TI4-dashboard-01.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](/img/ti4cover.jpeg)\n",
    "\n",
    "# Data and Twilight Imperium, Part 1: Introduction, Cleaning, and Graphing Game Completion\n",
    "I've been quite impressed with how the Twilight Imperium community handles [their match data](https://lookerstudio.google.com/u/0/reporting/3b435bf2-2100-488c-a424-130f1d22ebb0/page/pE58B?params=%7B%22df333%22:%22include%25EE%2580%25801%25EE%2580%2580IN%25EE%2580%25805%22%7D). No one would blame them for not keeping track of the torrents of data generated by such a complex game, but not only have they risen to the task, but the analysts in the community have managed to wrangle and present it in a compelling way that can inform the play-by-play decisions of avid players.\n",
    "\n",
    "Taking a bit of inspiration from them, I thought I might try my hand at taking the same data and recreating its presentation on my own to the best of my ability. Along the way, I hope to improve my facility with Jupyter Notebooks and Python's Pandas and Matplotlib libraries.\n",
    "\n",
    "## What is Twilight Imperium\n",
    "[Twilight Imperium](https://en.wikipedia.org/wiki/Twilight_Imperium) is an enormous board game of war, strategy, and diplomacy. Taking place in a colorful sci-fi setting, each player controls a unique faction and competes to be the first to reach ten (or sometimes fourteen) victory points. The game is famous for its complexity and length of play; the map of the galaxy is generated randomly, then players each choose one of twenty-five factions with completely unique abilities and play styles. A 'quick' game will only last four hours, but it's not unusual for games to last ten hours or more, especially offline.\n",
    "\n",
    "![](/img/ti4_example.jpeg)\n",
    "\n",
    "The game was first released in 1997 as a passion project of Christian T. Petersen, the founder and then-owner of Fantasy Flight Games (FFG). Although the game has always had a die-hard cult following since the very beginning, the game reached new heights of popularity with the release of its fourth edition (TI4) in August 2017, followed three years later with its first and so-far only expansion, *Prophecy of Kings*. A volunteer-created port of the game into [Tabletop Simulator](https://en.wikipedia.org/wiki/Tabletop_Simulator) (TTS) made it easier to host tournaments and pickup games, as well as to help potential new players try out TI4 without making a daunting $100+ investment. Because of this, it's no surprise that FFG themselves semi-officially condone the port, which they probably see as effective word-of-mouth advertising for a difficult product to sell. [Podcasts](https://spacecatspeaceturtles.com/) and [Youtube channels](https://www.youtube.com/@TIJunkies) have sprung up out of the TI4 community, and FFG has been slowly but steadily expanding the product line with [tie-in novels](https://www.fantasyflightgames.com/en/twilight-imperium-fiction/) and a [roll-and-write version of the game](https://www.fantasyflightgames.com/en/twilight-inscription/) (think Yahtzee, but much, much more complicated).\n",
    "\n",
    "## The Data\n",
    "As a consequence of online TTS games, fans have been able to collect tons and tons of data from each match. Data analysts among them have collated these data into a dashboard with useful and captivating visuals that update with every game played.\n",
    "\n",
    "Here's a description of the data from the [dashboard](https://lookerstudio.google.com/u/0/reporting/3b435bf2-2100-488c-a424-130f1d22ebb0/page/pE58B?params=%7B%22df333%22:%22include%25EE%2580%25801%25EE%2580%2580IN%25EE%2580%25805%22%7D):\n",
    "\n",
    ">A snapshot of the board state is saved at the start of every round (and at the end of the game when the roundplusfinal column = 'final'). In this table, each row represents one of these snapshots of one of the players. A 6 player game that goes for 5 rounds will have 36 rows total (one snapshot per round per player, plus another snapshot at the final end state of the game). The setupTimestamp field is used to differentiate between different matches.\n",
    "\n",
    "I downloaded the data at exactly 3:31pm on July 5th, 2023. The resulting CSV file is 320.3 MB and contains over 286,290 rows and twenty-seven columns. The oldest data were added on February 28, 2021. Since one game can cover multiple rows and each game is uniquely identified with the setupTimestamp column, a .nunique() method shows that this dataset represents 9,082 games."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8p/sbpv06dx3b576tlsdyc4cs9c0000gn/T/ipykernel_4032/2064672778.py:5: DtypeWarning: Columns (21) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('TI4 Tabletop Simulator Stats Dashboard.csv')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 286290 entries, 0 to 286289\n",
      "Data columns (total 27 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   Date                         286290 non-null  object \n",
      " 1   setupTimestamp               286290 non-null  float64\n",
      " 2   Timestamp                    286290 non-null  float64\n",
      " 3   mapString                    65737 non-null   object \n",
      " 4   isHomebrewMatch              286290 non-null  bool   \n",
      " 5   isPOK                        286290 non-null  bool   \n",
      " 6   roundPlusFinal               286290 non-null  object \n",
      " 7   color                        286290 non-null  object \n",
      " 8   active                       286233 non-null  object \n",
      " 9   score                        286290 non-null  int64  \n",
      " 10  custodiansPoints             286239 non-null  float64\n",
      " 11  Winning Faction              286290 non-null  object \n",
      " 12  isPOK.1                      286290 non-null  bool   \n",
      " 13  Player Count                 286290 non-null  int64  \n",
      " 14  factionName                  286171 non-null  object \n",
      " 15  factionNameWinner            286290 non-null  object \n",
      " 16  objectives                   210235 non-null  object \n",
      " 17  Public_Objectives_I          286260 non-null  object \n",
      " 18  Public_Objectives_II         245634 non-null  object \n",
      " 19  leaders                      286290 non-null  object \n",
      " 20  technologies                 286290 non-null  object \n",
      " 21  hexSummaryImageURLHyperlink  261850 non-null  object \n",
      " 22  strategyCards                277754 non-null  object \n",
      " 23  strategyCards2               45440 non-null   object \n",
      " 24  isSpeaker                    286290 non-null  bool   \n",
      " 25  factionsInGame               284465 non-null  object \n",
      " 26  gameDuration                 286290 non-null  float64\n",
      "dtypes: bool(4), float64(4), int64(2), object(17)\n",
      "memory usage: 51.3+ MB\n",
      "None\n",
      "                             Date  setupTimestamp     Timestamp mapString   \n",
      "286280  Feb 28, 2021, 12:11:30 AM    1.614471e+09  1.614471e+09       NaN  \\\n",
      "286281  Feb 28, 2021, 12:11:30 AM    1.614471e+09  1.614471e+09       NaN   \n",
      "286282  Feb 28, 2021, 12:11:30 AM    1.614471e+09  1.614471e+09       NaN   \n",
      "286283  Feb 28, 2021, 12:11:30 AM    1.614471e+09  1.614471e+09       NaN   \n",
      "286284  Feb 28, 2021, 12:11:30 AM    1.614471e+09  1.614471e+09       NaN   \n",
      "286285  Feb 28, 2021, 12:11:30 AM    1.614471e+09  1.614471e+09       NaN   \n",
      "286286  Feb 28, 2021, 12:11:30 AM    1.614471e+09  1.614471e+09       NaN   \n",
      "286287  Feb 28, 2021, 12:11:30 AM    1.614471e+09  1.614471e+09       NaN   \n",
      "286288  Feb 28, 2021, 12:11:30 AM    1.614471e+09  1.614471e+09       NaN   \n",
      "286289  Feb 28, 2021, 12:11:30 AM    1.614471e+09  1.614471e+09       NaN   \n",
      "\n",
      "        isHomebrewMatch  isPOK roundPlusFinal   color   active  score  ...   \n",
      "286280            False   True          final     Red     true      8  ...  \\\n",
      "286281            False   True          final  Yellow    false      7  ...   \n",
      "286282            False   True              3     Red  unknown      2  ...   \n",
      "286283            False   True          final  Purple     true      8  ...   \n",
      "286284            False   True          final    Blue     true     10  ...   \n",
      "286285            False   True              3  Yellow  unknown      3  ...   \n",
      "286286            False   True              2    Blue  unknown      1  ...   \n",
      "286287            False   True              1  Yellow  unknown      0  ...   \n",
      "286288            False   True              1    Blue  unknown      0  ...   \n",
      "286289            False   True              1   White  unknown      0  ...   \n",
      "\n",
      "                                      Public_Objectives_I   \n",
      "286280  ['Engineer a Marvel', 'Populate the Outer Rim'...  \\\n",
      "286281  ['Engineer a Marvel', 'Populate the Outer Rim'...   \n",
      "286282  ['Engineer a Marvel', 'Populate the Outer Rim'...   \n",
      "286283  ['Engineer a Marvel', 'Populate the Outer Rim'...   \n",
      "286284  ['Engineer a Marvel', 'Populate the Outer Rim'...   \n",
      "286285  ['Engineer a Marvel', 'Populate the Outer Rim'...   \n",
      "286286  ['Engineer a Marvel', 'Populate the Outer Rim'...   \n",
      "286287  ['Engineer a Marvel', 'Populate the Outer Rim'...   \n",
      "286288  ['Engineer a Marvel', 'Populate the Outer Rim'...   \n",
      "286289  ['Engineer a Marvel', 'Populate the Outer Rim'...   \n",
      "\n",
      "          Public_Objectives_II                                     leaders   \n",
      "286280  ['Found a Golden Age']    {\"hero\":\"purged\",\"commander\":\"unlocked\"}  \\\n",
      "286281  ['Found a Golden Age']  {\"hero\":\"unlocked\",\"commander\":\"unlocked\"}   \n",
      "286282  ['Found a Golden Age']    {\"hero\":\"locked\",\"commander\":\"unlocked\"}   \n",
      "286283  ['Found a Golden Age']    {\"hero\":\"purged\",\"commander\":\"unlocked\"}   \n",
      "286284  ['Found a Golden Age']    {\"hero\":\"locked\",\"commander\":\"unlocked\"}   \n",
      "286285  ['Found a Golden Age']      {\"hero\":\"locked\",\"commander\":\"locked\"}   \n",
      "286286  ['Found a Golden Age']    {\"hero\":\"locked\",\"commander\":\"unlocked\"}   \n",
      "286287  ['Found a Golden Age']      {\"hero\":\"locked\",\"commander\":\"locked\"}   \n",
      "286288  ['Found a Golden Age']      {\"hero\":\"locked\",\"commander\":\"locked\"}   \n",
      "286289  ['Found a Golden Age']      {\"hero\":\"locked\",\"commander\":\"locked\"}   \n",
      "\n",
      "                                             technologies   \n",
      "286280  [\"Plasma Scoring\",\"Prototype War Sun II\",\"Dest...  \\\n",
      "286281  [\"Antimass Deflectors\",\"Sarween Tools\",\"Dark E...   \n",
      "286282                                                  3   \n",
      "286283  [\"Antimass Deflectors\",\"Neural Motivator\",\"Gra...   \n",
      "286284  [\"Antimass Deflectors\",\"Sarween Tools\",\"Dark E...   \n",
      "286285                                                  3   \n",
      "286286                                                  3   \n",
      "286287                                                  1   \n",
      "286288                                                  2   \n",
      "286289                                                  2   \n",
      "\n",
      "       hexSummaryImageURLHyperlink strategyCards strategyCards2 isSpeaker   \n",
      "286280                         NaN       Warfare            NaN     False  \\\n",
      "286281                         NaN      Politics            NaN     False   \n",
      "286282                         NaN    Technology            NaN     False   \n",
      "286283                         NaN     Diplomacy            NaN     False   \n",
      "286284                         NaN      Imperial            NaN     False   \n",
      "286285                         NaN    Leadership            NaN     False   \n",
      "286286                         NaN  Construction            NaN     False   \n",
      "286287                         NaN       Warfare            NaN     False   \n",
      "286288                         NaN         Trade            NaN      True   \n",
      "286289                         NaN  Construction            NaN     False   \n",
      "\n",
      "                                           factionsInGame gameDuration  \n",
      "286280  Titans of Ul, Emirates of Hacan, Federation of...          7.5  \n",
      "286281  Titans of Ul, Emirates of Hacan, Federation of...          7.5  \n",
      "286282  Titans of Ul, Emirates of Hacan, Federation of...          7.5  \n",
      "286283  Titans of Ul, Emirates of Hacan, Federation of...          7.5  \n",
      "286284  Titans of Ul, Emirates of Hacan, Federation of...          7.5  \n",
      "286285  Titans of Ul, Emirates of Hacan, Federation of...          7.5  \n",
      "286286  Titans of Ul, Emirates of Hacan, Federation of...          7.5  \n",
      "286287  Titans of Ul, Emirates of Hacan, Federation of...          7.5  \n",
      "286288  Titans of Ul, Emirates of Hacan, Federation of...          7.5  \n",
      "286289  Titans of Ul, Emirates of Hacan, Federation of...          7.5  \n",
      "\n",
      "[10 rows x 27 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Date                             9082\n",
       "setupTimestamp                   9084\n",
       "Timestamp                        9084\n",
       "mapString                        1204\n",
       "isHomebrewMatch                     2\n",
       "isPOK                               2\n",
       "roundPlusFinal                     13\n",
       "color                              12\n",
       "active                              3\n",
       "score                              16\n",
       "custodiansPoints                    8\n",
       "Winning Faction                    94\n",
       "isPOK.1                             2\n",
       "Player Count                        7\n",
       "factionName                       128\n",
       "factionNameWinner                  94\n",
       "objectives                     104076\n",
       "Public_Objectives_I              9171\n",
       "Public_Objectives_II             1718\n",
       "leaders                            38\n",
       "technologies                    44489\n",
       "hexSummaryImageURLHyperlink     46272\n",
       "strategyCards                      19\n",
       "strategyCards2                     16\n",
       "isSpeaker                           2\n",
       "factionsInGame                  16992\n",
       "gameDuration                     2592\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "df = pd.read_csv('TI4 Tabletop Simulator Stats Dashboard.csv')\n",
    "print(df.info())\n",
    "print(df.tail(10))\n",
    "df.nunique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning\n",
    "As one might expect from such copious amounts of data from such a complex game, there are more null values than I think most data analysts would be comfortable with. This might reflect an issue with how data is collected from TTS games or even player knowledge of how to properly send it. For now, though, the only column that we should be careful with is the `mapString` data, which describe board states at each round for every game.\n",
    "\n",
    "As we can see, column 21 (`hexSummaryImageURLHyperlink`) has mixed data types. Normally, this would need to be corrected, but considering that this data stores a hyperlink to help display board states, it might be best to ignore it for now. Of more concern is that the date column holds strings instead of the `datetime` datatype. Let's fix that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of 0           Jul 1, 2023, 3:09:34 PM\n",
       "1           Jul 1, 2023, 3:09:34 PM\n",
       "2           Jul 1, 2023, 3:09:34 PM\n",
       "3           Jul 1, 2023, 3:09:34 PM\n",
       "4           Jul 1, 2023, 3:09:34 PM\n",
       "                    ...            \n",
       "286285    Feb 28, 2021, 12:11:30 AM\n",
       "286286    Feb 28, 2021, 12:11:30 AM\n",
       "286287    Feb 28, 2021, 12:11:30 AM\n",
       "286288    Feb 28, 2021, 12:11:30 AM\n",
       "286289    Feb 28, 2021, 12:11:30 AM\n",
       "Name: Date, Length: 286290, dtype: object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'].head\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of 0        2023-07-01 15:09:34\n",
       "1        2023-07-01 15:09:34\n",
       "2        2023-07-01 15:09:34\n",
       "3        2023-07-01 15:09:34\n",
       "4        2023-07-01 15:09:34\n",
       "                 ...        \n",
       "286285   2021-02-28 00:11:30\n",
       "286286   2021-02-28 00:11:30\n",
       "286287   2021-02-28 00:11:30\n",
       "286288   2021-02-28 00:11:30\n",
       "286289   2021-02-28 00:11:30\n",
       "Name: DateFixed, Length: 286290, dtype: datetime64[ns]>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DateFixed'] = pd.to_datetime(df['Date'], format='%b %d, %Y, %I:%M:%S %p')\n",
    "df['DateFixed'].head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 286290 entries, 0 to 286289\n",
      "Series name: DateFixed\n",
      "Non-Null Count   Dtype         \n",
      "--------------   -----         \n",
      "286290 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1)\n",
      "memory usage: 2.2 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df['DateFixed'].info())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing and Visualizing the Data\n",
    "Let's try to recreate one of the first charts on the [dashboard](https://lookerstudio.google.com/u/0/reporting/3b435bf2-2100-488c-a424-130f1d22ebb0/page/pE58B?params=%7B%22df333%22:%22include%25EE%2580%25801%25EE%2580%2580IN%25EE%2580%25805%22%7D): Match Completion Rates. The site describes the data thusly:\n",
    "\n",
    ">This is the percentage of submitted matches that are considered complete. A game is considered incomplete if noone has reached the VP target (e.g. 10 or 14 VP), and can occur if players leave the match without updating the score tracker to reflect the final scores. Placing a command token on the final notch of the score tracker will trigger a stats update.\n",
    "\n",
    "It's no surprise that a long game like Twilight Imperium would have less than a 70% completion rate. However, as mentioned in the description above, some of the low completion rates might be inaccurate simply because players don't know how to properly use the TTS mod to report data.\n",
    "\n",
    "First, since each observation is represents one player per round, we need to aggregate each game by the unique `setupTimestamp` series. We'll need to add a Boolean series to determine whether or not someone reached the final score of 10 or 14. The simplest way to do this would be to use the `Winning Faction` series.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9084 entries, 0 to 9083\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype         \n",
      "---  ------           --------------  -----         \n",
      " 0   setupTimestamp   9084 non-null   float64       \n",
      " 1   DateFixed        9084 non-null   datetime64[ns]\n",
      " 2   Winning Faction  9084 non-null   object        \n",
      " 3   Complete         9084 non-null   bool          \n",
      "dtypes: bool(1), datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 221.9+ KB\n",
      "None\n",
      "   setupTimestamp           DateFixed    Winning Faction  Complete\n",
      "0    1.614471e+09 2021-02-28 00:11:30  Emirates of Hacan      True\n",
      "1    1.614476e+09 2021-02-28 01:31:56   Incomplete Match     False\n",
      "2    1.614493e+09 2021-02-28 06:16:36   Incomplete Match     False\n",
      "3    1.614499e+09 2021-02-28 07:50:19       Titans of Ul      True\n",
      "4    1.614499e+09 2021-02-28 07:57:52   Incomplete Match     False\n",
      "      setupTimestamp           DateFixed   Winning Faction  Complete\n",
      "9079    1.688170e+09 2023-06-30 23:58:38  Incomplete Match     False\n",
      "9080    1.688184e+09 2023-07-01 04:00:50  Incomplete Match     False\n",
      "9081    1.688216e+09 2023-07-01 12:50:10  Incomplete Match     False\n",
      "9082    1.688223e+09 2023-07-01 14:50:35  Incomplete Match     False\n",
      "9083    1.688224e+09 2023-07-01 15:09:34  Incomplete Match     False\n"
     ]
    }
   ],
   "source": [
    "# Creating a new data frame from columns from the original dataframe.\n",
    "completionRate = df[['DateFixed','Winning Faction','setupTimestamp']].copy()\n",
    "\n",
    "# Adding a column with Boolean data types to indicate whether the game is complete or not.\n",
    "completionRate['Complete'] = completionRate['Winning Faction'] != 'Incomplete Match'\n",
    "\n",
    "# Aggregating the data by setupTimestamp\n",
    "completionRate1 = completionRate.groupby('setupTimestamp', as_index=False).first()\n",
    "\n",
    "print(completionRate1.info())\n",
    "print(completionRate1.head())\n",
    "print(completionRate1.tail())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data frame has been reduced to 9,084 rows, which matches the current number of games in the database. Seeing as there are no null values, I think this data is ready for analysis.\n",
    "\n",
    "Let's go back to the original chart from the dashboard:\n",
    "\n",
    "<img src=\"completionrate_screenshot.png\" style=\"width: 500px;\"/>\n",
    "\n",
    "The chart's x-axis represents time, the left y-axis represents completed game count, and the right y-axis represents the completion rate itself. It also seems that the x-axis is delimited by month. Keeping that in mind, let's try to recreate that visualization with Pandas and Matplotlib:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouping the results by month.\n",
    "grouper = pd.Grouper(key='DateFixed', freq='M')\n",
    "CompleteCount = completionRate1.groupby(grouper)['Complete'].sum().reset_index()\n",
    "CompleteRate = completionRate1.groupby(grouper)['Complete'].apply(lambda x: np.sum(x)/len(x))\n",
    "\n",
    "# Setting up the axes, labels and colors.\n",
    "fig, ax1 = plt.subplots(facecolor='#2d2f38')\n",
    "ax1.plot(CompleteCount['DateFixed'], CompleteCount['Complete'], color='#0d67d1')\n",
    "ax1.set_yticks([0,100,200,300,400])\n",
    "ax1.set_yticklabels([0,100,200,300,400], color='#e2e2e4')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(CompleteCount['DateFixed'], CompleteRate, color='#0aaec2')\n",
    "ax2.set_yticks([0.0,0.20,0.4,0.6,0.8,1.0])\n",
    "ax2.set_yticklabels(['0%','20%','40%','60%','80%','100%'], color='#e2e2e4')\n",
    "ax1.set_facecolor('#2d2f38')\n",
    "\n",
    "ax1.set_xticks(['2021-02','2021-05','2021-08','2021-11','2022-02','2022-05','2022-08','2022-11','2023-02','2023-05'])\n",
    "ax1.set_xticklabels(['Feb 2021','May 2021','Aug 2021','Nov 2021','Feb 2022','May 2022','Aug 2022','Nov 2022','Feb 2022','May 2022'], color='#e2e2e4')\n",
    "for tick in ax1.xaxis.get_major_ticks()[1::2]:\n",
    "    tick.set_pad(15)\n",
    "\n",
    "# Setting up the legend\n",
    "legend1 = mlines.Line2D([], [], color='#0d67d1', label='Complete Game Count')\n",
    "legend2 = mlines.Line2D([], [], color='#0aaec2', label='Complete Game %')\n",
    "ax1.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc='lower left', fontsize = 'small', facecolor = '#2d2f38', labelcolor = '#e2e2e4', edgecolor = '#2d2f38', ncols=2, handles=[legend1,legend2])\n",
    "#plt.gca().legend(('Completion Rate %','Completion Game Count'))\n",
    "\n",
    "# Show Plot\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](completion_graph.png)\n",
    "# Conclusion\n",
    "\n",
    "There's a few things missing from the chart to make it complete, notably the annotations for each point plotted. I'm satisfied for now, though. It was really neat digging into the documentation of Matplotlib to get everything working just right. I also think that this is a chart that can be used to make decisions or at least guide future inquiry. For example, I'd be interested to see if the declines in complete game percentage track with influxes of new players, who typically aren't aware of how to correctly report their data. I look forward to working with this dataset in the future and uncovering more insights into this intriguing game."
   ]
  }
 ],
 "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": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}