Python
Google Colaboratory
California Ab 2426
Mbox
Anaconda

Data Science Sunday Episode 42, 2024

Oyesina Oyerinde Anuoluwapo

9 min readOct 27, 2024

Statistics accounts for uncertainties, putting a figure to uncertainties
— Peter Okwukogu

Thanks to Everyone at Colab kaduna Data Science Sundays, and most especially the convener Sanusi, and the facilitator Peter Okwukogu.

It was a moment where we had Data scientists from across the globe, from Africa, Asia, South America etc. all in one call.
This was the 42nd meeting for individuals interested in learning more about Python from scratch, grasping key Data Science, and Data Analysis Concepts.

We engaged in Hands-on Coding Exercises, Participated in Q&A Sessions, and Networked with like-minded individuals.

We were taken through Jupyter Notebook.

We went through Python Docs and Glossary.

We were given links to resources on Data Science, Statistics, Anaconda, and Python Basics on YouTube, and Links to previous episodes.

This was the first in the series with a practical Google Colaboratory.

We started talking about California law AB 2426, “software as rent”.
The law, AB 2426, essentially expands upon existing laws against false advertising by restricting the use of any terms “which a reasonable person would understand to confer an unrestricted ownership.” To ensure people understand that digital ownership isn’t really ownership, sellers will need to either receive “an affirmative acknowledgement from the purchaser” at the time of the transaction or — and this is obviously the more practical approach — put a warning, “in a manner that clearly calls attention to the language,” on relevant products.

This brought about the question
“Are you developing a solution or a time-bound license?”

The new California law, AB 2426, will require any company offering to California consumers online-only digital goods — including a broad category of online media and content, such as games, video, audio, digital books and other digital assets (potentially including NFTs and other blockchain-based digital assets) — to disclose conspicuously that the consumer is purchasing a license of limited duration, and to separately provide the terms of that license. The law takes effect on January 1, 2025, and a violation constitutes a misdemeanour that could give rise to civil penalties. Before this date, covered companies may need to update their licenses, as well as the purchase flow for digital content, to provide the requisite notice and obtain customer acknowledgements at the point of sale.

Ray Dalio in the book “Principle of changing world Order “… looks back not to 10 years but to 60 -100 years.

To build you have to grab the abstraction from the data, Data Science Sunday is hinged on this too.

Excuses even though valid don’t care about your goals like your goals don’t care about your excuses.

The obstacle is the way — Ryan Holiday

The Obstacle Is the Way: The Timeless Art of Turning Trials into Triumph is the third book by author Ryan Holiday. It was published in 2014. It is a book which offers individuals a framework to flip obstacles into opportunities, an approach crafted by Holiday. It was inspired by the philosophy of stoicism.

It takes a lot of research to know where you are and where you want to be in the tech space.

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We looked at how to use the Google Colaboratory for Python.

A question was asked, “What are life-changing roles in the data space?”

Question:

We hypothetically asked, “What are the highest-paying data roles?”

We took a close look at the roles picking the Chief Data Officer (CDO) first, his responsibilities and tasks.

Question:

We searched for who is a Chief data officer (CDO).

The result from “Indeed website” was written by the Indeed Editorial Team

We looked at what is Data Analytics:

Data analytics is the process of examining data to discover patterns, trends, and correlations, and to use that information to make decisions.
It gave us a picture of its definition, purpose, benefits, applications, techniques, and types.

Question:

What kind of problems do data analystics solve?
One of them is Logistics

Question:

What is logistics?

Answer

The overall process of managing how resources are acquired, stored, and transported to a final destination.

What Is Logistics in Business?

In business, logistics is the process of transporting and storing raw materials, finished goods, inventory, and other resources. Logistics in a business is typically made up of many components, including customer service, demand forecasting, warehousing, material handling, inventory control, order processing, and transportation.

Why Is Logistics Important?

Logistics is critical to a company’s bottom line. It enables the movement of materials or goods, the satisfaction of contracts, and the fulfilment of services.
Effective logistics management ensures smooth movement along the supply chain and can provide a competitive advantage.

What Jobs Are Available in the Logistics Industry?

Email Marketing Analysis

On Google Colab Python we did an Email Marketing Analysis using a Google Takeout Mbox file.

import pandas as pd
import numpy as np
import matplotlib-pyplot as plt
import seaborn as sns
import mailbox

# Put the mbox file into a variable
mboxfile = mailbox. mbox ('mbox.mbox')

# Create a mailbox object
mbox = mailbox. mbox (mboxfile)

#List of available keys in the mbox object

# Import the CSV package
import csv

#Create a CSV file with the required attribute

# Create a CSV file with the required attribute
with open( mailbox.csv',
'w') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(I'subject', "from', 'date', 'to', "label', "thread'1) for message in mbox:
writer.writerow[messagg['subject'1, messagel'from 1, message!'date'], messagel'to'1, message|'X-Gmail-Labels'], message

#Create a CSV file with the required attribute
with open 'mailbox! csv', 'w') as csvfile:
writer = csv.writer (csvfile)
writer.writerow(['subject', "from', 'date', 'to', 'label', "thread']) for message in mbox: writer.writerow( [message I'subject'], message['from'], message['date'], message['to'], message ['X-Gmail-Labels'], message ['X-GM-THRID']]))

# Load the CSV file
gmail_data = pd. read_csv('/content/mailbox.csv')

# A summary info
gmail_data. info()

#Look at the date column
gmail_data ['date'].head ()

# Convert the date feature to a DateTime format
gmail_data['date'] = gmail_data['date'].apply(lambda x: pd. to_datetime(x, errors='coerce', utc=True))

# A summary info
gmail_data. info()

# Check for missing values in the date column
gmail_datal 'date'l.isnull().sum()

# Read the first 5 rows
gmail_data. head ()

#Import regular expression
import re

# Function to refactor the "from column
def extract_email (string):
email = re. findall(r'<(.+?)>', string)
if not email:
email = list(filter(lambda y: '@' in y, string.split()))
return email[0] if email else np.nan

# Apply function to the 'from' column
gmail_datal'from'1 = gmail_datal'from ].apply(Lambda x: extract_email(x))

gmail_data.head()

# I must apply a filter to the label column to extract inbox and sent
myemail = 'peter.okwukogu@gmail.com'
gmail_datal'label'] = gmail_data['from'].apply]lambda x: 'inbox' if x == myemail else 'sent'

My takeaway from this meeting :

- You can make use of Google Collaboratory for Python coding
- Google collaboratory now has AI embedded to make smart suggestions for programmers.
- You can do faster coding by using some of the suggested codes by the AI

You can be part of the next episode, which is episode 43, The registration link to the meeting. 📺 is below ⬇️

https://forms.gle/kSc6nqoQGiNNsRN18