Pandas & NumPy Academy · Lesson

isin() and between() Filters

Select rows where a column value is in a list with isin() or within a numeric range with between().

The isin() Method Overview

isin() checks whether each element of a Series is contained in a given list (or set) of values. It returns a boolean Series that you can use directly for row filtering. This is more concise and often faster than chaining multiple == comparisons with |.

For example, instead of (df['country'] == 'USA') | (df['country'] == 'UK'), you write df['country'].isin(['USA', 'UK']).

import pandas as pd

df = pd.DataFrame({
    'country': ['USA', 'Germany', 'UK', 'France', 'USA'],
    'sales': [400, 200, 150, 300, 600]
})

# Check membership
mask = df['country'].isin(['USA', 'UK'])
print(mask.tolist())  # [True, False, True, False, True]

result = df[mask]
print(result)
#   country  sales
# 0     USA    400
# 2      UK    150
# 4     USA    600

isin() with Sets for Speed

You can pass a Python set instead of a list to isin(). Membership look-up in a set is O(1) — it doesn't get slower as the list grows. This matters when your allowed values list has thousands of entries, making set-based isin() significantly faster than list-based isin().

import pandas as pd

df = pd.DataFrame({
    'sku': ['A001', 'B002', 'A003', 'C004', 'B005'],
    'qty': [10, 5, 3, 8, 12]
})

# Use a set for fast membership check
allowed_skus = {'A001', 'A003', 'B005'}
result = df[df['sku'].isin(allowed_skus)]
print(result)
#     sku  qty
# 0  A001   10
# 2  A003    3
# 4  B005   12

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