create read update delete using Tkinter

Estimated reading time: 3 minutes

CRUD ( create, read, update, delete) is a programming acronym notably for how to manage updates to a database.

It is commonly used when talking about storing data on a database and follows the following rules:

  • The ability to create or add new records.
  • Be able to read and or retrieve the records.
  • If an update is needed, then allow those updates to be posted to the database successfully.
  • To ensure records are maintained correctly and deleted where a delete request is requested.

Generally speaking, it is related to persistent storage principles and you can find more information here basic functions of persistent storage (datacadamia.com)

Given these points when using graphical user interfaces, and trying to introduce crud functionality to your database applications, together with the design of the application, should yield:

  • Good database design.
  • Reducing complexity.
  • Eliminate duplication.
  • Have consistency.

We have spoken about the use of CRUD, but what are its benefits?

Security roles can be enabled

In addition, CRUD brings they bring structure to what actually can happen on a server, in essence, the ability to apply updates is managed tightly.

Helps put structure around what an application can do

Equally important, in building an application for use by users, knowing what they will do can prompt the designers to ask questions about what the user will actually do.

For example probably when Twitter was been designed, the things that probably where thought of :

Create a tweet – Functionality for the user to create and post a tweet, that gets saved to the database.

Read a tweet – Load all tweets from the database to the users interface.

Update – Allow a user to update their account profile or tweet timeline.

Delete – A user can delete their own tweets, their profile and or account.

Data flows between servers can be managed easier

Similarly, with the modern use of technology and processes moving online, data flows around between lots of people and organizations.

With this in mind, the need to send and receive data has to be managed more efficiently and securely.

As a result, data coming into the server can be controlled as to where it is received, and updated to. This can be accomplished by security roles discussed above.

Using classes with CRUD

The use of Python classes is commonly seen in many applications, for this reason we have used them again below.

Uniquely classes will only help to enhance your computer programme and organization of code, as a result of removing duplication and simplifying the code.

Classes are a very useful way to manage the structure of your code, thus keeping everything centralized.

Sooner or later if this was not implemented the project would become too difficult to manage, and maintenance and updates would become difficult to manage.

In the below video we take you through the steps involved in applying this methodology, using an SQLite database.

We use Python Classes to manage the different requests by the user, the details can be found here

  • Main Tkinter window creation
  • Exporting data to Excel
  • Updating the records
  • Filter the records based on user choice
  • Deletion of a record.

When applying these updates, we have written the code that will apply the following SQL updates:

  • Select
  • Update
  • Insert
  • Delete

As can be seen these four SQL commands are the commonly used across any application to perform these requests in a CRUD application.

Tkinter GUI tutorial python – how to clean excel data

Estimated reading time: 2 minutes

Tkinter is an application within Python that allows users to create GUI or graphical user interfaces to manage data in a more user-friendly way.

We have tested this code over 100,000 records sitting on the Microsoft OneDrive network so in a way, its speeds were quite good, over five tests, they all were under 100s from start to finish.

data cleansing data cleansing fixed

In this Tkinter GUI tutorial python, you will be shown how to find the data errors, clean them and then export the final result to excel.

We will take you through the following:

  • Creation of the Tkinter interface.
  • Methods/ functions to find errors.
  • Methods/functions to clean the data.
  • Exporting the clean data to an excel file.

The video walks through the creation of a Tkinter window using a canvas and a frame to store the data frame.

Then it looks at importing the data through pd.read_excel, to load the data into a pandas data frame.

Next, there is a function and or method that will extract the errors through str.extract , which is loaded into separate columns

Finally, I have exported the clean dataset using rawdata.to_excel , and saved the file as a separate new spreadsheet.

 

planning your machine learning model

Estimated reading time: 1 minute

Planning your machine learning model is one of the most important steps you will take in order to achieve the best results you are looking for.

In looking at how to plan a machine learning project, this video takes you through 3 steps:

a. Researching

b. Building your model

c. Testing your model

❤Subscribe for more free YouTube tips: Subscribe to Data analytics Ireland

❤Share this video with a YouTuber friend: Planning your machine learning model

 

python constructor self and __init__explained

Here in python constructor self and __init__ explained, we will take you through an explanation of self and __init__, how you can use, and the concepts behind them.

We show how to create an object, initiate a class and pass the parameters to the __init__ constructor.

Also we take you through the concept of self and how it can be used to process an object’s parameters and return values where necessary.

❤Subscribe for more free YouTube tips: Subscribe to Data analytics Ireland

❤Share this video with a YouTuber friend: Python constructors explained

 

how to create charts in Tkinter

Estimated reading time: 1 minute

In how to create charts in Tkinter, it is one of many libraries that can be used to create different chart types and graphical user interfaces

With python programming, its ease of use and ability to quickly roll out GUI applications, make it one of the most popular in use today.

Because the application allows the creation of interfaces that users will be familiar with, its roll out across an organization will make adoption a lot easier.

In fact, the applications created can also have the ability to create an executable file that can be placed on users’ desktops.

In the below video, after importing Tkinter we use many of its built-in functions to create:

  • Pie chart, a bar chart, and a line chart.
  • We also use functions and classes to help manage the creation of the charts.
  • It is recommended you follow the video to the end as it will give you a great insight into how these can be used.

❤Subscribe for more free YouTube tips: Subscribe to Data analytics Ireland

❤Share this video with a YouTuber friend: How to create Tkinter charts

 

how to add sine and cosine in python code

Explaining what this post is about
I was recently online and providing help to a fellow python coder, and a query came up about how you would rewrite some code that had included the sine function  and the cosine function

We were asked to see if we could translate this code:

L_1=1;
L_2=1.5;
q_0=0.5;
q_1=pi()/4;
q_2=pi()/6;
%Position of the end effetor
x= q_0+L_1*cos(q_1)-L_2*cos(pi()-q_1-q_2)
y=L_1*sin(q_1)+L_2*sin(pi()-q_1-q_2)

into its python equivalent.

Some background about the output

In order to get the desired result, there is a need to import a package to provide some of the mathematical analysis required, and was achieved through using Numpy statistical analysis

This package allows the following functions to be used in the logic:

  • Pi
  • Cosine
  • Sine

And the result is

As a result, the below shows the output of the above question converted to its Python equivalent:

import numpy as np

a= np.pi
print("PI value is ", a)

L_1=1
L_2=1.5
q_0=0.5
q_1=a/4
q_2=a/6

print("L_1 value is",L_1)
print("L_2 value is",L_2)
print("q_0 value is",q_0)
print("q_1 value is",q_1)
print("q_2 value is",q_2)


x= (q_0+(L_1*(np.cos(q_1)))-(L_2*(np.cos(a-q_1-q_2))))
y= (L_1*(np.sin(q_1))+(L_2*(np.sin(a-q_1-q_2))))

print("x value is " , x)
print("y value is " , y)

with its output showing:

PI value is  3.141592653589793
L_1 value is 1
L_2 value is 1.5
q_0 value is 0.5
q_1 value is 0.7853981633974483
q_2 value is 0.5235987755982988
0.7071067811865476
-0.25881904510252085
x value is  1.5953353488403288
y value is  2.15599552062015

 

how to create an instance of a class

Estimated reading time: 1 minute

Here in how to create an instance of a class, as described herein, how to create a class in Python, we will further explore the instance of class and how this can be used within a program to assign values to an object. This allows that object to inherit those values contained within the class, making it easier to have consistency regards functionality and data.

This video covers off

(a) creating an instance of a class

(B) Using the __init__ within the class

(C) define the constructor method __init__

(D) Creating an object that calls a class and uses the class to process some piece of data.

What are the benefits of this?

  • You only need to create one class that holds all the attributes required.
  • That class can be called from anywhere within a program, once an instance of it is created.
  • You can update the class, and once completed, those new values will become available to an instance of that class.
  • Makes for better management of objects and their properties, not multiple different versions contained within a program

 

 

How to create a class in Python

Estimated reading time: 1 minute

How to create a class in Python : In this video explaining classes will be the main topic on how they are constructed,  we explain how to create an instance of a class. Also, we look at what class attributes are and how they can be used to assign key data that can be called anywhere within a program.

The steps involve the following:

(a) Create a class

(B) Assign attributes to the class

(C) Create a method within the class ( similar to a function)

(D) Create an instance of a class to call its attributes and methods.

This video is a follow on from object oriented programming – Python Classes explained

Python Tutorial: Pandas groupby columns ( video 2)

Pandas groupby using column values

In this second video how to groupby using pandas and as part of expanding the data analytics information of this website, we are looking to explain how you can use a groupby selection but only using the column values and not the column names.

Below we import our data into a dataframe, and then group as follows:

  • Aggregate function
  • Using the cut function and assigning values to bins.
  • Assigning labels to the data frame output based on the bin values.

 

 

Why would you want to use Pandas groupby and column values?

This video looks to help understand the why going by values might be easier than column names:

  • Column names can change from project to project, using by values allows easy implementation of getting the output regardless of the names used.
  • You could apply this to any Python class, and as long as you can inherit will allow the code to run smoothly.
  • Implementing by value allows a clear understanding of the desired output as the values are clearly understood to generate what is required.
  • You need to understand how data within your data set falls within a particular cohort:
    • This use of values in different programs just needs to change, the underlying logic remains the same.
    • Using column names still means that to group them, the logic still needs to be written.

Python tutorial: Pandas groupby ( Video 1)

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:

  • Directly naming the column
  • Through get_group
  • Using a loop
  • Utilising a lambda function