When I was working on a data analysis project where I needed to process some text data stored in a NumPy array. When I tried to use the .split() method on my array, I got this frustrating error: AttributeError: ‘numpy.ndarray’ object has no attribute ‘split’.
This is a common error that many Python developers encounter when transitioning between string operations and NumPy arrays. The issue is simple: NumPy arrays don’t have a built-in .split() method like Python strings do.
In this article, I’ll walk you through why this error occurs and share five practical methods to solve it. These solutions have saved me countless hours of debugging, and I’m confident they’ll help you too!
Why Does This Error Occur?
The error occurs because of a fundamental difference between Python strings and NumPy arrays:
- Python strings have a built-in
.split()method that splits the string into a list of substrings. - NumPy arrays are different objects that don’t inherit string methods, even if they contain string data.
Let’s look at a simple example that triggers this error:
import numpy as np
# Create a NumPy array with a string
text_array = np.array("Hello world from Python")
# This will cause the error
words = text_array.split() # AttributeError: 'numpy.ndarray' object has no attribute 'split'Now, let’s see how to fix this common issue.
Methods to solve AttributeError: ‘numpy.ndarray’ object has no attribute ‘split’
Let me show you some important methods to solve the AttributeError: ‘numpy.ndarray’ object has no attribute ‘split’ error.
Read NumPy Array to a String in Python
Method 1: Convert NumPy Array to String First
The simplest solution is to convert your NumPy array to a Python string before applying the .split() method in Python.
import numpy as np
# Create a NumPy array with a string
text_array = np.array("Hello world from Python")
# Convert to string first, then split
words = str(text_array).split()
print(words)Output:
['Hello', 'world', 'from', 'Python']I executed the above example code and added the screenshot below.
This method works great when your array contains a single string. It’s straightforward and doesn’t require any special NumPy knowledge.
Check out np.add.at() Function in Python
Method 2: Use NumPy’s array_split() Function
If you’re trying to split a NumPy array into sub-arrays (rather than splitting string content), you should use NumPy’s array_split() function in Python:
import numpy as np
# Create a NumPy array
num_array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
# Split into 3 sub-arrays
split_arrays = np.array_split(num_array, 3)
print(split_arrays)Output:
[array([1, 2, 3, 4]), array([5, 6, 7]), array([8, 9, 10])]I executed the above example code and added the screenshot below.
This method is perfect when you need to divide a larger array into smaller chunks of approximately equal size.
Read Replace Values in NumPy Array by Index in Python
Method 3: Apply String Methods Element-wise with NumPy’s vectorize
If you have an array of strings and want to split each one, you can use Python NumPy’s vectorize to apply the string .split() method to each element:
import numpy as np
# Create a NumPy array of strings
phrases = np.array(["Hello world", "Python programming", "Data analysis"])
# Define a function to split strings
def split_str(s):
return s.split()
# Vectorize the function with output type set to object
vectorized_split = np.vectorize(split_str, otypes=[object])
result = vectorized_split(phrases)
print(result)Output:
[list(['Hello', 'world']) list(['Python', 'programming'])
list(['Data', 'analysis'])]I executed the above example code and added the screenshot below.
This approach is ideal when dealing with arrays containing multiple strings that each need splitting.
Check out np.diff() Function in Python
Method 4: Use list() Before Applying split()
Another simple approach is to convert your NumPy array to a list first, especially if you’re working with a 1D array in Python:
import numpy as np
# Create a NumPy array with a string
text_array = np.array("Data science with Python")
# Convert to list, then to string, then split
words = str(list(text_array)[0]).split()
print(words) # ['Data', 'science', 'with', 'Python']This method can be particularly useful when working with arrays that have been extracted from complex data structures.
Read NumPy Filter 2D Array by Condition in Python
Method 5: Use NumPy’s char.split() for String Arrays
For arrays of strings, NumPy provides specialized functions in the numpy.char module:
import numpy as np
# Create a NumPy array of strings
text_array = np.array(['Hello world', 'Python programming', 'NumPy is great'])
# Use numpy.char.split
split_arrays = np.char.split(text_array)
print(split_arrays)
# [list(['Hello', 'world']) list(['Python', 'programming']) list(['NumPy', 'is', 'great'])]This method is perfect for arrays of strings and provides a clean, NumPy-native solution.
Real-World Example: Processing Customer Feedback
Let’s look at a real-world example where this error might occur. Imagine we’re analyzing customer feedback for a retail company based in New York:
import numpy as np
# Customer feedback stored in a NumPy array
feedback = np.array([
"Great service and fast shipping to Manhattan",
"Product quality could be better but arrived on time in Brooklyn",
"Excellent customer support for my Chicago delivery"
])
# Wrong approach - will cause error
# words = feedback.split() # AttributeError!
# Correct approach using numpy.char.split
split_feedback = np.char.split(feedback)
# Process the feedback - e.g., count words per feedback
word_counts = np.array([len(comment) for comment in split_feedback])
print("Word count for each feedback:", word_counts)
# Word count for each feedback: [7 10 7]
# Further analysis - check if certain locations are mentioned
def contains_location(feedback_list, location):
return np.array([location.lower() in ' '.join(f).lower() for f in feedback_list])
ny_mentions = contains_location(split_feedback, "New York")
manhattan_mentions = contains_location(split_feedback, "Manhattan")
brooklyn_mentions = contains_location(split_feedback, "Brooklyn")
print("Feedback mentioning Manhattan:", manhattan_mentions)
# Feedback mentioning Manhattan: [ True False False]This example demonstrates how to properly handle text analysis on customer feedback stored in NumPy arrays.
Read Use np.argsort in Descending Order in Python
Common Mistakes to Avoid
When working with NumPy arrays and string operations, watch out for these common pitfalls:
- Forgetting that NumPy arrays are not Python strings – Even if an array contains string data, it doesn’t inherit string methods.
- Using
.split()directly on multi-dimensional arrays – Always consider the shape of your array before applying string methods. - Not checking array data types – Make sure your array contains string data before attempting string operations.
- Ignoring NumPy’s specialized string functions – The
numpy.charmodule provides many string operations that work directly on arrays.
I hope you found this article helpful in understanding and fixing the ‘AttributeError: ‘numpy.ndarray’ object has no attribute ‘split” error. Remember, the key is understanding the difference between Python’s native string objects and NumPy arrays containing string data.
If you encounter this error, try one of the five methods I’ve outlined above, based on your specific use case. Each approach has its strengths, whether you’re working with single strings, arrays of strings, or simply need to split an array into chunks.
Other Python articles you may also like:
- Convert the DataFrame to a NumPy Array Without Index in Python
- np.count() function in Python
- Copy Elements from One List to Another in Python
I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.
