I would like to read the following html,
import pandas as pd
daily_info=pd.read_html(\'https://www.investing.com/earnings-calendar/\',flavor=\'html5lib\')
pr
Pretend to be a browser:
import requests
url = 'https://www.investing.com/earnings-calendar/'
header = {
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36",
"X-Requested-With": "XMLHttpRequest"
}
r = requests.get(url, headers=header)
dfs = pd.read_html(r.text)
Result:
In [201]: len(dfs)
Out[201]: 7
In [202]: dfs[0]
Out[202]:
0 1 2 3
0 NaN NaN NaN NaN
In [203]: dfs[1]
Out[203]:
Unnamed: 0 Company EPS / Forecast Revenue / Forecast.1 Market Cap Time \
0 Monday, April 24, 2017 NaN NaN NaN NaN NaN NaN NaN
1 NaN Acadia (AKR) -- / 0.11 -- / -- 2.63B NaN
2 NaN Agree (ADC) -- / 0.39 -- / -- 1.34B NaN
3 NaN Alcoa (AA) -- / 0.53 -- / -- 5.84B NaN
4 NaN American Campus (ACC) -- / 0.27 -- / -- 6.62B NaN
5 NaN Ameriprise Financial (AMP) -- / 2.52 -- / -- 19.76B NaN
6 NaN Avacta Group (AVTG) -- / -- 1.26M / -- 47.53M NaN
7 NaN Bank of Hawaii (BOH) 1.2 / 1.08 165.8M / -- 3.48B NaN
8 NaN Bank of Marin (BMRC) 0.74 / 0.8 -- / -- 422.29M NaN
9 NaN Banner (BANR) -- / 0.68 -- / -- 1.82B NaN
10 NaN Barrick Gold (ABX) -- / 0.2 -- / -- 22.44B NaN
11 NaN Barrick Gold (ABX) -- / 0.28 -- / -- 30.28B NaN
12 NaN Berkshire Hills Bancorp (BHLB) -- / 0.54 -- / -- 1.25B NaN
13 NaN Brookfield Canada Office Properties (BOXC) -- / -- -- / -- NaN NaN
...