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# 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.

<br/>

## Converting Strings to Datetime

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

```python title="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.

<br/>

## Useful Date Components

Once converted, you can extract parts like:

```python title="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.

<br/>

## Time-Based Filtering

You can also filter by date ranges:

```python title="Filter after a certain date"
df[df["Date"] > "2023-01-01"]
```

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

Pandas provides a straightforward way to transform string representations of dates into datetime objects using the pd.to_datetime() function. This conversion is crucial as it enables more advanced operations like sorting, filtering, and extracting specific date components, which are essential for time series analysis.

### Pandas allows you to convert string data directly into datetime objects for easier analysis.