使用 Beautiful Soup 和 Selenium 向 CSV 插入數據

Question

我正在使用beautifulsoup和selenium來抓取 python 中的一些數據。這是我通過 url https://www.flashscore.co.uk/match/YwbnUyDn/#/match-summary/point-by-point/10運行的代碼https://www.flashscore.co.uk/match/YwbnUyDn/#/match-summary/point-by-point/10 ：

from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from bs4 import BeautifulSoup

DRIVER_PATH = '$PATH/chromedriver.exe'

options = Options()
options.headless = True
options.add_argument("--window-size=1920,1200")

driver = webdriver.Chrome(options=options, executable_path=DRIVER_PATH)

class_name = "matchHistoryRow__dartThrows"

def write_to_output(url):  
    driver.get(url)
    soup = BeautifulSoup(driver.page_source, 'html.parser')
    print(soup.find_all("div", {"class": class_name}))
    return

這是我試圖抓取的模式——我想獲取冒號之間的一對跨度並將它們放入 csv 的單獨列中，問題是class出現在冒號之前或之后，所以我不是確定如何 go 了解如何執行此操作。 例如：

<div class="matchHistoryRow__dartThrows"><span><span class="matchHistoryRow__dartServis">321</span>:<span>501</span>
        <span class="dartType dartType__180" title="180 thrown">180</span></span>, <span><span>321</span>:<span
            class="matchHistoryRow__dartServis">361</span><span class="dartType dartType__140"
            title="140+ thrown">140+</span></span>, <span><span
            class="matchHistoryRow__dartServis">224</span>:<span>361</span></span></div>

我希望在 csv 中以這種方式表示：

player_1_score,player_2_score
321,501
321,361
224,361

go 的最佳方式是什么？

Answer 1

您可以使用正則表達式來解析分數（最簡單的方法，如果文本結構相應）：

import re
import pandas as pd
from bs4 import BeautifulSoup


html_doc = """
<div class="matchHistoryRow__dartThrows"><span><span class="matchHistoryRow__dartServis">321</span>:<span>501</span>
        <span class="dartType dartType__180" title="180 thrown">180</span></span>, <span><span>321</span>:<span
            class="matchHistoryRow__dartServis">361</span><span class="dartType dartType__140"
            title="140+ thrown">140+</span></span>, <span><span
            class="matchHistoryRow__dartServis">224</span>:<span>361</span></span></div>
"""

soup = BeautifulSoup(html_doc, "html.parser")

# 1. parse whole text from a row
txt = soup.select_one(".matchHistoryRow__dartThrows").get_text(
    strip=True, separator=" "
)

# 2. find scores with regex
scores = re.findall(r"(\d+)\s+:\s+(\d+)", txt)

# 3. create dataframe from regex
df = pd.DataFrame(scores, columns=["player_1_score", "player_2_score"])
print(df)
df.to_csv("data.csv", index=False)

印刷：

  player_1_score player_2_score
0            321            501
1            321            361
2            224            361

---

這data.csv （來自 LibreOffice 的屏幕截圖）：

---

另一種方法，不使用re ：

scores = [
    s.get_text(strip=True)
    for s in soup.select(
        ".matchHistoryRow__dartThrows > span > span:nth-of-type(1), .matchHistoryRow__dartThrows > span > span:nth-of-type(2)"
    )
]

df = pd.DataFrame(
    {"player_1_score": scores[::2], "player_2_score": scores[1::2]}
)

print(df)

Answer 2

使用Selenium和player_1_score 的 css-selectors你需要span:first-child和player_2_score你需要span:nth-child(2) 。 因此，您可以使用以下解決方案：

driver.get('https://www.flashscore.co.uk/match/YwbnUyDn/#/match-summary/point-by-point/10')
WebDriverWait(driver, 20).until(EC.element_to_be_clickable((By.CSS_SELECTOR, "button#onetrust-accept-btn-handler"))).click()
player_1_scores = [my_elem.text for my_elem in WebDriverWait(driver, 20).until(EC.visibility_of_all_elements_located((By.CSS_SELECTOR, "div.matchHistoryRow__dartThrows span span:first-child")))[:3]]
player_2_scores = [my_elem.text for my_elem in WebDriverWait(driver, 20).until(EC.visibility_of_all_elements_located((By.CSS_SELECTOR, "div.matchHistoryRow__dartThrows span span:nth-child(2)")))[:3]]
df = pd.DataFrame(data=list(zip(player_1_scores, player_2_scores)), columns=['player_1_score', 'player_2_score'])
print(df)

控制台 Output：

  player_1_score player_2_score
0            501            321
1            361            321
2            361            181

寫入CSV ：

df = pd.DataFrame(data=list(zip(player_1_scores, player_2_scores)), columns=['player_1_score', 'player_2_score'])
df.to_csv("my_data.csv", index=False)

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