Working with Date and Time Columns

Many datasets include timestamps such as sales logs, sensor data, or user activity.

Pandas makes it easy to convert strings to datetime objects, extract date parts, and filter by time ranges.

Converting Strings to Datetime

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

Convert column to datetime

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

This allows pandas to treat dates as real datetime objects so you can sort, filter, and extract specific components.

Useful Date Components

Once converted, you can extract parts like:

Access date components

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.

Time-Based Filtering

You can also filter by date ranges:

Filter after a certain date

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

This is useful for analyzing data over time or comparing different periods.

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