1810に戻すとつながるがよく切れるらしい、省電力はチェックを外して様子見
接続が切れているのでこれと交換 1903でしばらく使ってみたが安定して動いてます
インテル デュアルバンド 高速 Wi-Fi 通信Band Wireless-802.11 AC Intel 7260 無線LANカード 7260HMW
貯水率がないのですが計算しているのかな？
[http://i.river.go.jp/-p01-/p/ktm1801060/?mtm=0&swd=&prf=2301&twn=2301221&rvr=&den=1920300700002]
http://163.49.30.82/cgi-bin/DspDamData.exe?ID=1368050651020&KIND=3&PAGE=0
利水容量28420千m3
import re import requests from bs4 import BeautifulSoup dam_name = '宇連ダム' url = 'http://i.river.go.jp/_-p01-_/p/ktm1801060/?mtm=0&swd=&prf=2301&twn=2301221&rvr=&den=1920300700002' headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; rv:11.0) like Gecko' } r = requests.get(url, headers=headers) if r.status_code == 200: soup = BeautifulSoup(r.content, 'html5lib') contents = soup.find('a', {'name': 'contents'}).get_text('\n', strip=True) # print(contents) # サブタイトル・単位を除去 _temp = re.sub(r'■\d{1,2}時間履歴\n単位：千m3\n', '', contents) lines = [i.split() for i in _temp.splitlines()] for line in lines: try: _time, _rate = line rate = float(_rate) except: continue else: rate = round(rate / 28420 * 100, 1) print('ただいまの{0}の貯水率は{1}%です（{2}）'.format(dam_name, rate, _time)) break else: print('取得できませんでした')
import csv import time from urllib.parse import urljoin import gspread import pandas as pd import requests from bs4 import BeautifulSoup from oauth2client.service_account import ServiceAccountCredentials from tqdm import tqdm def cleaning(info, team, data): result = [] for trs in data: temp = [i.get_text(strip=True) for i in trs.select("th, td")] # 時間の分を除去後、延長時間を計算 temp[0] = eval(temp[0].rstrip("分")) # 選手名のPKを削除 temp[2] = temp[2].replace("(PK)", "").strip() result.append(info + [team] + temp) return result def scraping(n, url): r = requests.get(url) if r.status_code == requests.codes.ok: soup = BeautifulSoup(r.content, "html5lib") # シーズン・節 score_season = soup.select_one( "div.score-header > h2.score-meta > span.score-season" ).get_text(strip=True) # 節 score_season = score_season.strip("第節") # print(score_season) # 日時 score_date = ( soup.select_one("div.score-header > h2.score-meta > span.score-date") .get_text(strip=True) .split() ) # print(score_date) # チーム名 score_table = soup.select_one("table.score-table") home_team = score_table.select_one("th.score-team1").get_text(strip=True) away_team = score_table.select_one("th.score-team2").get_text(strip=True) # print(home_team, away_team) # 試合情報 game_info = [n, score_season] + score_date + [home_team, away_team] # 得点 for i in soup.select("div.section > h3"): # 得点のテーブルか確認 if i.text == "得 点": table = [ trs for trs in i.parent.select( "div.score-frame > div.score-left > table > tbody > tr" ) ] home_data = cleaning(game_info, home_team, table) table = [ trs for trs in i.parent.select( "div.score-frame > div.score-right > table > tbody > tr" ) ] away_data = cleaning(game_info, away_team, table) score_data = home_data + away_data return score_data return None if __name__ == "__main__": url = "http://www.jfl.or.jp/jfl-pc/view/s.php?a=1411&f=2019A001_spc.html" r = requests.get(url) if r.status_code == requests.codes.ok: soup = BeautifulSoup(r.content, "html5lib") with open("result.csv", "w") as fw: writer = csv.writer(fw, dialect="excel", lineterminator="\n") # ヘッダー writer.writerow( ["試合", "節", "日付", "時刻", "ホーム", "アウェイ", "チーム名", "時間", "背番号", "選手名"] ) n = 0 links = soup.select("td.detail-link > a") for link in tqdm(links): # 詳細のリンクか確認 if link.text == "詳細": n += 1 spc_url = urljoin(url, link.get("href")) # 詳細をスクレイピング score_data = scraping(n, spc_url) # CSVに保存 if score_data: writer.writerows(score_data) # 3秒待機 time.sleep(3) df = pd.read_csv("result.csv") df["得点"] = 1 # ゴール数ランキング pv_goal = df.pivot_table( values="得点", index=["選手名", "チーム名", "背番号"], aggfunc=sum, fill_value=0 ) pv_goal = pv_goal.reset_index() # オウンゴールを削除 pv_goal.drop(pv_goal.index[pv_goal["選手名"] == "オウンゴール"], inplace=True) pv_goal["背番号"] = pv_goal["背番号"].astype(int) # ランキング pv_goal["順位"] = pv_goal["得点"].rank(ascending=False, method="min").astype(int) # チーム jfl_2019 = [ "Ｈｏｎｄａ ＦＣ", "ＦＣ大阪", "ソニー仙台ＦＣ", "ＦＣ今治", "東京武蔵野シティＦＣ", "ＭＩＯびわこ滋賀", "奈良クラブ", "ヴェルスパ大分", "ラインメール青森", "ヴィアティン三重", "テゲバジャーロ宮崎", "ＦＣマルヤス岡崎", "ホンダロックＳＣ", "流経大ドラゴンズ龍ケ崎", "松江シティＦＣ", "鈴鹿アンリミテッド", ] team = {name: i for i, name in enumerate(jfl_2019, 1)} pv_goal["チームID"] = pv_goal["チーム名"].map(team) # 順位・チーム名・選手名で昇順 pv_goal.sort_values( ["順位", "チームID", "背番号"], ascending=[True, True, True], inplace=True ) pv_goal.drop(["チームID", "背番号"], axis=1, inplace=True) pv_goal.set_index("順位", inplace=True) # スプレッドシート scope = [ "https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive", ] credentials = ServiceAccountCredentials.from_json_keyfile_name( "jfl-ranking.json", scope ) gc = gspread.authorize(credentials) workbook = gc.open_by_key("xxxxxxxxxxxxxxxxxxxxxxxxxxxxx") # ランキング更新 rank_data = pv_goal.reset_index().values.flatten().tolist() worksheet = workbook.worksheet("得点ランキング") cell_list = worksheet.range("A2:D201") for cell, v in zip(cell_list, rank_data): cell.value = v worksheet.update_cells(cell_list)
![できる仕事がはかどるPython自動処理全部入り。 [ 森本哲也 ]](https://thumbnail.image.rakuten.co.jp/@0_mall/book/cabinet/6251/9784295006251.jpg?_ex=128x128 "できる仕事がはかどるPython自動処理全部入り。 [ 森本哲也 ]")
![日経ソフトウエア 2019年 07月号 [雑誌]](https://thumbnail.image.rakuten.co.jp/@0_mall/book/cabinet/0799/4910071890799.jpg?_ex=128x128 "日経ソフトウエア 2019年 07月号 [雑誌]")