แยกวิเคราะห์วันที่และกำหนด dtypes ขณะโหลด

When pandas reads a file it guesses each column's dtype. Get types right at load time and every later step gets simpler. 🧱

A date like 2024-03-01 looks like a date to you, but pandas reads it as a plain string unless you ask otherwise.

The parse_dates option converts listed columns into real datetime values during the read, no extra step needed.

Once a column is a real datetime, you can sort by time, filter date ranges, and pull out the month with ease.

For unusual layouts, pass a format string so pandas parses fast and never guesses wrong on day-month order.

The dtype option lets you declare a column's type yourself, overriding pandas guesses with a clean dictionary.

Codes like 00123 lose their zeros if read as numbers. Forcing the column to string preserves them exactly as written.

Reading whole numbers as int32 instead of the default int64 can halve memory on big files. Choose the smallest type that fits.

Columns with few repeated values, like a status field, shrink dramatically when stored as the category dtype.

Always confirm the result. Checking dtypes shows exactly how pandas typed each column so surprises never reach your analysis.

Missed one? You can still convert afterward with astype, but doing it at load is cleaner and a little faster.

A column of dates is loading as plain text. What fixes it during read?

You can now parse dates, force string and numeric types, and use category for repeats, all at load time. Verify with dtypes and move on. 🎉