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Matplotlib Articles
Page 18 of 91
How to plot the difference of two distributions in Matplotlib?
To plot the difference between two distributions in Matplotlib, we use kernel density estimation (KDE) to create smooth probability density functions from our data, then visualize both distributions and their difference. Understanding Kernel Density Estimation Kernel Density Estimation creates a continuous probability density function from discrete data points using Gaussian kernels. This allows us to compare distributions smoothly. Complete Example Here's how to plot two distributions and their difference ? import numpy as np import matplotlib.pyplot as plt import scipy.stats # Set figure size plt.rcParams["figure.figsize"] = [10, 6] plt.rcParams["figure.autolayout"] = True # ...
Read MoreHow to visualize 95% confidence interval in Matplotlib?
To visualize a 95% confidence interval in Matplotlib, you need to plot your main data line and fill the area around it representing the uncertainty range. This technique is commonly used in statistical plots to show the reliability of predictions or measurements. Basic 95% Confidence Interval Plot Here's how to create a confidence interval visualization with synthetic data ? import matplotlib.pyplot as plt import numpy as np # Set figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create sample data x = np.arange(0, 10, 0.05) y = np.sin(x) # Define the ...
Read MorePlotting an imshow() image in 3d in Matplotlib
To plot an imshow() image in 3D in Matplotlib, you can display 2D data as both a traditional image and as a 3D surface plot. This technique is useful for visualizing data from different perspectives. Step-by-Step Approach Here's the process to create 3D visualizations of imshow data ? Create xx and yy coordinate grids using numpy Generate 2D data using mathematical functions Create a figure with multiple subplots Display data as a 2D image using imshow() Display the same data as a 3D contour plot Show the combined visualization Basic Example This example ...
Read MoreAdding extra contour lines using Matplotlib 2D contour plotting
To add extra contour lines using Matplotlib 2D contour plotting, we can control the density and positioning of contour lines by specifying custom levels. This allows us to highlight specific values or create more detailed visualizations. Steps to Add Extra Contour Lines Set the figure size and adjust the padding between and around the subplots. Create a function f(x, y) to get the z data points from x and y. Create x and y data points using NumPy. Make a list of levels using NumPy to define where contour lines appear. Make a contour plot using contour() ...
Read MoreHow to remove the digits after the decimal point in axis ticks in Matplotlib?
To remove the digits after the decimal point in axis ticks in Matplotlib, you can use several approaches. The most common methods involve formatting the tick labels to display only integer values. Method 1: Using set_xticklabels() with astype(int) This method converts the tick values to integers, effectively removing decimal places ? import matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([1.110, 2.110, 4.110, 5.901, 6.00, 7.90, 8.90]) y = np.array([2.110, 1.110, 3.110, 9.00, 4.001, 2.095, 5.890]) fig, ax = plt.subplots() ax.plot(x, y) # Remove ...
Read MoreHiding major tick labels while showing minor tick labels in Matplotlib
To hide major tick labels while showing minor tick labels in Matplotlib, you can use the setp() method to control the visibility of specific tick label types. This is useful when you want a cleaner plot appearance with less cluttered labels. Basic Example Here's how to hide major tick labels while keeping minor ones visible ? import matplotlib.pyplot as plt import numpy as np # Set figure size plt.figure(figsize=(8, 4)) # Create data x = np.linspace(1, 10, 100) y = np.log(x) # Plot the data plt.plot(x, y, 'b-', linewidth=2) # Hide major ...
Read MoreHow to mark a specific level in a contour map on Matplotlib?
To mark a specific level in a contour map on Matplotlib, you can use the contour() method with specific level values and highlight them using different colors or line styles. This technique is useful for emphasizing particular data ranges or thresholds in your visualization. Basic Contour Plot with Labeled Levels First, let's create a basic contour plot and label the contour lines ? import matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True def f(x, y): return np.sin(x) ** 10 + np.cos(10 + y * ...
Read MoreHow to label a patch in matplotlib?
To label a patch in matplotlib, you need to add a label parameter when creating the patch and then use legend() to display it. This is useful for creating annotated plots with geometric shapes. Basic Patch Labeling Here's how to create and label a rectangle patch ? import matplotlib.pyplot as plt import matplotlib.patches as patches # Set figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Initialize patch position x = y = 0.1 # Create figure and axis fig = plt.figure() ax = fig.add_subplot(111) # Add rectangle patch with label ...
Read MoreHow to sort bars in a bar plot in ascending order (Matplotlib)?
To sort bars in a bar plot in ascending order, we can take the following steps − Set the figure size and adjust the padding between and around the subplots. Make a list of data for bar plots. Create a bar plot using bar() method, with sorted data. To display the figure, use show() method. Example Here's how to create a bar plot with bars sorted in ascending order ? import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = [3, 5, 9, 15, 12] plt.bar(range(len(data)), sorted(data), ...
Read MoreHow to add titles to the legend rows in Matplotlib?
In Matplotlib, adding titles to legend rows helps organize multiple data series into logical groups. This technique uses phantom plot handles to create section headers within the legend. Basic Approach The key steps are creating phantom handles (invisible plot elements) and inserting them as section dividers in your legend ? import matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create sample data y = np.exp(-np.arange(5)) markers = ["s", "o", "*"] labels = ["Series A", "Series B"] fig, ax = plt.subplots() # Plot multiple lines ...
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