Web-scraping investment fund data into Pandas

Just starting out trying to do some python for data analysis. I am a total beginner and have figure out how to brute force somethings, but I know it is inefficient but don't know how to do it without messing up what i have.

I have multiple webpages to scrape and then store data in a data frame. The code is identical for all of the pages. How do I set it up as a routine instead just repeating the same code over and over again.

As an example the two urls are: https://etfdb.com/etf/IWD/ https://etfdb.com/etf/IWF/

The html is identical so the web scraping working exactly the same for both.

Once scraped, I want them in a single data frame.

The below works, but is me taking the least sophisticated approach since I know so little. The actual code is likely not pretty but it works.

Any help appreciated on how this should be improved.

import bs4
from urllib.request import urlopen as uReq
from bs4 import BeautifulSoup as soup
import pandas as pd
import numpy as np
from IPython.display import display

iwd_url = 'https://etfdb.com/etf/IWD/'
uClient = uReq(iwd_url)
uClient.close()
page_soup = soup(page_html, "html.parser")

#Isolate header to get name and symbol
h1 = page_soup.h1

#Isolate stock symbol
title = h1.findAll("span",{"class":"label-primary"})
titlet = title[0].text

#print(titlet)

#strip space and line break
strip1 = h1.text.strip()

#strip stock symbol
strip2 = strip1.strip(titlet)

#strip remaining line break
strip3 = strip2.strip()

#print(strip3)

IWD = page_soup.findAll("table",{"class":"chart base-table"})[1]
#Create lists to fill
sectordata=[]
sectorname=[]
sectorweight=[]
for row in IWD.findAll("td"):

sectordata.append(row.text)
#list created

#Assign every other value to proper list to get 2 columns
sectorname = sectordata[::2]
sectorweight = sectordata[1::2]

#Insert name/symbol for clarification/validation
sectorweight.insert(0,titlet)
sectorname.insert(0,strip3)

# create empty data frame in pandas
df = pd.DataFrame()
#Add the first column to the empty dataframe.
df['Sector']  = sectorname

df['Weight'] = sectorweight
##display(df)

### NEXT

iwf_url = 'https://etfdb.com/etf/IWF/'
uClient = uReq(iwf_url)
uClient.close()
page_soup = soup(page_html, "html.parser")

#Isolate header to get name and symbol
h1 = page_soup.h1

#Isolate stock symbol
title = h1.findAll("span",{"class":"label-primary"})
titlet = title[0].text

#print(titlet)

#strip space and line break
strip1 = h1.text.strip()

#strip stock symbol
strip2 = strip1.strip(titlet)

#strip remaining line break
strip3 = strip2.strip()

#print(strip3)

IWD = page_soup.findAll("table",{"class":"chart base-table"})[1]
#Create lists to fill
sectordata=[]
sectorname=[]
sectorweight=[]
for row in IWD.findAll("td"):

sectordata.append(row.text)
#list created

#Assign every other value to proper list to get 2 columns
sectorname = sectordata[::2]
sectorweight = sectordata[1::2]

#Insert name/symbol for clarification/validation
sectorweight.insert(0,titlet)
sectorname.insert(0,strip3)

# create empty data frame in pandas
df2 = pd.DataFrame()
#Add the first column to the empty dataframe.
df2['Sector']  = sectorname

df2['Weight'] = sectorweight
#display(df2)

results = df.merge(df2, on = "Sector")
results.columns = ['Sector', 'IWD', 'IWF']
display(results)


Like I said, this works, but it isn't automated and its ham-handed way of getting there. Please help me to get better!

I created function get_soup because this code is offen used many times in code. In this code I could put all in get_data.

get_data gets url and uses get_soup with this url. Later it scrapes data from html, creates DataFrame and returns it

Main part uses get_data with two urls and gets two dataframes.

I put some other comments in code.

# <-- remove not used modules
# <-- use more popular names
from urllib.request import urlopen
from bs4 import BeautifulSoup as BS
import pandas as pd
from IPython.display import display

# --- functions ---
# <-- all functions before main part

def get_soup(url):
# <-- more readable names (and more popular)
response = urlopen(url)
response.close()
soup = BS(html, "html.parser")
return soup

def get_data(url):

soup = get_soup(url)

#Isolate header to get name and symbol
h1 = soup.h1

#Isolate stock symbol
# <-- find() to get only first item
title = h1.find("span",{"class":"label-primary"}).text

#print(title)

#strip space and line break
# <-- use the same variable instead strip,strip2, strip3
# <-- maybe too much comments

#strip stock symbol

#strip remaining line break

#print(strip)

# <-- use better name 'table'
table = soup.find_all("table",{"class":"chart base-table"})[1]

#Create lists to fill
#sector_data = [row.text for row in table.find_all("td")]
sector_data = []
# <-- remove lists which will be created later

for row in table.find_all("td"):
sector_data.append(row.text)

#Assign every other value to proper list to get 2 columns
sector_name = sector_data[::2]
sector_weight = sector_data[1::2]

#Insert name/symbol for clarification/validation
sector_weight.insert(0, title)

# create dataframe in pandas
# <-- create DF directly with data
df = pd.DataFrame({
'Sector': sector_name,
'Weight': sector_weight,
})

#display(df)

return df

# --- main ---

# <-- the same variable url because it keep the same type of data
#     and I will no need this value later - so I can resuse this name.
url = 'https://etfdb.com/etf/IWD/'
df1 = get_data(url)

url = 'https://etfdb.com/etf/IWF/'
df2 = get_data(url)

results = df1.merge(df2, on="Sector")
results.columns = ['Sector', 'IWD', 'IWF']
display(results)


Pandas read_html allows to

Read HTML tables into a list of DataFrame objects.

Using this we can store the urls in a list.

l=['https://etfdb.com/etf/IWD/','https://etfdb.com/etf/IWF/']


Then we read the urls and store them in a list:

dfs=[pd.read_html(i)[5].rename(columns={'Percentage':i.split('/')[-2]}) for i in l]


Once we have this list of dataframes, we can use a reduce merge to merge all the dataframes in the list:

from functools import reduce
df_final = reduce(lambda left,right: pd.merge(left,right,on='Sector'), dfs)
print(df_final)


Output

                    Sector     IWD     IWF
0               Financials  23.02%   3.21%
1               Healthcare  12.08%  14.04%
2              Industrials   9.27%   9.39%
3                   Energy   8.98%   0.35%
4   Consumer, Non-Cyclical   8.85%   4.69%
5           Communications    7.7%  11.27%
6               Technology   6.13%  36.46%
7       Consumer, Cyclical   5.86%  14.24%
8              Real Estate   5.15%   2.31%
9                    Other   3.54%   2.55%
10         Basic Materials   2.74%   1.34%
11      ETF Cash Component   0.33%   0.14%