Data Analytics and Video Tutorials
Use python data analytics to get a better insight into your data. As a result, you’ll better understand your data and make better decisions.
In this first video about pandas groupby and as part of expanding the data analytics information of this website, we are looking to explain how you can use a groupby selection to sort your data into similar datasets better so they can be better analysed. In the video below, we import our data into a dataframe, and then group as follows:
There may be an occasion as you are building out a data science or data analytics project, checks need to be performed on the dataset as follows:
and this is where input boxes and Tkinter can help!
In the below video, we are demonstrating an introduction to using an input box and validating the input.
We demonstrate how to validate the data entered into the tkinter input box and return a message, this will ensure the user gets the correct data.
When starting your data analytics projects, one of the critical considerations is how to present your results quickly and understandably?
Undoubtedly this is true if you are only going to look at the results yourself.
If the work you do is a repeatable process, a more robust longer-term solution needs to be applied, this is where Tkinter can help, which is a python graphical user interface.
When you are importing tkinter, some of the functionality that can be used include:
While going through a set of data and getting some meaning to it can be challenging, using the python graphical user interface tutorial below can help build the screens that will allow a repeatable process to display in a meaningful way.
Using the tkinter widget could help achieve the following:
There are many more ways that you could do this, but one of the most important things is that data analytics can be built into a windows environment using Tkinter.
These GUI applications are what the user would be used to currently seeing. As a result, this could help to distribute a solution across an enterprise to lots of different users.
Also, another benefit is that they will work on many different operating systems.
The only thing that needs to happen is that the requirements the user needs are defined, and the developer then builds on those, with the data analytics code run in the background of this program with Tkinter and output into a user-friendly screen for review.
Estimated reading time: 2 minutes
Here we have delivered a complimentary video on How to create a graphical user interface in Tkinter .
It will demonstrate how a Combobox can be used to select values and then validate the entry chosen.
Using a Combobox in the computer programming world has been around for some time.
It is a useful way to select from a choice and could in many ways in data analytics help as the following examples show:
In this video, we use ttk, written to help split the behavior of code from the code implementing its appearance.
You can see plenty more on it here ttk information. This is a handy piece of functionality as styling an object can interfere with how it works.
We also have a function that helps with the validation. In the below, it accomplishes the following:
def checkifireland ():
x = combolist.get() # asssigns the value inside the combobox to x so it can be processed
if x == "Ireland":
messagebox.showinfo("Correct answer", "You will love it in Ireland")
else:
messagebox.showinfo("Incorrect answer", "You should visit Ireland first!")
The effectiveness is especially handy as it helps to ensure that the code returned from the Combobox to the function is correct
The below video will take through this step by step and explain the concepts discussed above.
There are many informative Python – working with excel videos that are on our YouTube channel.
We are looking to bring them in and show them on a graphical user interface tutorial.
To see how to load a set of values into a combobox from an SQLite database table, have a look at how to load values into a combobox