Instructions Getting Started With Essential Python Libraries
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Instructions: Getting Started With Python
Adapted from “Hands-On Machine Learning with Scikit-Learn and TensorFlow:
Concepts, Tools, and Techniques to Build Intelligent Systems” by Aurélien Géron
The goal of the following instructions is to get you ready with the essential python
libraries. After going through the steps in this document, you should refer to jupyter
notebook series on “Data Scientist’s Handbook”.
The instructions are based on following python libraries. Note there are more instructions
and advanced techniques that we will learn in the course. Following instructions are
meant for the first few weeks of the semester. Then, we will dive into more sophisticated
techniques.
NumPy: Base n-dimensional array package
SciPy: Fundamental library for scientific computing
Matplotlib: Comprehensive 2D/3D plotting
Pandas: Data structures and analysis
Scikit-Learn: Production-ready Python framework to implement many Machine
Learning algorithms efficiently
Note: for the course assignments, you are not allowed to use the Scikit-Learn or any
Machine Learning libraries (unless being told to do so). However, once you have
learned the fundamentals of frequently used Machine Learning models, it is extremely
useful to include Scikit-Learn, TensorFlow and other learning libraries in your toolbox.
1. Install Python
https://www.python.org/
2. Create the Workspace
Next you need to create a workspace directory for your Machine Learning code and
datasets. Open a terminal and type the following commands (after the $ prompts):
$ export ML_PATH="$HOME/ml"
$ mkdir -p $ML_PATH
# You can change the path if you prefer
3. Install Python Modules
You will need a number of Python modules: Jupyter, NumPy, Pandas, Matplotlib, and
Scikit-Learn.
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To install these modules, use Python’s own packaging system “pip”. pip is a recursive
acronym that can stand for either “Pip Installs Packages” or “Pip Installs Python”. pip is a
package management system used to install and manage software packages written in
Python. One major advantage of pip is the ease of its command-line interface, which
makes installing Python software packages as easy as issuing one command. Python 3.4
and later include pip (pip3 for Python 3) by default.
First check whether pip is already installed.
$ pip3 --version
You should make sure to have a recent version of pip installed, at the very least >1.4 to
support binary module installation (a.k.a. wheels). To upgrade the pip module, type:
$ pip3 install --upgrade pip
Now you can install all the required modules and their dependencies using this simple pip
command:
$ pip3 install --upgrade jupyter matplotlib numpy pandas scipy scikit-learn
To check your installation, try to import every module like this:
$ python3 -c "import jupyter, matplotlib, numpy, pandas, scipy, sklearn"
After successful installation, explore these modules starting with NumPy and Pandas.
Note on NumPy: Make sure that you are well-versed in Linear Algebra operations with
NumPy.
Note on Pandas: The Pandas library in built on NumPy. It is one of the most preferred
tools for data scientists to do data manipulation and analysis, next to matplotlib for data
visualization and NumPy, the fundamental library for scientific computing in Python on
which Pandas was built. There should be no output and no error.
Pandas deals with the following three data structures: Series, DataFrame and Panel.
These data structures are built on top of NumPy array, which means they are fast.
Frequently you will use its DataFrame structure.
A DataFrame is a general 2D labeled, size-mutable tabular structure with potentially
heterogeneously typed columns. Building and handling two or more dimensional arrays
is a tedious task, burden is placed on the user to consider the orientation of the data set
when writing functions. But using Pandas data structures, the mental effort of the user is
reduced.
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Note on Jupyter Notebook: The Jupyter Notebook App is a server-client application that
allows editing and running notebook documents via a web browser. The Jupyter
Notebook App can be executed on a local desktop requiring no Internet access (as
described in this document) or can be installed on a remote server and accessed through
the Internet.
With this brief background on the Python modules, you are ready to fire up Jupyter by
typing:
$ jupyter notebook
A Jupyter server is now running in your terminal, listening to port 8888. You can visit
this server by opening your web browser to http://localhost:8888/ (this usually happens
automatically when the server starts). You should see your empty workspace directory.
4. Create Python Notebook
Create a new Python notebook by clicking on the “New” button and selecting the
appropriate Python version.
This does three things.
First, it creates a new notebook file called Untitled.ipynb in your workspace.
Second, it starts a Jupyter Python kernel to run this notebook.
Third, it opens this notebook in a new tab. You should start by renaming this notebook to,
for example, “Grade” (this will automatically rename the file to Grade.ipynb) by clicking
Untitled and typing the new name.
Note: A notebook contains a list of cells. Each cell can contain executable code or
formatted text. Right now the notebook contains only one empty code cell, labeled “In
[1]:”. Try typing print("Life is Beautiful!") in the cell, and click on the play button or
press Shift-Enter. This sends the current cell to this notebook’s Python kernel, which
runs it and returns the output. The result is displayed below the cell, and since we reached
the end of the notebook, a new cell is automatically created.
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Go through the User Interface Tour from Jupyter’s Help menu to learn the basics.
If you have finished up to this point, then turn to jupyter notebook series on “Data
Scientist’s Handbook”.
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