<jupyter_start><jupyter_text>What is Jupyter Notebooks?Jupyter is a web-based interactive development environment that supports multiple programming languages, however most commonly used with the Python programming language.The interactive environment that Jupyter provides enables students, scientists, and researchers to create reproducible analysis and formulate a story within a single document.Lets take a look at an example of a completed Jupyter Notebook: [Example Notebook](http://nbviewer.jupyter.org/github/cossatot/lanf_earthquake_likelihood/blob/master/notebooks/lanf_manuscript_notebook.ipynb)  Jupyter Notebook Features* File Browser* Markdown Cells & Syntax* Kernels, Variables, & Environment* Command vs. Edit Mode & Shortcuts  What is Markdown?Markdown is a markup language that uses plain text formatting syntax.  This means that we can modify the formatting our text with the use of various symbols on our keyboard as indicators.Some examples include:* Headers* Text modifications such as italics and bold* Ordered and Unordered lists* Links* Tables* Images* Etc.Now I'll showcase some examples of how this formatting is done: Headers: H1 H2 H3 H4 H5 H6 Text modifications:Emphasis, aka italics, with *asterisks* or _underscores_.Strong emphasis, aka bold, with **asterisks** or __underscores__.Combined emphasis with **asterisks and _underscores_**.Strikethrough uses two tildes. ~~Scratch this.~~ Lists:1. First ordered list item2. Another item  * Unordered sub-list. 1. Actual numbers don't matter, just that it's a number  1. Ordered sub-list4. And another item.* Unordered list can use asterisks- Or minuses+ Or pluses Links:http://www.umich.edu[The University of Michigan's Homepage](www.http://umich.edu/)To look into more examples of Markdown syntax and features such as tables, images, etc. head to the following link: [Markdown Reference](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)  Kernels, Variables, and EnvironmentA notebook kernel is a “computational engine” that executes the code contained in a Notebook document. There are kernels for various programming languages, however we are solely using the python kernel which executes python code.When a notebook is opened, the associated kernel is automatically launched for our convenience.<jupyter_code>### This is python
print("This is a python code cell")<jupyter_output>This is a python code cell<jupyter_text>A kernel is the back-end of our notebook which not only executes our python code, but stores our initialized variables.<jupyter_code>### For example, lets initialize variable x

x = 1738

print("x has been set to " + str(x))
### Print x

print(x)<jupyter_output><empty_output><jupyter_text>Issues arrise when we restart our kernel and attempt to run code with variables that have not been reinitialized.If the kernel is reset, make sure to rerun code where variables are intialized.<jupyter_code>## We can also run code that accepts input

name = input("What is your name? ")

print("The name you entered is " + name)<jupyter_output><empty_output><jupyter_text>It is important to note that Jupyter Notebooks have in-line cell execution.  This means that a prior executing cell must complete its operations prior to another cell being executed.  A cell still being executing is indicated by the [*] on the left-hand side of the cell.<jupyter_code>print("This won't print until all prior cells have finished executing.")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.06491633006347375, 'fraction_numerical': 0.004327755337564916, 'mean_word_length': 5.591254752851711, 'pattern_counts': {'":': 0, '<': 16, '<?xml version=': 0, '>': 16, 'https://': 1, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '16381', 'license': 'MIT', 'n_tokens_mistral': 956, 'n_tokens_neox': 898, 'n_words': 433, 'path': 'Understanding_and_Visualizing_Data_with_Python-master/week1/introduction_jupyter.ipynb', 'repo_name': 'rezapci/UofM_Statistics_with_Python_Specialization', 'size': '3466'}