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Multiple Conditions
Real-world filtering rarely involves just one condition. Pandas lets you combine conditions using the logical operators & ("and") and | ("or").
AND Condition
Say I want to filter for basketball players averaging between 20 and 30 points per game:
solid_scorers = df[(df["ppg"] >= 20.0) & (df["ppg"] <= 30.0)]
print(solid_scorers)
# player team ppg status
# 0 Jayson Tatum Celtics 26.9 active
# 13 Devin Booker Suns 27.1 active
# 15 Jrue Holiday Celtics 21.4 injured
Each condition must be wrapped in its own parentheses when combined: (condition_1) & (condition_2). Leaving them out causes a Python operator precedence error.
Don't Confuse Operators
In Pandas, always use & and | instead of and and or.
We can't use the regular Python logical operators because they work with single True/False values. When filtering a DataFrame, we're comparing entire Series at once.
OR Condition
Say we want the records for players at the extremes – either bench warmers (under 5 ppg) or superstars (over 35 ppg):
extreme_scorers = df[(df["ppg"] < 5) | (df["ppg"] > 35)]
print(extreme_scorers)
# player team ppg status
# 4 Joel Embiid 76ers 35.3 active
# 9 Payton Pritchard Celtics 4.1 active
# 16 Luka Doncic Mavericks 38.7 active
You can mix & and | in a single filter – just keep each piece wrapped in parentheses so the grouping is clear.
Assignment
SnackStack's support team knows a device needs a technician's attention when it's online and either a fridge running too warm or an oven running too cold.
Complete the find_devices_needing_attention function. It accepts a DataFrame of device readings and returns only the rows that need attention.
The DataFrame has device_id, device_type, temperature_celsius, and status columns.