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To fill the area under a curve in a Seaborn distribution plot, we can use distplot() and fill_between() methods.StepsSet the figure size and adjust the padding between and around the subplots.Create a list of data points.Plot a univariate distribution of observations.To fill the area under the curve, use fill_between() method.Set or retrieve autoscaling margins, x=0 and y=0.To display the figure, use show() method.Exampleimport seaborn as sns import scipy.stats as stats import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = [2.0, 7.5, 9.0, 8.5] ax = sns.distplot(x, fit_kws={"color": "red"}, kde=False, fit=stats.gamma, hist=None, label="label 1") l1 = ... Read More
A variable is used to store and add parameters in a request, Collection, scripts and so on. An Environment in Postman comprises a key-value pair. The key in an Environment is known as the Environment variable.An Environment variable has a local scope which means a variable defined within an Environment can be accessed in the same Environment in which it is created. In case we try to access that variable outside the Environment in which it is created, we shall encounter errors.To create an Environment variable we have to follow the below steps −Step 1 − Click on the New ... Read More
To adjust tick frequency for X-axis, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N, for number of sample data points.Create x and y data points using numpy.Plot x and y data points using plot() method.Initialize a variable freq_x to adjust the frequency of the xticks.Use xticks() method to set the xticks.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 N = 10 x = np.random.randint(low=0, high=N, size=N) y = np.random.randint(low=0, high=N, ... Read More
To save scatterplot animations with matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize four variables, steps, nodes, positions and solutions.Append positions and solutions values in the list.Create a figure and a set of subplots.Initialize a variable for marker size.Configure the grid lines.Make an animation by repeatedly calling a function *animate*, to clear the axis, add new axis sublot, and plot scatter points on the axis.Save the animated scatter plot as a .gif file.Exampleimport matplotlib.pyplot as plt import matplotlib.animation as animation import numpy as np plt.rcParams["figure.figsize"] = [7.50, ... Read More
The command-line runner used to execute requests and verify Response in Postman is known as Newman. Apart from the Collection Runner, the Newman can also be used for triggering requests in a Postman Collection.Newman works well with NPM registry and GitHub. It can also be integrated with Continuous Integration tools like Jenkin. Newman generates the code 0 if all the requests get executed successfully.The code 1 is generated in case of errors. These codes can be interpreted by the CI tool. Newman is developed on node.js and utilizes the npm package manager. To install Newman follow the below steps −Step ... Read More
To plot a line (polygonal chain) with matplotlib with minimal smoothing, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initalize a variable, N, to get the number of data points.Create x and y data points using numpy.Get 1-D monotonic cubic interpolation, using pchip() method.Plot (x, interp(x)) and (x, y) data points using numpy.To display the figure, use show() method.Exampleimport numpy as np from scipy.interpolate import pchip import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True N = 50 x = np.linspace(-10, 10, N) y = np.sin(x) ... Read More
To check if points are inside ellipse faster than contains_point method, 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.Set the aspect ratios, equal.Create x and y data points using numpy.Initialize center, height, width and angle of the ellipse.Get a scale free ellipse.Add a '~.Patch' to the axes' patches; return the patch.If the point lies inside an ellipse, change its color to "red" else "green".Plot x and y data points using scatter() method, with colors.To display the figure, use show() method.Exampleimport matplotlib.pyplot as ... Read More
We can handle infinite loops in the Postman workflow. The requests in a Collection get executed in the order in which they appear. However, we can change the workflow with the help of the function - postman.setNextRequest().It is used to direct the next request to be executed. The name of the next request to be executed is passed as a parameter to this function.Syntax postman.setNextRequest("next request name")Let us take an example of a Collection having four requests -We would like Postman to change to a workflow such that the below requests are executed from top to bottom in the order listed ... Read More
To change the color of a single X-axis tick label in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure.Add an '~.axes.Axes' to the figure as part of a subplot arrangement.Create x and y data points using numpy.Plot x and y data points using plot() method.To set the color of X-axis tick label in matplotlib, we can use tick_params() method with axis='x' and color='red'.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] ... Read More
To appropriately plot losses values acquired by (loss_curve_) from MLPCIassifier, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a params, a list of dictionaries.Make a list of labels and plot arguments.Create a figure and a set of subplots, with nrows=2 and ncols=Load and return the iris dataset (classification).Get x_digits and y_digits from the dataset.Get customized data_set, list of tuples.Iterate zipped, axes, data_sets and the list of name of titles.In the plot_on_dataset() method; set the title of the current axis.Get the Multi-layer Perceptron classifier instance.Get mlps, i.e a list of ... Read More