between Method¶

The between() method filters values within a range, inclusive of both endpoints.

Basic Usage¶

Filter values in a range.

1. Numeric Range¶

import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'age': [22, 28, 35, 42, 55]
})

# Filter age between 25 and 40
result = df[df['age'].between(25, 40)]
print(result)

      name  age
1      Bob   28
2  Charlie   35

2. Inclusive by Default¶

Both endpoints are included (25 and 40).

3. Boolean Result¶

mask = df['age'].between(25, 40)
print(mask)

0    False
1     True
2     True
3    False
4    False
Name: age, dtype: bool

inclusive Parameter¶

Control endpoint inclusion.

1. Both Endpoints (Default)¶

df['age'].between(25, 40, inclusive='both')
# Includes 25 and 40

2. Neither Endpoint¶

df['age'].between(25, 40, inclusive='neither')
# Excludes 25 and 40

3. Left or Right Only¶

df['age'].between(25, 40, inclusive='left')   # Includes 25 only
df['age'].between(25, 40, inclusive='right')  # Includes 40 only

Date Ranges¶

Filter dates within a period.

1. Date between¶

activity = pd.DataFrame({
    'activity_date': pd.to_datetime([
        '2019-06-27', '2019-06-28', '2019-07-01',
        '2019-07-27', '2019-07-28'
    ]),
    'user_id': [1, 2, 3, 4, 5]
})

# Filter 30-day period
result = activity[
    activity['activity_date'].between('2019-06-28', '2019-07-27')
]
print(result)

  activity_date  user_id
1    2019-06-28        2
2    2019-07-01        3
3    2019-07-27        4

2. String Dates¶

Pandas automatically converts string dates.

3. Datetime Objects¶

from datetime import datetime

start = datetime(2019, 6, 28)
end = datetime(2019, 7, 27)
result = activity[activity['activity_date'].between(start, end)]

LeetCode Example: User Activity¶

Filter activity within date range.

1. Sample Data¶

activity = pd.DataFrame({
    'activity_date': pd.to_datetime([
        '2019-06-27', '2019-06-28', '2019-07-01',
        '2019-07-15', '2019-07-27', '2019-07-28', '2019-08-01'
    ]),
    'user_id': [1, 2, 3, 4, 5, 6, 7]
})

2. Filter with between¶

filtered = activity[
    activity['activity_date'].between('2019-06-28', '2019-07-27')
]

3. Count Unique Users¶

active_users = filtered['user_id'].nunique()

vs Comparison Operators¶

Equivalent operations.

1. between Syntax¶

df[df['age'].between(25, 40)]

2. Comparison Syntax¶

df[(df['age'] >= 25) & (df['age'] <= 40)]

3. Advantages¶

between: cleaner, more readable
Comparison: more flexible (exclusive bounds, etc.)

String Ranges¶

Filter string values alphabetically.

1. Alphabetic Range¶

df = pd.DataFrame({'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve']})
result = df[df['name'].between('B', 'D')]
print(result)

     name
1     Bob
2  Charlie

2. Lexicographic Order¶

Strings are compared lexicographically.

3. Case Sensitivity¶

# Uppercase letters come before lowercase in ASCII
# 'A' < 'Z' < 'a' < 'z'

Numeric Precision¶

Handling float comparisons.

1. Float Range¶

df = pd.DataFrame({'value': [0.1, 0.5, 1.0, 1.5, 2.0]})
result = df[df['value'].between(0.5, 1.5)]

2. Precision Issues¶

# Be aware of floating point precision
# 0.1 + 0.2 != 0.3 in floating point

3. Round First¶

df['value_rounded'] = df['value'].round(2)
result = df[df['value_rounded'].between(0.5, 1.5)]

Combining with Other Filters¶

Use between with additional conditions.

1. AND Condition¶

result = df[
    (df['age'].between(25, 40)) & 
    (df['city'] == 'NY')
]

2. OR Condition¶

result = df[
    (df['age'].between(25, 40)) | 
    (df['salary'] > 100000)
]

3. Multiple between¶

result = df[
    (df['age'].between(25, 40)) & 
    (df['salary'].between(50000, 80000))
]