Working with Date and Time Columns

Many datasets include timestamps — like sales records, sensor readings, or user activity logs.

Pandas simplifies working with these by letting you convert strings into datetime objects, extract specific components, and filter by time periods.

To work with dates, first convert them from string format using:

Copy
df["Date"] = pd.to_datetime(df["Date"])

This converts the column into true datetime objects, allowing you to easily sort, filter, and extract specific components.

Once converted, you can extract parts like:

Copy
df["Year"] = df["Date"].dt.year
df["Weekday"] = df["Date"].dt.day_name()

You can now analyze trends by year, month, or day of the week.

You can also filter by date ranges:

Copy
df[df["Date"] > "2023-01-01"]

This technique is especially useful for analyzing trends over time or comparing specific time periods.