我正试图抓住BBC足球比赛结果网站,以获得球队,投篮,进球,卡牌和事故。我目前有3个团队数据传入URL。
我用Python编写脚本并使用Beautiful soup bs4
包。将结果输出到屏幕时,将打印第一个团队,第一个和第二个团队,然后是第一个,第二个和第三个团队。因此,当我试图让三支球队只进行一次时,第一支队伍有效地被打印了3次。
我将此问题排序后,我会将结果写入文件。我将团队数据添加到数据框中然后添加到列表中(我不确定这是否是最好的方法)。
我确定是否与for
循环有关,但我不确定如何解决问题。
代码:
from bs4 import BeautifulSoup
import urllib2
import pandas as pd
out_list = []
for numb in('EFBO839787', 'EFBO839786', 'EFBO815155'):
url = 'http://www.bbc.co.uk/sport/football/result/partial/' + numb + '?teamview=false'
teams_list = []
inner_page = urllib2.urlopen(url).read()
soupb = BeautifulSoup(inner_page, 'lxml')
for report in soupb.find_all('td', 'match-details'):
home_tag = report.find('span', class_='team-home')
home_team = home_tag and ''.join(home_tag.stripped_strings)
score_tag = report.find('span', class_='score')
score = score_tag and ''.join(score_tag.stripped_strings)
shots_tag = report.find('span', class_='shots-on-target')
shots = shots_tag and ''.join(shots_tag.stripped_strings)
away_tag = report.find('span', class_='team-away')
away_team = away_tag and ''.join(away_tag.stripped_strings)
df = pd.DataFrame({'away_team' : [away_team], 'home_team' : [home_team], 'score' : [score], })
out_list.append(df)
for shots in soupb.find_all('td', class_='shots'):
home_shots_tag = shots.find('span',class_='goal-count-home')
home_shots = home_shots_tag and ''.join(home_shots_tag.stripped_strings)
away_shots_tag = shots.find('span',class_='goal-count-away')
away_shots = away_shots_tag and ''.join(away_shots_tag.stripped_strings)
dfb = pd.DataFrame({'home_shots': [home_shots], 'away_shots' : [away_shots] })
out_list.append(dfb)
for incidents in soupb.find("table", class_="incidents-table").find("tbody").find_all("tr"):
home_inc_tag = incidents.find("td", class_="incident-player-home")
home_inc = home_inc_tag and ''.join(home_inc_tag.stripped_strings)
type_inc_goal_tag = incidents.find("td", "span", class_="incident-type goal")
type_inc_goal = type_inc_goal_tag and ''.join(type_inc_goal_tag.stripped_strings)
type_inc_tag = incidents.find("td", class_="incident-type")
type_inc = type_inc_tag and ''.join(type_inc_tag.stripped_strings)
time_inc_tag = incidents.find('td', class_='incident-time')
time_inc = time_inc_tag and ''.join(time_inc_tag.stripped_strings)
away_inc_tag = incidents.find('td', class_='incident-player-away')
away_inc = away_inc_tag and ''.join(away_inc_tag.stripped_strings)
df_incidents = pd.DataFrame({'home_player' : [home_inc],'event_type' : [type_inc_goal],'event_time': [time_inc],'away_player' : [away_inc]})
out_list.append(df_incidents)
print "end"
print out_list
我是python和堆栈溢出的新手,关于格式化我的问题的任何建议也很有用。
提前致谢!
答案 0 :(得分:1)
这3个for循环应该在你的main for循环中。
[{'score': ['1-3'], 'away_team': ['Man City'], 'home_team': ['Dynamo Kiev']},
{'score': ['1-0'], 'away_team': ['Zenit St P'], 'home_team': ['Benfica']},
{'score': ['1-2'], 'away_team': ['Boston United'], 'home_team': ['Bradford Park Avenue']}]
效果很好 - 只出现一次团队。
这是第一个for循环的输出:
var canvas = document.getElementById("canvas");
var context = canvas.getContext("2d");
var img = new Image();
img.src = "data:image/png;base64,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";
context.resetTransform();
context.translate(205, 205);
context.drawImage(img, -201,-201,402,402);
var angle = 0;
var angularVelocity = .01;
function draw() {
// ease out
angularVelocity += .0035;
angle -= (1 / angularVelocity);
context.resetTransform();
context.clearRect(0, 0, canvas.width, canvas.height);
context.translate(205, 205);
context.rotate(angle * Math.PI/180);
context.drawImage(img, -201,-201,402,402);
if (angularVelocity+0.1<1){
spin = requestAnimationFrame(draw);
}
}
function startSpin() {
angularVelocity = .01;
spin = requestAnimationFrame(draw);
}
答案 1 :(得分:0)
这看起来像打印问题,在打印out_list的缩进级别是什么?
它应该是零缩进,一直到代码中的左边。
或者你想将out_list移动到最顶层的循环中,以便在每次迭代后重新分配。