I am trying to scrape IB website. So, what I am doing, I have created the urls to iterate over, and I am able to extract the required information, but seems the dataframe keeps being overwritten vs appending.
import pandas as pd
from pandas import DataFrame as df
from bs4 import BeautifulSoup
import csv
import requests
base_url = "https://www.interactivebrokers.com/en/index.phpf=2222&exch=mexi&showcategories=STK&p=&cc=&limit=100"
n = 1
url_list = []
while n <= 2:
url = (base_url + "&page=%d" % n)
url_list.append(url)
n = n+1
def parse_websites(url_list):
for url in url_list:
html_string = requests.get(url)
soup = BeautifulSoup(html_string.text, 'lxml') # Parse the HTML as a string
table = soup.find('div',{'class':'table-responsive no-margin'}) #Grab the first table
df = pd.DataFrame(columns=range(0,4), index = [0]) # I know the size
for row_marker, row in enumerate(table.find_all('tr')):
column_marker = 0
columns = row.find_all('td')
try:
df.loc[row_marker] = [column.get_text() for column in columns]
except ValueError:
# It's a safe way when [column.get_text() for column in columns] is empty list.
continue
print(df)
df.to_csv('path_to_file\\test1.csv')
parse_websites(url_list)
Can you please take a look at my code at advise what I am doing wrong ?
One solution if you want to append the data frames on the file is to write in append mode:
df.to_csv('path_to_file\\test1.csv', mode='a', header=False)
otherwise you should create the data frame outside as mentioned in the comments.
If you define a data structure from within a loop, each iteration of the loop
will redefine the data structure, meaning that the work is being rewritten.
The dataframe should be defined outside of the loop if you do not want it to be overwritten.
Related
I'm looking to iterate through the URL with "count" as variables between 1 and 65.
Right now, I'm close but really struggling to figure out the last piece. I'm receiving the same table (from variable 1) 65 times, instead of receiving the different tables.
import requests
import pandas as pd
url = 'https://basketball.realgm.com/international/stats/2023/Averages/Qualified/All/player/All/desc/{count}'
res = []
for count in range(1, 65):
html = requests.get(url).content
df_list = pd.read_html(html)
df = df_list[-1]
res.append(df)
print(res)
df.to_csv('my data.csv')
Any thoughts?
A few errors:
Your URL was templated incorrectly. It remains at .../{count} literally, without substituting or updating from the loop variable.
If you want to get page 1 to 65, use range(1, 66)
Unless you want to export only the last dataframe, you need to concatenate all of them first
# No count here, we will add it later
url = 'https://basketball.realgm.com/international/stats/2023/Averages/Qualified/All/player/All/desc'
res = []
for count in range(1, 66):
# pd.read_html accepts a URL too so no need to make a separate request
df_list = pd.read_html(f"{url}/{count}")
res.append(df_list[-1])
pd.concat(res).to_csv('my data.csv')
i'm currently trying to web scraping a dataframe (about sctack exchange of a companie) in a website in order to make a new dataframe in python this data.
I've tried to scrap the row of the dataframe in order to store in a csv file and use the method pandas.read_csv().
I meet some trouble because the csv file is not as good as i thought.
How can i manage to get the exactly same dataframe in python with web-scraping it
Here's my code :
from bs4 import BeautifulSoup
import urllib.request as ur
import csv
import pandas as pd
url_danone = "https://www.boursorama.com/cours/1rPBN/"
our_url = ur.urlopen(url_danone)
soup = BeautifulSoup(our_url, 'html.parser')
with open('danone.csv', 'w') as filee:
for ligne in soup.find_all("table", {"class": "c-table c-table--generic"}):
row = ligne.find("tr", {"class": "c-table__row"}).get_text()
writer = csv.writer(filee)
writer.writerow(row)
The dataframe in the website
The csv file
You can use pd.read_html to read the required table:
import pandas as pd
url = "https://www.boursorama.com/cours/1rPBN/"
df = pd.read_html(url)[1].rename(columns={"Unnamed: 0": ""}).set_index("")
print(df)
df.to_csv("data.csv")
Prints and saves data.csv (screenshot from LibreOffice):
Please try this for loop instead:
rows = []
headers = []
# loop to get the values
for tr in soup.find_all("tr", {"class": "c-table__row"})[13:18]:
row = [td.text.strip() for td in tr.select('td') if td.text.strip()]
rows.append(row)
# get the header
for th in soup.find_all("th", {"class": "c-table__cell c-table__cell--head c-table__cell--dotted c-table__title / u-text-uppercase"}):
head = th.text.strip()
headers.append(head)
This would get your values and header in the way you want. Note that, since the tables don't have ids or any unique identifiers, you need to proper stabilish which rows you want considering all tables (see [13:18] in the code above).
You can check your content making a simple dataframe from the headers and rows as below:
# write csv
df = pd.DataFrame(rows, columns=headers)
print(df.head())
Hope this helps.
This question already has answers here:
scraping data from wikipedia table
(3 answers)
Closed 2 years ago.
I want to apply some statistics to data tables obtained directly from specific internet pages.
This tutorial https://towardsdatascience.com/web-scraping-html-tables-with-python-c9baba21059 helped me creating a data frame from a table at the webpage http://pokemondb.net/pokedex/all. However, I want to do the same for geographic data, such as population and gdp of several countries.
I found some tables at wikipedia, but it doesn't work quite well and I don't understand why. Here's my code, that follows the above mentioned tutorial:
import requests
import lxml.html as lh
import pandas as pd
url = 'https://en.wikipedia.org/wiki/List_of_African_countries_by_population'
#Create a handle, page, to handle the contents of the website
page = requests.get(url)
#Store the contents of the website under doc
doc = lh.fromstring(page.content)
#Parse data that are stored between <tr>..</tr> of HTML
tr_elements = doc.xpath('//tr')
#Check the length of the first 12 rows
print('Length of first 12 rows')
print ([len(T) for T in tr_elements[:12]])
#Create empty list
col=[]
i=0 #For each row, store each first element (header) and an empty list
for t in tr_elements[0]:
i+=1
name=t.text_content()
print ('%d:"%s"'%(i,name))
col.append((name,[]))
#Since out first row is the header, data is stored on the second row onwards
for j in range(1,len(tr_elements)):
#T is our j'th row
T=tr_elements[j]
#If row is not of size 10, the //tr data is not from our table
if len(T)!=10:
break
#i is the index of our column
i=0
#Iterate through each element of the row
for t in T.iterchildren():
data=t.text_content()
#Check if row is empty
if i>0:
#Convert any numerical value to integers
try:
data=int(data)
except:
pass
#Append the data to the empty list of the i'th column
col[i][1].append(data)
#Increment i for the next column
i+=1
print('Data gathering: done!')
print('Column lentgh:')
print([len(C) for (title,C) in col])
Dict={title:column for (title,column) in col}
df=pd.DataFrame(Dict)
print(df.head())
The output is the following:
Length of first 12 rows
[5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
1:"Ranks
"
2:"Countries(or dependent territory)
"
3:"Officialfigure(whereavailable)
"
4:"Date oflast figure
"
5:"Source
"
Data gathering: done!
Column lentgh:
[0, 0, 0, 0, 0]
Empty DataFrame
Columns: [Ranks
, Countries(or dependent territory)
, Officialfigure(whereavailable)
, Date oflast figure
, Source
]
Index: []
The length of the columns shouldn't be null. The format is not the same as the one of the tutorial. Any idea of how to make it right? Or maybe another data source that doesn't return this strange output format?
The length of your rows, as you've shown by your print statement in line 16 (which corresponds to the first line of your output), is not 10. It is 5. And your code breaks out of the loop in the very first iteration, instead of populating your col.
changing this statement:
if len(T)!=10:
break
to
if len(T)!=5:
break
should fix the problem.
Instead of using requests, use pandas to read the url data.
‘df = pd.read_html(url)
On Line 52 you are trying to edit a tuple. This is not possible in Python.
To correct this, use a list instead.
Change line 25 to col.append([name,[]])
In addition, when using the break it breaks the for loop, this causes it to have no data inside the array.
When doing these sorts of things you also must look at the html. The table isn't formatting as nice as one would hope. For example, it has a bunch of new lines, and also has the images of the countries flag. You can see this example of North America for how the format is different every time.
It seems like you want an easy way to do this.I would look into BeautifulSoup4. I have added a way that I would do this with bs4. You'll have to do some editing to make it look better
import requests
import bs4 as bs
import pandas as pd
url = 'https://en.wikipedia.org/wiki/List_of_African_countries_by_population'
column_names = []
data = []
#Create a handle, page, to handle the contents of the website
page = requests.get(url)
#Store the html in the soup object
soup = bs.BeautifulSoup(page.content, 'html.parser')
#Gets the table html
table = soup.find_all('table')[0]
#gets the table header
thead = table.find_all('th')
#Puts the header into the column names list. We will use this for the dict keys later
for th in thead:
column_names.append(th.get_text())
#gets all the rows of the table
rows = table.find_all('tr')
#I do not take the first how as it is the header
for row in rows[1:]:
#Creates a list with each index being a different entry in the row.
values = [r for r in row]
#Gets each values that we care about
rank = values[1].get_text()
country = values[3].get_text()
pop = values[5].get_text()
date = values[7].get_text()
source = values[9].get_text()
temp_list = [rank,country,pop,date,source]
#Creates a dictionary with keys being the column names and the values being temp_list. Appends this to list data
data.append(dict(zip(column_names, temp_list)))
print(column_names)
df = pd.DataFrame(data)
Need some help here. Plan to extract all the statistical data of this site https://lotostats.ro/toate-rezultatele-win-for-life-10-20
My issue is that I am not able to read the table. I can't do this nor for the first page.
Can someone pls help?
import requests
import lxml.html as lh
import pandas as pd
from bs4 import BeautifulSoup
soup = BeautifulSoup(html_doc, 'html.parser')
url='https://lotostats.ro/toate-rezultatele-win-for-life-10-20'
#Create a handle, page, to handle the contents of the website
page = requests.get(url)
#Store the contents of the website under doc
doc = lh.fromstring(page.content)
#Parse data that are stored between <tr>..</tr> of HTML
tr_elements = doc.xpath('//tr')
#Create empty list
col=[]
i=0
#For each row, store each first element (header) and an empty list
for t in tr_elements[0]:
i+=1
name=t.text_content()
print ('%d:"%s"'%(i,name))
col.append((name,[]))
#Since out first row is the header, data is stored on the second row onwards
for j in range(1,len(tr_elements)):
#T is our j'th row
T=tr_elements[j]
#If row is not of size 10, the //tr data is not from our table
# if len(T)!=10:
# break
#i is the index of our column
i=0
#Iterate through each element of the row
for t in T.iterchildren():
data=t.text_content()
#Check if row is empty
if i>0:
#Convert any numerical value to integers
try:
data=int(data)
except:
pass
#Append the data to the empty list of the i'th column
col[i][1].append(data)
#Increment i for the next column
i+=1
Dict={title:column for (title,column) in col}
df=pd.DataFrame(Dict)
df.head()
print(df)
Data is dynamically added. You can find the source, returning json, in network tab
import requests
r = requests.get('https://lotostats.ro/all-rez/win_for_life_10_20?draw=1&columns%5B0%5D%5Bdata%5D=0&columns%5B0%5D%5Bname%5D=&columns%5B0%5D%5Bsearchable%5D=true&columns%5B0%5D%5Borderable%5D=false&columns%5B0%5D%5Bsearch%5D%5Bvalue%5D=&columns%5B0%5D%5Bsearch%5D%5Bregex%5D=false&columns%5B1%5D%5Bdata%5D=1&columns%5B1%5D%5Bname%5D=&columns%5B1%5D%5Bsearchable%5D=true&columns%5B1%5D%5Borderable%5D=false&columns%5B1%5D%5Bsearch%5D%5Bvalue%5D=&columns%5B1%5D%5Bsearch%5D%5Bregex%5D=false&start=0&length=20&search%5Bvalue%5D=&search%5Bregex%5D=false&_=1564996040879').json()
You can decode that and likely (investigate that) remove timestamp part (or simply replace with random number)
import requests
r = requests.get('https://lotostats.ro/all-rez/win_for_life_10_20?draw=1&columns[0][data]=0&columns[0][name]=&columns[0][searchable]=true&columns[0][orderable]=false&columns[0][search][value]=&columns[0][search][regex]=false&columns[1][data]=1&columns[1][name]=&columns[1][searchable]=true&columns[1][orderable]=false&columns[1][search][value]=&columns[1][search][regex]=false&start=0&length=20&search[value]=&search[regex]=false&_=1').json()
To see the lottery lines:
print(r['data'])
The draw parameter seems to be related to page of draws e.g. 2nd page:
https://lotostats.ro/all-rez/win_for_life_10_20?draw=2&columns[0][data]=0&columns[0][name]=&columns[0][searchable]=true&columns[0][orderable]=false&columns[0][search][value]=&columns[0][search][regex]=false&columns[1][data]=1&columns[1][name]=&columns[1][searchable]=true&columns[1][orderable]=false&columns[1][search][value]=&columns[1][search][regex]=false&start=20&length=20&search[value]=&search[regex]=false&_=1564996040880
You can alter the length to retrieve more results. For example, I can deliberately oversize it to get all results
import requests
r = requests.get('https://lotostats.ro/all-rez/win_for_life_10_20?draw=1&columns[0][data]=0&columns[0][name]=&columns[0][searchable]=true&columns[0][orderable]=false&columns[0][search][value]=&columns[0][search][regex]=false&columns[1][data]=1&columns[1][name]=&columns[1][searchable]=true&columns[1][orderable]=false&columns[1][search][value]=&columns[1][search][regex]=false&start=0&length=100000&search[value]=&search[regex]=false&_=1').json()
print(len(r['data']))
Otherwise, you can set the length param to a set number, do an initial request, and calculate the number of pages from the total (r['recordsFiltered']) records count divided by results per page.
import math
total_results = r['recordsFiltered']
results_per_page = 20
num_pages = math.ceil(total_results/results_per_page)
Then do a loop to get all results (remembering to alter draw param). Obviously, the less requests the better.
I have extracted a table from a site with the help of BeautifulSoup. Now I want to keep this process going in a loop with several different URL:s. If it is possible, I would like to extract these tables into different excel documents, or different sheets within a document.
I have been trying to put the code through a loop and appending the df
from bs4 import BeautifulSoup
import requests
import pandas as pd
xl = pd.ExcelFile(r'path/to/file.xlsx')
link = xl.parse('Sheet1')
#this is what I can't figure out
for i in range(0,10):
try:
url = link['Link'][i]
html = requests.get(url).content
df_list = pd.read_html(html)
soup = BeautifulSoup(html,'lxml')
table = soup.select_one('table:contains("Fees Earned")')
df = pd.read_html(str(table))
list1.append(df)
except ValueError:
print('Value')
pass
#Not as important
a = df[0]
writer = pd.ExcelWriter('mytables.xlsx')
a.to_excel(writer,'Sheet1')
writer.save()
I get a 'ValueError'(no tables found) for the first nine tables and only the last table is printed when I print mylist. However, when I print them without the for loop, one link at a time, it works.
I can't append the value of df[i] because it says 'index out of range'