In this program, we will plot a bar graph using the matplotlib library. The most important Step in solving matplotlib related problems using the matplotlib library is importing the matplotlib library. The syntax is:import matplotlib.pyplot as pltPyplot is a collection of command style functions that make Matplotlib work like MATLAB. In addition to plotting the bar graphs, we will also add some textures to the graphs. The 'hatch' parameter in the bar() function is used to define the texture of the barAlgorithmStep 1: Define a list of values. Step 2: Use the bar() function and define parameters like xaxis, yaxis, ... Read More

In this program, we will plot a bar graph using the matplotlib library. The most important Step in solving matplotlib related problems using the matplotlib library is importing the matplotlib library. The syntax is:import matplotlib.pyplot as pltPyplot is a collection of command style functions that make Matplotlib work like MATLAB. We will use the function barh() for plotting the horizontal bar chartsAlgorithmStep 1: Define a list of values. Step 2: Use the barh() function in the matplotlib.pyplot library and define different parameters like height width, etc. Step 3: Label the axes using xlabel() and ylabel(). Step 3: Plot the graph ... Read More

We can use the method, plt.figure(), to create the figures, and then, set their titles by passing strings as arguments.StepsCreate a new figure, or activate an existing figure, with the window title “Welcome to figure 1”.Draw a line using plot() method, over the current figure.Create a new figure, or activate an existing figure, with the window title “Welcome to figure 2”.Draw a line using plot() method, over the current figure.Using plt.show(), show the figures.Examplefrom matplotlib import pyplot as plt plt.figure("Welcome to figure 1") plt.plot([1, 3, 4]) plt.figure("Welcome to figure 2") plt.plot([11, 13, 41]) plt.show()OutputRead More

Get fig from plt.figure() and create three different axes using add_subplot, where projection=3d.Set up the figure title using ax.set_title("name of the figure"). Use the method ax.quiver to plot vector projection, plot3D for cube, and plot_wireframe for sphere after using sin and cos.StepsCreate a new figure, or activate an existing figure.To draw vectors, get a 2D array.Get a zipped object.Add an ~.axes.Axes to the figure as part of a subplot arrangement, with 3d projection, where nrows = 1, ncols = 3 and index = 1.Plot a 3D field of arrows.Set xlim, ylim and zlim.Set the title of the axis (at index ... Read More

In this program, we will plot a bar graph using the matplotlib library. The most important Step in solving matplotlib related problems using the matplotlib library is importing the matplotlib library. The syntax is −import matplotlib.pyplot as pltPyplot is a collection of command style functions that make Matplotlib work like MATLABAlgorithmStep 1: Define a list of values. Step 2: Use the bar() function in the matplotlib.pyplot library and define different parameters like height, width, etc. Step 3: Label the axes using xlabel() and ylabel(). Step 3: Plot the graph using show().Example Codeimport matplotlib.pyplot as plt data_x = ['Mumbai', 'Delhi', ... Read More

Using plt.rcParams["figure.figsize"], we can get the width setting.StepsTo get the plot width setting, use plt.rcParams["figure.figsize"] statement.Override the plt.rcParams["figure.figsize"] with a tuple (12, 9).After updating the width, get the updated width using plt.rcParams["figure.figsize"].ExamplesIn IDEExampleimport matplotlib.pyplot as plt print("Before, plot width setting:", plt.rcParams["figure.figsize"]) plt.rcParams["figure.figsize"] = (12, 9) print("Before, plot width setting:", plt.rcParams["figure.figsize"])OutputBefore, plot width setting: [6.4, 4.8] Before, plot width setting: [12.0, 9.0]In IPythonExampleIn [1]: from matplotlib import pyplot as plt In [2]: plt.rcParams["figure.figsize"]OutputOut[2]: [6.4, 4.8]

In Python to create a logarithmic bin, we can use Numpy library to generate logarithmically spaced bins, and using matplotlib for creating a histogram. Logarithmic bins in a Python histogram refer to bins that are spaced logarithmically rather than linearly. We can set the logarithmic bins while plotting histograms by using plt.hist(bin="") Steps to Create Logarithmic Bins To set logarithmic bins in a Python histogram, the steps are as follows. Import Libraries: Importing 'matplotlib' for plotting and 'numpy' for performing numerical computations. ... Read More

First, we can get the axes. Then, ax.spines could help to set the color by specifying the name of the axes, i.e., top, bottom, right and left.StepsAdd an axes to the current figure and make it the current axes.Using step 1 axes, we can set the color of all the axes.Using ax.spines[axes].set_color(‘color’), set the color of the axes. Axes could be bottom, top, right, and left. Color could be yellow, red, black, and blue.To show the figure, use the plt.show() method.Examplefrom matplotlib import pyplot as plt ax = plt.axes() ax.spines['bottom'].set_color('yellow') ax.spines['top'].set_color('red') ax.spines['right'].set_color('black') ax.spines['left'].set_color('blue') plt.show()OutputRead More

We can first create bars and then, by using set_color, we can set the color of the bars.StepsPass two lists consisting of four elements, into the bars method argument.Step 1 returns bars.Return values (0, 1, 2, 3) can be set with different colors, using set_color() method. Green, Black, Red color will be set and one bar will have the default color.To show the figure, use plt.show() method.Examplefrom matplotlib import pyplot as plt bars = plt.bar([1, 2, 3, 4], [1, 2, 3, 4]) bars[0].set_color('green') bars[1].set_color('black') bars[2].set_color('red') plt.show()Output

Using the figure() method, we can set the current figure.StepsCreate a new figure, or activate an existing figure, with the window title “Welcome to figure 1”.Create a new figure, or activate an existing figure, with the window title “Welcome to figure 2”.Using plt.show(), show the figures.Examplefrom matplotlib import pyplot as plt plt.figure("Welcome to figure 1") plt.figure("Welcome to figure 2")    # Active Figure plt.show()Output