To plot a step function with matplotlib in Python, we can take the following steps −Create data points for x and y.Make a step plot using step() method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([1, 3, 4, 5, 7]) y = np.array([1, 9, 16, 25, 49]) plt.step(x, y, 'r*') plt.show()Output

To rotate xtick labels through 90 degrees, we can take the following steps −Make a list (x) of numbers.Add a subplot to the current figure.Set ticks on X-axis.Set xtick labels and use rotate=90 as the arguments in the method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = [1, 2, 3, 4] ax1 = plt.subplot() ax1.set_xticks(x) ax1.set_xticklabels(["one", "two", "three", "four"], rotation=90) plt.show()Output

To make a simple 3D line with matplotlib, we can take the following steps −Create a new figure or activate an existing figure.Add axes to the figure as part of a subplot arrangement.Create data points for theta, z, r, x and y using numpy.Plot x, y and z using plot() method.Place a legend on the figure using legend() method.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 fig = plt.figure() ax = fig.add_subplot(projection='3d') theta = np.linspace(-4 * np.pi, 4 * np.pi, 100) z = np.linspace(-2, 2, 100) r ... Read More

To plot multiple histograms on same plot with Seaborn, we can take the following steps −Create two lists (x and y).Create a figure and add a set of two subplots.Iterate a list consisting of x and y.Plot a histogram with histplot() method using the data in the list (step 3).Limit the X-axis range from 0 to 10.To display the figure, use show() method.Exampleimport seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = [1, 5, 1, 4, 2] y = [7, 5, 6, 4, 5] fig, ax = plt.subplots() for a in [x, y]: ... Read More

To change the color of a plot frame, we can set axes ticklines and spine value into a specific color.StepsCreate a figure and add a set of subplots, using subplots method with value 4.Zip colors with axes and iterate them together.In the iteration, set the color for spines values and ticklines (x, y).Adjust the padding between and around the subplots.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, (ax1, ax2, ax3, ax4) = plt.subplots(4) for ax, color in zip([ax1, ax2, ax3, ax4], ['green', 'red', 'yellow', 'blue']):    plt.setp(ax.spines.values(), color=color)    ax.plot([8, 3], ... Read More

To annotate time series plot in matplotlib, we can take the following steps −Create lists for time and numbers.Using subplots() method, create a figure and a set of subplots.Using plot_date() method, plot the data that contains dates with linestyle "-.".Annotate a point in the plot using annotate() method.Date ticklabels often overlap, so it is useful to rotate them and right-align them.To display the figure, use show() method.Exampleimport datetime as dt from matplotlib import pyplot as plt, dates as mdates plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = [dt.datetime(2021, 1, 1), dt.datetime(2021, 1, 2),    dt.datetime(2021, 1, 3), dt.datetime(2021, 1, 4)] y = ... Read More

To insert statistical annotation, we can take the following steps −Create lists (x and y) of numbers.Using subplots() method, create a figure and a set of subplots.Using plot() method, plot the data that contains dates with linestyle "-.".Annotate a point in the plot using annotate() method, mean of 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, 1, 5) y = np.linspace(-2, 2, 5) mean_x = np.mean(x) mean_y = np.mean(y) fig, ax = plt.subplots() ax.plot(x, y, linestyle='-.') ax.annotate('*', (mean_y, mean_y), xytext=(-.50, 1), arrowprops=dict(arrowstyle='-|>')) ... Read More

To annotate data points while plotting from pandas data frame, we can take the following steps −Create df using DataFrame with x, y and index keys.Create a figure and a set of subplots using subplots() method.Plot a series of data frame using plot() method, kind='scatter', ax=ax, c='red' and marker='x'.To annotate the scatter point with the index value, iterate the data frame.To display the figure, use show() method.Exampleimport numpy as np import pandas as pd from matplotlib import pyplot as plt import string plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({'x': np.random.rand(10), 'y': np.random.rand(10)}, index=list(string.ascii_lowercase[:10])) fig, ax = plt.subplots() df.plot('x', ... Read More

To fill color below a curve, we can take the following steps −StepsInitialize variable n. Initialize x and y data points using numpy.Create a figure and a set of subplots, fig and ax.Plot the curve using plot() method.Use fill_between() method to fill the area between the two curves, with -1 value.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 n = 256 X = np.linspace(-np.pi, np.pi, n, endpoint=True) Y = np.sin(2 * X) fig, ax = plt.subplots() ax.plot(X, Y, color='blue', alpha=1.00) ax.fill_between(X, Y, 0, color='blue', alpha=.1) plt.show()OutputRead More

To plot a histogram from pre-counted data in matplotlib, we can take the following steps −Create a list of numbers.Make a pre-counted list with the help of input data.Plot a histogram with data, color=red, and label=data, using hist() method.Plot another histogram with counted data, color=default, and label=counted_data, using hist() method.To place the legend, use legend() method.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 data = [1, 2, 2, 3, 4, 5, 5, 5, 5, 6, 10] counted_data = {1: 1, 2: 2, 3: 1, 4: 1, 5: 4, 6: 1, ... Read More