Fixing Python Function Error with Positional Only Argument

Load custom #Python modules into #Excel 🤯 The *real* Python in Excel i.e., #xlwings Lite (https://lnkd.in/eMuEm773), just got better! Felix Zumstein recently announced the new feature in xlwings Lite to drag and drop data files straight into Excel (https://lnkd.in/ePVR9jUd), that was exciting! Now you can mount your own personal #Python modules into #Excel! See here (https://lnkd.in/epPcXYPw) how to do it. This is proof that you can import your own custom Python modules into Excel. Really exciting! 🤩

I haven't properly announced this feature yet, but yes, you can now import or mount local Python files that automatically register as scripts in your Run button or as custom functions across all workbooks (and you can import them into main.py). It works like the venerable "Personal.xlsb" file with VBA. It's a little smarter though, as you can filter modules to only load with certain workbooks. E.g., whenever I open a Stripe report, my cleanup_stripe script automatically appears. Since these files are on your local machine, you can track them with Git or store them on OneDrive, etc. See https://lnkd.in/e_CU9tmP #pythoninexcel #python #microsoftexcel

As far as i know, xlwings from Felix Zumstein is still the Best way to use Python in Excel sheets. It's been a while Microsoft said Python will be embedded in Excel... but where is it ?

Calling astype on #Python #Pandas series with unconvertible values? Use errors='ignore' to skip the error: s = pd.Series([10, np.nan, 30]) s.astype('int8') # raises IntCastingNaNError s.astype('int8', errors='ignore') # keeps original dtype

How many NaNs in a #Python #Pandas series? Use isna (returns True/False) then sum/value_counts: s = pd.Series([10, 20, np.nan, 40, 50, np.nan, 70, 80]) s.isna().sum() # returns 2 s.isna().value_counts() # count True/False Hint: Pass normalize=True for the percentage

When retrieving a slice with loc in #Python #Pandas, the end point is included, highly unusual in Python! s = pd.Series([10, 20, 30, 40, 50], index=list('abcde')) s.loc['a':'c'] # a, b, c (inclusive!) s.iloc[0:2] # a, b (exclusive, like Python)

How many NaNs in a #Python #Pandas data frame? 1. Use isna, returning a data frame of booleans 2. Use sum, returning the number of NaNs in each column 3. Use sum again, adding those numbers df.isna().sum().sum()

While working on the Valid Palindrome problem recently, I noticed a small detail that was easy to overlook. I initially wrote: `s.lower()` expecting the string to update. But in Python, strings are immutable — lower() returns a new string, it doesn’t modify the original one. The correct step was: `s = s.lower()` What stood out to me wasn’t the fix itself, but how easy it is to miss behaviors like this when focusing only on the overall logic. It was a good reminder that many bugs don’t come from complex algorithms, but from not fully understanding how basic operations behave. Still learning, and trying to be more deliberate about these small but important details. #LeetCode #Python #Strings

Most Python code I see still counts things manually. Something like this: counts = {} for item in data: counts[item] = counts.get(item, 0) + 1 It works. But Python has had a built-in solution for years: from collections import Counter counts = Counter(data) Cleaner code is nice. But the real advantage is clarity of intent. When someone reads Counter(data), they don’t have to figure out what the code is doing. They immediately know why it exists. Small differences like this scale more than people realize. #Python #DataAnalytics

Want to guarantee that no one can run "from .. import *" on your #Python module? Include a non-existent name in the __all__ list: x = 100 y = [10, 20, 30] __all__ = ['YOU_SHOULD_NOT_USE_IMPORT_*'] Using "from .. import *" on this module will result in an error!