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Articles by Mukul Latiyan
Page 10 of 37
How to Correctly Access Elements in a 3D Pytorch Tensor?
PyTorch is a popular open−source machine learning framework that provides efficient tensor operations on both CPUs and GPUs. A tensor is a multi−dimensional array in PyTorch, and it is the fundamental data structure used for storing and manipulating data in PyTorch. In this context, a 3D tensor is a tensor with three dimensions, and it can be represented as a cube−like structure with rows, columns, and depth. To access elements in a 3D PyTorch tensor, you need to know its dimensions and the indices of the elements you want to access. The indices of a tensor are specified using square ...
Read MoreHow to Convert Unstructured Data to Structured Data Using Python ?
Unstructured data is data that does not follow any specific data model or format, and it can come in different forms such as text, images, audio, and video. Converting unstructured data to structured data is an important task in data analysis, as structured data is easier to analyse and extract insights from. Python provides various libraries and tools for converting unstructured data to structured data, making it more manageable and easier to analyse. In this article, we will explore how to convert unstructured biometric data into a structured format using Python, allowing for more meaningful analysis and interpretation of the ...
Read MoreHow to Convert to Best Data Types Automatically in Pandas?
Pandas is a popular data manipulation library in Python, used for cleaning and transforming data. It provides various functionalities for converting data types, such as the astype() method. However, manually converting data types can be time−consuming and prone to errors. To address this, Pandas introduced a new feature in version 1.0 called convert_dtypes(), which allows automatic conversion of columns to their best−suited data types based on the data in the column. This feature eliminates the need for manual type conversion and ensures that the data is appropriately formatted. Converting the Datatype of a Pandas Series Consider the code shown below ...
Read MoreHow To Convert Sklearn Dataset To Pandas Dataframe in Python?
Scikit−learn (sklearn) is one of the most popular machine learning libraries for Python. It provides a range of efficient tools for machine learning and statistical modelling, including a variety of datasets. These datasets are provided in the form of numpy arrays, which can be difficult to work with for certain tasks, such as exploratory data analysis. Pandas is a popular data manipulation library that provides powerful tools for data analysis and manipulation. It provides data structures for efficiently storing and manipulating large datasets, and provides a wide range of tools for data cleaning, transformation, and analysis. Below are the two ...
Read MoreHow to Convert Scrapy items to JSON?
Web scraping is the process of extracting data from websites. It involves parsing HTML or XML code and extracting relevant information from it. Scrapy is a popular Python−based web scraping framework that allows you to easily build web scrapers to extract structured data from websites. Scrapy provides a robust and efficient framework for building web crawlers that can extract data from websites and store it in various formats. One of the key features of Scrapy is its ability to parse and store data using custom Item classes. These Item classes define the structure of the data that will be extracted ...
Read MoreCleaning Data with Dropna in Pyspark
In order to make sure that the data is accurate, trustworthy, and appropriate for the intended analysis, cleaning the data is a crucial step in any data analysis or data science endeavour. The data cleaning functions in Pyspark, like dropna, make it a potent tool for working with big datasets. The dropna function in Pyspark allows you to remove rows from a DataFrame that contain missing or null values. Missing or null values can occur in a DataFrame for various reasons, such as incomplete data, data entry errors, or inconsistent data formats. Removing these rows can help ensure the quality ...
Read MoreChange Value in Excel Using Python
In this article we will learn different approaches with which we can change the value of data present in an excel sheet using python. Openxypl Openpyxl is a Python library used for working with Excel spreadsheets. It is a popular choice for working with Excel files in Python because it is easy to use, has an active developer community, and provides many features for working with spreadsheets. Openpyxl allows you to create, read, write, and modify Excel files using Python. It supports the following file formats: XLSX (Microsoft Excel Open XML Spreadsheet) XLSM (Microsoft Excel Open XML Macro−Enabled ...
Read MoreCluster Sampling in Pandas
In this article, we will learn how we can perform cluster sampling in Pandas. But before we deep dive into that, let's explore a little about what sampling is in Pandas, as well as how pandas help us to do that. Sampling In Pandas, sampling refers to the process of selecting a subset of rows or columns from a DataFrame or Series object. Sampling can be useful in many data analysis tasks, such as data exploration, testing, and validation. Pandas provides several methods for sampling data, including: DataFrame.sample(): This method returns a random sample of rows from a ...
Read MoreClear LRU Cache in Python
In this article, we will learn how to clear an LRU cache implemented in Python. Before we dive deep into the coding aspect, let's explore a little about what an LRU cache is and why it is popular. LRU Cache, also known as the Least Recently Used Cache, is a data structure that is widely used in computer science to improve the performance of applications by reducing the time it takes to access frequently−used data. The LRU Cache stores a limited number of items and removes the least recently used item when the cache becomes full. This allows ...
Read MoreCheck if a Thread has Started in Python
Multithreading is a powerful technique used in modern programming languages to execute multiple threads simultaneously. In Python, the threading module is used to implement multithreading. Multithreading allows programs to perform multiple tasks at once and can improve the performance of applications. When working with multithreading in Python, it's essential to know how to check if a thread is running or not. The is_alive() method provided by the Thread class is a simple and effective way to check the status of a thread. By using this method, you can determine whether a thread has started, is running, or has finished its ...
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