iiinova — where your digital self keeps evolving

Reuven M. Lerner @lernerpython.com · 55m

AI coding is easy, right? Write a #ClaudeCode prompt, and you're done. Um, no: It's new techniques to learn and master Join the 6th cohort of HOPPy (Hands-on projects in #Python). You'll implement a game of your own design, with me mentoring. More info: buff.ly/cKGgcyf

000

Reuven M. Lerner @lernerpython.com · 11h

Replace NaN values in a #Python #Pandas data frame with fillna: 1. Pass it a single value to replace everywhere df.fillna(999) 2. Pass a series/dict whose keys/index match columns; values fill matching columns. df.fillna(df.mean()) df.fillna({'x':999, 'y':888, 'z':777})

000

Reuven M. Lerner @lernerpython.com · 29/09/2026

Are you using #ClaudeCode? As you know, building robust, maintainable software is more than prompts. At tomorrow's Advanced Claude Code workshop, you'll learn to build more robust, maintainable code. Take your Claude Code to the next level: Learn more: buff.ly/qwOKjsW

011

Reuven M. Lerner @lernerpython.com · 29/09/2026

Drop rows with *only* NaN values in a #Python #Pandas data frame with the how='all' keyword argument: df.dropna(how='all') This removes completely empty rows, leaving rows containing even one non-NaN value (aka thresh=1).

020

Reuven M. Lerner @lernerpython.com · 28/09/2026

I'm excited to announce the next HOPPy (Hands-On Projects in #Python), a 5-week course in which you'll use #ClaudeCode to write a game that *you* design. No theory — just learning by doing. Want to hear more? Come to Tuesday's free HOPPy 5 info session: buff.ly/VvPbxHO

020

Reuven M. Lerner @lernerpython.com · 28/09/2026

Drop rows containing NaN in a #Python #Pandas data frame, but only in specific columns, with "subset": df.dropna() # drops rows containing NaN df.dropna(subset='x') # drops rows where column x is NaN df.dropna(subset=['x', 'y']) # drops rows where x or y is NaN

030

Reuven M. Lerner @lernerpython.com · 27/09/2026

You can remove NaN from a #Python #Pandas data frame with dropna, but be careful: It removes rows with even one NaN, which can be overkill. Pass "thresh" to allow some rows to remain, even if they contain NaN: df.dropna(thresh=2) # keeps rows with 2 or more non-NaN values

150

Reuven M. Lerner @lernerpython.com · 26/09/2026

Want to remove NaN from a #Python #Pandas series? Three basic options, all returning a new series: - s.dropna, NaNs (and their indexes) go away - s.fillna(n), NaNs are replaced by n - s.interpolate, NaNs are replaced via a linear function

020

Reuven M. Lerner @lernerpython.com · 25/09/2026

Coming to #Python #Pandas from NumPy? You'll reach for np.isnan: s = Series([10, np.nan, 30]) s.loc[ ~np.isnan(s) ] # returns 10, 30 Unfortunately, this works. Better, use s.isna (or s.isnull). But the best way to drop NaN? Use dropna: s.dropna() # returns 10, 30

020

Reuven M. Lerner @lernerpython.com · 25/09/2026

It's easy to write #Python with AI. But it's not as easy to build maintainable, well-engineered applications. On Wednesday, join my hands-on, advanced Claude Code workshop. Topics include custom skills, testing, and telemetry. More info at: buff.ly/qwOKjsW

010

Reuven M. Lerner @lernerpython.com · 24/09/2026

Want to write NaN in #Python #Pandas, but are annoyed that NumPy removed it? Or: Tired of importing NumPy, just for np.nan? I learned *yesterday* that you can say: float('nan') or float('NaN') Both return a perfectly usable NaN, good in NumPy and Pandas. Wow. 🤯

021

Reuven M. Lerner @lernerpython.com · 23/09/2026

How often are AI incidents happening? Are they becoming more frequent? Who is getting harmed? In the latest Bamboo Weekly, we analyze the AI incident database using #Python #Pandas — plus joins, JSON, pivot tables, and Plotly. More info: buff.ly/BO8uMFy

000

Reuven M. Lerner @lernerpython.com · 23/09/2026

PyArrow dtypes in #Python #Pandas are nullable (with pd.NA): s = Series([10, pd.NA, 30], dtype='int64[pyarrow]') s is: 0 10 1 <NA> 2 30 dtype: int64[pyarrow] The dtype is int64, but allows nulls. (Use np.nan? It's turned into pd.NA.)

010

Reuven M. Lerner @lernerpython.com · 22/09/2026

You have a #Python #Pandas series with ints + NaN. You don't want float forced on you. Solution: Use the "extension" type Int64 (note Initial Caps) and pd.NA: s = Series([10, pd.NA, 30], dtype='Int64') # must name explicitly s is: 0 10 1 <NA> 2 30 dtype: Int64

020

Reuven M. Lerner @lernerpython.com · 21/09/2026

Values in a #Python #Pandas series all have the same dtype. np.nan is a float, so (1) the series gets a float dtype, or (2) it's "object," meaning "something unspecific." Annoying for integers. Awful for other types. For this, we need nullable types! (Coming tomorrow...)

020

Reuven M. Lerner @lernerpython.com · 20/09/2026

Missing data? NumPy calls it nan. #Python #Pandas displays it as NaN. But: Pandas doesn't define pd.nan or pd.NaN. NumPy removed np.NaN in version 2.0. So you have to refer to np.nan from within Pandas, and it'll be displayed as NaN.

020

Reuven M. Lerner @lernerpython.com · 19/09/2026

NumPy is very strict about nan. But #Python #Pandas throws nan values away: s = Series([10, 20, 30, np.nan, 50]) s.sum() # np.float64(110.0) s.mean() # np.float64(27.5) s.count() # np.int64(4) Get stricter behavior with skipna=False: s.sum(skipna=False) # np.float64(nan)

030

Reuven M. Lerner @lernerpython.com · 18/09/2026

Missing data in #Python #Pandas? We use nan ("not a number"), which comes from NumPy. np.nan is a float, but not a normal one: np.nan + 10 # nan np.nan * 12345 # nan sum([10, 20, np.nan]) # nan np.nan == 0 # False np.nan == np.nan # False

010

Reuven M. Lerner @lernerpython.com · 17/09/2026

What is a "callable" in #Python? Typically, a function or class. But really, it's anything with __call__ defined: class MyClass: def __call__(self): return 'Hi!' m = MyClass() m() # 'Hi!' The "callable" builtin basically returns True if it finds __call__.

010

Reuven M. Lerner @lernerpython.com · 16/09/2026

If you invoke +=, #Python prefers __iadd__ to __add__: __iadd__ returns (a modified) self, not a new object -- so id doesn't change: class MyClass: def __init__(self, x): self.x = x def __iadd__(self, other): self.x = self.x + other.x return self

130

Reuven M. Lerner @lernerpython.com · 15/09/2026

In just 24 hours, this month's hands-on Claude Code #Python workshops begin — 2 intro and 1 advanced. Come and learn how to create maintainable, robust, and secure software — not just vibe-coding on a whim. Learn more at buff.ly/Ra3TBkG.

010

Reuven M. Lerner @lernerpython.com · 15/09/2026

If you invoke +=, #Python can use __add__. MyClass implements __add__ (calling print for debugging): m1 = MyClass(10) m2 = MyClass(20) m1 += m2 # prints "Now in MyClass.__add__" m1 now refers to a new object, and its repr is: MyClass instance, vars(self)={'x': 30}

010

Reuven M. Lerner @lernerpython.com · 14/09/2026

Reading lots of newsletters? Want to convert them into a personal PDF? Check out Newsprint, my #Python project that reads your mailbox, grabs starred messages, lets you choose non-starred ones, and creates a PDF. Give it a whirl: buff.ly/s5Bm02C

010

Reuven M. Lerner @lernerpython.com · 14/09/2026

How does the "in" operator work in #Python? - If an object defines __contains__, then its (boolean) result is returned (coerced to bool). - If not, then Python iterates over it with __iter__ Can you find and return a result faster than __iter__? Then define __contains__.

030

Reuven M. Lerner @lernerpython.com · 13/09/2026

Consider this #Python code: s1 = {10, 20, 30} s1_alias = s1 s2 = {20, 30, 40} s1 = s1 | s2 What is s1_alias? Still {10, 20, 30}, because a new set was assigned to s1. If we used s1 |= s2, then s1_alias would change -- because |= (aka __ior__) mutates the left-hand value.

130

Reuven M. Lerner @lernerpython.com · 12/09/2026

Sets in #Python use the | operator (aka the __or__ magic method) for "union": s1 = {10, 20, 30} s2 = {20, 30, 40} s1 | s2 # returns {10, 20, 30, 40} Want to assign the result to s1? Use |= instead, which runs the __ior__ method: s1 |= s2 s1 is now {10, 20, 30, 40}

031

Reuven M. Lerner @lernerpython.com · 11/09/2026

I used Buffer to post this, and didn't have an image of my own. I guess it substituted one, even though I told it not to. Argh. I guess I should make a logo for the project, to avoid such problems in the future!

020

Reuven M. Lerner @lernerpython.com · 11/09/2026

In #Python, we use "or" for conditions. | is bitwise (not boolean) "or": x = 10 # 0b1010 y = 13 # 0b1101 x | y # 15, or 0b1111 | runs __or__. On dicts, | combines. d1 = {'a':10, 'b':2} d2 = {'b':10, 'c':30} d1 | d2 # {'a': 10, 'b': 10, 'c': 30}, right side wins

010

Reuven M. Lerner @lernerpython.com · 11/09/2026

Do you receive lots of e-mail newsletters? Then check out Newsprint, my latest open-source #Python project. Newsprint turns e-mail newsletters into a personalized PDF "newspaper," suitable for printing and offline reading. Check it out, and let me know what you think: buff.ly/s5Bm02C

pypi.org

newsprint

Fetch starred newsletters over IMAP, reduce them to article content, and print them quarter-sheet, four to a side, duplex.

210

Reuven M. Lerner @lernerpython.com · 10/09/2026

The % in #Python, on numbers, is modulo: x = 10 y = 3 x % y # 1 x.__mod__(y) # same thing, 1 But str uses __mod__ for interpolation: s = 'Hi, %d' s % y # 'Hi, 3' s.__mod__(y) # Same thing, 'Hi, 3' Same operator, same magic method — but totally different.

020

Reuven M. Lerner @lernerpython.com · 09/09/2026

How are the schools in your country? The results from PISA 2025 are out, ranking education systems in dozens of countries. In the latest Bamboo Weekly, we use #Python #Pandas to better understand some of this data. Level up your data-analysis skills every Wednesday: buff.ly/cMTVKZf

020

Reuven M. Lerner @lernerpython.com · 09/09/2026

Just as + in #Python invokes __add__, other operators invoke other magic methods: • - is __sub__ • * is __mul__ • / is __truediv__ • // is __floordiv__ • % is __mod__ • ** is __pow__ Your methods can do whatever you want. But do try to stick to conventional expectations.

020

Reuven M. Lerner @lernerpython.com · 08/09/2026

If you tell #Python x + 5 it'll run x.__add__(5), which handles the int 5. But what about 5 + x int.__add__ doesn't know how to handle x. So it returns NotImplemented — and Python turns it around, calling: x.__radd__(5) That's the "reverse add" magic method!

040

Reuven M. Lerner @lernerpython.com · 08/09/2026

"Computers don't do what you want them to do. They do what you tell them to do." That's still true with agents. The gap just got bigger — and validation matters more than the results. I'm giving 3 #ClaudeCode + #Python workshops this month. Join my free info session on Sept 14: buff.ly/IKiazYj

010

Reuven M. Lerner @lernerpython.com · 07/09/2026

Use duck typing, and your #Python object knows how to + with other types: class C: def __init__(self, x): self.x = x def __add__(self, o): if hasattr(o, 'x'): return C(self.x + o.x) else: return C(self.x + o)

010

Reuven M. Lerner @lernerpython.com · 06/09/2026

If your #Python class implements __add__, it should usually return a new instance of your class, not an int: class C: def __init__(self, x): self.x = x def __add__(self, other): return C(self.x + other.x) c1 = C(10) c2 = C(15) vars(c1 + c2) # {'x':25}

020

Reuven M. Lerner @lernerpython.com · 05/09/2026

How can your #Python object support +? Implement __add__: class C: def __init__(self, x): self.x = x def __add__(self, other): return self.x + other.x c1 = C(10) c2 = C(15) c1 + c2 # 25

000

Reuven M. Lerner @lernerpython.com · 04/09/2026

Operators in #Python are turned into "magic" method calls: x + y # becomes x.__add__(y) So: x = 10 y = 'hello' x + y # unsupported operand type(s) for +: 'int' and 'str' y + x # can only concatenate str (not "int") to str Different errors from different methods!

120

Reuven M. Lerner @lernerpython.com · 03/09/2026

A #Python function's code is on its __code__ attribute. Which means you can do this: def add(a, b): return a + b def sub(a, b): return a - b add.__code__, sub.__code__ = sub.__code__, add.__code__ add(50, 20) # 30 sub(50, 20) # 70 Impractical ... but fun!

031

Reuven M. Lerner @lernerpython.com · 03/09/2026

I'm sure that there are ways AI can be used to help teach, learn, and reinforce ideas in every field. I trust MIT to ensure people and fundamental skills aren't lost in the hype, and that AI is used to assist in thinking and human creativity, rather than to replace it.

000

Reuven M. Lerner @lernerpython.com · 02/09/2026

Because #Python functions' defaults are kept in __defaults__, avoid mutable defaults: def add1(x=[]): x.append(1) # same as: add1.__defaults__[0].append(1) ‼️ return x add1() # [1] add1() # [1, 1] add1() # [1, 1, 1] 🤯 add1.__defaults__ # ([1, 1, 1],)

000

Reuven M. Lerner @lernerpython.com · 01/09/2026

Python uses #Python to implement Python! def add(x=5, y=10): return x + y Now you can call it as: add() # 15 add(8) # 18 add(8, 3) # 11 The defaults are stored in a tuple: add.__defaults__ # (5, 10) Missing args are assigned from __defaults__.

010

Reuven M. Lerner @lernerpython.com · 31/08/2026

If a #Python function adds 10 to an argument, co_consts includes 10. But the compiler sees a tiny number, and emits LOAD_SMALL_INT instead of LOAD_CONST. But if you're adding 1m to an argument? That's big enough to use LOAD_CONST, using the index in co_consts.

020

Reuven M. Lerner @lernerpython.com · 30/08/2026

Python uses #Python to implement Python! An inline int/string in your function is stored in co_consts (a tuple) at compile time. Each constant is stored only once, even if used multiple times in the function: def plus10(n): return n + 10 plus10.__code__.co_consts # (10,)

020

Reuven M. Lerner @lernerpython.com · 29/08/2026

Class attributes in #Python are normally in a dict called __dict__: class MyClass: x = 100 def __init__(self): self.y = 200 Notice: 'x' in MyClass.__dict__ # True '__init__' in MyClass.__dict__ # True 'y' in MyClass.__dict__ # False - instance, not class!

030

Reuven M. Lerner @lernerpython.com · 28/08/2026

When a #Python generator yields, its stack frame remains — inspect it for the current line and local variables: def fib(): x = 0 y = 1 while True: yield x x, y = y, x+y g = fib() next(g) g.gi_frame.f_locals # returns {'x': 0, 'y': 1}

021

Reuven M. Lerner @lernerpython.com · 27/08/2026

Ugh -- the US and Canada are in a trade war. But what does each country export to the other? How much? Which states/provinces trade the most? And what do they trade, anyway? The latest Bamboo Weekly poses 5 #Python #Pandas challenges about US/Canada trade data. Try it: bambooweekly.com

000

Reuven M. Lerner @lernerpython.com · 27/08/2026

Python uses #Python to implement Python! Given: def add(a:int, b:int) -> dict[str,int]: return {'a':a, 'b':b, 'total':a+b} Where is this stored? in a dict, add.__annotations__: {'a': int, 'b': int, 'return': dict[str, int]}

010

Reuven M. Lerner @lernerpython.com · 26/08/2026

Python uses #Python to implement Python! class A: pass class B(A): pass class C(A): pass class D(B, C): pass Parent classes are in the __bases__ tuple: print(B.__bases__) # (<class '__main__.A'>,) print(D.__bases__) # (<class '__main__.B'>, <class '__main__.C'>)

030