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Introduction to Python for Data Science

Introduction
Missing values
Duplicate rows
Outliers
Joining datasets
Summary
17. Summary

Instruction

It's time to wrap things up!

In this part, we learned how to deal with the following problems:

1. Missing values - you can use 

dataframe.fillna(value)
to replace all NaNs with value.

2. Duplicate rows - you can use 

dataframe.drop_duplicates()
to get rid of duplicate rows.

3. Outliers - one of the ways of dealing with duplicates is to remove them using 

dataframe.drop(index)

4. Joining DataFrames - to join two DataFrames, use:

dataframe_a.merge(dataframe_b, on='column')

Okay, are you ready for a short quiz?

Exercise

Click Next exercise to continue.