Statistics and Data Visualisation with Python – First Chapter Done

As you know I am writing a new book. This time it is a book about statistics and data visualisation using Python as the main language to analyse data. It was thinking that I was a bit behind with my plan for the book, but I managed to surprise myself by being bang on time completing the first chapter.

This is the introductory chapter where we cover some background on the importance of statistics, a bit of history and the personalities behind some concepts widely used in stats and data visualisation. We then cover some background in formulating questions to be answered with data and how to communicate our results.

On to the next chapter! 🐍📊📖

Advanced Data Science and Analytics with Python – Discount

I am reaching out as volume 2 of my data science book will be out for publication in May and my publisher has made it possible for me to offer 20% off. You can order the book here.

This follows from “Data Science and Analytics with Python” and both books are intended for practitioners in data science and data analytics in both academic and business environments.

The new book aims to present the reader with concepts in data science and analytics that were deemed to be more advanced or simply out of scope in the author’s first book, and are used in data analytics using tools developed in Python such as SciKit Learn, Pandas, Numpy, etc. The use of Python is of particular benefit given its recent popularity in the data science community. The book is therefore a reference to be used by seasoned programmers and newcomers alike and the key benefit is the practical approach presented throughout the book

More information about the first book can be found here.

Advanced Data Science and Analytics with Python – Proofreading

Super excited to have received the proofread version of Advanced Data Science and Analytics with Python. They all seem to be very straightforward corrections: a few missing commas, some italics here and there and capitalisation bits and bobs.

I hope to be able to finish the corrections before my deadline for March 25th, and then enter the last phase before publication in May 2020.

Cover Draft for “Advanced Data Science and Analytics with Python”

I have received the latest information about the status of my book “Advanced Data Science and Analytics with Python”. This time reviewing the latest cover drafts for the book.

This is currently my favourite one.

Awaiting the proofreading comments, and I hope to update you about that soon.

Pandas 1.0 is out

If you are interested in #DataScience you surely have heard of #pandas and you would be pleased to hear that version 1.0 finally out. With better integration with bumpy and improvements with numba among others. Take a look!
— Read on

Advanced Data Science and Analytics with Python – Submitted!

There you go, the first checkpoint is completed: I have officially submitted the completed version of “Advanced Data Science and Analytics with Python”.

The book has been some time in the making (and in the thinking…). It is a follow up from my previous book, imaginatively called “Data Science and Analytics with Python” . The book covers aspects that were necessarily left out in the previous volume; however, the readers in mind are still technical people interested in moving into the data science and analytics world. I have tried to keep the same tone as in the first book, peppering the pages with some bits and bobs of popular culture, science fiction and indeed Monty Python puns. 

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow up from the topics discuss in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The development is also supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications.

The book can be read independently form the previous volume and each of the chapters in this volume is sufficiently independent from the others proving flexibiity for the reader. Each of the topics adressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book

Time series analysis, natural language processing, topic modelling, social network analysis, neural networds and deep learning are comprehensively covrered in the book. The book discusses the need to develop data products and tackles the subject of bringing models to their intended audiences. In this case literally to the users fingertips in the form of an iPhone app.

While the book is still in the oven, you may want to take a look at the first volume. You can get your copy here:

Furthermore you can see my Author profile here.

Adding new conda environment kernel to Jupyter and nteract

I know there are a ton of posts out there covering this very topic. I am writing this post more for my out benefit, so that I have a reliable place to check the commands I need to add a new conda environment to my Jupyter and nteract IDEs.

First to create an environment that contains, say TensorFlow, Pillow, Keras and pandas we need to type the following in the command line:

$ conda create -n tensorflow_env tensorflow pillow keras pandas jupyter ipykernel nb_conda

Now, to add this to the list of available environments in either Jupyter or nteract, we type the following:

$ conda activate tensor_env

$ python -m ipykernel install --name tensorflow_env

$ conda deactivate

Et voilà, you should now see the environment in the dropdown menu!

Data Science and Analytics with Python – Social Network Analysis

Using the time wisely during the Bank Holiday weekend. As my dad would say, “resting while making bricks”… Currently reviewing/editing/correcting Chapter 3 of “Advanced Data Science and Analytics with Python”. Yes, that is volume 2 of “Data Science and Analytics with Python“.


Python – Pendulum

Working with dates and times in programming can be a painful test at times. In Python, there are some excellent libraries that help with all the pain, and recently I became aware of Pendulum. It is effectively are replacement for the standard datetime class and it has a number of improvements. Check out the documentation for further information.

Installation of the packages is straightforward with pip:

$ pip install pendulum

For example, some simple manipulations involving time zones:

import pendulum

now ='Europe/Paris')

# Changing timezone

# Default support for common datetime formats

# Shifting

Duration can be used as a replacement for the standard timedelta class:

dur = pendulum.duration(days=15)

# More properties

# Handy methods
'2 weeks 1 day'

It also supports the definition of a period, i.e. a duration that is aware of the DateTime instances that created it. For example:

dt1 =
dt2 = dt1.add(days=3)

# A period is the difference between 2 instances
period = dt2 - dt1


# A period is iterable
for dt in period:

Give it a go, and let me know what you think of it.