Set Python Pandas Datetime to Midnight

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.

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.

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.

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

🚀 Day 13 of My Python + DSA Journey Today’s problem focused on frequency counting 👇 ✅ Majority Element (#169) 💡 Majority Element Find the element that appears more than n/2 times 🔍 Approach: Used hashmap → counted frequency and returned element exceeding n/2 ⏱ O(n) time | O(n) space 🔥 What I learned today: • Hashmaps make counting problems simple • Frequency-based logic is very common in arrays • Early exit improves efficiency Getting faster at recognizing patterns ⚡ #Day13 #LeetCode #Python #DSA #CodingJourney #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.

Want to check if two #Python #Pandas datetimes are in the same month/quarter/year? Convert them both to periods, and compare with ==: date1.to_period('M') == date2.to_period('M') # same month? date1.to_period('Q') == date2.to_period('Q') # same quarter?

Friday brings #Python Let me share another trick. Are you ready for a simple one? You can merge two dictionaries in Python by using the | operator. Isn't that elegant?

Have you used #Python's `divmod()` function before? If not, `divmod()` takes two numbers as arguments and returns a tuple containing their quotient and remainder in a single operation

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')