How to get started with Python for data analysts
The multi-purpose programming language Python is used by development teams around the world, primarily for its simplicity, flexibility, and readability. Python also provides a plethora of useful options for data analysts and data scientists, as it has large number of libraries dedicated to analytics — from data mining, data processing, and data modeling to data visualization.
An integrated development environment (IDE) is a coding tool that makes it easy for users to write, test, and debug code under a single umbrella. For data analysts, choosing the right Python IDE can make a difference both in their overall adoption and in their ability to explain and share analysis.
Today I’ll tell you about my favorite Python IDEs and libraries for advanced data analytics and share some of the advantages of each.
Top 3 Python IDEs for data analysis
JupyterLab is a web-based IDE for notebooks, code, and data that features a flexible interface and a modular design that enables users to expand functionality according to their needs.
If you’re just starting your journey in Python for data analysis, JupyterLab can be a great fit as it provides an interactive output, allowing you to write your code and test it in the same place.
Notebooks offer a natural way to tell share your analytic line of thinking and tell stories with data. As the next generation of Jupyter Notebook, JupyterLab aims to fix many of Notebook’s usability issues and dramatically broadens its scope. It delivers a general framework for data science and interactive computing in the browser using Python, Julia, R, or any one of several other languages.
Spyder is, according to their website, “a free and open source scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts.”
Spyder’s features like syntax highlighting, code completion, and real-time code analysis highlight potential problems or syntax errors in your code. The static code analysis feature detects style issues, bad practices, potential bugs, and other quality problems. These capabilities make it one of the best IDEs to consider for data analysis.
If you work on data-driven projects that require presenting data to a non-technical audience, Jupyter is probably a better option. If you’re building data science applications with multiple scripts that reference each other, consider Spyder.
PyCharm is a “freemium” IDE, available as a free Community version and a paid Professional version, featuring a keyboard-centric approach and a broad range of built-in developer tools. It also offers syntax and error highlighting, code analysis, auto-code generation, auto indentation, and a code folder, making it an ideal choice for developers wanting to create data analysis applications with Python.
My favorite Python libraries for data analysis
As a data analyst, most of the time your responsibilities involve data processing, data cleaning, data modeling, and data visualizations. Here are a few of the most common libraries targeting these responsibilities:
NumPy is an open-source project was developed with the goal of enabling numerical computing with Python. This powerful library mainly works with numerical data in arrays whose objects can have up to n dimensions. It also delivers high-performance multi-dimensional homogeneous data objects (NumPy Arrays).
SciPy is a free, open-source Python library features modules for an array of tasks common in data science and engineering. It’s a compilation of mathematical algorithms and convenience functions built on the Python extension NumPy. SciPy adds serious power to interactive Python sessions by equipping the user with high-level commands and classes for manipulating and visualizing data.
pandas is a Python software library developed specifically for data manipulation and analysis. This is the most widely used package for data manipulation and transformation. Thanks to pandas’ built-in functions and support for user-defined operations, all groups of users can easily prepare their data for downstream tasks.
Matplotlib is a comprehensive Python library designed for creating static, animated, and interactive visualizations. This is the most popular plotting (data visualization) routine package for Python, featuring line, bar, scatter, histogram, and many other types of plots that help users understand trends and patters and make correlations.
Python has taken a strong hold of the analytics community and for good reason. Python brings the simplicity and power of open-source libraries to help you tackle any data problem. Python is also very cloud friendly and integrates well with low level languages.
If you like the idea of expanding your data skills with Python, you’re definitely our type. We’re tackling the world’s hardest problems with the best analytics engineers. Come join us in helping our clients solve their data challenges!