In the latest version of pandas (0.19.2) you can directly pass the url

import pandas as pd

url = "https://raw.githubusercontent.com/cs109/2014_data/master/countries.csv"
c = pd.read_csv(url)

pd.read_csv does not parse HTML. You might try pd.read_html, but would find that it works on <table> tags, not <pre> tags.

On inspecting the HTML content of the given URL, it is evident that the data is contained in a <pre> tag.

Use something like requests to get the page content, and BeautifulSoup4 to parse the HTML page contents (with an appropriate parsing engine, either lxml or html5lib). Then pull out the content of the <pre> tag, splitting on newlines, slicing to ignore unwanted lines, and then splitting on whitespace.

Minimal working code:

import pandas as pd
import requests
from bs4 import BeautifulSoup

url = 'https://psl.noaa.gov/cgi-bin/data/timeseries/timeseries.pl?ntype=1&var=Zonal+Wind&level=1000&lat1=50&lat2=25&lon1=-135&lon2=-65&iseas=0&mon1=0&mon2=0&iarea=0&typeout=1&Submit=Create+Timeseries'
res = requests.get(url)

# get the text from the 'pre' tag, split it on newlines
# slice off 1 head and 5 tail rows
# (inspect the contents of 'soup.find('pre').text' to determine correct values)
soup = BeautifulSoup(res.content, "html5lib")
data = soup.find('pre').text.split("\n")[1:-5]

df = pd.DataFrame([row.split() for row in data]).apply(pd.to_numeric)
df = df.set_index(df.iloc[:,0])

results in

>>> print(df.head(5))
        0      1      2      3      4      5      6      7      8      9      10     11     12
0
1948  1948  0.878  0.779  0.851  0.393  0.461  0.747  0.867  0.539 -0.106  0.045  0.819  1.506
1949  1949  0.386  1.197  1.154  1.054  0.358  0.645  0.643  0.477  0.128 -0.091  1.500  0.390
1950  1950  0.674  0.973  1.640  0.821  0.572  1.002  0.635  0.196 -0.020  0.268  0.844  1.045
1951  1951  1.524  0.698  0.971  0.790  0.789  0.587  0.682  0.238  0.256  0.035  0.906  1.268
1952  1952  1.524  1.510  1.353  0.705  0.710  1.188  0.412  0.432 -0.091  0.415  0.443  1.509

and

>>> print(df.dtypes)
0       int64
1     float64
2     float64
...
12    float64

This answer is a good starting point for what you're trying to accomplish.

The reader works well but you don’t have the right number of columns in your header. You can get the other columns back using .reset_index() and then rename the columns:

>>> df = pd.read_csv(url, sep='\t')
>>> n_missing_headers = df.index.nlevels
>>> cols = df.columns.to_list() + [f'col{n}' for n in range(n_missing_headers)]
>>> df.reset_index(inplace=True)
>>> df.columns = cols
>>> df.head()
   industry_code SIC_code publishing_status    industry_name  col0 col1  col2
0              0      NaN                 B    Total nonfarm     1    T     1
1           5000      NaN                 A    Total private     1    T     2
2           5100      NaN                 A  Goods-producing     1    T     3
3         100000    10-14                 A           Mining     2    T     4
4         101000       10                 A     Metal mining     3    T     5

You can then keep the first 4 columns if you want:

>>> df.iloc[:, :-n_missing_headers].head()
   industry_code SIC_code publishing_status    industry_name
0              0      NaN                 B    Total nonfarm
1           5000      NaN                 A    Total private
2           5100      NaN                 A  Goods-producing
3         100000    10-14                 A           Mining
4         101000       10                 A     Metal mining

Just pass the url to pandas.read_csv

import  pandas as pd

df = pd.read_csv("http://www.nasdaq.com/screening/companies-by-industry.aspx?exchange=NASDAQ&render=download")

print(df)

Seems like there was some encoding issues with df.read_csv() it never splitted the code:

#!/usr/bin/env python3
import requests
import pandas as pd
url = "https://cdn.upgrad.com/UpGrad/temp/d934844e-5182-4b58-b896-4ba2a499aa57/companies.txt"
r = requests.get(url)
df = None
if r.status_code == 200: 
    rows  = r.text.split('\r\n')
    header = rows[0].split('\t')
    data = []
    for n in range(1, len(rows)):
        cols = rows[n].split('\t')
        data.append(cols)
    df = pd.DataFrame(columns=header, data=data)
else:
    print("error: unable to load {}".format(url))
    sys.exit(-1)
print(df.shape)
print(df.head(2))

    $ ./test.py
(66369, 10)
                permalink      name            homepage_url                                      category_list     status country_code state_code      region           city  founded_at
0     /Organization/-Fame     #fame      http://livfame.com                                              Media  operating          IND         16      Mumbai         Mumbai
1  /Organization/-Qounter  :Qounter  http://www.qounter.com  Application Platforms|Real Time|Social Network...  operating          USA         DE  DE - Other  Delaware City  04-09-2014