Difference between revisions of "Jupyter"
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== Tutorial == | == Tutorial == | ||
− | There's a great tutorial at [https://www.dataquest.io/blog/jupyter-notebook-tutorial/ https://www.dataquest.io/blog/jupyter-notebook-tutorial/]! A few notes: | + | There's a great tutorial at [https://www.dataquest.io/blog/jupyter-notebook-tutorial/ https://www.dataquest.io/blog/jupyter-notebook-tutorial/]! You do not need to sign in or click the Start Free button to follow the tutorial. |
− | * CTRL-Enter runs the current cell; SHIFT-Enter runs the current cell and provides a new empty cell below it. SHIFT-Enter is generally the way to go | + | |
− | * ESC and ENTER toggle between command mode and edit mode. In edit mode, there is a pencil icon at the top right; in command mode, there isn't. Also, if you click in the edit part of a cell you enter edit mode; if you click in the space between the In []: and the >| you enter command mode. | + | A few notes: |
− | * In addition to the commands shown, Markdown understands basic LaTeX (Greek letters, fractions, integrals, etc). Use single $ around commands for inline and $$ around commands for displaymath. | + | * What is a Jupyter Notebook? |
− | * For the commands that print formatted strings, the tutorial uses the string modulo method. To relate this to using format, and also to using the new (as of Python 3.6) f-string, here are three ways of printing the same information:<syntaxhighlight lang=python> | + | ** Nothing to add |
+ | * How to Follow This Tutorial | ||
+ | ** Nothing to add | ||
+ | * Installation | ||
+ | ** You can skip this if you already have Anaconda and have already started Jupyter Notebook in a browser. | ||
+ | * Creating Your First Notebook | ||
+ | ** You should already be at the '''Running Jupyter''' phase. | ||
+ | ** The New-> Python 3 might look like New->Python 3 (ipykernel) | ||
+ | ** In the '''Cells''' part: | ||
+ | *** CTRL-Enter or the $$\blacktriangleright\!\shortmid$$ runs the current cell; SHIFT-Enter runs the current cell and provides a new empty cell below it. SHIFT-Enter is generally the way to go as it runs the cell and gives you a new input line (rather than having to insert a new one) | ||
+ | *** ESC and ENTER toggle between command mode and edit mode. In edit mode, there is a pencil icon at the top right; in command mode, there isn't. Also, if you click in the edit part of a cell you enter edit mode; if you click in the space between the In []: and the >| you enter command mode. | ||
+ | ** In the '''Markdown''' part: | ||
+ | *** In addition to the commands shown, Markdown understands basic LaTeX (Greek letters, fractions, integrals, etc). Use single $ around commands for inline and $$ around commands for displaymath. | ||
+ | ** In the '''Kernels''' part: | ||
+ | *** For the commands that print formatted strings, the tutorial uses the string modulo method. To relate this to using format, and also to using the new (as of Python 3.6) f-string, here are three ways of printing the same information:<syntaxhighlight lang=python> | ||
+ | a = 2 | ||
+ | b = 4 | ||
+ | c = 2.5 | ||
+ | d = 6.25 | ||
# string modulo | # string modulo | ||
print('%d squared is %d and %0.2e squared is %0.2e' % (a, b, c, d)) | print('%d squared is %d and %0.2e squared is %0.2e' % (a, b, c, d)) | ||
Line 20: | Line 38: | ||
print(f'{a:d} squared is {b:d} and {c:0.2e} squared is {d:0.2e}') | print(f'{a:d} squared is {b:d} and {c:0.2e} squared is {d:0.2e}') | ||
</syntaxhighlight>You can see that all three are similar; the f-string puts the variable at the same location it will end up printing in the string rather than way at the end. | </syntaxhighlight>You can see that all three are similar; the f-string puts the variable at the same location it will end up printing in the string rather than way at the end. | ||
− | * The "Setup" section in the middle of the page starts to go into some advanced data analysis with Pandas; they always give you the code, but it may be confusing! Also: | + | |
− | ** You will need to have saved their data file to the folder where you are saving your notebook. The file is in the "Example Data Analysis in a Jupyter Notebook" section way at the top of the page, or you can get it from [https://s3.amazonaws.com/dq-blog-files/fortune500.csv https://s3.amazonaws.com/dq-blog-files/fortune500.csv]. | + | * Example Analysis |
− | ** There needs to be a carriage return after "import seaborn as sns" | + | ** The "Setup" section in the middle of the page starts to go into some advanced data analysis with Pandas; they always give you the code, but it may be confusing! Also: |
+ | *** You will need to have saved their data file to the folder where you are saving your notebook. The file is in the "Example Data Analysis in a Jupyter Notebook" section way at the top of the page, or you can get it from [https://s3.amazonaws.com/dq-blog-files/fortune500.csv https://s3.amazonaws.com/dq-blog-files/fortune500.csv]. | ||
+ | *** There needs to be a carriage return after "import seaborn as sns" |
Revision as of 03:35, 13 January 2023
This page is meant to be a startup guide for using Jupyter Notebooks with Python. It assumes you have installed Anaconda from https://www.anaconda.com/. Most of this guide was written running Python 3.9 and Jupyter Notebooks 6.4.12.
Starting Up
- To start Jupyter Notebooks with Anaconda:
- On Windows, go to the Anaconda folder in the Start Menu or open the Anaconda Navigator and start Jupyter Notebooks from there.
- On macOS, open the Anaconda Navigator and start Jupyter Notebooks from there.
Depending on your settings, you may get a new browser that points to your localhost or you may get a window with a web address that you need to copy and paste into a web browser (in which case, do that). In either case, the end result should be a web page open to the jupyter page with tabs for Files, Running, and Clusters.
Tutorial
There's a great tutorial at https://www.dataquest.io/blog/jupyter-notebook-tutorial/! You do not need to sign in or click the Start Free button to follow the tutorial.
A few notes:
- What is a Jupyter Notebook?
- Nothing to add
- How to Follow This Tutorial
- Nothing to add
- Installation
- You can skip this if you already have Anaconda and have already started Jupyter Notebook in a browser.
- Creating Your First Notebook
- You should already be at the Running Jupyter phase.
- The New-> Python 3 might look like New->Python 3 (ipykernel)
- In the Cells part:
- CTRL-Enter or the $$\blacktriangleright\!\shortmid$$ runs the current cell; SHIFT-Enter runs the current cell and provides a new empty cell below it. SHIFT-Enter is generally the way to go as it runs the cell and gives you a new input line (rather than having to insert a new one)
- ESC and ENTER toggle between command mode and edit mode. In edit mode, there is a pencil icon at the top right; in command mode, there isn't. Also, if you click in the edit part of a cell you enter edit mode; if you click in the space between the In []: and the >| you enter command mode.
- In the Markdown part:
- In addition to the commands shown, Markdown understands basic LaTeX (Greek letters, fractions, integrals, etc). Use single $ around commands for inline and $$ around commands for displaymath.
- In the Kernels part:
- For the commands that print formatted strings, the tutorial uses the string modulo method. To relate this to using format, and also to using the new (as of Python 3.6) f-string, here are three ways of printing the same information:You can see that all three are similar; the f-string puts the variable at the same location it will end up printing in the string rather than way at the end.
a = 2 b = 4 c = 2.5 d = 6.25 # string modulo print('%d squared is %d and %0.2e squared is %0.2e' % (a, b, c, d)) # format print('{:d} squared is {:d} and {:0.2e} squared is {:0.2e}'.format(a, b, c, d)) # f-string print(f'{a:d} squared is {b:d} and {c:0.2e} squared is {d:0.2e}')
- For the commands that print formatted strings, the tutorial uses the string modulo method. To relate this to using format, and also to using the new (as of Python 3.6) f-string, here are three ways of printing the same information:
- Example Analysis
- The "Setup" section in the middle of the page starts to go into some advanced data analysis with Pandas; they always give you the code, but it may be confusing! Also:
- You will need to have saved their data file to the folder where you are saving your notebook. The file is in the "Example Data Analysis in a Jupyter Notebook" section way at the top of the page, or you can get it from https://s3.amazonaws.com/dq-blog-files/fortune500.csv.
- There needs to be a carriage return after "import seaborn as sns"
- The "Setup" section in the middle of the page starts to go into some advanced data analysis with Pandas; they always give you the code, but it may be confusing! Also: