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Data Visualization Articles
Page 42 of 68
Text alignment in a Matplotlib legend
Text alignment in a Matplotlib legend allows you to control how the legend text is positioned. You can set horizontal alignment using the set_ha() method on legend text objects. Basic Legend Text Alignment Here's how to align legend text to the left ? import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-5, 5, 100) plt.plot(x, np.sin(x), label="$y=sin(x)$") plt.plot(x, np.cos(x), label="$y=cos(x)$") legend = plt.legend(loc='upper right') for t in legend.get_texts(): t.set_ha('left') plt.show() Different Alignment Options You ...
Read MorePlot Matplotlib 3D plot_surface with contour plot projection
To create a 3D surface plot with contour projections in Matplotlib, we combine plot_surface() for the main surface and contourf() for projecting contours onto the coordinate planes. Understanding the Components A surface plot with contour projections consists of: A 3D surface using plot_surface() Contour projections on the XY, XZ, and YZ planes using contourf() The zdir parameter controls which plane the contour is projected onto Basic Example Here's how to create a surface plot with contour projections ? import numpy as np from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import ...
Read MoreHow to add Matplotlib Colorbar Ticks?
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 MoreHow to change the font properties of a Matplotlib colorbar label?
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 MoreHow can I draw a scatter trend line using Matplotlib?
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 MoreHow to plot hexbin histogram in Matplotlib?
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 MoreHow to make joint bivariate distributions in Matplotlib?
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 MoreHow to align the bar and line in Matplotlib two Y-axes chart?
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 MoreDraw a border around subplots in Matplotlib
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 MorePlotting a cumulative graph of Python datetimes in Matplotlib
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, ...
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