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More Sorting
The .sort_index() method sorts a DataFrame by its index (the row labels) instead of by a column's values.
After filtering, those labels are often out of order. Sorting by index is also particularly handy when you've set a custom index like string IDs or timestamps.
# Sort by index
df_sorted = df.sort_index()
# Sort by index in descending order
df_sorted = df.sort_index(ascending=False)
In-Place Sorting
By default, .sort_values() returns a new DataFrame. Pass inplace=True to modify the original instead:
df.sort_values("close_price", inplace=True)
Handling Missing Values
Real market data has gaps. You can control where NaN ("Not a Number") values land in the sorted output:
# NaN values at the end (the default)
df_sorted = df.sort_values("close_price", na_position="last")
# NaN values at the beginning
df_sorted = df.sort_values("close_price", na_position="first")
This matters any time you sort by a numeric column that has missing values.
Assignment
SnackStack's field technicians need a daily service route. Devices that failed to report a temperature (a NaN reading) are the most suspicious, and everything else follows, hottest first.
Complete the build_service_route function. It accepts a DataFrame and returns a DataFrame ordered for the technician's route.
Sort by temperature_celsius in descending order with NaNs positioned first.