BigDataScalability.com – Big data analytics can be defined as a process to reveal meaningful insights using analytics tools. There’s a set of programs that can be used for data analysis, among which is Python. Is analyzing big data in Python worth the try for data analysts and data scientists?
Choosing a data analysis tool is not a simple process as it requires many considerations, such as business needs and who will use it. Python, for some reason, is considered one of the best tools for data analysis. Let’s have a closer look!
Table of Contents
What is Python?
Python is a programming language designed for general purposes, from building websites to conducting data analysis. Thanks to its versatility, it becomes a widely used programming language with millions of active users nowadays.
Unlike most programming languages with a steep learning curve, Python is beginner-friendly. It is relatively easy to learn, making it suitable for everyday tasks. Having that said, it is powerful enough to analyze a large dataset.
Big data in Python can be manipulated and analyzed so that you can extract meaningful insights to take action in the future. Besides, it has a wide selection of libraries that allow users to write programs for efficient data analysis and machine learning.
Python also lets data analysts and professional users perform complex calculations, build a variety of data visualizations, and also create algorithms for machine learning. Not to mention it can help you accomplish data-related tasks.
Furthermore, Python has many other functions in technical fields. Other than analyzing big data, it is often used to develop applications or websites, particularly the backends. It can also be used for scripting, a process to write code for automation.
With millions of users worldwide, Python has a large community that contributes to its libraries. Not only does it serve as a useful resource for programmers, but also it helps coders to solve problems in case of getting stumbled.
Big Data in Python: Benefits
Python jibes with data analysis for several reasons. The main point is that working with data in Python can save a lot of time and effort as it allows data analysts to write fewer code lines, making it easier to read. Here’s why Python and big data can be a perfect combination.
Python’s open-source nature makes it possible for any programmer to access this programming language, as well as modify and distribute the code. It runs excellently on multiple platforms so that you can analyze big data in Python either on Windows, Linux, macOS, and other platforms.
2. Library Support
This general-purpose programming language boasts a huge number of analytics libraries that make it ideal for numerical computing, statistical analysis, data analysis, machine learning, and visualization. This is why Python is often used in both industry and academic fields.
3. Data Processing Support
Python comes packed with a built-in feature that supports data processing for unconventional and unstructured data such as images and voice. For this reason, it can be an ideal analytics tool for big data that is often sourced from social media.
A quick analysis is another benefit to find in Python. This tool accelerates code development by enabling prototyping so that it promotes faster coding while maintaining code transparency. Thus, the code maintenance and code-adding process become easier.
Why You Should Analyze Big Data in Python
Python is on the top of the list when it comes to big data analytics tools. Why use Python for big data and why is it essential for data analysis? In addition to a couple of benefits it has, this programming language has a few key secrets you should know.
1. Tons of Library Packages
As mentioned earlier in the benefits section, Python has got robust library packages. This meets the demands of data science and analysis needs, making it a great choice in big data analytics. Among the popular libraries for big data are Pandas, NumPy, SciPy, Mlpy, Scikit-learn, and many others.
Thanks to these libraries, scientists find it easier to extract big data in Python. For instance, the integration between Spark and Python’s library Scikit-learn allows big data scientists to write code and test smaller data sets before going to the larger set of data.
To use the Python library, data scientists will need to search online by typing “Python + (required library)” (type without quotation marks). This action will show the testing code, required documentation, and examples to help you out.
2. Low Learning Curve
Python is widely known as the easiest programming language, making it ideal for programmers of any level including beginners. The language is straightforward so the readability is relatively high, especially for English speakers.
Moreover, Python has a high-level language that allows programmers or data scientists to focus on what they can do instead of how they should do it. For this reason, working with big data in Python takes less time.
With all the flexibility and versatility, it is not surprising that Python is among the most favored analytics tools. Not to mention the Python syntaxes are much easier to remember.
Python is designed to handle a large set of data which makes it the perfect choice for big data. Excellent scalability does matter when it comes to analyzing massive data. Fortunately, this programming language is more scalable and flexible than its rivals, such as Java or R.
Python can easily handle the increase in data volume by increasing the speed of data processing which can improve work efficiency. As a comparison, data scientists who work using Java and R will find it tough to get a similar experience.
Analyzing big data in Python allows data analysts and scientists to save a lot of time. Thanks to a great scale of flexibility that makes this programming language compatible with big data.
Things can go wrong with your data analysis process. While finding help can be a challenge with some programming languages, Python is an exception. As one of the most widely used languages in academic and technical fields, Python has vast community support that offers solutions.
Users who need help can obtain expert support by turning to Stack Overflow, mailing list, documentation, and also user-contributed code. The larger the community the more users can contribute information about their experience. This also means more support at no cost.
5. Various IDEs
Python has data visualization support that allows data analysts to reveal the patterns and layers hidden in the data, thanks to various IDEs it has. In addition to data visualization, it supports machine learning, data analysis, and natural language processing.
This explains why Python is suitable for data science. Some of the IDEs available in this programming language are Spyder which can be integrated with many Python packages; Pycharm which supports various libraries; and Rodeo which supports syntax highlighting and auto-completion.
6. Compatible with Hadoop
Hadoop is another popular programming language with millions of active users. It is compatible with Python so many data analysts and scientists often use them together. They prefer combining Hadoop and Python rather than Java for many reasons, one of which is the large libraries Python has.
Moreover, Python comes packed with PyDoop which gives decent support for Hadoop. This package gives access to the HDFS API that makes it possible to read and write data from the global system. It also gives you an option to solve complex data science using minimal programming.
How Python Helps with Big Data Analytics
Processing large datasets can make your computer struggle. Python as one of the best programming languages is designed to help you solve the problem in three main ways. Here’s how Python deals with big data in Python without slowing everything down, thanks to Pandas library support.
1. Optimize Data Types to Reduce Memory Usage
The larger the data the more memory it uses, which means the slower your computer gets. Pandas help reduce memory usage during the process by optimizing data types. When it loads data, it automatically infers data type.
In some cases, specifying the type of data will lead to a significant reduction in memory usage. However, you need to make sure the dataset is not in an exploratory phase.
2. Split Data
Working with big data in Python gets easier as it offers an option to split data into chunk sizes. Most large datasets won’t fit into your memory so you can benefit from Pandas’ split option to break the data into smaller sets. This is much simpler rather than processing one big block.
Furthermore, an iterator object appears when you use this option. Using this object allows you to go through chunks and filter or analyze data just like you would do with the full set.
3. Use Lazy Evaluation
Lazy evaluation means the strategy that delays expression evaluation until the value is needed. Often used in functional programming, lazy evaluation is an important concept because it is the basis where distributed computation frameworks are built.
Learning Python for Data Science
Before you deal with big data in Python, you’ll want to learn how to use it. Whether you want to have a career in data analytics or data science, learning Python becomes essential. It takes several steps to learn Python, so take a closer look at the following pointers.
1. Learn Python Basics
First thing first, learning Python should start from the basics. You will want to know what it is, how it works, and what it has. You can join an online course or college program, learn autonomously, or do anything that comes to your preferences.
Stay consistent and motivated are keys to success. Join a community so that you can read and share experiences during the process.
The perfect practice makes perfect. Hands-on learning is considered the best way to excel in your knowledge and skills in working with big data in Python. Practicing with some small projects available in the Dataquest course, such as Exploring Hacker News and Exploring eBay Car Sales.
You might have several questions during the practices but as you progress, you will be able to figure them out. Enhance your coursework by reading some guidebooks or blog posts.
3. Learn Python Libraries
Dealing with big data in Python requires you to understand the library packages. There are 4 main libraries often used for data science, including NumPy, Pandas, Matplotlib, and Scikit-learn. Learn and explore these library packages to improve your Python comprehension.
4. Apply Advanced Techniques
During your journey with Python, you will need to improve your skills with advanced techniques. Get to know more about regression and classification so that you can step into the next level of machine learning.
In a nutshell, Python is a decent programming language that offers a lot of benefits for data analytics. Not only does it offer a bunch of library packages, but also it is quite easy to learn and it has vast community support to help you out.
Analyzing big data in Python should give a better experience of data analysis, especially for beginners. Learn this programming language for data science and reveal hidden secrets behind your data.