To decouple hatch and edge color in matplotlib, we can use hatch color “o” and edge color “red”.−StepsCreate a new figure or activate existing figure.Add a subplot arrangement to the current axes.Create two lists of data points.Use bar() method with hatch and edgecolor.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 fig = plt.figure() ax1 = fig.add_subplot(111) x = [3, 6, 1] y = [4, 6, 1] ax1.bar(x, y, color='black', edgecolor='red', hatch="o", lw=1., zorder=0) plt.show()Output

If a 4-tuple or B box Base is given, then it specifies the b box (x, y, width, height) that the legend is placed in.StepsCreate x and y data points using numpy.Plot x and y using plot() method, with label y=sin(x) and color=green.To place the legend at a specific location, use location 'upper left' and use legend box dimension with four tuples that was defined in the above description.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-2, 2, 10) y = np.sin(x) plt.plot(x, ... Read More

To make the angles in a matplotlib polar plot go clockwise with 00 at the top, we can take the followingsteps−StepsAdd a subplot to the current figure ax.To set polar plot clockwise with top 0o, set the theta direction as −1 using set_theta_direction()method. And, use set_theta_offset() method to set the offset for the location of 0 in radians.Create theta, using numpy.Plot theta and sin(theta) on the current axis.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True ax = plt.subplot(1, 1, 1, projection='polar') ax.set_theta_direction(-1) ax.set_theta_offset(np.pi / 2.0) ... Read More

To plot over an image background, we can take the following steps−Read an image from a file into an array.Create a figure (fig) and add a set of subplots (ax) with extent [0, 300, 0, 300].Create an array x of range (300).Plot x using plot() method with linestyle=dotted, linewidth=2, and color=red.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True im = plt.imread("bird.jpg") fig, ax = plt.subplots() im = ax.imshow(im, extent=[0, 300, 0, 300]) x = np.array(range(300)) ax.plot(x, x, ls='dotted', linewidth=2, color='red') plt.show()OutputRead More

To make equivalent imagesc, we can use extent [left, right, bottom, top].StepsCreate random data using numpy.Display the data as an image, i.e., on a 2D regular raster, with data and extent [−1, 1, −1, 1] arguments.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(4, 4) plt.imshow(data, extent=[-1, 1, -1, 1]) plt.show()Output

To plot multiple graphs in matplotlib, we will use the following steps −StepsCreate x, y1 and y2 data points using numpy.Add a subplot to the current figure at index 1.Plot curve 1 using x and y1.Add a subplot to the current figure at index 2.Plot curve 2 using x and y2.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 x = np.linspace(-2, 2, 10) y1 = np.sin(x) y2 = np.cos(x) plt.subplot(211) plt.plot(y1) plt.subplot(212) plt.plot(y2) plt.show()OutputRead More

To fix the extension of margin at the bottom of a figure, we can take the following steps −Using Pandas dataframe, create a df with the keys, time and speed.Plot df.time and df.speed using plot() method.Tick_params() is a convenience method for changing the appearance of ticks and tick labels. rotation=90 extends the tick labels at the bottom.To fix the bottom extension, use tight_layout() method.Exampleimport numpy as np import pandas as pd from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame(dict(time=list(pd.date_range("2021-01-01 12:00:00", periods=10)), speed=np.linspace(1, 10, 10))) plt.plot(df.time, df.speed) plt.tick_params(rotation=90) plt.show()OutputRead More

To shade an area between two points in matplotlib, we can take the following steps−Create x and y data points using numpy.Plot x and y data points, with color=red and linewidth=2.To shade an area parallel to X-axis, initialize two variables, y1 and y2.To add horizontal span across the axes, use axhspan() method with y1, y2, green as shade color, and alpha for transprency of the shade.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.linspace(0, 20, 500) y = np.cos(3*x) + np.sin(2*x) plt.plot(x, y, c='red', lw=2) ... Read More

To set the background color on specific areas of a pyplot, we can take the following steps −Using subplots() method, create a figure and a set of subplots, where nrows=1.Using rectangle, we can create a rectangle, defined via an anchor point and its width and height. Where, edgecolor=orange, linewidth=7, and facecolor=green.To plot a diagram over the axis, we can create a line using plot() method, where line color is red.To color a specific portion of the plot, add a rectangle patch on the diagram using add_patch() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, patches plt.rcParams["figure.figsize"] = ... Read More

To set the xlabel at the end of X-axis in matplotlib, we can take the following steps −Create data points for x using numpy.Using subplot() method, add a subplot to the current figure.Plot x and log(x) using plot() method.Set the label on X-axis using set_label() method, with fontsize=16, loc=left, and color=red.To set the xlabel at the end of X-axis, use the coordinates, x and y.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(1, 2, 5) ax = plt.subplot() ax.plot(x, np.log(x)) ax.set_xticks(x) label = ax.set_xlabel('X ->', fontsize=16, loc="left", c="red") ax.xaxis.set_label_coords(1.0, -0.025) plt.show()OutputRead More