Many individuals find Python to be the preferred programming language for their needs. It is not uncommon, however, for people to inquire about the language’s usability for specific purposes. One such question that often arises is: “Can Python be used for web scraping and data wrangling?” The answer is yes, web scraping and data wrangling are both common use cases for Python.
Interested in learning how Python may help you with web scraping, data wrangling, and processing? Don’t give up!
Shadrack Mwangi, a software developer at Works, recently granted an interview in which he shared his expertise on a range of topics related to Python, data scraping, and data processing. His insights provided readers with valuable information on the topics.
You’ll find the most important takeaways here.
Explain what Python is.
Python is a scripting language that stands out from the rest due to its high-level, interpreted, and object-oriented nature. Moreover, its syntax is straightforward and easy to learn, making it an ideal choice for anyone looking for a scripting language. Python is also free to use and distribute, meaning that there is no need to worry about any additional costs.
Python is an incredibly versatile programming language, making it useful for a wide array of applications. These applications include, but are not limited to, data analysis, web development, automation, scripting, software testing, prototyping, creating high-level data structures, data wrangling, and web scraping.
Can you explain the concept of “data scraping” to me?
Websites, business software, legacy systems, and databases can all be exploited to extract pertinent data that can then be imported into a local file, such as an Excel spreadsheet. This data scraping process ensures that the desired information is effectively gathered and is available for further analysis.
In order to integrate the data into the existing workflow of your company, the utilisation of data scraping tools or software is recommended in order to gather and import the data into an appropriate programme.
Why should we scrape data and how?
The advantages of data scraping are its low price, high reliability of information, rapid processing, and simple installation.
How does one go about scraping data?
Steps in the process of data scraping include:
- Locating Scrape-Friendly URLs
- Examination of the Page
- Indicating which information has to be extracted
- Developing the required software
- Processing the instructions
- Keeping the records safe
What languages do you use, and what languages do developers have access to, for data scraping?
In response to your queries, Shadrack indicated that he prefers Python due to the fact that the scraping tools in Python are well-developed and there is a great amount of support available for scraping use cases.
How important is Python while scraping data?
A number of modules and frameworks exist in Python specifically for data scraping. The following are some of those:
- Elegant broth: It’s a library that can extract information from XML and HTML documents. To scrape a screen, nothing beats a bowl of beautiful soup.
- LXML: There are two C libraries that it supports: libxml2 and libxslt.
- The Mechanic’s Stew It’s code that keeps cookies, clicks on links, and fills out and submits forms automatically.
- Inquiries In Python: This library is equipped with a range of features, including proxy functionality compatible with the Hypertext Transfer Protocol (HTTP), decompression, content decoding, and verification of Secure Sockets Layer (SSL) encryption. Furthermore, this is the only non-Genetically Modified Organism (GMO) HTTP library that offers these characteristics.
- Selenium: This tool provides a straightforward application programming interface (API) for creating acceptability or functional tests.
- Urllib: It’s a toolkit for accessing and analysing URLs.
- Scrapy: It’s an API-enabled, open-source web crawling platform.
Scraping data using Python is straightforward and can be automated throughout the whole process. It involves parsing, extending, importing, merging, and gathering data from different sources. Python is a programming language that provides the ability to automate scripting, data transfer, and storage operations.
The difficulties of data scraping are discussed.
Scraping data can be a challenging task, particularly when working with a new library which does not permit client-side rendering and has a non-synchronous loading process. Furthermore, certain websites are equipped with anti-scraping features which might impede IP address identification, proxy checks, and redirect captures, as Shadrack explains.
In order to effectively address these issues, Shadrack suggests taking the following steps. Firstly, one should determine the desired target for web scraping and evaluate whether the website possesses any anti-scraping measures or not. Subsequently, an appropriate method must be utilised to circumvent the existing anti-scraping protections. Finally, the data can be retrieved in either HTML or JSON format.
He further explains that in order to properly execute the script, it is necessary for IR locators to locate the data, transform it, and then send it to storage. Additionally, the web drivers should be used to verify the following addresses and replicate them accordingly. Furthermore, web drivers can be leveraged to simulate client-side rendering, providing an accurate imitation of the entire browsing experience.
Data scraping authentication can be a complex challenge, requiring the establishment of a session which requires a login name and password. To resolve these authentication issues, one must manually log into the website and take advantage of the cookies stored. Shadrack suggests examining the headers delivered during the authentication requests and comparing them to the headers used by the software being implemented.
The value of Python data scraping to the end user.
By utilising the power of Python, businesses can gain valuable insights into their target market and their competitors. Through the process of web scraping, a range of data can be collected from listing websites which can be used to identify market trends, set suitable pricing, and determine the most popular products. This can provide a strategic advantage in the marketplace, as businesses can use this information to make informed decisions about their products and services.
When it comes to Python, what does the future hold for data mining?
In the near future, Python is set to be widely utilised for web scraping, resulting in an abundance of new data structures. This shift in data collection methods will necessitate businesses to seek the expertise of data scraping specialists to effectively analyse customer behaviour and generate efficient machine learning and artificial intelligence models.
Data wrangling is defined as what exactly?
Data wrangling is the process of consolidating data from various sources into a form that is more appropriate for further analysis and interpretation. The main objective of data wrangling is to transform raw data into a structured, clean and usable dataset that can be used for various purposes, such as understanding the data better, creating visualisations, and developing predictive models. In order to achieve this goal, the data must be carefully sorted and organised, which can include tasks such as deduplication, normalisation, parsing, and data type conversion. Once the data is properly wrangled, it can be used for a variety of analytical activities.
Is Python useful for data wrangling, and how?
Data wrangling is one of the main activities that can be executed using the pre-built capabilities of Python. This includes tasks such as grouping data, concatenating data, merging data, and combining data frames. Each of these activities can be used to transform and manipulate data sets in order to produce more accurate and useful insights.
When working with Python, what library do you recommend most for manipulating data?
Pandas is an open-source Python library developed with the purpose of providing a comprehensive platform for data analysis and manipulation. It is capable of accessing and manipulating both labelled and relational databases, making it an ideal choice for quantitative analysis. Pandas is built on two powerful libraries: NumPy for mathematical operations and matplotlib for visualisation of data. This library has gained strong support from different developer communities due to its robust feature set.
How do you define stemming and lemmatization?
Stemming is a process that separates the initial sounds of words. For instance, words such as “drinking,” “drinks,” and others all have the word “drink” as their stem. By employing stemming, it is possible to extract the roots of various terms from a search engine’s database.
Lemmatization is a process of morphological analysis that uses a dictionary to determine the synonyms of a word in a similar context to obtain precise search results. For instance, the lemma of the word ‘run’ could include ‘ran’, ‘running’, and ‘runs’. This method allows us to identify different variations of the same word that have different inflections.
Why do we need to stem and lemmatize words?
The following are only some of the things that may be accomplished with the use of stemming and lemmatization:
- Analysing texts via computational means: Text Mining is the process of extracting useful information from texts written in natural language. To do this in a precise manner, it is beneficial to use natural language processing techniques such as stemming and lemmatization. These techniques help to extract data from text in an accurate and efficient way.
- Getting info out of the computer: By employing techniques such as stemming and lemmatization, documents can be categorised into a variety of topics. These techniques help to ensure that the data obtained through a search is as precise and accurate as possible, allowing for the most effective presentation of the information.
- Emotional intelligence: Sentiment analysis is the process of evaluating the customer feedback that relates to various products and services. To effectively prepare the texts for sentiment analysis, it is important to first perform stemming and then lemmatization. Stemming is a process that involves reducing words to their root form, while lemmatization is the process of reducing a word to its base form. Both of these processes help to simplify the language used in customer feedback, making it easier to extract meaningful insights from the data.
- Clustering of documents: The practice of document clustering involves the utilisation of cluster analysis on written texts. Before document clustering can be conducted, the document must first be tokenized and de-stopped.
Using a stemmer or a lemmatizer may cut down on filler words. Moreover, they mandate tokenization in order to ease document clustering.
Can you recommend a Python package for stemming and lemmatization?
Python’s Natural Language Toolkit (NLTK) is a comprehensive library designed to provide users with a range of natural language processing (NLP) tools and data. If you are looking for assistance with data categorization, semantic reasoning, tokenizing, parsing, or tagging, NLTK is the perfect library for you. It offers a rich collection of functions and resources to help you get the most out of your NLP tasks.
Can I use Python to earn a well-paying job?
Works specialises in helping Python engineers find high-paying, long-term positions at some of the most respected organisations in the United States. To learn more about the opportunities we offer, please visit the Works employment page.