Python: Webscraping using BeautifulSoup and Requests

I covered an introduction to webscraping with Requests in an earlier post. You can check it out here: Requests

As a quick refresher, the requests module allows you call on a website through Python and retrieve the HTML behind the website. In this lesson we are going to add on to this functionality by adding the module BeautifulSoup.


BeautifulSoup provides an useful HTML parser that makes it much easier to work with the HTML results from Requests. Let’s start by importing our libraries we will need for this lesson

Next we are going to use requests to call on my website.
We will then pass the HTML code to BeautifulSoup

The syntax is BeautifulSoup(HTML, ‘html.parser’)

The HTML I am sending to BeautifulSoup comes from my request.get() call. In the last lesson, I used r.text to print out the HTML to view, here I am passing r.content to BeautifulSoup and printing out the results.

Note I am also using the soup.prettify() command to ensure my printout is easier to read for humans

BeautifulSoup makes parsing the HTML code easier. Below I am asking to see soup.title – this returns the HTML code with the “title” markup.

To take it even another step, we can add soup.title.string to just get the string without the markup tags

soup.get_text() returns all the text in the HTML code without the markups or other code

In HTML ‘a’ and ‘href’ signify a link

We can use that to build a for loop that reads all the links on the webpage.

Python: Rename Pandas Dataframe Columns

Renaming columns is easy using pandas, first lets build a quick dataframe:

import pandas as pd
x= {'Job Title' :['Manager', 'Tech', 'Supervisor'],
    'Employee' : ['Jill', 'Will', 'Phil']}

df = pd.DataFrame(x)

now to rename, we have a few options

df.rename(columns={'oldName1': 'newName1', 'oldName2': 'newName2'}, inplace=True)
#or you can move it to a new dataframe if you want to keep the original intact
df1 = df.rename(columns={'oldName1': 'newName1', 'oldName2': 'newName2'})
#note I left off the inplace=True argument on the second since I didn't want to 
#overwrite the original

Here are a few other ways to do it, each will give you the same results

df2 = df.rename({'Job Title': 'Job_title', 'Employee': 'Emp'}, axis=1)  
df3 = df.rename({'Job Title': 'Job_title', 'Employee': 'Emp'}, axis='columns')
df4 = df.rename(columns={'Job Title': 'Job_title', 'Employee': 'Emp'})   

More Python tips click here:

Python: Convert Datetime to Date using Pandas

To convert a datetime in a pandas dataframe to date use the function:

df['column'] = pd.to_datetime(df['column'])

To demonstrate, first let’s build a dataframe

import pandas as pd
df = pd.DataFrame({'Job_Start': ['Demolition','Construction', 'Cleanup'],
                   'time': ['2022-05-20 08:07:22', '2022-05-27 07:34:01', 
                   '2022-06-01 09:12:11']})

Now lets convert the “time” column to date instead of datetime

df['time'] = pd.to_datetime(df['time'])

Python Web Scraping: Using Selenium to automate web

This is follow up to how to connect to Chrome using Selenium. If you do not know how to get to a website on Chrome using Selenium, go here

To refresh. here is the code we used to open up a web page (in this case Wikipedia’s home page)

If you run this code, you should find yourself on the home page for Wikipedia

Okay, so now lets learn how to interact with page, the first thing I am going to do is to select the English language version of the page. There are a few ways go about this, but one of the easier approaches is to look at the HTML code that creates the page and to use xpaths or titles to find the object you are looking at.

Right click on the link for English and click inspect from the drop down.

If you get a body link first, you might need to right click and hit inspect again

To check if you have the right element, hover your mouse over it, and it will be highlighted on the webpage

Once you have the right element, right click on it, go to copy>Copy Xpath

Chose Xpath, not full Xpath, it makes for easier coding. You XPath should look something like this: //*[@id=”js-link-box-en”]/strong * When you go to try this, your XPath may look different. As websites are constantly updated, many of the Xpaths get updated as well. Go with the one you find when you Inspect the HTML code yourself

Now we are going to use selenium to “Find” the element we want. The code is dr.find_element_by_XPath(‘//*[@id=”js-link-box-en”]/strong’) *Note the use of single quote around the XPath, it is better to use them as many XPaths will contain double quotes

Once you have run that code, Selenium knows what element you are looking at, you can interact with it now. Let’s “click” the link

Note something i did in the code, I added a link= before my find element command. This assigned the element now to a variable. I can now use the “click()” method the variable inherited from the selenium.webdriver object to click on the English link

I could have just done this: dr.find_element_by_xpath(‘//*[@id=”js-link-box-en”]/strong’).click()

But by assigning the variable it is a) cleaner code and b) the link can be reused by my code later. Remember, it is a law of programming that you will always have to go back and fix something you haven’t seen in 6 months, so make the code as clean as possible to make future you less likely to develop a drinking problem due to having to fix poorly written code.

If you run the code above, you will move to the home English page

Lets try one more thing, lets typing a search into the search bar:

Right click > inspect the search bar, then right click>copy>copy xpath the selection in the HTML code

Now that you have the XPath, lets use the find_element_by_xpath code and a new command, send_keys() to input characters into the search box

Finally, right click on the magnifying glass>inspect>copy>copy Xpath and let us click on it to finish our search. (remember to hover over to make sure you have the right link)

Now you should find yourself on the Data Science page of Wikipedia

Now remember — the xpaths I have on this page will likely be out of date by the time you try this, so make sure to inspect the elements and get the correct XPaths for this work for you.

Python Web Scraping / Automation: Connecting to Chrome with Selenium

Selenium is a Python package that allows you to control web browsers through Python. In this tutorial (and the following tutorials), we will be connecting to Googles Chrome browser, Selenium does work with other browsers as well.

First you will need to download Selenium, you can use the following commands depending on your Python distribution

c:\> Pip install selenium

c:\> Conda install selenium

If you are on a work computer or dealing with a restrictive VPN, the offline install option may help you: Selenium_Install_Offline

Next you need to download the driver that let’s you manage Chrome through Python.

Start by determining what version of Chrome you have on your computer

Click on the three dots in the right corner of your Chrome browser, select Help> About Google Chrome

Go to to download the file that matches your Chrome version. (note, this is something you will need to do every time Chrome is updated, so get used to it.)

Open up the zipfile you downloaded, you will find a file called chromedriver.exe

Put it somewhere you can find, put in the following code to let Python know where to find it.

from selenium import webdriver
options = webdriver.ChromeOptions()
dr = webdriver.Chrome('C:/Users/larsobe/Desktop/chromedriver.exe',chrome_options=options)

Now to see if this works, use the following line, (you can try another website if you choose)   

Note the message Chrome is being controlled by automated test software.

You are now running a web browser via Python.

Python: Simulate Blockchain Mining

In my earlier tutorial, I demonstrated how to use the Python library hashlib to create a sha256 hash function. Now, using Python, I am going to demonstrate the principle of blockchain mining. Again using BitCoin as my model, I will be trying to find a nonce value that will result in a hash value below a predetermined target.

We will start by simply enumerating an integer through our sha 256 hash function until we find a hash with 4 leading zeros.

I used a while loop, passing the variable “y” through my hashing function each time the loop runs. I then inspect the first 4 digits [:4] of my hash value. If the first four digits equal 0000 then I exit the loop by setting the found variable to 1

(*note, a hash value is a string – hence the need for quotes around ‘0000’)


As you can see in the version above, it took 88445 iterations to find an acceptable hash value

Now, using the basic example of a blockchain I gave in an earlier lesson, let’s simulate mining a block


You’ll see, I am now combining the block number, nonce, data, and previous hash of my simulated block and passing it through my encryption function.  Just like in BitCoin, the only value I change per iteration is the Nonce. I keep passing my block through the hashing function until I find the Nonce that gives me a hash below the target.


Now, let’s lower the target value to 6 leading zeros. This should result in a longer runtime to get your hash


To measure the run time difference, let’s add some time stamps to our code


So, I am using the timestamp function twice. D1 will be our start time, d2 will be our end time, and I am subtracting d1 from d2 to get our elapsed time. In the example below, my elapsed time was 5 secs


Now, let’s bump the target down to 7 leading zeros. Now this brings my elapsed computing time to 20 minutes. That is a considerable commitment of resources. You can see why they call it a “proof of work” now.



Python: Create a Blockchain Hash Function

If you are at all like me, reading about a concept is one thing. Actually practicing it though, that helps me to actually understand it. If you have been reading my blockchain tutorial, or if you came from an outside tutorial, then you have undoubtedly read enough about cryptographic hashes.

Enough reading, let’s make one:

( if you are unfamiliar with crytographic hashes, you can reference my tutorial on them here: Blockchain: Cryptographic Hash )

For this example, I am using the Anaconda Python 3 distribution.

Like most things in Python, creating a hash is as simple as importing a library someone has already created for us. In this case, that library is: hashlib

So our first step is to import hashlib

import hashlib

Now let us take a moment to learn the syntax require to create a cryptographic hash with hashlib. In this example, I am using the SHA 256 hashing algorithm. I am using this because it is the same algorithm used by BitCoin.

Here is the syntax used


To understand the syntax, we are calling the hashlib method sha256(): hashlib.sha256()

Inside the brackets, we are entering the string we want to encode in the hash. Yes it must be a string for this function to work.

Still inside the brackets we use the method .encode() to (surprise, surprise) ENCODE the string as a hash

Finally, I added the method .hexdigest() to have the algorithm return our hash in hexadecimal format. This format will help in understanding future lessons on blockchain mining.

So in the example below, you can see that I assigned the variable x the string ‘doggy’. I then passed x to our hash function. The output can be seen below.


Now a hash can hold much more than just a simple word. Below, I have passed the Gettysburg Address to the hashing function.

(**note the ”’ ”’ triple quotes. Those are used in Python if your string takes up more than one line **)


Now I try passing a number. You will notice I get an error.


To avoid the error, I turn the integer 8 into a string with the str() function


Below I concatenation a string and an integer.


Last I want to show the avalanche effect of the hash function.


By simply changing the first letter from an uppercase T to a lowercase t the hash changes completely. This is a requirement for hashing functions. If the hash did not change dramatically from a small change to the string, it would be easy to reverse engineer the hash. This is known as the avalanche effect.



Python: An Interesting Problem with Pandas

I was writing a little tongue and cheek article for LinkedIn on fraud detection using frequency distributions (you can read the article here: LinkedIn). While this was a non-technical article, I wanted to use some histograms from a real data set, so I uploaded a spread sheet into Python and went to work.

While working with the data I ran into an interesting problem that had me chasing my tail for about 10 minutes before I figured it out. It is a fun little problem involving Series and Dataframes.

As always, you can upload the data set here: FraudCheck1

Upload the data.

import pandas as pd
df = pd.read_excel


The data is pretty simple here. We are concerned with our answer column and the CreatedBy (which is the employee ID).  What I am trying to do is see if the “answer”  (a reading from an electric meter) are really random or if they have been contrived by someone trying to fake the data.

First, I want to get the readings for all the employees, so I used pop() to place the answer column into a separate list.

df1 = df

y = df1.pop("answer")


Then, to make my histogram more pleasant looking, I decided to only use the last digit before the decimal. That way I will have 10 bars (0-9). (Remember, this is solely for making charts for an article. So I was not concerned with any more stringent methods of normalization)

What I am doing below is int(199.7%10). Remember % is the modulus – leaves you with the remainder and int converts your float to an integers. So 199.7 is cut to 199. The 199/10 remainder = 9.

a= []
i = 0 
while i < len(y):
     i += 1


Then I created my histogram.

%matplotlib inline
from matplotlib import pyplot as plt


Now my problem

Now I want graph only the answers from employee 619, so first I filter out all rows but the ones for employee 619.

df2 = df.query('CreatedBy == 619')
y1 =df2.pop("answer")

Then I ran my loop to turn my answers into a single digit.

And I get an error.  Why?


Well the answer lies in the datatypes we are working with. Pandas read_excel function creates a Dataframe.

When you pop a column from a dataframe, you end up with a Series. And remember a series is an indexed listing of values.

Let’s look at our Series. Check out the point my line is pointing to below. Notice how my index jumps from 31 to 62. My while loop counts by 1, so after 31, I went looking for y1[32] and it doesn’t exist.


Using .tolist() converts our Series to a list and now our while loop works.


And now we can build another histogram.


The Code

import pandas as pd
df = pd.read_excel

df1 = df
y =df1.pop("answer")

a= []
i = 0 
while i < len(y):
   i += 1

%matplotlib inline
from matplotlib import pyplot as plta1 = []
i = 0
while i < len(y2):
 i = i+1


df2 = df.query('CreatedBy == 619')
y1 =df2.pop("answer")

y2= y1.tolist()

a1 = []
i = 0
while i < len(y2):
    i = i+1



Python: Naive Bayes’

Naive Bayes’ is a supervised machine learning classification algorithm based off of Bayes’ Theorem. If you don’t remember Bayes’ Theorem, here it is:


Seriously though, if you need a refresher, I have a lesson on it here: Bayes’ Theorem

The naive part comes from the idea that the probability of each column is computed alone. They are “naive” to what the other columns contain.

You can download the data file here: logi2

Import the Data

import pandas as pd
df = pd.read_excel("C:\Users\Benjamin\Documents\logi2.xlsx")


Let’s look at the data. We have 3 columns – Score, ExtraCir, Accepted. These represent:

  • Score – Student Test Score
  • ExtraCir – Was Student in an Extra Circular Activity
  • Accepted – Was the Student Accepted

Now the Accepted column is our result column – or the column we are trying to predict. Having a result in your data set makes this a supervised machine learning algorithm.

Split the Data

Next split the data into input(score and extracir) and results (accepted).

y = df.pop('Accepted')
X = df




Fit Naive Bayes

Lucky for us, scikitlearn has a bit in Naive Bayes algorithm – (MultinomialNB)

Import MultinomialNB and fit our split columns to it (X,y)

from sklearn.naive_bayes import MultinomialNB
classifier = MultinomialNB(),y)


Run the some predictions

Let’s run the predictions below. The results show 1 (Accepted) 0 (Not Accepted)

#--score of 1200, ExtraCir = 1

#--score of 1000, ExtraCir = 0


The Code

import pandas as pd
df = pd.read_excel("C:\Users\Benjamin\Documents\logi2.xlsx")

y = df.pop('Accepted')
X = df


from sklearn.naive_bayes import MultinomialNB
classifier = MultinomialNB(),y)

#--score of 1200, ExtraCir = 1

#--score of 1000, ExtraCir = 0


Python: K Means Cluster

K Means Cluster will be our introduction to Unsupervised Machine Learning. What is Unsupervised Machine Learning exactly? Well, the simplest explanation I can offer is that unlike supervised where our data set contains a result, unsupervised does not.

Think of a simple regression where I have the square footage and selling prices (result) of 100 houses. Taking that data, I can easily create a prediction model that will predict the selling price of a house based off of square footage. – This is supervised machine learning

Now, take a data set containing 100 houses with the following data: square footage, house style, garage/no garage, but no selling price. We can’t create a prediction model since we have no knowledge of prices, but we can group the houses together based on commonalities. These groupings (clusters) can be used to gain knowledge of your data set.

I think seeing it in action will help.

If you want to play along, download the data set here: KMeans1

The data set contains a 1 year repair history of 197 Ultrasound medical devices.

Data dictionary (ID Tag – asset number assigned device, Model – model name of device, WO Count – count of repair work orders, AVG Labor – average labor minutes per repair, Labor Cost – average labor cost per repair, No Problem-  count of repairs where no problem was found, Avg Cost -average cost of parts, Travel – average travel hours per repair, Travel Cost – average travel cost per repair, Department – department that owns the ultrasound device)


We want to see what kind of information we can extract from this data.

To do so, we are going to use K Means Clustering.

How does K Means Clustering work? Each row in the table is converted to a vector. Imagine the vectors now graphed in N-dimension space. Next pick the number of clusters you want to create. For each cluster, you will place a  point(a centroid) in space and the vectors are grouped based on their proximity to their nearest centroid.

The calculation to tell proximity is made using geometric means (not arithmetic)- hence the name K-Means Cluster

(each dot below is a row in your table, the colors represent a cluster)


Let’s do it in Python

Import the data.

import pandas as pd

df = pd.read_excel("C:\Users\Benjamin\Documents\KMeans1.xlsx")


Now, we are going to drop a few columns: ID Tag – is a random number, has no value in clustering. Then Model and Department,as they are text and while there are ways to work with the text, it is more complicated so for now, we are just going to drop the columns

df1 = df.drop(["ID Tag", "Model", "Department"], axis = 1)


Now lets import KMeans from sklearn.cluster

We then initialize KMeans (n_clusters= 4 -no of clusters you want, init=’k-means++’ -sets how the centroids are places. k-means++ is one of the faster methods of centroid placement, n_init=10 – number times the algorithm with run placing new centroids each iteration)

from sklearn.cluster import KMeans
km = KMeans(n_clusters=4, init='k-means++', n_init=10)


Choosing number of clusters is a bit of an art. Play with it a bit and see how different values play out for you.

Now fit the model


Now, export the cluster identifiers to a list. Notice my values are 0 -3. One value for each cluster.

x = km.fit_predict(df1)


Create a new column on the original dataframe called Cluster and place your results (x) in that column

df["Cluster"]= x


Sort your dataframe by cluster

df1 = df.sort(['Cluster'])


Now as you start to examine the data in each cluster, you show start to see patterns emerge.

Below is an example of the patterns I found in the clusters.


Now remember, this is just an INTRODUCTION to unsupervised learning. We will learn more tricks to help you discover the patterns as we move forward.