How to Pass Python Variables to Javascript

Estimated reading time: 4 minutes

In our recent blog posting How to Pass a Javascript Variable to Python using JSON, we demonstrated how to easily use AJAX to pass whatever data you wanted and then manipulate it with Python.

In this blog posting, we are going to show how to do this the other way around. The scenario is that you have an application and or website that wants to use data generated through Python, but let Javascript then use it within the application.

As Python can be connected to numerous databases and files ( txt, excel) etc, this piece of logic is very useful for the programmer looking to integrate both programming languages.

Let’s start looking at the code, and see how this can be achieved.

Step 1 – What Files are generated?

This program uses Python Flask to create a web page, that has a drop-down menu. The two files used to generate this are as follows:

(A) – This is the python file that creates a website and loads a template HTML file as outlined below.

(B) Index.html – This is the template file that loads into the browser and runs all the javascript. The javascript loaded here also loads the python data passed over from

Step 2 – APP.PY code overview

The Python library that enables webpage creation is called Flask, and as can be seen below it has to be imported.

In addition, we need to also import render_template which tells the program to go to the templates folder and load “Index.HTML”

The variable that is been passed to JavaScript is called name, and these are the values that you will see in the web browser when it is loaded.

from flask import Flask, render_template

app = Flask(__name__)

def index():
    name = ['Joe','John','Jim','Paul','Niall','Tom']
    return render_template('index.html', name=name)

if __name__ == "__main__":

Step 3 – Index.HTML overview

Here is the template HTML file that runs in the browser. You can add CSS etc to this to make it look nicer and more user friendly.

As you can see it has the usual HTML tags appear as part of a website.

Well look at some of the code further:

In this bit <select id =’select’> </select>, this is the dropdown menu that will appear when Index.html is opened. It will store all the values passed from python. Note that its id is “select”, this will be used later on.

The main parts to focus on next is between <script></script>. This is what reads in the python data and populates it to the dropdown menu.

In step 2 we mentioned that there was a variable called “name”, with values to be passed over.

This is acheived on this line:

var select = document.getElementById(“select”), test = {{ name | tojson }};

Notice that name appears here, and this is referencing back to the exact same value that was discussed in step 2.

For the rest of the lines, I have explained with comments what each does.

<!DOCTYPE html>
<html lang="en">
    <meta charset="UTF-8">
    <title>Pass Python variable to Javascript</title>

<select id ='selectvalue'>
    //name = ['Joe','John','Jim','Paul','Niall','Tom']

    var selectvalue = document.getElementById("selectvalue"), test = {{ name | tojson }};
    //The increment operator (++) increments (adds one to) its operand and returns a value.
    for(var i = 0; i < test.length; i++) // This line checks for the length of the data you feeding in i.e the no of items
var selection = document.createElement("OPTION"), // This line creates a variable to store the different values fed in from the JSON object "TEST"
txt = document.createTextNode(test[i]); // This just reads each value from the test JSON variable above
selection.appendChild(txt); // This line appends each value as it is read.
selection.setAttribute("value",test[i]); // This line sets each value read in as a value for the drop down
selectvalue.insertBefore(selection,selectvalue.lastChild); //This reads eah value into the dropdown based on the order in the "TEST" above.

Step 4 – What the output looks like!

From step 2, these are values we asked to be used in Javascript to populate a dropdown:

name = [‘Joe’,’John’,’Jim’,’Paul’,’Niall’,’Tom’]

Python variable passed to Javascript

How to Add Formulas to Excel using Python

Estimated reading time: 3 minutes

You may be working on automating some exports to Excel using Python to compare files or just pure and simple adding formulas to an Excel file before you open it up.

Here we explain adding formulas to your Excel output using Numpy or adding the calculations to specific cells in the output.

Adding formulas to specific cells

First of all, let’s look at the normal spreadsheet with some calculations, these have the formulas typed in. The ultimate objective is to have the Python code do this for us, one less step.

As can be seen, the cells have the formulas in them, but this would be a very time-consuming process if you had to do it multiple times, in multiple spreadsheets.

To get around this we can write the Python logic as follows:

  1. Create three lists and three dataframes as follows.
datasetA_list = np.array([1,2,3,4,5,6,7,8,9,10])

datasetB_list = np.array([9,8,65,43,3,21,3,2,1,7])

dataset_list = ('sum','average','median','standard deviation','count','correlation')

datasetA = pd.DataFrame(datasetA_list,columns=['ValueA'])
datasetB = pd.DataFrame(datasetB_list,columns=['ValueB'])
dataset_list_calcs = pd.DataFrame(dataset_list, columns=['Calcs'])

2. Next create a path to where you are going to store the data as follows:

path = 'output.xlsx'

3. In this next step create the workbook and location where the data will be stored. This will load the headings created in step 1 to a particular location on the spreadsheet.

workbook = pd.ExcelWriter(path, engine='openpyxl') = load_workbook(path)
workbook.sheets = dict((ws.title,ws) for ws in

datasetA.to_excel(workbook,sheet_name="Sheet1", startrow=1,index=False, header=True,)
datasetB.to_excel(workbook,sheet_name="Sheet1", startrow=1, startcol=2,index=False, header=True)
dataset_list_calcs.to_excel(workbook,sheet_name="Sheet1", startrow=1, startcol=4,index=False, header=True)

4. Load the formulas into cells besides their relevant headings. This should line post these formulas beside the relevant heading created in step 1.

###Creating calculations for datasetA

sheet = workbook.sheets['Sheet1']
sheet['E2'] = 'CalcsA'
sheet['F3'] = '=SUM(A3:A12)'
sheet['F4'] = '=AVERAGE(A3:A12)'
sheet['F5'] = '=MEDIAN(A3:A12)'
sheet['F6'] = '=STDEV(A3:A12)'
sheet['F7'] = '=COUNT(A3:A12)'
sheet['F8'] = '=CORREL(A3:A12,C3:C12)'

###Creating calculations for datasetB

sheet = workbook.sheets['Sheet1']
sheet['H2'] = 'CalcsB'
sheet['H3'] = '=SUM(C3:C12)'
sheet['H4'] = '=AVERAGE(C3:C12)'
sheet['H5'] = '=MEDIAN(C3:C12)'
sheet['H6'] = '=STDEV(C3:C12)'
sheet['H7'] = '=COUNT(C3:C12)'
sheet['H8'] = '=CORREL(A3:A12,C3:C12)'

Use Numpy to create the calculations

a. Create the calculations that you will populate into the spreadsheet, using Numpy

a = np.sum(datasetA_list)
b = np.average(datasetA_list)
c = np.median(datasetA_list)
d = np.std(datasetA_list,ddof=1) ## Setting DDOF = 0 will give a differnt figure, this corrects to match the output.
f = np.count_nonzero(datasetA_list)
g = np.corrcoef(datasetA_list,datasetB_list)

b. Create the headings and assign them to particular cells

sheet['E14'] = 'Numpy Calculations'
sheet['E15'] = 'Sum'
sheet['E16'] = 'Average'
sheet['E17'] = 'Median'
sheet['E18'] = 'Standard Deviation'
sheet['E19'] = 'Count'
sheet['E20'] = 'Correlation'

c. Assign the variables in step a to a set of cells

sheet['F15'] = a
sheet['F16'] = b
sheet['F17'] = c
sheet['F18'] = d
sheet['F19'] = f
sheet['F20'] = str(g)

d. Save the workbook and close it – This step is important, and always include.

And the final output looks like…

TypeError: the first argument must be callable

Estimated reading time: 3 minutes

So you may be using Python Classes and have encountered the problem TypeError: First Argument Must be Callable. So what does the problem mean and how can you fix it?

In this article, we are looking to explain how it may occur and the easy fix you can apply to stop the problem in the future.

Let us understand calling Functions/Methods first

Normally in a computer python program, you will have a need to use a function/method as it has the functionality that will save you time, and can continuously be reused.

A classic example is print(“Hello”) that handles all the required logic to show this on a screen. Its output is quickly viewable, and there is not any need to understand what is going on in the background.

Here in this example we have run the function on its own, as a result it does not need to pointed to somehwhere else to run the logic it contains.

For the error we are looking to resolve, this is part of the problem. A function/method can be run on its own, or from within a Class.

The defining differene is that on its own it needs the parenthensis i.e. (), but if if you calling it from within a class, then it has to be handled differently.

Seeing the Type Error First Argument must be callable and fixing it

In the below we have a block of code, that produces the error we are trying to fix.

Following on from what was discussed above, the offending line is highlighted.

In particular the problem lies with printprogress() . This is been called , but in actual fact the problem is that when you have the logic written like this, it gives the error this blog post was setup for.

In essence it is trying to run the program from that exact point, which python does not allow.

Removing the parenthesis then allows the program to go and find the module it is referencing and then run the logic contained within that.

import schedule
import time

class scheduleprint():

    def printprogress(self):
        print("Start of Processing")
        print("Processing Complete")

    def schedule_a_print_job(self, type="Secs", interval=5):

        if type == "Secs": # Fed from the function paramaters
            schedule.every(interval)> The problem is here, remove the () after printprogress.
            # Including the parentheses  after printprogess will throw an error as you cant run that method directly from there you can only call it.

        if type == "Mins": # Fed from the function paramaters
            # Including the parentheses  after printprogess will throw an error as you cant run that method directly from there you can only call it.

        while True:
            time.sleep(1) # The number of seconds the Python program should pause execution.

run = scheduleprint() # initiating an instance of the class "scheduleprint"
run.schedule_a_print_job() # running the function contained within the class.

In summary to help troubleshoot this problem:

(A) Check your code to see where it is calling a module within a class.

(B) Next make sure that in that call no parenthesis are present, otherwise it wont be able to find the module.

how to build a machine learning model

Estimated reading time: 1 minute

In this video on how to build a machine learning model, the first of eight, we will take you through the initial questions you should as before looking to build your model.

This is the first video of eight that will explain what is machine learning, the benefits of it and some examples of companies doing it at the moment.

This series of machine learning model tutorials, will look to explain the concepts and steps involved to help get a better understanding of how you would go about building the model to better understand your data and customers.

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

❤Share this video with a YouTuber friend: What is machine learning?




What are the reserved keywords in Python

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

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 Tutorial: How to create charts in Excel

Estimated reading time: 4 minutes

Taking it further with a python excel chart.

We have created some video content here using Python with Excel that illustrates the different ways you can leverage Python to

  • Data cleanse
  • Find unwanted characters
  • And see if the file is empty before import!

What this video is giving as output

Here we are looking to introduce charts in Excel, and how how to use Python to easily work with your data and export to an excel sheet.

Below is the final output of our two charts, this is for illustrative purposes, and is taken from the Irish Government’s website as at 1st May 2020, importing the cell ranges associated with them.

A barchart of covid cases bullt in Python
a line chart of covid cases built in Python

How we went about it

Step 1 – Importing the data

import pandas as pd
import openpyxl

importfile = openpyxl.load_workbook("C://dataanalyticsireland/Python Code/youtube/codeused/files/Covid19CountyStatisticsHPSCIreland01052020.xlsx")

#Telling the program to go to each individual sheet
Dublin = importfile["Dublin"]
Cork = importfile["Cork"]
Limerick = importfile["Limerick"]
Galway = importfile["Galway"]

#Telling the program for each sheet , create a dataframe.
dfDublin = pd.DataFrame(Dublin.values)
dfCork = pd.DataFrame(Cork.values)
dfLimerick = pd.DataFrame(Limerick.values)
dfGalway = pd.DataFrame(Galway.values)

Step 2 – Splitting the different sheets out into different data frames

### Showing to break down here, further on I cocatenate the dataframes  ###

#This brings in all the values from the sheet into a variable so they can be read.
datadublin = Dublin.values
datacork = Cork.values
datalimerick = Limerick.values
datagalway = Galway.values
#This tells the program that the headers are on row 0.
columnsdublin = next(datadublin)[0:]
columnscork = next(datacork)[0:]
columnslimerick = next(datalimerick)[0:]
columnsgalway = next(datagalway)[0:]
# The imported file has the column headings on row 0, moving them to be the actual column headings instead of numerical values.
dfDublin = pd.DataFrame(datadublin, columns=columnsdublin)
dfCork = pd.DataFrame(datacork, columns=columnscork)
dfLimerick = pd.DataFrame(datalimerick, columns=columnslimerick)
dfGalway = pd.DataFrame(datagalway, columns=columnsgalway)

Step 3 – Exports all three data frames to a separate tab each to the below file

# This starts the process of where to save the output by creating a writer variable
writer = pd.ExcelWriter('C://dataanalyticsireland/Python Code/youtube/codeused/files/Covid19output01052020.xlsx')
# Save the dataframe "Dublin" to Dublin sheet etc...
dfDublin.to_excel(writer, 'Dublin')
dfCork.to_excel(writer, 'Cork')
dfLimerick.to_excel(writer, 'Limerick')
dfGalway.to_excel(writer, 'Galway')
# save the excel file

Step 4 – Merging all the data to allow the charts to be created easily

mergedPDList = [dfDublin,dfCork,dfLimerick,dfGalway]  # Creating a new merged list of the dataframes, easier when doing charts below
mergeddf = pd.concat(mergedPDList) # physical merging of the dataframes

Step 5 – Creating a bar chart

import matplotlib.pyplot as plt
import matplotlib.pyplot as pltline
import seaborn as sns
#from openpyxl.drawing.image import Image
#This makes the seaborn library over ride the matplotlib properites for styling

plt.figure(figsize =(9,6)) = mergeddf['CountyName'],
       height = mergeddf['ConfirmedCovidCases']
plt.xticks(rotation = 45)
plt.title("No of Confirmed Covid Cases in the Republic of Ireland", fontsize=25, fontweight='bold')
plt.ylabel('No of cases')
plt.width = 35
img = plt.savefig('C://dataanalyticsireland/Python Code/youtube/codeused/files/barchart.png',bbox_inches='tight')

Step 6 – Creating a line chart

x = dfDublin['ConfirmedCovidCases']
x1 = dfCork['ConfirmedCovidCases']
x2 = dfLimerick['ConfirmedCovidCases']
x3 = dfGalway['ConfirmedCovidCases']
#Sets the size of the output before it is created
pltline.legend(['Dublin', 'Cork','Limerick','Galway'], fontsize="x-large")
pltline.ylabel('No of cases')
pltline.xlabel('No of days elapsed')
img1 = plt.savefig('C://dataanalyticsireland/Python Code/youtube/codeused/files/linegraph.png')

Step 7 – Exporting charts to the XLSX file

from openpyxl import load_workbook
#from openpyxl.drawing.image import Image
import openpyxl
#This step opens the existing file.
wsnew = load_workbook('C://dataanalyticsireland/Python Code/youtube/codeused/files/Covid19output01052020.xlsx')
#This step creates a new worksheet called Barchart
newsheet = wsnew.create_sheet("Bar Chart",4) #Creates a new sheet in fourth position
newsheet1 = wsnew.create_sheet("Line Graph",5) #Creates a new sheet in fifth position
#Adding the image to Newsheet which is called "Barchart" in the excel file
img = openpyxl.drawing.image.Image('C://dataanalyticsireland/Python Code/youtube/codeused/files/barchart.png')
img1 = openpyxl.drawing.image.Image('C://dataanalyticsireland/Python Code/youtube/codeused/files/linegraph.png')
img.anchor = 'B2' # Tells what cell to put the imahe in
img1.anchor = 'B2' # Tells what cell to put the imahe in
newsheet.add_image(img) # Adds the image
newsheet1.add_image(img1) #Adds the image'C://dataanalyticsireland/Python Code/youtube/codeused/files/Covid19output01052020.xlsx')

In the below video on creating a python excel chart, we have approached this as follows:

  • Created four separate data frames, they are the four regions that will feed into the creation of the graphs.
  • Separate to this, we merged the four data frames into one, to use with the bar chart.

And to finish off

If you like what this video has explained, please click to see our YouTube channel for more informative videos.

Data Analytics Ireland

How to check if a file is empty

Estimated reading time: 2 minutes

Ever wondered how to go about checking if a file is empty?

A problem you may come across in Data Analytics that when you are importing a file as outlined in this post Python – How to import data from files is how do we know if the files are empty or not before import?

In the world of data, there are several reasons to check :

  • You have an automated process relying on the import not been empty.
  •  A process that preceded you receiving the file did not work.
  • The amount of time and effort to investigate the problem causes undue work to fix.

The nuts and bolts of it all

Here we have a video that looks at different scenarios on how to bring in files. The following functionality appears in this video:

  • os.path.getsize – This looks to get the file size attached to the file. * Please see note below
  • pd.read_csv
  • pd.read_excel

The add on bits

*One note about os.path.getsize, which we found:

  • It only works in the logic provided if the size of the file is zero KB.
  •  CSV and XLSX files even though they where created empty, when saved had a file size greater than zero.
  •  TXT files, when created empty and saved, had a file size of zero.


Hope this video helps explain further how empty files can be checked in python before they are processed.


Data Analytics Ireland

hide a column from a data frame

Estimated reading time: 2 minutes

They say there is nowhere to hide, we disagree!
As an addition to How to add a column to a dataframe would you like to learn to go and hide it?! This video has several steps in it; following each one will give you a good introduction.

To start why you would like to hide a column?

  • You may not want to reveal its output as it is sensitive information.
  • The data in the column is not in the correct format, you will want to repurpose it, so it is the way you want it.
  •  The column could be a calculated column. Hence it serves as an intermediary step before your data frame is output.

Finding the best way to hide unwanted data:

In this video, we introduce several concepts to help not show a column:

  • Specify the actual columns you want to include in the data frame, by default doing this you are excluding the column or columns you don’t want to see.
  •  We use drop, to explicitly tell the data frame not to show a particular column.
  •  Also, we display a scenario whereby you have a calculated column but do not want to show its output, based on one of the reasons outlined above.
  • Finally, the index of the column can appear in the output, so we have shown through set_index how to hide it from what is displayed.

This latest in the Python Dataframe series looks to build on the knowledge in the previous examples. We hope as you learn python online, it will increase your programming skills.

Thanks for watching and don’t forget to like and share through our social media buttons to the right of this page.

Data Analytics Ireland

Python Tutorial: How to sort lists

Estimated reading time: 2 minutes

Following on from our post on how to use Python lists have you ever wondered how to sort lists for your Python project?

Our latest video on lists will go through some of the techniques available so that you can get an idea of how to structure your data and sort.

Getting to understand how to implement

In this latest video we will look at:

  • sort() method
  • sorted() function
  • sorting a list through a function


Adding in those extra bits to help make the process smoother

Have you thought about sorting ascending/descending?

  • There is also a discussion on this topic as well, and while an index is available for the list, which you may feel does not merit sorting, there could be other logical reasons to implement sorting.
  • Leaving out the reverse = True/False in the sorted method can have an impact, though if you require it left out of the list you have created, automatic ascending will be the default.

On this channel, we have discussed a number of different ways to manage your data. In thinking about sorting a list, why would you want to do this?

Some common reasons are:

  • To visually see if there are duplicates, either on the screen or printed out.
  • If other objects are dependant on the list, say a combo box, then having duplicates visible can help to reduce the size of their contents.
  • Iteration – If you are looking to iterate over a list, it will be quicker if it is sorted.

If you want to learn about lists, using them, and how how they can be iterated over, why not visit Data Analytics Ireland YouTube channel, there are lots of videos there that will help explain the concepts discussed here further.

To get some more links on this topic click here python sort method, it is a blog posting from our website that has some useful links and explanations for you.