#!/usr/bin/env python
# coding: utf-8

# # Python and Jupyter Overview
# 
# ![](images/python_jupyter.png)

# ## Python

# ### Python is...

# * a *high-level*, *structured*, *open-source*, *interpreted* programming language

# * a really good choice for almost any programming task

# * a very popular and effective choice for data science tasks

# According to StackOverflow Trends, more than 11 percent of Stack Overflow questions were tagged with "python" in late 2018. All other languages fell well short of this number:
# 
# | Language | Percent |
# |----------|---------|
# | Python   | 11.2%   |
# | Java     | 7.7%    |
# | C++      | 2.75%   |
# | R        | 2.7%    |
# | Matlab   | < 1%    |
# | Scala    | < 1%    |
# | SAS      | < 1%    |
# | Julia    | < 1%    |

# More recently, the [2021 Stack Overflow Developer Survey](https://insights.stackoverflow.com/survey/2021) shows that Python is used by over 48% of all "developers"
# 
# <center>
# <img src="images/so_dev_survey.png" alt="Stack Overflow Dev Survey" width="50%" height="50%">
# </center>

# ### Python in the real world
# 
# Python is one of the *the most popular programming languages in the world*. It's commonly used for:
# 
# * Application development
# * Scripting
# * Automation
# * Testing
# * Data science

# ### Python in data science
# 
# As previously mentioned, Python is also a popular choice in data science. For reference:

#   * According to KDNuggets, 65.6 percent of data scientists used Python regularly in 2018. This was an *increase* from 54 percent in 2017.

#   * In contrast, R was used by 48.5 percent of data scientists in 2018. This was a *decrease* from 63 percent in 2017.

# <div class="admonition note alert alert-info">
#     <b><p class="first admonition-title" style="font-weight: bold">Note</p></b>
#     <p><b>Disclaimer</b>: R is terrific and an <i>excellent</i> tool for data science.</p>
# </div>

# ### Why are data scientists choosing Python?

#   * It can do anything...so everybody uses it
#  

# * Consistency across engineering and data science teams

#   * Open-source and community support

#   * Concise syntax, readability and ease-of-use

#   * Strength in numeric computations and cutting edge data science libraries

# ## Jupyter

# ### JupyterLab is...

# * a *language-agnostic integrated development environment (IDE)* specializing in **notebooks**

# * a popular choice among data scientists using Python

# <div class="admonition note alert alert-info">
#     <b><p class="first admonition-title" style="font-weight: bold">Note</p></b>
#     <p>We've chosen to use Jupyter over other popular IDEs for this course but that does not mean it is always the best IDE for writing Python.</p>
# </div>

# ### Why are data scientists choosing Jupyter?

# * Ad-hoc analyses and science development

# * Synchronous data visualizations

# * Documentation of code with accompanying comments

# * Flexibility through extensions

# ### Reasons you may want to use another IDE...

# * Jupyter is not good for developing *scripts*, or Python code that isn't run interactively

# * It's not trivial to install and launch Jupyter without a tech background

# <div class="admonition note alert alert-info">
#     <b><p class="first admonition-title" style="font-weight: bold">Note</p></b>
#     <p>We've minimized these challenges for this workshop by using Binder.  If you are interested in installing and using Jupyter and Python on your own machine, we recommend using <a href="https://jupyter.readthedocs.io/en/latest/install.html">Anaconda</a> to do so.</p>
# </div>

# ## Jupyter Basics

# ### Launching Jupyter 
# 
# Let's begin by launching Jupyter by opening the [Binder repository](https://mybinder.org/v2/gh/uc-python/intro-python-datasci/master?urlpath=lab). This is how we will access Jupyter throughout the course.

# You should see a screen like this:
# 
# ![binder-launching.png](images/binder-launching.png)

# Jupyter can also be launched via Anaconda Navigator:
# 
# <center>
# <img src="images/navigator-jupyter.png" alt="navigator-jupyter.png" width="900" height="900">
# </center>

# Note that we want to launch the JupyterLab option for this class.

# ### Jupyter File Structure

# As you can see, Jupyter displays a file browser when it launches:
# 
# ![jupyter-file-structure.png](images/jupyter-file-structure.png)

# There is just one directory for you to worry about:
# 
# **notebooks** - interactive slideshows and code for you to follow along with

# ### Jupyter Notebooks

# The **notebook** is the core file used to interact with Python from Jupyter. A few details:

# * Notebooks allow the writing AND running of Python code

# * Notebooks are organized by **cells** - code and commentary text goes in the cells

# * All notebook files have the extension `.ipynb` (**i**nteractive **py**thon **n**ote**b**ook)

# A new Jupyter notebook can be opened from the "launcher" page, which opens automatically when you start JupyterLab.
# 
# There may be multiple options listed in the Notebooks section, as seen here. These are different Python installations available on your computer. You should choose **Python 3** in this case.
# 
# ![open-jupyter-notebook](images/open-jupyter-notebook.png)

# This will open a new notebook with a Python 3 kernel:
# 
# ![new-jupyter-notebook](images/new-jupyter-notebook.png)

# ### Notebook Cells

# As previously mentioned, Jupyter notebooks are organized by **cells**. These cells are at the core of a notebook:

# * When using Jupyter, all Python code is typed into and run from a cell

# * Comments, markdown, HTML, LaTeX can also be rendered within a cell

# ### Code Cells
# 
# By default, all cells are code cells. This means Python code can be run by simply:
# 
# 1. Clicking on a cell's input area
# 2. Typing Python code into the cell
# 3. Pressing CTRL + RETURN (or SHIFT + RETURN)

# The results of the code will be printed to the output area:
# 
# ![python-code-cell.png](images/python-code-cell.png)

# ### Comment/Markdown/HTML/LaTeX Cells
# 
# Cells can be converted to text-oriented cells by:
# 
# 1. Selecting a cell by clicking on it
# 2. Clicking the "Code" dropdown on the edit panel
# 3. Clicking the "Markdown" option
# 
# ![markdown-cell-selection.png](images/markdown-cell-selection.png)

# Text can then be typed into these cells (regular text, markdown, HTML, LaTeX):
# 
# ![markdown-cell-unrendered.png](images/markdown-cell-unrendered.png)

# And it can be rendered by pressing CTRL + RETURN:
# 
# ![markdown-cell-rendered.png](images/markdown-cell-rendered.png)

# ### Inserting New Cells
# 
# New cells can be inserted by selecting a cell by clicking on it and
# 
# * Clicking the "+" menu button add a new cell below the selected one
# * Or using the keyboard shortcut `"a"` to insert cell above or `"b"` to insert cell below
# 
# ![insert-new-cell.png](images/insert-new-cell.png)

# ### Your Turn
# 
# 1. Create or open a notebook in Jupyter.
# 2. Create a new *markdown* cell. Write your name in it.
# 3. Create a new *code* cell. Write `x = 5` and run it.

# ### Additional Tips

# * Notebooks can be saved by clicking "File" -> "Save Notebook As..."

# * An easy way to find a feature or its related keyboard shortcut is "View" -> "Activate Command Palette"

# * Notebooks can be downloaded from Binder in numerous formats by clicking "File" -> "Download"
#   * This is a great way to save your work when using Binder. These notebooks can then be reloaded at any time.
#   
#      <div class="admonition warning alert alert-danger">
#       <b><p class="first admonition-title" style="font-weight: bold">Caution!</p></b>
#       <p>New content created in Binder does not persist across sessions. Also, Binder tends to shutdown after a few minutes of inactivity. Save and download your work accordingly!</p>
#      </div>

# ## Questions
# 
# Are there any questions before moving on?