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Articles by Priya Mishra
Page 2 of 14
How are variables stored in Python (Stack or Heap)?
In Python, variables are stored differently based on their type and scope in two main memory regions: the stack and the heap. Python, being a high-level programming language, abstracts away many low-level memory management details from the programmer. However, understanding how variables are stored is essential for writing efficient and optimized code. In this article, we will explore how variables are stored in Python and the difference between stack and heap memory allocation. Stack Memory The stack is a region of memory used for storing local variables and function call information. It operates on a Last-In-First-Out (LIFO) ...
Read MoreHover Text and Formatting in Python-Plotly
Python-Plotly is a powerful data visualization library that provides interactive hover text and formatting features. These features allow you to display additional information and customize tooltips when users hover over data points, making your visualizations more informative and engaging. Understanding Hover Modes Plotly offers different hover modes that control how tooltips are displayed when hovering over data points. The three main hover modes are: Closest − Shows hover information for the nearest data point (default) X unified − Shows hover information for all points with the same x-value Y unified − Shows hover information for all ...
Read MoreHouse Price Prediction using Machine Learning in Python
House price prediction using machine learning has revolutionized the real estate industry by leveraging Python's powerful data analysis capabilities. This comprehensive guide explores how to build predictive models that help buyers, sellers, and investors make informed decisions in the dynamic housing market. Linear Regression for House Price Prediction Linear regression is a widely used technique for house price prediction due to its simplicity and interpretability. It assumes a linear relationship between independent variables (bedrooms, bathrooms, square footage) and the dependent variable (house price). By fitting a linear regression model to historical data, we estimate coefficients that represent ...
Read MoreHorizontal Stripplot with Jitter using Altair in Python
A horizontal stripplot with jitter is an effective visualization for displaying the distribution of continuous variables across different categories. Altair, a powerful Python library for declarative statistical visualization, makes creating these plots straightforward and customizable. What are Stripplot and Jitter? A stripplot displays individual data points in a horizontal arrangement, allowing us to observe their distribution across different categories. However, when multiple data points share the same horizontal position, they can overlap and make it difficult to distinguish individual points. Jitter is a technique that adds a small amount of random noise to the vertical position of ...
Read MoreHorizontal Boxplots with Points using Seaborn in Python
Boxplots are one of the most popular tools for data visualization, mainly created using Seaborn, which provides a simple and powerful way to create both horizontal and vertical boxplots and other types of visualizations. In this article, we will focus on how to create a horizontal boxplot with points using Seaborn in Python. What is a Boxplot? A boxplot is a graphical representation of a dataset that shows the distribution of data using quartiles, median, and outliers. The box in the middle represents the interquartile range (IQR), with whiskers extending to the minimum and maximum values within ...
Read Morehomogeneity_score using sklearn in Python
While working with clustering algorithms in Python, it is important to be able to evaluate the performance of the models, and one of the popular metrics for evaluating the performance of the clustering model is the homogeneity score using sklearn. It measures how well the labels assigned by a clustering algorithm match the true labels of a dataset. The higher the homogeneity score, the better the clustering algorithm performed. In this article, we'll take a closer look at the homogeneity score and how to compute it using Scikit-learn in Python. What is the Homogeneity Score? The homogeneity ...
Read MoreHolistically-Nested Edge Detection with OpenCV and Deep Learning
Holistically-Nested Edge Detection (HED) is a deep learning-based method for detecting edges in images using convolutional neural networks. First introduced by Xie and Tu in 2015, HED has gained popularity for producing accurate and high-quality edge maps by learning edge features directly from image data. In this article, we will explore the basics of HED, how it works, and how to implement it using OpenCV and deep learning. What is HED (Holistically-Nested Edge Detection)? Edge detection is an important computer vision task that involves identifying sharp discontinuities in images. These edges serve as the foundation for more ...
Read MoreHistogram Plotting and stretching in Python
Histogram plotting and stretching is a powerful technique in image processing and data visualization that allows you to represent the distribution of pixel intensities and improve image contrast. This process enhances visibility by spreading pixel values across the full intensity range (0-255). A histogram shows the frequency distribution of pixel intensities in an image. Histogram stretching (also called contrast stretching) improves image contrast by mapping the current range of pixel values to utilize the full available range. Method 1: Using Built-in Functions OpenCV and Matplotlib provide built-in functions for histogram equalization and plotting. Here's how to plot ...
Read MoreHighlight the negative values red and positive values black in Pandas Dataframe
Analyzing data is a fundamental aspect of any data science task. One common requirement during data exploration is to visually highlight negative and positive values in a pandas DataFrame for effective interpretation. In this article, we will explore powerful techniques using the Pandas library in Python to visually highlight negative values in red and positive values in black within a DataFrame. By employing these approaches, data analysts can efficiently distinguish between positive and negative trends, aiding in insightful data interpretation. Methods to Highlight Values There are several methods to highlight negative values in red and positive values ...
Read MoreHighlight the NaN values in Pandas Dataframe
Working with incomplete or missing data is a common challenge in data analysis, and the initial step towards addressing this problem is to identify the NaN (missing) values in data structures like a Pandas DataFrame. In a Pandas DataFrame, these missing values are often represented as NaN (Not a Number) values, which can occur due to various reasons like errors during data entry, extraction, or processing. Fortunately, Pandas offers a range of effective techniques for detecting and managing missing values. This article will explore multiple approaches to identify NaN values within a Pandas DataFrame, including utilizing built-in functions like ...
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