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Why Do Data Analysts Use Python?
In the world of data analysis, Python has emerged as a prominent programming language. According to the TIOBE Index, which measures the popularity of programming languages, Python is the most popular programming language in the world. Its popularity among data analysts stems from its versatility, extensive libraries, and intuitive syntax.
This article explores the key reasons why pro and amateur data analysts alike choose Python as their go-to tool for data exploration, manipulation, visualization, and modeling.
How Do Data Analysts Use Python?
Its ease of use and impressive range of libraries make it popular among data analysts. The language helps data analysts with each of the steps of analysing data.
Here’s how data experts use it in the data analysis world:
Data professionals use Python libraries like BeautifulSoup and Scrapy to mine - or gather - data. Scrapy, for instance, helps create programs to collect organised and structured data from the web.
BeautifulSoup, even more than Scrapy, is focused on scraping web pages. Data professionals use BeautifulSoup to scrape complex web pages effectively.
Data analysts can also use Python libraries to structure large datasets and make mathematical operations more manageable. Pandas, a Python library, offers a data structure called data frame to effectively work with large tables of data.
Pandas allows you to load the data into the data frame, and then add new calculated columns based on various simple but powerful operations. Overall, it helps perform various data analytics functions to improve work efficiency.
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Data analysts need to present meaningful insights in an understandable format. This helps the company’s decision-makers identify the trends and make better business decisions.
Python libraries like Matplotlib allow data analysts to convert numbers into pie charts, graphics, histograms, etc.
This makes it easier for data analysts to make their driven data visually appealing and comprehensible.
Why Do Data Analysts Prefer Using Python?
The highly cross-functional language offers several perks to its users. It helps data analysts to make sense of complicated data sets and make them easier to understand.
Another pro of using Python is its high readability. Python code is easier for collaborating with other analysts, for communicating with other technical stakeholders, and it makes it more maintainable when it comes time to adapt it for new data sources and needs.
The language fits well for data analyst professionals as it provides heavy support and offers an extensive range of libraries for several tasks.
An Easy Learning Curve
Python is known for its simple syntax and readability, which is a major benefit. It cuts down the time data analysts otherwise spend familiarising themselves with a programming language.
The gentle learning curve makes it stand out among old programming languages with complicated syntax.
For example, languages like Java, C+, and Ruby require a steep learning curve, especially for beginner data analysts. On the other hand, the simplicity of Python helps data analysts perform various data-related tasks simultaneously.
Therefore, if you’re dipping your toes in the data analytics field, you’ll enjoy working with a simple yet effective programming language. Luckily, Python offers solutions for most complexities encountered when handling data.
Vast Collection of Libraries
Python offers an extensive list of free libraries to its users. What’s more enticing about the libraries is that they grow consistently, offering powerful solutions.
Numpy, Scikit-learn, Pandas, and Matplotlib are a few popular libraries which help expedite data analytics tasks.
Despite Python’s simplicity, you will be in situations where you’ll need help with the programming language. Fortunately, it offers an array of useful libraries with helpful support material. What’s more, you can use them free of charge.
Besides, you can access user-contributed codes, from mailing lists to documentation and more. In addition, users globally can reach out to skilled programmers to ask for help and advice when needed.
Epic Visualisation Tools
Our brains process visuals better than text. Therefore, data analysts present meaningful insights into graphs, charts, or graphics to make them understandable. Fortunately, you do not need to be an expert at data visualisation as a beginner.
Python has you covered with its diverse data visualisation tools. You can convert your complicated datasets into graphics, charts, or interactive plots.
The better you present the data to the company’s decision-makers, the better they’ll understand what you managed to extract from the web. This will help them identify the trends and see what they can do to change the existing business strategies.
Extended Data Analytics Tools
Although data collection is essential to data analysis, you must also handle the extracted data effectively. Python has several built-in analytics tools to help you identify patterns and spot helpful information for better insights.
You can use handy tools to evaluate a company’s performance based on several critical metrics.
Tips to Get Started With Python as a Data Analyst
Data analysts need a reliable platform to write and execute their code. Python offers several features to accomplish regular tasks efficiently as a data analyst. We’ll share a few tips for beginner data analysts to help them understand how to work with Python.
Set Up the Python Environment
To begin with, you must set up an environment that allows you to work in Python easily. Several programs offer you the programming environment to make your work convenient.
For instance, the Anaconda Python Platform caters to your needs by providing different libraries, including Numpy, IPython, Pandas, Matplotlib, and more.
You can download and install Anaconda to your system. It includes various in-built programs, allowing you to gather and run your code hassle-free.
For example, Jupyter Notebook is one of the most widely used programs you can open in your browser and use on the go.
Know How the Libraries Work
Python libraries are known to simplify the work of data analysts. Therefore, it is crucial to understand their primary features.
Some libraries that can help you with data analysis include the following.
Numpy. This Python library offers computational tools to help you optimise your statistical and mathematical functions. Numpy Arrays (whether multidimensional or one-dimensional) allow you to effectively apply an operation to the entire array at once, making the code both shorter and more efficient. In addition, Numpy makes it easy to perform matrix operations, which are central to many machine learning algorithms.
Matplotlib. Matplotlib allows you to make data visualisations by offering graph-based representations. It also lets you control the graphs. Hence, you can alter the shape, thickness, colours, and style per your preferences.
Pandas is one of the most significant Python libraries concerning data analytics. It entails high-level data manipulation tools and structures that help during data cleaning and manipulation processes. You can clean your error-filled data and remove what’s not needed. The library allows storing data in tabular format using Data Frame.
Scikit-learn. This Python library helps you execute ML models. It automates the process of generating helpful insights from massive volumes of data.
You’ll typically find these libraries pre-installed on your system, particularly if you've used Anaconda to set up Jupyter. However, if you need to, you can always install libraries using the pip command.
Practice with Different Datasets
It is essential to practise with different datasets and see if you can manage data analysis. You’ll find several datasets in the StatsModel Python library.
You can download them and apply fundamental analytical and statistical operations. This will help you track your progress as a data analyst and identify your weaknesses.
Here are some processes to practise:
Data cleaning. This involves cleaning the data to remove errors and correct inaccuracies that may affect the results.
Preprocessing. Here you must convert the data into formats appropriate for performing data analytic tasks with Python.
Manipulation. This involves implementing ML models to gain the desired results.
Data visualisation. Here you’ll convert your valuable insights into understandable charts, graphics, maps, and more.
Is Python Required for Data Analysis?
A comprehensive understanding of Python programming is extremely beneficial for data analysts. Employers likely expect data analysts to know how Python libraries work to simplify data-related tasks. Therefore, learning Python is a wise career choice.
Can Python Replace Spreadsheets?
Although Python may enhance the functionalities of Excel in certain instances, given its versatility, it cannot replace Excel. Both play a unique role in performing data analytics tasks.
Despite the vast range of programming languages, most data analysts choose to work with Python.
If you’re a beginner in data analytics with Python, you can take a Python Programming course at UCD Professional Academy. The comprehensive course will give you an in-depth knowledge of Python programming and how you can use it during data analysis.