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To make a multicolored point in matplotlib, we can take the following steps−Initialize two varuables, x and y.Use scatter method with x and y data points with green color having marker size 2000.Use scatter method with x and y data points with red color having marker size 1000.Use scatter method with x and y data points with blue color having marker size 500.Use scatter method with x and y data points with white color having marker size 10.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x, y = 0, ... Read More
To add percentages on top of bars in Seaborn, we can take the following steps −Create the lists, x, y and percentages to plot using Seaborn.Using barplot, show point estimates and confidence intervals with bars. Store the returned axis.Find patches from the returned axis (In step 2).Iterate the patches (returned in step 3).Find x and y from the patches to place the percentage value at the top of the bars.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import seaborn as sns plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = ['A', 'B', 'C', 'D', 'E'] y = [1, 3, 2, 0, ... Read More
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
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 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
To draw axis in the middle of a figure, we can take the following steps −Create x and sqr data points using numpy.Create a new figure, or activate an existing figure, using figure() method.Add an axis to the figure as a part of a subplot arrangement.Set the postion of left and bottom spines.Set the color of the right and top spines.Plot x and sqr, using plot() method, with label y=x2 and color=red.Place the legend using legend() method. Set the location at upper right corner.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 ... Read More
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