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To display the count over the bar in matplotlib histogram, we can iterate each patch and use text() method to place the values over the patches. Steps Set the figure size and adjust the padding between and around the subplots. Make a list of numbers to make a histogram plot. Use hist() method to make histograms. Iterate the patches and calculate the mid-values of each patch and height of the patch to place a text. To display the figure, use show() method. ... Read More
In Matplotlib pie charts, auto-labelled values show percentages by default. To display absolute values instead, we can use a custom autopct function with lambda expressions. Understanding the Problem By default, Matplotlib pie charts show percentages. When you have actual data values like [5, 3, 4, 1], the chart displays percentages (38%, 23%, 31%, 8%) instead of the original values. Solution Using Lambda Function Use autopct=lambda p: '{:.0f}'.format(p * total / 100) to convert percentages back to absolute values ? import matplotlib.pyplot as plt # Configure figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = ... Read More
Creating double head arrows on Matplotlib axes helps visualize relationships and measurements between points. You can achieve this using the annotate() method with specific arrow properties. Basic Double Head Arrow Use the annotate() method with arrowstyle="" to create arrows pointing in both directions ? import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.annotate('Double Arrow', xy=(0.1, 0.1), xytext=(0.5, 0.5), arrowprops=dict(arrowstyle='', color='red')) plt.show() Customizing Arrow Properties You can customize the arrow appearance by modifying properties like ... Read More
To add legends and title to grouped histograms generated by Pandas, we can use the DataFrame's plot() method with kind='hist' and then customize the plot using Matplotlib functions. Basic Grouped Histogram with Title First, let's create a DataFrame and plot a basic grouped histogram with a title ? import matplotlib.pyplot as plt import pandas as pd # Set figure size for better visibility plt.rcParams["figure.figsize"] = [10, 6] # Create sample data df = pd.DataFrame({ 'Math': [85, 92, 78, 88, 95, 82, 90], 'Science': [88, 85, 92, ... Read More
To plot scatter points on polar axis in Matplotlib, we can create a polar coordinate system where points are positioned using angles (theta) and radial distances (r). This is useful for visualizing circular or angular data patterns. Basic Polar Scatter Plot Let's start with a simple example that demonstrates the key components ? 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 # Generate sample data N = 150 r = 2 * np.random.rand(N) theta = 2 * np.pi * np.random.rand(N) area = 200 ... Read More
When creating plots in Matplotlib within Jupyter notebooks, you often see unwanted printed output like []. This happens because Matplotlib functions return objects that Jupyter displays. Here are three effective methods to suppress this output. Method 1: Using Semicolon The simplest approach is adding a semicolon at the end of your plot command ? import numpy as np import matplotlib.pyplot as plt x = np.linspace(1, 10, 1000) y = np.sin(x) # Without semicolon - shows output plt.plot(x, y) [] import numpy as np import matplotlib.pyplot as plt ... Read More
To save figures to PDF as raster images in Matplotlib, you need to use the rasterized=True parameter when creating plots. This converts vector graphics to bitmap format, which can be useful for complex plots with many data points. Basic Setup First, let's set up the basic requirements and create a simple rasterized plot − import numpy as np import matplotlib.pyplot as plt # Set figure size and layout plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create figure and subplot with rasterization fig = plt.figure() ax = fig.add_subplot(111, rasterized=True) # Generate random data ... Read More
In Matplotlib, you can retrieve the color of any plotted line using the get_color() method. This is particularly useful when you want to identify the automatically assigned colors or when working with multiple plots in the same figure. Basic Example Let's start with a simple example to understand how get_color() works ? 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 points x = np.arange(10) # Plot multiple lines p = plt.plot(x, x, x, x ** 2, x, x ** ... Read More
In Matplotlib, you can create contour plots with logarithmic color scaling using contourf() combined with LogLocator(). This is particularly useful when your data spans several orders of magnitude. Basic Setup First, let's understand the key components needed for logarithmic contour plots − Use contourf() method for filled contour plots Apply ticker.LogLocator() for logarithmic color scale Handle negative or zero values with masked arrays Add a colorbar to visualize the scale Example Here's how to create a contour plot with logarithmic color scaling − import matplotlib.pyplot as plt import numpy as np ... Read More
Customizing edge labels in NetworkX with Matplotlib allows you to control the appearance and positioning of text displayed along graph edges. You can adjust label position, font properties, and styling to create clear, professional network visualizations. Basic Edge Label Display First, let's create a simple graph with edge labels ? import matplotlib.pyplot as plt import networkx as nx # Create a directed graph G = nx.DiGraph() G.add_nodes_from([1, 2, 3, 4]) G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 1), (1, 3)]) # Position nodes using spring layout pos = nx.spring_layout(G, seed=42) # Draw the ... Read More