Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
To display the matrix value and colormap in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Initialize max and min values for matrix.Plot the values of a 2D matrix or array as color-coded image.Iterate each cell of the color-code image and place value at the center.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() min_val, max_val = 0, 5 matrix = np.random.randint(0, 5, size=(max_val, ... Read More
To annotate a range of the X-axis in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create xx and yy data points using numpy.Create a figure and a set of subplots.Plot xx and yy data points using plot() method.Set ylim of the axis.Use annotate method to place arrow heads and range tag name.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True xx = np.linspace(0, 10) yy = np.sin(xx) fig, ax = plt.subplots(1, 1) ... Read More
To add annotated text in Matplotlib for several points, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.To set the label for each scattered point, make a list of labels.Plot xpoints, ypoints using scatter() method. For color, use xpoints.Iterate zipped labels, xpoints and ypoints.Use annotate() method with bold LaTeX representation in a for loop.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True xpoints = np.linspace(1, 10, 10) ... Read More
To plot a line in Matplotlib with an interval at each data point, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make an array of means and standard deviations.Plot means using plot() method.Fill the area between means+stds and means-stds, alpha=0.7 and color='yellow'.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True means = np.array([3, 5, 1, 8, 4, 6]) stds = np.array([1.3, 2.6, 0.78, 3.01, 2.32, 2.9]) plt.plot(means, color='red', lw=7) plt.fill_between(range(6), means - stds, means ... Read More
To create a line chart using matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make lists of years and population growth.Plot years and population on the line using plot() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True years = [1901, 1911, 1921, 1931, 1941, 1951, 1961, 1971, 1981, 1991, 2001, 2011] population = [237.4, 238.4, 252.09, 251.31, 278.98, 318.66, 361.09, 439.23, 548.16, 683.33, 846.42, 1028.74] plt.plot(years, population, color='red', marker='o') plt.show()Output
To use different markers for different points in a Pylab (Pyplot) scatter plot, we can use the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N, for number of sample data.Create x and y random data points.Make a list of markers.Zip the x, y and markers.Iterate the zipper objects and plot the data points with different markers.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True N = 10 x = np.random.rand(N) y = np.random.rand(N) ... Read More
To slice an image into Red, Green and Blue channels with misc.imread, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Read an image from a file into an array.Make lists of colormaps and titles.Create a figure and a set of subplots.Zip the axes, images, titles and colormaps.Iterate zipped objs and set the title of each channel image.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True image = plt.imread('bird.png') titles = ['With red channel', 'With green channel', 'With blue channel'] cmaps ... Read More
To pixelate a square image to 256 big pixels with Python, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Open and identify the given image file.Resize the image samples.Make resultant images and resize them.Save the resultant figure.Examplefrom PIL import Image from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True img = Image.open("bird.png") imgSmall = img.resize((16, 16), resample=Image.BILINEAR) result = imgSmall.resize(img.size, Image.NEAREST) result.save('result.png')Input ImageOutput Image
To maximize plt.show() using Python on Mac, we can use full_screen_toggle().StepsSet the figure size and adjust the padding between and around the subplots.Add a subplot to the current figure.Make a piechart with input list.Get the Figure Manager of the current figure.Use full_screen_toggle() to create a full-screen pop-up window.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.subplot(1, 1, 1) plt.pie([1, 2, 3]) mng = plt.get_current_fig_manager() mng.full_screen_toggle() plt.show()Output
To animate a Seaborn heatmap or correlation matrix, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure.Make a dimension tuple.Make a Seaborn heatmap.Create an init() method for the first heatmap.Use FuncAnimation() class to make an animation by repeatedly calling a function animate that will create a random dataset and create a heatmap.To display the figure, use show() method.Exampleimport numpy as np import seaborn as sns import matplotlib.pyplot as plt from matplotlib import animation plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True ... Read More