Rishikesh Kumar Rishi

Rishikesh Kumar Rishi

1,016 Articles Published

Articles by Rishikesh Kumar Rishi

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How to convert data values into color information for Matplotlib?

Rishikesh Kumar Rishi
Rishikesh Kumar Rishi
Updated on 25-Mar-2026 1K+ Views

Converting data values into color information for Matplotlib allows you to create visually appealing scatter plots where colors represent data patterns. This process involves using colormaps to map numerical values to specific colors. Basic Color Mapping with Colormaps The most straightforward approach uses Matplotlib's built-in colormaps to automatically map data values to colors ? import numpy as np import matplotlib.pyplot as plt # Set figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create sample data values = np.random.rand(100) x = np.random.rand(len(values)) y = np.random.rand(len(values)) # Create scatter plot with automatic color ...

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Interactive plotting with Python Matplotlib via command line

Rishikesh Kumar Rishi
Rishikesh Kumar Rishi
Updated on 25-Mar-2026 1K+ Views

Interactive plotting in Matplotlib allows you to modify plots dynamically and see changes in real-time. Using plt.ion() and plt.ioff(), you can enable or disable interactive mode to control when plot updates are displayed. Setting Up Interactive Mode First, you need to activate the matplotlib backend and enable interactive mode. Open IPython shell and enter the following commands − %matplotlib auto import matplotlib.pyplot as plt plt.ion() # Enable interactive mode Creating an Interactive Plot Let's create a figure and demonstrate how interactive plotting works − # Create figure and axis fig, ...

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Displaying different images with actual size in a Matplotlib subplot

Rishikesh Kumar Rishi
Rishikesh Kumar Rishi
Updated on 25-Mar-2026 3K+ Views

To display different images with actual size in a Matplotlib subplot, we can take the following steps − Set the figure size and adjust the padding between and around the subplots. Read two images using imread() method (im1 and im2) Create a figure and a set of subplots. Turn off axes for both the subplots. Use imshow() method to display im1 and im2 data. To display the figure, use show() method. Example ...

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How do I make bar plots automatically cycle across different colors?

Rishikesh Kumar Rishi
Rishikesh Kumar Rishi
Updated on 25-Mar-2026 890 Views

To make bar plots automatically cycle across different colors, we can use matplotlib's cycler to set up automatic color rotation for each bar group. This ensures each data series gets a distinct color without manual specification. Steps to Create Color-Cycling Bar Plots Set the figure size and adjust the padding between and around the subplots Configure automatic cycler for different colors using plt.cycler() Create a Pandas DataFrame with the data to plot Use plot() method with kind="bar" to create the bar chart Display the figure using show() method Example Here's how to create a ...

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How to change the line color in a Seaborn linear regression jointplot?

Rishikesh Kumar Rishi
Rishikesh Kumar Rishi
Updated on 25-Mar-2026 961 Views

To change the line color in a Seaborn linear regression jointplot, we can use the joint_kws parameter in the jointplot() method. This allows us to customize the appearance of the regression line by passing styling arguments. Steps Set the figure size and adjust the padding between and around the subplots Create x and y data points using NumPy to make a Pandas DataFrame Use jointplot() method with joint_kws parameter to specify line color Display the figure using show() method Example Here's how to create a regression jointplot with a custom line color ? ...

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Controlling the width of bars in Matplotlib with per-month data

Rishikesh Kumar Rishi
Rishikesh Kumar Rishi
Updated on 25-Mar-2026 700 Views

To control the width of bars in Matplotlib with per-month data, you can dynamically calculate bar widths based on the actual time intervals between dates. This ensures consistent visual representation regardless of varying month lengths. Steps to Control Bar Width Set the figure size and adjust the padding between and around the subplots Create a list of datetime objects representing monthly data points Calculate bar widths based on the actual days between consecutive months Plot the bar chart with calculated widths using plt.bar() Display the figure using show() method Example Here's how to create ...

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How to animate a sine curve in Matplotlib?

Rishikesh Kumar Rishi
Rishikesh Kumar Rishi
Updated on 25-Mar-2026 3K+ Views

To create an animated sine curve in Matplotlib, we use the animation module to continuously update the curve's position. This creates a smooth wave motion effect by shifting the sine wave over time. Steps to Create Animated Sine Curve Follow these steps to animate a sine curve − Set the figure size and adjust the padding between and around the subplots Create a new figure and add axes with appropriate limits Initialize an empty line plot that will be updated during animation Define an initialization function to reset the line data Create an animation function that ...

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Matplotlib histogram with multiple legend entries

Rishikesh Kumar Rishi
Rishikesh Kumar Rishi
Updated on 25-Mar-2026 4K+ Views

Creating a histogram with multiple legend entries in Matplotlib allows you to categorize data visually with colored bars and corresponding legend labels. This is useful for highlighting different data ranges or categories within your distribution. Basic Histogram with Multiple Legend Entries Here's how to create a histogram where different bars are colored according to categories ? import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Rectangle # Set figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Generate sample data data = np.random.rayleigh(size=1000) * 35 # Create histogram N, bins, ...

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Differentiate the orthographic and perspective projection in Matplotlib

Rishikesh Kumar Rishi
Rishikesh Kumar Rishi
Updated on 25-Mar-2026 693 Views

In Matplotlib 3D plotting, you can use two different projection types: perspective and orthographic. Perspective projection simulates how objects appear to the human eye with depth perception, while orthographic projection maintains parallel lines and consistent object sizes regardless of distance. Key Differences Aspect Perspective Projection Orthographic Projection Depth Perception Objects farther away appear smaller Objects maintain size regardless of distance Parallel Lines Converge to vanishing points Remain parallel Use Case Realistic 3D visualization Technical drawings, measurements Example: Comparing Both Projections Here's how to create side-by-side plots ...

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How to create a Matplotlib bar chart with a threshold line?

Rishikesh Kumar Rishi
Rishikesh Kumar Rishi
Updated on 25-Mar-2026 4K+ Views

To create a Matplotlib bar chart with a threshold line, we use the axhline() method to draw a horizontal reference line across the chart. This visualization helps identify which data points exceed a specific threshold value. Basic Bar Chart with Threshold Line Here's how to create a simple bar chart with a threshold line ? import numpy as np import matplotlib.pyplot as plt # Sample data categories = ['A', 'B', 'C', 'D', 'E'] values = [8, 15, 12, 6, 18] threshold = 10 # Create bar chart plt.figure(figsize=(8, 5)) bars = plt.bar(categories, values, color='lightblue', ...

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