Summary
Keywords
Full Transcript
Most datasets contain "missing values", meaning that the data is incomplete. Deciding how to handle missing values can be challenging! In this video, I'll cover all of the basics: how missing values are represented in pandas, how to locate them, and options for how to drop them or fill them in. 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 "read_csv" documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html "isnull" documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.isnull.html "notnull" documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.notnull.html "dropna" documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dropna.html "value_counts" documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html "fillna" documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html Working with missing data: http://pandas.pydata.org/pandas-docs/stable/missing_data.html == 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/
Continue this lesson in the app
Install CourseHive on Android or iOS to keep learning while you move.
