Python is a modern, highly successful programming language which, since its creation in 1991, has gained enormous popularity in various areas of software development, including web applications, scientific research and, above all, data analytics. Its creator is Guido van Rossum, who designed Python as a language that would be easy to learn and at the same time powerful enough for complex tasks.
Python differs from other programming languages in its readability and the simplicity of its syntax, which makes it an ideal choice for beginners and experienced programmers alike. Thanks to its extensive standard library and rich collection of external libraries it provides tools for almost any task, from processing text and numbers to complex mathematical calculations and machine learning.
Interesting Facts and Statistics About Python
In recent years Python has become an indispensable tool in the field of data analytics. Its ability to easily manipulate data, carry out statistical analyses and visualise results makes it the preferred language for data scientists and analysts around the world. These are some of the many reasons why we also like to use Python when working with data.
Before we go into describing the ways Python can be used when working with data, it may be interesting to look at some information and statistics. Did you know that:
- According to several surveys, Python is the most popular language among data scientists and analysts. In 2021, more than 69% of respondents in a Kaggle survey said they use Python as their main programming language (Azure Lessons).
- Python is the dominant language in machine learning. According to the 2021 Stack Overflow survey, Python is the most widely used language for machine learning, with a 57% share (Azure Lessons).
- Python has more than 200,000 packages available in the PyPI repository, many of which focus on data analytics and machine learning. Libraries such as Pandas, NumPy, Scikit-Learn and TensorFlow are widely used by data scientists (Azure Lessons).
- Python offers a wide range of tools and frameworks for integration with databases, such as SQLAlchemy for SQL databases and PyMongo for MongoDB, which makes it easier for data analysts to work with different data sources (Azure Lessons).
- Many universities and research institutions use Python as the main language for teaching data science and analytics. For example, MIT and Stanford use Python in their machine learning and data science courses (Azure Lessons).
- Python is often listed in the requirements for jobs in data science and analytics. According to Indeed, in 2021 more than 70% of job ads for data scientist positions listed knowledge of Python as a requirement (Azure Lessons).
- Many major technology companies, such as Google, Facebook and Netflix, use Python for various aspects of data analytics and machine learning, which testifies to its robustness and reliability (Azure Lessons).
These statistics show how important a tool Python has become for data analysts and scientists, and underline its wide adoption and popularity in various areas of data science.
And How Do We Use Python?
As has been said, Python has a really wide range of uses across the work of a data analyst. And we work with it daily. The most common tasks we solve with Python are described below.
Web Scraping
Python lets us efficiently collect data from websites using libraries such as BeautifulSoup, Scrapy and Selenium. These tools help us extract structured information from various online sources.
We use web scraping to obtain information from websites that the client does not have available elsewhere. It may be, for example, information about product ratings on a website, job advertisements or any other information relevant to the client.
Advantages of using Python for web scraping:
- Automation: It makes it possible to automate data collection, which saves time and costs.
- Flexibility: The ability to adapt web scraping scripts to specific needs.
- Wide library support: There are many libraries that make it easier to work with different types of websites.
Disadvantages:
- Changes in website structure: Websites can change, which may require constant maintenance of the scripts.
Machine Learning
Python is our main tool for developing machine learning models. We use libraries such as scikit-learn, TensorFlow and Keras, which let us train and deploy models for predictive analysis, classification and regression.
There are really many uses of machine learning, and the requests we meet most often are for customer segmentation, building various predictive models, or building product recommendation models for a website.
Advantages of using Python for machine learning:
- Rich library ecosystems: There are many libraries for different types of models and algorithms.
- Simplicity: Python syntax is easy to read and allows rapid prototyping.
- Community support: A strong community offers rich documentation and support.
Disadvantages:
- Performance: Python can be slower than some other languages in computationally demanding tasks, although this problem can often be worked around by using optimised libraries.
Processing Larger Volumes of Data
For the processing and analysis of large data sets we use libraries such as Pandas, Dask and PySpark. These tools let us manipulate large datasets and carry out complex queries and aggregations.
Essentially, you could say that using Python in this respect is very similar to using SQL for processing queries within a database. The difference lies mainly in the speed that Python offers and in the volume of data it can process quickly. It can also handle more complex tasks that would be difficult to solve with SQL.
Advantages of using Python for data processing:
- Efficiency: Python allows efficient manipulation and analysis of data.
- Parallel processing: Libraries such as Dask and PySpark support parallel processing, which improves performance when working with big data.
- Simplicity and readability: Python scripts for data processing are easy to read and maintain.
Disadvantages:
- Memory demands: Processing large datasets can be memory-intensive, which can lead to problems when working on machines with limited memory.
Building Data Applications
Python lets us build interactive data applications using frameworks such as Dash, Streamlit and Flask. These applications are used for data visualisation and for interactively exploring datasets.
Advantages:
- Rapid development: Frameworks for building applications allow rapid prototyping and deployment.
- Interactivity: They allow users to work interactively with data and visualisations.
- Flexibility: Easy integration with other tools and services.
Disadvantages:
- Performance: For very demanding applications, Python's performance can be limiting.
- Complexity: Building more complex applications may require deeper knowledge of the frameworks and of programming.
Building APIs with Python
Python is also our tool for developing RESTful APIs using frameworks such as Flask and FastAPI. These APIs give external applications and services access to our data and analytical models.
We most often meet requests to build various API solutions for external services that have not yet been integrated into applications that allow easy access to them (such as Keboola). For these external services it is then necessary to develop custom API solutions, which include, for example, Ecomail and Abra.
Advantages:
- Simplicity: Python frameworks for building APIs are easy to use and allow rapid development.
- Flexibility: They allow easy integration with various data sources and services.
- Performance: Frameworks such as FastAPI are designed with performance and scalability in mind.
Disadvantages:
- Security: Securing an API can be demanding and requires careful planning and implementation.
- Maintenance: APIs require regular maintenance and updates to stay functional and secure.
Python is a key tool in our company for various data tasks, thanks to its versatility, wide range of libraries and strong community. Although it has its disadvantages, its benefits clearly outweigh them and allow us to solve complex problems effectively and bring value to our clients.
Conclusion
Python is simply an indispensable part of our daily work, which it makes simpler and faster. Thanks to Python we can automate even more complicated tasks that SQL falls short on, and so it complements our technology stack beautifully.
Using Python in data analytics allows us to react quickly to market needs and to provide our clients with valuable insights and solutions. With Python's growing popularity and the constant development of new libraries and tools, we look forward to further innovations and improvements that this language will bring us. Python is and will remain a key element of our technology strategy and an indispensable tool for success in the field of data analytics.