SAVE_FILENAME = 'stats.csv'
FULL_AP_ITEMS = util.FULL_AP_ITEMS
REGIONS = util.REGIONS_SHORT
REGION_DICT = util.REGION_DICT
REVERSE_REGION_DICT = util.REVERSE_REGION_DICT
PATCHES = util.PATCHES
# Create dictionaries from codes to names and vice-versa for items and champs.
item_data = rawpi.get_item_list('na').json()['data']
ITEM_DICT = {i: item_data[i]['name'] for i in item_data.keys()}
REVERSE_ITEM_DICT = {ITEM_DICT[i]: i for i in ITEM_DICT}
champ_data = rawpi.get_champion_list('na').json()['data']
CHAMP_DICT = {champ_data[name]['id']: name for name in champ_data}
# Define methods for creating DataFrame rows. These are structured as dicts
# with keys being column names and values being DataFrame values. These methods
# function by passing a dictionary and adding to them. This dictionary can then
# be added to a list that a DataFrame can be created from.
def get_item_purchases(timeline):
"""
Given a timeline json object, returns a dictionary of
participant ID: [list of item purchases] pairs.
List will be sorted in chronological order.
Keyword argument:
import itertools
import json
import os
import sys
sys.path.append('C:\\Python33\\RIOTAPICHALLENGE\\lolapi-master')
import rawpi
import math
data = "C:\\Python33\\RIOTAPICHALLENGE\\GAME_DATA_FILES"
pre_ap_changes = "\\5.11"
post_ap_changes = "\\5.14"
norms = "\\NORMAL_5X5"
ranked = "\\RANKED_SOLO"
champion_list = rawpi.get_champion_list('na', dataById=True).json()["data"]
final_data = open('C:\\Python33\\RIOTAPICHALLENGE\\final_data.json')
working_data = json.load(final_data)
final_data.close()
final_polished_data = open("C:\\Python33\\RIOTAPICHALLENGE\\db_data.json")
working_final_polished_data = json.load(final_polished_data)
final_polished_data.close()
#input should be a list of stuff
def sample_mean(sample):
return sum(sample)/len(sample)
def sample_variance(sample):
FULL_AP_ITEMS = util.FULL_AP_ITEMS
REGIONS = util.REGIONS_SHORT
REGION_DICT = util.REGION_DICT
REVERSE_REGION_DICT = util.REVERSE_REGION_DICT
PATCHES = util.PATCHES
# Create dictionaries from codes to names and vice-versa for items and champs.
item_data = rawpi.get_item_list('na').json()['data']
ITEM_DICT = {i: item_data[i]['name'] for i in item_data.keys()}
REVERSE_ITEM_DICT = {ITEM_DICT[i]: i for i in ITEM_DICT}
champ_data = rawpi.get_champion_list('na').json()['data']
CHAMP_DICT = {champ_data[name]['id']: name for name in champ_data}
# Define methods for creating DataFrame rows. These are structured as dicts
# with keys being column names and values being DataFrame values. These methods
# function by passing a dictionary and adding to them. This dictionary can then
# be added to a list that a DataFrame can be created from.
def get_item_purchases(timeline):
"""
Given a timeline json object, returns a dictionary of
participant ID: [list of item purchases] pairs.
List will be sorted in chronological order.
