Programming Articles - Page 1201 of 3363

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

Let's take an example. We create a set of data points such that it would generate some warnings. We will create data points x from −1 to 1 and try to find log in that range, which means it will throw an error at value 0, while calculating logs.StepsCreate data points for x and calculate log(x), using numpy.Plot x and y using plot() method.Use warnings.filterwarnings("ignore") to suppress the warning.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt import warnings plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True warnings.filterwarnings("ignore") x = np.linspace(-1, 1, 10) y ... Read More

To insert a small image on the corner of a plot with matplotlib, we can take the following steps−Read an image from a file into an array using imread() method.Using subplots() method, create a figure and add a set of subplots.Plot a line on the current axis.Create newax (new axis) to show the image array (Step 1).Turn off the newly created axis, created for an image insert.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 im = plt.imread('bird.jpg') # insert local path of the image. fig, ax = plt.subplots() ax.plot(range(10)) newax = ... Read More

To draw a rectangle with only border in matplotlib, we can take the following steps−Create a figure and a set of subplots.Get the current axes, creating one if necessary.Add a patch, i.e., a rectangle to the current axes that is returned in step 2. Set the facecolor attribute to 'none'.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, patches plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True figure, _ = plt.subplots() ax = plt.gca() ax.add_patch(patches.Rectangle((.25, .25), .50, .50, edgecolor='orange', facecolor='none', linewidth=2)) plt.show()Output

To change the color of the ticks in the colorbar in matplotlib, we can take the following steps−Create a random 2D−Array using numpy, with 4☓4 dimension.Use imshow() method to display the data as an image.Create a colorbar using colorbar() method with scalar mappable instance of imshow().Use getp() method to return the value of an object's property or print all of them.Set the property of an artist object.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) im = plt.imshow(data, cmap="twilight_shifted_r") cbar = plt.colorbar(im) ... Read More