To make a scatter plot for clustering in Python, 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, Cluster and centers using numpy.Create a new figure or activate an existing figure.Add a subplot arrangement to the current figure.Plot the scatter data points using scatter() method.Iterate centers data and place marker using scatter() method.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.random.randn(10) y = np.random.randn(10) Cluster = np.array([0, 1, ... Read More

To put a circle with annotation in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create data points using numpy.Get the point coordinate to put circle with annotation.Get the current axis.Plot the data and data points using plot() method.Set X and Y axes scale.To put a circled marker, use the plot() method with marker='o' and some properties.Annotate that circle (Step 7) with arrow style.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.array([[5, ... Read More

To create a 3D animation using matplotlib, we can take the following steps −Import the required packages. For 3D animation, you need to import Axes3D from mpl_toolkits.mplot3d and matplotlib.animation.Set the figure size and adjust the padding between and around the subplots.Create t, x, y and data points using numpy.Create a new figure or activate an existing figure.Get the instance of 3D axes.Turn off the axes.Plot the lines with data.Create an animation by repeatedly calling a function *animate*.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation from mpl_toolkits.mplot3d import Axes3D plt.rcParams["figure.figsize"] ... Read More

To set labels in matplotlib.hlines, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Add a horizontal line across the axis, y=1, with y=1 label, color='orange'.Add a horizontal line across the axis, y=2, with y=2 label, color='red'.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Add horizontal line plt.hlines(y=1, xmin=1, xmax=4, lw=7, color='orange') plt.text(4, 1, 'y=1', ha='left', va='center') # Add another horizontal line plt.hlines(y=2, xmin=2, xmax=5, lw=7, color='red') plt.text(2, 2, 'y=2', ha='right', va='center') ... Read More

To create broken horizontal bar graphs 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.Plot a horizontal sequence of rectangles.Scale X and Y axes limit.Configure the grid lines.Annotate the broken bars.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() # Horizontal sequence of rectangles ax.broken_barh([(110, 30), (150, 10)], (10, 9), facecolors='tab:blue') ax.broken_barh([(10, 50), (100, 20), (130, 10)], (20, 9),    facecolors=('tab:orange', 'tab:green', 'tab:red')) # Scale ... Read More

To modify a 2d scatterplot to display color based on a third array in a CSV file, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Read the CSV file with three headers.Create a new figure or activate an existing figure.Add an 'ax' to the figure as part of a subplot arrangement.Make a scatter plot with CSV file data points.To display the figure, use show() method.Exampleimport pandas as pd from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True columns = ["data1", "data2", "data3"] df = ... Read More

To turn off the ticks and marks of a matplotlib axes, 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.Plot x and y data points using plot() method.Get the current axis of the plot.Use set_tick_params() to hide X and Y axes label marks.Use set_xticks() and set_yticks() to hide X and Y axes tick marks.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True ... Read More

To make a multiline plot from .CSV file in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a list of columns to fetch the data from a .CSV file. Make sure the names match with the column names used in the .CSV file.Read the data from the .CSV file.Plot the lines using df.plot() method.To display the figure, use show() method.Exampleimport pandas as pd from matplotlib import pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Make a list of ... Read More

To plot two different arrays of different lengths in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create y1, x1, y2 and x2 data points using numpy with different array lengths.Plot x1, y1 and x2, y2 data points using plot() method.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True y1 = (np.random.random(100) - 0.5).cumsum() y2 = y1.reshape(-1, 10).mean(axis=1) x1 = np.linspace(0, 1, 100) x2 = np.linspace(0, 1, 10) plt.plot(x1, y1) plt.plot(x2, y2) ... Read More

To shift a graph along 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 x and y data points using numpy.Plot the x and y data points for the original curve.Plot the shifted graph, in the range of (1, 1+len(y)) with y data points.Place a legend on the figure.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # x and y data points x = np.linspace(-5, 5, ... Read More