When writers hit a block, they read. When musicians get stuck, they seek stimulation by listening to music. It's the same with any craft. The more you surround yourself with the work of others in your field, the more opportunities you have to learn, be inspired, and channel those influences back into your own work.

As your business grows, maintaining excellent customer service requires delivering high-quality solutions efficiently and effectively. But balancing the goals that lead to great customer service is a challenge—overemphasize efficiency and your quality may suffer; provide a white-glove experience on every interaction and your customer service cost might bankrupt the entire company.

No matter how you're looking to present your data, there's a seemingly endless number of Python libraries and resources to use (check out ten of our favorites).

You may know Plotly as an easy-to-use online data visualization tool, but did you know they also offer a Python API so data scientists can use Plotly and Python together offline? We're excited to add Plotly to the growing list of supported libraries in Mode Python Notebooks.

With Plotly and Mode you can build beautiful, interactive data visualizations on top of a Python dataset and share it with teammates instantly.

Sometimes you just need to share a data set. A list of app users who meet certain criteria. A call list for a sales team. You get the idea. Often the next step with shared data sets like these is for someone to export the data as a CSV to do simple explorations in Excel.

Scroll through the Python Package Index and you'll find libraries for practically every data visualization need—from GazeParser for eye movement research to pastalog for realtime visualizations of neural network training. And while many of these libraries are intensely focused on accomplishing a specific task, some can be used no matter what your field.

Presenting new data always raises more questions than it answers. Your coworkers want to better understand results to inform decision-making. Business teams have insatiable appetites for slices of data—a sales team wants the numbers segmented by account age, while a product manager wants them grouped by product type.