Too many indices for array питон ошибка

I know there is a ton of these threads but all of them are for very simple cases like 3×3 matrices and things of that sort and the solutions do not even begin to apply to my situation. So I’m trying to graph G versus l1 (that’s not an eleven, but an L1). The data is in the file that I loaded from an excel file. The excel file is 14×250 so there are 14 arguments, each with 250 data points. I had another user (shout out to Hugh Bothwell!) help me with an error in my code, but now another error has surfaced.

So here is the code in question:

# format for CSV file:
header = ['l1', 'l2', 'l3', 'l4', 'l5', 'EI',
      'S', 'P_right', 'P1_0', 'P3_0',
      'w_left', 'w_right', 'G_left', 'G_right']

def loadfile(filename, skip=None, *args):
    skip = set(skip or [])
    with open(filename, *args) as f:
        cr = csv.reader(f, quoting=csv.QUOTE_NONNUMERIC)
        return np.array(row for i,row in enumerate(cr) if i not in skip)
#plot data
outputs_l1 = [loadfile('C:\Users\Chris\Desktop\Work\Python Stuff\BPCROOM - Shingles analysis\ERR analysis\l_1 analysis//BS(1) ERR analysis - l_1 - P_3 = {}.csv'.format(p)) for p in p3_arr]

col = {name:i for i,name in enumerate(header)}

fig = plt.figure()
for data,color in zip(outputs_l1, colors):
    xs  = data[:, col["l1"     ]]
    gl = data[:, col["G_left" ]] * 1000.0    # column 12
    gr = data[:, col["G_right"]] * 1000.0    # column 13
    plt.plot(xs, gl, color + "-", gr, color + "--")
for output, col in zip(outputs_l1, colors):
    plt.plot(output[:,0], output[:,11]*1E3, col+'--')
plt.ticklabel_format(axis='both', style='plain', scilimits=(-1,1))
plt.xlabel('$l1 (m)$')
plt.ylabel('G $(J / m^2) * 10^{-3}$')
plt.xlim(xmin=.2)
plt.ylim(ymax=2, ymin=0)

plt.subplots_adjust(top=0.8, bottom=0.15, right=0.7)

After running the entire program, I recieve the error message:

Traceback (most recent call last):
  File "C:/Users/Chris/Desktop/Work/Python Stuff/New Stuff from Brenday 8 26 2014/CD_ssa_plot(2).py", line 115, in <module>
    xs  = data[:, col["l1"     ]]
IndexError: too many indices for array

and before I ran into that problem, I had another involving the line a few below the one the above error message refers to:

Traceback (most recent call last): File "FILE", line 119, in <module> 
gl = data[:, col["G_left" ]] * 1000.0 # column 12 
IndexError: index 12 is out of bounds for axis 1 with size 12

I understand the first error, but am just having problems fixing it. The second error is confusing for me though. My boss is really breathing down my neck so any help would be GREATLY appreciated!

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If you define an array and try to index it with more dimensions than the array has, you will raise the error: IndexError: too many indices for array. You need to recheck the array’s dimensions and index it with those dimensions to solve this error.

This tutorial will go through the error in detail and an example to learn how to solve it.

IndexError: too many indices for array

What is an IndexError?

Python’s IndexError occurs when the index specified does not lie in the range of indices in the bounds of a list. In Python, index numbers start from 0 and end at n-1, where n is the number of elements present in the list. Let’s look at an example of a Python array:

pets = ["cat", "dog", "hamster"]

This array contains three values, and the first element, cat, has an index value of 0. The second element, dog, has an index of 1. The third element, hamster, has an index of 2.

If we try to access an item at index position 3, we will raise an IndexError, because the list range is 0 to 2.

print(pets[3])

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
1 print(pets[3])

IndexError: list index out of range

When accessing a list, remember that Python list indexing starts with 0.

Indexing a Multidimensional Array Using Numpy

To access elements in an n-dimensional array, we can use comma-separated integers representing the array’s dimension and index. Let’s look at an example with a two-dimensional array. We can think of a two-dimensional array as a table with rows and columns, and the row represents the dimension, and the index represents the column.

import numpy as np

arr = np.array([[2,3,4,5,6], [8, 4, 3, 2, 1]])

print('3rd element on 1st row: ', arr[0,2])

In the above code, the value 0 means we are accessing the first dimension or row, and the value 2 mean we are accessing the element in the third column of the first row. Let’s run the code to see the result:

3rd element on 1st row:  4

Example: Indexing a 1-Dimensional Array

Let’s look at an example where we define a numpy array in a single dimension and try to access the elements of the array in two dimensions.

import numpy as np

x = np.array([45, 12, 55, 99, 10, 5, 2])

print(x[0, 3])

Let’s run the code to get the output:

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
      3 x = np.array([45, 12, 55, 99, 10, 5, 2])
      4 
      5 print(x[0, 3])

IndexError: too many indices for array

By using two numbers in square brackets separated by a comma [0, 3], we tell the Python interpreter to access the third element of the first array. However, there is only one array; therefore, we raise the IndexError.

Solution

To solve the error, you can use print statements to get the array’s shape and dimensions. Once you know the array’s dimensions, you must index using those dimensions. In this case, the array is one-dimensional; therefore, we only need to specify one index value. Let’s look at the revised code:

x = np.array([45, 12, 55, 99, 10, 5, 2])

print('Shape of the array is: ', np.shape(x))

print('Dimension of the array is: ', len(np.shape(x)))

print(x[0])

The function np.shape gives us the shape of the array. You can pass the numpy array shape to the len() function, returning the array’s dimension. Let’s run the code to see what happens:

Shape of the array is: (7,)
Dimension of the array is: 1
45

The code successfully runs and prints the element at the 0th index of the numpy array.

Summary

Congratulations on reading to the end of this tutorial! If you try to access an array using more dimensions than the array, you will raise the error: IndexError: too many indices for array. You can access multiple dimensions by adding commas between the index values. If you are accessing a one-dimensional array, you cannot index the array with two index values as if it were two-dimensional. To solve this error, first get the dimensions of your array by passing the shape of the array, using np.shape(array), to the len() function. Once you have the number of dimensions for the array you must index using those dimensions.

For further reading on IndexError, you can read the following articles:

• How to Solve Python IndexError: list index out of range
• How to Solve Python IndexError: single positional indexer is out-of-bounds

To learn more about Python for data science and machine learning, go to the online courses page on Python for the most comprehensive courses available.

Arrays in Python are one dimensional and two dimensional. Two-dimensional arrays consist of one or more arrays inside it. You can access the elements in a 2D array by mentioning two indices. The first index represents the position of the inner array. The other index represents the element within this inner array.

Python IndexError: too many indices for array

While using a numpy array, you might come across an error IndexError: too many indices for an array. This occurs when you are trying to access the elements of a one-dimensional numpy array as a 2D array. To avoid this error, you need to mention the correct dimensions of the array.

This error is thrown by Python ‘numpy array’ library when you try to access a single-dimensional array into multiple dimensional arrays.

Example

# Code with error

# Importing the numpy library
import numpy as np

# Declaring and Initializing the one Dimension Array
x = np.array([212,312,2,12,124,142,12])

# Printing the result
print(x[0,3])

OUTPUT:

Traceback (most recent call last):
  File "main.py", line 5, in <module>
    print(x[0,3])
IndexError: too many indices 

In the above example, we have declared a single dimensional array using numpy library array, but later in the code, we are trying to print it as a two-dimensional array.

Solution

To resolve this error, you have re-check the dimensions which you are using and the dimension you are declaring and initializing 

# Code without errors

# Importing the numpy library
import numpy as np

# Declaring and initializing the one dimension array
x = np.array([212,312,2,12,124,142,12])

# To check the dimension of the array
print("Shape of the array = ",np.shape(x));

# Printing the result
print(x[0])

OUTPUT:

Shape of the array =  (7,)
212 

How to check the Dimension of Array

To check the dimension of your declared array use len(x.shape) function of the numpy library. 

import numpy as np
myarray = np.array([[[2,4,4],[2,34,9]],[[12,3,4],[2,1,3]],[[2,2,2],[2,1,3]]])

print("Array Dimension = ",len(myarray.shape))

Output

Array Dimension =  3

Conclusion

The indices of the numpy array have to be mentioned according to the size. For a one dimensional array, mention one index. For 2D arrays, specify two indices. The best way to go about it is by using the len(myarray.shape).