Data analysis in Python with pandas - How do I make my pandas DataFrame smaller and faster?

Are you working with a large dataset in pandas, and wondering if you can reduce its memory footprint or improve its efficiency? In this video, I'll show you how to do exactly that in one line of code using the "category" data type, introduced in pandas 0.15. I'll explain how it works, and how to know when you shouldn't use it. SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool == RESOURCES == GitHub repository for the series: https://github.com/justmarkham/pandas-videos "info" documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.info.html "memory_usage" documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.memory_usage.html "astype" documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.astype.html Overview of categorical data in pandas: http://pandas.pydata.org/pandas-docs/stable/categorical.html API reference for categorical methods: http://pandas.pydata.org/pandas-docs/stable/api.html#categorical == LET'S CONNECT! == Newsletter: https://www.dataschool.io/subscribe/ Twitter: https://twitter.com/justmarkham Facebook: https://www.facebook.com/DataScienceSchool/ LinkedIn: https://www.linkedin.com/in/justmarkham/