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Articles by Rishikesh Kumar Rishi
Page 101 of 102
Creating a Heatmap in matplotlib with pcolor
First, we can create an image using imshow method, taking a harvest matrix. After that, we can mark those image pixels with some value.StepsCreate a list of subjects.Create a list of students.Create a harvest matrix.Create fig and ax variables using subplots method, where default nrows and ncols are 1.Display data as an image, i.e., on a 2D regular raster, with step 1 data.Get or set the current tick locations and labels of the X-axis, with the length of students.Get or set the current tick locations and labels of the Y-axis, with the length of subjects.Set X-axis tick labels of the ...
Read MoreHow can I plot a confusion matrix in matplotlib?
Using imshow method, we can create an image with an input (5, 5) array dimension. After that, we can use the xticks and yticks method to mark the ticks on the axes.StepsReturn random floats in the half-open interval [5, 5) and interpolation='nearest'.Display data as an image, i.e., on a 2D regular raster, with step 1 data.Get or set the current tick locations and labels of the X-axis, using xticks method.Get or set the current tick locations and labels of the Y-axis, using yticks method.Use plt.show() to show the figure.Exampleimport matplotlib.pyplot as plt import numpy as np plt.imshow(np.random.random((5, 5)), interpolation='nearest') ...
Read MoreHow to rotate X-axis tick labels in Pandas bar plot?
Using plt.xticks(x, labels, rotation='vertical'), we can rotate our tick’s label.StepsCreate two lists, x, and y.Create labels with a list of different cities.Adjust the subplot layout parameters, where bottom = 0.15.Add a subplot to the current figure, where nrow = 1, ncols = 2 and index = 1.Plot the line using plt.plot(), using x and y (Step 1).Get or set the current tick locations and labels of the X-axis. Pass no arguments to return the current values without modifying them, with x and label data.Set or retrieve auto-scaling margins, value is 0.2.Set the title of the figure at index 1, the ...
Read MoreHow to make a discrete colorbar for a scatter plot in matplotlib?
Using plt.colorbar(ticks=np.linspace(-2, 2, 5)), we can create a discrete color bar.StepsReturn random floats in the half open interval, i.e., x, using np.random.random method.Return random floats in the half open interval, i.e., y, using np.random.random method.Return random integers from `low` (inclusive) to `high` (exclusive), i.e., z, using np.random.randint(-2, 3, 20) method.Set the X-axis label using plt.xlabel().Set the Y-axis label using plt.ylabel().Use the built-in rainbow colormap.Generate a colormap index based on discrete intervals.A scatter plot of *y* vs. *x* with varying marker size and/or color, with x, y and z are created (Steps 1, 2, 3).Create a colorbar for a ScalarMappable instance, ...
Read MoreHow to share x axes of two subplots after they have been created in Matplotlib?
First, we can create two axes using the subplot method where nrows=2, ncols=1. That means, we can have two indices to plot the desired plot. We can use ax1.get_shared_x_axes().join(ax1, ax2) method for our plot.StepsCreate two lists of the numbers.Add a subplot to the current figure, ax1, where nrows = 2, ncols = 1, and index is 1 for ax1.Add a subplot to the current figure, ax2, where nrows = 2, ncols = 1, and index is 2 for ax2.Plot x and y using points that are created in step 1.Using get_shared_x_axes().join(ax1, ax2), return a reference to the shared axes Grouper ...
Read MoreAdding value labels on a matplotlib bar chart
In this program, we can initialize some input values and then try to plot a bar using those values. We can instantiate a figure and axis so that we could set the label, ticks, and annotate the height and width of the bar.StepsMake a list of years.Make a list of populations in that year.Get the number of labels using np.arrange(len(years)) method.Set the width of the bars.Create fig and ax variables using subplots() method, where default nrows and ncols are 1.Set the Y-axis label of the figure using set_ylabel().Set the title of the figure, using set_title().Set the X-ticks with x that ...
Read MorePlot different colors for different categorical levels using matplotlib
We can plot a diagram where a number of students will be plotted on the X-axis and the marks obtained by them will be plotted on the Y-axis. Also, we can set the color for different marks obtained by the students.StepsMake a list of the number of students.Make a list of marks that have been obtained by the students.To represent the color of each scattered point, we can have a list of colors.Using Panda, we can have a list representing the axes of the data frame.Create fig and ax variables using subplots method, where default nrows and ncols are 1.Set ...
Read MoreWhat is the difference between drawing plots using plot, axes or figure in matplotlib?
Let’s understand the difference between plot, axes, and figure with an example.Plot − Plot helps to plot just one diagram with (x, y) coordinates.Axes − Axes help to plot one or more diagrams in the same window and sets the location of the figure.Figure − This method provides a top-level container for all the plot elements.We can follow these steps to replicate the differences among them −Create a new figure, or activate an existing figure, using plt.figure().Add an axis to the figure as part of a subplot arrangement, using plt.add_subplot(xyz) where x is nrows, y is ncols and z is ...
Read MorePlotting with seaborn using the matplotlib object-oriented interface
Seaborn is used to visualizing the random distribution and we can use matplotlib interface to show this distribution over a diagram.We can take the following steps to show the diagram −Figure level interface for drawing distribution plots onto a Face Grid. This function provides access to several approaches for visualizing the univariate or bivariate distribution of data, including subsets of data defined by semantic mapping and faceting across multiple subplots.List of numbers can be passed in the above-defined method, i.e., displot().To show the diagram, plt.show() can be used whereas plot was drawn using Seaborn.Exampleimport matplotlib.pyplot as plt import seaborn as ...
Read MoreHow to set the matplotlib figure default size in ipython notebook?
To set the matplotlib figure default size in iPython, use the following steps −To check the default figure size, use plt.rcParams["figure.figsize"] over the ipython shell.Now to set the figure size, override the plt.rcParams["figure.figsize"] variable with a tuple i.e., (20, 10).After overriding the plt.rcParams["figure.figsize"] variable, you can use it to get changed figure size.Exampleimport matplotlib.pyplot as plt print("Before, figure default size is: ", plt.rcParams["figure.figsize"]) plt.rcParams["figure.figsize"] = (20, 10) print("After, figure default size is: ", plt.rcParams["figure.figsize"])OutputBefore, figure default size is: [6.4, 4.8] After, figure default size is: [20.0, 10.0]
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