Tkinter is Python’s standard GUI (Graphical User Interface) toolkit, providing an easy and intuitive way to build desktop applications. As part of Python’s standard library, it requires no additional installation and works seamlessly across Windows, macOS, and Linux. Whether you’re building simple tools or complex applications, Tkinter offers the flexibility and simplicity needed for rapid Continue reading
How to Master Seaborn FacetGrid and Regression Plots
Master Seaborn’s most powerful visualization techniques: create multi-panel statistical visualizations with FacetGrid using sns.FacetGrid(data, col=”category”, row=”group”) to compare conditional relationships across subsets; build comprehensive regression analysis with sns.lmplot(data=df, x=”variable1″, y=”variable2″, hue=”group”, col=”condition”) combining scatterplots with fitted regression lines; and leverage advanced features like polynomial regression, robust fitting, and confidence intervals for publication-quality statistical graphics that Continue reading
How to Master Seaborn Color Palettes, Boxplots, and Clustermaps
Master three essential Seaborn visualization techniques: create perceptually uniform color palettes with sns.color_palette() for qualitative, sequential, and diverging data; build statistical boxplots using sns.boxplot() to show distribution quartiles and outliers; and generate hierarchically-clustered heatmaps with sns.clustermap() to reveal data patterns through dendrogram-based clustering—all with practical code examples and statistical best practices.
Complete Seaborn tutorial: master statistical data visualization with Python
Seaborn is Python’s premier statistical visualization library, built on matplotlib with a high-level, dataset-oriented API that makes complex statistical plots accessible in just a few lines of code; install with pip install seaborn, load data into pandas DataFrame, use functions like sns.heatmap(), sns.pairplot(), and sns.boxplot() with built-in themes and color palettes for publication-ready graphics that Continue reading
How to Use Seaborn in Python: A Fast, Practical Guide
Seaborn is a high‑level statistical visualization library built on matplotlib, designed to turn tidy data into clear, publication‑quality charts with minimal code; install Seaborn, pick a plot function like scatterplot/histplot/catplot, pass a pandas DataFrame and column names, then theme with set_theme() and color palettes for consistent, attractive output.
How to change plotly figure size
Plotly allows you to create interactive, publication-quality figures in Python. You can customize the width and height of your figures through multiple approaches: using update_layout, Plotly Express parameters, default templates, or configuration options when exporting. This guide covers each method with code examples.
Understanding Plotly Traces in Python
In Plotly, a trace represents a single dataset or graphical element within a chart. Each trace is an object that defines how data is displayed—such as lines, bars, markers, or other shapes. You can use multiple traces to layer and customize complex plots.
Understanding fill in Tkinter
In Tkinter, the fill option is commonly used with the pack() geometry manager. It controls how a widget expands to fill the space allocated to it. This helps you build responsive and adaptive layouts by controlling expansion in horizontal, vertical, or both directions.
Creating Round Buttons in Tkinter
Tkinter doesn’t natively support round buttons, but with a few creative techniques you can still design buttons with a circular shape. This is useful when building more modern or custom-styled interfaces.
Resolving TypeError: ufunc isnan not supported for the input types
I will explain how to resolve the error TypeError: ufunc ‘isnan’ not supported for the input types. That may occur when you try to plot a seaborn heatmap with pandas data. This error happens when some of the values in your data are not numeric, and seaborn tries to coerce them into numbers, but fails. Continue reading
Profiling Python Code for Performance Optimization
Performance profiling is essential for optimizing Python applications. It helps identify bottlenecks and areas that consume the most resources. I will show you several tools and techniques for profiling Python code to enhance its performance.
Saving Plotly Plots to Local Files
Plotly is renowned for its interactive web-based plots, but you often need to save these plots as static image files (PNG, JPEG, SVG) or as interactive HTML files for sharing or embedding. See how to save your Plotly figures to local files.
How to Set Same Scale for X and Y Axis in Plotly
When creating plots, especially scatter plots or scientific visualizations, it’s often crucial to ensure that the x and y axes have the same scale. This means that a unit of distance on the x-axis represents the same magnitude as a unit of distance on the y-axis. In Plotly, this is achieved by setting the aspect Continue reading
Setting Background Color to Transparent in Plotly Plots
Plotly is a versatile charting library that allows for extensive customization. One common requirement is to make the background of a plot transparent, which is particularly useful when embedding plots on websites with custom backgrounds or when layering plots. See how to set both the plot area and the overall figure background to transparent in Continue reading
How to remove space between subplots in Plotly
Plotly’s subplot functionality is incredibly useful for displaying multiple related plots in a single figure. By default, make_subplots adds a certain amount of spacing between these plots for readability. However, there are many cases where you might want to reduce or completely remove this space to create a more compact or visually continuous layout. See Continue reading