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Scraping Website: How to Write a Script in Python

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Here's a python script that you can use as a model to scrape a website. Python script The below logic uses BeautifulSoup Package for web scraping. import requests from bs4 import BeautifulSoup url = 'https://www.example.com' response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') # Print the title of the webpage print(soup.title.text) # Print all the links in the webpage for link in soup.find_all('a'):     print(link.get('href')) In this script, we first import the Requests and Beautiful Soup libraries. We then define the URL we want to scrape and use the Requests library to send a GET request to that URL. We then pass the response text to Beautiful Soup to parse the HTML contents of the webpage. We then use Beautiful Soup to extract the title of the webpage and print it to the console. We also use a for loop to find all the links in the webpage and print their href attributes to the console. This is just a basic example, but
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How to use Pandas Series Method top ideas

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How to use Pandas Series Method top ideas

Here is an example of how to use a Series constructor in Pandas. A one-dimensional array capable of holding any data type (integers, strings, floating-point numbers, Python objects, etc.) is called a Series object in pandas.

Sample DataFrame




Single dimension data


Below is the single dimension data of Index and Value.


 Index Value
 1 10           
 2 40
 3 01
 4 99

Having single value for an index is called Single dimensional data. On the other hand, when one index has multiple values, it is called multi-dimensional array.  

Below is the example for Multi-dimensional array. 

a = (1, (10,20))
mySeries = pd.Series(data, index=index)
Here, pd is a Pandas object. The data and index are two arguments. The data refers to a Python dictionary of "ndarray"  and index is index of data.

Generating DataFrame from single dimension data

The below example shows, how to construct single dimension data (Values and Index).

>>>mySeries = pd.Series([10,20,30], index=[1,2, 'a'])

Special Notes: In the above index list the 'a' represents alpha type.

Once mySeries object created, you can verify Values and Index. Do follow the steps in the screen.

series data 

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