Introduction

Create standings from JFL match results continued

Scraping

Same as the scraping of the previous Create standings from JFL match results

import requests
from bs4 import BeautifulSoup

url = "http://www.jfl.or.jp/jfl-pc/view/s.php?a=1542&f=2020A001_spc.html"

r = requests.get(url)
r.raise_for_status()

soup = BeautifulSoup(r.content, "html.parser")

data = []

for table in soup.find_all("table", class_="table-data"):

    trs = table.find_all("tr")

    th = int(trs[0].th.get_text(strip=True).strip("Section"))

    for i, tr in enumerate(trs[1:], 1):

        tds = [td.get_text(strip=True) for td in tr.find_all("td")]

        data.append([th, i] + tds)

Data wrangling

import pandas as pd

df = pd.DataFrame(
    data, columns=["section", "number", "date", "time", "home", "Score", "Away", "Stadium", "Remarks"]
)

df.set_index(["section", "number"], inplace=True)

df

df_score = (
    df["Score"].str.split("-", expand=True).rename(columns={0: "Home score", 1: "Away score"})
)

df_score["Home score"] = pd.to_numeric(df_score["Home score"], errors="coerce").astype("Int64")
df_score["Away score"] = pd.to_numeric(df_score["Away score"], errors="coerce").astype("Int64")

df1 = pd.concat([df, df_score], axis=1).dropna(subset=["Home score", "Away score"])

#Home results only
df_home = df1.loc[:, ["home", "Away", "home得点", "Away得点"]].copy()
df_home.rename(
    columns={"home": "Team name", "Away": "Opponent", "homescore": "score", "Awayscore": "Conceded"}, inplace=True
)
df_home["War"] = "H"
df_home.head()

#Away results only
df_away = df1.loc[:, ["Away", "home", "Away得点", "home得点"]].copy()
df_away.rename(
    columns={"Away": "Team name", "home": "Opponent", "Awayscore": "score", "homescore": "Conceded"}, inplace=True
)
df_away["War"] = "A"
df_away.head()

#Combine home and away
df_total = pd.concat([df_home, df_away])

df_total

jfl_2020 = [
    "Ｈｏｎｄａ ＦＣ",
    "Sony Sendai FC",
    "Tokyo Musashino City FC",
    "Tegevajaro Miyazaki",
    "Honda Lock SC",
    "Verspah Oita",
    "FC Osaka",
    "MIO Biwako Shiga",
    "Veertien Mie",
    "FC Maruyasu Okazaki",
    "Suzuka Point Getters",
    "Line mail Aomori",
    "Nara club",
    "Matsue City FC",
    "Iwaki FC",
    "Kochi United SC",
]

df_total

df_total["result"] = df_total.apply(
    lambda x: f'{x["score"]}△{x["Conceded"]}'
    if x["score"] == x["Conceded"]
    else f'{x["score"]}○{x["Conceded"]}'
    if x["score"] > x["Conceded"]
    else f'{x["score"]}●{x["Conceded"]}',
    axis=1,
)

df_total

#Battle record table total
pv_senseki = df_total.pivot(values="result", index=["Team name", "War"], columns="対War相手").fillna("")

new_idx = pd.MultiIndex.from_product(
    [jfl_2020, ["H", "A"]], names=pv_senseki.index.names
)

jfl_senseki = pv_senseki.reindex(new_idx, columns=jfl_2020)

jfl_senseki

print(jfl_senseki.to_markdown())

	ＨｏｎｄａＦＣ	Sony Sendai FC	Tokyo Musashino City FC	Tegevajaro Miyazaki	Honda Lock SC	Verspah Oita	FC Osaka	MIO Biwako Shiga	Veertien Mie	FC Maruyasu Okazaki	Suzuka Point Getters	Line mail Aomori	Nara club	Matsue City FC	Iwaki FC	Kochi United SC
('ＨｏｎｄａＦＣ', 'H')			1△1					3○1	1△1			1○0
('ＨｏｎｄａＦＣ', 'A')		4○0			3○0		1△1				1○0
('Sony Sendai FC', 'H')	0●4												1△1			3○1
('Sony Sendai FC', 'A')			0●2			4○2				2○1		2○0		1●2
('Tokyo Musashino City FC', 'H')		2○0		1●2			2○1
('Tokyo Musashino City FC', 'A')	1△1										0●1					1△1
('Tegevajaro Miyazaki', 'H')					1△1								1●2	2○0
('Tegevajaro Miyazaki', 'A')			2○1					4○1	0△0		1○0
('Honda Lock SC', 'H')	0●3					1●2	0●4						0●3
('Honda Lock SC', 'A')				1△1										3○1	2○1	1○0
('Verspah Oita', 'H')		2●4								1○0		1●2			4○1
('Verspah Oita', 'A')					2○1				2○0				2○1
('FC Osaka', 'H')	1△1							1○0	1●2	0△0				2○1
('FC Osaka', 'A')			1●2		4○0										2●3
('MIO Biwako Shiga', 'H')				1●4							3△3	6○0		1○0
('MIO Biwako Shiga', 'A')	1●3						0●1		2○1	3○1
('Veertien Mie', 'H')				0△0		0●2		1●2		3○2
('Veertien Mie', 'A')	1△1						2○1						0●1	1●2
('FC Maruyasu Okazaki', 'H')		1●2						1●3				1△1			0●2
('FC Maruyasu Okazaki', 'A')						0●1	0△0		2●3							2○0
('Suzuka Point Getters', 'H')	0●1		1○0	0●1										1●2	1○0
('Suzuka Point Getters', 'A')								3△3					1○0			2△2
('Line mail Aomori', 'H')		0●2												3○0	1△1
('Line mail Aomori', 'A')	0●1					2○1		0●6		1△1
('Nara club', 'H')						1●2			1○0		0●1					1△1
('Nara club', 'A')		1△1		2○1	3○0										1●2
('Matsue City FC', 'H')		2○1			1●3				2○1							2○1
('Matsue City FC', 'A')				0●2			1●2	0●1			2○1	0●3
('Iwaki FC', 'H')					1●2		3○2						2○1			4○3
('Iwaki FC', 'A')						1●4				2○0	0●1	1△1
('Kochi United SC', 'H')			1△1		0●1					0●2	2△2
('Kochi United SC', 'A')		1●3											1△1	1●2	3●4

[PYTHON] Create a record table from JFL match results

Introduction

Scraping

Data wrangling