\nWorkflows

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

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\n Preface\n
- \n Introduction\n
  - \n What is this book?\n
  - \n Who is it for?\n
  - \n What will you learn?\n
  - \n The state of the book\n
  \n
- \n An introduction to Advanced Python\n
  - \n What is Advanced Python?\n
  - \n Tech Requirements\n
  - \n Helpful Themes\n
  - \n Readings\n
  \n
\n
\n Workflows\n
- \n Continuous science\n
  - \n Debugging\n
  - \n “Bug Report” Rules\n
  - \n Primers\n
  - \n Higher Levels\n
  - \n Testing\n
  - \n Readings\n
  \n
- \n Scientific workflows\n
  - \n What is Python?\n
  - \n Config\n
  - \n Decorators\n
  - \n Bootstraping\n
  - \n Readings\n
  \n
- \n Packages and iteration\n
  - \n Packages\n
  - \n Versioning\n
  - \n Functional Programming\n
  \n
- \n Avoiding the for loop\n
  - \n Primitives\n
  - \n Vectorizing\n
  - \n Einstein Summation\n
  - \n Iteration Primitives\n
  - \n Vectorization: A Case Study\n
  - \n Iterators\n
  - \n Readings\n
  \n
\n
\n Skeletons\n
- \n Classes, composition, and graphs\n
  - \n Inheritance\n
  - \n Composition\n
  \n
- \n The DAG\n
  - \n Graphical Programs\n
  - \n What does Data Science look like?\n
  - \n The Revelation\n
  \n
- \n Luigi\n
  - \n Project scaffolding\n
  - \n The Task\n
  - \n The Pieces\n
  - \n The Big Picture\n
  - \n Atomicity\n
  - \n Atomicity\n
  - \n Readings\n
  \n
- \n Graphs\n
  - \n Luigi\n
  - \n The Big Picture\n
  - \n Salted Graphs\n
  - \n The Sorry State of Stateful Data\n
  - \n Advanced Luigi\n
  \n
\n
\n Data\n
- \n Dask and Parquet\n
  - \n Micro Sciences\n
  - \n Dask - Basics\n
  - \n Rookie Mistakes\n
  - \n Executive Summary\n
  - \n Split, Apply, Combine\n
  - \n Data Containers\n
  - \n Parquet\n
  - \n Dask - Partitioning\n
  - \n Case Study - Fancy Indexing\n
  - \n Dask and Luigi\n
  - \n Readings\n
  \n
- \n Django and SQL\n
  - \n Mutability\n
  - \n Living Data\n
  - \n Django\n
  - \n ORM\n
  - \n Django Code\n
  - \n ORM Breakdowns\n
  - \n The Competition\n
  - \n Readings\n
  \n
- \n API’s and Data\n
  - \n Metaprogramming\n
  - \n DB Design\n
  - \n Atomic Targets\n
  - \n Migrations\n
  - \n The Web\n
  - \n APIs\n
  - \n Reading\n
  \n
- \n More Meta\n
  - \n Api’s and Clients\n
  - \n Factories\n
  - \n Optimization\n
  - \n Readings\n
  \n
\n
\n Algorithms\n
- \n Smart & Lazy Coding\n
  - \n Parallel Code\n
  - \n Memory Views\n
  - \n Memoization\n
  - \n Sketching\n
  - \n Readings\n
  \n
- \n Visualization\n
  - \n Data Viz\n
  - \n Declarative Grammars\n
  - \n Javascript and HTML5\n
  - \n Colormaps\n
  - \n Data Shading\n
  - \n Readings\n
  \n
- \n Where We Are\n
  - \n “Python”\n
  - \n Testing\n
  - \n Workflows\n
  - \n Higher Levels\n
  - \n Deployment\n
  - \n Looping\n
  - \n Functional Coding\n
  - \n Composition\n
  - \n Graphical Programs\n
  - \n Data Scaling\n
  - \n The Web\n
  - \n DB’s\n
  - \n API’s\n
  - \n Meta\n
  - \n Optimization\n
  - \n Visualization\n
  \n
\n
\n Appendix\n
- \n Changelog\n
\n

\nPreface

\n\n

\nIntroduction

\n\n

\nWhat is this book?

\n\n

Today, most people enter the world of Data Science through the buzz and allure of “AI.” We tackle Kaggle challenges, voraciously consume Stack Overflow, and eat, live, and breathe through the Jupyter Notebook. Python, along with its “killer app” of Machine Learning, has done nothing short of revolutionize the way we “do data science,” and the world is a more interesting place because of it!

\n\n

The Big Cloud providers, and many open source tools, have done wonders to democratize this technology. But, ‘easy access’ to high technology comes with a cost - we can easily go too far, rely too much on the tools we have today, and forget how to build the tools we need to truly transform our individual projects.

\n\n

Most of the time, your impact as a Data Scientist is limited by your ability to enact your ideas - not by the ideas themselves. You can train a model on ‘clean’ data using Scikit Learn or FastAI, or run an ANOVA, in a notebook. Enacting that idea means getting to the data in the first place. It means knowing how to store it. It means processing your data at scale. It means running your processing script, reliably, every day on fresh data. It means testing that script. It means collaborating on that script with a coworker - or 10 - as the project scales. It means curating a library and building tools to solve the same problem for 5 new projects. It means packaging a model up for distribution - sharing with another data scientist, or deploying it as a service.

\n\n

It means changing the way you think about problems by adopting new paradigms that accelerate you - and your work - across your organization. It means building an approach to data science within the broader python ecosystem.

\n\n

This book is about python, and how to be an effective python programmer, as a Data Scientist. We learn the advanced python skills we need to accelerate you, and solve the real, daily problems you face in your DS role.

\n\n

\nWho is it for?

\n\n

The ideal reader has at least 1 year of active daily python use and is ready to learn more. You should be comfortable writing functions, classes, and reading source code. You should not be confused about basic syntax, data types, etc, and when you see something you don’t understand, you should be comfortable reading the docs or source code yourself. You should know how to import a package or module. You should know how to define a class. You should know how to look up python syntax and how to answer questions about python. You should be comfortable reading the source code for packages you work with, rather than reverting to StackOverflow for every question. You should know how to debug an error message - if you are not used to reading and interpreting errors, launching a line-by-line debugger, you should get comfortable breaking and debugging code before you dive into a book like this.

\n\n

Often, I get feedback from students who find this material too challenging - “it’s like sitting down to a piano lesson and learning how to tune a piano” and “it’s like showing up for an intro to Russian class only to learn the class is taught in Russian.” These, in fact, are very appropriate descriptions - as an advanced subject, this book is written in native python, and we learn exactly when and how to tune python for advanced, but very common, needs. It is extremely technical, and earns the advanced designation. I only hope you will find the material valuable.

\n\n

\nWhat will you learn?

\n\n

Congratulations on starting this journey - it is tough, but I promise it will be rewarding. Roughly speaking we will cover 4 main themes in this book, learning advanced Python syntax and tools in the context of solving real-world Data Science problems. The themes are:

\n\n

Workflows: How to test, deploy, share, merge, version, review, and iterate on your code. We will look at build systems - pipenv, conda, poetry - versioning tools, templating with cookiecutter, how to work with git, and more.
Skeletons: The bones upon which we build projects. These frameworks give us new perspectives and approaches - how to build ETL’s with Airflow and Luigi, how to structure and train a model with MLOps tools, how to compose new frameworks and build repeatable tools using context managers, decorators, and descriptors.
Data: How to store, process, search in the context of big data and small, row based or columnar, using parquet or SQL, scaling with tools like Dask
Algorithms: The universal CS concepts that help many projects, including hashing, memoization techniques, JIT and static compilation, visualization, data sketching, and more

\n\n

I hope this whet’s your appetite, and you join me and your fellow scientists on this journey.

\n\n

\nThe state of the book

\n\n

This book originated as slides and lecture notes I developed while teaching CSCI E-29, Advanced Python for Data Science, at Harvard Extension School. Currently, the book contains light transformation of those notes from slide into prose, which leaves a lot of opportunity to improve and flesh out the text. After all the notes are transcribed, diagrams reformatted for the book medium, etc, I will come back to rewrite the text as needed for textbook form.

\n\n\n

\nAn introduction to Advanced Python

\n\n\n

\nWhat is Advanced Python?

\n\n

\n… is it an awesome notebook?

\n\n

\n… or knowing every function?

\n\n

\n… and writing faster code?

\n\n

\nIt’s about higher levels

\n\n

\n\n\n

\nIt’s about the science

\n\n

Columnar & Sparse Representations

\n\n

Probabilistic Data Structures

\n\n

Parallel & Distributed Algorithms

\n\n

\nIt’s about repeatability

\n\n

A manual install

\n\n\n

pip install numpy\nipython\n

\n\n\n\n\n

No env
No data flow

\n\n

Minimally repeatable

\n\n\n

pip install -r reqs.txt\npython script.py in.csv\n

\n\n\n\n\n

Minimal reqs
Implicit flow

\n\n

A fully repeatable process

\n\n\n

pipenv install\nmake output.parquet\n

\n\n\n\n\n

Frozen env
Explicit flow

\n\n

\n… and standards/enforcement

\n\n

Why learn “best practices” if you can build them?

\n\n

Each of these examples represents a design decision - and best practice - wrapped up in code

\n\n\n

with atomic_write('output.csv') as f:\n    df.to_csv(f)\n

\n\n\n\n\n\n

mlflow.sklearn.save_model(model, \"my_model\")\n

\n\n\n\n\n\n

with submit_pset('Pset 0') as quiz_submission:\n    quiz_submission.answer_submission_questions(...)\n

\n\n\n\n\n

\nIt’s about collaboration

\n\n

Workflows that facilitate development
Code that is consistent and self-documenting
Units of work that build on each other seamlessly

\n\n

\nIt’s about the ecosystem

\n\n

The modern Data Scientist leverages a broader ecosystem of tools than anyone else in the history of man

\n\n

Python is a glue language, and is popular because it is the best interface to all of these tools

\n\n

We will embrace the ecosystem, rather than have you work in a sandbox

\n\n

\nIt’s about the deliverable

\n\n

Your output is not enough; show your work.

\n\n

\nIt’s about you\n

\n\n

You are the most valuable asset, and usually the bottleneck. Python gives you wings.

\n\n\n

\nTech Requirements

\n\n

\nPrerequisites

\n\n

You should already know Python! This book assumes fluency in basic to intermediate python syntax.

\n\n

You should know how to use libraries, functions, classes, and basics of python packaging!

\n\n

You should be familiar with numpy, scipy, and pandas

\n\n\n\n

Students have the most difficulty understanding python packages vs modules vs scripts, how to build and configure their environments, and set up repositories and cloud accounts. Make sure you are comfortable with the basic daily tools (the things used in real life that they don’t teach you in Python 101) and can debug and communicate problems effectively.

\n\n

\nPython 3.8

\n\n

This book will target 3.8.

\n\n

The focus of this book is python. New versions bring new features, but we are focused on a much bigger picture - what makes python python, not what you can do with any new feature.

\n\n

The vast majority of things you can and should do as an advanced Python programmer are achievable in any version, pending library support, perhaps with minor syntax variations. We are chasing the forest here, not the trees.

\n\n

\nIDE

\n\n

Pick one that helps with:

\n\n

Debugging!
Source code/doc lookups
Syntax checks, autocomplete, runtime config…

\n\n

If you have never line-by-line debugged a program before, this is the place to start!

\n\n

This is your chance to learn to debug!

\n\n

Debugging provides most of what you want from Jupyter, but much, much better experience:

\n\n

Jump to source
Line-by-line stepping
Stack inspection
…

\n\n

… not Jupyter!

\n\n

You should not be using Jupyter Notebooks for this course!

\n\n

You will get the impression that I hate Jupyter. That is not true.

\n\n

It’s great for EDA, but lacks repeatability, composeability, and cooperativity.

\n\n

Use the right tool for the right job. Jupyter is not the right tool for Advanced Python!

\n\n

\nDocumentation

\n\n

You need to document and comment your code!

\n\n

Pick one of:

\n\n

\n\n\n

def fetch_bigtable_rows(big_table):\n    \"\"\"Fetches rows from a Bigtable\n\n    More notes...\n\n    Args:\n        big_table: A Bigtable Table\n        ...\n\n    Returns:\n        A dict...\n\n    \"\"\"\n

\n\n\n\n\n

\nBlack

\n\n

https://black.readthedocs.io/en/stable/

\n\n

Why spend time formatting code?

\n\n

Code format is about more than just style. Everyone has different aesthetics, but we would not spend much time discussing it if it were merely preferences.

\n\n

When code is merged or committed, we see deltas regardless if the change is functional or stylistic. Adopting an automatic, strict style minimizes the latter. If your code is always formatted consistently, you can easily merge work from another branch without seeing or dealing with distracting stylistic deltas. It helps you collaborate.

\n\n

Plus, it removes the mental energy of learning or conforming to a style. Just auto format all the time!

\n\n

This is an example of how we don’t learn or set a ‘best practice;’ rather, we pick a framework that automates or enforces the practice for us.

\n\n\n

\nHelpful Themes

\n\n

\nTyping

\n\n

Python doesn’t require types, but you shouldn’t forget them!

\n\n

The following all ‘safely’ handle a basic lookup, but make different decisions about the type and content of the default value

\n\n\n

ENCODING = {\n    1: 'one',\n    2: 'two',\n}\n\ndef encode(x: int) -> str:\n    # Preserves type, but is a new 'magic' value\n    return ENCODING.get(x, 'none')\n

\n\n\n\n\n\n

def encode(x: int) -> Union[str, None]:\n    # Standard tombstone value, but\n    # may cause problems eg encode(9).upper()\n    return ENCODING.get(x, None)\n

\n\n\n\n\n\n

def encode(x: int) -> str:\n    # Preserve type and boolean eg 'if encode(x)'\n    return ENCODING.get(x, '')\n

\n\n\n\n\n

Functions should operate on an expected data type, and return an expected type.

\n\n

If this changes depending on the arguments, mass confusion will follow.

\n\n

Strive for consistent, self-explanatory argument and return types

\n\n

Never ever ever:

\n\n\n

def sqrt(x: float) -> Union[float, str]:\n    if x < 0:\n        return 'invalid'\n    return x**0.5\n

\n\n\n\n\n

Typing (Overkill): Let your IDE and common sense do the heavy lifting

\n\n

Good practices in the past are not as necessary today; see: Hungarian Notation.

\n\n\n

def float_square_root(float_x: float) -> float:\n    assert isinstance(float_x, float)\n    return x**0.5\n

\n\n\n\n\n

\n
Encoding the type of a function into the name … is brain damaged - the compiler knows the types anyway … and it only confuses the programmer.
\n\n
– Linus Torvalds
\n

\n\n

\nCardinality & Ownership

\n\n

Try to isolate and shrink the responsibility of your code

\n\n

Smaller, isolated functions are easier to test and easier to reuse in new situations.

\n\n\n

def process(df: DataFrame) -> DataFrame:\n    # Hard codes column name and iteration method\n    for i, x in enumerate(df['col']):\n        df['col'].iloc[i] = x + 1\n    return df\n\n>>> df = process(df)\n

\n\n\n\n\n\n

def process(vec: List) -> List:\n    # Hard codes iteration method\n    return [x + 1 for x in vec]\n\n>>> df['col'] = process(df['col'])\n

\n\n\n\n\n\n

def process(x: float) -> float:\n    # Operates on a unit\n    return x + 1\n\n>>> df['col'].apply(process)\n>>> process(df['col']) # If vectorizable\n>>> process(3)\n

\n\n\n\n\n

One cognitive burden when reading or designing DS code is the cardinality of your data - are you processing a single element, or a bunch of them?

\n\n

Strive to make it clear whether your function is designed to work on elemental data (eg, a single primitive) or a collection (eg, an iterable, a series/dataframe, numpy array, etc).

\n\n

In general, write code to operate on data primitives - this is much easier to test, a better assumption for how a function should work, and more likely that you can plug it in to other parts of your code. It also allows you to use other mechanisms for applying the same thing to the container, eg using pandas .apply, or map objects.

\n\n

The big exception to this is if you need numpy vectorization for speed, and your function cannot be naively applied to a scalar vs a numpy array in the same way (eg, you need to access properties of the vector to perform the calculation).

\n\n

\nCode as Data

\n\n

Try to write every program as an operation on data.

\n\n

Here is ‘good’ code:

\n\n\n

print('one')\nprint('two')\n

\n\n\n\n\n

But this code is ‘better’:

\n\n\n

for arg in ['one', 'two']:\n    print(arg)\n

\n\n\n\n\n

And this is better still:

\n\n\n

def map(func, seq):\n    for el in seq:\n        func(el)\n\nmap(print, ['one', 'two'])\n

\n\n\n\n\n

Code-as-data, flow-based, object-oriented, and other higher-level programming concepts will be major focus areas in this book.

\n\n\n

\nReadings

\n\n

\nNow

\n\n

\nSoon

\n\n

\nReference

\n\n

Python Modules and Packages

\n\n\n

\nWorkflows

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nContinuous science

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nDebugging

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nHow to ask for help

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nWhat did you do?

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nWhat did you expect?

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nWhat happened?

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\n5 Why’s

\n\n\n

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\n\n\n

\nWhat did you do?

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nWhat did you expect?

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nWhat happened?

\n\n\n

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\n\n\n

\nDebugging - making progress

\n\n\n

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\n\n\n

\nPset 0 Frequent Issues

\n\n\n

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\n\n\n

\n“Bug Report” Rules

\n\n\n

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\n\n\n

\nA report template

\n\n\n

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\n\n\n

\nPrimers

\n\n\n

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\n\n\n

\nLogging

\n\n\n

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\n\n\n

\nPrint

\n\n\n

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\n\n\n

\nLoggers

\n\n\n

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\n\n\n

\nCode as Data

\n\n\n

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\n\n\n

\nFunctional programming

\n\n\n

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\n\n\n

\nHigher Levels

\n\n\n

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\n\n\n

\nDecorators

\n\n\n

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\n\n\n

\nRegistries

\n\n\n

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\n\n\n

\nWrappers

\n\n\n

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\n\n\n

\nWrapper Case - Typecasting

\n\n\n

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\n\n\n

\nWrapper Case - Lazy Loading

\n\n\n

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\n\n\n

\nContext Managers

\n\n\n

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\n\n\n

\nCtx Mgrs

\n\n\n

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\n\n\n

\nWhen to use Contexts

\n\n\n

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\n\n\n

\nTesting

\n\n\n

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\n\n\n

\nUnit Tests

\n\n\n

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\n\n\n

\nUnit Tests - Example 1

\n\n\n

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\n\n\n

\nUnit Tests - Example 2

\n\n\n

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\n\n\n

\nUnit Tests - Do’s and Don’ts

\n\n\n

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\n\n\n

\nWhat to test

\n\n\n

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\n\n\n

\nTwo faces of testing

\n\n\n

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\n\n\n

\nPlugins - Coverage

\n\n\n

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\n\n\n

\nTest Discovery

\n\n\n

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\n\n\n

\nTest Driven Development

\n\n\n

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\n\n\n

\nTesting tools

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\n\n\n

\nHypothesis

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\n\n\n

\nMock

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\n\n\n

\nMoto

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\n\n\n

\nReadings

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\n\n\n

\nScientific workflows

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\n\n\n

\nWhat is Python?

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\n\n\n

\nWhat is wrong with your root python?

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\n\n\n

\nThe Vision

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\n\n\n

\nVirtual envs

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\n\n\n

\nEnvironments, Apps, Libraries

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\n\n\n

\nVirtual Environments

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\n\n\n

\nFor the Purist:

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\n\n\n

\nThe Scientist:

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\n\n\n

\nThe Bold:

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\n\n\n

\nWhat are your reqs?

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\n\n\n

\nPipenv

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\n\n\n

\nPackages

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\n\n\n

\nDev Packages

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\n\n\n

\n(Ana) Conda

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\n\n\n

\nThe Good

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\n\n\n

\nThe Bad

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\nThe Ugly

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\nThe Unsolved

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\nThe Promising

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\n\n\n

\nWhat else?

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\n\n\n

\nPoetry

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\n\n\n

\nPip-tools

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\n\n\n

\nµPipenv

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\n\n\n

\nMy Recommendations

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\n\n\n

\nConfig

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\n\n\n

\nWhy Config?

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\n\n\n

\nAbsolute Nothing

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\n\n\n

\n… except for relatives

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\n\n\n

\nConfig Basics

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\nDotenvs

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\nOther systems

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\nExtras

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\n\n\n

\nDecorators

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\n\n\n

\nDecorating Classes

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\nWrapping Classes

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\nThe Wrapper Pattern

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\n\n\n

\nWhat is up with `@wraps(func)`?

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\n\n\n

\nDecorators – With Args

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\n\n\n

\nA look at `wraps()`\n

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\nDecorators – Uniting Parameters

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\nAn Alternate - Class-Based Decorators

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\n\n\n

\nBootstraping

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\nJinja2

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\n\n\n

\nCookiecutter

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\n\n\n

\nReadings

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\n\n\n

\nNow

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\n\n\n

\nUpcoming

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\n\n\n

\nPackages and iteration

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\n\n\n

\nPackages

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\n\n\n

\nFiles vs Packages

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\n\n\n

\nRelative Imports

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\n\n\n

\nSay my `name`!

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\n\n\n

\nA Misunderstood Idiom

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\n\n\n

\nWhat’s in a `name`?

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\n\n\n

\nDo not directly run packages or submodules

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\n\n\n

\nVersioning

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\n\n\n

\nWhat’s in a number?

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\n\n\n

\nSemantic versions

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\n\n\n

\nRequirements w.r.t Semver

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\n\n\n

\nTracking the version

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\n\n\n

\nBump

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\n\n\n

\nWhy not just git?

\n\n\n

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\n\n\n

\n`setuptools_scm`\n

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\n\n\n

\nLogging

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\n\n\n

\nFunctional Programming

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\n\n\n

\nCode as Data

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\n\n\n

\nState

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\n\n\n

\nStateful

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\n\n\n

\nStateless

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\n\n\n

\nLambdas

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\n\n\n

\nEasy Shorthand

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\n\n\n

\nExamples

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\n\n\n

\nProcedural

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\n\n\n

\nFunctional

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\n\n\n

\nExample - Reduction

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\n\n\n

\nProcedural

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\n\n\n

\nFunctional

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\n\n\n

\nDeclarative vs Procedural

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\n\n\n

\nAvoiding the `for` loop

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\n\n\n

\nPrimitives

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\n\n\n

\nPure python

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\n\n\n

\nPandas/Numpy

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\n\n\n

\nMap

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\n\n\n

\nFilter

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\n\n\n

\nZip

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\n\n\n

\nVectorizing

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\n\n\n

\nBroadcasting

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\n\n\n

\nEinstein Summation

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\n\n\n

\nIteration Primitives

\n\n\n

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\n\n\n

\nVectorization: A Case Study

\n\n\n

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\n\n\n

\nLiberty’s Telematics

\n\n\n

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\n\n\n

\nSolution – The Loop

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\n\n\n

\nSolution – The `apply`\n

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\n\n\n

\nSolution – Broadcasting

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\n\n\n

\nSolution – Einstein!

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\n\n\n

\nIterators

\n\n\n

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\n\n\n

\nEven Higher Levels

\n\n\n

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\n\n\n

\nTwo Faces of Iteration

\n\n\n

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\n\n\n

\nContainers

\n\n\n

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\n\n\n

\nCoroutines

\n\n\n

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\n\n\n

\nIterables vs Iterators

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\n\n\n

\nGenerators

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\n\n\n

\nInfinite Iterables

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\n\n\n

\nThe Coroutine

\n\n\n

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\n\n\n

\nIterators - The Point

\n\n\n

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\n\n\n

\nReadings

\n\n\n

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\n\n\n

\nNow

\n\n\n

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\n\n\n

\nUpcoming

\n\n\n

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\n\n\n

\nSkeletons

\n\n\n

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\n\n\n

\nClasses, composition, and graphs

\n\n\n

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\n\n\n

\nInheritance

\n\n\n

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\n\n\n

\nClass Inheritance (Refresher)

\n\n\n

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\n\n\n

\nAbstracts and Interfaces

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\n\n\n

\nMixins

\n\n\n

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\n\n\n

\n… and then we jump the shark

\n\n\n

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\n\n\n

\nMy Example

\n\n\n

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\n\n\n

\nInheritance Revisited

\n\n\n

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\n\n\n

\nComposition

\n\n\n

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\n\n\n

\nSingle Responsibility Principle

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\n\n\n

\nProperties

\n\n\n

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\n\n\n

\nThe mechanics…

\n\n\n

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\n\n\n

\nThe usage…

\n\n\n

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\n\n\n

\nDescriptors

\n\n\n

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\n\n\n

\nParameter Example

\n\n\n

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\n\n\n

\nPandas Example

\n\n\n

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\n\n\n

\nDescriptors

\n\n\n

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\n\n\n

\nDeclarative Classes

\n\n\n

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\n\n\n

\nThe DAG

\n\n\n

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\n\n\n

\nGraphical Programs

\n\n\n

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\n\n\n

\nDigraph Symmetry

\n\n\n

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\n\n\n

\nWhat does Data Science look like?

\n\n\n

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\n\n\n

\nDAG’s to the Rescue

\n\n\n

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\n\n\n

\nThe Revelation

\n\n\n

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\n\n\n

\nLuigi

\n\n\n

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\n\n\n

\nProject scaffolding

\n\n\n

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\n\n\n

\nThe Task

\n\n\n

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\n\n\n

\nThe Pieces

\n\n\n

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\n\n\n

\nTasks

\n\n\n

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\n\n\n

\nTargets

\n\n\n

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\n\n\n

\nScheduler

\n\n\n

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\n\n\n

\nSkeleton

\n\n\n

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\n\n\n

\nThe Big Picture

\n\n\n

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\n\n\n

\nAtomicity

\n\n\n

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\n\n\n

\nIdeally

\n\n\n

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\n\n\n

\nOtherwise

\n\n\n

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\n\n\n

\nAtomicity

\n\n\n

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\n\n\n

\nReadings

\n\n\n

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\n\n\n

\nGraphs

\n\n\n

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\n\n\n

\nLuigi

\n\n\n

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\n\n\n

\nTask Graphs

\n\n\n

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\n\n\n

\nParameters

\n\n\n

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\n\n\n

\nSpecification

\n\n\n

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\n\n\n

\nComposition

\n\n\n

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\n\n\n

\nDeclarative

\n\n\n

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\n\n\n

\nAtomic Targets

\n\n\n

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\n\n\n

\n`target.exists()`\n

\n\n\n

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\n\n\n

\n`target.open()`\n

\n\n\n

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\n\n\n

\nTask Anatomy

\n\n\n

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\n\n\n

\nSpecial Task Types

\n\n\n

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\n\n\n

\nExternal

\n\n\n

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\n\n\n

\nWrappers

\n\n\n

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\n\n\n

\nExternal Programs

\n\n\n

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\n\n\n

\nThe Big Picture

\n\n\n

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\n\n\n

\nThe Big Picture

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\n\n\n

\nSalted Graphs

\n\n\n

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\n\n\n

\nThree Approaches To State Management

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\n\n\n

\nLatest - new outputs

\n\n\n

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\n\n\n

\nMakefile - always update

\n\n\n

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\n\n\n

\nLuigi - write once, deterministically

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\n\n\n

\nSummary of approaches

\n\n\n

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\n\n\n

\nThe Sorry State of Stateful Data

\n\n\n

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\n\n\n

\nData Dependency Hell

\n\n\n

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\n\n\n

\nContent Addressable Filesystems

\n\n\n

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\n\n\n

\nGit, Blockchain, Merkle

\n\n\n

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\n\n\n

\nData Documents

\n\n\n

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\n\n\n

\nThe Pseudocode

\n\n\n

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\n\n\n

\nThe resolution

\n\n\n

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\n\n\n

\nThe Point

\n\n\n

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\n\n\n

\nThe Workflow

\n\n\n

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\n\n\n

\nMinimal Implementation

\n\n\n

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\n\n\n

\nAdvanced Luigi

\n\n\n

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\n\n\n

\nThe Inevitable Creep

\n\n\n

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\n\n\n

\nMy Example

\n\n\n

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\n\n\n

\nTask Skeleton

\n\n\n

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\n\n\n

\nOutput

\n\n\n

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\n\n\n

\nRequirements

\n\n\n

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\n\n\n

\nThe Point of Requirement

\n\n\n

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\n\n\n

\nHow to test a `Task`?

\n\n\n

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\n\n\n

\nData

\n\n\n

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\n\n\n

\nDask and Parquet

\n\n\n

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\n\n\n

\nMicro Sciences

\n\n\n

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\n\n\n

\nMonolithic Environments

\n\n\n

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\n\n\n

\nMicroservices

\n\n\n

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\n\n\n

\nVenvs for Apps

\n\n\n

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\n\n\n

\nVenvs for (some) Tasks

\n\n\n

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\n\n\n

\nMicro Environments

\n\n\n

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\n\n\n

\nDask - Basics

\n\n\n

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\n\n\n

\nComputational Graphs

\n\n\n

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\n\n\n

\nDelayed

\n\n\n

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\n\n\n

\n`dask.DataFrame`\n

\n\n\n

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\n\n\n

\nBasic Usage

\n\n\n

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\n\n\n

\nWhat does not work?

\n\n\n

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\n\n\n

\nData peeking

\n\n\n

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\n\n\n

\nDynamic schemas

\n\n\n

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\n\n\n

\nMeta Management

\n\n\n

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\n\n\n

\nRookie Mistakes

\n\n\n

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\n\n\n

\nAvoid `compute`!

\n\n\n

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\n\n\n

\nAvoid `repartition`!

\n\n\n

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\n\n\n

\nExecutive Summary

\n\n\n

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\n\n\n

\nSplit, Apply, Combine

\n\n\n

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\n\n\n

\nA Result in Pieces

\n\n\n

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\n\n\n

\nComputational Graphs

\n\n\n

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\n\n\n

\nMatrix

\n\n\n

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\n\n\n

\nWhy??

\n\n\n

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\n\n\n

\nScalability

\n\n\n

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\n\n\n

\nSymbolism

\n\n\n

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\n\n\n

\nSmart Speed

\n\n\n

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\n\n\n

\nData Containers

\n\n\n

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\n\n\n

\nRow and Column Stores

\n\n\n

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\n\n\n

\n2D

\n\n\n

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\n\n\n

\nRow Storage

\n\n\n

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\n\n\n

\nColumn Storage

\n\n\n

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\n\n\n

\nPartitions

\n\n\n

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\n\n\n

\nRow Storage

\n\n\n

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\n\n\n

\nColumnar Storage

\n\n\n

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\n\n\n

\nKey-Value Stores

\n\n\n

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\n\n\n

\nParquet

\n\n\n

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\n\n\n

\nApache Parquet

\n\n\n

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\n\n\n

\nDataframes & Parquet

\n\n\n

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\n\n\n

\nStatistics

\n\n\n

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\n\n\n

\nApache Arrow

\n\n\n

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\n\n\n

\nThe Players

\n\n\n

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\n\n\n

\nParquet

\n\n\n

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\n\n\n

\nArrow

\n\n\n

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\n\n\n

\nFeather

\n\n\n

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\n\n\n

\nColumn stats

\n\n\n

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\n\n\n

\nIn each partfile

\n\n\n

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\n\n\n

\nExplicit Metadata

\n\n\n

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\n\n\n

\nPredicate Pushdown

\n\n\n

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\n\n\n

\nA Tale of Two (Parquet) Engines

\n\n\n

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\n\n\n

\n Fastparquet\n

\n\n\n

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\n\n\n

\n Pyarrow\n

\n\n\n

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\n\n\n

\nDask - Partitioning

\n\n\n

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\n\n\n

\nPredicates

\n\n\n

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\n\n\n

\nParquet Filters

\n\n\n

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\n\n\n

\nThe Index

\n\n\n

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\n\n\n

\nDask DataFrames as Key/Value Stores

\n\n\n

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\n\n\n

\nIndexed DataFrames

\n\n\n

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\n\n\n

\nPandas

\n\n\n

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\n\n\n

\nSurrogate Index

\n\n\n

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\n\n\n

\nPartition Structures

\n\n\n

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\n\n\n

\nImplicit

\n\n\n

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\n\n\n

\nExplicit

\n\n\n

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\n\n\n

\nPartitions

\n\n\n

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\n\n\n

\nCase Study - Fancy Indexing

\n\n\n

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\n\n\n

\nA Dask Example

\n\n\n

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\n\n\n

\nThe Problem

\n\n\n

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\n\n\n

\nThe (Ideal) Solution

\n\n\n

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\n\n\n

\nSpace Filling Curves

\n\n\n

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\n\n\n

\nCells

\n\n\n

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\n\n\n

\nThe Index

\n\n\n

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\n\n\n

\nExamples

\n\n\n

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\n\n\n

\nCoverings

\n\n\n

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\n\n\n

\nShapes to Slices

\n\n\n

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\n\n\n

\nConvolutions

\n\n\n

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\n\n\n

\nThe Point

\n\n\n

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\n\n\n

\nDask and Luigi

\n\n\n

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\n\n\n

\nTargets

\n\n\n

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\n\n\n

\nReadings

\n\n\n

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\n\n\n

\nDask

\n\n\n

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\n\n\n

\nParquet

\n\n\n

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\n\n\n

\nDjango and SQL

\n\n\n

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\n\n\n

\nMutability

\n\n\n

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\n\n\n

\nMutability vs State

\n\n\n

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\n\n\n

\nMutable

\n\n\n

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\n\n\n

\nStateful

\n\n\n

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\n\n\n

\nMutable Objects

\n\n\n

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\n\n\n

\nMutable

\n\n\n

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\n\n\n

\nImmutable

\n\n\n

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\n\n\n

\nPop Quiz

\n\n\n

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\n\n\n

\nMutable Objects

\n\n\n

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\n\n\n

\nMutable

\n\n\n

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\n\n\n

\nImmutable

\n\n\n

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\n\n\n

\nImmutable Workflows

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\n\n\n

\nDeterministic Workflows

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\n\n\n

\nLiving Data

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\n\n\n

\nNot every workflow is deterministic

\n\n\n

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\n\n\n

\nWeak Mutability

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\n\n\n

\nStrong Mutability

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\n\n\n

\nSnapshots

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\n\n\n

\nCustom Salts

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\n\n\n

\nData-Driven Salts

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\n\n\n

\nBeware!

\n\n\n

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\n\n\n

\nDjango

\n\n\n

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\n\n\n

\nA Framework

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\n\n\n

\nFull Stack Django

\n\n\n

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\n\n\n

\nWhen you need Django

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\n\n\n

\nWeb App

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\n\n\n

\nYour DB

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\n\n\n

\nMicroservice

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\n\n\n

\nORM

\n\n\n

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\n\n\n

\nThe Python <-> DB Layer

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\n\n\n

\nSQL

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\n\n\n

\nExpression Language

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\n\n\n

\nORM

\n\n\n

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\n\n\n

\nTwo Types of Data Use

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\n\n\n

\nAnalytics

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\n\n\n

\nObjects

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\n\n\n

\nAnalytics - Data Mapper

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\n\n\n

\nObjects - Active Record

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\n\n\n

\nDjango ORM

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\n\n\n

\nDjango Idioms

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\n\n\n

\nDB management

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\n\n\n

\nUniversal Django

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\nORM as Translation

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\n\n\n

\nORM as LCD

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\n\n\n

\nCommon DB’s

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\nDjango Code

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\n\n\n

\nBasic Models

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\nInheritable Models

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\nThe Point

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\nORM Breakdowns

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\nThe Competition

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\n\n\n

\nDjango

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\n\n\n

\nSQLAlchemy

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\n\n\n

\nReadings

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\nAPI’s and Data

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\nMetaprogramming

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\n\n\n

\nWe’ve already seen code as data…

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\n\n\n

\n… and templated code

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\nMetaclasses

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\nStandard Form

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\nThe Metaclass

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\nHold up! What’s the constructor??

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\nMetaclasses

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\n\n\n

\nCase Study - Django Meta Models

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\nDjango Model Inheritance

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\nMulti-Table

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\n\n\n

\nAbstract

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\nProxy

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\nModel Options

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\nModelBase

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\n\n\n

\nExample Use Case

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\nThe Point

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\nPrimitive

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\n\n\n

\nDenormalized

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\n\n\n

\nNormalized

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\n\n\n

\nNormalization

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\n\n\n

\nDenormalized

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\n\n\n

\nNormalized

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\nLive, rolling

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\nAPIs

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\nORM-as-a-Service

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\n\n\n

\nReading

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\n\n\n

\nNow

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\n\n\n

\nUpcoming

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\nThe API

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\nSnippet Example

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\nSerializers

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\n\n\n

\n Requests\n

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\nCLI

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\nPython

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\nMeta Factories

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\n\n\n

\n Factory Boy\n

\n\n\n

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\n\n\n

\nGeneric

\n\n\n

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\n\n\n

\nDjango

\n\n\n

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\n\n\n

\nAlchemy

\n\n\n

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\n\n\n

\nFaker

\n\n\n

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\n\n\n

\nThe Point

\n\n\n

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\n\n\n

\nOptimization

\n\n\n

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\n\n\n

\nRoot of all Evil

\n\n\n

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\n\n\n

\nTale of Two Compilers

\n\n\n

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\n\n\n

\nRaw C/Fortran

\n\n\n

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\n\n\n

\n Cython\n

\n\n\n

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\n\n\n

\n Numba\n

\n\n\n

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\n\n\n

\nCython

\n\n\n

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\n\n\n

\nPure Python

\n\n\n

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\n\n\n

\nVanilla Cython

\n\n\n

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\n\n\n

\nStatic Cython

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\n\n\n

\nNumpy

\n\n\n

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\n\n\n

\nCython Standard

\n\n\n

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\n\n\n

\nNumba

\n\n\n

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\n\n\n

\nJIT

\n\n\n

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\n\n\n

\nSimplified Threading

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\n\n\n

\nSIMD Vectorization

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\n\n\n

\nGPU Acceleration

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\n\n\n

\nReadings

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\n\n\n

\nREST

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\n\n\n

\nFactories

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\n\n\n

\nOptimization

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\n\n\n

\nAlgorithms

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\n\n\n

\nSmart & Lazy Coding

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\n\n\n

\nParallel Code

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\n\n\n

\nMake it faster!

\n\n\n

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\n\n\n

\nPerformance Bottlenecks

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\n\n\n

\nIs Complex

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\n\n\n

\nIs Limited

\n\n\n

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\n\n\n

\nIs Chunky

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\n\n\n

\nIs Waiting

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\n\n\n

\nThreads and Processes

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\n\n\n

\nProcesses

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\n\n\n

\nThreads

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\n\n\n

\nWhat is shared?

\n\n\n

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\n\n\n

\nParallel TL/DR

\n\n\n

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\n\n\n

\nThreads

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\n\n\n

\nProcesses

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\n\n\n

\nMachines

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\n\n\n

\nBasic Pools

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\n\n\n

\nThreads

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\n\n\n

\nProcesses

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\n\n\n

\nGIL and Parallelism

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\n\n\n

\nFrameworks

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\n\n\n

\nLuigi

\n\n\n

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\n\n\n

\nDask

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\n\n\n

\nDask and Parallelism

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\n\n\n

\nMemory Views

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\n\n\n

\nMemoization

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\n\n\n

\nLazy Props

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\n\n\n

\nGlobal Caches

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\n\n\n

\nInstance Cache

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\n\n\n

\nWhy memoize?

\n\n\n

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\n\n\n

\nTime/Memory Tradeoff

\n\n\n

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\n\n\n

\nLazy is Simpler

\n\n\n

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\n\n\n

\nLazy Properties

\n\n\n

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\n\n\n

\nGlobal Cache

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\n\n\n

\nInstance Cache

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\n\n\n

\nKey Equivalence

\n\n\n

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\n\n\n

\nCache Backends

\n\n\n

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\n\n\n

\nBackend Considerations

\n\n\n

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\n\n\n

\nMetadata and Alg

\n\n\n

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\n\n\n

\nMedium

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\n\n\n

\nAuto Purging

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\n\n\n

Least Recently Used

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\n\n\n

Least Frequently Used

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\n\n\n

\nPersistent Caches

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\n\n\n

\nSketching

\n\n\n

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\n\n\n

\nSummary Stats

\n\n\n

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\n\n\n

\nSketches

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\n\n\n

\nSet Testing

\n\n\n

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\n\n\n

\nBloom Filters

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\n\n\n

\nSketches

\n\n\n

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\n\n\n

\nSummarizing Rarity

\n\n\n

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\n\n\n

\nMeasuring Rarity

\n\n\n

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\n\n\n

\nCardinality Estimation

\n\n\n

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\n\n\n

\nUnnecessary!

\n\n\n

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\n\n\n

\nDaily Aggregates

\n\n\n

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\n\n\n

\nThe Point

\n\n\n

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\n\n\n

\nReadings

\n\n\n

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\n\n\n

\nVisualization

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\n\n\n

\nData Viz

\n\n\n

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\n\n\n

\nData Scientists Wear Two Hats

\n\n\n

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\n\n\n

\nCapturing value

\n\n\n

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\n\n\n

\nDemonstrating Value

\n\n\n

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\n\n\n

\nVisualization: Working Modes

\n\n\n

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\n\n\n

\nExploration

\n\n\n

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\n\n\n

\nInteractivity

\n\n\n

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\n\n\n

\nStatic Deploys

\n\n\n

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\n\n\n

\nOne Lib to Rule Them All

\n\n\n

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\n\n\n

\nMPL Backend

\n\n\n

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\n\n\n

\nImperative

\n\n\n

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\n\n\n

\nDeclarative

\n\n\n

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\n\n\n

\nDeclarative Grammars

\n\n\n

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\n\n\n

\nGrammar of Graphics

\n\n\n

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\n\n\n

\nSeaborn - Pidgin (?) of Graphics

\n\n\n

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\n\n\n

\n ggplot2\n

\n\n\n

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\n\n\n

\nR code

\n\n\n

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\n\n\n

\n plotnine\n

\n\n\n

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\n\n\n

\nJavascript and HTML5

\n\n\n

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\n\n\n

\nWhy?

\n\n\n

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\n\n\n

\nInteractivity!

\n\n\n

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\n\n\n

\nDeployability!

\n\n\n

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\n\n\n

\nHTML Deploys

\n\n\n

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\n\n\n

\n D3\n

\n\n\n

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\n\n\n

\n Vega-Lite\n

\n\n\n

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\n\n\n

\nBut Wait! There’s More!

\n\n\n

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\n\n\n

\nHybrids

\n\n\n

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\n\n\n

\nPyViz - The New Breed

\n\n\n

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\n\n\n

\nMicro apps for viz

\n\n\n

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\n\n\n

\nBokeh

\n\n\n

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\n\n\n

\nPyViz HoloViz

\n\n\n

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\n\n\n

\nHoloViews, hvPlot, GeoViews

\n\n\n

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\n\n\n

\nPanel for Apps

\n\n\n

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\n\n\n

\n… and, of course, plotly/dash

\n\n\n

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\n\n\n

\n Streamlit\n

\n\n\n

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\n\n\n

\nColormaps

\n\n\n

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\n\n\n

\nColor for Science

\n\n\n

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\n\n\n

\nKinds of Color

\n\n\n

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\n\n\n

\nQualitative

\n\n\n

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\n\n\n

\nSequential

\n\n\n

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\n\n\n

\nDivergent

\n\n\n

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\n\n\n

\nColorspace

\n\n\n

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\n\n\n

\nQualitative Color

\n\n\n

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\n\n\n

\nSequential Color

\n\n\n

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\n\n\n

\nPerceptually Uniform Maps

\n\n\n

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\n\n\n

\nNever `jet`\n

\n\n\n

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\n\n\n

\nDivergent

\n\n\n

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\n\n\n

\nData Shading

\n\n\n

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\n\n\n

\nBig Data Pitfalls

\n\n\n

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\n\n\n

\nVisualization As Map Reduce

\n\n\n

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\n\n\n

\n Datashader\n

\n\n\n

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\n\n\n

\nReadings

\n\n\n

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\n\n\n

\nWhere We Are

\n\n\n

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\n\n\n

\n“Python”

\n\n\n

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\n\n\n

\nTesting

\n\n\n

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\n\n\n

\nWorkflows

\n\n\n

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\n\n\n

\nHigher Levels

\n\n\n

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\n\n\n

\nDeployment

\n\n\n

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\n\n\n

\nLooping

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nFunctional Coding

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nComposition

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nGraphical Programs

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nData Scaling

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nThe Web

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nDB’s

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nAPI’s

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nMeta

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nOptimization

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nVisualization

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nAppendix

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n

\nChangelog

\n\n\n

This content is not available in the sample book. The book can be purchased on Leanpub at http://leanpub.com/apds.

\n\n\n\n

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\nWorkflows

Table of Contents

\nPreface

\nIntroduction

\nWhat is this book?

\nWho is it for?

\nWhat will you learn?

\nThe state of the book

\nAn introduction to Advanced Python

\nWhat is Advanced Python?

\n… is it an awesome notebook?

\n… or knowing every function?

\n… and writing faster code?

\nIt’s about higher levels

\nIt’s about the science

\nIt’s about repeatability

\n… and standards/enforcement

\nIt’s about collaboration

\nIt’s about the ecosystem

\nIt’s about the deliverable

\nIt’s about you\n

\nTech Requirements

\nPrerequisites

\nPython 3.8

\nIDE

\nDocumentation

\nBlack

\nHelpful Themes

\nTyping

\nCardinality & Ownership

\nCode as Data

\nReadings

\nNow

\nSoon

\nReference

\nWorkflows

\nContinuous science

\nDebugging

\nHow to ask for help

\nWhat did you do?

\nWhat did you expect?

\nWhat happened?

\n5 Why’s

\nWhat did you do?

\nWhat did you expect?

\nWhat happened?

\nDebugging - making progress

\nPset 0 Frequent Issues

\n“Bug Report” Rules

\nA report template

\nPrimers

\nLogging

\nPrint

\nLoggers

\nCode as Data

\nFunctional programming

\nHigher Levels

\nDecorators

\nRegistries

\nWrappers

\nWrapper Case - Typecasting

\nWrapper Case - Lazy Loading

\nContext Managers

\nCtx Mgrs

\nWhen to use Contexts

\nTesting

\nUnit Tests

\nUnit Tests - Example 1

\nUnit Tests - Example 2

\nUnit Tests - Do’s and Don’ts

\nWhat to test

\nTwo faces of testing

\nPlugins - Coverage

\nTest Discovery

\nTest Driven Development

\nTesting tools

\nHypothesis

\nMock

\nMoto

\nReadings

\nWhat is up with `@wraps(func)`?

\nA look at `wraps()`\n

\nSay my `name`!

\nWhat’s in a `name`?

\n`setuptools_scm`\n