Article Categories
- All Categories
-
Data Structure
-
Networking
-
RDBMS
-
Operating System
-
Java
-
MS Excel
-
iOS
-
HTML
-
CSS
-
Android
-
Python
-
C Programming
-
C++
-
C#
-
MongoDB
-
MySQL
-
Javascript
-
PHP
-
Economics & Finance
Matplotlib Articles
Page 50 of 91
Adjusting the spacing between the edge of the plot and the X-axis in Matplotlib
To adjust the spacing between the edge of the plot and the X-axis in Matplotlib, we can use several methods including tight_layout(), subplots_adjust(), or configure padding parameters. This is useful for preventing axis labels from being cut off or creating better visual spacing. Using tight_layout() Method The tight_layout() method automatically adjusts subplot parameters to give specified padding around the plot ? import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] x = np.linspace(-2, 2, 100) y = np.exp(x) plt.plot(x, y, c='red', lw=1) plt.tight_layout() plt.show() Using subplots_adjust() Method ...
Read MoreHow to add footnote under the X-axis using Matplotlib?
To add a footnote under the X-axis using Matplotlib, we can use the figtext() method to place text at specific coordinates on the figure. This is useful for adding citations, data sources, or explanatory notes. Using figtext() Method The figtext() method allows you to place text anywhere on the figure using normalized coordinates (0 to 1). Position (0, 0) is the bottom-left corner, and (1, 1) is the top-right corner. Example Here's how to add a footnote with a styled box under the X-axis − import numpy as np import matplotlib.pyplot as plt ...
Read MorePlot yscale class linear, log, logit and symlog by name in Matplotlib?
Matplotlib provides several Y-axis scaling options to better visualize data with different characteristics. The yscale() method allows you to apply linear, log, symlog, and logit scales by name to transform how data appears on the Y-axis. Setting Up the Data First, let's create sample data and configure the plot layout ? import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Generate sample data y = np.random.normal(loc=0.5, scale=0.4, size=1000) y = y[(y > 0) & (y < 1)] y.sort() x = np.arange(len(y)) print(f"Data range: {y.min():.3f} to {y.max():.3f}") ...
Read MoreHow to locate the median in a (Seaborn) KDE plot?
A Kernel Density Estimation (KDE) plot shows the probability density of data points. To locate and highlight the median in a Seaborn KDE plot, we can calculate the median value and draw a vertical line at that position. Steps to Add Median Line Create or load your dataset Calculate the median using np.median() Plot the KDE using sns.kdeplot() Add a vertical line at the median using plt.axvline() Example Here's how to create a KDE plot with the median highlighted − import numpy as np import seaborn as sns import matplotlib.pyplot as plt ...
Read MoreHow to set the margins of a Matplotlib figure?
To set the margins of a matplotlib figure, we can use the margins() method. This method controls the padding around the data in your plots, allowing you to adjust how much whitespace appears between the data and the plot boundaries. Syntax The margins() method accepts the following parameters: plt.margins(x=None, y=None, tight=None) x − Margin for the x-axis (default: 0.05) y − Margin for the y-axis (default: 0.05) tight − Whether to use tight layout (True/False) Example Here's how to create subplots with different margin settings ? import numpy ...
Read MoreAutomatically setting Y-axis limits for a bar graph using Matplotlib
Setting Y-axis limits for bar graphs helps improve visualization by focusing on the data range and removing unnecessary whitespace. Matplotlib provides the ylim() method to automatically calculate and set appropriate Y-axis boundaries. Steps Set the figure size and adjust the padding between and around the subplots. Create two lists for data points. Make two variables for max and min values for Y-axis. Use ylim() method to limit the Y-axis range. Use bar() method to plot the bars. To display ...
Read MoreHow do you improve Matplotlib image quality?
To improve Matplotlib image quality, you can increase the DPI (dots per inch) value and use vector formats like PDF or EPS. Higher DPI values (600+) produce sharper images, while vector formats maintain quality at any zoom level. Key Methods for Better Image Quality There are several approaches to enhance your Matplotlib output ? Increase DPI: Use values like 300, 600, or 1200 for high-resolution output Vector formats: Save as PDF, EPS, or SVG for scalable quality Figure size: Set appropriate dimensions before plotting Font settings: Adjust font sizes and families for clarity Example: ...
Read MoreHow to close a Python figure by keyboard input using Matplotlib?
To close a Python figure by keyboard input, we can use plt.pause() method, an input prompt, and close() method. This approach allows interactive control over when the figure window closes. Steps Set the figure size and adjust the padding between and around the subplots Create random data points using NumPy Create a new figure using figure() method Plot the data points using plot() method Set the title of the plot Redraw the current figure using draw() method Use pause() to display the figure briefly Take input from the user to proceed to the next statement Use close() ...
Read MoreHow can I display an np.array with pylab.imshow() using Matplotlib?
To display a NumPy array as an image using Matplotlib's imshow() function, you need to create a 2D array and use the appropriate display parameters. The imshow() function treats each array element as a pixel value and maps it to colors based on the specified colormap. Basic Steps Create a 2D NumPy array containing your data Use plt.imshow() to display the array as an image Specify interpolation method and colormap for better visualization Display the figure using plt.show() Example import numpy as np import matplotlib.pyplot as plt # Set figure size and ...
Read MoreHow to plot a stacked event duration using Python Pandas?
To plot a stacked event duration using Python Pandas, you create horizontal lines that represent different events over time periods. This visualization is useful for displaying timelines, project schedules, or any data with start and end times. Steps to Create a Stacked Event Duration Plot Set the figure size and adjust the padding between and around the subplots Create a DataFrame with lists of xmin (start times) and corresponding xmax (end times) Use hlines() method to plot horizontal lines representing event durations Display the figure using show() method Example Here's how to create a ...
Read More