Positron IDE: Data Analysis with Python in Jupyter Notebooks and Python Script Files (Public Beta)

Timeline ---
00:00 Intro to Positron IDE for data science.
00:15 Focus on Python; other videos cover R.
00:27 Check Python interpreter or environments.
01:10 View environment and interpreter details.
01:23 Switch environments; auto-detects/install dependencies.
01:54 Auto-installs ipykernel if missing.
02:02 Transition between environments.
02:35 Use the new project wizard if needed.
02:45 Create Jupyter notebook or Python project.
03:32 Set up Python environment with Venv or Conda.
04:12 Skip adding new environment; already set up.
04:30 New Notebook vs. New File options.
04:40 Selecting New File lists document options.
05:10 Create Python Notebook on welcome screen.
05:20 Run code immediately in the new notebook.
05:30 Describe code cell execution and output display.
06:03 Cell toolbar actions available.
06:35 Save notebook with Cmd+S (macOS) or Ctrl+S (Windows).
06:55 Create a new folder, save the notebook.
07:25 Saved notebook path and breadcrumb navigation.
08:10 Add code and markdown cells from the toolbar.
09:10 Switch between Python environments and kernels.
10:05 Run cells, view variable values in the session tab.
10:39 Environment issues when switching kernels.
11:09 Variables and data types in the session tab.
11:33 Separation between Jupyter notebook kernel and console.
13:00 Open folder, refresh Positron to execute code.
13:13 Trust authors to allow code execution.
13:57 Set and start interpreter for workspace.
14:14 Reopen notebook, restart kernel, run all cells.
14:45 Hide Explorer tab for more screen space.
15:21 Clear outputs, run cells again.
15:55 Use a pre-made notebook with data analysis code.
16:40 Launch pre-made notebook from Explorer tab.
16:45 Select notebook kernel, start running code.
17:00 Running code updates variables tab with function data.
17:20 Print statements for all cell outputs.
17:47 Keyboard shortcuts for Jupyter Notebook.
18:10 Run cell to download data, clickable URLs.
18:45 Download and access dataset in project directory.
18:55 Use automagic commands to navigate notebook and data.
19:13 Load data into 'penguins' variable, view in session tab.
19:25 Variable viewer details, data frame variables, observations.
20:12 Trigger data viewer inline with %view.
21:33 Interactive data viewer features.
24:07 Actions in data viewer don't affect pandas' data frame.
24:35 Use pandas commands to view data.
24:50 Function help documentation under Help tab.
26:22 Visualization libraries: matplotlib, plotnine, seaborn.
26:45 Use %%capture to suppress output, %pip to install package.
27:48 Interactive visualization libraries: Bokeh, Plotly, Altair.
28:00 Interactivity in Bokeh plot.
28:48 Set render for Plotly plots in Positron.
29:19 Altair for visualization.
29:30 Display summaries.
29:45 Help entry and correlation matrix.
30:10 Handle missing data.
30:30 Create linear regression models with statsmodels, visualize with Seaborn.
31:16 Export notebook to PDF, HTML, Python script.
33:03 Exporting to Python script generates non-runnable script.
33:39 Workable Python script with # %% or line by line execution.
35:04 Console shows Pandas table with HTML formatting.
35:28 Graphs shown in lower right plot window.
36:00 Plot history viewer, navigate previous plot iterations.
36:50 Interactive plots work.
37:23 Final notes.
Summary ---
We look at the Positron IDE's Python capabilities in terms of data analysis within a Jupyter Notebook and Python Script. We aim to use a pre-existing Python interpreter and associate the code files within a workspace directory. We explore many features from static to interactive plots and using pandas data frames within a notebook and console session. Moreover, we discuss the notebook session being detached from the console session.
Links ---
Data location:
github.com/coatless/raw-data/...
Relevant script file:
github.com/coatless-videos/po...
Positron Interactive Data Viewer Wiki Page
github.com/posit-dev/positron...
Positron can be obtained from:
github.com/posit-dev/positron
Version information ----
This was demonstrated on:
Positron Version: 2024.06.1 (Universal) build 27
Code - OSS Version: 1.90.0
Commit: a893e5b282612ccb2200102957ac38d3c14e5196
Date: 2024-06-26T02:08:06.673Z
Electron: 29.4.0
Chromium: 122.0.6261.156
Node.js: 20.9.0
V8: 12.2.281.27-electron.0
OS: Darwin arm64 23.5.0
#positron #posit #rstudio #jupyternotebook #plotnine #seaborn #pandas #plotly #csv

[@arthuryounger9340]

Thanks for an excellent practical introduction to python in Positron!

[@thecoatlessprofessor7674]

Glad it was helpful!

[@ortayakarsk705]

Do you think 4 gb rtx3050 enough for data analysis?

[@thecoatlessprofessor7674]

The Positron IDE and Python are RAM and CPU hungry. Unless you are fitting a neural network/doing deep learning, the amount of RAM on a GPU is unlikely to play a role in being able to do data analysis effectively.

[@_jhc]

Is there support for using this IDE with Databricks?

[@thecoatlessprofessor7674]

You can establish a connection using odbc to a Databricks cluster via its connection panel, c.f.
solutions.posit.co/connections/db/databases/databricks/

[@_jhc]

@@thecoatlessprofessor7674 Thank you!