Programming Articles - Page 857 of 3363

We can parse JSON Response with Rest Assured. To parse a JSON body, we shall use the JSONPath class and utilize the methods of this class to obtain the value of a specific attribute.We shall first send a GET request via Postman on a mock API URL and observe the Response body.ExampleCode Implementationimport org.testng.annotations.Test; import static io.restassured.RestAssured.*; import io.restassured.RestAssured; import io.restassured.path.json.JsonPath; import io.restassured.response.Response; import io.restassured.response.ResponseBody; import io.restassured.specification.RequestSpecification; public class NewTest {    @Test    void responseParse() {       //base URI with Rest Assured class       RestAssured.baseURI = "https://run.mocky.io/v3";       //input details   ... Read More

We can validate XML response in Rest Assured. For obtaining an XML response, we have to pass the parameter ContentType.XML to the accept method. We shall first send a GET request via Postman on a mock API URL.Using Rest Assured, we shall validate its XML Response containing the name of the subjects Rest Assured, Postman, and their prices 10 and 6 respectively.In the above XML Response, we shall obtain the values of the name and price tags by traversing the paths - courses.subject.name and courses.subject.price respectively.We shall perform the assertion with the help of the Hamcrest framework which uses the ... Read More

We can validate the response time of a request in Rest Assured. The time elapsed after a request is sent to the server and then receiving the response is known as the response time.The response time is obtained in milliseconds by default. To validate the response time with Matchers, we need to use the below-overloaded methods of the ValidatableResponseOptions −time(matcher) - it verifies the response time in milliseconds with the matcher passed as a parameter to the method.time(matcher, time unit) - it verifies the response time with the matcher and time unit is passed as parameters to the method.We shall ... Read More

We can get the response time of a request in Rest Assured. The time elapsed after a request is sent to the server and then receiving the response is known as the response time.The response time is obtained in milliseconds by default. However, we can also obtain in other time units. The below methods of the ResponseOptions interface can be used to get the response time −getTime - it gets the response time in milliseconds.getTimeIn(time unit) - it gets the response time in the time unit passed as a parameter to this method.time() - it gets the response time in ... Read More

A PUT request is used to pass data to the server for the creation or modification of a resource. The difference between POST and PUT is that POST request is not idempotent.This means invoking the same PUT request numerous times will always yield the same output. But invoking the same POST request numerous times will create a similar resource more than one time.The status codes for PUT requests are −200 - request is successful along with modification in the Response body.400 - request is unsuccessful.204 - request is successful without content.ExampleCode Implementationimport org.testng.annotations.Test; import static io.restassured.RestAssured.*; import io.restassured.RestAssured; public class ... Read More

The agg() method in pandas Series is used to apply one or more functions on a series object. By using this agg() method we can apply multiple functions at a time on a series.To use multiple functions at once we need to send those function names as a list of elements to the agg() function.Example# import pandas package import pandas as pd # create a pandas series s = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) print(s) # Applying agg function result = s.agg([max, min, len]) print('Output of agg method', result)ExplanationThe object “s” has 10 ... Read More

The add_suffix is the panda Series function which is used to add a string suffix to the series index labels. this method will return a new series object with updated labels.This add_suffic method takes a string as a parameter, and using that string will update the series labels. It will add the given string after the index labels of the series.Example# import pandas package import pandas as pd # create a pandas series s = pd.Series([2, 4, 6, 8, 10]) print(series) result = s.add_suffix('_Index') print("Resultant series with updated labels: ", result)ExplanationIn this following example, we created a series ... Read More

In pandas Series functionalities we have a function called add() which is used to add a series object with another series object. It is also used to add a Series object with an integer value and with a python list.The series.add() method has a fill_values parameter. Which is used to handle the missing values effectively by substituting a float value to this parameter. By default the input to this fill_value parameter is Nan.Exampleimport pandas as pd import numpy as np sr1 = pd.Series(np.arange(1, 6)) print('Series Object 1:', sr1, sep='') sr2 = pd.Series(np.random.randint(10, 20, 4)) print('Series Object 2:', ... Read More

The basic operation of this add() method in series is used to add a series with another series, or with a list of values, or with a single integer. And it will return a new series with resultant elements.It supports the substitution of fill_values for handling missing data. We can fill Nan Values using the fill_value parameter of the series.add() method.If you want to add a series with a list, then the elements in the list must be equal to the number of elements in the series.Example# import the required packages import pandas as pd import numpy as np ... Read More

Pandas used to deal with large data sets, in that large data tables columns and rows are indexed with some names and those names are called labels. When we are working with datasets there may be some duplicate labels present in the data set.The duplication can lead to making incorrect conclusions on our data, it may impact our desired outputs. Here we are talking about label duplication, nothing but rows and column index names repeated more than 1 time.Let’s take an example to identify the duplicate labels in a DataFrame.Identifying duplicates in column labelsExampledf1 = pd.DataFrame([[6, 1, 2, 7], [8, ... Read More