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Programming Articles
Page 2132 of 2547
How to parameterize tests with multiple data sets using Rest Assured?
We can parameterize tests with multiple data sets using Rest Assured. Using data providers we can execute a single test case in multiple runs. To know more about TestNG data providers visit the below link −https://www.tutorialspoint.com/testng/testng_parameterized_test.htmThis technique can be used for dynamic payloads. For this, we shall create a Java class containing the payload.Then in the second Java class (having the implementation of the POST request), we shall pass the dynamic fields of the payload as parameters to the request body.Please find the project structure for the implementation below.Code Implementation in NewTest.javaimport org.testng.annotations.DataProvider; import org.testng.annotations.Test; import static io.restassured.RestAssured.*; import io.restassured.RestAssured; ...
Read MoreHow to create a project with Cucumber and Rest Assured dependencies?
We can create a project with Cucumber and Rest Assured dependencies. This can be done by following the below steps −Step 1 − Create a Maven project. The details on how to create a Maven project is discussed in detail in the below link −https://www.tutorialspoint.com/maven/index.htmStep 2 − Add the following dependencies in the pom.xml file in a project for Cucumber.Cucumber JVM - Java dependencyCucumber JVM - JUnit dependencyhttps://mvnrepository.com/artifact/io.cucumber/cucumber-junitStep 3 − Add the following dependencies in the pom.xml file in the project for Rest Assured.Rest Assured dependencyhttps://mvnrepository.com/artifact/io.rest-assured/rest-assuredJackson Databind dependencyhttps://mvnrepository.com/artifact/com.fasterxml.jackson.core/jacksondatabind
Read MoreWhat does the all() method do in pandas series?
The all() method in the pandas series is used to identify whether any False values are present in the pandas Series object or not. The typical output for this all method is boolean values (True or False).It will return True if elements in the series object all are valid elements (i.e. Non-Zero values) otherwise, it will return False. This means the pandas series all() method checks for whether all elements are valid or not.Exampleimport pandas as pd series = pd.Series([1, 2, 3, 0, 4, 5]) print(series) #applying all function print(series.all())ExplanationHere we have created a pandas series object ...
Read MoreHow to create a pandas DataFrame using a dictionary?
DataFrame is used to represent the data in two-dimensional data table format. Same as data tables, pandas DataFrames also have rows and columns and each column and rows are represented with labels.By using the python dictionary we can create our own pandas DateFrame, here keys of the dictionary will become the column labels, and values will be the row data.Here we will create a DataFrame using a python dictionary, Let’s see the below example.Example# importing the pandas package import pandas as pd data = {"int's":[1, 2, 3, 4], "float's":[2.4, 6.67, 8.09, 4.3]} # creating DataFrame df = pd.DataFrame(data) ...
Read MoreHow to create a pandas DataFrame using a list of tuples?
The pandas DataFrame constructor will create a pandas DataFrame object using a python list of tuples. We need to send this list of tuples as a parameter to the pandas.DataFrame() function.The Pandas DataFrame object will store the data in a tabular format, Here the tuple element of the list object will become the row of the resultant DataFrame.Example# importing the pandas package import pandas as pd # creating a list of tuples list_of_tuples = [(11, 22, 33), (10, 20, 30), (101, 202, 303)] # creating DataFrame df = pd.DataFrame(list_of_tuples, columns= ['A', 'B', 'C']) # displaying resultant DataFrame ...
Read MoreHow to prefix string to a pandas series labels?
In pandas Series functionalities we have a function called add_prefix that is used to add a string prefix to the labels. Particularly in pandas series the row labels will be prefixed with string.The add_prefix method will return a new series object with prefixed labels. It will add the given string before the row labels of the series.Exampleimport pandas as pd series = pd.Series(range(1, 10, 2)) print(series) # add Index_ prefix to the series labels result = series.add_prefix('Index_') print("Prefixed Series object with a string: ", result)ExplanationIn this following example, we have created a pandas series using python range ...
Read MoreHow to create a pandas DataFrame using a list of lists?
The pandas DataFrame can be created by using the list of lists, to do this we need to pass a python list of lists as a parameter to the pandas.DataFrame() function.Pandas DataFrame will represent the data in a tabular format, like rows and columns. If we create a pandas DataFrame using a list of lists, the inner list will be transformed as a row in the resulting list.Example# importing the pandas package import pandas as pd # creating a nested list nested_list = [[1, 2, 3], [10, 20, 30], [100, 200, 300]] # creating DataFrame df = pd.DataFrame(nested_list, columns= ...
Read MoreHow to create a pandas DataFrame using a list?
DataFrame is a two-dimensional pandas data structure, and it has heterogeneous tabular data with corresponding labels(Rows and Columns).In general pandas, DataFrame is used to deal with real-time tabular data such as CSV files, SQL Database, and Excel files. If you want to create a DataFrame there are many ways like: by using a list, Numpy array, or a dictionary.We can create a DataFrame by using a simple list.Exampleimport pandas as pd # importing the pandas package Li = [100, 200, 300, 400, 500] # Assigning the value to list(Li) df = pd.DataFrame(Li) # Creating the DataFrame print(df) ...
Read MoreWhat does count method do in the pandas series?
The count method in the pandas series will return an integer value, that value represents the total number of elements present in the series object. It counts only the valid element and neglects the invalid elements from series data.Invalid elements are nothing but missing values like Nan, Null, and None. The count method won’t count missing values as an element, it will neglect the missing values and count the remaining values.Example# importing pandas packages import pandas as pd d = {'a':'A', 'c':"C", 'd':'D', 'e':'E'} #creating a series with null data s_obj = pd.Series(d, index=list('abcdefg')) print(s_obj) # ...
Read MoreWhat is the use of tail() methods in Pandas series?
The tail method in pandas series object is used to retrieve bottom elements from a series. And this tail method takes an integer as a parameter which is represented by variable n.Based on that n value the pandas series tail method will return a series object with n number of bottom elements from the actual series object.Let’s take an example and see how this tail method will work on our pandas series object.Example# importing required packages import pandas as pd # creating pandas Series object series = pd.Series(list(range(10, 100, 4))) print(series) print('Result from tail() method:') # ...
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