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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 More
Log helps to make a plot with log scaling on both the X and Y axis, whereas symlog (symmetric log) is used for axis scaling.StepsFirst, we can adjust the subplot layout parameters.Return an evenly spaced value (t) within a given interval, using the numpy.arrange() method.Add a subplot to the current figure, with nrows = 1, ncols = 2 and current index is 1.Make a plot with log scaling on the Y axis, using the semilogy() method.Set the title for the axes, using the plt.title() method.Configure the grid lines, using the grid(True) method.Create two evenly spaced values within a given interval ... Read More
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 More
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 More
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 More
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]Read More
To fill color between two vertical lines, use the following steps −Using plt.subplots() method, create a figure and a set of subplots. This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call.To draw two vertical lines, initialize x = 3 and x = 5.Using the created ax, axvspan would help to add vertical span(rectangle) across the axes.This rectangle spans from xmin to xmax horizontally, and, by default, the whole Y-axis vertically.To show the figure, use the plt.show() method.Exampleimport matplotlib.pyplot as plt fig, ax = plt.subplots() line1 = ... Read More
To remove or adapt the border of the frame of legend we can follow the following steps −Set the X-axis label using the plt.xlabel() method.Set the Y-axis label using the plt.ylabel() method.Plot the lines using the plt.plot() method with [9, 5], [2, 5] and [4, 7, 8] array.Initializing two variables; location = 0 for the best location and border_drawn_flag = True (True, if border to be drawn for legend. False, if border is not drawn).Use the plt.legend() method for the legend and set the location and border_drawn_flag accordingly to get the perfect legend in the diagram.plt.show() method would help to ... Read More
To plot a histogram using Matplotlib, we can follow the steps given below −Make a list of numbers and assign it to a variable x.Use the plt.hist() method to plot a histogram.Compute and draw the histogram of *x*.We can pass n-Dimensional arrays in the hist argument also.To show the plotted figure, use the plt.show() method.Examplefrom matplotlib import pyplot as plt x = [300, 400, 500, 2000, 10] plt.hist(x, 10) plt.show()Output
To display multiple images in one figure, we can follow the steps given below −Initialize the number of rows and cols. nrows*ncols subplot will be created in the current figure. nrows = 2 and ncols = 2, i.e., 2*2 = 4 subplots can be created.Now add the figures at different indices from 1 to 4.Use plt.subplot(2, 2, 1) to add new images, i.e., pie at index 1.To plot a pie chart, pass a list of numbers. Pie charts will be split into the size of list and %age section will depend upon the values in the list.Set the title of ... Read More