 # IndexError: Only Integers, Slices (:), Ellipsis (…), Numpy.newaxis (None) And Integer Or Boolean Arrays Are Valid Indices – How To Fix It? If you are in trouble with problem “IndexError: only integers, slices (:), ellipsis (…), numpy.newaxis (None) and integer or boolean arrays are valid indices” when working with indexes in pandas or numpy library, follow our tutorial below to fix it completely.

## Reason for “IndexError: only integers, slices (:), ellipsis (…), numpy.newaxis (None) and integer or boolean arrays are valid indices”

This is a common error when you try to access indexes of an array in numpy or the content of columns in pandas incorrectly.

The following examples display common mistakes when everyone work with numpy and pandas.

Mistake with NumPy:

from random import randint
import numpy as np

def binarySearch(array, x, left, right):
if right >= left:
middle = (right + left) / 2
if array[middle] == x:
return middle
elif array[middle] > x:
return binarySearch(array, x, left, middle - 1)
else:
return binarySearch(array, x, middle + 1, right)
else:
return -1

# Create random array
arr = np.random.randint(40, size = 10)

# Random select a number in array
x = arr[randint(0, len(arr) - 1)]

print("My random array:", arr)
print("My hidden number:", x)

arr = np.sort(arr)
print("My sorted array:", arr)

a = binarySearch(arr, x, 0, len(arr) - 1)
print("Position of my number in sorted array:", a + 1)

When you run the above code, you will get the following error message:

Traceback (most recent call last):
File "code.py", line 28, in <module>
a = binarySearch(arr, x, 0, len(arr)-1)
File "code.py", line 7, in binarySearch
if array[middle] == x:
IndexError: only integers, slices (:), ellipsis (...), numpy.newaxis (None) and integer or boolean arrays are valid indices

Mistake in Pandas:

import pandas as pd
import numpy as np

array = np.array(
[
[1, 1, 1],
[2, 4, 8],
[3, 9, 27],
[4, 16, 64],
[5, 25, 125],
[6, 36, 216],
[7, 49, 343],
]
)

column_values = ["number", "squares", "cubes"]
df = pd.DataFrame(data = array, columns = column_values)
print("My DataFrame:\n", df)
print("My squares column\n", df.columns["squares"])

The same error for this above code:

Traceback (most recent call last):
File "code.py", line 19, in <module>
print("My squares column\n", df.columns["square"])
IndexError: only integers, slices (:), ellipsis (...), numpy.newaxis (None) and integer or boolean arrays are valid indices

## The solution to the problem

### The solution to NumPy’s error

In the example, variable middle is assigned to the middle index. Obviously, it must be an integer value, but the result of the / operator is a float number.

There are some methods to receive an integer value for middle such as using type casting, round number. Simply, we will use the // operator to get an integer result.

Code:

from random import randint
import numpy as np

def binarySearch(array, x, left, right):
if right >= left:
middle = (right + left) // 2  # Using // operator
if array[middle] == x:
return middle
elif array[middle] > x:
return binarySearch(array, x, left, middle - 1)
else:
return binarySearch(array, x, middle + 1, right)
else:
return -1

# Create random array
arr = np.random.randint(40, size = 10)

# Random select a number in array
x = arr[randint(0, len(arr) - 1)]

print("My random array:", arr)
print("My hidden number:", x)

arr = np.sort(arr)
print("My sorted array:", arr)

a = binarySearch(arr, x, 0, len(arr) - 1)
print("Position of my number in sorted array:", a + 1)

Result:

My random array: [16 36 14 27 33 27 31 16  9 39]
My hidden number: 27
My sorted array: [ 9 14 16 16 27 27 31 33 36 39]
Position of my number in sorted array: 5

You can also try the round() function:

def binarySearch(array, x, left, right):
if right >= left:
middle = round((right + left) // 2) # Use round() function
if array[middle] == x:
return middle

Or type casting to get the same result:

def binarySearch(array, x, left, right):
if right >= left:
middle = int((right + left) / 2) # Use type casting
if array[middle] == x:
return middle

### The solution to Pandas’s error

In this example, people seem to want to access the value of the “squares” column, but they are wrong. pandas.DataFrame.columns return column labels of the DataFrame

To get the value of columns in DataFrame, you can pass a list of columns to [] to select columns in that order

Syntax:

df[“columns_name”]

You can also access it directly as an attribute.

df.column_name

Code:

import pandas as pd
import numpy as np

array = np.array(
[
[1, 1, 1],
[2, 4, 8],
[3, 9, 27],
[4, 16, 64],
[5, 25, 125],
[6, 36, 216],
[7, 49, 343],
]
)

columnValues = ["number", "squares", "cubes"]
df = pd.DataFrame(data = array, columns = columnValues)
print("My DataFrame:\n", df)
print("My squares column\n", df.squares)

Result:

My DataFrame:
number  squares  cubes
0       1        1      1
1       2        4      8
2       3        9     27
3       4       16     64
4       5       25    125
5       6       36    216
6       7       49    343
My squares column
0     1
1     4
2     9
3    16
4    25
5    36
6    49
Name: squares, dtype: int64

## Summary

You have been through our article and know how to solve “IndexError: only integers, slices (:), ellipsis (…), numpy.newaxis (None) and integer or boolean arrays are valid indices”. Hope you understand clearly the purpose of the function and use them correctly.

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