5 Striking Pandas Tips and Tricks for Analysts and Data Scientists

Efficiency has become a key ingredient for the timely completion of work. One is not expected to spend more than a reasonable amount of time to get things done. Especially when the task involves basic coding. One such area where data scientists are expected to be the fastest is when using the Pandas library in Python.

Pandas is an open-source package. It helps to perform data analysis and data manipulation in Python language. Additionally, it provides us with fast and flexible data structures that make it easy to work with Relational and structured data.

If you’re new to Pandas then go ahead and enroll in this free course. It will guide you through all the in’s and out’s of this wonderful Python library. And set you up for your data analysis journey. This is the sixth part of my Data Science hacks, tips, and tricks series. I highly recommend going through the previous articles to become a more efficient data scientist or analyst.

I have also converted my learning into a free course that you can check out:

Also, if you have your own Data Science hacks, tips, and tricks, you can share it with the open community on this GitHub repository: Data Science hacks, tips and tricks on GitHub.

To begin with, data exploration is an integral step in finding out the properties of a dataset. Pandas provide a quick and easy way to perform all sorts of analysis. One such important analysis is the conditional selection of rows or filtering of data.

The conditional selection of rows can be based on a single condition or multiple conditions in a single statement separated by logical operators.

For example, I’m taking up a dataset on loan prediction. You can check out the dataset here.

We are going to select the rows of customers who haven’t graduated and have an income of less than 5400. Let us see how do we perform it.

Note: Remember to put each of the conditions inside the parenthesis. Else you’ll set yourself up for an error.

The data can be of 2 types – Continuous and categorical depending on the requirement of our analysis. Sometimes we do not require the exact value present in our continuous variable. But the group it belongs to. This is where Binning comes into play.

For instance, you have a continuous variable in your data – age. But you require an age group for your analysis such as – child, teenager, adult, senior citizen. Indeed, Binning is perfect to solve our problem here.

To perform binning, we use the cut() function. This useful for going from a continuous variable to a categorical variable.