Saturday, January 23, 2021

Data Analytics Ireland

Data Analytics and Video Tutorials

Data Analytics

Take data and present it in a format that is easily understandable.

Articles 

What is data analytics?

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Estimated reading time: 4 minutes

In recent years and in the future, the importance of data analysis has gone up significantly. Here in Data Analytics Ireland we will look to explain the concept, please share or link to this article so others can see its contents. The reason for this is that the digital economy has taken off for several reasons:

(A) Automation of tasks has become easier.

(B) Less use of paper, becoming a greener economy.

(C) Technology improvements have meant that storage and big data processing make the process of delivering services easier.

(D) Career opportunities for professionals with good skills have increased.

(E) A wide range of open source and paid tools are now easily available that help to process and report on the data.

(F) Entry requirements are easy, and this coupled with an ability to quickly acquire knowledge and skills helps entry whether you want to be full time or part-time.

As a result of all this:

(A) Large data volumes need to be analysed.

(B) Consumers’ habits about how they use a service or the information they look for now has a digital footprint.

(C) Now once a consumer has used a service ( whether purchased or not), the ability to understand their habits can be captured to deliver them better:

  1. The services they want.
  2. The products they want.
  3. Quicker turnaround time.

How can this help with all the data that is captured and stored?

So in this article, we have already outlined what the background is as to how the industry has evolved to where it is now.

As outlined, all information traditionally would not have been stored in a format that was not easily accessible.

Now,  the work can be broken down as follows:

Step 1 – Data Capture

To understand what you want to analyse, a data analyst will work with their technical colleagues to ensure that the correct data is captured.

Once the completeness and accuracy are fulfilled, your data quality will become less of an issue.

Step 2 – Analysing


The next step is to understand the data, which can happen in several ways:

(A) You create visual charts of it; this allows the viewer of the information to get an initial view of the information without looking at the underlying data. Sometimes this will show patterns in data or clusters.

(B) Using data science statistics to see if they can explain the data. This could show information such as how data is correlated or otherwise. Also, probabilities could be calculated to show what outcomes might happen in the future.

(C) Data analysts might also need to understand how to build a machine learning model to use complex algorithms, to explain the data better, sometimes patterns that are not immediately understood can be unearthed and investigated further. There are two approaches commonly used for this, namely supervised and unsupervised machine learning; this link will explain thoroughly what they are about.

Step3 – Presenting


Now that all the steps have happened to have the data you want, as a decision-maker will it be feasible to look over rows and columns of data, and get a sense of what it all means? An emphatic NO is an answer, but this is where some of the visualisation tools come in!

Visually presenting data points, very quickly allows a viewer of the information to come to a decision quickly, and the tools that are outlined below will help with that process. These are a handful that will allow the data to be sliced and diced, there are many more out there, but they all allow data to be drilled down into and get to a real understanding what is going on.

Some of the tools include Tableau, Power BI, and Python( it has libraries that do a nice job)

Step4 – Decision Making

So after all this analysing, there need to be decisions made:

(A) Have the data in the correct format?

(B) In a place that it can be accessed and reviewed.

(C) Relevant to when the decision needs to be made

From the outset, as part of the work of performing the data analytics, an assessment needs to be made as to how often a decision will need to be made, with what data, and when.

At this point, the decision-makers should have a set of data ready for them to look over and reliably make a decision based on what they have in front of them, if they can’t make a decision then possibly, steps 1-3 should be reviewed and revisited.

Often what happens, is what information was required to make a change needs to be updated, improved upon, or additional data needs to be added on.

It is the job of the teams that manage the data sets to source that data and or change how they present it, to now reflect the decision that needs to be made.

class Python 

how to create an instance of a class

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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

 

 

class Python 

How to create a class in Python

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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 Python Tutorial 

Python Tutorial: Pandas groupby columns ( video 2)

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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 regular expressions 

Regular expressions python

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Estimated reading time: 3 minutes

Regular expressions explained

Regular expressions are a set of characters usually in a particular sequence that helps find a match/pattern for a specific piece of data in a dataset.

The purpose is to allow a uniform of set characters that can be reused multiple times, based on the requirements of the user, without having to build each time.

The patterns are similar to those that you would find in Perl.

How are regular expressions built?

To start, in regular expressions, there are metacharacters, which are characters that have a special meaning. Their values are as follows:

. ^ $ * + ? { } [ ] \ | ( )

.e = All occurrences which have one “e”, and value before that e. There can be multiple e, eg ..e means check two characters before e.

^ =Check if a string starts with a particular pattern.

*  = Match zero or more occurrences of a pattern, at least one of the characters can be found.

+ = Looks to match exact patterns, one or more times, and if they are not precisely equal, then nothing is returned.

? =Check if a string after ? exists in a pattern and returns it. If a value before the ? is directly beside the value after ? then returns both values.

—> e.g. t?e is the search pattern. “The” is the string. The result will return only the value e, but if the string is “te”, then it will return te, as the letters are directly beside each other.

da{2} = Check to see if a character has a set of other characters following it. E.g. sees if d has two “a” following it.

[abc] = These are the characters you are looking for in the data. Could also use [a-c] and will give you the same result. Change to uppercase to get only those with uppercase.

\ = Denoting a backslash used to escape all metacharacters, so if they need to be found in a string, they can be. Used to escape $ in a string so they can be found as a literal value.

| = This is used when you want an “or” operator in the logic, i.e. check for one or more values from a pattern, either or both can be present.

() = Looks to group pattern searches or a partial match, to see if they are together or not.

 

Special sequences, making it easier again

\a = Matches if the specified characters are at the start of the string been searched.

\b = Matches if the specified characters are at the beginning or the end of the string been searched.

\B = Matches if the specified characters are NOT at the beginning or the end of the string been searched.

\d = Matches any digits 0-9.

\D = Matches any character is not a digit.

\s = Matches where a string contains a whitespace character.

\S = Matches where a string contains a non-whitespace character.

\w = Matches if digits or character or _ found

\W = Matches if non-digits and or characters or _found

\z = matches if the specified characters are at the end of the string.

 

 

For further references and reading materials, please see the below websites, the last one is really useful in testing any regular expressions you would like to build:

See further reading material here: regular expression RE explained

Another complementary page to the link above regular expression REGEX explained

I found this link on the internet, and would thoroughly recommend you bookmark it. It will also allow you to play around with regular expressions and test them before you put into your code, a very recommended resource Testing regular expressions

 

Definition Python 

What are the reserved keywords in Python

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What are python reserved keywords?

When coding in the Python language there are particular python reserved words that the system uses, which cannot be accessed as a variable or a function as the computer program uses them to perform specific tasks.

When you try to use them, the system will block it and throws out an error. Running the below code in Python

import keyword
keywordlist = keyword.kwlist
print(keywordlist)


Produces the below keyword values
['False', 'None', 'True', 'and', 'as', 'assert', 'async', 'await', 'break', 'class', 'continue', 'def', 'del',
'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal',
'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']

When writing your code, it is important to follow the following guidelines:

(A) Research the keywords first for the language you are writing in.

(B) Ensure that your programming language highlights keywords when used, so you can fix the issue.

(C) Setup your computer program in debug mode to highlight keywords use.

With some programs running into thousands of lines of code, with additional functions and variables, it can become harder to spot the problem, so good rigour in the initial stages of coding will help down the road any issues that you may find that need to fixed.

This code was run in Python version 3.8

Python Python Tutorial 

Python tutorial: Create an input box in Tkinter

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Using an tkinter input box for your data projects

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:

  •  Big data sets and speed requirements in conjunction with
  • The need to reduce the volume of data returned which is impeding performance

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.

Types of uses for a tkinter input box are varied, here are some thoughts:

  • Use an input box to return a set of data for a particular day.
  • Using them to filter down the results to a particular cohort of data.
  • Conduct a string search to find data quality issues to be fixed.
Python Python Tutorial 

Python tutorial: How to create a graphical user interface in tkinter

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How would you like to present your data analytics work better?

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.

There are many applications for using Tkinter, such as:

  • Use them to build calculators.
  • They can show graphs and bar charts.
  • Show graphics on a screen.
  • Validate user input.

Where this all fits in with data analytics?

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.

Ultimately, you could do the following:

  • Build a screen that shows data analytics errors in a data set, e.g. The number of blank column values in a dataset.
  • Another application is to run your analytics to show the results on a screen that can be printed or exported.
  • Similarly, you could also have a screen where a user selects several parameters that are fed into the data analytics code and produces information for the user to analyse.

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 that the user would be used to seeing. As a result, this could help to distribute a solution across an enterprise to lots of different users.

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.

 

Python Python Tutorial 

How to create a combobox in tkinter

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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:

  • Select a date to filter a data set down to values that are in the dataset.
  • Using matplotlib to plot data points in charts, you could have dynamic values that change the diagram based on values chosen from the Combobox.
  • Utilizing data analytics reports that the user accesses, the Combobox could be used to change the data shown dynamically to allow comparisons.
  • When looking to fix data quality issues, use the Combobox to select values for a date that needs to be fixed, apply the fixes on screen, and then save back to the database.

Developing a Tkinter GUI and the possibilities it brings

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:

  • Allows the combobox value selected to be retrieved.
  • Validates the entry chosen in the combobox using an if statement.
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.

The next steps

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

R Programming 

how to remove unwanted characters from your data

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Using r programming functions to cleanse data
In our recent post  Python Tutorial: How do I remove unwanted characters  we walked through the concepts behind data cleansing.

We demonstrated several different approaches to data cleansing, and the use of regular expressions was also shown.

Here we look at that approach using rstudio and the following functions:

How to approach data cleansing

In removing unwanted characters, you want to ensure that you have a defined list of what should not appear and will cause you errors. It is also essential to understand the type of data errors that can occur as follows:

  • Data entry errors
  • Data columns have the incorrect format, e.g. Telphone numbers which have non-numerical characters in them
  • Missing data that is required – e.g. null values
  • Data that does not make sense, e.g. date of birth that is beyond the range of what you would typically expect to see
  • Duplicate values for the same piece of data. The problem here is that this can inflate the no of data errors, and not give a true count of the actual errors.

In the below video we utilise r programming code using the above functions, but also use an if statement to check if an unwanted character is in the data set first before proceeding to remove it and return the cleansed data.

Some of the strategies to help counteract data errors could include:

  • Eliminate manual inputs
  • Controls at point of entry for data, e.g. for dates only allow date formats in the field.
  • Reduce duplication of the data across multiple systems, reduces the no of places that data differences can occur.
  • If integrating different systems with the same data into one network, perform a data cleanse beforehand, reduces the work needed afterwards to clean up the problems that brings.