Extract First Value from Comma Separated Column in Pandas

Use dt.day_of_week to get the day number from a #Python #Pandas datetime series. But is 0 Sunday or Monday? Or not used at all? Better: Use the day_name method: df['x'].dt.day_name() Good news: It returns a series of strings! Bad news: It's 4x slower.

Generate a #Python #Pandas DatetimeIndex of datetime values, specifying the frequency, by passing freq and a code: pd.date_range(start='2026-03-01', end='2026-05-01', freq='8h') This returns a 184-element DatetimeIndex with evenly-spaced datetime values in that period.

Want to take a #Python #Pandas series of strings, and get datetime values? Use pd.to_datetime: pd.to_datetime(df['x']) Notice: It's not a method! It's a top-level pd function. Specify a non-standard "format" with a strftime string: pd.to_datetime(df['x'], format='%d/%m/%Y')

Any #Python function parameters after *args are keyword-only: def myfunc(*args, k): return f'{args=}, {k=}' Want to give k a value? Use a keyword argument: myfunc(10, 20, 30, k=100) Don't need *args? Just use *, and k is keyword only: def myfunc(*, k): return f'{k=}'

A lot of developers try to make their code “clean” by following a checklist. Small classes. Short functions. Everything abstracted behind services and containers. The result often looks tidy, but changing the behaviour of the program is still hard. The real logic is scattered across too many layers. In today’s video, I refactor a small Python program and show why optimizing for smallness often leads to worse designs. What actually matters is cohesion: grouping things that change together and making the workflow of the program easy to understand. 👉 Watch the full video here: https://lnkd.in/g-PFkk3C. #python #softwaredesign #cleancode #refactoring #arjancodes

Day 38/100 – #100DaysOfCode 🚀 Solved LeetCode #2011 – Final Value of Variable After Performing Operations (Python). Today I practiced string handling and simple iteration to compute the final value after a series of operations. Approach: 1) Initialize a variable x = 0. 2) Traverse through each operation in the list. 3) If the operation contains "++", increment x by 1. 4) Otherwise, decrement x by 1. 5) Return the final value of x. Time Complexity: O(n) Space Complexity: O(1) Simple logic but great practice for handling strings and loops 💪 #LeetCode #Python #DSA #Strings #ProblemSolving #100DaysOfCode

Reading a CSV into #Python #Pandas, and want a column to be treated as datetime values? Use parse_dates: df = pd.read_csv(filename, parse_dates=['x', 'y']) Bonus: The PyArrow engine often (not always) parses columns that look like dates.

Many Python beginners get confused with this concept. List vs Tuple. At first, they look the same. But they are not. Here’s the simple difference: List: • Mutable (can be changed) • Uses square brackets [] • Example: numbers = [1, 2, 3] Tuple: • Immutable (cannot be changed) • Uses parentheses () • Example: numbers = (1, 2, 3) So when should you use them? ✓ Use a List when your data can change ✓ Use a Tuple when your data should stay fixed Understanding this small difference can make your code better and more efficient. 😳 Did you know this difference before? #python #listvstuple #blujay #blujaytechnologies

How rustystats makes use of Python & Rust to fit GLMs. Faster, less memory required, and more accurate*. I've had this conversation a few times over the last couple of weeks - it is quite common to use other languages for heavy computation within Python libraries. This is how I used Rust to make fitting GLMs as fast and memory efficient as I could. You can find the code here: https://lnkd.in/e4cR7gwc *based on the single conversion GLM I migrated from statsmodel that made use of ordered target encoding for categorical factors and a monotonic spline for one of the continuous factors.

Generate a #Python #Pandas DatetimeIndex of datetime values with pd.date_range: pd.date_range(start='2026-03-01', end='2026-05-01', periods=10) This returns a 10-element series with evenly-spaced datetime values in that period.