What are the different ways to filter rows in a Pandas DataFrame?

import pandas as pd

df = pd.DataFrame({
    'city':     ['NYC', 'LA', 'NYC', 'Chicago', 'LA'],
    'revenue':  [120, 85, 200, 55, 140],
    'category': ['A', 'B', 'A', 'C', 'B'],
})

# Boolean indexing — most common
df[df['revenue'] > 100]

# Compound conditions — use & | ~ (not and/or)
df[(df['city'] == 'NYC') & (df['revenue'] > 100)]

# isin — membership test
df[df['city'].isin(['NYC', 'LA'])]

# between — inclusive range
df[df['revenue'].between(80, 150)]

# str methods for text filtering
df[df['city'].str.startswith('N')]
df[df['city'].str.contains('C', case=False)]

# query() — string-based, readable for complex conditions
df.query('city == "NYC" and revenue > 100')
df.query('revenue > @threshold', local_dict={'threshold': 100})
# @ prefix references a Python variable inside query string

# filter() — filter columns or index labels (NOT rows by content)
df.filter(like='rev')    # columns whose name contains 'rev'
df.filter(regex='^c')   # columns starting with 'c'