{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false, "scrolled": true }, "source": [ "#Ploting my Caves of Qud data\n", "\n", "Having previously cleaned the data it's now time to pull it into a pandas dataframe and get some info from it." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "data": { "text/html": [ "
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NameEnd TimeGame End TimeEnemyx hitDamageWeaponPVPos DamScoreTurnsZonesStoried ItemsArtifact
0Goethe IIThursday, August 13, 2015 6:04:58 PM20th of Uru UxWahmahcalcalit00lase beam0048753352352601HE Missile
1Kant XVIIISunday, August 30, 2015 7:34:00 PM27th of Tuum Utchute crab12crab claw71d240178371452221Fix-It spray foam x2
2O'Brien IIIWednesday, September 02, 2015 3:50:10 AM6th of Tebet UxKumukokumu the Stylish, legendary ogre ape851ape fist203d320556211141300force bracelet 0 0 <> [no cell]
3Kant XIIFriday, August 28, 2015 11:14:48 PM7th of Iyur UtPutus Templar warden13folded carbide long sword92d517061170661180electrobow <> ->10 1d6 [no cell]
4Nietzsche IIIWednesday, August 05, 2015 8:00:46 PM19th of Tishru ii Uxeyeless king crab620massive king crab claw201d616607161241150ubernostrum injector <>
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" ], "text/plain": [ " Name End Time \\\n", "0 Goethe II Thursday, August 13, 2015 6:04:58 PM \n", "1 Kant XVIII Sunday, August 30, 2015 7:34:00 PM \n", "2 O'Brien III Wednesday, September 02, 2015 3:50:10 AM \n", "3 Kant XII Friday, August 28, 2015 11:14:48 PM \n", "4 Nietzsche III Wednesday, August 05, 2015 8:00:46 PM \n", "\n", " Game End Time Enemy x hit \\\n", "0 20th of Uru Ux Wahmahcalcalit 0 \n", "1 27th of Tuum Ut chute crab 1 \n", "2 6th of Tebet Ux Kumukokumu the Stylish, legendary ogre ape 8 \n", "3 7th of Iyur Ut Putus Templar warden 1 \n", "4 19th of Tishru ii Ux eyeless king crab 6 \n", "\n", " Damage Weapon PV Pos Dam Score Turns Zones \\\n", "0 0 lase beam 0 0 48753 35235 260 \n", "1 2 crab claw 7 1d2 40178 37145 222 \n", "2 51 ape fist 20 3d3 20556 21114 130 \n", "3 3 folded carbide long sword 9 2d5 17061 17066 118 \n", "4 20 massive king crab claw 20 1d6 16607 16124 115 \n", "\n", " Storied Items Artifact \n", "0 1 HE Missile \n", "1 1 Fix-It spray foam x2 \n", "2 0 force bracelet 0 0 <> [no cell] \n", "3 0 electrobow <> ->10 1d6 [no cell] \n", "4 0 ubernostrum injector <> " ] }, "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", "%pylab inline\n", "\n", "col_names = [\"Name\", \"End Time\", \"Game End Time\", \"Enemy\", \"x hit\", \"Damage\", \"Weapon\", \"PV\", \"Pos Dam\", \"Score\", \"Turns\", \"Zones\", \"Storied Items\", \"Artifact\"]\n", "\n", "#read in the data from the text file, setting the seperator between each column as \"\\t\". \n", "qud = pd.read_csv(\"Cleaned_Qud_HighScores_1.txt\", sep=r\"\\t+\", names = col_names, engine='python')\n", "qud.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Game summary for' 'Friday, August 21, 2015 11:25:56 PM'\n", " 'died on the 8th of Nivvun Ut' 'Warden Ualraig' 0 0 'Freezes' 0 '0' -1451\n", " 19 1 0 'no artifact']\n", "['Napoleon' 'Monday, August 03, 2015 3:23:01 PM' '18th of Tuum Ut' 'quit'\n", " 0 0 'quit' 0 '0' -1588 95 1 0 'no artifact']\n" ] }, { "data": { "text/html": [ "
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NameEnd TimeGame End TimeEnemyx hitDamageWeaponPVPos DamScoreTurnsZonesStoried ItemsArtifact
50GoetheSunday, August 09, 2015 7:43:13 PMGoethe died on the 22nd of Tuum Utboar26bite71d3-125312130no artifact
51MalenkovSunday, August 02, 2015 1:34:01 PM9th of Tishru ii Uxtraipsing mortar00explosion00-131813050no artifact
52Khrushchev IIISunday, August 02, 2015 4:19:46 PM1st of Nivvun Utscalding steam00scalding steam00-135132440no artifact
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" ], "text/plain": [ " Name End Time \\\n", "50 Goethe Sunday, August 09, 2015 7:43:13 PM \n", "51 Malenkov Sunday, August 02, 2015 1:34:01 PM \n", "52 Khrushchev III Sunday, August 02, 2015 4:19:46 PM \n", "\n", " Game End Time Enemy x hit Damage \\\n", "50 Goethe died on the 22nd of Tuum Ut boar 2 6 \n", "51 9th of Tishru ii Ux traipsing mortar 0 0 \n", "52 1st of Nivvun Ut scalding steam 0 0 \n", "\n", " Weapon PV Pos Dam Score Turns Zones Storied Items \\\n", "50 bite 7 1d3 -1253 121 3 0 \n", "51 explosion 0 0 -1318 130 5 0 \n", "52 scalding steam 0 0 -1351 324 4 0 \n", "\n", " Artifact \n", "50 no artifact \n", "51 no artifact \n", "52 no artifact " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Dropping these two values\n", "print qud.iloc[53].values #Forgot to name my character. Deceided to quit by attacking either Mehmet or Warden Ualraig\n", "print qud.iloc[54].values #Took one step and Ualraig wasted Mehmet. Walked around for a while but quit as I could not longer start the \"What's Eating The Watervine? mission\n", "\n", "#As these are my two lowest scores I can the set the dataframe to be rows 0 to 53 (does not include 53)\n", "qud = qud[0:53]\n", "qud.tail(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Further Cleaning\n", "\n", "With the data now pulled into a dataframe there is still a small bit of cleaning to do. Below are three functions to convert the End Time of each game to a datetime, to pull the game month from the Game End Time (I left the day in as at a later time I might check am I more likely to die early or late in the month) and to clean up the Artifact column" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import re\n", "from datetime import datetime\n", "from time import strptime\n", "\n", "def convert_to_date(date_in):\n", " date_search = re.search(\"(\\w{6,9}),\\s*(\\w{3,9})\\s*(\\d{2}),\\s*(\\d{4})\\s*(\\d{1,2}):(\\d{2}):(\\d{2})\\s*(\\w{2})\", date_in)\n", " #date_search.group(1) = Day as word(ie Sunday), 2 = Month as word (ie August), 3 = day of month, 4 = year, 5 = hour, 6 = minute, 7 = second, 8 = AM or PM\n", " \n", " #In End Time hour is expressed from 1 to 12, ie 1 AM or 1 PM. The below code converts that to 0 to 23, ie 1 or 13\n", " hour = int(date_search.group(5))\n", "\n", " if date_search.group(8) == \"PM\":\n", " if hour == 12:\n", " pass\n", " else:\n", " hour += 12\n", " \n", " if date_search.group(8) == \"AM\":\n", " if hour == 12:\n", " hour = 0\n", " \n", " \n", " #Create a datetime. strptime is used to take the first 3 letters of the Month as word and get the int value for that month, ie August = Aug, is month 8 of 12\n", " new_date = datetime(int(date_search.group(4)), strptime(date_search.group(2)[:3], \"%b\").tm_mon, int(date_search.group(3)), hour, int(date_search.group(6)), int(date_search.group(7)))\n", " \n", " return new_date\n", "\n", "qud[\"End Time\"] = qud[\"End Time\"].apply(convert_to_date)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "#Pull the month out of Game Time\n", "\n", "def convert_game_month(date_in):\n", " date_search = re.search(\"of\\s*((\\w*\\s*)*)\", date_in)\n", " return date_search.group(1)\n", "\n", "qud[\"Game End Time\"] = qud[\"Game End Time\"].apply(convert_game_month)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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NameEnd TimeGame End TimeEnemyx hitDamageWeaponPVPos DamScoreTurnsZonesStoried ItemsArtifact
0Goethe II2015-08-13 18:04:58Uru UxWahmahcalcalit00lase beam0048753352352601HE Missile
1Kant XVIII2015-08-30 19:34:00Tuum Utchute crab12crab claw71d240178371452221Fix-It spray foam
2O'Brien III2015-09-02 03:50:10Tebet UxKumukokumu the Stylish, legendary ogre ape851ape fist203d320556211141300force bracelet
3Kant XII2015-08-28 23:14:48Iyur UtPutus Templar warden13folded carbide long sword92d517061170661180electrobow
4Nietzsche III2015-08-05 20:00:46Tishru ii Uxeyeless king crab620massive king crab claw201d616607161241150ubernostrum injector
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" ], "text/plain": [ " Name End Time Game End Time \\\n", "0 Goethe II 2015-08-13 18:04:58 Uru Ux \n", "1 Kant XVIII 2015-08-30 19:34:00 Tuum Ut \n", "2 O'Brien III 2015-09-02 03:50:10 Tebet Ux \n", "3 Kant XII 2015-08-28 23:14:48 Iyur Ut \n", "4 Nietzsche III 2015-08-05 20:00:46 Tishru ii Ux \n", "\n", " Enemy x hit Damage \\\n", "0 Wahmahcalcalit 0 0 \n", "1 chute crab 1 2 \n", "2 Kumukokumu the Stylish, legendary ogre ape 8 51 \n", "3 Putus Templar warden 1 3 \n", "4 eyeless king crab 6 20 \n", "\n", " Weapon PV Pos Dam Score Turns Zones Storied Items \\\n", "0 lase beam 0 0 48753 35235 260 1 \n", "1 crab claw 7 1d2 40178 37145 222 1 \n", "2 ape fist 20 3d3 20556 21114 130 0 \n", "3 folded carbide long sword 9 2d5 17061 17066 118 0 \n", "4 massive king crab claw 20 1d6 16607 16124 115 0 \n", "\n", " Artifact \n", "0 HE Missile \n", "1 Fix-It spray foam \n", "2 force bracelet \n", "3 electrobow \n", "4 ubernostrum injector " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def clean_artifacts(artifact):\n", " x_search = re.search(\"(x\\d+)\", artifact) #remove multipliers like \"x2\"\n", " if x_search != None:\n", " artifact = artifact.replace(x_search.group(0), \"\").strip()\n", " \n", " mul_search = re.search(\"((-?\\d+\\s*\\d+d\\d+)+)\", artifact) #removes pv and possible weapon damage like \"2 1d3\"\n", " if mul_search != None:\n", " artifact = artifact.replace(mul_search.group(0), \"\").strip()\n", " \n", " artifact = artifact.replace(\"->\", \"\").replace(\"<>\", \"\").strip() #removes -> and <> which should be empty from previous cleaning\n", " \n", " cell_search = re.search(\"(\\[(\\w*\\s*)*\\])\", artifact) #removes [no cell], [shotgun shell] etc\n", " if cell_search != None:\n", " artifact = artifact.replace(cell_search.group(0), \"\").strip()\n", " \n", " digit_search = re.search(\"((\\d+\\s*)+)\", artifact) #removes any remaining digits such as av dv ie 2 4\n", " if digit_search != None:\n", " artifact = artifact.replace(digit_search.group(0), \"\").strip()\n", " \n", " return artifact\n", "\n", "qud[\"Artifact\"] = qud[\"Artifact\"].apply(clean_artifacts)\n", "qud.head() #print new, clean dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Sorting by date and by score\n", "\n", "With the End Time now cleaned up and converted to a datetime the entire dataframe can be sorted on this column, giving the dataframe in order of my earliest game to my most recent game. I can then print off my highscore progression and after sorting the dataframe by score I can print my 5 highest scores. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Highscore Progression\n", "1 Stalin 2015-08-01 14:04:38 -1131\n", "2 Stalin 2015-08-01 15:28:05 -71\n", "4 Lenin 2015-08-01 16:08:30 902\n", "16 Khrushchev VIII 2015-08-02 18:19:47 1760\n", "27 Nietzsche III 2015-08-05 20:00:46 16607\n", "29 Goethe II 2015-08-13 18:04:58 48753\n", "\n", "\n", "Highest Scores\n", "Goethe II 2015-08-13 18:04:58 48753\n", "Kant XVIII 2015-08-30 19:34:00 40178\n", "O'Brien III 2015-09-02 03:50:10 20556\n", "Kant XII 2015-08-28 23:14:48 17061\n", "Nietzsche III 2015-08-05 20:00:46 16607\n" ] } ], "source": [ "sorted_qud = qud.sort([\"End Time\"]).reset_index(drop = True) #Sort by End Time, reset the index and drop the old index\n", "highscore = -10000\n", "print \"Highscore Progression\" #Game Number, Name, Date, Score\n", "for score in sorted_qud[\"Score\"]:\n", " if int(score) > highscore:\n", " highscore = int(score)\n", " print \"%d %s %s %d\" % (\n", " int(sorted_qud.index[sorted_qud[\"Score\"] == score][0])+ 1, #the index value of the game + 1. My first game is at index 0 so add 1 and this becomes game 1\n", " sorted_qud[\"Name\"][sorted_qud[\"Score\"] == score].tolist()[0], #Character's name\n", " sorted_qud[\"End Time\"][sorted_qud[\"Score\"] == score].tolist()[0], #End Time of game\n", " int(score) #Score\n", " )\n", "print \"\\n\" \n", "print \"Highest Scores\"\n", "sorted_scores = qud.sort([\"Score\"], ascending = False).reset_index(drop = True) #sort by score\n", "for i in range(5):\n", " print sorted_scores[\"Name\"].iloc[i], sorted_scores[\"End Time\"].iloc[i], sorted_scores[\"Score\"].iloc[i] #print Name, End Time and Score for first 5 rows" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Ploting my score data\n", "\n", "There are now a number of plots I can build using the data I have pulled down. Two simple scatter plots can be made, one containing points for my score and the number of turns taken in each game and another for my score and the number of zones visited. Using the sklearn library I can plot a linear line to each plot and also use this to predict the score of my current game.\n", "\n", "Using the dataframe sorted on date I can plot a bar for the score in each game and then plot a 5 game simple moving average. Red vertical lines are added for patch updates to see if my score is affected by these. The lines represent the 4th, 8th and 15/21st of August (I didn't play any games between the 15th and 21st) and I remember it took me a while to get to grips with the game after the 21st of August patch. I blame Ctesiphus.\n", "\n", "The final plot is a histogram of my highscores. Not impressive." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TcmF5SQdIly5w5JHQsGHRkakeiwhSSvWiHck8WZKkSixYALffDj175kaIiy7K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\ntN6OvyvxOnuQzv5n8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn import linear_model\n", "\n", "fig = plt.figure(figsize = (20,10))\n", "p1 = fig.add_subplot(221) # 2x2, plot 1 (top left)\n", "p2 = fig.add_subplot(222) # 2x2, plot 2 (top right)\n", "p3 = fig.add_subplot(223) # 2x2, plot 3 (bottom left)\n", "p4 = fig.add_subplot(224) # 2x2, plot 4 (bottom right)\n", "\n", "#Turns to Score\n", "p1.scatter(qud[\"Turns\"], qud[\"Score\"], color=\"green\") #Turns on x axis, score on y axis, color green (this is Qud after all)\n", "\n", "X = np.array(qud[\"Turns\"]).reshape(len(qud),1) #variable X is an np.array of the turns, len(qud) rows, 1 column\n", "y= np.array(qud[\"Score\"]).reshape(len(qud),1) #variable y is an np.array of the scores, len(qud) rows, 1 column\n", "\n", "turns_score = linear_model.LinearRegression()\n", "turns_score.fit(X, y) #fit turns and score using linear regression\n", "\n", "#plot a line with turns on the x axis and predicted score for that many turns from the linear regression model on the y axis\n", "p1.plot(qud[\"Turns\"], turns_score.predict(X), color=\"red\") \n", "p1.set_title(\"Score per Turn\")\n", "p1.set_xlabel(\"Turns\")\n", "p1.set_ylabel(\"Score\")\n", "p1.axis('tight')\n", "\n", "#Zones to Score\n", "p2.scatter(qud[\"Zones\"], qud[\"Score\"], color=\"green\")\n", "X= np.array(qud[\"Zones\"]).reshape(len(qud),1) #Update X to be an np.array of zones, y stays as score above\n", "\n", "zones_score = linear_model.LinearRegression()\n", "zones_score.fit(X, y) #fit zones to score\n", "\n", "#plot a line with zones on the x axis and predicted score for that many zones from the linear regression model on the x axis\n", "p2.plot(qud[\"Zones\"], zones_score.predict(X), color=\"red\")\n", "p2.set_title(\"Score per Zone\")\n", "p2.set_xlabel(\"Zones\")\n", "p2.set_ylabel(\"Score\")\n", "p2.axis('tight')\n", "\n", "#using the sorted by date dataframe plot a bar chart of the scores. sorted_qud.index.values starts at 0, not 1\n", "p3.bar(sorted_qud.index.values, sorted_qud[\"Score\"], color=\"green\")\n", "p3.plot(pd.rolling_mean(sorted_qud[\"Score\"].values, window=5, min_periods=1), color=\"red\", linewidth=2) #plot a 5 game simple moving average\n", "\n", "p3.set_title(\"5 Game Moving Average\")\n", "p3.set_xlabel(\"Game (Vertical lines represent patches: Aug 4, Aug 8, Aug 15/21)\")\n", "p3.set_ylabel(\"Score\")\n", "p3.axis('tight')\n", "#These numbers are plotted manually from looking at the dataframe and seeing when was the first game I played on/after each patch release\n", "p3.axvline(24, color = \"red\", linewidth = 2) #first game on/after Aug 4th\n", "p3.axvline(27, color = \"red\", linewidth = 2) #first game on/after Aug 8th\n", "p3.axvline(29, color = \"red\", linewidth = 2) #first game on/after Aug 15th and 21st\n", "\n", "#Histogram. Depressing\n", "p4.hist(qud[\"Score\"], bins = 50);\n", "p4.axis('tight')\n", "p4.set_title(\"Score Frequency\")\n", "p4.set_xlabel(\"Score (50 bins)\")\n", "p4.set_ylabel(\"Frequency\")\n", "\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the linear regression models I can now get the coefficient, intercept and root mean square error for the score per turn line and the score per zone line. Also, as the number of turns is displayed when a game is saved I can calculate how many points my current save game is worth. However, I would not expect this figure to be very accurate due to the small number of points available and I'm not willing to have my character die to check how right the figure is!\n", "\n", "In linear regression y = a + Xb where a is the intercept and b is the coefficient. So using the below data:\n", "\n", "score = -2171.04135919 + Turns(1.21948537419)\n", "\n", "The root mean square error is found by taking the root of the mean_squared_error of the score compared to the predicted score\n", "\n", "With a bit of moving around of figures I get to equation turns = (score + intercept)/coefficient which allows me to predict the number of turns needed for 100,000 points and 1,000,000 points. I have a bit of work to do yet! (I expect these numbers to change substantially as the model gets more data points in the high game points range, over half the data points are in the minus range at the moment)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For Score Per Turn\n", "Total turns multiplied by the coefficient plus the intercept = my score\n", "Coefficient: 1.21948537419\n", "Intercept: -2171.04135919\n", "RMSE: 1532.50022362\n", "Predicted score from my current game (59924 turns): 70905\n", "Turns needed for 100,000 points: 83783\n", "Turns needed for 1,000,000 points: 821799\n" ] } ], "source": [ "from sklearn.metrics import mean_squared_error\n", "from math import sqrt\n", "print \"For Score Per Turn\"\n", "print \"Total turns multiplied by the coefficient plus the intercept = my score\"\n", "print \"Coefficient: \", turns_score.coef_[0][0]\n", "print \"Intercept: \", turns_score.intercept_[0]\n", "print \"RMSE: \", sqrt(mean_squared_error(y, turns_score.predict(np.array(qud[\"Turns\"]).reshape(len(qud),1))))\n", "print \"Predicted score from my current game (59924 turns): \", int(turns_score.predict(59924)[0][0])\n", "print \"Turns needed for 100,000 points: \", int(math.ceil(((100000 + abs(turns_score.intercept_))/turns_score.coef_)[0][0]))\n", "print \"Turns needed for 1,000,000 points: \", int(math.ceil(((1000000 + abs(turns_score.intercept_))/turns_score.coef_)[0][0]))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For Score Per Zone\n", "Total zones visited multiplied by the coefficient plus the intercept = my score\n", "Coefficient: 187.832983072\n", "Intercept -2433.84828011\n", "RMSE: 1141.29924083\n" ] } ], "source": [ "print \"For Score Per Zone\"\n", "print \"Total zones visited multiplied by the coefficient plus the intercept = my score\"\n", "print \"Coefficient: \", zones_score.coef_[0][0]\n", "print \"Intercept \", zones_score.intercept_[0]\n", "print \"RMSE: \", sqrt(mean_squared_error(y, zones_score.predict(np.array(qud[\"Zones\"]).reshape(len(qud),1))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#A look at some of the remaining data\n", "\n", "I took in a lot of data into the dataframe but have only looked at End Time, score, zone and turns. As time goes on and I get more entries I may be able to do more with the following bits of data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Iyur Ut' 'Kisu Ux' 'Nivvun Ut' 'Shwut Ux' 'Simmun Ut' 'Tebet Ux'\n", " 'Tishru i Ux' 'Tishru ii Ux' 'Tuum Ut' 'Ubu Ut' 'Uru Ux' 'Uulu Ut']\n", "12\n" ] } ], "source": [ "#Each month mentioned in the Game End Time\n", "game_months = qud[\"Game End Time\"]\n", "print np.unique(game_months)\n", "print len(np.unique(game_months))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Game End Time\n", "Tuum Ut 8\n", "Uru Ux 7\n", "Nivvun Ut 7\n", "Tishru i Ux 6\n", "Uulu Ut 5\n", "Tishru ii Ux 5\n", "Iyur Ut 4\n", "Shwut Ux 3\n", "Kisu Ux 3\n", "Ubu Ut 2\n", "Tebet Ux 2\n", "Simmun Ut 1\n", "Name: Game End Time, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Use groupby to find most mentioned month, ie the month I have died most in. Nivvun Ut is the very first month...\n", "qud['Game End Time'].groupby(qud['Game End Time']).count().order(ascending = False)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Artifact\n", "no artifact 37\n", "Fix-It spray foam 3\n", "semi-automatic pistol 2\n", "acid gas grenade mk I 2\n", "ubernostrum injector 1\n", "stun gas grenade mk I 1\n", "pump shotgun 1\n", "poison gas grenade mk I 1\n", "force bracelet 1\n", "electrobow 1\n", "compass bracelet 1\n", "blaze injector 1\n", "HE Missile 1\n", "Name: Artifact, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Use group by to find the most advanced artifact I held when I died. Lots of no artifacts and lots of artifacts awarded for finishing the first 2 missions in Joppa\n", "qud['Artifact'].groupby(qud['Artifact']).count().order(ascending = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I have done slightly more work with the Enemy column. First I print it off as is but then make some changes. \n", "Deaths from bleeding were always the result of young ivory. In my early games I took hemophilia as a defect which basicly meant instant death at low toughness levels and no bandages when I stared bleeding, so \"bleeding\" and \"young ivory\" are combined into a single group.\n", "\n", "Deaths from scalding steam were always the result of fire ants setting the water around me on fire and I, like an idiot, taking a step forward. Only once was there another reason...I set the water on fire with my Flaming Hands and walked into it. Since then I have only used Freezing Hands.\n", "\n", "All snapjaws, including the two faction leaders, are put into a single catagory.\n", "\n", "\"Wahmahcalcalit\", \"Umchuum\", \"Duhmahcaluhcal\" are evidence my character was suffering confusion when he died, these were 3 wizard faction leaders.\n", "\n", "I could do further grouping. Chute crab and king crab could be combined, the two named Snapjaws could be put in with the ogre instead to form a Named or Faction Leader catagory, dawnglider could be added to the fire ant/scalding steam catagory etc." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Enemy\n", "bleeding 9\n", "unknown 8\n", "salthopper 4\n", "scalding steam 3\n", "equimax 2\n", "snapjaw scavenger 2\n", "jilted lover 2\n", "Wahmahcalcalit 1\n", "chute crab 1\n", "cave spider 1\n", "boar 1\n", "young ivory 1\n", "dawnglider 1\n", "Ruf-ohoubub, the stalwart Snapjaw Bear-baiter 1\n", "Putus Templar warden 1\n", "Kumukokumu the Stylish, legendary ogre ape 1\n", "Groubuubu-wof-wofuz, the stalwart Snapjaw Tot-eater 1\n", "Umchuum 1\n", "eyeless king crab 1\n", "explosion 1\n", "fire ant 1\n", "giant amoeba 1\n", "girshling 1\n", "horned chameleon 1\n", "napjaw scavenger 1\n", "salamander 1\n", "scrap shoveler 1\n", "snapjaw hunter 1\n", "traipsing mortar 1\n", "Duhmahcaluhcal 1\n", "Name: Enemy, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qud['Enemy'].groupby(qud['Enemy']).count().order(ascending = False)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Name\n", "young ivory/bleeding 10\n", "unknown 8\n", "snapjaw 6\n", "salthopper 4\n", "fire ant/scalding steam 4\n", "wizard 3\n", "jilted lover 2\n", "equimax 2\n", "explosion 1\n", "Putus Templar warden 1\n", "boar 1\n", "cave spider 1\n", "chute crab 1\n", "dawnglider 1\n", "giant amoeba 1\n", "eyeless king crab 1\n", "girshling 1\n", "horned chameleon 1\n", "salamander 1\n", "scrap shoveler 1\n", "traipsing mortar 1\n", "Kumukokumu the Stylish, legendary ogre ape 1\n", "Name: Name, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#create a list called enemies, add new values to it, convert to a dataframe and groupby name\n", "enemies = qud[\"Enemy\"].tolist()\n", "for i in range(len(enemies)):\n", " name = enemies[i].strip()\n", " if name in [\"Wahmahcalcalit\", \"Umchuum\", \"Duhmahcaluhcal\"]:\n", " enemies[i] = \"wizard\"\n", " if name in [\"snapjaw scavenger\", \"napjaw scavenger\", \"snapjaw hunter\", \"Groubuubu-wof-wofuz, the stalwart Snapjaw Tot-eater\", \"Ruf-ohoubub, the stalwart Snapjaw Bear-baiter\"]:\n", " enemies[i] = \"snapjaw\"\n", " if name in [\"young ivory\", \"bleeding\"]:\n", " enemies[i] = \"young ivory/bleeding\" \n", " if name in [\"scalding steam\", \"fire ant\"]:\n", " enemies[i] = \"fire ant/scalding steam\"\n", "\n", "enemy_df = pd.DataFrame(enemies, columns=[\"Name\"])\n", "enemy_df['Name'].groupby(enemy_df['Name']).count().order(ascending = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are a number of highscores that have an empty line where the death description should be and these are marked unknown. Also, others will just read \"died from bleeding\". Here bleeding is added as both the enemy name and the weapon.\n", "\n", "Below is a look at the weapons that have killed my characters. Rending mandibles. Giant pseudopod. The things I've seen." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Weapon\n", "bleeding 9\n", "unknown 8\n", "rending mandibles 4\n", "bite 4\n", "explosion 3\n", "scalding steam 3\n", "lase beam 2\n", "bronze two-handed sword 2\n", "thorns 2\n", "Umumerchacal 1\n", "ape fist 1\n", "carbide battle axe 1\n", "claw 1\n", "crab claw 1\n", "flames 1\n", "fangs 1\n", "fire 1\n", "folded carbide long sword 1\n", "giant pseudopod 1\n", "impalement 1\n", "iron dagger 1\n", "massive king crab claw 1\n", "scrap shovel 1\n", "steel battle axe 1\n", "Tusks 1\n", "Name: Weapon, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qud['Weapon'].groupby(qud['Weapon']).count().order(ascending = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is the complete dataframe sorted by date" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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NameEnd TimeGame End TimeEnemyx hitDamageWeaponPVPos DamScoreTurnsZonesStoried ItemsArtifact
0Stalin2015-08-01 14:04:38Nivvun UtUmchuum24Umumerchacal00-113136230poison gas grenade mk I
1Stalin2015-08-01 15:28:05Uru Uxsalthopper39rending mandibles111d4-713412300no artifact
2Stalin2015-08-01 15:41:26Tishru i Uxexplosion00explosion00-224106180no artifact
3Lenin2015-08-01 16:08:30Iyur Utequimax39bite82d29021019120no artifact
4Malenkov2015-08-01 16:18:54Tishru i UxRuf-ohoubub, the stalwart Snapjaw Bear-baiter210bronze two-handed sword41d8-115568780no artifact
5Malenkov2015-08-01 16:35:17Tuum Utnapjaw scavenger00explosion00-1004911120no artifact
6Malenkov2015-08-02 13:34:01Tishru ii Uxtraipsing mortar00explosion00-131813050no artifact
7Khrushchev2015-08-02 14:13:37Uru Uxbleeding00bleeding00-5311863170no artifact
8Khrushchev2015-08-02 15:52:52Tishru ii UxGroubuubu-wof-wofuz, the stalwart Snapjaw Tot-...211carbide battle axe52d37082789210no artifact
9Khrushchev II2015-08-02 16:13:32Uru Uxbleeding00bleeding0013164090no artifact
10Khrushchev III2015-08-02 16:19:46Nivvun Utscalding steam00scalding steam00-135132440no artifact
11Khrushchev IV2015-08-02 16:35:26Nivvun Utsnapjaw scavenger11iron dagger21d4-9361134120no artifact
12Khrushchev V2015-08-02 16:45:07Uulu Utsalthopper410rending mandibles101d4-91851060no artifact
13Khrushchev VI2015-08-02 17:12:49Tuum Utfire ant00flames00-1261705210no artifact
14Khrushchev VII2015-08-02 17:17:56Uru Uxsalamander13bite31d3-112740350no artifact
15Khrushchev VIII2015-08-02 18:19:47Tuum Utbleeding00bleeding0017604198250no artifact
16Khrushchev IX2015-08-03 13:56:40Tishru i Uxbleeding00bleeding00-114333640no artifact
17Khrushchev X2015-08-03 13:59:50Tishru ii Uxunknown00unknown00-120928740no artifact
18Khrushchev XI2015-08-03 15:18:32Kisu Uxunknown00unknown0011364121210blaze injector
19Napoleon2015-08-03 15:41:37Uulu Utbleeding00bleeding00-5471536110no artifact
20Napoleon II2015-08-03 15:57:39Tishru i Uxbleeding00bleeding00-5901298110no artifact
21Napolen III2015-08-03 16:20:16Shwut Uxdawnglider00fire00-5431773160no artifact
22Napoleon IV2015-08-03 16:28:33Uulu Utsalthopper25rending mandibles101d4-117127540no artifact
23Napoleon V2015-08-03 16:31:59Ubu Utbleeding00bleeding00-117532030no artifact
24Nietzsche2015-08-04 20:18:00Tuum Uthorned chameleon14Tusks42d315033719270no artifact
25Nietzsche II2015-08-04 20:25:57Simmun Utjilted lover23thorns51d4-124613630no artifact
26Nietzsche III2015-08-05 20:00:46Tishru ii Uxeyeless king crab620massive king crab claw201d616607161241150ubernostrum injector
27Goethe2015-08-09 19:43:13Tuum Utboar26bite71d3-125312130no artifact
28Goethe II2015-08-13 18:04:58Uru UxWahmahcalcalit00lase beam0048753352352601HE Missile
29Kant2015-08-21 23:08:22Tuum Utgiant amoeba24giant pseudopod101d323723013210no artifact
30Kant II2015-08-21 23:14:45Ubu Utsnapjaw hunter316bronze two-handed sword41d8-121414540no artifact
31Kant II2015-08-21 23:23:56Shwut Uxscrap shoveler58scrap shovel151d2-98672060no artifact
32Kant III2015-08-21 23:25:01Kisu Uxbleeding00bleeding00-125210530no artifact
33Kant IV2015-08-22 00:00:38Iyur Utsnapjaw scavenger16steel battle axe31d613933347160acid gas grenade mk I
34Kant V2015-08-22 18:22:36Tishru i Uxunknown00unknown0066628118470semi-automatic pistol
35Kant VI2015-08-27 16:36:27Shwut Uxyoung ivory00impalement00-91161370no artifact
36Kant VII2015-08-27 16:50:50Uulu Utunknown00unknown00-24377080no artifact
37Kant VIII2015-08-27 20:01:17Tishru i Uxscalding steam00scalding steam0078487719350Fix-It spray foam
38Kant IX2015-08-27 20:51:15Iyur Utjilted lover12thorns51d4-801807130acid gas grenade mk I
39Kant X2015-08-27 21:17:34Kisu Uxcave spider12fangs21d22652287160no artifact
40Kant XI2015-08-27 23:14:09Uru Uxscalding steam00scalding steam00997111105600Fix-It spray foam
41Kant XII2015-08-28 23:14:48Iyur UtPutus Templar warden13folded carbide long sword92d517061170661180electrobow
42Kant XIII2015-08-29 00:05:24Tebet Uxgirshling16claw21d618492853180pump shotgun
43Kant XIV2015-08-29 00:23:09Tuum Utbleeding00bleeding00-3672230140no artifact
44Kant XV2015-08-29 00:42:44Nivvun Utunknown00unknown00-4341332100no artifact
45Kant XVI2015-08-29 00:46:03Uulu Utequimax25bite92d2-112040150no artifact
46Kant XVII2015-08-29 01:44:21Tishru ii Uxunknown00unknown0031697239370compass bracelet
47Kant XVIII2015-08-30 19:34:00Tuum Utchute crab12crab claw71d240178371452221Fix-It spray foam
48O'Brien2015-09-02 00:03:29Nivvun Utunknown00unknown00-117428750no artifact
49O'Brien II2015-09-02 00:53:32Nivvun UtDuhmahcaluhcal00lase beam0054716356420stun gas grenade mk I
50O'Brien III2015-09-02 03:50:10Tebet UxKumukokumu the Stylish, legendary ogre ape851ape fist203d320556211141300force bracelet
51O'Brien IV2015-09-02 12:47:21Uru Uxsalthopper37rending mandibles111d41991810140semi-automatic pistol
52O'Brien V2015-09-02 12:58:51Nivvun Utunknown00unknown00-6181538140no artifact
\n", "
" ], "text/plain": [ " Name End Time Game End Time \\\n", "0 Stalin 2015-08-01 14:04:38 Nivvun Ut \n", "1 Stalin 2015-08-01 15:28:05 Uru Ux \n", "2 Stalin 2015-08-01 15:41:26 Tishru i Ux \n", "3 Lenin 2015-08-01 16:08:30 Iyur Ut \n", "4 Malenkov 2015-08-01 16:18:54 Tishru i Ux \n", "5 Malenkov 2015-08-01 16:35:17 Tuum Ut \n", "6 Malenkov 2015-08-02 13:34:01 Tishru ii Ux \n", "7 Khrushchev 2015-08-02 14:13:37 Uru Ux \n", "8 Khrushchev 2015-08-02 15:52:52 Tishru ii Ux \n", "9 Khrushchev II 2015-08-02 16:13:32 Uru Ux \n", "10 Khrushchev III 2015-08-02 16:19:46 Nivvun Ut \n", "11 Khrushchev IV 2015-08-02 16:35:26 Nivvun Ut \n", "12 Khrushchev V 2015-08-02 16:45:07 Uulu Ut \n", "13 Khrushchev VI 2015-08-02 17:12:49 Tuum Ut \n", "14 Khrushchev VII 2015-08-02 17:17:56 Uru Ux \n", "15 Khrushchev VIII 2015-08-02 18:19:47 Tuum Ut \n", "16 Khrushchev IX 2015-08-03 13:56:40 Tishru i Ux \n", "17 Khrushchev X 2015-08-03 13:59:50 Tishru ii Ux \n", "18 Khrushchev XI 2015-08-03 15:18:32 Kisu Ux \n", "19 Napoleon 2015-08-03 15:41:37 Uulu Ut \n", "20 Napoleon II 2015-08-03 15:57:39 Tishru i Ux \n", "21 Napolen III 2015-08-03 16:20:16 Shwut Ux \n", "22 Napoleon IV 2015-08-03 16:28:33 Uulu Ut \n", "23 Napoleon V 2015-08-03 16:31:59 Ubu Ut \n", "24 Nietzsche 2015-08-04 20:18:00 Tuum Ut \n", "25 Nietzsche II 2015-08-04 20:25:57 Simmun Ut \n", "26 Nietzsche III 2015-08-05 20:00:46 Tishru ii Ux \n", "27 Goethe 2015-08-09 19:43:13 Tuum Ut \n", "28 Goethe II 2015-08-13 18:04:58 Uru Ux \n", "29 Kant 2015-08-21 23:08:22 Tuum Ut \n", "30 Kant II 2015-08-21 23:14:45 Ubu Ut \n", "31 Kant II 2015-08-21 23:23:56 Shwut Ux \n", "32 Kant III 2015-08-21 23:25:01 Kisu Ux \n", "33 Kant IV 2015-08-22 00:00:38 Iyur Ut \n", "34 Kant V 2015-08-22 18:22:36 Tishru i Ux \n", "35 Kant VI 2015-08-27 16:36:27 Shwut Ux \n", "36 Kant VII 2015-08-27 16:50:50 Uulu Ut \n", "37 Kant VIII 2015-08-27 20:01:17 Tishru i Ux \n", "38 Kant IX 2015-08-27 20:51:15 Iyur Ut \n", "39 Kant X 2015-08-27 21:17:34 Kisu Ux \n", "40 Kant XI 2015-08-27 23:14:09 Uru Ux \n", "41 Kant XII 2015-08-28 23:14:48 Iyur Ut \n", "42 Kant XIII 2015-08-29 00:05:24 Tebet Ux \n", "43 Kant XIV 2015-08-29 00:23:09 Tuum Ut \n", "44 Kant XV 2015-08-29 00:42:44 Nivvun Ut \n", "45 Kant XVI 2015-08-29 00:46:03 Uulu Ut \n", "46 Kant XVII 2015-08-29 01:44:21 Tishru ii Ux \n", "47 Kant XVIII 2015-08-30 19:34:00 Tuum Ut \n", "48 O'Brien 2015-09-02 00:03:29 Nivvun Ut \n", "49 O'Brien II 2015-09-02 00:53:32 Nivvun Ut \n", "50 O'Brien III 2015-09-02 03:50:10 Tebet Ux \n", "51 O'Brien IV 2015-09-02 12:47:21 Uru Ux \n", "52 O'Brien V 2015-09-02 12:58:51 Nivvun Ut \n", "\n", " Enemy x hit Damage \\\n", "0 Umchuum 2 4 \n", "1 salthopper 3 9 \n", "2 explosion 0 0 \n", "3 equimax 3 9 \n", "4 Ruf-ohoubub, the stalwart Snapjaw Bear-baiter 2 10 \n", "5 napjaw scavenger 0 0 \n", "6 traipsing mortar 0 0 \n", "7 bleeding 0 0 \n", "8 Groubuubu-wof-wofuz, the stalwart Snapjaw Tot-... 2 11 \n", "9 bleeding 0 0 \n", "10 scalding steam 0 0 \n", "11 snapjaw scavenger 1 1 \n", "12 salthopper 4 10 \n", "13 fire ant 0 0 \n", "14 salamander 1 3 \n", "15 bleeding 0 0 \n", "16 bleeding 0 0 \n", "17 unknown 0 0 \n", "18 unknown 0 0 \n", "19 bleeding 0 0 \n", "20 bleeding 0 0 \n", "21 dawnglider 0 0 \n", "22 salthopper 2 5 \n", "23 bleeding 0 0 \n", "24 horned chameleon 1 4 \n", "25 jilted lover 2 3 \n", "26 eyeless king crab 6 20 \n", "27 boar 2 6 \n", "28 Wahmahcalcalit 0 0 \n", "29 giant amoeba 2 4 \n", "30 snapjaw hunter 3 16 \n", "31 scrap shoveler 5 8 \n", "32 bleeding 0 0 \n", "33 snapjaw scavenger 1 6 \n", "34 unknown 0 0 \n", "35 young ivory 0 0 \n", "36 unknown 0 0 \n", "37 scalding steam 0 0 \n", "38 jilted lover 1 2 \n", "39 cave spider 1 2 \n", "40 scalding steam 0 0 \n", "41 Putus Templar warden 1 3 \n", "42 girshling 1 6 \n", "43 bleeding 0 0 \n", "44 unknown 0 0 \n", "45 equimax 2 5 \n", "46 unknown 0 0 \n", "47 chute crab 1 2 \n", "48 unknown 0 0 \n", "49 Duhmahcaluhcal 0 0 \n", "50 Kumukokumu the Stylish, legendary ogre ape 8 51 \n", "51 salthopper 3 7 \n", "52 unknown 0 0 \n", "\n", " Weapon PV Pos Dam Score Turns Zones Storied Items \\\n", "0 Umumerchacal 0 0 -1131 362 3 0 \n", "1 rending mandibles 11 1d4 -71 3412 30 0 \n", "2 explosion 0 0 -224 1061 8 0 \n", "3 bite 8 2d2 902 1019 12 0 \n", "4 bronze two-handed sword 4 1d8 -1155 687 8 0 \n", "5 explosion 0 0 -1004 911 12 0 \n", "6 explosion 0 0 -1318 130 5 0 \n", "7 bleeding 0 0 -531 1863 17 0 \n", "8 carbide battle axe 5 2d3 708 2789 21 0 \n", "9 bleeding 0 0 13 1640 9 0 \n", "10 scalding steam 0 0 -1351 324 4 0 \n", "11 iron dagger 2 1d4 -936 1134 12 0 \n", "12 rending mandibles 10 1d4 -918 510 6 0 \n", "13 flames 0 0 -126 1705 21 0 \n", "14 bite 3 1d3 -1127 403 5 0 \n", "15 bleeding 0 0 1760 4198 25 0 \n", "16 bleeding 0 0 -1143 336 4 0 \n", "17 unknown 0 0 -1209 287 4 0 \n", "18 unknown 0 0 1136 4121 21 0 \n", "19 bleeding 0 0 -547 1536 11 0 \n", "20 bleeding 0 0 -590 1298 11 0 \n", "21 fire 0 0 -543 1773 16 0 \n", "22 rending mandibles 10 1d4 -1171 275 4 0 \n", "23 bleeding 0 0 -1175 320 3 0 \n", "24 Tusks 4 2d3 1503 3719 27 0 \n", "25 thorns 5 1d4 -1246 136 3 0 \n", "26 massive king crab claw 20 1d6 16607 16124 115 0 \n", "27 bite 7 1d3 -1253 121 3 0 \n", "28 lase beam 0 0 48753 35235 260 1 \n", "29 giant pseudopod 10 1d3 2372 3013 21 0 \n", "30 bronze two-handed sword 4 1d8 -1214 145 4 0 \n", "31 scrap shovel 15 1d2 -986 720 6 0 \n", "32 bleeding 0 0 -1252 105 3 0 \n", "33 steel battle axe 3 1d6 1393 3347 16 0 \n", "34 unknown 0 0 6662 8118 47 0 \n", "35 impalement 0 0 -911 613 7 0 \n", "36 unknown 0 0 -243 770 8 0 \n", "37 scalding steam 0 0 7848 7719 35 0 \n", "38 thorns 5 1d4 -80 1807 13 0 \n", "39 fangs 2 1d2 265 2287 16 0 \n", "40 scalding steam 0 0 9971 11105 60 0 \n", "41 folded carbide long sword 9 2d5 17061 17066 118 0 \n", "42 claw 2 1d6 1849 2853 18 0 \n", "43 bleeding 0 0 -367 2230 14 0 \n", "44 unknown 0 0 -434 1332 10 0 \n", "45 bite 9 2d2 -1120 401 5 0 \n", "46 unknown 0 0 3169 7239 37 0 \n", "47 crab claw 7 1d2 40178 37145 222 1 \n", "48 unknown 0 0 -1174 287 5 0 \n", "49 lase beam 0 0 5471 6356 42 0 \n", "50 ape fist 20 3d3 20556 21114 130 0 \n", "51 rending mandibles 11 1d4 199 1810 14 0 \n", "52 unknown 0 0 -618 1538 14 0 \n", "\n", " Artifact \n", "0 poison gas grenade mk I \n", "1 no artifact \n", "2 no artifact \n", "3 no artifact \n", "4 no artifact \n", "5 no artifact \n", "6 no artifact \n", "7 no artifact \n", "8 no artifact \n", "9 no artifact \n", "10 no artifact \n", "11 no artifact \n", "12 no artifact \n", "13 no artifact \n", "14 no artifact \n", "15 no artifact \n", "16 no artifact \n", "17 no artifact \n", "18 blaze injector \n", "19 no artifact \n", "20 no artifact \n", "21 no artifact \n", "22 no artifact \n", "23 no artifact \n", "24 no artifact \n", "25 no artifact \n", "26 ubernostrum injector \n", "27 no artifact \n", "28 HE Missile \n", "29 no artifact \n", "30 no artifact \n", "31 no artifact \n", "32 no artifact \n", "33 acid gas grenade mk I \n", "34 semi-automatic pistol \n", "35 no artifact \n", "36 no artifact \n", "37 Fix-It spray foam \n", "38 acid gas grenade mk I \n", "39 no artifact \n", "40 Fix-It spray foam \n", "41 electrobow \n", "42 pump shotgun \n", "43 no artifact \n", "44 no artifact \n", "45 no artifact \n", "46 compass bracelet \n", "47 Fix-It spray foam \n", "48 no artifact \n", "49 stun gas grenade mk I \n", "50 force bracelet \n", "51 semi-automatic pistol \n", "52 no artifact " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_qud" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }