Author name: cndro

                                                         Photo by Joshua Sortino on Unsplash This article will discuss why Selenium is the best automation tool. Selenium is an open-source automation testing tool used for automating tests to validate web applications on different web browsers. It supports different programming languages to create test scripts, such as Python, Java, C#, e.t.c. It also supports various browsers like Chrome, Mozilla, Firefox, Safari, e.t.c. We can easily automate browser testing across these browsers using Selenium WebDriver. Selenium is well used by Developers because of the result derived from it. Now we will move further to discuss key reasons Developers use Selenium tool. After that, we’ll implement how we can use the tool. Key Reasons to use Selenium Tool Open Source Availability: One of the most exceptional reasons many Developers use this tool daily is that it’s publicly accessible and free. To use this tool, you don’t have to make any payment. The community keeps helping developers improve the web browser features and functionalities. Language and Framework Support: We also have the aspect of language and framework support. For a Developer to utilize this tool, he can use any of his desired programming languages: Python, Java, C# e.t.c. As a Developer, you don’t have to worry about language support. You can use any major languages to carry out your test automation. Also, for every supported language in Selenium , there is an allocated framework designed to assist in writing test scripts. This was put in place to make the tool easier to use. Ease of Implementation: Selenium has a friendly user interface making it very easy to implement test scripts effectively. With Selenium we can add different configurations on how we want our test to run. We can also reduce code duplication and improve code readability. In addition, new users who just started learning the tool find it easier to use and it doesn’t take much time to learn. Certainly, Developers who use Selenium sure do excellent feedbacks to give on this tool. Support Across Various Operating Systems: It supports various Operating systems; it isn’t OS-dependent. This tool can work across this different OS; Windows, Linux, Mac OS, UNIX, etc. With Selenium WebDriver, we can create a test case on Windows and execute it on Mac and other OS. Constant Update: Just like we mentioned earlier that a community supports Selenium. The selenium community releases regular updates and upgrades to make this tool more effective for Developers. Constant improvement on this tool drives more users daily. Now that we’ve seen the different reasons this tool is very effective and why it’s one of the best automation tools. We’ll discuss how we can use this tool in Python. Selenium With Python Let’s implement how we to run a Selenium test script with Python programming language. Follow the installation steps carefully. Install Python on Windows To demonstrate this, we will use a windows machine. If you don’t have Python on your machine, please visit this link to download, preferably Python 3. After installing python, go to your command prompt to confirm by typing python, just as seen below. You will have your python version, just as ours shows python 3.10.5. Install Selenium We can install Selenium using the command below. This will install the latest package of Selenium. Python -m pip install -U Selenium You can also confirm your installation using the same command we used below. Download Selenium Web Driver The next step is to download the Selenium Web driver for the browser we’ll be using. We’re using Google Chrome Browser, so we will download the chrome driver based on our chrome’s version. We can download the driver here. If you’re using other browsers, you can get Firefox, Safari, and Edge via each link. Here, we download the chrome driver for Windows as shown below. Our Code After downloading the zip file, we will extract the .exe file inside our working environment folder. To make things look cleaner, we can create a folder named Browser and put the .exe file inside. Like what we have below, our code will reside in the code.py file. This is what we have in our code as shown below. In this code, we demonstrated a simple test case of how Selenium get driver was used to open Google and then we searched for our Name “Cndro”. Selenium performs the action of searching and clicking on search button automatically, after then, close the browser. 1 #we import our library 2 from selenium import webdriver 3 import time 4 from selenium.webdriver.common.keys import Keys 5 6 #here we paste link to our exe file in the webdriver.chrome method 7 driver = webdriver.Chrome(r’C:\Users\CNDRO\Documents\Selenium_project\Browser\chromedriver.exe’) 8 9 #we maximize our window 10 driver.maximize_window() 11 12 #We will use the driver.get to open google 13 driver.get(“https://www.google.com/”) 14 15 #we use the driver to find name of the search bar name, so we can paste in our search item name 16 driver.find_element_by_name(“q”).send_keys(“cndro”) 17 18 #we use time.sleep to make selenium work at a balance by not being too fast 19 time.sleep(3) 20 21 #Now, we also get the name of the button where selenium will click automatically 22 #and then use send keys.enter to automatically click it 23 driver.find_element_by_name(“btnK”).send_keys(Keys.ENTER) 24 25 #we put it on sleep again 26 time.sleep(3) 27 28 #we then close the brower 29 driver.close()   So we run our code with the command python code.py as seen below; Then it opens a new browser, just as seen below, to carry out our designated action. Which automation tool do you consider the best? Drop your thoughts in the comments section below. Thanks for reading.

Photo by Shunya Koide on Unsplash Pandas is a software library written for the Python programming language for data manipulation and analysis. Wes McKinney first started this library in 2008 out of the demand for a high performance and flexible tools to perform quantitative analysis on financial data. Pandas is built on top of two core python libraries. We have the Matplotlib, used for data visualization, and Numpy, used for mathematical operations. With Pandas, Developers can easily work with tabular data (like spreadsheets) within a Python script. Now, let’s discuss why Pandas library is a useful tool for data exploration and manipulation. Data cleansing: It isn’t new that Data Scientist/ Analyst spends a huge amount of time on cleaning datasets and preprocessing to the actual form they want. Pandas provides an easy pathway to be able to handle this messy data, deal with missing values and remove irrelevant data. With pandas we can read, process and write to (CSV, TSV, Excel, HDF, JSON, THML , database data files) and any other formats. Pandas gives us two different forms of representing our data. We have the Series, a list-like structure, and DataFrames, which has a tabular structure. Data Exploration: Data exploration refers to the initial step in data analysis in which Data Analysts use data visualization and statistical techniques to describe dataset characterizations. In this phase, a data analyst can explore and identify relationships between different data variables, the presence of outliers and what the structure looks like. Data Exploration is usually carried out to get insight and recognize patterns from the data. With that being said, Pandas is an excellent tool when it comes to Data Exploration in a dataset. All Data Scientists/ Analysts should have this in their skill set. Here are different functions we can use in pandas for exploring data; .info(): The info function is used to return information about our DataFrame. The information contains the number of columns, column labels, column data types, memory usage, range index, and the number of cells in each column. .head(): With this function, we can print the first 6 rows of our data and change it to print other values . i.e df.head(10); this will print the first 10 rows of data in our DataFrame. .tail(): We can use .tail() in the reverse order of .head(). Here we’ll print the last 6 rows of our data, or the tail end of our data. .describe(): We can use the .describe() method to calculate statistical data such as the Percentile, Mean & Standard Deviation of numerical values in our DataFrame or series. .sort_values(): When working with our data, a need might arise for us to want to sort our DataFrame in a particular order. We can sort by column, ascending order and descending order. .groupby(): the .groupby() function is usually used to split data into separate groups, so as to perform computations such as; .mean(), .sum() e.t.c for better analysis Data visualization: Pandas is one of the efficient ways to visualize data. It provides several different functions to use with the help of the .plot() function. With this .plot() function we can plot all kinds of graphs such as Bar graph, Histogram, Box Plot, Area Plot, Scatter Plot e.t.c. when working with series or DataFrame. Bar graph — df.plot(kind=”bar”) Histogram — df.plot.hist() Box Plot — df.plot.box() Area Plot — df.plot.area() Scatter Plot — df.plot.scatter(); Merges and Joins: Another way Pandas is helpful is the ability to merge and join datasets. Pandas provides us an effective method for easily combining DataFrame or Series based on different forms of logic. With pandas, we can merge, join, and concatenate in our datasets, thereby providing a meaningful structure for our dataset. .merge() — Pandas use the .merge() method to combine two different datasets. We can also merge with any join types(inner, left, right, and outer). .join() — Here also, pandas uses .join() method to combine differently-indexed DataFrames into a new DataFrame. We can join using the argument ‘on’ , or join on two differently indexed DataFrames. .concatenate() — The .concat() method is a very efficient way of handling large datasets. We can concatenate DataFrames both vertically(axis=0) and horizontally(axis=1). Data Normalization: Another area where we use Pandas is the aspect of Data Normalization. Data analysts or scientists usually carry out normalization to adjust values existing on different scales to a common scale. The Pandas library provides multiple built-in methods through maximum absolute scaling which makes data normalization techniques very easy to implement. Some of these methods includes; .max() method .abs() method Statistical Analysis: Pandas has also proven many times as the best tool for carrying out statistical analysis of data. In Pandas, different built-in statistics are made available, which we can apply to our numerical data. Some of these methods we can use are listed below; .value_counts() — This function returns object containing counts of unique values .count() — The count() method is used for counting the number of not empty values for each row, or column. .mean() — The mean() method is used to calculate the mean of the values for the requested axis. .median() — The median() method is used to calculate the median values for the requested axis. .argmax() — This can be used to calculate the maximum value present in the input Index. If we have more than one maximum value (i.e., the maximum value is present more than once), then it returns the index of the first occurrence of the maximum value. .max() — This is used to calculate the maximum value of the requested axis. Hope you enjoyed this article. Thanks for reading.

Class or Static Variables in Python                                                            Photo by Shahadat Rahman on Unsplash In this tutorial, we’ll discuss class or static variables in Python. Class or Static variables are the variables that belong to a class and not objects. We can also say they are shared amongst objects of the class. Therefore, all variables assigned a value in the class declaration are called class variables, and variables given inside the class methods are referred to as instance variables. Whenever we have a class, all class objects will share the static variable. This static variables can be accessed either by class name or through object reference such as ClassName.static_variable_name. Let’s check the examples below to understand how this is done; Example1 There is an Employee class with a class variable known as role in this example. We assigned a variable to our class variable called “Software Engineer”. Next, we defined a class constructor which accepts two new arguments. We then created our instance variable(_id and name). After this, we created 2 different objects for the Employee class. Through this object, we could access our static/class variable “role”. We were also able to call each of the instance variables using each of the individual objects created. 1 class Employee: 2 # Here is a Class Variable 3 role = ‘Software Engineer’ 4 def __init__(self,_id,name): 5 #this is an Instance Variable 6 self._id= _id 7 #this is an Instance Variable 8 self.name = name 9 10 11 # our objects of the Employee class 12 a = Employee(1, ‘Adams’) 13 b = Employee(2, ‘Janes’) 14 15 print(a.role) 16 print(b.role) 17 print(a._id) 18 print(b._id) 19 print(a.name) 20 print(b.name) 21 print(Employee.role)   Output 1 Software Engineer 2 Software Engineer 3 1 4 2 5 Adams 6 Janes 7 Software Engineer We can change the value in a class variable by using class object but mind you; this doesn’t affect another object of the class that might just be created. Let’s see the demonstration below; 1 a.role = ‘Data Engineer’ 2 print(a.role) 3 print(b.role) 4 print(Employee.role) Here, we use the  a object to change the value in the role(our class variable). We observe that this code won’t affect the second object and the main class variable whenever we run this code. The reason is that this occurs at the level of that particular object. We generated the output below; Output 1 Data Engineer 2 Software Engineer 3 Software Engineer Note: You can access the class variable by using either the class name or the object reference, just as seen below; 1 print(a.role) 2 print(Employee.role) Output 1 Software Engineer 2 Software Engineer Let’s check another example below on class or static variable; Example 2 Here is another example on how to use the class or static variable. In this example, we demonstrated using a particular animals class called carnivorous. We defined our static variable in our class and also created our constructor which has an argument called name. We defined another new method used for printing all the objects of the animal class we created just as seen below; 1 class Animals: 2 # class or static variable 3 category = ‘carnivorous’ 4 5 def __init__(self, name): 6 # instance variable 7 self.name = name 8 9 10 # method to show data 11 def show(self): 12 # accessing class variable 13 print(self.name) 14 15 a = Animals(‘Lion.’) 16 b = Animals(‘Wolf.’) 17 c = Animals(‘Leopard.’) 18 d = Animals(‘Hyena.’) 19 e = Animals(‘Polar Bear.’) 20 21 print(‘These animals belong to the class: ‘, Animals.category) 22 print(‘These animals are:’) 23 a.show() 24 b.show() 25 c.show() 26 d.show() 27 e.show() Output We generated the output below; 1 These animals belong to the class: carnivorous 2 These animals are: 3 Lion. 4 Wolf. 5 Leopard. 6 Hyena. 7 Polar Bear. Hope you enjoyed this post. Thanks for reading.

.                                                                  Photo by Rubaitul Azad on Unsplash This articles explains how you can get started with Docker in Python. Docker is a free software developed by Docker Inc. This containerization platform helps us package our application and its dependencies in the form of containers. This application can work effortlessly in any environment, either development, test, or production. Docker was developed to tackle many problems developers face, including the issue of an application working on a computer but not starting on a staging server or development. Let’s discuss some of the different terminologies in docker before moving to the installation aspect. Docker Container — The containers are created from images. We can also refer to them as the runtime instances of Docker Images. The Containers also have the equipment required for an application to be run in an isolated way. Docker Image — Docker images refer to the template for creating our Docker containers. The starting point of every Docker image is a base image such as ubuntu14.04 LTS. Docker File: Docker file is usually a text file that has series of instructions on how Docker Image should be created. Volume — This can be defined as a shared folder. They are usually initialized when a container is created. Registry — The registry can be referred to as the server which stores the Docker Images. It provides us the ability to pull up any docker image. Installation Now, we need to install Docker Engine on our machine. To install Docker Engine on your machine either Linux, Windows, or Mac, you can visit this link . After you’ve done your installation, you verify with the command docker –version on your terminal. I will be using a windows machine to demonstrate in this tutorial. Create your Project Open your VS Code or any other IDE to create a new project, let’s create a folder call docker_demo. In this folder we have our two files, main.py and requirements.txt just as seen below: Main.py file Inside this main.py file, we use the Numpy library. We want to create NumPy ndarray object by using the array() function as seen below: 1 import numpy as np 2 3 arr = np.array([1, 2, 3, 4, 5]) 4 5 print(arr) Requirements.txt file 1 -i https://pypi.org/simple 2 numpy>=1.18.1 3 pandas>=1.0.1 4 python-dateutil>=2.8.1 5 pytz>=2019.3 6 six>=1.14.0 Our requirements.txt also contains all the necessary libraries we need. Let’s create our Docker File now. Create Docker File Here, we’ll create a new file that has no extension and name it Docker File inside our current working directory, just as seen below: We then write the following command inside our Docker file: 1 #all docker file starts with a “FROM” instruction for setting parent image, here we are pulling latest python to run our application 2 FROM python:latest 3 4 #we copy the requirements file into the app folder we are just creating now 5 COPY requirements.txt /app/ 6 7 #here we set this app folder as our working directory 8 WORKDIR /app 9 10 #Now we use the RUN command to pip install –upgrade pip and install the requirements.txt in our parent image here 11 RUN pip install –upgrade pip \ 12 && pip install –requirement requirements.txt 13 14 #After installation, we copy our python file(main.py) inside our working directory in the parent image 15 COPY main.py . 16 17 #here, we defined the command to be executed to run our python file 18 #just like we do “python main.py” to run our python file in our VS Code terminal. 19CMD [“python”, “main.py”] Build Docker Image To build our Docker image, we will write this command below in our terminal docker build -t image_name . The image-name stands for the name you want to assign to your image . The command I used is shown below: docker build -t my_docker . A local image with my_docker will be automatically created for us, just as shown below. Run local Docker image What we need to do now is to run our local docker image. We can run with the command below by using the docker image name; Therefore our code is ready to be launched. 1 $ docker run my_docker After running the docker image, we generated our result as shown in the image below; Docker Commands Check All Containers You can also view all running containers you have by using the command below: 1 docker ps -a   Show Running Containers To show only running containers, you use this command: 1 docker ps Show Latest Created Container To show the latest created container, you use the command below. 1 docker ps -l Hope you found this post helpful. Thanks for reading.

                                                                  Photo by Clément Hélardot on Unsplash Constructors in Python are methods used for initializing an object. We can say they are methods called whenever an object is created. A constructor also has one simple task, which is to assign values to data members of a class whenever an object has been created. In explaining constructor better, a constructor is called by the __init__() method, and it is invoked whenever an object is created. For instance, if we create three objects, our class constructor will be called by that same three times. Now, let’s discuss the different types of constructors we have in Python; Types Of Constructors We have two types of Constructors and they are: Default constructor Parameterized constructor Default Constructor The default constructor is the type of constructor that takes no arguments. This constructor holds only one argument “self”, which is a reference to the instance that is being established. Example 1 In the example below, we created a class named Fruits and defined a constructor that takes no arguments. Inside the constructor, we initialized two variables known as “first_fruit” and “second_fruit”. Then we created a method for printing our data members and also created an objects of the class named “our_object”. Using these objects, we were able to call our instance method. 1 class Fruits: 2 3 # our default constructor 4 def __init__(self): 5 self.first_fruit = “Pineapple” 6 self.second_fruit =”Apple” 7 8 # we define a method for printing our data members 9 def print_fruit(self): 10 print(self.first_fruit) 11 print(self.second_fruit) 12 13 14 # created object of the class here 15 our_object = Fruits() 16 17 # let’s call the instance method using our object of the class 18 our_object.print_fruit()   Output We generated the output below; 1 Pineapple 2 Apple Let’s look at this other example below on Default Constructor. In our example below, we can observe that it has no argument as well. Example 2 1 class Customer: 2 3 #our default constructor 4 def __init__(self): 5 self.name = “Scott” 6 self.age = 40 7 self.address = “Elizabeth strt” 8 9 # we define a method for printing our data members 10 def details(self): 11 print(‘Name:’, self.name, ‘Age:’, self.age,’Address:’, self.address) 12 13 # created object of the class here 14 the_cust= Customer() 15 16 # let’s call the instance method using our object of the class 17the_cust.details()   Output We generated this output. 1 Name: Scott Age: 40 Address: Elizabeth strt 2​ Parameterized constructor This constructor contains parameters just as the name depicted. It has a reference to the instance being created called self just like that of the default constructor but here, it can allow more arguments and the self serves as the first argument. Let’s dive in different examples on Parameterized constructors to understand it better. Example 1 In this example, we created a Customer class with constructor that has one more argument apart from the self, which acts as the first argument. Here, we can see how we used the “name” arguments after we created the object of our class. We can observe that the name (Mr Scott) was passed in as an argument when we created the object. 1 class Customer: 2 # Our parameterized Constructor with more argument 3 def __init__(self, name): 4 self.name = name 5 self.age = 40 6 7 # we define a method for printing our data members 8 def details(self): 9 print(“Hi “,self.name) 10 11 # created object of the class here 12 the_cust = Customer(“Mr Scott”) 13 14 # let’s call the instance method using our object of the class 15the_cust.details()   Output We should have this output after running our code 1 Hi Mr Scott Let’s check one more example on the Parameterized Constructor Example2 Here is also another way of defining more than one arguments in a constructor just as seen below 1 class Customer: 2 # parameterized constructor 3 def __init__(self, name, age, address): 4 self.name = name 5 self.age = age 6 self.address = address 7 8 # display object 9 def details(self): 10 print(self.name, self.age, self.address) 11 12 # creating object of the Employee class 13 Mike = Customer(‘Mike Howard’, 40, ‘520 N MADISON LOS ANGELES’) 14 Rose = Customer(‘Rose Cooper’, 32, ‘521 N MADISON LOS ANGELES’) 15 Benjamin = Customer(‘Benjamin Smith’,35,’522 N MADISON LOS ANGELES’) 16 Mike.details() 17 Rose.details() 18 Benjamin.details()   Output 1 Mike Howard 40 520 N MADISON LOS ANGELES 2 Rose Cooper 32 521 N MADISON LOS ANGELES 3 Benjamin Smith 35 522 N MADISON LOS ANGELES Hope you enjoyed this post. If you do, Comment, share, and follow for more informative posts. Thanks for reading.

This tutorial will demonstrate how we can create a calendar table in Power BI Report using the DAX(Data Analysis Expressions) function. Power BI is a Business Intelligence tool used for sharing insights in different companies. It is very efficient for creating reports and dashboards. Dates are crucial in data; it helps us gain information about events, sales reports, and so many other useful references in our data. There are two different functions that can be used for creating a Power BI calendar. These are; the CALENDAR DAX function and CALENDARAUTO function. CALENDAR Function We can use the Calendar function to put a date range in data. Writing this function gives us a range of dates: the start date and finish date of what we set. The Syntax 1 CALENDAR(<start_date>, <end_date>) 2 The start_date specifies the start date, while the end_date specifies the end of our date.   Let’s see an example below; The date range we specified here should give us the date from May 1st, 2014 till January 1st, 2021. CALENDAR (DATE (2014, 5, 1), DATE (2021, 1, 10))   CALENDARAUTO Function CALENDARAUTO function generates the list of dates containing all the data model dates. The argument needed here is the fiscal year-end month. The Syntax looks like this; CALENDARAUTO([fiscal_year_end_month])   [fiscal_year_end_month]: This is a DAX expression that returns an integer from 1 to 12. When omitted, it defaults to the value specified in the calendar table template for the current user, if present; otherwise, it defaults to 12. Let’s see the example below; In this example, we click on New table and write the CALENDARAUTO function in the empty box. On writing that, Power BI will then create a start and end date calendar by looking at the date range of your table dates. Now that we’ve examined the two different functions we can use, we can discuss how to add columns to the date table with DAX. The ADDCOLUMNS Function With the ADDCOLUMNS function, we can create new columns in our table. The syntax looks like this; ADDCOLUMNS ( <Table>, <Name>, <Expression> [, <Name>, <Expression> [, … ] ] )   Let’s look at this example below. This example shows us how we can create many columns. We created the Year column, the month_number, days of the week, Quarter, YearQuarter, and others. We use the Format function to specify how we want our date to be. 1 Calendar = 2 3 ADDCOLUMNS ( 4 5 CALENDAR (DATE(2014,5,1), DATE(2021,1,10)), 6 7 “DateAsInteger”, FORMAT ( [Date], “YYYYMMDD” ), 8 9 “Year”, YEAR ( [Date] ), 10 11 “Monthnumber”, FORMAT ( [Date], “MM” ), 12 13 “YearMonthnumber”, FORMAT ( [Date], “YYYY/MM” ), 14 15 “YearMonthShort”, FORMAT ( [Date], “YYYY/mmm” ), 16 17 “MonthNameShort”, FORMAT ( [Date], “mmm” ), 18 19 “MonthNameLong”, FORMAT ( [Date], “mmmm” ), 20 21 “DayOfWeekNumber”, WEEKDAY ( [Date] ), 22 23 “DayOfWeek”, FORMAT ( [Date], “dddd” ), 24 25 “DayOfWeekShort”, FORMAT ( [Date], “ddd” ), 26 27 “Quarter”, “Q” & FORMAT ( [Date], “Q” ), 28 29 “YearQuarter”, FORMAT ( [Date], “YYYY” ) & “/Q” & FORMAT ( [Date], “Q” ) 30 31) Our table should resemble what we have below; And that’s a wrap. See you in our next post.