Python’s role in data analytics

The purpose behind the topic:

Being in an IT background taught me to stay updated on all the technologies and programming languages. By far Python in my opinion is a language that promotes readability and makes coding simple. Because of its high-level, interpreted, and object-oriented architecture, it is suitable for a wide range of software applications and a wonderful tool for data analytics. So let’s learn more about it –


Python was first launched in 1990, but it was only recently that it gained prominence. It surpassed JavaScript, HTML/CSS, and SQL as the fourth most popular programming language in 2020, with 44.1% of developers using it. It is used for API development, AI, web development, the Internet of Things, etc. It is unusual that it has outstanding libraries that perform properly for every stage of data science. These libraries are intended for data mining, data processing, modeling, and data visualization, which are the three most common applications of Python in data analysis.

Data Mining

Python-based data mining libraries such as Scrapy and BeautifulSoup are available.

· Scrapy can be used to create custom applications that scrape structured data from the web. It’s also used for gathering data from APIs.

· When data cannot be retrieved through APIs, BeautifulSoup is used: it scrapes data and arranges it in the desired format.

Data Processing and Modelling

NumPy and Pandas are the two main libraries utilized at this point.

· NumPy (Numerical Python) is a programming language that is used to organize large data sets and makes math operations and vectorization on arrays easier.

· Pandas have two data structures: series (a list of objects) and data frames (a collection of elements) (a table with multiple columns). This library converts data to a data frame, which you can then manipulate by deleting or adding new columns and performing different operations. It’s a lot easier to use this on multiple arrays.

Data Visualization

Python data visualization libraries such as Matplotlib and Seaborn are commonly used.

· They assist in the conversion of large lists of numbers into easy-to-understand visualizations, such as histograms, pie charts, and heatmaps.

Pros & Cons:

Pros — Great community and connections, Easy to learn, Flexible and scalable, Wide range of libraries and constant updates

Cons — Dynamic typing, underdeveloped database access layer, and limited speed

Career Path:

1. Data Science/Machine learning

2. Application development

3. Data processing and analysis

4. Robotics and automation

5. DevOps

6. Scientific computation

7. Digital Project Manager


Python is used by world-class organizations like Google, Facebook, Instagram, Spotify, Quora, Netflix, Dropbox, and Reddit for their everyday coding and business needs, and they know the importance of how it grasps and relates to practical business and real-world applications. Many sectors including the healthcare sector, finance, aerospace, and banking rely heavily on Python. There are other options, including R, SQL, and Scala available based on an organization’s needs; nonetheless, Python remains the most common language for data analysis.

Fun fact: In my research, I came across a prediction that indicates, Python is not the only programming language in the future. As per the stack overflow trends, within a few years, there is a possibility for its downfall similar to C, Java, C++, R, and Javascript.






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Bipin Chandra Rayilla

Bipin Chandra Rayilla

Passionate towards education, profession, and healthy living.

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