Scraping_a_Table.py

Lukman Omotosho

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Data Scraper

Data Analyst

BeautifulSoup

Python

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Consumer Complaints Analysis
Houses_in_Nigeria.Rmd
Houses_in_Nigeria.html
Nigeria_Agric_Export_Analysis.Rmd
Nigeria_Agric_Export_Analysis.html
Nigeria_Agric_Export_Analysis.pbix
Order and Sales Analysis.pbix
README.md
Retail Strategy Analytics 1.Rmd
Sales_Intro.Rmd
Sales_Intro.html
Scraping_a_Table.py
hospital_patients_analysis.sql
nigeria_houses.sql

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from bs4 import BeautifulSoup import pandas as pd import requests url = "https://en.wikipedia.org/wiki/List_of_largest_companies_by_revenue" page = requests.get(url) soup = BeautifulSoup(page.text, "html.parser") table = soup.find_all("table")[0] rows = table.find_all("tr") table_header = table.find_all("th")[0:7] clean_table_header = [header.text.strip() for header in table_header] df = pd.DataFrame(columns= clean_table_header) for row in rows[1:]: columns = row.find_all("td") if len(columns)>= 7: row_data = [data.text.strip() for data in columns[:7]] length = len(df) df.loc[length] = row_data df.to_csv("Top_companies.csv", index=False, encoding= 'utf-8') df.info() print(clean_table_header) table_header clean_table_header
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Extracted data from with web using python. The data contained the top 50 companies in the world based on revenue and profit.

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Data Scraper

Data Analyst

BeautifulSoup

Python

Lukman Omotosho

Data analysis, entry, virtual assistance and web scraping.

Nigeria Agric Export Analysis
Nigeria Agric Export Analysis
hospital patients analysis
hospital patients analysis
Consumer Complaints Analysis
Consumer Complaints Analysis