The ability to efficiently work with big datasets and extract valuable information is an indispensable tool for every aspiring data scientist. When working with a small amount of data, we often don’t realize how slow code execution can be. This course will build on your knowledge of Python and the pandas library and introduce you to efficient built-in pandas functions to perform tasks faster. Pandas’ built-in functions allow you to tackle the simplest tasks, like targeting specific entries and features from the data, to the most complex tasks, like applying functions on groups of entries, much faster than Python's usual methods. By the end of this course, you will be able to apply a function to data based on a feature value, iterate through big datasets rapidly, and manipulate data belonging to different groups efficiently. You will apply these methods on a variety of real-world datasets, such as poker hands or restaurant tips.
This chapter will give you an overview of why efficient code matters and selecting specific and random rows and columns efficiently.
This chapter shows the usage of the replace() function for replacing one or multiple values using lists and dictionaries.
This chapter presents different ways of iterating through a Pandas DataFrame and why vectorization is the most efficient way to achieve it.
This chapter describes the groupby() function and how we can use it to transform values in place, replace missing values and apply complex functions group-wise.
In the following tracksPython Programming
PrerequisitesData Manipulation with pandas
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