Using Cython, you can speed up existing Python code by an order of 100x or more. This is possible because Cython converts some of the Python code to C by doing some basic code changes. Even without any code change a speed up of 2x is commonly observed, like in this post example. Because, everything …
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Let’s see how to cythonize Python code inside Jupyter notebooks step by step. In this post we will see how to: Define and time a Python Function to benchmark How to run Python using Cython in Jupyter Notebook Let’s cythonize the function But, let’s first answer a basic question: What is the difference between CPython …
How to convert Python to Cython inside Jupyter Notebooks? Read More »
OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. OpenCV-Python is a Python wrapper for the original OpenCV C++ library. Let’s see how it install OpenCV in python. Introduction OpenCV enables users to perform image and video processing tasks with ease. In this blog post, we will provide …
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Pip is a widely used package manager for Python, allowing you to install and manage Python packages easily. In this blog post, we’ll explore various methods to install Pip on MacOS. I’ll provide clear, reproducible code examples for each method, making it easy for you to get started with Pip on your MacOS system. Using …
install pip mac – How to install pip in MacOS?: A Comprehensive Guide Read More »
Web scraping is the technique of extracting data from a specific website or web page. This has wide applications in: Research and publication purposes Competitor and market studies Creating data for machine learning models The extracted data can be stored in any format be it a csv, txt, json, API etc so that it can …
Scrapy vs. Beautiful Soup: Which is better for web scraping? Read More »
1. What is the purpose of adding Python to the PATH environment variable? Adding Python to the PATH environment variable in Windows allows you to run Python commands from any directory within the command prompt. Here are the steps to add Python to the PATH variable: 2. What is the PATH environment variable in Windows? …
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numpy.random.randint function is used to get random integers from low to high values. The low value is included while the high value is excluded in the calculations. The output values are taken from the discrete uniform distribution of the range values. random.randint(low, high=None, size=None, dtype=int) Purpose: The numpy random randint function used for creating a …
The np.random.uniform() function is used to create an array with random samples from a uniform probability distribution of given low and high values. random.uniform(low=0.0, high=1.0, size=None) Purpose: The numpy random uniform function used for creating a numpy array with random float values from low to high interval. Parameteres: Low: float or array-like of floats,optional: Lowest …
The np.sort() function is used to sort the array along a specified axis. Numpy.sort (a, axis=- 1, kind=None, order=None) Purpose: This function is used for sorting the array. Parameters: arr:a:array_like array to be sorted. axis: None or int,optional Axis on which we perform the arithmetic mean if specified. otherwise, the arr will be flattened. kind: …
numpy.median function is used to calculate the median of an array along a specific axis or multiple axes. Median is defined as the middle value separating the higher half from the lower half of a data sample in other words median is a value in the middle when you sort the values. In this post, …
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The size of the data you can load and train in your local computer is typically limited by the memory in your computer. Let’s explore various options of how to deal with big data in python for your ML projects. In certain situations, you might have a hard time working on large data, that is …
How to deal with Big Data in Python for ML Projects (100+ GB)? Read More »
Decorators in python allow you to dynamically change the functionality of another function, without altering it’s code. What? Is that possible? Yes. This covers: 1. What is a decorator and how to create one? 2. Easier way to decorate functions 3. Class decorators 4. Problem with docstrings on decorated functions and how to solve. What …
Decorators in Python – How to enhance functions without changing the code? Read More »
Generators in python provide an efficient way of generating numbers or objects as and when needed, without having to store all the values in memory beforehand. Introduction You can think of Generators as a simple way of creating iterators without having to create a class with __iter__() and __next__() methods. So how to create a …
Generators in Python – How to lazily return values only when needed and save memory? Read More »
Iterable in Python is any object that can be looped over. In order for an iterable to be looped over, an iterable needs to be converted to an iterator using the __iter__() method. Once created, it is actually the iterator that gets iterated over. This is also what happens internally when you run a for-loop. …
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Any Python file with a .py extension is a Module in Python. A python package is a collection of such modules along with a __init__.py file. Let’s understand how to work with modules and packages with clear examples. Introduction When you work on python projects, it’s not a good practice to have all you python …
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Object oriented programming is an effective way of writing code. You create classes which are python objects, that represented meaningful entities which defines its own behaviour (via methods) and attributes. Let’s understand what a class is and the concepts behind Object Oriented Programming in Python Everything you have encountered so far in Python, such as …
How to reshape a numpy array? The numpy.reshape() function is used to reshape a numpy array without changing the data in the array. It is a very common practice to reshape arrays to make them compatible for further calculations.
Vaex Python is an alternative to the Pandas library that take less time to do computations on huge data using Out of Core Dataframe. It has fast, interactive visualization capabilities as well. Pandas is the most widely used python library for dealing with dataframes and processing. The popularity is due to the convenient, easy to …
Modin is a python library that can be used to handle large datasets using parallelisation. The syntax is similar to pandas and its astounding performance has made it a promising solution. By changing just one line of code. This article shows you why you should start using Modin and how to use it with hands-on …
Modin – How to speedup pandas by changing one line of code Read More »
Dask provides efficient parallelization for data analytics in python. Dask Dataframes allows you to work with large datasets for both data manipulation and building ML models with only minimal code changes. It is open source and works well with python libraries like NumPy, scikit-learn, etc. Let’s understand how to use Dask with hands-on examples. Dask …
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