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steamMarket_dataAnalysis
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# -*- coding: utf-8 -*-
"""
Created on Thu Mar 7 22:02:10 2019
@author: Blake Porter
www.blakeporterneuro.com
This code is made available with the CC BY-NC-SA 4.0 license
https://creativecommons.org/licenses/by-nc-sa/4.0/
While the dataCollection program can work on all games, this analysis program cannot.
This analysis program is specific to Rust. It will work on Rust data from the Steam Market but it will not work with other games.
For example, a lot of the data cleaning is to catagorize specific skins for specific items in Rust. CSGO does not
have an eoka pistol and as such you may get errors.
I hope you will use this code to help you in analyzing your own data for your own purposes by using my methods and formats for data
cleaning, organizing, and analysis. I do not encourage you to just copy and paste this code as 1) it only works for Rust
and 2) it is specific to my questions.
"""
import pandas as pd
import numpy as np
import scipy.stats as sci
import pickle
import matplotlib.pyplot as plt
import matplotlib.ticker as tkr
import seaborn as sns
import ptitprince as pt
import matplotlib.dates as mdates
import matplotlib.cbook as cbook
from statsmodels.stats.multicomp import pairwise_tukeyhsd
gameID = '252490';
# open file with all names
with open(gameID+'PriceData_master_priceFill.pkl', "rb") as file: # Unpickling
data = pickle.load(file)
with open(gameID+'PriceData_3.pkl', "rb") as file: # Unpickling
combo = pickle.load(file)
with open(gameID+'PriceData_2.pkl', "rb") as file: # Unpickling
combo2 = pickle.load(file)
with open(gameID+'marketDelta.pkl', "rb") as file: # Unpickling
marketDelta = pickle.load(file)
with open(gameID+'marketPrice.pkl', "rb") as file: # Unpickling
marketPrice = pickle.load(file)
with open(gameID+'marketVol.pkl', "rb") as file: # Unpickling
marketVol = pickle.load(file)
roughConversion = 1.49;
#with open(gameID+'PriceData_backUp_manualPrices09032019.pkl', "rb") as file: # Unpickling
# data = pickle.load(file)
# convert these from datetimes and indexes to numbers
data['timeOnMarket'] = pd.to_numeric(data['timeOnMarket']);
data['maxIdx'] = pd.to_numeric(data['maxIdx']);
data['minIdx'] = pd.to_numeric(data['minIdx']);
data['tier'] = pd.to_numeric(data['tier']);
data['OG_price'] = pd.to_numeric(data['OG_price']);
data['itemName'] = data['itemName'].str.lower();
data['itemTypeGeneral'] = "";
data['tier'] = "";
data['OG_price_NZ'] = data['OG_price'] * roughConversion;
data = data[['tier','itemTypeGeneral','itemType','OG_price','OG_price_NZ','initial','timeOnMarket','priceAvg','priceSD','priceIncrease','smoothChange','maxPrice','maxIdx','minPrice','minIdx','maxMinIdxDiff','swing','volAvg','volSD','slope','rr']];
#for currName in data.loc[:,'itemName']:
#
test['OG_price'][test['itemType'].str.contains('ak47')&test['OG_price'].isnull()] = 1.99;
uniqueItems = data.itemType.unique().tolist();
for currItem in uniqueItems:
data['OG_price'][data['itemType'].str.contains(currItem)&data['OG_price'].isnull()] = np.mean(data['OG_price'][data['itemType'].str.contains(currItem)&data['OG_price'].notnull()]);
data['OG_to_avg'] = data['priceAvg'] - data['OG_price_NZ'];
data['itemTypeGeneral'][data['itemName'].str.contains('hat')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('guitar')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('box')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('trunk')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('storage')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('door')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('bag')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('furnace')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('fridge')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('locker')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('carpet')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('rug')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('bed')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('machine')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('chair')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('table')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('barricade')] = 'RP';
data['itemTypeGeneral'][data['itemName'].str.contains('hatchet')] = 'tool';
data['itemTypeGeneral'][data['itemName'].str.contains('pickaxe')] = 'tool';
data['itemTypeGeneral'][data['itemName'].str.contains('axe')] = 'tool';
data['itemTypeGeneral'][data['itemName'].str.contains('hammer')] = 'tool';
data['itemTypeGeneral'][data['itemName'].str.contains('pick')] = 'tool';
data['itemTypeGeneral'][data['itemName'].str.contains('rock')] = 'tool';
data['itemTypeGeneral'][data['itemName'].str.contains('sword')] = 'melee';
data['itemTypeGeneral'][data['itemName'].str.contains('club')] = 'melee';
data['itemTypeGeneral'][data['itemName'].str.contains('blade')] = 'melee';
data['itemTypeGeneral'][data['itemName'].str.contains('knife')] = 'melee';
data['itemTypeGeneral'][data['itemName'].str.contains('shotgun')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('dbs')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('pump')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('python')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('mp5')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('sar')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('rifle')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('pistol')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('revolver')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('smg')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('ak47')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('sap')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('smg')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('gun')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('bolt')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('47')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('bar')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('300')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('eoka')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('pipe')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains(' ak')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('grenade')] = 'explosive';
data['itemTypeGeneral'][data['itemName'].str.contains('satchel')] = 'explosive';
data['itemTypeGeneral'][data['itemName'].str.contains('rocket')] = 'explosive';
data['itemTypeGeneral'][data['itemName'].str.contains('launcher')] = 'explosive';
data['itemTypeGeneral'][data['itemName'].str.contains('crossbow')] = 'crossbow';
data['itemTypeGeneral'][data['itemType'].str.contains('crossbow')] = 'gun';
data['itemTypeGeneral'][data['itemName'].str.contains('hoodie')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('shirt')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('gloves')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('pants')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('bandana')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('vest')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('boots')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('boonie')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('cap')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('shoes')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('jacket')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('balaclava')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('beenie')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('shorts')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('headwrap')] = 'clothing';
data['itemTypeGeneral'][data['itemName'].str.contains('poncho')] = 'armor';
data['itemTypeGeneral'][data['itemName'].str.contains('helmet')] = 'armor';
data['itemTypeGeneral'][data['itemName'].str.contains('chest')] = 'armor';
data['itemTypeGeneral'][data['itemName'].str.contains('mask')] = 'armor';
data['itemTypeGeneral'][data['itemName'].str.contains('kilt')] = 'armor';
data['itemTypeGeneral'][data['itemName'].str.contains('bucket')] = 'armor';
######################
data['tier'][data['itemName'].str.contains('hat')] = 0;
data['tier'][data['itemName'].str.contains('hatchet')] = 1;
data['tier'][data['itemName'].str.contains(' ak')] = 3;
data['tier'][data['itemName'].str.contains('guitar')] = 0;
data['tier'][data['itemName'].str.contains('box')] = 0;
data['tier'][data['itemName'].str.contains('trunk')] = 0;
data['tier'][data['itemName'].str.contains('storage')] = 0;
data['tier'][data['itemName'].str.contains('door')] = 0;
data['tier'][data['itemName'].str.contains('armored')] = 3;
data['tier'][data['itemName'].str.contains('bag')] = 0;
data['tier'][data['itemName'].str.contains('furnace')] = 0;
data['tier'][data['itemName'].str.contains('fridge')] = 1;
data['tier'][data['itemName'].str.contains('locker')] = 2;
data['tier'][data['itemName'].str.contains('carpet')] = 0;
data['tier'][data['itemName'].str.contains('rug')] = 0;
data['tier'][data['itemName'].str.contains('bed')] = 1;
data['tier'][data['itemName'].str.contains('machine')] = 1;
data['tier'][data['itemName'].str.contains('chair')] = 1;
data['tier'][data['itemName'].str.contains('table')] = 0;
data['tier'][data['itemName'].str.contains('barricade')] = 2;
data['tier'][data['itemName'].str.contains('garage')] = 1;
data['tier'][data['itemName'].str.contains('hammer')] = 0;
data['tier'][data['itemName'].str.contains('rock')] = 0;
data['tier'][data['itemName'].str.contains('club')] = 0;
data['tier'][data['itemName'].str.contains('knife')] = 0;
data['tier'][data['itemName'].str.contains('dbs')] = 1;
data['tier'][data['itemName'].str.contains('pump')] = 2;
data['tier'][data['itemName'].str.contains('python')] = 2;
data['tier'][data['itemName'].str.contains('mp5')] = 3;
data['tier'][data['itemName'].str.contains('sar')] = 2;
data['tier'][data['itemName'].str.contains('revolver')] = 1;
data['tier'][data['itemName'].str.contains('smg')] = 2;
data['tier'][data['itemName'].str.contains('ak47')] = 3;
data['tier'][data['itemName'].str.contains('sap')] = 2;
data['tier'][data['itemName'].str.contains('smg')] = 2;
data['tier'][data['itemName'].str.contains('bolt')] = 3;
data['tier'][data['itemName'].str.contains('47')] = 3;
data['tier'][data['itemName'].str.contains('bar')] = 3;
data['tier'][data['itemName'].str.contains('300')] = 4;
data['tier'][data['itemName'].str.contains('eoka')] = 0;
data['tier'][data['itemName'].str.contains('pipe')] = 1;
data['tier'][data['itemName'].str.contains('grenade')] = 2;
data['tier'][data['itemName'].str.contains('satchel')] = 2;
data['tier'][data['itemName'].str.contains('rocket')] = 3;
data['tier'][data['itemName'].str.contains('launcher')] = 3;
data['tier'][data['itemName'].str.contains('crossbow')] = 1;
data['tier'][data['itemName'].str.contains('hoodie')] = 2;
data['tier'][data['itemName'].str.contains('gloves')] = 0;
data['tier'][data['itemName'].str.contains('pants')] = 1;
data['tier'][data['itemName'].str.contains('bandana')] = 0;
data['tier'][data['itemName'].str.contains('boots')] = 2;
data['tier'][data['itemName'].str.contains('boonie')] = 0;
data['tier'][data['itemName'].str.contains('cap')] = 0;
data['tier'][data['itemName'].str.contains('shoes')] = 0;
data['tier'][data['itemName'].str.contains('jacket')] = 1;
data['tier'][data['itemName'].str.contains('balaclava')] = 0;
data['tier'][data['itemName'].str.contains('beenie')] = 0;
data['tier'][data['itemName'].str.contains('shorts')] = 0;
data['tier'][data['itemName'].str.contains('headwrap')] = 0;
data['tier'][data['itemName'].str.contains('shirt')] = 0;
data['tier'][data['itemName'].str.contains('shorts')] = 0;
data['tier'][data['itemName'].str.contains('poncho')] = 0;
data['tier'][data['itemName'].str.contains('chest plate')] = 3;
data['tier'][data['itemName'].str.contains('mask')] = 3;
data['tier'][data['itemName'].str.contains('kilt')] = 2;
data['tier'][data['itemName'].str.contains('bucket')] = 1;
data['tier'][data['itemName'].str.contains('roadsign')] = 2;
data['tier'][data['itemName'].str.contains('sign')] = 2;
data['tier'][data['itemName'].str.contains('vest')] = 2;
data['tier'][data['itemType'].str.contains('rs')] = 2;
data['tier'][data['itemType'].str.contains('vest')] = 2;
######
data['itemType'][data['itemName'].str.contains('hat')] = 'hat';
data['itemType'][data['itemName'].str.contains('hatchet')] = 'hatchet';
data['itemType'][data['itemName'].str.contains('stone hatchet')] = 'stone hatchet';
data['itemType'][data['itemName'].str.contains('axe')] = 'pickaxe';
data['itemType'][data['itemName'].str.contains('pickaxe')] = 'pickaxe';
data['itemType'][data['itemName'].str.contains('stone pickaxe')] = 'stone pickaxe';
data['itemType'][data['itemName'].str.contains('guitar')] = 'guitar';
data['itemType'][data['itemName'].str.contains('trunk')] = 'lw box';
data['itemType'][data['itemName'].str.contains('storage')] = 'lw box';
data['itemType'][data['itemName'].str.contains('wooden door')] = 'wooden door';
data['itemType'][data['itemName'].str.contains('bag')] = 'sleeping bag';
data['itemType'][data['itemName'].str.contains('furnace')] = 'furnace';
data['itemType'][data['itemName'].str.contains('fridge')] = 'fridge';
data['itemType'][data['itemName'].str.contains('locker')] = 'locker';
data['itemType'][data['itemName'].str.contains('carpet')] = 'rug';
data['itemType'][data['itemName'].str.contains('rug')] = 'rug';
data['itemType'][data['itemName'].str.contains('bed')] = 'bed';
data['itemType'][data['itemName'].str.contains('machine')] = 'vending';
data['itemType'][data['itemName'].str.contains('chair')] = 'chair';
data['itemType'][data['itemName'].str.contains('table')] = 'table';
data['itemType'][data['itemName'].str.contains('barricade')] = 'barricade';
data['itemType'][data['itemName'].str.contains('garage')] = 'garage door';
data['itemType'][data['itemName'].str.contains('sheet metal door')] = 'sheet metal door';
data['itemType'][data['itemName'].str.contains('hammer')] = 'hammer';
data['itemType'][data['itemName'].str.contains('rock')] = 'rock';
data['itemType'][data['itemName'].str.contains('sword')] = 'sword';
data['itemType'][data['itemName'].str.contains('club')] = 'club';
data['itemType'][data['itemName'].str.contains('blade')] = 'sword';
data['itemType'][data['itemName'].str.contains('knife')] = 'knife';
data['itemType'][data['itemName'].str.contains('dbs')] = 'dbs';
data['itemType'][data['itemName'].str.contains('pump')] = 'pump';
data['itemType'][data['itemName'].str.contains('python')] = 'python';
data['itemType'][data['itemName'].str.contains('mp5')] = 'mp5';
data['itemType'][data['itemName'].str.contains('sar')] = 'sar';
data['itemType'][data['itemName'].str.contains('revolver')] = 'revolver';
data['itemType'][data['itemName'].str.contains('smg')] = 'smg';
data['itemType'][data['itemName'].str.contains('ak47')] = 'ak47';
data['itemType'][data['itemName'].str.contains('sap')] = 'sap';
data['itemType'][data['itemName'].str.contains('smg')] = 'smg';
data['itemType'][data['itemName'].str.contains('bolt')] = 'bar';
data['itemType'][data['itemName'].str.contains('47')] = 'ak47';
data['itemType'][data['itemName'].str.contains('bar')] = 'bar';
data['itemType'][data['itemName'].str.contains('300')] = 'lr300';
data['itemType'][data['itemName'].str.contains('eoka')] = 'eoka';
data['itemType'][data['itemName'].str.contains('pipe')] = 'waterpipe';
data['itemType'][data['itemName'].str.contains(' ak')] = 'ak47';
data['itemType'][data['itemName'].str.contains('ak ')] = 'ak47';
data['itemType'][data['itemName'].str.contains('grenade')] = 'grenade';
data['itemType'][data['itemName'].str.contains('satchel')] = 'satchel';
data['itemType'][data['itemName'].str.contains('rocket')] = 'launcher';
data['itemType'][data['itemName'].str.contains('launcher')] = 'launcher';
data['itemType'][data['itemName'].str.contains('crossbow')] = 'crossbow';
data['itemType'][data['itemName'].str.contains('hoodie')] = 'hoodie';
data['itemType'][data['itemName'].str.contains('bandana')] = 'bandana';
data['itemType'][data['itemName'].str.contains('boonie')] = 'boonie';
data['itemType'][data['itemName'].str.contains('cap')] = 'cap';
data['itemType'][data['itemName'].str.contains('shoes')] = 'hide boots';
data['itemType'][data['itemName'].str.contains('jacket')] = 'jacket';
data['itemType'][data['itemName'].str.contains('balaclava')] = 'balaclava';
data['itemType'][data['itemName'].str.contains('beenie')] = 'beenie';
data['itemType'][data['itemName'].str.contains('headwrap')] = 'headwrap';
data['itemType'][data['itemName'].str.contains('pants')] = 'pants';
data['itemType'][data['itemName'].str.contains('boots')] = 'boots';
data['itemType'][data['itemName'].str.contains('shirt')] = 'tshirt';
data['itemType'][data['itemName'].str.contains('gloves')] = 'leather gloves';
data['itemType'][data['itemName'].str.contains('short')] = 'shorts';
data['itemType'][data['itemName'].str.contains('poncho')] = 'poncho';
data['itemType'][data['itemName'].str.contains('mask')] = 'metal face mask';
data['itemType'][data['itemName'].str.contains('kilt')] = 'rs kilt';
data['itemType'][data['itemName'].str.contains('bucket')] = 'bucket';
data['itemType'][data['itemName'].str.contains('chest plate')] = 'metal chest plate';
data['itemType'][data['itemName'].str.contains('sign vest')] = 'rs jacket';
data['itemType'][data['itemName'].str.contains('sign pants')] = 'rs kilt';
data['itemType'][data['itemName'].str.contains('sign jacket')] = 'rs jacket';
data['itemType'][data['itemName'].str.contains('sign kilt')] = 'rs kilt';
data['itemType'][data['itemName'].str.contains('vest')] = 'rs jacket';
data['itemType'][data['itemType'].str.contains('beenie')] = 'beanie';
##############
# Graphs for blog post
# Store vs Market
test = data;
index = range(0,data.shape[0]*2+1);
columns = ['cat','price'];
holder = pd.DataFrame(index=index,columns=columns)
holder['cat'][0:data.shape[0]] = "OG_price_NZ";
holder['price'][0:data.shape[0]] = data["OG_price_NZ"];
holder['cat'][data.shape[0]:data.shape[0]*2] = "priceAvg";
holder['price'][data.shape[0]:data.shape[0]*2] = data["priceAvg"];
f, ax = plt.subplots(figsize=(7, 5))
for index, row in test.iterrows():
#ax = sns.pointplot(x="cat", y="price", data=test[row])
plt.plot([0,1],[row['OG_price_NZ'],row['priceAvg']], 'ko-',alpha=0.05)
sns.pointplot(x="cat", y="price", data=holder,estimator=np.median,capsize=.1,color="#cf432c")
ax.set_xlim(-0.5,1.5);
ax.set_ylim(0,10);
plt.ylabel('NZD $');
plt.xlabel('Prices');
plt.title('Change in price from Store to Market')
ax.set_xticks([-0.5,0,1,1.5]);
ax.set_xticklabels(["","Original","Average"]);
fmt = '${x:,.0f}'
tick = tkr.StrMethodFormatter(fmt)
ax.yaxis.set_major_formatter(tick)
sci.wilcoxon(data['OG_price_NZ'], data['priceAvg'])
f, ax = plt.subplots(figsize=(7, 5))
sns.kdeplot(data['OG_price_NZ'],shade=True, color="k",bw = 1)
sns.kdeplot(data['priceAvg'], shade=True, color="#cf432c",bw = 1,clip=[0,50])
ax.set_xlim(0,10);
plt.title('Store prices vs Market prices')
fmt = '${x:,.0f}'
tick = tkr.StrMethodFormatter(fmt)
ax.xaxis.set_major_formatter(tick)
plt.ylabel('KDE');
plt.xlabel('NZD $');
plt.legend(['Store','Market']);
plt.text(7,0.25,'Avg change: $'+str(round(data['OG_to_avg'].mean(),2)),fontsize=12);
plt.text(7,0.23,'Med change: $'+str(round(data['OG_to_avg'].median(),2)),fontsize=12);
# Initial
# Initial dist
f, ax = plt.subplots(1)
ax.hist(data['initial'],bins=800,color ="#cf432c");
plt.xlim(-20, 20)
ax.axvline(x=0, ymin=0, ymax=175,linewidth=0.5,color='k')
plt.ylabel('# of Items')
plt.xlabel('Initial price change')
plt.text(10,575,'Avg: $'+str(round(data['initial'].mean(),2)),fontsize=12);
plt.text(10,545,'Med: $'+str(round(data['initial'].median(),2)),fontsize=12);
plt.title('Day 1 to Day 2 price change')
fmt = '${x:,.0f}'
tick = tkr.StrMethodFormatter(fmt)
ax.xaxis.set_major_formatter(tick)
#Max
f, ax = plt.subplots(1)
ax.hist(data['maxIdx'],bins=100,color ="#cf432c");
plt.xlim(-0,800)
plt.ylabel('# of Items')
plt.xlabel('Day of max price')
plt.text(600,445,'Avg max: '+str(int(round(data['maxIdx'].mean(),0))),fontsize=12);
plt.text(600,420,'Med max: '+str(int(round(data['maxIdx'].median(),0))),fontsize=12);
plt.text(600,395,'Mode max: '+str(int(round(data['maxIdx'].mode()[0],2))),fontsize=12);
plt.title('Distribution of max price days')
(data['maxIdx'] < 2).sum()
(data['maxIdx'] == 0).sum()
(data['maxIdx'] == 1).sum()
(data['maxIdx'] == 2).sum()
#Min
f, ax = plt.subplots(1)
ax.hist(data['minIdx'],bins=100,color ="#cf432c");
plt.xlim(-0,800)
plt.ylabel('# of Items')
plt.xlabel('Day of min price')
plt.text(600,300,'Avg max: '+str(int(round(data['minIdx'].mean(),0))),fontsize=12);
plt.text(600,285,'Med max: '+str(int(round(data['minIdx'].median(),0))),fontsize=12);
plt.text(600,270,'Mode max: '+str(int(round(data['minIdx'].mode()[0],2))),fontsize=12);
plt.title('Distribution of min price days')
# smooth/appreciation
a = data[data['smoothChange'].notnull()];
f, ax = plt.subplots(1)
ax.hist(a['smoothChange'],bins=1000,color ="#cf432c");
plt.xlabel('Change in Price')
plt.ylabel('# of items')
plt.axvline(x=0, ymin=0, ymax=175,linewidth=0.5,color='k')
ax.set_xlim(-50, 50)
formatter = tkr.FormatStrFormatter('$%1.2f')
ax.xaxis.set_major_formatter(formatter)
plt.text(30,265,'Avg: $'+str(round(a['smoothChange'].mean(),2)),fontsize=12);
plt.text(30,252,'Med: $'+str(round(a['smoothChange'].median(),2)),fontsize=12);
sci.wilcoxon(data['OG_to_avg'], data['smoothChange'])
## swing
f, ax = plt.subplots(1)
ax.hist(data['swing'],bins=2000,color ="#cf432c");
plt.xlim(0,50)
plt.ylabel('# of Items')
plt.xlabel('Swing in price $')
plt.text(39,84,'Avg: $'+str(round(data['swing'].mean(),2)),fontsize=12);
plt.text(39,79,'Med: $'+str(round(data['swing'].median(),2)),fontsize=12);
plt.title('Distribution of price swings')
formatter = tkr.FormatStrFormatter('$%1.2f')
ax.xaxis.set_major_formatter(formatter)
## maxMinIdxDiff
f, ax = plt.subplots(1)
ax.hist(data['maxMinIdxDiff'],bins=300,color ="#cf432c");
plt.xlim(-800,800)
plt.ylabel('# of Items')
plt.xlabel('Swing duration (days)')
plt.text(515,40,'Avg: '+str(int(round(data['maxMinIdxDiff'].mean(),0))),fontsize=12);
plt.text(515,38,'Med: '+str(int(round(data['maxMinIdxDiff'].median(),0))),fontsize=12);
plt.text(515,36,'Mode: '+str(int(round(data['maxMinIdxDiff'].mode()[0],2))),fontsize=12);
plt.title('Distribution of swing durations')
sci.normaltest(data['maxMinIdxDiff'])
sci.skew(data['maxMinIdxDiff'])
sci.skewtest(data['maxMinIdxDiff'])
## Index fund
# marketDelta
# marketPrice
# marketVol
deltaVector = marketDelta.mean(axis=1,skipna = True);
priceVector = marketPrice.mean(axis=1,skipna = True);
priceCount = marketPrice.count(axis=1)
volVector = marketVol.mean(axis=1,skipna = True);
volCount = marketVol.count(axis=1)
goodDates = volCount > 0;
deltaVector = deltaVector[goodDates];
priceVector = priceVector[goodDates];
priceCount = priceCount[goodDates];
volVector = volVector[goodDates];
volCount = volCount[goodDates];
priceVolNorm = priceVector/priceCount;
volItemNorm = volVector*volCount;
priceRoll = priceVector.rolling('5d').mean();
years = mdates.YearLocator() # every year
months = mdates.MonthLocator() # every month
yearsFmt = mdates.DateFormatter('%Y')
f, ax = plt.subplots(figsize=(12, 5))
plt.plot(volItemNorm,color ="#cf432c")
plt.ylabel('Index volume')
plt.xlabel('Date')
plt.title('COBALT Index Fund')
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
ax.axvline(x='2015-12-25', ymin=0, ymax=10,linewidth=0.5,color='r',alpha=0.5)
ax.axvline(x='2016-12-25', ymin=0, ymax=10,linewidth=0.5,color='r',alpha=0.5)
ax.axvline(x='2017-12-25', ymin=0, ymax=10,linewidth=0.5,color='r',alpha=0.5)
ax.axvline(x='2018-12-25', ymin=0, ymax=10,linewidth=0.5,color='r',alpha=0.5)
ax.axvline(x='2016-06-20', ymin=0, ymax=10,linewidth=0.5,color='k',alpha=0.5)
ax.axvline(x='2017-06-20', ymin=0, ymax=10,linewidth=0.5,color='k',alpha=0.5)
ax.axvline(x='2018-06-20', ymin=0, ymax=10,linewidth=0.5,color='k',alpha=0.5)
plt.xlim('2015-07-16','2019-03-31')
f, ax = plt.subplots(1,1,figsize=(12, 5))
plt.plot(priceVector,color ="#cf432c")
plt.ylabel('Index ($)')
plt.xlabel('Date')
plt.title('COBALT Index Fund')
fmt = '${x:,.0f}'
tick = tkr.StrMethodFormatter(fmt)
ax.yaxis.set_major_formatter(tick)
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
ax.axvline(x='2015-12-25', ymin=0, ymax=10,linewidth=0.5,color='r',alpha=0.5)
ax.axvline(x='2016-12-25', ymin=0, ymax=10,linewidth=0.5,color='r',alpha=0.5)
ax.axvline(x='2017-12-25', ymin=0, ymax=10,linewidth=0.5,color='r',alpha=0.5)
ax.axvline(x='2018-12-25', ymin=0, ymax=10,linewidth=0.5,color='r',alpha=0.5)
ax.axvline(x='2016-06-20', ymin=0, ymax=10,linewidth=0.5,color='k',alpha=0.5)
ax.axvline(x='2017-06-20', ymin=0, ymax=10,linewidth=0.5,color='k',alpha=0.5)
ax.axvline(x='2018-06-20', ymin=0, ymax=10,linewidth=0.5,color='k',alpha=0.5)
plt.xlim('2015-07-16','2019-03-31')
f, ax = plt.subplots(1,2,figsize=(12, 5))
plt.bar(pd.date_range('2015-07-16','2019-03-31'),volItemNorm,align='edge',color ="#cf432c",edgecolor="#cf432c")
plt.ylabel('Index volume')
plt.xlabel('Date')
plt.title('COBALT Index Fund')
# THE plot
f, (ax1, ax2) = plt.subplots(2, 1,figsize=(12, 5),gridspec_kw = {'height_ratios':[4, 1],'wspace':0.0, 'hspace':0.0})
ax1.plot(priceVector,color ="#cf432c",alpha=0.5)
ax1.plot(priceRoll,color ="#cf432c")
ax1.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=False) # labels along the bottom edge are off
ax1.set_ylabel('Index ($)')
ax1.set_ylim(0,9)
ax1.set_yticks([0, 3, 6, 9]);
ax1.set_title('COBALT Index Fund')
fmt = '${x:,.0f}'
tick = tkr.StrMethodFormatter(fmt)
ax1.yaxis.set_major_formatter(tick)
ax1.axvline(x='2015-12-25', ymin=0, ymax=10,linewidth=0.5,color='r',alpha=0.5)
ax1.axvline(x='2016-12-25', ymin=0, ymax=10,linewidth=0.5,color='r',alpha=0.5)
ax1.axvline(x='2017-12-25', ymin=0, ymax=10,linewidth=0.5,color='r',alpha=0.5)
ax1.axvline(x='2018-12-25', ymin=0, ymax=10,linewidth=0.5,color='r',alpha=0.5)
ax1.axvline(x='2016-06-20', ymin=0, ymax=10,linewidth=0.5,color='k',alpha=0.5)
ax1.axvline(x='2017-06-20', ymin=0, ymax=10,linewidth=0.5,color='k',alpha=0.5)
ax1.axvline(x='2018-06-20', ymin=0, ymax=10,linewidth=0.5,color='k',alpha=0.5)
ax1.set_xlim('2015-07-16','2019-03-31')
ax2.bar(pd.date_range('2015-07-16','2019-03-31'),volItemNorm,align='edge',color ="#222423",edgecolor="#222423")
ax2.set_ylabel('Index volume')
ax2.set_xlabel('Date')
ax2.set_xlim('2015-07-16','2019-03-31')
ax2.xaxis.set_major_locator(years)
ax2.xaxis.set_major_formatter(yearsFmt)
ax2.xaxis.set_minor_locator(months)
ax2.set_yticks([0,15000,30000]);
ax2.set_yticklabels(['0','15k','30k']);
plt.bar(pd.date_range('2015-07-16','2019-03-31'),volItemNorm,align='edge',color ="#222423",edgecolor="#222423")
# summer june 20
s = pd.date_range('2017-01-01','2019-03-31', freq='D').to_series();
daysWeek = s.dt.dayofweek;
volItemNormNew = volItemNorm[s];
priceVectorNew = priceVector[s];
#plt.scatter(daysWeek,volItemNormNew,color ="#cf432c",alpha=0.5);
#mon = np.mean(volItemNormNew[daysWeek == 0])
f, ax = plt.subplots(figsize=(7, 5))
sns.violinplot(x=daysWeek,y=volItemNormNew)
plt.ylabel('Volume of trades')
plt.xlabel('Day of the week')
plt.title('Trading volume by day')
sci.f_oneway(volItemNormNew[daysWeek == 0], volItemNormNew[daysWeek == 1], volItemNormNew[daysWeek == 2], volItemNormNew[daysWeek == 3], volItemNormNew[daysWeek == 4], volItemNormNew[daysWeek == 5], volItemNormNew[daysWeek == 6])
print(pairwise_tukeyhsd(volItemNormNew, daysWeek))
f, ax = plt.subplots(figsize=(7, 5))
sns.violinplot(x=daysWeek,y=priceVectorNew)
plt.ylabel('Price of Index')
plt.xlabel('Day of the week')
plt.title('Prices by day')
fmt = '${x:,.0f}'
tick = tkr.StrMethodFormatter(fmt)
ax.yaxis.set_major_formatter(tick)
sci.f_oneway(priceVectorNew[daysWeek == 0], priceVectorNew[daysWeek == 1], priceVectorNew[daysWeek == 2], priceVectorNew[daysWeek == 3], priceVectorNew[daysWeek == 4], priceVectorNew[daysWeek == 5], priceVectorNew[daysWeek == 6])
print(pairwise_tukeyhsd(priceVectorNew, daysWeek))
f, ax = plt.subplots(figsize=(12, 5))
plt.plot(priceVolNorm,color ="#cf432c")
plt.ylabel('Price change ($)')
plt.xlabel('Date')
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
# groups
# tiers
dataT = data[data['tier'].notnull()];
f, ax = plt.subplots(figsize=(7, 5))
g = sns.boxplot(x='tier',y='priceAvg',data=dataT)
ax.set_ylim(0,15)
plt.ylabel('Avg Price')
plt.xlabel('Tier')
plt.title('Avg prices by tier')
fmt = '${x:,.0f}'
tick = tkr.StrMethodFormatter(fmt)
ax.yaxis.set_major_formatter(tick)
sci.f_oneway(dataT['priceAvg'][dataT['tier'] == 0], dataT['priceAvg'][dataT['tier'] == 1], dataT['priceAvg'][dataT['tier'] == 2], dataT['priceAvg'][dataT['tier'] == 3], dataT['priceAvg'][dataT['tier'] == 4])
print(pairwise_tukeyhsd(dataT['priceAvg'], dataT['tier']))
# armor
dataArmor = data[data['itemTypeGeneral'] == 'armor'];
dataArmor = dataArmor[dataArmor['itemType'] != ''];
dataArmor['itemType'][dataArmor['itemType'].str.contains('bone helmet')] = 'bone';
dataArmor['itemType'][dataArmor['itemType'].str.contains('metal chest plate')] = 'MCP';
dataArmor['itemType'][dataArmor['itemType'].str.contains('metal face mask')] = 'MFM';
dataArmor['itemType'][dataArmor['itemType'].str.contains('rs jacket')] = 'RSJ';
dataArmor['itemType'][dataArmor['itemType'].str.contains('rs kilt')] = 'RSK';
(dataArmor['itemType']=='bone').sum()
(dataArmor['itemType']=='poncho').sum()
(dataArmor['itemType']=='RSJ').sum()
(dataArmor['itemType']=='MCP').sum()
sci.f_oneway(dataArmor['priceAvg'][dataArmor['itemType'] == 'RSJ'], dataArmor['priceAvg'][dataArmor['itemType'] == 'RSK'], dataArmor['priceAvg'][dataArmor['itemType'] == 'MFM'], dataArmor['priceAvg'][dataArmor['itemType'] == 'MCP'], dataArmor['priceAvg'][dataArmor['itemType'] == 'coffee'])
sci.f_oneway(dataArmor['swing'][dataArmor['itemType'] == 'RSJ'], dataArmor['swing'][dataArmor['itemType'] == 'RSK'], dataArmor['swing'][dataArmor['itemType'] == 'MFM'], dataArmor['swing'][dataArmor['itemType'] == 'MCP'], dataArmor['swing'][dataArmor['itemType'] == 'coffee'])
sci.f_oneway(dataArmor['OGNZAvgDiff'][dataArmor['itemType'] == 'RSJ'], dataArmor['OGNZAvgDiff'][dataArmor['itemType'] == 'RSK'], dataArmor['OGNZAvgDiff'][dataArmor['itemType'] == 'MFM'], dataArmor['OGNZAvgDiff'][dataArmor['itemType'] == 'MCP'], dataArmor['OGNZAvgDiff'][dataArmor['itemType'] == 'coffee'])
f, ax = plt.subplots(figsize=(7, 5))
sns.boxplot(x='itemType',y='priceAvg',data=dataArmor)
ax.set_ylim(0,25)
plt.ylabel('Avg Price')
plt.xlabel('')
plt.title('Avg prices by armor type')
fmt = '${x:,.0f}'
tick = tkr.StrMethodFormatter(fmt)
ax.yaxis.set_major_formatter(tick)
a = dataArmor.describe();
# guns
dataGuns = data[data['itemTypeGeneral'] == 'gun'];
dataGuns['itemType'][dataGuns['itemType'].str.contains('crossbow')] = 'crossy';
dataGuns['itemType'][dataGuns['itemType'].str.contains('revolver')] = 'rev';
dataGuns['itemType'][dataGuns['itemType'].str.contains('waterpipe')] = 'wp';
dataGuns['itemType'][dataGuns['itemType'].str.contains('thompson')] = 'tomy';
f, ax = plt.subplots(figsize=(7, 5))
sns.boxplot(x='itemType',y='priceAvg',data=dataGuns)
ax.set_ylim(0,50)
plt.ylabel('Avg Price')
plt.xlabel('')
plt.title('Avg prices by gun type')
fmt = '${x:,.0f}'
tick = tkr.StrMethodFormatter(fmt)
ax.yaxis.set_major_formatter(tick)
sci.f_oneway(dataGuns['priceAvg'][dataGuns['itemType'] == 'eoka'],dataGuns['priceAvg'][dataGuns['itemType'] == 'crossy'],dataGuns['priceAvg'][dataGuns['itemType'] == 'dbs'],dataGuns['priceAvg'][dataGuns['itemType'] == 'rev'],dataGuns['priceAvg'][dataGuns['itemType'] == 'wp'],dataGuns['priceAvg'][dataGuns['itemType'] == 'pump'],dataGuns['priceAvg'][dataGuns['itemType'] == 'python'],dataGuns['priceAvg'][dataGuns['itemType'] == 'sap'],dataGuns['priceAvg'][dataGuns['itemType'] == 'sar'],dataGuns['priceAvg'][dataGuns['itemType'] == 'smg'],dataGuns['priceAvg'][dataGuns['itemType'] == 'tomy'],dataGuns['priceAvg'][dataGuns['itemType'] == 'ak47'],dataGuns['priceAvg'][dataGuns['itemType'] == 'bar'],dataGuns['priceAvg'][dataGuns['itemType'] == 'mp5'],dataGuns['priceAvg'][dataGuns['itemType'] == 'lr300'])
sci.f_oneway(dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'eoka'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'crossy'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'dbs'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'rev'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'wp'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'pump'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'python'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'sap'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'sar'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'smg'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'tomy'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'ak47'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'bar'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'mp5'],dataGuns['OGNZAvgDiff'][dataGuns['itemType'] == 'lr300'])
sci.f_oneway(dataGuns['swing'][dataGuns['itemType'] == 'eoka'],dataGuns['swing'][dataGuns['itemType'] == 'crossy'],dataGuns['swing'][dataGuns['itemType'] == 'dbs'],dataGuns['swing'][dataGuns['itemType'] == 'rev'],dataGuns['swing'][dataGuns['itemType'] == 'wp'],dataGuns['swing'][dataGuns['itemType'] == 'pump'],dataGuns['swing'][dataGuns['itemType'] == 'python'],dataGuns['swing'][dataGuns['itemType'] == 'sap'],dataGuns['swing'][dataGuns['itemType'] == 'sar'],dataGuns['swing'][dataGuns['itemType'] == 'smg'],dataGuns['swing'][dataGuns['itemType'] == 'tomy'],dataGuns['swing'][dataGuns['itemType'] == 'ak47'],dataGuns['swing'][dataGuns['itemType'] == 'bar'],dataGuns['swing'][dataGuns['itemType'] == 'mp5'],dataGuns['swing'][dataGuns['itemType'] == 'lr300'])
print(pairwise_tukeyhsd(dataGuns['priceAvg'], dataGuns['itemType']))
a = dataGuns.describe();
#RP
dataRP = data[data['itemTypeGeneral'] == 'RP'];
dataRP = dataRP[dataRP['itemType'] != 'building'];
dataRP = dataRP[dataRP['itemType'] != 'decoration'];
dataRP = dataRP[dataRP['itemType'] != 'table'];
dataRP = dataRP[dataRP['itemType'] != 'water purifier'];
dataRP = dataRP[dataRP['itemType'] != 'target'];
dataRP = dataRP[dataRP['itemType'] != 'lantern'];
dataRP = dataRP[dataRP['itemType'] != 'fireplace'];
dataRP['itemType'][dataRP['itemType'].str.contains('sheet metal door')] = 'sm door';
dataRP['itemType'][dataRP['itemType'].str.contains('sleeping bag')] = 'bag';
dataRP['itemType'][dataRP['itemType'].str.contains('wooden door')] = 'w door';
dataRP['itemType'][dataRP['itemType'].str.contains('wood door')] = 'w door';
dataRP['itemType'][dataRP['itemType'].str.contains('garage')] = 'g';
dataRP['itemType'][dataRP['itemType'].str.contains('garage door')] = 'g';
f, ax = plt.subplots(figsize=(7, 5))
sns.boxplot(x='itemType',y='priceAvg',data=dataRP)
ax.set_ylim(0,20)
plt.ylabel('Avg Price')
plt.xlabel('')
plt.title('Avg prices by role playing type')
fmt = '${x:,.0f}'
tick = tkr.StrMethodFormatter(fmt)
ax.yaxis.set_major_formatter(tick)
a = dataRP.describe();
# clothing
dataCloth = data[data['itemTypeGeneral'] == 'clothing'];
dataCloth = dataCloth[dataCloth['itemType'] != 'beanie'];
dataCloth = dataCloth[dataCloth['itemType'] != 'burlap shoes'];
dataCloth = dataCloth[dataCloth['itemType'] != 'burlap gloves'];
dataCloth = dataCloth[dataCloth['itemType'] != 'burlap shows'];
dataCloth = dataCloth[dataCloth['itemType'] != 'crop top'];
dataCloth = dataCloth[dataCloth['itemType'] != 'hide boots'];
dataCloth = dataCloth[dataCloth['itemType'] != 'hide halterneck'];
dataCloth = dataCloth[dataCloth['itemType'] != 'hide poncho'];
dataCloth = dataCloth[dataCloth['itemType'] != 'leather boots'];
dataCloth = dataCloth[~dataCloth['itemType'].str.contains('miner')];
dataCloth = dataCloth[dataCloth['itemType'] != 'rs jacket'];
dataCloth = dataCloth[dataCloth['itemType'] != 'rs kilt'];
dataCloth = dataCloth[dataCloth['itemType'] != 'shorts'];
dataCloth = dataCloth[dataCloth['itemType'] != 'snow jacket'];
dataCloth = dataCloth[dataCloth['itemType'] != 'skirt'];
dataCloth['itemType'][dataCloth['itemType'].str.contains('sheet metal door')] = 'sm door';
f, ax = plt.subplots(figsize=(7, 5))
sns.boxplot(x='itemType',y='OGNZAvgDiff',data=dataCloth)
ax.set_ylim(0,15)
plt.ylabel('Avg Price')
plt.xlabel('')
plt.title('Avg prices by clothing')
fmt = '${x:,.0f}'
tick = tkr.StrMethodFormatter(fmt)
ax.yaxis.set_major_formatter(tick)
a = dataCloth.describe();
# make sure you sort by priceAvg (or whatever) first
# top
dataTop = data.head(50);
a = dataTop.describe();
dataTop['itemTypeGeneral'].str.contains('clothing').sum()
dataTop['itemTypeGeneral'].str.contains('armor').sum()
dataTop['itemTypeGeneral'].str.contains('gun').sum()
dataTop['itemTypeGeneral'].str.contains('RP').sum()
# bot
dataBot = data.tail(100);
dataBot = dataBot[dataBot['itemType'] != 'decoration'];
dataBot = dataBot[dataBot['itemType'] != 'building'];
dataBot = dataBot[dataBot['itemType'] != 'crafting'];
dataBot = dataBot[dataBot['itemType'] != 'loot'];
b = dataBot.describe();
dataBot['itemTypeGeneral'].str.contains('clothing').sum()
dataBot['itemTypeGeneral'].str.contains('armor').sum()
# tests
sci.mannwhitneyu(dataTop['tier'], dataBot['tier'])
sci.mannwhitneyu(dataTop['timeOnMarket'], dataBot['timeOnMarket'])
sci.mannwhitneyu(dataTop['OGNZAvgDiff'], dataBot['OGNZAvgDiff'])
# gold
golden = data.index.tolist();
golden = pd.Series(golden)
goldenIdx = golden.str.contains('gold');
goldenMoney = pd.Series(data['OGNZAvgDiff'])
goldenMoney = goldenMoney.reset_index();
dataGold = goldenMoney[goldenIdx];
b = dataGold.describe();
sci.mannwhitneyu(dataGold['OGNZAvgDiff'], data['OGNZAvgDiff'])
#############
# Rainn test
f, ax = plt.subplots(figsize=(7, 5))
dy="tier"; dx="OGNZAvgDiff"; ort="h"; pal = sns.color_palette(n_colors=1)
pal = "Set2"
f, ax = plt.subplots(figsize=(7, 5))
ax=pt.half_violinplot( x = dx, y = dy, data = data, palette = pal, bw = 0.2, cut = 0,
scale = "area", width = .6, inner = None, orient = ort)
ax=sns.stripplot( x = dx, y = dy, data = data, palette = pal, edgecolor = "white",
size = 3, jitter = 1, zorder = 0, orient = ort)
ax=sns.boxplot( x = dx, y = dy, data = data, color = "black", width = .15, zorder = 10,\
showcaps = True, boxprops = {'facecolor':'none', "zorder":10},\
showfliers=True, whiskerprops = {'linewidth':2, "zorder":10},\
saturation = 1, orient = ort)
ax.set_xlim(0, 50);
dy="tier"; dx="priceAvg"; ort="h"; pal = "Set2"; sigma = .2;
ax=pt.RainCloud(x = dx, y = dy, data = data, palette = pal, bw = sigma,
width_viol = .6, figsize = (7,5), orient = ort)
ax.set_xlim(0, 50)
############
# clean up data
data = data[data['priceIncrease'] > -100]
data = data[data['priceAvg'] > 0.5] # get rid of random drop skins
# make my plots pretty
sns.set_palette("Blues_r");
# for graphs
labels = ['tier','itemTypeGeneral','itemType','OG_price','OG_price_NZ','initial','timeOnMarket','priceAvg','priceSD','priceIncrease','smoothChange','maxPrice','maxIdx','minPrice','minIdx','maxMinIdxDiff','swing','volAvg','volSD','slope','rr','OGNZAvgDiff'];
# handy descriptive stats from Pandas
dataDesc = data.describe();
sns.pairplot(data)
data.median()
data.sem()
forCorr = data.loc[:,['priceAvg','OG_price_NZ','initial','priceIncrease','smoothChange','maxPrice','maxIdx','minIdx','maxMinIdxDiff','swing','volAvg']];
corrLabels = ['priceAvg','OG_price_NZ','initial','priceIncrease','smoothChange','maxPrice','maxIdx','minIdx','maxMinIdxDiff','swing','volAvg'];
# Correlation to see how data is related
correlations = forCorr.corr()
f, ax = plt.subplots(1)
f.set_size_inches(4,3)
plt.set_cmap('RdYlBu_r')
cax=ax.matshow(correlations,vmin=-1,vmax=1)
cb = f.colorbar(cax)
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=13)
ticks=np.arange(0,len(corrLabels),1)
ax.set_xticks(ticks[0:len(correlations)])
ax.set_yticks(ticks[0:len(correlations)])
ticks=correlations.index.tolist() # label axes from corr index which is labels
ax.set_xticklabels(ticks[0:len(correlations)])
ax.set_yticklabels(ticks[0:len(correlations)])
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.xticks(rotation=-90)
#plt.title('Correlations', fontsize=32)
# Minor ticks
ax.set_xticks(np.arange(-.5, len(correlations)-1, 1), minor=True);
ax.set_yticks(np.arange(-.5, len(correlations)-1, 1), minor=True);
# Gridlines based on minor ticks
ax.grid(which='minor', color='k', linestyle='-', linewidth=2)
plt.savefig(gameID+'Corr.png', bbox_inches='tight')
plt.figure()
g = sns.distplot(data['timeOnMarket'],bins = 100,kde=False)
ax = g.axes
ax.set_xlim(0,data["timeOnMarket"].max())
plt.ylabel('# of Skins')
plt.xlabel('Time on Market (days)')
plt.savefig(gameID+'timeMarketDist.png', bbox_inches='tight')
a = data[data['smoothChange'].notnull()];
f, ax = plt.subplots(1)
ax.hist(a['smoothChange'],bins=1000);
plt.xlabel('Change in Price')
plt.ylabel('# of items')
plt.axvline(x=0, ymin=0, ymax=175,linewidth=0.5,color='k')
ax.set_xlim(-50, 50)
formatter = tkr.FormatStrFormatter('$%1.2f')
ax.xaxis.set_major_formatter(formatter)
plt.text(-48,250,'Avg change: $'+str(round(a['smoothChange'].mean(),2)),fontsize=12);
plt.text(-48,235,'Med change: $'+str(round(a['smoothChange'].median(),2)),fontsize=12);
plt.savefig(gameID+'smoothChange.png', bbox_inches='tight')
f, ax = plt.subplots(1)
sns.distplot(a['smoothChange'],bins = 1000,kde=False)
ax.set_xlim(-50, 50)
formatter = tkr.FormatStrFormatter('$%1.2f')
ax.xaxis.set_major_formatter(formatter)
plt.xlabel('Change in Price')
plt.ylabel('# of items')
plt.axvline(x=0, ymin=0, ymax=175,linewidth=0.5,color='k')
plt.text(-48,250,'Avg change: $'+str(round(a['smoothChange'].mean(),2)),fontsize=12);
plt.text(-48,235,'Med change: $'+str(round(a['smoothChange'].median(),2)),fontsize=12);
plt.savefig(gameID+'smoothChange.png', bbox_inches='tight')
plt.figure()
g = sns.distplot(data['priceIncrease'],bins = 1000,kde=False)
plt.xlim(-50, 50)
ax = g.axes
ax.axvline(x=0, ymin=0, ymax=175,linewidth=0.5)
plt.ylabel('# of Items')
plt.xlabel('Total price change')
plt.text(-48,185,'Avg change: $'+str(round(data['priceIncrease'].mean(),2)),fontsize=12);
plt.text(-48,165,'Med change: $'+str(round(data['priceIncrease'].median(),2)),fontsize=12);
#fmt = '${y:,.0f}'
#tick = tkr.StrMethodFormatter(fmt)
#ax.xaxis.set_major_formatter(tick)
plt.savefig(gameID+'priceIncreaseDist.png', bbox_inches='tight')
f, ax = plt.subplots(1)
ax.scatter(x='timeOnMarket',y='priceIncrease',data=data,s=0.5,color='black',alpha=0.5)
m, b = np.polyfit(data['timeOnMarket'],data['priceIncrease'],1)
ax.plot(data['priceIncrease'],m*data['priceIncrease']+b,'-',color='black')
plt.title('Time vs Price Change')
plt.xlabel('timeOnMarket')
plt.ylabel('priceIncrease')
ax.set_ylim(0,100)
fmt = '${x:,.0f}'
tick = tkr.StrMethodFormatter(fmt)
ax.yaxis.set_major_formatter(tick)
f, ax = plt.subplots(1)
ax.scatter(x='timeOnMarket',y='priceAvg',data=data,s=0.5,color='black',alpha=0.5)
m, b = np.polyfit(data['timeOnMarket'],data['priceAvg'],1)
ax.plot(data['timeOnMarket'],m*data['timeOnMarket']+b,'-',color='black')
plt.title('Time vs Average Price')
plt.xlabel('timeOnMarket')
plt.ylabel('priceAvg')
ax.set_ylim(0,20)