Python holds a special place among all other programming languages when it comes to data analysis. It comes with a rich set of tools and libraries that can make data evaluation assignments a walk in the park.
But how can you implement it? In this article, we explore a few essential steps to consider when performing data analysis using Python.
Let’s dig in!
Install the correct Python version for data analysis
It’s a no-brainer; before analyzing any data using Python, you first need to install the most suitable version for such tasks. An appropriate example is python 2.7 that features identical syntax to the latest version Python 3.6. This version also enjoys lots of support from the Python community.
However, you may not need to install it if your operating system has its pre-installed version.
To install Python 2.7, you need to use Anaconda, a Python distribution program that provides you with the core language as well as the accompanying essential data analytics tools.
- A standard Anaconda package comes packed with the following:
- More than 1000 data science packages
- Core Python language
Package management with Conda
Alternatively, you can use Miniconda which is a minimized Anaconda prototype. It features basic requirements needed to install the necessary data analysis packages, ultimately reducing file sizes and eventual download times.
Either of these two programs will work just fine so it’s your choice.
We recommend installing Miniconda to save up on time. Here are the steps to follow:
- Search and locate the Miniconda download page
- Select a Python 2.7 installer compatible with your operating system environment
- Download it
- Locate your downloaded installer using Finder (in Mac OS) and explorer (in windows)
- Run the installer
Upon installation, test your system to determine if the downloaded software is now your default Python interpreter to enjoy the Anaconda package maximally.
You can type “which python” in the terminal to determine so on a Linux or Mac environment. Alternatively, you can run your installed interpreter to make a conclusion.
Learn about Python fundamentals and libraries
To successfully perform data analysis using this language, you don’t need high-level Python coding skills. However, you need to at least have some basics as follows:
For starters, you need to acquaint yourself with some of the essential libraries including Numpy which enables efficient use of data analysis tools. Pandas is another important one that features manipulation tools as well as high-level data structures for easy and fast data analysis. Finally, Matplotlib provides an object-oriented interface that can integrate plots into applications thereby allowing better analysis.
We recommend that you study each of these libraries one at a time. Trying to understand all of them at once can frustrate your efforts as you will be forced to switch between different ideas which can be confusing.
Install the libraries using Conda install to maximize on Anaconda/Miniconda
Other than that, you’ll also need to acquaint yourself with Python fundamentals such as data structures to get started. For example, learn about lists that house various data types such as strings, objects, and integers, as well as split functions.
Integrate important data analytics tools
Incorporating robust data analysis tools into your setup can significantly improve your results. A good place to start is the Scikit-learn that offers elaborate machine learning capabilities and also provides up-to-date analytics tools.
Implementing such systems will enable you to perform more multivariate and complex regressions as well as data preprocessing and the development of more advanced models. If combined with Python libraries such as Numpy and iPython, these tools can create the basis of a robust data analytics set.
Not only that, but you can also develop your own Python data analysis algorithms which can be directly incorporated into your organization’s intelligence tools via an Application Programming Interface (API).
Practice mini Python data analysis exercises
Using basic models, start practicing what you’ve known about machine learning and data analysis knowledge. As time goes by, move to more advanced techniques to understand even deeper strategies for analyzing data.
Python is increasingly becoming popular among data scientists due to its easy integration with other data analytics tools and databases. It’s also simple to code, easy to understand and features powerful data analysis libraries and excellent computational intensity. This makes it important for you to understand how to implement if your goal is to improve your data analysis efforts for increased business profits.