Mastering the little things in Python, NumPy, and Pandas
If you’ve ever found yourself looking up the same question, concept, or syntax over and over again when programming, you’re not alone.
I find myself doing this constantly.
While it’s not unnatural to look things up on StackOverflow or other resources, it does slow you down a good bit and raise questions as to your complete understanding of the language.
We live in a world where there is a seemingly infinite amount of accessible, free resources looming just one search away at all times. However, this can be both a blessing and a curse. When not managed effectively, an over-reliance on these resources can build poor habits that will set you back long-term.
Personally, I find myself pulling code from similar discussion threads several times, rather than taking the time to learn and solidify the concept so that I can reproduce the code myself the next time.
This approach is lazy and while it may be the path of least resistance in the short-term, it will ultimately hurt your growth, productivity, and ability to recall syntax (cough, interviews) down the line.
Recently, I’ve been working through an online data science course titled Python for Data Science and Machine Learning (Oh God, I sound like that guy on Youtube). Over the early lectures in the series, I was reminded of some concepts and syntax that I consistently overlook when performing data analysis in Python.
In the interest of solidifying my understanding of these concepts once and for all and saving you guys a couple of StackOverflow searches, here’s the stuff that I’m always forgetting when working with Python, NumPy, and Pandas.
I’ve included a short description and example for each, however for your benefit, I will also include links to videos and other resources that explore each concept more in-depth as well.
Writing out a for loop every time you need to define some sort of list is tedious, luckily Python has a built-in way to address this problem in just one line of code. The syntax can be a little hard to wrap your head around but once you get familiar with this technique you’ll use it fairly often.
See the example above and below for how you would normally go about list comprehension with a for loop vs. creating your list with in one simple line with no loops necessary.
x = [1,2,3,4] out =  for item in x: out.append(item**2) print(out) [1, 4, 9, 16] # vs. x = [1,2,3,4] out = [item**2 for item in x] print(out) [1, 4, 9, 16]
Ever get tired of creating function after function for limited use cases? Lambda functions to the rescue! Lambda functions are used for creating small, one-time and anonymous function objects in Python. Basically, they let you create a function, without creating a function.
The basic syntax of lambda functions is:
lambda arguments: expression
Note that lambda functions can do everything that regular functions can do, as long as there’s just one expression. Check out the simple example below and the upcoming video to get a better feel for the power of lambda functions:
double = lambda x: x * 2 print(double(5)) 10
Once you have a grasp on lambda functions, learning to pair them with the map and filter functions can be a powerful tool.
Specifically, map takes in a list and transforms it into a new list by performing some sort of operation on each element. In this example, it goes through each element and maps the result of itself times 2 to a new list. Note that the list function simply converts the output to list type.
# Map seq = [1, 2, 3, 4, 5] result = list(map(lambda var: var*2, seq)) print(result) [2, 4, 6, 8, 10]
The filter function takes in a list and a rule, much like map, however it returns a subset of the original list by comparing each element against the boolean filtering rule.
# Filter seq = [1, 2, 3, 4, 5] result = list(filter(lambda x: x > 2, seq)) print(result) [3, 4, 5]
For creating quick and easy Numpy arrays, look no further than the arange and linspace functions. Each one has their specific purpose, but the appeal here (instead of using range), is that they output NumPy arrays, which are typically easier to work with for data science.
Arange returns evenly spaced values within a given interval. Along with a starting and stopping point, you can also define a step size or data type if necessary. Note that the stopping point is a ‘cut-off’ value, so it will not be included in the array output.
# np.arange(start, stop, step) np.arange(3, 7, 2) array([3, 5])
Linspace is very similar, but with a slight twist. Linspace returns evenly spaced numbers over a specified interval. So given a starting and stopping point, as well as a number of values, linspace will evenly space them out for you in a NumPy array. This is especially helpful for data visualizations and declaring axes when plotting.
# np.linspace(start, stop, num) np.linspace(2.0, 3.0, num=5) array([ 2.0, 2.25, 2.5, 2.75, 3.0])
You may have ran into this when dropping a column in Pandas or summing values in NumPy matrix. If not, then you surely will at some point. Let’s use the example of dropping a column for now:
df.drop('Column A', axis=1) df.drop('Row A', axis=0)
I don’t know how many times I wrote this line of code before I actually knew why I was declaring axis what I was. As you can probably deduce from above, set axis to 1 if you want to deal with columns and set it to 0 if you want rows. But why is this? My favorite reasoning, or atleast how I remember this:
df.shape (# of Rows, # of Columns)
Calling the shape attribute from a Pandas dataframe gives us back a tuple with the first value representing the number of rows and the second value representing the number of columns. If you think about how this is indexed in Python, rows are at 0 and columns are at 1, much like how we declare our axis value. Crazy, right?
If you’re familiar with SQL, then these concepts will probably come a lot easier for you. Anyhow, these functions are essentially just ways to combine dataframes in specific ways. It can be difficult to keep track of which is best to use at which time, so let’s review it.
Concat allows the user to append one or more dataframes to each other either below or next to it (depending on how you define the axis).
Merge combines multiple dataframes on specific, common columns that serve as the primary key.
Join, much like merge, combines two dataframes. However, it joins them based on their indices, rather than some specified column.
Check out the excellent Pandas documentation for specific syntax and more concrete examples, as well as some special cases that you may run into.
Think of apply as a map function, but made for Pandas DataFrames or more specifically, for Series. If you’re not as familiar, Series are pretty similar to NumPy arrays for the most part.
Apply sends a function to every element along a column or row depending on what you specify. You might imagine how useful this can be, especially for formatting and manipulating values across a whole DataFrame column, without having to loop at all.
Last but certainly not least is pivot tables. If you’re familiar with Microsoft Excel, then you’ve probably heard of pivot tables in some respect. The Pandas built-in pivot_table function creates a spreadsheet-style pivot table as a DataFrame. Note that the levels in the pivot table are stored in MultiIndex objects on the index and columns of the resulting DataFrame.
That’s it for now. I hope a couple of these overviews have effectively jogged your memory regarding important yet somewhat tricky methods, functions, and concepts you frequently encounter when using Python for data science. Personally, I know that even the act of writing these out and trying to explain them in simple terms has helped me out a ton.
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An overview of using Python for data science including Numpy, Scipy, pandas, Scikit-Learn, XGBoost, TensorFlow and Keras.
An overview of using Python for data science including Numpy, Scipy, pandas, Scikit-Learn, XGBoost, TensorFlow and Keras.
So you’ve heard of data science and you’ve heard of Python.
You want to explore both but have no idea where to start — data science is pretty complicated, after all.
If you look at the contents of this article, you may think there’s a lot to master, but this article has been designed to gently increase the difficulty as we go along.
One article obviously can’t teach you everything you need to know about data science with python, but once you’ve followed along you’ll know exactly where to look to take the next steps in your data science journey.
Python, as a language, has a lot of features that make it an excellent choice for data science projects.
It’s easy to learn, simple to install (in fact, if you use a Mac you probably already have it installed), and it has a lot of extensions that make it great for doing data science.
Just because Python is easy to learn doesn’t mean its a toy programming language — huge companies like Google use Python for their data science projects, too. They even contribute packages back to the community, so you can use the same tools in your projects!
You can use Python to do way more than just data science — you can write helpful scripts, build APIs, build websites, and much much more. Learning it for data science means you can easily pick up all these other things as well.
There are a few important things to note about Python.
Right now, there are two versions of Python that are in common use. They are versions 2 and 3.
Most tutorials, and the rest of this article, will assume that you’re using the latest version of Python 3. It’s just good to be aware that sometimes you can come across books or articles that use Python 2.
The difference between the versions isn’t huge, but sometimes copying and pasting version 2 code when you’re running version 3 won’t work — you’ll have to do some light editing.
The second important thing to note is that Python really cares about whitespace (that’s spaces and return characters). If you put whitespace in the wrong place, your programme will very likely throw an error.
There are tools out there to help you avoid doing this, but with practice you’ll get the hang of it.
If you’ve come from programming in other languages, Python might feel like a bit of a relief: there’s no need to manage memory and the community is very supportive.
If Python is your first programming language you’ve made an excellent choice. I really hope you enjoy your time using it to build awesome things.
The best way to install Python for data science is to use the Anaconda distribution (you’ll notice a fair amount of snake-related words in the community).
It has everything you need to get started using Python for data science including a lot of the packages that we’ll be covering in the article.
If you click on Products -> Distribution and scroll down, you’ll see installers available for Mac, Windows and Linux.
Even if you have Python available on your Mac already, you should consider installing the Anaconda distribution as it makes installing other packages easier.
If you prefer to do things yourself, you can go to the official Python website and download an installer there.
Packages are pieces of Python code that aren’t a part of the language but are really helpful for doing certain tasks. We’ll be talking a lot about packages throughout this article so it’s important that we’re set up to use them.
Because the packages are just pieces of Python code, we could copy and paste the code and put it somewhere the Python interpreter (the thing that runs your code) can find it.
But that’s a hassle — it means that you’ll have to copy and paste stuff every time you start a new project or if the package gets updated.
To sidestep all of that, we’ll instead use a package manager.
If you chose to use the Anaconda distribution, congratulations — you already have a package manager installed. If you didn’t, I’d recommend installing pip.
No matter which one you choose, you’ll be able to use commands at the terminal (or command prompt) to install and update packages easily.
Now that you’ve got Python installed, you’re ready to start doing data science.
But how do you start?
Because Python caters to so many different requirements (web developers, data analysts, data scientists) there are lots of different ways to work with the language.
Python is an interpreted language which means that you don’t have to compile your code into an executable file, you can just pass text documents containing code to the interpreter!
Let’s take a quick look at the different ways you can interact with the Python interpreter.
If you open up the terminal (or command prompt) and type the word ‘python’, you’ll start a shell session. You can type any valid Python commands in there and they’d work just like you’d expect.
This can be a good way to quickly debug something but working in a terminal is difficult over the course of even a small project.
If you write a series of Python commands in a text file and save it with a .py extension, you can navigate to the file using the terminal and, by typing python YOUR_FILE_NAME.py, can run the programme.
This is essentially the same as typing the commands one-by-one into the terminal, it’s just much easier to fix mistakes and change what your program does.
An IDE is a professional-grade piece of software that helps you manage software projects.
One of the benefits of an IDE is that you can use debugging features which tell you where you’ve made a mistake before you try to run your programme.
Some IDEs come with project templates (for specific tasks) that you can use to set your project out according to best practices.
None of these ways are the best for doing data science with python — that particular honour belongs to Jupyter notebooks.
Jupyter notebooks give you the capability to run your code one ‘block’ at a time, meaning that you can see the output before you decide what to do next — that’s really crucial in data science projects where we often need to see charts before taking the next step.
If you’re using Anaconda, you’ll already have Jupyter lab installed. To start it you’ll just need to type ‘jupyter lab’ into the terminal.
If you’re using pip, you’ll have to install Jupyter lab with the command ‘python pip install jupyter’.
It probably won’t surprise you to learn that data science is mostly about numbers.
The NumPy package includes lots of helpful functions for performing the kind of mathematical operations you’ll need to do data science work.
It comes installed as part of the Anaconda distribution, and installing it with pip is just as easy as installing Jupyter notebooks (‘pip install numpy’).
The most common mathematical operations we’ll need to do in data science are things like matrix multiplication, computing the dot product of vectors, changing the data types of arrays and creating the arrays in the first place!
Here’s how you can make a list into a NumPy array:
Here’s how you can do array multiplication and calculate dot products in NumPy:
And here’s how you can do matrix multiplication in NumPy:
With mathematics out of the way, we must move forward to statistics.
The Scipy package contains a module (a subsection of a package’s code) specifically for statistics.
You can import it (make its functions available in your programme) into your notebook using the command ‘from scipy import stats’.
This package contains everything you’ll need to calculate statistical measurements on your data, perform statistical tests, calculate correlations, summarise your data and investigate various probability distributions.
Here’s how to quickly access summary statistics (minimum, maximum, mean, variance, skew, and kurtosis) of an array using Scipy:
Data scientists have to spend an unfortunate amount of time cleaning and wrangling data. Luckily, the Pandas package helps us do this with code rather than by hand.
The most common tasks that I use Pandas for are reading data from CSV files and databases.
It also has a powerful syntax for combining different datasets together (datasets are called DataFrames in Pandas) and performing data manipulation.
You can see the first few rows of a DataFrame using the .head method:
You can select just one column using square brackets:
And you can create new columns by combining others:
In order to use the pandas read_sql method, you’ll have to establish a connection to a database.
The most bulletproof method of connecting to a database is by using the SQLAlchemy package for Python.
Because SQL is a language of its own and connecting to a database depends on which database you’re using, I’ll leave you to read the documentation if you’re interested in learning more.
Sometimes we’d prefer to do some calculations on our data before they arrive in our projects as a Pandas DataFrame.
If you’re working with databases or scraping data from the web (and storing it somewhere), this process of moving data and transforming it is called ETL (Extract, transform, load).
You extract the data from one place, do some transformations to it (summarise the data by adding it up, finding the mean, changing data types, and so on) and then load it to a place where you can access it.
There’s a really cool tool called Airflow which is very good at helping you manage ETL workflows. Even better, it’s written in Python.
It was developed by Airbnb when they had to move incredible amounts of data around, you can find out more about it here.
Sometimes ETL processes can be really slow. If you have billions of rows of data (or if they’re a strange data type like text), you can recruit lots of different computers to work on the transformation separately and pull everything back together at the last second.
This architecture pattern is called MapReduce and it was made popular by Hadoop.
Nowadays, lots of people use Spark to do this kind of data transformation / retrieval work and there’s a Python interface to Spark called (surprise, surprise) PySpark.
Both the MapReduce architecture and Spark are very complex tools, so I’m not going to go into detail here. Just know that they exist and that if you find yourself dealing with a very slow ETL process, PySpark might help. Here’s a link to the official site.
We already know that we can run statistical tests, calculate descriptive statistics, p-values, and things like skew and kurtosis using the stats module from Scipy, but what else can Python do with statistics?
One particular package that I think you should know about is the lifelines package.
Using the lifelines package, you can calculate a variety of functions from a subfield of statistics called survival analysis.
Survival analysis has a lot of applications. I’ve used it to predict churn (when a customer will cancel a subscription) and when a retail store might be burglarised.
These are totally different to the applications the creators of the package imagined it would be used for (survival analysis is traditionally a medical statistics tool). But that just shows how many different ways there are to frame data science problems!
The documentation for the package is really good, check it out here.Machine Learning in Python
Now this is a major topic — machine learning is taking the world by storm and is a crucial part of a data scientist’s work.
Simply put, machine learning is a set of techniques that allows a computer to map input data to output data. There are a few instances where this isn’t the case but they’re in the minority and it’s generally helpful to think of ML this way.
There are two really good machine learning packages for Python, let’s talk about them both.
Most of the time you spend doing machine learning in Python will be spent using the Scikit-Learn package (sometimes abbreviated sklearn).
This package implements a whole heap of machine learning algorithms and exposes them all through a consistent syntax. This makes it really easy for data scientists to take full advantage of every algorithm.
The general framework for using Scikit-Learn goes something like this –
You split your dataset into train and test datasets:
Then you instantiate and train a model:
And then you use the metrics module to test how well your model works:
The second package that is commonly used for machine learning in Python is XGBoost.
Where Scikit-Learn implements a whole range of algorithms XGBoost only implements a single one — gradient boosted decision trees.
This package (and algorithm) has become very popular recently due to its success at Kaggle competitions (online data science competitions that anyone can participate in).
Training the model works in much the same way as a Scikit-Learn algorithm.Deep Learning in Python
The machine learning algorithms available in Scikit-Learn are sufficient for nearly any problem. That being said, sometimes you need to use the most advanced thing available.
Deep neural networks have skyrocketed in popularity due to the fact that systems using them have outperformed nearly every other class of algorithm.
There’s a problem though — it’s very hard to say what a neural net is doing and why it’s making the decisions that it is. Because of this, their use in finance, medicine, the law and related professions isn’t widely endorsed.
The two major classes of neural network are convolutional neural networks (which are used to classify images and complete a host of other tasks in computer vision) and recurrent neural nets (which are used to understand and generate text).
Exploring how neural nets work is outside the scope of this article, but just know that the packages you’ll need to look for if you want to do this kind of work are TensorFlow (a Google contibution!) and Keras.
Keras is essentially a wrapper for TensorFlow that makes it easier to work with.
Once you’ve trained a model, you’d like to be able to access predictions from it in other software. The way you do this is by creating an API.
An API allows your model to receive data one row at a time from an external source and return a prediction.
Because Python is a general purpose programming language that can also be used to create web services, it’s easy to use Python to serve your model via API.
If you need to build an API you should look into the pickle and Flask. Pickle allows you to save trained models on your hard-drive so that you can use them later. And Flask is the simplest way to create web services.
Finally, if you’d like to build a full-featured web application around your data science project, you should use the Django framework.
Django is immensely popular in the web development community and was used to build the first version of Instagram and Pinterest (among many others).
And with that we’ve concluded our whirlwind tour of data science with Python.
We’ve covered everything you’d need to learn to become a full-fledged data scientist. If it still seems intimidating, you should know that nobody knows all of this stuff and that even the best of us still Google the basics from time to time.
Learn Data Science | How to Learn Data Science for Free. In this post, I have described a learning path and free online courses and tutorials that will enable you to learn data science for free.
The average cost of obtaining a masters degree at traditional bricks and mortar institutions will set you back anywhere between $30,000 and $120,000. Even online data science degree programs don’t come cheap costing a minimum of $9,000. So what do you do if you want to learn data science but can’t afford to pay this?
I trained into a career as a data scientist without taking any formal education in the subject. In this article, I am going to share with you my own personal curriculum for learning data science if you can’t or don’t want to pay thousands of dollars for more formal study.
The curriculum will consist of 3 main parts, technical skills, theory and practical experience. I will include links to free resources for every element of the learning path and will also be including some links to additional ‘low cost’ options. So if you want to spend a little money to accelerate your learning you can add these resources to the curriculum. I will include the estimated costs for each of these.
The first part of the curriculum will focus on technical skills. I recommend learning these first so that you can take a practical first approach rather than say learning the mathematical theory first. Python is by far the most widely used programming language used for data science. In the Kaggle Machine Learning and Data Science survey carried out in 2018 83% of respondents said that they used Python on a daily basis. I would, therefore, recommend focusing on this language but also spending a little time on other languages such as R.
Before you can start to use Python for data science you need a basic grasp of the fundamentals behind the language. So you will want to take a Python introductory course. There are lots of free ones out there but I like the Codeacademy ones best as they include hands-on in-browser coding throughout.
I would suggest taking the introductory course to learn Python. This covers basic syntax, functions, control flow, loops, modules and classes.
Next, you will want to get a good understanding of using Python for data analysis. There are a number of good resources for this.
To start with I suggest taking at least the free parts of the data analyst learning path on dataquest.io. Dataquest offers complete learning paths for data analyst, data scientist and data engineer. Quite a lot of the content, particularly on the data analyst path is available for free. If you do have some money to put towards learning then I strongly suggest putting it towards paying for a few months of the premium subscription. I took this course and it provided a fantastic grounding in the fundamentals of data science. It took me 6 months to complete the data scientist path. The price varies from $24.50 to $49 per month depending on whether you pay annually or not. It is better value to purchase the annual subscription if you can afford it.
If you have chosen to pay for the full data science course on Dataquest then you will have a good grasp of the fundamentals of machine learning with Python. If not then there are plenty of other free resources. I would focus to start with on scikit-learn which is by far the most commonly used Python library for machine learning.
When I was learning I was lucky enough to attend a two-day workshop run by Andreas Mueller one of the core developers of scikit-learn. He has however published all the material from this course, and others, on this Github repo. These consist of slides, course notes and notebooks that you can work through. I would definitely recommend working through this material.
Then I would suggest taking some of the tutorials in the scikit-learn documentation. After that, I would suggest building some practical machine learning applications and learning the theory behind how the models work — which I will cover a bit later on.
SQL is a vital skill to learn if you want to become a data scientist as one of the fundamental processes in data modelling is extracting data in the first place. This will more often than not involve running SQL queries against a database. Again if you haven’t opted to take the full Dataquest course then here are a few free resources to learn this skill.
Codeacamdemy has a free introduction to SQL course. Again this is very practical with in-browser coding all the way through. If you also want to learn about cloud-based database querying then Google Cloud BigQuery is very accessible. There is a free tier so you can try queries for free, an extensive range of public datasets to try and very good documentation.
To be a well-rounded data scientist it is a good idea to diversify a little from just Python. I would, therefore, suggest also taking an introductory course in R. Codeacademy have an introductory course on their free plan. It is probably worth noting here that similar to Dataquest Codeacademy also offers a complete data science learning plan as part of their pro account (this costs from $31.99 to $15.99 per month depending on how many months you pay for up front). I personally found the Dataquest course to be much more comprehensive but this may work out a little cheaper if you are looking to follow a learning path on a single platform.
It is a good idea to get a grasp of software engineering skills and best practices. This will help your code to be more readable and extensible both for yourself and others. Additionally, when you start to put models into production you will need to be able to write good quality well-tested code and work with tools like version control.
There are two great free resources for this. Python like you mean it covers things like the PEP8 style guide, documentation and also covers object-oriented programming really well.
The scikit-learn contribution guidelines, although written to facilitate contributions to the library, actually cover the best practices really well. This covers topics such as Github, unit testing and debugging and is all written in the context of a data science application.
For a comprehensive introduction to deep learning, I don’t think that you can get any better than the totally free and totally ad-free fast.ai. This course includes an introduction to machine learning, practical deep learning, computational linear algebra and a code-first introduction to natural language processing. All their courses have a practical first approach and I highly recommend them.
Whilst you are learning the technical elements of the curriculum you will encounter some of the theory behind the code you are implementing. I recommend that you learn the theoretical elements alongside the practical. The way that I do this is that I learn the code to be able to implement a technique, let’s take KMeans as an example, once I have something working I will then look deeper into concepts such as inertia. Again the scikit-learn documentation contains all the mathematical concepts behind the algorithms.
In this section, I will introduce the key foundational elements of theory that you should learn alongside the more practical elements.
The khan academy covers almost all the concepts I have listed below for free. You can tailor the subjects you would like to study when you sign up and you then have a nice tailored curriculum for this part of the learning path. Checking all of the boxes below will give you an overview of most elements I have listed below.
Calculus is defined by Wikipedia as “the mathematical study of continuous change.” In other words calculus can find patterns between functions, for example, in the case of derivatives, it can help you to understand how a function changes over time.
Many machine learning algorithms utilise calculus to optimise the performance of models. If you have studied even a little machine learning you will probably have heard of Gradient descent. This functions by iteratively adjusting the parameter values of a model to find the optimum values to minimise the cost function. Gradient descent is a good example of how calculus is used in machine learning.
What you need to know:
Many popular machine learning methods, including XGBOOST, use matrices to store inputs and process data. Matrices alongside vector spaces and linear equations form the mathematical branch known as Linear Algebra. In order to understand how many machine learning methods work it is essential to get a good understanding of this field.
What you need to learn:
Vectors and spaces
Here is a list of the key concepts you need to know:
The third section of the curriculum is all about practice. In order to truly master the concepts above you will need to use the skills in some projects that ideally closely resemble a real-world application. By doing this you will encounter problems to work through such as missing and erroneous data and develop a deep level of expertise in the subject. In this last section, I will list some good places you can get this practical experience from for free.
“With deliberate practice, however, the goal is not just to reach your potential but to build it, to make things possible that were not possible before. This requires challenging homeostasis — getting out of your comfort zone — and forcing your brain or your body to adapt.”, Anders Ericsson, Peak: Secrets from the New Science of Expertise
Machine learning competitions are a good place to get practice with building machine learning models. They give access to a wide range of data sets, each with a specific problem to solve and have a leaderboard. The leaderboard is a good way to benchmark how good your knowledge at developing a good model actually is and where you may need to improve further.
The UCI machine learning repository is a large source of publically available data sets. You can use these data sets to put together your own data projects this could include data analysis and machine learning models, you could even try building a deployed model with a web front end. It is a good idea to store your projects somewhere publically such as Github as this can create a portfolio showcasing your skills to use for future job applications.
One other option to consider is contributing to open source projects. There are many Python libraries that rely on the community to maintain them and there are often hackathons held at meetups and conferences where even beginners can join in. Attending one of these events would certainly give you some practical experience and an environment where you can learn from others whilst giving something back at the same time. Numfocus is a good example of a project like this.
In this post, I have described a learning path and free online courses and tutorials that will enable you to learn data science for free. Showcasing what you are able to do in the form of a portfolio is a great tool for future job applications in lieu of formal qualifications and certificates. I really believe that education should be accessible to everyone and, certainly, for data science at least, the internet provides that opportunity. In addition to the resources listed here, I have previously published a recommended reading list for learning data science available here. These are also all freely available online and are a great way to complement the more practical resources covered above.
Thanks for reading!
Python For Data Analysis - Build a Data Analysis Library from Scratch - Learn Python in 2019
Immerse yourself in a long, comprehensive project that teaches advanced Python concepts to build an entire library