Python Tutorial

A way to learn python through examples that show how better improve your code and python output.

Python Python Tutorial Python working with excel Python working with files XML 

How to Create an XML file from Excel using Python

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Estimated reading time: 3 minutes

Are you working on a data analytics project where you need to feed your data to a location that is able to process an XML file?

The ability to get your data into a structured format like XML has many benefits:

(A) You can transfer the data to a web service for processing.

(B) Multiple different formats of your raw data can be standardised, enabling quick conversion and processing.

(C) XML files can be read by multiple different programs, as long as you deliver them in the correct format.

(D) The receiver of data can easily read the XML file and store it on their database.

The ability to use this method to read and transfer data is a very powerful way to help a data analyst process large data sets.

In fact, if you are using cloud-based applications to analyse the information you are storing, this will quickly enable you to deliver the data.

What packages do I need in Python?

The first step is to import the following:

import pandas as pd
from lxml import etree as et

Next we want to read in the source data

In this instance, we are reading an excel file

raw_data = pd.read_excel(r'Link to where your data is stored including the full file name')

Now we want to start building the XML structure

The FIRST STEP is to define the root

root = et.Element('root')

The root is the parent of all the data items (tags) contained in the XML file and is needed as part of the structure

The SECOND STEP is to define the tag names that will store each row of the source data

for row in raw_data.iterrows(): ==> This is a loop that takes runs through each record and populates for each tag.
    root_tags = et.SubElement(root, 'ExportData') #=== > Root name
# These are the tag names for each row (SECTION 1)
    Column_heading_1 = et.SubElement(root_tags, 'Name')
    Column_heading_2 = et.SubElement(root_tags, 'Area')
    Column_heading_3 = et.SubElement(root_tags, 'NoPurchases')
    Column_heading_4 = et.SubElement(root_tags, 'Active')

###These are the values that will be populated for each row above
# The values inside the [] are the raw file column headings.(SECTION 2)
    Column_heading_1.text = str(row[1]['Name'])
    Column_heading_2.text = str(row[1]['Area'])
    Column_heading_3.text = str(row[1]['No Purchases'])
    Column_heading_4.text = str(row[1]['Active'])

The raw file looks like this:

NameAreaNo PurchasesActive
JohnDublin2Y
MaryGalway3N
JoeLimerick4N
JimmyKilkenny55Y
JenniferBelfast6N
SusanWaterford3Y
JakeCork1Y
BobbyDundalk11N
SarahSligo9N
CianEnnis8Y
Raw file data that will be imported into the XML file

The THIRD STEP is to create the XML file and populate it with the data from the source file

# This Section outputs the data to an xml file
# Unless you tell it otherwise it saves it to the same folder as the script.
tree = et.ElementTree(root) ==> The variable tree is to hold all the values of "root"
et.indent(tree, space="\t", level=0) ===> This just formats in a way that the XML is readable
tree.write('output.xml', encoding="utf-8") ==> The data is saved to an XML file

The XML output should look like the below

<root>
	<ExportData>
		<Name>John</Name>
		<Area>Dublin</Area>
		<NoPurchases>2</NoPurchases>
		<Active>Y</Active>
	</ExportData>
	<ExportData>
		<Name>Mary</Name>
		<Area>Galway</Area>
		<NoPurchases>3</NoPurchases>
		<Active>N</Active>
	</ExportData>
	<ExportData>
		<Name>Joe</Name>
		<Area>Limerick</Area>
		<NoPurchases>4</NoPurchases>
		<Active>N</Active>
	</ExportData>
	<ExportData>
		<Name>Jimmy</Name>
		<Area>Kilkenny</Area>
		<NoPurchases>55</NoPurchases>
		<Active>Y</Active>
	</ExportData>
	<ExportData>
		<Name>Jennifer</Name>
		<Area>Belfast</Area>
		<NoPurchases>6</NoPurchases>
		<Active>N</Active>
	</ExportData>
	<ExportData>
		<Name>Susan</Name>
		<Area>Waterford</Area>
		<NoPurchases>3</NoPurchases>
		<Active>Y</Active>
	</ExportData>
	<ExportData>
		<Name>Jake</Name>
		<Area>Cork</Area>
		<NoPurchases>1</NoPurchases>
		<Active>Y</Active>
	</ExportData>
	<ExportData>
		<Name>Bobby</Name>
		<Area>Dundalk</Area>
		<NoPurchases>11</NoPurchases>
		<Active>N</Active>
	</ExportData>
	<ExportData>
		<Name>Sarah</Name>
		<Area>Sligo</Area>
		<NoPurchases>9</NoPurchases>
		<Active>N</Active>
	</ExportData>
	<ExportData>
		<Name>Cian</Name>
		<Area>Ennis</Area>
		<NoPurchases>8</NoPurchases>
		<Active>Y</Active>
	</ExportData>
</root>

Additional XML data can be added

  1. Add more rows – All you need to do is add onto the source file and save. When you rerun the logic it will read in the extra information.
  2. Add more columns – All you need to do is go to the second step above add in a tag name to SECTION 1. Seperately you will need to add an additional column with data to the source file, and then add that column name to SECTION 2 as well

CSV Python Python Dataframe 

How To Compare CSV Files for Differences

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Estimated reading time: 5 minutes

Often a lot of data analytics projects involve comparisons, some of it could be to look at data quality problems, other times it could be to check that data you are loading has been saved to the database with no errors.

As a result of this, there is a need to quickly check all your data is correct. But rather r than do visual comparisons, wouldn’t it be nice to use Python to quickly resolve this?

Luckily for you in this blog post, we will take you through three ways to quickly get answers, they could be used together or on their own.

Let’s look at the data we want to compare

We have two CSV files, with four columns in them:

The objective here is to compare the two and show the differences in the output.

Import the files to a dataframe.

import pandas as pd
import numpy as np
df1 = pd.read_csv('CSV1.csv')
df2 = pd.read_csv('CSV2.csv')

The above logic is very straightforward, it looks for the files in the same folder as the python script and looks to import the data from the CSV files to the respective data frames.

The purpose of this is that the following steps will use these data frames for comparison.

Method 1 – See if the two data frames are equal

In the output for this, it shows differences through a boolean value in this instance “True” or “False”.


array1 = np.array(df1) ===> Storing the data in an array will allow the equation below to show the differences.
array2 = np.array(df2)

df_CSV_1 = pd.DataFrame(array1, columns=['No','Film','Year','Length (min)'])
df_CSV_2 = pd.DataFrame(array2, columns=['No','Film','Year','Length (min)'])

df_CSV_1.index += 1 ===> This resets the index to start at 1 not 0, helps with the output when trying to understand the differences.
df_CSV_2.index += 1

df_CSV_1.index += 1 ===> This resets the index to start at 1 not 0, helps with the output when trying to understand the differences.
df_CSV_2.index += 1

print(df_CSV_1.eq(df_CSV_2).to_string(index=True)) ===> This shows the differences between the two arrays.

Your output will look like this, and as can be seen on lines 3 and 13 are false, these are the yellow values in the CSV2 file that are different to the CSV1 file values, all other data is the same which is correct.

The obvious advantage of the below is that you can quickly what is different and on what line, but now what values are different, we will explore that in the next methods.

        No  Film   Year  Length (min)
1   True  True   True          True
2   True  True   True          True
3   True  True  False          True
4   True  True   True          True
5   True  True   True          True
6   True  True   True          True
7   True  True   True          True
8   True  True   True          True
9   True  True   True          True
10  True  True   True          True
11  True  True   True          True
12  True  True   True          True
13  True  True  False          True
14  True  True   True          True
15  True  True   True          True
16  True  True   True          True
17  True  True   True          True
18  True  True   True          True
19  True  True   True          True
20  True  True   True          True

Method 2 – Find and print the values only that are different

So in the first approach, we could see there are differences, but not what lines are different between the two files.

In the below code it will again look at the data frames but this time print the values from the CSV1 file that have different values in the CSV2 file.

a = df1[df1.eq(df2).all(axis=1) == False] ===> This compares the data frames, but only returns the rows from DF1 that have a different value in one of the columns on DF2

a.index += 1 ===>This resets the index to start at 1, not 0, which helps with the output when trying to understand the differences. 

print(a.to_string(index=False))

As a result, the output from this as expected is:

No        Film  Year  Length (min)
  3    Parasite  2019           132
 13  Midsommar   2019           148

Method 3 – Show your differences and the value that are different

The final way to look for any differences between CSV files is to use some of the above but show where the difference is.

In the below code, the first line compares the two years between the two sets of data, and then applies a true to the column if they match, otherwise a false.


df1['Year_check_to_DF2'] = np.where(df1['Year'] == df2['Year'], 'True', 'False')
df1.index += 1 #resets the index to start from one.

df2_year = df2['Year'] ===> We create this column to store the DF2 year value.
df2_year = pd.Series(df2_year) #Series is a one-dimensional labeled array capable of holding data of any type.

df1 = df1.assign(df2_year=df2_year.values) = This adds the DF2 year value to the DF1 data frame
print(df1.to_string(index=False))

In this instance, this returns the below output. As can be seen, it allows us to complete a line visual of what is different.

So in summary we have completed a comparison of what is different between files.

There are a number of practical applications of this:

(A) If you are loading data to a database table, the uploaded data can be exported and compared to the original CSV file uploaded.

(B) Data Quality – Export key data to a CSV file and compare.

(C) Data Transformations – Compare two sets of data, and ensure transformations have worked as expected. In this instance, differences are expected, but as long as they are what you programmed for, then the validation has worked.

If you like this blog post, there are others that may interest you:

How to Compare Column Headers in CSV to a List in Python

How to count the no of rows and columns in a CSV file

Python type error 

TypeError: ‘float’ object is not callable

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So you have been presented with the TypeError: ‘float’ object is not callable in Python, but unsure how to fix? Well read on so we can get you a solution.

First things an overview of a Float

A float is any number that can contain a decimal place separating an integer from the fractions to the right of the decimal point.

As an example, in the below code, we have the exact same number. But when a computer comes to read it, it treats them differently, due to the fact that b has a decimal point and a fraction to the right of it.

a = 1
b = 1.0

print (type(a))
print (type(b))

Output:
<class 'int'>
<class 'float'>

So how in a piece of code would this error occur?

In the below code, the error occurs. The reason behind this is that float is an actual function that changes a value to a float data type.

As a result, you cannot assign a variable name as “float” and then call it separately in its code. Python specifically does not allow a programmer to use a function as a variable name.

a= float(10)
b= float(11)
c= float(12)
float = a

d = float(a/b*c)
print(d)

Output:
d = float(a/b*c)
TypeError: 'float' object is not callable

So how is this fixed?

In order to fix this, we rename the variable “float” to something else, and make sure that the new name is not another function name!

As can be seen when we do this, the below output runs as expected with no errors:

a= float(10)
b= float(11)
c= float(12)
float_value = a   ===> This line was corrected from above from "float" to "float_value"

d = float(a/b*c)
print(d)

Output: 
10.909090909090908

In summary the crux of this problem is that when creating a variable, it cannot be a function name.

You can see similar posts here on TypeErrors.

Have you seen these posts?

TypeError: ‘float’ object is not callable

TypeError: ‘int’ object is not callable

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 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 Python Tutorial 

Python tutorial: Pandas groupby ( Video 1)

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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

 

 

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.

When you are importing tkinter, some of the functionality that can be used include:

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

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.

Using the tkinter widget could help achieve 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.

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.

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