1. Aprender
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  2. Cursos
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  3. Writing Efficient Code with pandas
  • 1

    Selecting columns and rows efficiently

    Gratuito

    This chapter will give you an overview of why efficient code matters and selecting specific and random rows and columns efficiently.

  • 2

    Replacing values in a DataFrame

    This chapter shows the usage of the replace() function for replacing one or multiple values using lists and dictionaries.

  • 3

    Efficient iterating

    This chapter presents different ways of iterating through a Pandas DataFrame and why vectorization is the most efficient way to achieve it.

  • 4

    Data manipulation using .groupby()

    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.

Initializing

Exercicio

Validation of normalization

For this exercise, we will perform a z-score normalization and verify that it was performed correctly.

A distinct characteristic of normalized values is that they have a mean equal to zero and standard deviation equal to one.

After you apply the normalization transformation, you can group again on the same variable, and then check the mean and the standard deviation of each group.

You will apply the normalization transformation to every numeric variable in the poker_grouped dataset, which is the poker_hands dataset grouped by Class.

Instruções 1/2

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  • Apply the normalization transformation to the grouped object poker_grouped.