team1\" or \"team2\", we just add everything to `teams`.)\n",
    "\n",
    "We'll use a `set` data type ([documentation here](https://docs.python.org/3/library/stdtypes.html#set-types-set-frozenset)) to ensure unique teams, then convert it to a sorted list at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Argentina', 'Australia', 'Belgium', 'Brazil', 'Colombia', 'Costa Rica', 'Croatia', 'Denmark', 'Egypt', 'England', 'France', 'Germany', 'Iceland', 'Iran', 'Japan', 'Mexico', 'Morocco', 'Nigeria', 'Panama', 'Peru', 'Poland', 'Portugal', 'Russia', 'Saudi Arabia', 'Senegal', 'Serbia', 'South Korea', 'Spain', 'Sweden', 'Switzerland', 'Tunisia', 'Uruguay']\n"
     ]
    }
   ],
   "source": [
    "# Replace None with appropriate code\n",
    "teams_set = set()\n",
    "\n",
    "for match in matches:\n",
    "    # Add team1 name value to teams_set\n",
    "    teams_set.add(match[\"team1\"][\"name\"])\n",
    "    # Add team2 name value to teams_set\n",
    "    teams_set.add(match[\"team2\"][\"name\"])\n",
    "\n",
    "teams = sorted(list(teams_set))\n",
    "print(teams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure the `assert`s pass before moving on to the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "# teams should be a list, not a set\n",
    "assert type(teams) == list\n",
    "\n",
    "# 32 teams competed in the 2018 World Cup\n",
    "assert len(teams) == 32\n",
    "\n",
    "# Each element of teams should be a string\n",
    "# (the name), not a dictionary\n",
    "assert type(teams[0]) == str"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1 complete. We have unique identifiers (names) for each of our records (countries) that we will be able to use to connect 2018 World Cup performance to 2018 population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Associating Countries with 2018 World Cup Performance\n",
    "\n",
    "> Create a data structure that connects a team name (country name) to its performance in the 2018 FIFA World Cup. We'll use the count of games won in the entire tournament (group stage as well as knockout stage) to represent the performance.\n",
    "\n",
    "> Also, create visualizations to help the reader understand the distribution of games won and the performance of each team.\n",
    "\n",
    "So, we are building a **data structure** that connects a country name to the number of wins. There is no universal correct format for a data structure with this purpose, but we are going to use a format that resembles the \"dataframe\" format that will be introduced later in the course.\n",
    "\n",
    "Specifically, we'll build a **dictionary** where each key is the name of a country, and each value is a nested dictionary containing information about the number of wins and the 2018 population.\n",
    "\n",
    "The final result will look something like this:\n",
    "```\n",
    "{\n",
    "  'Argentina': { 'wins': 1, 'population': 44494502 },\n",
    "  ...\n",
    "  'Uruguay':   { 'wins': 4, 'population': 3449299  }\n",
    "}\n",
    "```\n",
    "\n",
    "For the current step (step 2), we'll build a data structure that looks something like this:\n",
    "```\n",
    "{\n",
    "  'Argentina': { 'wins': 1 },\n",
    "  ...\n",
    "  'Uruguay':   { 'wins': 4 }\n",
    "}\n",
    "```\n",
    "\n",
    "### Initializing with Wins Set to Zero\n",
    "\n",
    "Start by initializing a dictionary called `combined_data` containing:\n",
    "\n",
    "* Keys: the strings from `teams`\n",
    "* Values: each value the same, a dictionary containing the key `'wins'` with the associated value `0`. However, note that each value should be a distinct dictionary object in memory, not the same dictionary linked as a value in multiple places.\n",
    "\n",
    "Initially `combined_data` will look something like this:\n",
    "```\n",
    "{\n",
    "  'Argentina': { 'wins': 0 },\n",
    "  ...\n",
    "  'Uruguay':   { 'wins': 0 }\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace None with appropriate code\n",
    "combined_data = {}\n",
    "for team in teams:\n",
    "    combined_data[team]={\"wins\":0}\n",
    "\n",
    "# Create the variable combined_data as described above\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that the `assert`s pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "# combined_data should be a dictionary\n",
    "assert type(combined_data) == dict\n",
    "\n",
    "# the keys should be strings\n",
    "assert type(list(combined_data.keys())[0]) == str\n",
    "\n",
    "# the values should be dictionaries\n",
    "assert combined_data[\"Japan\"] == {\"wins\": 0}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Wins from Matches\n",
    "\n",
    "Now it's time to revisit the `matches` list from earlier, in order to associate a team with the number of times it has won a match.\n",
    "\n",
    "This time, let's write some functions to help organize our logic.\n",
    "\n",
    "Write a function `find_winner` that takes in a `match` dictionary, and returns the name of the team that won the match.  Recall that a match is structured like this:\n",
    "\n",
    "```\n",
    "{\n",
    "  'num': 1,\n",
    "  'date': '2018-06-14',\n",
    "  'time': '18:00',\n",
    "  'team1': { 'name': 'Russia',       'code': 'RUS' },\n",
    "  'team2': { 'name': 'Saudi Arabia', 'code': 'KSA' },\n",
    "  'score1': 5,\n",
    "  'score2': 0,\n",
    "  'score1i': 2,\n",
    "  'score2i': 0,\n",
    "  'goals1': [\n",
    "    { 'name': 'Gazinsky',  'minute': 12, 'score1': 1, 'score2': 0 },\n",
    "    { 'name': 'Cheryshev', 'minute': 43, 'score1': 2, 'score2': 0 },\n",
    "    { 'name': 'Dzyuba',    'minute': 71, 'score1': 3, 'score2': 0 },\n",
    "    { 'name': 'Cheryshev', 'minute': 90, 'offset': 1, 'score1': 4, 'score2': 0 },\n",
    "    { 'name': 'Golovin',   'minute': 90, 'offset': 4, 'score1': 5, 'score2': 0 }\n",
    "  ],\n",
    "  'goals2': [],\n",
    "  'group': 'Group A',\n",
    "  'stadium': { 'key': 'luzhniki', 'name': 'Luzhniki Stadium' },\n",
    "  'city': 'Moscow',\n",
    "  'timezone': 'UTC+3'\n",
    "}\n",
    "```\n",
    "\n",
    "The winner is determined by comparing the values associated with the `'score1'` and `'score2'` keys. If score 1 is larger, then the name associated with the `'team1'` key is the winner. If score 2 is larger, then the name associated with the `'team2'` key is the winner. If the values are the same, there is no winner, so return `None`. (Unlike the group round of the World Cup, we are only counting *wins* as our \"performance\" construct, not 3 points for a win and 1 point for a tie.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace None with appropriate code\n",
    "\n",
    "\n",
    "def find_winner(match):\n",
    "    score_1 = match[\"score1\"]\n",
    "    score_2 = match[\"score2\"]\n",
    "    \n",
    "    if score_1 >score_2:\n",
    "        return match[\"team1\"][\"name\"]\n",
    "    elif score_2 > score_1:\n",
    "        return match[\"team2\"][\"name\"]\n",
    "    else:\n",
    "        return None\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "assert find_winner(matches[0]) == \"Russia\"\n",
    "assert find_winner(matches[1]) == \"Uruguay\"\n",
    "assert find_winner(matches[2]) == None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have this helper function, loop over every match in `matches`, find the winner, and add 1 to the associated count of wins in `combined_data`. If the winner is `None`, skip adding it to the dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Argentina': {'wins': 1},\n",
       " 'Australia': {'wins': 0},\n",
       " 'Belgium': {'wins': 6},\n",
       " 'Brazil': {'wins': 3},\n",
       " 'Colombia': {'wins': 2},\n",
       " 'Costa Rica': {'wins': 0},\n",
       " 'Croatia': {'wins': 3},\n",
       " 'Denmark': {'wins': 1},\n",
       " 'Egypt': {'wins': 0},\n",
       " 'England': {'wins': 3},\n",
       " 'France': {'wins': 6},\n",
       " 'Germany': {'wins': 1},\n",
       " 'Iceland': {'wins': 0},\n",
       " 'Iran': {'wins': 1},\n",
       " 'Japan': {'wins': 1},\n",
       " 'Mexico': {'wins': 2},\n",
       " 'Morocco': {'wins': 0},\n",
       " 'Nigeria': {'wins': 1},\n",
       " 'Panama': {'wins': 0},\n",
       " 'Peru': {'wins': 1},\n",
       " 'Poland': {'wins': 1},\n",
       " 'Portugal': {'wins': 1},\n",
       " 'Russia': {'wins': 2},\n",
       " 'Saudi Arabia': {'wins': 1},\n",
       " 'Senegal': {'wins': 1},\n",
       " 'Serbia': {'wins': 1},\n",
       " 'South Korea': {'wins': 1},\n",
       " 'Spain': {'wins': 1},\n",
       " 'Sweden': {'wins': 3},\n",
       " 'Switzerland': {'wins': 1},\n",
       " 'Tunisia': {'wins': 1},\n",
       " 'Uruguay': {'wins': 4}}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace None with appropriate code\n",
    "\n",
    "for match in matches:\n",
    "    # Get the name of the winner\n",
    "    winner = find_winner(match)\n",
    "    # Only proceed to the next step if there was\n",
    "    # a winner\n",
    "    if winner:\n",
    "        # Add 1 to the associated count of wins\n",
    "        combined_data[winner][\"wins\"] += 1\n",
    "\n",
    "# Visually inspect the output to ensure the wins are\n",
    "# different for different countries\n",
    "combined_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of Wins\n",
    "\n",
    "While we could try to understand all 32 of those numbers just by scanning through them, let's use some descriptive statistics and data visualizations instead\n",
    "\n",
    "#### Statistical Summary of Wins\n",
    "\n",
    "The code below calculates the mean, median, and standard deviation of the number of wins. If it doesn't work, that is an indication that something went wrong with the creation of the `combined_data` variable, and you might want to look at the solution branch and fix your code before proceeding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean number of wins: 1.5625\n",
      "Median number of wins: 1.0\n",
      "Standard deviation of number of wins: 1.5194057226429023\n"
     ]
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "import numpy as np\n",
    "\n",
    "wins = [val[\"wins\"] for val in combined_data.values()]\n",
    "\n",
    "print(\"Mean number of wins:\", np.mean(wins))\n",
    "print(\"Median number of wins:\", np.median(wins))\n",
    "print(\"Standard deviation of number of wins:\", np.std(wins))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizations of Wins\n",
    "\n",
    "In addition to those numbers, let's make a histogram (showing the distributions of the number of wins) and a bar graph (showing the number of wins by country)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set up figure and axes\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 7))\n",
    "fig.set_tight_layout(True)\n",
    "\n",
    "# Histogram of Wins and Frequencies\n",
    "ax1.hist(x=wins, bins=range(8), align=\"left\", color=\"green\")\n",
    "ax1.set_xticks(range(7))\n",
    "ax1.set_xlabel(\"Wins in 2018 World Cup\")\n",
    "ax1.set_ylabel(\"Frequency\")\n",
    "ax1.set_title(\"Distribution of Wins\")\n",
    "\n",
    "# Horizontal Bar Graph of Wins by Country\n",
    "ax2.barh(teams[::-1], wins[::-1], color=\"green\")\n",
    "ax2.set_xlabel(\"Wins in 2018 World Cup\")\n",
    "ax2.set_title(\"Wins by Country\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interpretation of Win Analysis\n",
    "\n",
    "Before we move to looking at the relationship between wins and population, it's useful to understand the distribution of wins alone. A few notes of interpretation:\n",
    "\n",
    "* The number of wins is skewed and looks like a [negative binomial distribution](https://en.wikipedia.org/wiki/Negative_binomial_distribution), which makes sense conceptually\n",
    "* The \"typical\" value here is 1 (both the median and the highest point of the histogram), meaning a typical team that qualifies for the World Cup wins once\n",
    "* There are a few teams we might consider outliers: Belgium and France, with 6x the wins of the \"typical\" team and 1.5x the wins of the next \"runner-up\" (Uruguay, with 4 wins)\n",
    "* This is a fairly small dataset, something that becomes more noticeable with such a \"spiky\" (not smooth) histogram\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Associating Countries with 2018 Population\n",
    "\n",
    "> Add to the existing data structure so that it also connects each country name to its 2018 population, and create visualizations comparable to those from step 2.\n",
    "\n",
    "Now we're ready to add the 2018 population to `combined_data`, finally using the CSV file\n",
    "\n",
    "Recall that `combined_data` currently looks something like this:\n",
    "```\n",
    "{\n",
    "  'Argentina': { 'wins': 1 },\n",
    "  ...\n",
    "  'Uruguay':   { 'wins': 4 }\n",
    "}\n",
    "```\n",
    "\n",
    "And the goal is for it to look something like this:\n",
    "```\n",
    "{\n",
    "  'Argentina': { 'wins': 1, 'population': 44494502 },\n",
    "  ...\n",
    "  'Uruguay':   { 'wins': 4, 'population': 3449299  }\n",
    "}\n",
    "```\n",
    "\n",
    "To do that, we need to extract the 2018 population information from the CSV data.\n",
    "\n",
    "### Exploring the Structure of the Population Data CSV\n",
    "\n",
    "Recall that previously we loaded information from a CSV containing population data into a list of dictionaries called `population_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12695"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "len(population_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "12,695 is a very large number of rows to print out, so let's look at some samples instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([{'': '9984', 'Country Name': 'Malta', 'Country Code': 'MLT', 'Year': '1983', 'Value': '330524'},\n",
       "       {'': '3574', 'Country Name': 'Bahrain', 'Country Code': 'BHR', 'Year': '1994', 'Value': '549583'},\n",
       "       {'': '8104', 'Country Name': 'Iran, Islamic Rep.', 'Country Code': 'IRN', 'Year': '1988', 'Value': '53077313'},\n",
       "       {'': '7905', 'Country Name': 'Iceland', 'Country Code': 'ISL', 'Year': '1966', 'Value': '195570'},\n",
       "       {'': '14678', 'Country Name': 'United Arab Emirates', 'Country Code': 'ARE', 'Year': '1966', 'Value': '159976'},\n",
       "       {'': '13998', 'Country Name': 'Thailand', 'Country Code': 'THA', 'Year': '1994', 'Value': '58875269'},\n",
       "       {'': '8448', 'Country Name': 'Jamaica', 'Country Code': 'JAM', 'Year': '1978', 'Value': '2105907'},\n",
       "       {'': '8979', 'Country Name': 'Kuwait', 'Country Code': 'KWT', 'Year': '1978', 'Value': '1224067'},\n",
       "       {'': '3180', 'Country Name': 'Argentina', 'Country Code': 'ARG', 'Year': '2013', 'Value': '42202935'},\n",
       "       {'': '7140', 'Country Name': 'Gibraltar', 'Country Code': 'GIB', 'Year': '1968', 'Value': '27685'}],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "np.random.seed(42)\n",
    "population_record_samples = np.random.choice(population_data, size=10)\n",
    "population_record_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are **2 filtering tasks**, **1 data normalization task**, and **1 type conversion task** to be completed, based on what we can see in this sample. We'll walk through each of them below.\n",
    "\n",
    "(In a more realistic data cleaning environment, you most likely won't happen to get a sample that demonstrates all of the data cleaning steps needed, but this sample was chosen carefully for example purposes.)\n",
    "\n",
    "### Filtering Population Data\n",
    "\n",
    "We already should have suspected that this dataset would require some filtering, since there are 32 records in our current `combined_data` dataset and 12,695 records in `population_data`. Now that we have looked at this sample, we can identify 2 features we'll want to use in order to filter down the `population_data` records to just 32. Try to identify them before looking at the answer below.\n",
    "\n",
    ".\n",
    "\n",
    ".\n",
    "\n",
    ".\n",
    "\n",
    "*Answer: the two features to filter on are* ***`'Country Name'`*** *and* ***`'Year'`***. *We can see from the sample above that there are countries in `population_data` that are not present in `combined_data` (e.g. Malta) and there are years present that are not 2018.*\n",
    "\n",
    "In the cell below, create a new variable `population_data_filtered` that only includes relevant records from `population_data`. Relevant records are records where the country name is one of the countries in the `teams` list, and the year is \"2018\".\n",
    "\n",
    "(It's okay to leave 2018 as a string since we are not performing any math operations on it, just make sure you check for `\"2018\"` and not `2018`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace None with appropriate code\n",
    "\n",
    "population_data_filtered = []\n",
    "\n",
    "for record in population_data:\n",
    "    # Add record to population_data_filtered if relevant\n",
    "    if (record[\"Country Name\"] in teams) and (record[\"Year\"] == \"2018\"):\n",
    "        population_data_filtered.append(record)\n",
    "\n",
    "len(population_data_filtered)  # 27"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmm...what went wrong? Why do we only have 27 records, and not 32?\n",
    "\n",
    "Did we really get a dataset with 12k records that's missing 5 of the data points we need?\n",
    "\n",
    "Let's take a closer look at the population data samples again, specifically the third one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'': '8104',\n",
       " 'Country Name': 'Iran, Islamic Rep.',\n",
       " 'Country Code': 'IRN',\n",
       " 'Year': '1988',\n",
       " 'Value': '53077313'}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "population_record_samples[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And compare that with the value for Iran in `teams`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Iran'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "teams[13]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ohhhh...we have a data normalization issue. One dataset refers to this country as `'Iran, Islamic Rep.'`, while the other refers to it as `'Iran'`. This is a common issue we face when using data about countries and regions, where there is no universally-accepted naming convention.\n",
    "\n",
    "### Normalizing Locations in Population Data\n",
    "\n",
    "Sometimes data normalization can be a very, very time-consuming task where you need to find \"crosswalk\" data that can link the two formats together, or you need to write advanced regex formulas to line everything up.\n",
    "\n",
    "For this task, there are only 5 missing, so we'll just go ahead and give you a function that makes the appropriate substitutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Russia\n",
      "Argentina\n"
     ]
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "def normalize_location(country_name):\n",
    "    \"\"\"\n",
    "    Given a country name, return the name that the\n",
    "    country uses when playing in the FIFA World Cup\n",
    "    \"\"\"\n",
    "    name_sub_dict = {\n",
    "        \"Russian Federation\": \"Russia\",\n",
    "        \"Egypt, Arab Rep.\": \"Egypt\",\n",
    "        \"Iran, Islamic Rep.\": \"Iran\",\n",
    "        \"Korea, Rep.\": \"South Korea\",\n",
    "        \"United Kingdom\": \"England\",\n",
    "    }\n",
    "    # The .get method returns the corresponding value from\n",
    "    # the dict if present, otherwise returns country_name\n",
    "    return name_sub_dict.get(country_name, country_name)\n",
    "\n",
    "\n",
    "# Example where normalized location is different\n",
    "print(normalize_location(\"Russian Federation\"))\n",
    "# Example where normalized location is the same\n",
    "print(normalize_location(\"Argentina\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, write new code to create `population_data_filtered` with normalized country names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace None with appropriate code\n",
    "\n",
    "population_data_filtered = []\n",
    "\n",
    "for record in population_data:\n",
    "    # Get normalized country name\n",
    "    normalized_name = normalize_location(record[\"Country Name\"])\n",
    "    # Add record to population_data_filtered if relevant\n",
    "    if (normalized_name in teams) and (record[\"Year\"] == \"2018\"):\n",
    "        # Replace the country name in the record\n",
    "        record[\"Country Name\"] = normalized_name\n",
    "        # Append to list\n",
    "        population_data_filtered.append(record)\n",
    "\n",
    "len(population_data_filtered)  # 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, now we should have 32 records instead of 27.\n",
    "\n",
    "### Type Conversion of Population Data\n",
    "\n",
    "We need to do one more thing before we'll have population data that is usable for analysis. Take a look at this record from `population_data_filtered` to see if you can spot it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'': '3185',\n",
       " 'Country Name': 'Argentina',\n",
       " 'Country Code': 'ARG',\n",
       " 'Year': '2018',\n",
       " 'Value': '44494502'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "population_data_filtered[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every key has the same data type (`str`), including the population value. In this example, it's `'44494502'`, when it needs to be `44494502` if we want to be able to compute statistics with it.\n",
    "\n",
    "In the cell below, loop over `population_data_filtered` and convert the data type of the value associated with the `\"Value\"` key from a string to an integer, using the built-in `int()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'': '14907',\n",
       " 'Country Name': 'Uruguay',\n",
       " 'Country Code': 'URY',\n",
       " 'Year': '2018',\n",
       " 'Value': 3449299}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace None with appropriate code\n",
    "for record in population_data_filtered:\n",
    "    # Convert the population value from str to int\n",
    "    record[\"Value\"] = int(record[\"Value\"])\n",
    "\n",
    "# Look at the last record to make sure the population\n",
    "# value is an int\n",
    "population_data_filtered[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that it worked with the assert statement below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "assert type(population_data_filtered[-1][\"Value\"]) == int"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Population Data\n",
    "\n",
    "Now it's time to add the population data to `combined_data`. Recall that the data structure currently looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Argentina': {'wins': 1},\n",
       " 'Australia': {'wins': 0},\n",
       " 'Belgium': {'wins': 6},\n",
       " 'Brazil': {'wins': 3},\n",
       " 'Colombia': {'wins': 2},\n",
       " 'Costa Rica': {'wins': 0},\n",
       " 'Croatia': {'wins': 3},\n",
       " 'Denmark': {'wins': 1},\n",
       " 'Egypt': {'wins': 0},\n",
       " 'England': {'wins': 3},\n",
       " 'France': {'wins': 6},\n",
       " 'Germany': {'wins': 1},\n",
       " 'Iceland': {'wins': 0},\n",
       " 'Iran': {'wins': 1},\n",
       " 'Japan': {'wins': 1},\n",
       " 'Mexico': {'wins': 2},\n",
       " 'Morocco': {'wins': 0},\n",
       " 'Nigeria': {'wins': 1},\n",
       " 'Panama': {'wins': 0},\n",
       " 'Peru': {'wins': 1},\n",
       " 'Poland': {'wins': 1},\n",
       " 'Portugal': {'wins': 1},\n",
       " 'Russia': {'wins': 2},\n",
       " 'Saudi Arabia': {'wins': 1},\n",
       " 'Senegal': {'wins': 1},\n",
       " 'Serbia': {'wins': 1},\n",
       " 'South Korea': {'wins': 1},\n",
       " 'Spain': {'wins': 1},\n",
       " 'Sweden': {'wins': 3},\n",
       " 'Switzerland': {'wins': 1},\n",
       " 'Tunisia': {'wins': 1},\n",
       " 'Uruguay': {'wins': 4}}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "combined_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal is for it to be structured like this:\n",
    "```\n",
    "{\n",
    "  'Argentina': { 'wins': 1, 'population': 44494502 },\n",
    "  ...\n",
    "  'Uruguay':   { 'wins': 4, 'population': 3449299  }\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, loop over `population_data_filtered` and add information about population to each country in `combined_data`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Argentina': {'wins': 1, 'population': 44494502},\n",
       " 'Australia': {'wins': 0, 'population': 24982688},\n",
       " 'Belgium': {'wins': 6, 'population': 11433256},\n",
       " 'Brazil': {'wins': 3, 'population': 209469333},\n",
       " 'Colombia': {'wins': 2, 'population': 49648685},\n",
       " 'Costa Rica': {'wins': 0, 'population': 4999441},\n",
       " 'Croatia': {'wins': 3, 'population': 4087843},\n",
       " 'Denmark': {'wins': 1, 'population': 5793636},\n",
       " 'Egypt': {'wins': 0, 'population': 98423595},\n",
       " 'England': {'wins': 3, 'population': 66460344},\n",
       " 'France': {'wins': 6, 'population': 66977107},\n",
       " 'Germany': {'wins': 1, 'population': 82905782},\n",
       " 'Iceland': {'wins': 0, 'population': 352721},\n",
       " 'Iran': {'wins': 1, 'population': 81800269},\n",
       " 'Japan': {'wins': 1, 'population': 126529100},\n",
       " 'Mexico': {'wins': 2, 'population': 126190788},\n",
       " 'Morocco': {'wins': 0, 'population': 36029138},\n",
       " 'Nigeria': {'wins': 1, 'population': 195874740},\n",
       " 'Panama': {'wins': 0, 'population': 4176873},\n",
       " 'Peru': {'wins': 1, 'population': 31989256},\n",
       " 'Poland': {'wins': 1, 'population': 37974750},\n",
       " 'Portugal': {'wins': 1, 'population': 10283822},\n",
       " 'Russia': {'wins': 2, 'population': 144478050},\n",
       " 'Saudi Arabia': {'wins': 1, 'population': 33699947},\n",
       " 'Senegal': {'wins': 1, 'population': 15854360},\n",
       " 'Serbia': {'wins': 1, 'population': 6982604},\n",
       " 'South Korea': {'wins': 1, 'population': 51606633},\n",
       " 'Spain': {'wins': 1, 'population': 46796540},\n",
       " 'Sweden': {'wins': 3, 'population': 10175214},\n",
       " 'Switzerland': {'wins': 1, 'population': 8513227},\n",
       " 'Tunisia': {'wins': 1, 'population': 11565204},\n",
       " 'Uruguay': {'wins': 4, 'population': 3449299}}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace None with appropriate code\n",
    "for record in population_data_filtered:\n",
    "    # Extract the country name from the record\n",
    "    country = record[\"Country Name\"]\n",
    "    # Extract the population value from the record\n",
    "    population = record[\"Value\"]\n",
    "    # Add this information to combined_data\n",
    "    combined_data[country][\"population\"] = population\n",
    "\n",
    "\n",
    "# Look combined_data\n",
    "combined_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that the types are correct with these assert statements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "assert type(combined_data[\"Uruguay\"]) == dict\n",
    "assert type(combined_data[\"Uruguay\"][\"population\"]) == int"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of Population\n",
    "\n",
    "Let's perform the same analysis for population that we performed for count of wins.\n",
    "\n",
    "#### Statistical Analysis of Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean population: 51687460.84375\n",
      "Median population: 34864542.5\n",
      "Standard deviation of population: 55195121.60871871\n"
     ]
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "populations = [val[\"population\"] for val in combined_data.values()]\n",
    "\n",
    "print(\"Mean population:\", np.mean(populations))\n",
    "print(\"Median population:\", np.median(populations))\n",
    "print(\"Standard deviation of population:\", np.std(populations))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizations of Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "# Set up figure and axes\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 7))\n",
    "fig.set_tight_layout(True)\n",
    "\n",
    "# Histogram of Populations and Frequencies\n",
    "ax1.hist(x=populations, color=\"blue\")\n",
    "ax1.set_xlabel(\"2018 Population\")\n",
    "ax1.set_ylabel(\"Frequency\")\n",
    "ax1.set_title(\"Distribution of Population\")\n",
    "\n",
    "# Horizontal Bar Graph of Population by Country\n",
    "ax2.barh(teams[::-1], populations[::-1], color=\"blue\")\n",
    "ax2.set_xlabel(\"2018 Population\")\n",
    "ax2.set_title(\"Population by Country\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interpretation of Population Analysis\n",
    "\n",
    "* Similar to the distribution of the number of wins, the distribution of population is skewed.\n",
    "* It's hard to choose a single \"typical\" value here because there is so much variation.\n",
    "* The countries with the largest populations (Brazil, Nigeria, and Russia) do not overlap with the countries with the most wins (Belgium, France, and Uruguay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Analysis of Population vs. Performance\n",
    "\n",
    "> Choose an appropriate statistical measure to analyze the relationship between population and performance, and create a visualization representing this relationship.\n",
    "\n",
    "### Statistical Measure\n",
    "So far we have learned about only two statistics for understanding the *relationship* between variables: **covariance** and **correlation**. We will use correlation here, because that provides a more standardized, interpretable metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07592816849178588"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "np.corrcoef(wins, populations)[0][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, interpret this number. What direction is this correlation? Is it strong or weak?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace None with appropriate code\n",
    "The country performance and the population have a weak correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization\n",
    "\n",
    "A **scatter plot** is he most sensible form of data visualization for showing this relationship, because we have two dimensions of data, but there is no \"increasing\" variable (e.g. time) that would indicate we should use a line graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "# Set up figure\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "# Basic scatter plot\n",
    "ax.scatter(x=populations, y=wins, color=\"gray\", alpha=0.5, s=100)\n",
    "ax.set_xlabel(\"2018 Population\")\n",
    "ax.set_ylabel(\"2018 World Cup Wins\")\n",
    "ax.set_title(\"Population vs. World Cup Wins\")\n",
    "\n",
    "# Add annotations for specific points of interest\n",
    "highlighted_points = {\n",
    "    \"Belgium\": 2,  # Numbers are the index of that\n",
    "    \"Brazil\": 3,  # country in populations & wins\n",
    "    \"France\": 10,\n",
    "    \"Nigeria\": 17,\n",
    "}\n",
    "for country, index in highlighted_points.items():\n",
    "    # Get x and y position of data point\n",
    "    x = populations[index]\n",
    "    y = wins[index]\n",
    "    # Move each point slightly down and to the left\n",
    "    # (numbers were chosen by manually tweaking)\n",
    "    xtext = x - (1.25e6 * len(country))\n",
    "    ytext = y - 0.5\n",
    "    # Annotate with relevant arguments\n",
    "    ax.annotate(text=country, xy=(x, y), xytext=(xtext, ytext))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization Interpretation\n",
    "\n",
    "Interpret this plot in the cell below. Does this align with the findings from the statistical measure (correlation), as well as the map shown at the beginning of this lab (showing the best results by country)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace None with appropriate text\n",
    "The plot aligns with what we found in the correlation. The values in the scatter plot are spreat out and do not indicate whether their is any correlation of a teams performance to its population.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Analysis\n",
    "\n",
    "> What is the relationship between the population of a country and their performance in the 2018 FIFA World Cup?\n",
    "\n",
    "Overall, we found a very weakly positive relationship between the population of a country and their performance in the 2018 FIFA World Cup, as demonstrated by both the correlation between populations and wins, and the scatter plot.\n",
    "\n",
    "In the cell below, write down your thoughts on these questions:\n",
    "\n",
    " - What are your thoughts on why you may see this result?\n",
    " - What would you research next?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace None with appropriate text\n",
    "\"\"\"\n",
    "None\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "That was a long lab, pulling together a lot of material. You read data into Python, extracted the relevant information, cleaned the data, and combined the data into a new format to be used in analysis. While we will continue to introduce new tools and techniques, these essential steps will be present for the rest of your data science projects from here on out."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (learn-env)",
   "language": "python",
   "name": "learn-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}