Spend some time in Python and you’ll likely encounter its bytecode files — those ‘.pyc’ files Python likes to leave behind after it runs.

Have you ever wondered what’s really going on in those files? Watch this video from PyCon 2018 to learn more about these files and what’s in them.

The pandas library is a powerful tool for multiple phases of the data science workflow, including data cleaning, visualization, and exploratory data analysis. However, proper data science requires careful coding, and pandas will not stop you from creating misleading plots, drawing incorrect conclusions, ignoring relevant data, including misleading data, or executing incorrect calculations.

In this tutorial session from PyCon Cleveland 2018, you’ll perform a variety of data science tasks on a handful of real-world datasets using pandas.