Jupyter notebooks from my YouTube videos

Do you enjoy my YouTube channel (https://YouTube.com/reuvenlerner)? If so, then you've probably noticed I use Jupyter and Marimo notebooks in nearly every video. This repo contains the notebooks from some of my videos. As I do more videos, I'll add them here.

If you have comments about my videos, or suggestions for topics I can/should address in the future, please let me know! You can reach me most easily at [email protected].

If you like my videos, you might also like:

"Better developers," with new, free articles about Python every week: https://BetterDevelopersWeekly.com/
"Bamboo Weekly," where I analyze data related to current events using Pandas: https://www.BambooWeekly.com/
My Bluesky feed: https://bsky.app/profile/lernerpython.com
LinkedIn, where I also post: https://linkedin.com/in/reuven
My first book, "Python Workout": https://PythonWorkout.com/
My second book, "Pandas Workout": https://PandasWorkout.com/

Want to get access to all of my courses, along with tons of exercises, live office hours, and a private Discord server where you can ask any questions you have? Check out my classes at https://LernerPython.com/ . The first two weeks are free!

Name		Name	Last commit message	Last commit date
Latest commit History 92 Commits
subdir		subdir
args and *kwargs.ipynb		args and *kwargs.ipynb
.gitignore		.gitignore
Advanced unpacking in Python loops.ipynb		Advanced unpacking in Python loops.ipynb
All about identifiers.ipynb		All about identifiers.ipynb
Assignment expression (aka "the walrus").ipynb		Assignment expression (aka "the walrus").ipynb
Averages in pandas.ipynb		Averages in pandas.ipynb
BW 106 — using Pandas to check flu infections.ipynb		BW 106 — using Pandas to check flu infections.ipynb
BW 107 — Consumer confidence.ipynb		BW 107 — Consumer confidence.ipynb
BW 108 — Measles.ipynb		BW 108 — Measles.ipynb
BW 109 - cocoa prices.ipynb		BW 109 - cocoa prices.ipynb
BW 110 -- credit access .ipynb		BW 110 -- credit access .ipynb
BW 111 -- state taxes.ipynb		BW 111 -- state taxes.ipynb
BW 112 -- number of programmers.ipynb		BW 112 -- number of programmers.ipynb
BW 113 -- US airports.ipynb		BW 113 -- US airports.ipynb
BW 115 -- Sahm rule.ipynb		BW 115 -- Sahm rule.ipynb
BW 116 -- Philadelphia Fed survey.ipynb		BW 116 -- Philadelphia Fed survey.ipynb
BW 117 - Electricity.ipynb		BW 117 - Electricity.ipynb
BW 126 - Electric vehicles.ipynb		BW 126 - Electric vehicles.ipynb
Better inputs with Rich.ipynb		Better inputs with Rich.ipynb
Boolean indexes.ipynb		Boolean indexes.ipynb
By value or by reference?.ipynb		By value or by reference?.ipynb
Commas in Python numbers — input and output.ipynb		Commas in Python numbers — input and output.ipynb
Comparing different types.ipynb		Comparing different types.ipynb
Comparing groupby and pivot tables.ipynb		Comparing groupby and pivot tables.ipynb
Constant vs. immutable.ipynb		Constant vs. immutable.ipynb
Context managers in Python.ipynb		Context managers in Python.ipynb
Data selection with filter.ipynb		Data selection with filter.ipynb
Datetime index superpowers.ipynb		Datetime index superpowers.ipynb
Dispatch tables.ipynb		Dispatch tables.ipynb
Don't use dict.keys to search in a dict.ipynb		Don't use dict.keys to search in a dict.ipynb
Don't use dot notation in Pandas!.ipynb		Don't use dot notation in Pandas!.ipynb
Dummies in Pandas.ipynb		Dummies in Pandas.ipynb
Exceptions.ipynb		Exceptions.ipynb
Extension DTypes.ipynb		Extension DTypes.ipynb
Filenames and paths.ipynb		Filenames and paths.ipynb
Five common Pandas mistakes.ipynb		Five common Pandas mistakes.ipynb
For loops vs. comprehensions.ipynb		For loops vs. comprehensions.ipynb
Fun with dtype_backend.ipynb		Fun with dtype_backend.ipynb
Function return values.py		Function return values.py
Grouping on dates.ipynb		Grouping on dates.ipynb
Grouping.ipynb		Grouping.ipynb
Immutable vs. hashable.ipynb		Immutable vs. hashable.ipynb
Intro to pivot tables in Pandas.ipynb		Intro to pivot tables in Pandas.ipynb
Jupyter keyboard hints video.ipynb		Jupyter keyboard hints video.ipynb
JupyterLite.ipynb		JupyterLite.ipynb
List comprehensions in Python.ipynb		List comprehensions in Python.ipynb
List sizes.ipynb		List sizes.ipynb
Lists in Python functions.ipynb		Lists in Python functions.ipynb
Lists vs. tuples.ipynb		Lists vs. tuples.ipynb
Loops and loop variables.ipynb		Loops and loop variables.ipynb
Magic commands.ipynb		Magic commands.ipynb
Method chaining in Pandas.ipynb		Method chaining in Pandas.ipynb
Mistakes learning Python.ipynb		Mistakes learning Python.ipynb
Modified variables.ipynb		Modified variables.ipynb
Most useful read_csv options.ipynb		Most useful read_csv options.ipynb
Multiple CSV to data frame.ipynb		Multiple CSV to data frame.ipynb
Multiple assignment gotchas.ipynb		Multiple assignment gotchas.ipynb
NumPy 2.0 and Pandas.ipynb		NumPy 2.0 and Pandas.ipynb
One function, Python three different errors.ipynb		One function, Python three different errors.ipynb
Optimizing your Pandas method chains.ipynb		Optimizing your Pandas method chains.ipynb
Pandas and slices.ipynb		Pandas and slices.ipynb
Pandas groupby gotchas.ipynb		Pandas groupby gotchas.ipynb
Pandas performance and Python built-in functions.ipynb		Pandas performance and Python built-in functions.ipynb
Parsing files with walruses, etc..ipynb		Parsing files with walruses, etc..ipynb
Passing keyword arguments as ** vs. a dict.ipynb		Passing keyword arguments as ** vs. a dict.ipynb
Peeking at Python bytecodes.ipynb		Peeking at Python bytecodes.ipynb
Pending input error in Jupyter.ipynb		Pending input error in Jupyter.ipynb
Python Pandas series vs. data frame.ipynb		Python Pandas series vs. data frame.ipynb
Python identifiers.ipynb		Python identifiers.ipynb
Python's "range" builtin.ipynb		Python's "range" builtin.ipynb
Python's ternary operator.ipynb		Python's ternary operator.ipynb
README.md		README.md
Randomly sampling your data frame.ipynb		Randomly sampling your data frame.ipynb
Regular expressions in Pandas.ipynb		Regular expressions in Pandas.ipynb
Removing dict pairs with a particular value.ipynb		Removing dict pairs with a particular value.ipynb
Replacing values in Pandas.ipynb		Replacing values in Pandas.ipynb
Retrieving from a multi-index with .loc and .xs.ipynb		Retrieving from a multi-index with .loc and .xs.ipynb
Searching in dicts and dict views.ipynb		Searching in dicts and dict views.ipynb
Shallow vs. deep copies.ipynb		Shallow vs. deep copies.ipynb
Simple interpolation.ipynb		Simple interpolation.ipynb
Sorting in Pandas.ipynb		Sorting in Pandas.ipynb
Sorting letter-number combinations.ipynb		Sorting letter-number combinations.ipynb
Sorting lists of strings vs. lists of ints.ipynb		Sorting lists of strings vs. lists of ints.ipynb
Stack and unstack.ipynb		Stack and unstack.ipynb
Stop using inplace=True!.ipynb		Stop using inplace=True!.ipynb
Strings, immutability, and +=.ipynb		Strings, immutability, and +=.ipynb
The "glob" module.ipynb		The "glob" module.ipynb
The "replace" method in Pandas.ipynb		The "replace" method in Pandas.ipynb
The columns keyword argument.ipynb		The columns keyword argument.ipynb
Underscores in numbers.ipynb		Underscores in numbers.ipynb
Understanding mask indexes.ipynb		Understanding mask indexes.ipynb
Understanding the SettingWithCopyWarning in Pandas.ipynb		Understanding the SettingWithCopyWarning in Pandas.ipynb
Unicode characters and Python.ipynb		Unicode characters and Python.ipynb
Untitled.ipynb		Untitled.ipynb
Untitled1.ipynb		Untitled1.ipynb
Untitled2.ipynb		Untitled2.ipynb
Untitled3.ipynb		Untitled3.ipynb
Untitled4.ipynb		Untitled4.ipynb
Untitled5.ipynb		Untitled5.ipynb

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