Python has rapidly become the language of choice for a large number of software developers. The utility added to the language with the introduction of the Pandas library is now causing a quiet revolution in the world of data science.

So what exactly is this Chinese bear-themed library and why, as a Python programmer, should you be using it today?

Let's find out.

What is Pandas anyway?

The Pandas software library is a free-to-use (BSD licensed), open-source add-on to the Python language. It enables the already famously useful language to easily retrieve information from datasets and present it in visually intuitive formats.

The library was created using a combination of Python, C, and Cython and compares very favorably to other libraries in key performance metrics, making it a logical choice for data scientists in general, but particularly for those who work hands-on with the Python language.

The goal of Pandas according to the kind folks at pandas.pydata.org is the following:

"pandas aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis/manipulation tool available in any language."

Since its creation in 2008, this highly accessible tool has steadily become the library of choice for data-conscious Python programmers worldwide.

A Brief History of Pandas

The story begins at AQR Capital Management. Wes McKinney, an employee at the time, saw a shortcoming in the software tools he was using to carry out high- performance quantitative analysis on financial data.

With typical developer gusto, Wes decided to create his own solution, and voilá, the birth of Pandas. When he parted ways with AQR, the persuasive Mr. McKinney convinced his bosses to allow the project to become open source. The year was 2009.

By 2012 Wes was joined by fellow AQR developer Chang She who quickly became a significant contributor to the project. This is also the year that the first edition of Python for Data Analysis was published in response to demand for greater information on the subject and introducing Pandas to a new audience of developers.

Why everyone loves Pandas

The library was quickly adopted by data scientists, Python developers, and all those interested in machine learning. The library's efficiency, high performance, and ease of use have made it extremely popular with anyone who needs to carry out advanced manipulations on large-scale data sets.

Here are some of the features we love about this software library.

Multi-format data read/write

Pandas enables Python to work directly with csv, txt, and Excel files as well as interfacing with SQL databases and the HDF5 format.

Intelligent data alignment, cleaning, and handling of missing data

This allows Python to seamlessly work with real-world 'messy' datasets and

manipulate information in an orderly way.

Sophisticated manipulation and filtering of large datasets

Pandas makes it easy for developers to execute a wide range of data engineering operations on large-scale datasets, including label-based slicing, subsetting, adding/dropping, and advanced indexing.

High-performance merging and joining

Efficiencies in the library make merge and join actions fast and accurate even on large mission-critical databases.

Time series functionality

Developers can easily include date range generation, moving window statistics, and other complex time series functions to their code.

Data aggregation

Pandas has an inbuilt powerful group by function that adds split-apply-combine operation functionality on large data sets to Python code

Hierarchical axis indexing

This allows developers to easily represent and work with high-dimensional data sets in lower-dimensional data structures.

There are many more reasons to use this library. These are some of our favourites and the ones we believe have the potential to change your world if you are a data scientist or developer who doesn't use Pandas yet.

How hard is it to learn?

Pandas is popular because of its ease of use and intuitive syntax, that's not to say there isn't a learning curve. The library is integrated with Python so to use it, your first step is to master the basics of that language.

Once you are comfortable with Python, you need to install the library into your Python language environment. If you are already comfortable with programming and data science then an online boot camp or following online tutorials while experimenting with real projects, such as those on Practity should be enough.

Pandas vs SQL vs R

SQL is a structural language that is excellent for non-programmers to interface with a database and carry out typical database and data manipulation. Although both SQL and Pandas have their uses, the latter truly excels in its efficient handling of detailed statistical, math, and procedural functions.

Although SQL can feel more intuitive to non-programmers, and its integrity is arguably more robust, Pandas with Python will certainly appeal more to developers and data scientists.

The R language comes from another era of programming. This versatile tool was developed in 1976 at the legendary Bell Labs by a team of Bell employees specifically for statistical analysis.

R is still widely by data scientists due to its wide range of functions. Increasingly however Pandas and Python have grown in popularity due to their relative simplicity, ease of use, and real-world applications. Where R wins in range of statistical applications, Pandas and Python win in producing usable finished products.

Final thoughts

If you already love Python, but you haven't used Pandas, then you really should jump all over this. There is a small learning curve, but you will be doing yourself a huge favour in the long term. Likewise, if you are a statistician working with R, you'll likely find Python and Pandas far easier to achieve the same results that you would in R.

Python and Pandas are becoming the tools of choice for data scientists, risk analysts, and other professionals who need to work efficiently with large datasets. If you are in any of these fields, why not be proactive? They aren't that hard to learn, they make your life easier and can also be the catalyst that propels your career forward.