Python provides users with an impressive range of different data types. On top of this, it allows for a high level of customization and expansion of its native data processing. This has resulted in third-party libraries that add a wide variety of additional functions to Python’s already impressive arsenal. NumPy is one of the more popular options for people interested in higher-level mathematical functions and multi-dimensional data types. However, this new functionality isn’t always self-explanatory. Users are often confronted with new error messages such as the “indexerror: too many indices for array”.
A Quick Explanation of the Error
An index error typically occurs when Python code tries to access data from a non-existent position within a data collection such as an array. This is somewhat analogous to telling a friend that you’ll meet them on the third floor of a one-story building. The code tries to move beyond the shape of the data collection and fails accordingly. Just as your friend would fail in any attempt to walk up to a non-existent floor.
Taking a Closer Look at the Error
Multidimensional arrays are one of NumPy’s most important features. In fact, homogeneous multidimensional arrays are NumPy’s main object type. The arrays aren’t just a convenient way to organize data either. They’re also specially tailored to work with many of the advanced mathematical systems which comprise NumPy. NumPy’s arrays are a voluminous subject. But in the scope of this error, we simply need to concentrate on a few important concepts related to NumPy’s arrays.
The first thing to keep in mind is that there’s a big difference between the standard Python array and NumPy’s arrays. There is some overlap between the two. But for the most part you can consider NumPy’s array to be a superset of Python’s standard array. The most notable difference is that Python’s arrays are one-dimensional while NumPy’s arrays are capable of multidimensional structures. These structures, or dimensions, are the array’s shape. Code can’t reach outside of the array’s shape to grab information. And that’s exactly what’s usually going on within this particular Python error.
How To Fix the Error
We can begin fixing the error by recreating it with the following Python code.
import numpy as np
a = np.array([1,2,3,4,5])
print(a[1,0])
This code will produce the “indexerror: too many indices for array” error message. The problem stems from the fact that we’re trying to access data from a one-dimensional NumPy array as if it had two dimensions. Consider the following code to see a different perspective on the array structure.
import numpy as np
a = np.array([[1,2,3,4,5],[6,7,8,9,10]])
print(a.shape)
print(a.shape[0]) # a’s x axis
print(a.shape[1]) # a’s y axis
print(a[1][0])
We begin by creating a similar NumPy array to the initial erroneous one. The main difference is that we’re now using a multidimensional array instead of a one-dimensional array. You can see this illustrated further in the lines following the declaration. We begin by printing the results of NumPy’s shape method. Shape will output the range of data on each axis. In short, it’s the two-by-five shape of the array structure.
We can narrow this down by specifically looking at the x-axis and y-axis provided by the shape method. The code prints the x axis as a.shape[0] and the y axis as a.shape[1]. And finally, we return to the intent of the original error-producing code with the final line. But this time around, now that it’s accessing a proper multidimensional array, it will return proper data rather than an error message. In this case, 6 is printed to the screen. This can be further simplified with the following code.
import numpy as np
a = np.array([[1,2,3,4,5],[6,7,8,9,10]])
print(a[1][0])
This code essentially reproduces the original erroneous code with a cleaned-up version. Likewise, the explanatory use of NumPy’s shape has been removed as well. You might be wondering about possible ways to safeguard against the error. However, that too is a subject unto itself.
NumPy’s arrays are one of the most important parts of the library. And they receive the amount of attention such a position demands. There’s a wide variety of ways to work with and manipulate NumPy’s multidimensional arrays. And there’s really no one true way to handle potential conflicts within them.
You can easily reshape arrays to grow or shrink, slice through them with specific rules, or use more complex processing to individually sort through the information within them before the code makes a decision. But the last example code is exactly what you need to get past the initial error and begin working on more advanced NumPy processing.