Python Pandas DatetimeIndex with pd.date_range

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 set the time on a #Python #Pandas series of datetimes to midnight? Use dt.normalize: df['x'].dt.normalize() You get back series of datetime values, but all times are 00:00:00.

Want to find which time period contains a #Python #Pandas datetime? Use to_period: pd.Timestamp.now().to_period('1D') pd.Timestamp.now().to_period('6W') This returns a Pandas "Period" object, useful (among other things) for checking if two dates are within the same 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 28 OF PYTHON CHALLENGE #Wapp for append() function (dynamically) l=[ ] n=int(input("enter how many entries u want?")) for i in range(n): a=input("enter the elements") l.append(a) print(l) output: enter how many entries u want 3 enter the element 100 enter the element abc enter the element 9.9 ['100','abc','9.9'] #coding #python #challenge #mca #30days

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?

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')

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.

Have a #Python #Pandas series with datetime values, and want all those until now? Compare with pd.Timestamp.now(): df.loc[ pd.col('when') < pd.Timestamp.now() ] This returns the rows from df where the "when" column is before now.

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