Return a boolean array which is True where the string element in array starts with prefix in Python

To return a boolean array which is True where string elements start with a specific prefix, use the numpy.char.startswith() method in NumPy. This function takes the input array as the first parameter and the prefix string as the second parameter.

Syntax

numpy.char.startswith(a, prefix, start=0, end=None)

Parameters

• a − Input array of strings
• prefix − String prefix to check for
• start − Optional start position (default: 0)
• end − Optional end position (default: None)

Basic Example

Let's create a string array and check which elements start with the prefix 'K' −

import numpy as np

# Create a string array
names = np.array(['KATIE', 'JOHN', 'KATE', 'KmY', 'BRAD'])
print("Array:", names)

# Check which names start with 'K'
result = np.char.startswith(names, 'K')
print("Starts with 'K':", result)

Array: ['KATIE' 'JOHN' 'KATE' 'KmY' 'BRAD']
Starts with 'K': [ True False  True  True False]

Multiple Prefix Examples

You can check for different prefixes on the same array −

import numpy as np

fruits = np.array(['apple', 'banana', 'apricot', 'berry', 'avocado'])
print("Fruits:", fruits)

# Check different prefixes
print("Starts with 'a':", np.char.startswith(fruits, 'a'))
print("Starts with 'b':", np.char.startswith(fruits, 'b'))
print("Starts with 'ap':", np.char.startswith(fruits, 'ap'))

Fruits: ['apple' 'banana' 'apricot' 'berry' 'avocado']
Starts with 'a': [ True False  True False  True]
Starts with 'b': [False  True False  True False]
Starts with 'ap': [ True False  True False False]

Using Start and End Parameters

You can specify start and end positions to check prefixes within a substring −

import numpy as np

words = np.array(['hello', 'world', 'help', 'welcome'])
print("Words:", words)

# Check if substring starting at position 1 begins with 'e'
result = np.char.startswith(words, 'e', start=1)
print("Position 1 starts with 'e':", result)

# Check first 3 characters start with 'hel'
result2 = np.char.startswith(words, 'hel', end=3)
print("First 3 chars start with 'hel':", result2)

Words: ['hello' 'world' 'help' 'welcome']
Position 1 starts with 'e': [ True False  True  True]
First 3 chars start with 'hel': [ True False  True False]

Practical Use Cases

Filter array elements based on prefix matching −

import numpy as np

emails = np.array(['admin@site.com', 'user@site.com', 'admin@test.com', 'guest@site.com'])
print("Emails:", emails)

# Find admin emails
is_admin = np.char.startswith(emails, 'admin')
admin_emails = emails[is_admin]

print("Admin emails:", admin_emails)
print("Boolean mask:", is_admin)

Emails: ['admin@site.com' 'user@site.com' 'admin@test.com' 'guest@site.com']
Admin emails: ['admin@site.com' 'admin@test.com']
Boolean mask: [ True False  True False]

Conclusion

The numpy.char.startswith() function efficiently checks string prefixes and returns a boolean array. Use the boolean result for filtering or conditional operations on string arrays.