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Scraping Top Crypto Currencies On Coincost

Scraping Top 100 Crypto-Currencies On Coincost



Data is the collection of facts. To perform any kind of analysis we need data. There are many different ways to collect data and perform analysis. One of them is web scraping.

Web Scraping

Web Scraping is a technique used to automatically extract large amount of data from websites and save it to a file or database. The scraped data usually stored in spreadsheet or tabular format. Here we will store the scraped data into a CSV file.

To scrape the data from Coincost we will be using requests, BeautifulSoup and pandas libraries of Python.

We will scrape data from


Coincost is a website that contains information about all crypto-currencies and the exchanges. Live prices, trades and volumes of crypto-cureencies can be easily monitor by the user. Using this website we can access each and every crypto-currency page for trading purpose.

Steps to be followed during the scraping data from Coincost:

  • Download the webpage of top 100 crypto-currencies using requests library.
  • Parse the HTML source code using BeautifulSoup library.
  • Extract the crypto-currency titles and URLs.
  • Compile the extracted information and create a dataframe using pandas
  • Download the currency page and parse the HTML source code for each currency.
  • Extract the name, price, market capitalization and 24 hours volume for each crypto-currency.
  • Compile the extracted information into dictionary of lists and create a dataframe.
  • Create a CSV file and save the data into it.
  • All the numbers are in dollars.

By the end of this project, we would have CSV file in the following format:

crypto_name, crypto_price, crypto_mkt_cap, crypto_24_vol <br>
Bitcoin BTC,50962,964659000000,29998100000<br>
Ethereum ETH,4441.09,527387000000,21849500000<br>
Binance Coin BNB,595.94,100262000000,2150060000

How to Run the code

You can execute the code using the "Run" button at the top of this page using "Run on binder". You can make changes and save your version of the notebook to Jovian by executing the following cells:

!pip install jovian --upgrade --quiet
import jovian
Akashdeep Pundir6 months ago