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A colorbar in Matplotlib displays the color scale used in a plot. By default, Matplotlib automatically places ticks on the colorbar, but you can customize these ticks to show specific values or improve readability. Basic Colorbar with Custom Ticks Here's how to add custom ticks to a colorbar using np.linspace() ? 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 data x, y = np.mgrid[-1:1:100j, -1:1:100j] z = (x + y) * np.exp(-5.0 * (x ** 2 + y ** 2)) ... Read More
To change the font properties of a Matplotlib colorbar label, you can customize the label using various font parameters like weight, size, and family. Here's how to modify colorbar label properties effectively. Steps to Change Colorbar Label Font Properties Set the figure size and adjust the padding between and around the subplots. Create x, y and z data points using numpy. Use imshow() method to display the data as an image, i.e., on a 2D regular raster. Create a colorbar for a ScalarMappable instance, mappable. Using colorbar axes, set the font properties such that the label is ... Read More
To draw a scatter trend line using Matplotlib, we can use polyfit() and poly1d() methods to calculate and plot the trend line over scattered data points. Basic Scatter Plot with Trend Line Here's how to create a scatter plot with a linear trend line − 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 x = np.random.rand(100) y = np.random.rand(100) # Create scatter plot fig, ax = plt.subplots() ax.scatter(x, y, c=x, cmap='plasma', alpha=0.7) # Calculate trend line using ... Read More
A hexbin histogram in Matplotlib displays the distribution of two-dimensional data using hexagonal bins. This visualization is particularly useful for large datasets where traditional scatter plots become cluttered with overlapping points. Basic Hexbin Plot The hexbin() method creates a hexagonal binning plot where the color intensity represents the density of data points in each hexagon ? import numpy as np import matplotlib.pyplot as plt # Generate sample data x = 2 * np.random.randn(5000) y = x + np.random.randn(5000) # Create hexbin plot fig, ax = plt.subplots(figsize=(8, 6)) hb = ax.hexbin(x[::10], y[::10], gridsize=20, cmap='plasma') ... Read More
Joint bivariate distributions visualize the relationship between two continuous variables. In Matplotlib, we can create these distributions using scatter plots with transparency to show density patterns. Basic Joint Bivariate Distribution Here's how to create a simple joint bivariate distribution using correlated data − import numpy as np import matplotlib.pyplot as plt # Set figure size plt.rcParams["figure.figsize"] = [8, 6] plt.rcParams["figure.autolayout"] = True # Generate correlated data np.random.seed(42) x = 2 * np.random.randn(5000) y = x + np.random.randn(5000) # Create scatter plot fig, ax = plt.subplots() scatter = ax.scatter(x, y, alpha=0.08, c=x, cmap="viridis") ... Read More
To align bar and line plots in matplotlib with two Y-axes, we use the twinx() method to create a twin axis that shares the X-axis but has an independent Y-axis. This allows overlaying different chart types on the same plot. Creating DataFrame and Basic Setup First, let's create sample data and set up the figure parameters ? import matplotlib.pyplot as plt import pandas as pd # Set figure size and layout plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create sample DataFrame df = pd.DataFrame({"col1": [1, 3, 5, 7, 1], "col2": [1, 5, 7, ... Read More
To draw a border around subplots in Matplotlib, we can use a Rectangle patch that creates a visible border around the subplot area. This technique is useful for highlighting specific subplots or creating visual separation in multi−subplot figures. Basic Border Around Single Subplot Here's how to add a simple border around one subplot ? import matplotlib.pyplot as plt import matplotlib.patches as patches # Create figure and subplot fig, ax = plt.subplots(1, 1, figsize=(6, 4)) # Plot some sample data ax.plot([1, 2, 3, 4], [1, 4, 2, 3], 'b-o') ax.set_title("Subplot with Border") # Get ... Read More
To plot a cumulative graph of Python datetimes in Matplotlib, you can combine Pandas datetime functionality with Matplotlib's plotting capabilities. This is useful for visualizing data trends over time, such as daily sales, website visits, or any time-series data that accumulates. Basic Setup First, let's create a dataset with datetime values and plot a cumulative sum ? import pandas as pd import matplotlib.pyplot as plt from datetime import datetime, timedelta import numpy as np # Set figure size plt.rcParams["figure.figsize"] = [10, 6] plt.rcParams["figure.autolayout"] = True # Create sample datetime data start_date = datetime(2024, 1, ... Read More
To programmatically select a specific subplot in Matplotlib, you can use several approaches including add_subplot(), subplots(), or subplot() methods. This allows you to modify individual subplots after creation. Method 1: Using add_subplot() Create subplots in a loop and then select a specific one by calling add_subplot() again with the same position ? import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [10, 4] plt.rcParams["figure.autolayout"] = True fig = plt.figure() # Create 4 subplots for index in [1, 2, 3, 4]: ax = fig.add_subplot(1, 4, index) ... Read More
Pcolormesh in Matplotlib creates pseudocolor plots with rectangular grids. By default, it produces blocky, pixelated output. To achieve smooth interpolation and eliminate harsh edges, you can use the shading="gouraud" parameter. Default Pcolormesh vs Smooth Interpolation Let's first see the difference between default shading and Gouraud shading ? import matplotlib.pyplot as plt import numpy as np # Create sample data data = np.random.random((5, 5)) x = np.arange(0, 5, 1) y = np.arange(0, 5, 1) x, y = np.meshgrid(x, y) # Create subplots for comparison fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5)) # Default ... Read More