I want to get League of Legends data ①

I want to get League of Legends data

This is the first post to commemorate.

Introduction

In this article, I wanted to get the data of "League of Legends" (hereinafter LOL), which is the game I'm addicted to, and numerically express the winning percentage and the champions I'm good at. (Well, sites like OPGG and YOURGG already exist, but my own For studying)

environment

As of January 29, 2020 OS:windows10 Anaconda:4.8.1 python:3.7.6

Advance preparation

Since the API is officially provided when collecting LOL data, we will use it this time. To do this, type the command below to install *** riotwather ***.

python -m pip install riotwatcher

You have to get the API KEY, so log in from here and remember the DEVELOPMENT API KEY. (This API KEY needs to be updated and rewritten if it becomes unusable due to the expiration date of 1 day.)

Get the data

(https://qiita.com/hyo_07/items/f106efbde82aeb47c8a0) I referred to this article.

sample_1.py

from riotwatcher import RiotWatcher

API_KEY="{The API KEY you just got}"
wather = RiotWatcher(API_KEY)
region = "jp1" #Server selection: Japanese mackerel is jp1
name = "Macha-kun" #Enter the summoner name
summoner = watcher.summoner.by_name(region,name) #Acquisition of player data
print(summoner)

When this is executed, the contents of the dictionary type summoner can be output. Actually, the contents are ["id": str, "accountId": str, "puuid": str, "fileIconId": int, "revisionDate": int, "summonerLevel": int].

sample_1.py

recentmatchlists = watcher.match.matchlist_by_account(region,summoner["accountId"]) #Get a list of data for the last 20 games
matches = recentmatchlists["matches"]
match_data = watcher.match.by_id(region,matches[0]["gameId"]) #Extract only one match
print(match_data)

Add these to the previous code and output the data for one game. As you can see from this, there is a huge amount of data even in one game. (I couldn't take a screenshot because it didn't fit on the screen)

Examine the contents of the data

Let's take a look at the contents of ** match_data ** one by one.

The data I want this time is the number of wins and losses, the number of kills, the number of deaths, the number of assists, the kill involvement rate, the champion used, the champion who played, the lane, CS, the gold earned, the damage done to the champion, the time of the game. To go. The game mode is narrowed down to rank.

Let's find the item just mentioned from the previous output. (It's hard to find with a lot of letters: D) By the way, match_data is a dict type.

It looks like the above when roughly divided. However, at this stage, the information I want is clear only for ** gameDuration ** and ** gameMode **. ** temas **, ** partitions ** and ** partial Identities ** are of * list type *, so it seems necessary to take a closer look.

So, let's take a look at the contents of ** teams ** one by one.

match_data["teams"][0] #Team 1
match_data["teams"][1] #Team 2

** bans ** is a * list type *, so let's dig deeper.

match_data["teams"][0]["bans"] #Team 1 BAN
match_data["teams"][1]["bans"] #Team 2 BAN

Now let's look at ** partials **.

match_data["participants"][0] #Take data for one person

There are ** stats ** and ** timeline ** of * dict type *, so let's check the details by following the example.

match_data["participants"][0]["stats"] #Output stats for one person

The amount is amazing, and I have no idea what the data represents from the middle.

Let's take a second look and look at the ** timeline ** in the same way.

match_data["participants"][0]["timeline"] #Output timeline for one person

Where it is * dict type *, data such as * "0-10" * and * "10-20" * at 10-minute intervals are stored in * float type *.

Finally, let's examine ** partial Identities **.

match_data["participantIdentities"][0] #Output partial Identities for one person

Check the contents of ** player **.

match_data["participantIdentities"][0]["player"] #Player information for one player

Finally

It takes a lot of patience to check this amount of data one by one. The place where the data description is blank is where it is not necessary this time. I want to fill it someday: D

Next, I would like to go to the work of analyzing using this extracted data.