df.query("salary > 100000")

df.query("name == 'john'")

df.query("name == 'john' | name == 'jack'")

df.query("name == 'john' and salary > 100000")

df.query("name.str.contains('a')")

# Grab top 1% of earners
df.query("salary > salary.quantile(.99)")

# Make more than the mean
df.query("salary > salary.mean()")

# Subset by top 3 most frequent products purchased
df.query("item in item.value_counts().nlargest(3).index")

# Query for null values
df.query("column.isnull()")

# Query for non-nulls
df.query("column.notnull()")

# @ - allows you to refer to variables in the environment
names = ["john", "fred", "jack"]
df.query("name in @names")

# Reference columns with spaces using backticks - Pandas >= 0.25
df.query("`Total Salary` > 100000")