Talks - Reuven M. Lerner: Times and dates in Pandas

Pandas can store dates/times as datetime64 objects which use 75% less memory than storing as strings and enable rich datetime functionality

Use pd.to_datetime() to convert string dates to datetime objects. For CSV imports, use parse_dates parameter in pd.read_csv()

Two key datetime concepts: specific moments in time (datetime objects) vs spans of time (timedelta objects)

Datetime columns can be accessed via the .dt accessor to extract components like year, month, day, hour etc.

Time series functionality is enabled by setting a datetime column as the index using set_index()

Chronological grouping can be done using pd.Grouper() with frequency codes like ‘1D’ (daily), ‘1M’ (monthly)

Resampling allows aggregating time series data at different frequencies, but requires datetime index

Avoid using inplace=True as it’s being deprecated and prevents method chaining

Time zones can be handled using .tz_localize() to assign zones and .tz_convert() to convert between zones

Invalid datetime parsing can be handled with errors='coerce' to convert bad values to NaT (Not a Time)

Datetime comparisons and sorting work naturally with both datetime strings and objects

Pivot tables and groupby operations work well with datetime components for temporal analysis

PyArrow backend generally handles datetime detection better than default CSV parser