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To plot a layered image in Matplotlib in Python, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create dx, dy, x, y and extent data using numpy.Create a new figure or activate an existing figure using figure() method.Create data1 and data2 to display the data as an image, i.e., on a 2D regular raster.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True dx, dy = 0.05, 0.05 x = np.arange(-3.0, 3.0, dx) y = np.arange(-3.0, 3.0, ... Read More
To save a plot in Seaborn, we can use the savefig() method.StepsSet the figure size and adjust the padding between and around the subplots.Make a two-dimensional, size-mutable, potentially heterogeneous tabular data.Plot pairwise relationships in a dataset.Save the plot into a file using savefig() method.To display the figure, use show() method.Exampleimport seaborn as sns import pandas as pd import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame(np.random.random((5, 5)), columns=["a", "b", "c", "d", "e"]) sns_pp = sns.pairplot(df) sns_pp.savefig("sns-heatmap.png")OutputWhen we execute the code, it will create the following plot and save it ... Read More
To remove grid lines from an image, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Load an image from a file.Convert the image from one color space to another.To remove grid lines, use ax.grid(False).Display the data as an image, i.e., on a 2D regular raster.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import cv2 plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True img = cv2.imread('bird.jpg') img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) plt.grid(False) plt.imshow(img) plt.show()OutputRead More
To customize the X-axis label, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N, to get the number of sample data.Create x and y data points using numpyPlot x and y data points using plot() method.Customize the X-axis labels with fontweight, color, fontsize, and alignment.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True N = 100 x = np.random.rand(N) y = np.random.rand(N) plt.plot(x, y, 'r*') plt.xlabel('X-axis Label', fontweight='bold', color='orange', ... Read More
The tables in MySQL consist of rows and columns. The columns specify the fields and the rows specify the records of data. The data in the tables needs to be fetched to use. We may at times need to fetch all the data from the MySQL table.All the rows can be fetched from the table using the SELECT * statement.SyntaxSELECT * FROM table_nameThe * in the above syntax means to fetch all the rows from the table.Steps you need to follow to select all the data from a table using MySQL in pythonimport MySQL connectorestablish connection with the connector using ... Read More
MultimeterAs the name implies, a multimeter is device that can be used to measure multiple quantities, i.e., when a single device is used to measure multiple quantities, the device is called multimeter. On the basis of output representation, there are two types of multimeters −Analog multimeterDigital multimeterAnalog MultimeterAn analog multimeter is a permanent magnet moving coil (PMMC) meter type measuring instrument. It works on the principle of d’Arsonval galvanometer. The analog multimeter has an analog display that uses the deflection of a pointer on the scale to indicate the level of measurement being made. The pointer deflects from its initial ... Read More
To customize X-axis ticks in Matplotlib, we can change the ticks length and width.StepsSet the figure size and adjust the padding between and around the subplots.Create lists for height, bars and y_pos data points.Make a bar plot using bar() method.To customize X-axis ticks, we can use tick_params() method, with color=red, direction=outward, length=7, and width=2.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True height = [3, 12, 5, 18, 45] bars = ('A', 'B', 'C', 'D', 'E') y_pos = np.arange(len(bars)) plt.bar(y_pos, height, color='yellow') plt.tick_params(axis='x', colors='red', direction='out', ... Read More
To reverse the colormap of an image, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data points using x and y.Get the blue color map using get_cmap() method.Add a subplot to the current figure at index 1.Plot x and y data points using scatter() method.Create a colorbar for a scalar mappable instance.Plot x and y data points using scatter() method, with reversed colormap.Set the title of both the axes.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] ... Read More
To plot single data with two Y-Axes (Two units) in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create speed and acceleration data points using numpy.Add a subplot to the current figure.Plot speed data points using plot() method.Create a twin Axes sharing the X-axis.Plot acceleration data point using plot() method.Place a legend on the figure.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True speed = np.array([3, 1, 2, 0, 5]) acceleration = np.array([6, 5, 7, ... Read More
To align axis label to the right (X-axis label) or top (Y-axis label), we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Initialize a variable, N, for number data samples.Plot x and y data points using plot() method.Set xlabel and ylabel at the right and top locations, respectively.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 fig, ax = plt.subplots() N = 10 x = np.random.rand(N) y = ... Read More