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We can execute Tests on Cookies in Postman. Once a request gets executed for an endpoint, a Response gets generated. Within a Response, the cookie information gets generated under the Cookies tab.We can add Tests script around cookies and apply Assertions on them for verification. Test scripts are incorporated under the Tests tab. Let us add the below script to verify the value of cookie – Cookie_Postman.pm.test("Verify Cookie value", function(){ pm.expect(pm.cookies.get('Cookie_Postman')).to.eql('value1')})Send the Request. After the Response is received, navigate to the Tests tab. It shows the Assertion in our Test script failed as the expected value of the Cookie_Postman is ... Read More
To add black border to matplotlib 2.0 'ax' object in Python, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Set axes edgecolor to black.Set axes linewidth to 2.50.Initialize a variable, N, to get the number of sample data.Create x and y data points using numpy.Plot x and y data points using plot() method.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 plt.rcParams["axes.edgecolor"] = "black" plt.rcParams["axes.linewidth"] = 2.50 N = 10 x = np.random.randint(low=0, high=N, size=N) y ... Read More
We can create a Mock Server in Postman. A Mock Server is used to simulate the working of an actual server to test APIs and Responses. These are very common if certain APIs require to be tested but they are presently unavailable on the web servers due to security concerns on the real server.The steps to create a Mock Server are listed below -Step 1 − Click on New from the top of the Postman application. Then click on the Mock Server link.Step 2 − Choose the option GET from the Method field, add a Request Path as /user/home, enter ... Read More
To adjust tick frequency for for Y-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_y to adjust the frequency of the yticks.Use yticks() method to set the yticks.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 plot a 3D patch collection 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.Get the current axes and set projection as 3d.Iterate ["x", "y", "z"] list, and set the circle patch using pathpatch_2d_to_3d() method to convert a PathPatch to a PathPatch3D object.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt from matplotlib.patches import Circle import mpl_toolkits.mplot3d.art3d as art3d plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.gca(projection='3d') for i in ["x", ... Read More
In Postman, sometimes we need to verify the eligibility of a user accessing a particular resource on the server. This is done by authenticating the credentials of a user by the system.Thus authentication helps to identify the identity of a user and is applied for the secured APIs. In Postman, this is carried out under the Authorization tab. The TYPE dropdown in the Authorization tab, lists down all the Authorization types.To carry out an encoded authentication, we have to choose the option No Auth from the TYPE dropdown in the Authorization tab and simultaneously from the Headers tab, we have ... Read More
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