课程

Writing Efficient Code with pandas

中级技能水平

更新时间 2022年8月

Learn efficient techniques in pandas to optimize your Python code.

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PythonProgramming4 小时14 视频45 练习3,500 经验值21,563成就声明

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课程描述

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.

先决条件

Data Manipulation with pandas

1

Selecting columns and rows efficiently

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

The need for efficient coding I

50 经验值

What does time.time() measure?

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Measuring time I

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Measuring time II

100 经验值

Locate rows: .iloc[] and .loc[]

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Row selection: loc[] vs iloc[]

100 经验值

Column selection: .iloc[] vs by name

100 经验值

Select random rows

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Random row selection

100 经验值

Random column selection

100 经验值

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.

Replace scalar values using .replace()

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Replacing scalar values I

100 经验值

Replace scalar values II

100 经验值

Replace values using lists

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Replace multiple values I

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Replace multiple values II

100 经验值

Replace values using dictionaries

50 经验值

Replace single values I

100 经验值

Replace single values II

100 经验值

Replace multiple values III

100 经验值

Most efficient method for scalar replacement

50 经验值

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.

Looping using the .iterrows() function

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Create a generator for a pandas DataFrame

100 经验值

The iterrows() function for looping

100 经验值

Looping using the .apply() function

50 经验值

.apply() function in every cell

100 经验值

.apply() for rows iteration

100 经验值

Vectorization over pandas series

50 经验值

Why vectorization in pandas is so fast?

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pandas vectorization in action

100 经验值

Vectorization with NumPy arrays using .values()

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Best method of vectorization

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Vectorization methods for looping a DataFrame

100 经验值

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.

Data transformation using .groupby().transform

50 经验值

The min-max normalization using .transform()

100 经验值

Transforming values to probabilities

100 经验值

Validation of normalization

100 经验值

When to use transform()?

50 经验值

Missing value imputation using transform()

50 经验值

Identifying missing values

100 经验值

Missing value imputation

100 经验值

Data filtration using the filter() function

50 经验值

When to use filtration?

50 经验值

Data filtration

100 经验值

Congratulations!

50 经验值

Writing Efficient Code with pandas

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