Data Science with Python explained
An overview of using Python for data science
including Numpy, Scipy, pandas, Scikit‑
Learn, XGBoost, TensorFlow andKeras.
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.
Don’t worry — Python is one of the easiest programming languages to
learn. And thanks to the hard work of thousands of open source
contributors, you can do data science, too.
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
Using Python for Data Science
Numeric computation in Python
Statistical analysis in Python
Data manipulation in Python
Working with databases in Python
13 min read
Data engineering in Python
Big data engineering in Python
Further statistics in Python
Machine learning in Python
Deep learning in Python
Data science APIs in Python
Applications in Python
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
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
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
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
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.
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