Extracting parts of a date or time from a timestamp

When working with timestamped market data, you often need to extract specific components like the date, time, or hour. Rather than using slower row-by-row loops, you can use vectorized operations for much better performance.

String slicing with lambda functions

The primary approach uses apply() with lambda functions to extract substrings from timestamp data:

df = liberator.get_dataframe(
    liberator.query(
        name='minute_bars',
        symbols='SPY',
        as_of='2024-07-26',
        back_to='2024-07-25'
    )
)

df['Date'] = df.apply(lambda row: str(row.timestamp)[0:10], axis=1)
df['Time'] = df.apply(lambda row: str(row.timestamp)[11:16], axis=1)
df['Hour'] = df.apply(lambda row: str(row.timestamp)[11:13], axis=1)
df['DateTime'] = df.apply(lambda row: str(row.timestamp)[0:16], axis=1)
df['DateHour'] = df.apply(lambda row: str(row.timestamp)[0:13], axis=1)

df[['timestamp', 'Date', 'Time', 'Hour', 'DateTime', 'DateHour']]

This creates new columns by slicing the timestamp string at specific positions:

Column	Slice	Example Output
`Date`	`[0:10]`	`2024-07-26`
`Time`	`[11:16]`	`14:30`
`Hour`	`[11:13]`	`14`
`DateTime`	`[0:16]`	`2024-07-26 14:30`
`DateHour`	`[0:13]`	`2024-07-26 14`

Alternative: FLOOR method using Pandas DateTime

You can also use pandas built-in datetime methods:

df['seconds'] = df['timestamp'].dt.floor('s')

df['Date'] = pd.to_datetime(df["timestamp"]).dt.date

String slicing with lambda functions provides fast processing for extracting timestamp components, making it preferable to manual loops for DataFrame operations.

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