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main.py
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from bs4 import BeautifulSoup
import re
import os
import requests
from typing import List
import pandas as pd
from tqdm import tqdm
from matplotlib import pyplot as plt
import japanize_matplotlib
import datetime
import collections
URL = "https://kakaku.com/pc/pc-memory/itemlist.aspx?pdf_Spec105=1&pdf_so=e2&pdf_vi=d&pdf_pg={int}"
DIR = os.path.dirname(__file__)
JP_SPACE = ' '
DF_COL = ['Manufacturer',
'ProductName',
'ReleaseYear',
'ReleaseMonth',
'ReleaseDay',
'DDR_ver',
'Bandwidth']
DDR2_PIN = 240
DDR3_PIN = 240
DDR4_PIN = 288
DDR5_PIN = 288
def gen_URL(id: int) -> str:
return f"https://kakaku.com/pc/pc-memory/itemlist.aspx?pdf_Spec105=1&pdf_so=e2&pdf_vi=d&pdf_pg={id}"
def soup_htmlPage(url: str) -> str:
texted = requests.get(url).text
souped = BeautifulSoup(texted, "html.parser")
return souped
def get_resultTable(souped_html: BeautifulSoup) -> pd.DataFrame:
df = pd.DataFrame(columns=DF_COL)
# manifacture, name, etc
item_names = souped_html.select("td[class=\"ckitemLink\"]")
# release data str
item_release = souped_html.select("td[class=\"swdate1\"]")
# detail specs
item_spec_detail = souped_html.select("div[class=\"ckitemSpecInnr\"]")
item_count = len(item_names)
for i in range(item_count):
# parse each info
item_names_str = item_names[i].text
release_str = item_release[i].text
# ex: ドスパラ 【直販モデル】 D4N3200-16G1A2 [SODIMM DDR4 PC4-25600 16GB]
print(item_names_str)
manufacturer = re.search(r"(^.*)\u3000", item_names_str).group(1)
if manufacturer != "ノーブランド":
productname = re.search(
r"\u3000(.*) [\[\(]", item_names_str).group(1)
else:
productname = None
# PC{DDR_VERSION}-{BAND_WIDTH}の体裁でないものは拒否
specs = re.search(r".* ?PC([0-9])L?-([0-9]+) ?.*", item_names_str)
if specs == None:
continue
ddr_ver = int(specs.group(1))
band_width = int(specs.group(2))
release_date = re.search(r"([0-9]+)/([0-9]+)/ ?([0-9]+)", release_str)
yyyy = int(release_date.group(1))
mm = int(release_date.group(2))
dd = int(release_date.group(3))
append_df = pd.DataFrame(data=[[manufacturer,
productname,
yyyy,
mm,
dd,
ddr_ver,
band_width]],
columns=DF_COL)
df = pd.concat([df, append_df], ignore_index=True)
return df
def scrape():
id = range(1, 48) # 2023/20/17現在，最大47ページ
df = pd.DataFrame(columns=DF_COL)
dt_now = datetime.datetime.now()
for i in id:
print(f"{i}")
url = gen_URL(i)
soup = soup_htmlPage(url)
append_df = get_resultTable(soup)
df = pd.concat([df, append_df], ignore_index=True)
df.to_csv(f"{DIR}/{dt_now.year}{dt_now.month}{dt_now.day}{dt_now.hour}.csv")
target_year_products = df.query("2020 <= ReleaseYear <= 2022")
target_year_products.to_csv(f"{DIR}/2020_2022.csv")
def read_localCsv(filepath: str) -> pd.DataFrame:
return pd.read_csv(filepath)
def create_date_band_table(df: pd.DataFrame, ddr_ver: int) -> List:
ddr_div = df[df["DDR_ver"] == ddr_ver]
ddr_div = ddr_div.reset_index(drop=True)
date_band = pd.DataFrame()
date_band["Name"] = ddr_div["ProductName"]
date_band["Band"] = ddr_div["Bandwidth"] / 1000 # MB/s -> GB/s
date_band["Date"] = pd.to_datetime(ddr_div["ReleaseYear"].astype(str)+'-' +
ddr_div["ReleaseMonth"].astype(str)+'-' +
ddr_div["ReleaseDay"].astype(str),
format="%Y-%m-%d")
return date_band
if __name__ == "__main__":
# scrape()
df = read_localCsv(f"{DIR}/202310174.csv")
mfacs = df[["ReleaseYear", "ReleaseMonth",
"ReleaseDay", "DDR_ver", "Bandwidth"]]
ddr2 = create_date_band_table(df, 2)
ddr3 = create_date_band_table(df, 3)
ddr4 = create_date_band_table(df, 4)
ddr5 = create_date_band_table(df, 5)
plt.rcParams['figure.subplot.bottom'] = 0.15 # xラベル見切れ対処
scat_size = 8
# plt.scatter(ddr2["Date"], ddr2["Band"], label="DDR2", s=scat_size)
# plt.scatter(ddr3["Date"], ddr3["Band"], label="DDR3", s=scat_size)
# plt.scatter(ddr4["Date"], ddr4["Band"], label="DDR4", s=scat_size)
# plt.scatter(ddr5["Date"], ddr5["Band"], label="DDR5", s=scat_size)
plt.scatter(ddr2["Band"] / DDR2_PIN * 1000,
ddr2["Band"],
label="DDR2",
s=scat_size)
plt.scatter(ddr3["Band"] / DDR3_PIN * 1000,
ddr3["Band"],
label="DDR3",
s=scat_size)
plt.scatter(ddr4["Band"] / DDR4_PIN * 1000,
ddr4["Band"],
label="DDR4",
s=scat_size)
plt.scatter(ddr5["Band"] / DDR5_PIN * 1000,
ddr5["Band"],
label="DDR5",
s=scat_size)
plt.title("メモリの種類とバンド幅・ピンごとの転送速度の関係")
plt.legend()
plt.grid(linewidth=0.5)
plt.yscale("log", base=2)
plt.ylabel("バンド幅[GB/s]")
plt.xticks(rotation=30)
plt.xscale("log", base=2)
plt.xlabel("ピン1つあたりの転送速度[MB/s]")
plt.savefig(f"{DIR}/Band_pin__memoryType_wholeYear.png", dpi=900)