Pandas Read_HTML Valueerror: No Tables Found

pandas read_html ValueError: No tables found

You can use requests and avoid opening browser.

You can get current conditions by using:

https://stationdata.wunderground.com/cgi-bin/stationlookup?station=KMAHADLE7&units=both&v=2.0&format=json&callback=jQuery1720724027235122559_1542743885014&_=15

and strip of 'jQuery1720724027235122559_1542743885014(' from the left and ')' from the right. Then handle the json string.

You can get summary and history by calling the API with the following

https://api-ak.wunderground.com/api/606f3f6977348613/history_20170201null/units:both/v:2.0/q/pws:KMAHADLE7.json?callback=jQuery1720724027235122559_1542743885015&_=1542743886276

You then need to strip 'jQuery1720724027235122559_1542743885015(' from the front and ');' from the right. You then have a JSON string you can parse.

Sample of JSON:

You can find these URLs by using F12 dev tools in browser and inspecting the network tab for the traffic created during page load.

An example for current, noting there seems to be a problem with nulls in the JSON so I am replacing with "placeholder":

import requests
import pandas as pd
import json
from pandas.io.json import json_normalize
from bs4 import BeautifulSoup

url = 'https://stationdata.wunderground.com/cgi-bin/stationlookup?station=KMAHADLE7&units=both&v=2.0&format=json&callback=jQuery1720724027235122559_1542743885014&_=15'
res = requests.get(url)
soup = BeautifulSoup(res.content, "lxml")
s = soup.select('html')[0].text.strip('jQuery1720724027235122559_1542743885014(').strip(')')
s = s.replace('null','"placeholder"')
data= json.loads(s)
data = json_normalize(data)
df = pd.DataFrame(data)
print(df)

Python Pandas - read_html No tables Found

There's no table but you're in luck because the data is coming from a fetch:

https://datacrunch.9c9media.ca/statsapi/sports/hockey/leagues/nhl/sortablePlayerSeasonStats/skater?brand=tsn&type=json&seasonType=regularSeason&season=2021

pandas read_html - no tables found

the page is dynamic which means you'll need to to render the page first. So you would need to use something like Selenium to render the page, then you can pull the table using pandas .read_html():

from selenium import webdriver
import pandas as pd

driver = webdriver.Chrome('C:/chromedriver_win32/chromedriver.exe')
driver.get("https://www.wunderground.com/history/daily/us/wi/milwaukee/KMKE/date/2013-6-26")

html = driver.page_source

tables = pd.read_html(html)
data = tables[1]

driver.close()

Output:

print (data)
        Time Temperature      ...       Precip Accum      Condition
0    6:52 PM        68 F      ...             0.0 in  Mostly Cloudy
1    7:52 PM        69 F      ...             0.0 in  Mostly Cloudy
2    8:52 PM        70 F      ...             0.0 in  Mostly Cloudy
3    9:52 PM        67 F      ...             0.0 in         Cloudy
4   10:52 PM        65 F      ...             0.0 in  Partly Cloudy
5   11:42 PM        66 F      ...             0.0 in  Mostly Cloudy
6   11:52 PM        68 F      ...             0.0 in  Mostly Cloudy
7   12:08 AM        68 F      ...             0.0 in         Cloudy
8   12:52 AM        68 F      ...             0.0 in  Mostly Cloudy
9    1:52 AM        70 F      ...             0.0 in         Cloudy
10   2:13 AM        70 F      ...             0.0 in         Cloudy
11   2:52 AM        71 F      ...             0.0 in         Cloudy
12   3:52 AM        70 F      ...             0.0 in  Mostly Cloudy
13   4:19 AM        70 F      ...             0.0 in         Cloudy
14   4:29 AM        70 F      ...             0.0 in         Cloudy
15   4:52 AM        70 F      ...             0.0 in         Cloudy
16   5:25 AM        70 F      ...             0.0 in  Mostly Cloudy
17   5:52 AM        71 F      ...             0.0 in         Cloudy
18   6:52 AM        73 F      ...             0.0 in         Cloudy
19   7:52 AM        74 F      ...             0.0 in         Cloudy
20   8:52 AM        73 F      ...             0.0 in         Cloudy
21   9:52 AM        71 F      ...             0.0 in         Cloudy
22  10:52 AM        71 F      ...             0.0 in         Cloudy
23  11:52 AM        70 F      ...             0.0 in         Cloudy
24  12:52 PM        72 F      ...             0.0 in  Mostly Cloudy
25   1:52 PM        70 F      ...             0.0 in  Mostly Cloudy
26   2:52 PM        71 F      ...             0.0 in  Mostly Cloudy
27   3:52 PM        71 F      ...             0.0 in  Partly Cloudy
28   4:52 PM        68 F      ...             0.0 in  Mostly Cloudy
29   5:52 PM        66 F      ...             0.0 in  Mostly Cloudy

[30 rows x 11 columns]

pd.read_html-ValueError: No tables found

pd.read_html() is for processing HTML documents, reading data contained within the <table> tag inside the HTML document to be processed.

To process CSV files, you need pd.read_csv()

This will accept a URL as an argument, so the following should work for you:

import pandas as pd
url = "https://raw.githubusercontent.com/hadley/data-baby-names/master/baby-names.csv"
df = pd.read_csv(url)
df.head()

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