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
Articles on Trending Technologies
Technical articles with clear explanations and examples
How to plot masked and NaN values in Matplotlib?
Matplotlib provides several approaches to handle masked and NaN values in data visualization. This is useful when you need to exclude certain data points from your plots based on specific conditions or handle missing data. Understanding Masked Arrays and NaN Values Masked arrays use NumPy's ma module to hide certain values without removing them from the dataset. NaN (Not a Number) values are treated as missing data and are automatically excluded from plot lines. Example: Plotting with Different Masking Approaches Let's create a dataset and demonstrate four different approaches to handle data exclusion ? ...
Read MoreHow to Zoom with Axes3D in Matplotlib?
Matplotlib's Axes3D allows you to create interactive 3D plots that support zooming, rotating, and panning. While basic zooming is handled automatically through mouse interaction, you can also control zoom programmatically by setting axis limits. Basic 3D Plot with Interactive Zoom Create a 3D scatter plot that supports interactive zooming ? from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = Axes3D(fig) x = [2, 4, 6, 3, 1] y = [1, 6, 8, 1, 3] z = [3, 4, 10, 3, 1] ...
Read MoreHow to move labels from bottom to top without adding "ticks" in Matplotlib?
To move labels from bottom to top without adding ticks in Matplotlib, we can use the tick_params() method to control label positioning. This is particularly useful for heatmaps where you want labels at the top for better readability. Step-by-Step Approach The process involves these key steps − Set the figure size and adjust the padding between and around the subplots Create data to visualize (we'll use a 5×5 matrix) Display the data as an image using imshow() method Use tick_params() method to move labels from bottom to top Display the figure using show() method ...
Read MoreHow to plot scatter masked points and add a line demarking masked regions in Matplotlib?
To plot scattered masked points and add a line to demark the masked regions, we can use matplotlib's masking capabilities along with the scatter() and plot() methods. This technique is useful for visualizing data that falls within or outside specific boundaries. Steps Set the figure size and adjust the padding between and around the subplots Create N, r0, x, y, area, c, r, area1 and area2 data points using NumPy Plot x and y data points using scatter() method with different markers for masked regions ...
Read MoreHow to refresh text in Matplotlib?
To refresh text in Matplotlib, you can dynamically update text elements by modifying their content and redrawing the canvas. This is useful for creating interactive plots or animations where text needs to change based on user input or data updates. Basic Text Refresh with Key Events Here's how to refresh text based on keyboard input − import matplotlib.pyplot as plt # Set figure size and enable automatic layout plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create figure and subplot fig, ax = plt.subplots() text = ax.text(0.5, 0.5, 'Press Z or C to change ...
Read MoreHow to make xticks evenly spaced despite their values? (Matplotlib)
When plotting data with irregular x-values, Matplotlib automatically spaces ticks according to their actual values. To create evenly spaced ticks regardless of the underlying values, we can use set_ticks() and set_ticklabels() methods. The Problem By default, Matplotlib positions x-ticks based on their actual values. If your data has irregular spacing (like [1, 1.5, 3, 5, 6]), the ticks will be unevenly distributed on the plot. Solution: Using set_ticks() and set_ticklabels() We can override the default behavior by setting custom tick positions and labels ? import numpy as np from matplotlib import pyplot as plt ...
Read MoreStuffing a Pandas DataFrame.plot into a Matplotlib subplot
When working with data visualization in Python, you often need to combine Pandas plotting capabilities with Matplotlib's subplot functionality. This allows you to create multiple related plots in a single figure for better comparison and analysis. Basic Setup First, let's create a simple example showing how to embed Pandas DataFrame plots into Matplotlib subplots ? import pandas as pd import matplotlib.pyplot as plt # Create sample data df = pd.DataFrame({ 'name': ['Joe', 'James', 'Jack'], 'age': [23, 34, 26], 'salary': [50000, 75000, 60000] }) ...
Read MoreDraw a parametrized curve using pyplot.plot() in Matplotlib
A parametrized curve is defined by equations where both x and y coordinates are expressed as functions of a parameter (usually t). Matplotlib's pyplot.plot() can easily visualize these curves by plotting the computed x and y coordinates. Basic Parametrized Curve Let's create a simple parametrized curve using trigonometric functions ? 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 # Number of sample points N = 400 # Parameter t from 0 to 2π t = np.linspace(0, 2 * np.pi, N) # ...
Read MoreHow to set different opacity of edgecolor and facecolor of a patch in Matplotlib?
In Matplotlib, you can set different opacity levels for edgecolor and facecolor of patches by using RGBA color tuples, where the fourth value (alpha) controls transparency. This allows you to create visually appealing graphics with varying opacity levels. Understanding RGBA Color Format RGBA color format uses four values: Red, Green, Blue, and Alpha (opacity). The alpha value ranges from 0 (completely transparent) to 1 (completely opaque). Basic Example Here's how to create a rectangle patch with different opacity for edge and face colors ? import matplotlib.pyplot as plt import matplotlib.patches as patches # ...
Read MoreHow do I plot a spectrogram the same way that pylab's specgram() does? (Matplotlib)
A spectrogram is a visual representation of the spectrum of frequencies of a signal as it varies with time. Matplotlib's specgram() function provides an easy way to create spectrograms similar to pylab's implementation. Creating Sample Data First, let's create a composite signal with different frequency components ? import matplotlib.pyplot as plt import numpy as np # Set figure parameters plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create time series data dt = 0.0005 t = np.arange(0.0, 20.0, dt) # Signal components s1 = np.sin(2 * np.pi * 100 * t) # 100 Hz sine wave s2 = 2 * np.sin(2 * np.pi * 400 * t) # 400 Hz sine wave s2[t
Read More