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Writing Efficient Python Code

Intermediate

4.7+

Updated 12/2024

Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.

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PythonSoftware Development4 hours15 videos52 exercises4,000 XP126,089Statement of Accomplishment

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Course Description

As a Data Scientist, the majority of your time should be spent gleaning actionable insights from data -- not waiting for your code to finish running. Writing efficient Python code can help reduce runtime and save computational resources, ultimately freeing you up to do the things you love as a Data Scientist. In this course, you'll learn how to use Python's built-in data structures, functions, and modules to write cleaner, faster, and more efficient code. We'll explore how to time and profile code in order to find bottlenecks. Then, you'll practice eliminating these bottlenecks, and other bad design patterns, using Python's Standard Library, NumPy, and pandas. After completing this course, you'll have the necessary tools to start writing efficient Python code!

Prerequisites

Data Types in Python Python Toolbox

1

Foundations for efficiencies

Pop quiz: what is efficient

A taste of things to come

Zen of Python

Building with built-ins

Built-in practice: range()

Built-in practice: enumerate()

Built-in practice: map()

The power of NumPy arrays

Practice with NumPy arrays

Bringing it all together: Festivus!

2

Timing and profiling code

Examining runtime

Using %timeit: your turn!

Using %timeit: specifying number of runs and loops

Using %timeit: formal name or literal syntax

Code profiling for runtime

Pop quiz: steps for using %lprun

Using %lprun: spot bottlenecks

Using %lprun: fix the bottleneck

Code profiling for memory usage

Pop quiz: steps for using %mprun

Using %mprun: Hero BMI

Using %mprun: Hero BMI 2.0

Bringing it all together: Star Wars profiling

3

Gaining efficiencies

Efficiently combining, counting, and iterating

Combining Pokémon names and types

Counting Pokémon from a sample

Combinations of Pokémon

Comparing Pokédexes

Searching for Pokémon

Gathering unique Pokémon

Eliminating loops

Gathering Pokémon without a loop

Pokémon totals and averages without a loop

Writing better loops

One-time calculation loop

Holistic conversion loop

Bringing it all together: Pokémon z-scores

4

Basic pandas optimizations

Intro to pandas DataFrame iteration

Iterating with .iterrows()

Run differentials with .iterrows()

Another iterator method: .itertuples()

Iterating with .itertuples()

Run differentials with .itertuples()

pandas alternative to looping

Analyzing baseball stats with .apply()

Settle a debate with .apply()

Optimal pandas iterating

Replacing .iloc with underlying arrays

Bringing it all together: Predict win percentage

Congratulations!

Writing Efficient Python Code

Course
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*4.7

from 53 reviews

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Most recent
Top reviews

Guorong C.

about 1 month

I like datacamp training format coding along to give me hands on experience. I learned a lot from this course. I am going to put those tricks from this course to practice.

Luis P.

3 months

excellent

Ghebremichael G.

4 months

Writing Efficient Python Code

Junseok C.

6 months

Structured course, learned efficient way of coding in Python

Lyndon H.

9 months

As someone who started coding in Assembler, I've always used looping when coding and was interested to see that alternative means were available. To paraphrase Monty Python "your loopings or your life" (Dennis Moore).

"I like datacamp training format coding along to give me hands on experience. I learned a lot from this course. I am going to put those tricks from this course to practice."

Guorong C.

"excellent"

Luis P.

"Writing Efficient Python Code"

Ghebremichael G.

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