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.

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== 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

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