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Python Efficient Code

Efficient code plays a crucial role in achieving optimal performance and resource utilization in software development. It focuses on minimizing execution time, reducing memory usage, and optimizing algorithms
Building with builtins

The builtins module in Python provides a set of functions and constants that are always available without the need for import statements. These built-in functions and constants can be useful for writing more efficient and concise code.

Here are some examples of using built-
Built-in function: range() with Efficient Code

The range() function is a built-in function in Python that generates a sequence of numbers. It is often used to create a loop
Built-in function: enumerate() with Efficient Code

The enumerate() function is a built-in function in Python that allows you to iterate over a sequence and keep track of the index of the current item. Here are some tips for using enumerate() to write efficient code:

Built-in function: map() with Efficient Code

The map() function is a built-in function in Python that applies a given function to each item of a sequence (such as a list, tuple, or set) and returns
The power of NumPy arrays with Efficient Code

NumPy is a Python library that provides support for large, multi-dimensional arrays and matrices, along with a wide range of mathematical
NumPy array broadcasting

NumPy array broadcasting allows you to perform operations between arrays with different shapes and sizes without having to explicitly loop over the arrays. Broadcasting can significantly improve the efficiency of your code by avoiding unnecessary loops
NumPy array boolean indexing

NumPy array boolean indexing allows you to select elements from an array based on a condition expressed as a boolean expression. Boolean indexing can be a powerful tool for filtering, masking, and modifying arrays.

Why should we time our code?

Timing our code is important for several reasons:

Performance optimization: Timing our code helps us identify which parts of our code are taking the most time to execute. This information ca
Using timeit

In Python, we can time our code using the %timeit magic command in Jupyter notebooks or the timeit module in regular Python scripts. Here
timeit output

The %timeit magic command in Jupyter notebooks and the timeit module in regular Python scripts return the average execution time of the code being timed, along with other information such as the standard deviation and the numb
Specifying number loops
In Python, we can specify the number of runs or loops when using %timeit in Jupyter notebooks or the timeit module in regular Python scripts.

Here's how to specify the number of run
Using timeit in line magic mode

In Jupyter notebooks, %timeit can be used in both line magic mode and cell magic mode.

In line magic mode, %timeit can be used to time a single line of code, like this:

Using timeit in cell magic mode

In Jupyter notebooks, %timeit can also be used in cell magic mode to time larger blocks of code or entire functions.

To use %timeit in cell magic mode, we simply add %%timeSaving output
In Jupyter notebooks, we can save the output of %timeit to a variable using the -o option. This is useful if we want to analyze the timing r Comparing times When writing efficient Python code, it's often useful to compare the execution times of different implementations of the same function Code profiling for runtime Code profiling is a technique used to identify performance bottlenecks and optimize the runtime of Python code. There are several profiling tools available in Python, including cProfile, line_profiler, and memory_profile %lprun output %lprun is a line-by-line profiler for Python code that can help you identify performance bottlenecks in your code. It is part of the line_profiler< Code profilling for memory usage To profile memory usage of your Python code, you can use the memory_profiler package. This package provides a decorator %mprun output %mprun is a line-by-line memory profiler for Python code that can help you identify memory usage issues in your code. It is p

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Built-in Functions

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