Array Attributes and Methods

Array Attributes

Attributes tell you information about the array.

Shape

Shows dimensions of the array.

code.py

import numpy as np

arr_1d = np.array([1, 2, 3, 4, 5])
print("Shape:", arr_1d.shape)

arr_2d = np.array([[1, 2, 3], [4, 5, 6]])
print("Shape:", arr_2d.shape)

Output:

Shape: (5,)
Shape: (2, 3)

What shape means:

(5,) means 1D array with 5 elements
(2, 3) means 2 rows and 3 columns

Size

Total number of elements.

code.py

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])
print("Size:", arr.size)

Output: 6

What size tells you: Total count of all numbers in array.

ndim

Number of dimensions.

code.py

import numpy as np

arr_1d = np.array([1, 2, 3])
arr_2d = np.array([[1, 2], [3, 4]])
arr_3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

print("1D dimensions:", arr_1d.ndim)
print("2D dimensions:", arr_2d.ndim)
print("3D dimensions:", arr_3d.ndim)

Output:

1D dimensions: 1
2D dimensions: 2
3D dimensions: 3

dtype

Data type of elements.

code.py

import numpy as np

integers = np.array([1, 2, 3])
floats = np.array([1.5, 2.5, 3.5])

print("Integer type:", integers.dtype)
print("Float type:", floats.dtype)

Output:

Integer type: int64
Float type: float64

itemsize

Size of each element in bytes.

code.py

import numpy as np

arr = np.array([1, 2, 3])
print("Item size:", arr.itemsize, "bytes")

Output: Item size: 8 bytes

Why this matters: Helps estimate memory usage for large arrays.

Reshaping Arrays

Change the shape without changing data.

reshape()

code.py

import numpy as np

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

Output:

[[1 2 3]
 [4 5 6]]

What reshape does: Converts 1D array (6 elements) to 2D array (2 rows, 3 columns).

Important: Total elements must match. 6 elements = 2×3 = 6.

reshape with -1

Let NumPy calculate one dimension.

code.py

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
reshaped = arr.reshape(2, -1)
print(reshaped)

Output:

[[1 2 3 4]
 [5 6 7 8]]

What -1 does: NumPy figures out it needs 4 columns (8 elements ÷ 2 rows = 4).

flatten()

Convert any array to 1D.

code.py

import numpy as np

arr_2d = np.array([[1, 2, 3], [4, 5, 6]])
flattened = arr_2d.flatten()
print(flattened)

Output: [1 2 3 4 5 6]

ravel()

Similar to flatten but faster.

code.py

import numpy as np

arr_2d = np.array([[1, 2], [3, 4]])
raveled = arr_2d.ravel()
print(raveled)

Output: [1 2 3 4]

Difference: flatten() creates copy, ravel() creates view (usually faster).

Statistical Methods

Calculate statistics on arrays.

sum()

code.py

import numpy as np

numbers = np.array([10, 20, 30, 40])
total = numbers.sum()
print("Sum:", total)

Output: Sum: 100

mean()

code.py

import numpy as np

scores = np.array([85, 90, 78, 92])
average = scores.mean()
print("Average:", average)

Output: Average: 86.25

min() and max()

code.py

import numpy as np

temperatures = np.array([72, 68, 75, 70, 73])
print("Lowest:", temperatures.min())
print("Highest:", temperatures.max())

Output:

Lowest: 68
Highest: 75

std() and var()

code.py

import numpy as np

data = np.array([2, 4, 4, 4, 5, 5, 7, 9])
print("Standard deviation:", data.std())
print("Variance:", data.var())

What these measure:

std: How spread out numbers are
var: Square of standard deviation

Finding Positions

argmin() and argmax()

Find position of minimum and maximum.

code.py

import numpy as np

prices = np.array([45, 32, 67, 28, 51])
print("Cheapest at index:", prices.argmin())
print("Most expensive at index:", prices.argmax())
print("Cheapest price:", prices[prices.argmin()])
print("Highest price:", prices[prices.argmax()])

Output:

Cheapest at index: 3
Most expensive at index: 2
Cheapest price: 28
Highest price: 67

Sorting

sort()

code.py

import numpy as np

numbers = np.array([5, 2, 8, 1, 9])
numbers.sort()
print(numbers)

Output: [1 2 5 8 9]

Warning: This changes the original array!

np.sort()

Returns sorted copy without changing original.

code.py

import numpy as np

numbers = np.array([5, 2, 8, 1, 9])
sorted_numbers = np.sort(numbers)
print("Sorted:", sorted_numbers)
print("Original:", numbers)

Output:

Sorted: [1 2 5 8 9]
Original: [5 2 8 1 9]

Copying Arrays

copy()

Create independent copy.

code.py

import numpy as np

original = np.array([1, 2, 3])
copy = original.copy()
copy[0] = 99

print("Original:", original)
print("Copy:", copy)

Output:

Original: [1 2 3]
Copy: [99 2 3]

Without copy:

code.py

import numpy as np

original = np.array([1, 2, 3])
reference = original
reference[0] = 99

print("Original:", original)
print("Reference:", reference)

Output:

Original: [99 2 3]
Reference: [99 2 3]

Both changed! They point to same data.

Practice Example

The scenario: Analyze student test scores.

code.py

import numpy as np

scores = np.array([78, 85, 92, 88, 76, 95, 82, 90, 87, 91])

print("Score Analysis")
print("=" * 40)

print("Number of students:", scores.size)
print("Data type:", scores.dtype)
print("Dimensions:", scores.ndim)
print()

print("Statistics:")
print("Average score:", scores.mean())
print("Highest score:", scores.max())
print("Lowest score:", scores.min())
print("Standard deviation:", round(scores.std(), 2))
print()

print("Top student at position:", scores.argmax())
print("Lowest student at position:", scores.argmin())
print()

sorted_scores = np.sort(scores)
print("Scores (sorted):", sorted_scores)
print()

reshaped = scores.reshape(2, 5)
print("Scores in 2 groups:")
print(reshaped)
print()

above_85 = scores[scores > 85]
print("Scores above 85:", above_85)
print("Count above 85:", above_85.size)

What this analyzes:

Basic array information
Statistical measures
Best and worst performers
Sorted view of scores
Grouping students
Filtering high scorers

Converting Data Types

astype()

code.py

import numpy as np

floats = np.array([1.7, 2.3, 3.9])
integers = floats.astype(int)
print("Floats:", floats)
print("Integers:", integers)

Output:

Floats: [1.7 2.3 3.9]
Integers: [1 2 3]

Warning: Decimal parts are lost, not rounded!

Transposing Arrays

Swap rows and columns.

code.py

import numpy as np

matrix = np.array([[1, 2, 3], [4, 5, 6]])
print("Original:")
print(matrix)
print("Shape:", matrix.shape)

transposed = matrix.T
print("Transposed:")
print(transposed)
print("Shape:", transposed.shape)

Output:

Original:
[[1 2 3]
 [4 5 6]]
Shape: (2, 3)

Transposed:
[[1 4]
 [2 5]
 [3 6]]
Shape: (3, 2)

Key Points to Remember

Use .shape to check dimensions, .size for total elements, .dtype for data type.

reshape() changes shape but total elements must stay same. Use -1 to let NumPy calculate one dimension.

Statistical methods: sum(), mean(), min(), max(), std(). Work on entire array or specific axis.

argmin() and argmax() return position (index), not the value itself.

Use .copy() to create independent copy. Without it, you get reference to same data.

Common Mistakes

Mistake 1: reshape with wrong total

code.py

arr = np.array([1, 2, 3, 4, 5])
arr.reshape(2, 3)  # Error! 5 elements can't fit 2×3=6

Mistake 2: Forgetting copy

code.py

arr1 = np.array([1, 2, 3])
arr2 = arr1  # Not a copy!
arr2[0] = 99  # Changes both!

Mistake 3: In-place sort

code.py

numbers.sort()  # Changes original
sorted_nums = np.sort(numbers)  # Keeps original

Mistake 4: Wrong axis

code.py

matrix.sum()  # Sums everything
matrix.sum(axis=0)  # Sums each column
matrix.sum(axis=1)  # Sums each row

What's Next?

You now know array attributes and methods. Next, you'll learn about array indexing and slicing - how to access and extract specific parts of arrays.