NumPy Operations

Overview link

NumPy provides a wide range of mathematical and array operations that enable efficient manipulation and analysis of data. These operations include arithmetic, statistical, logical, aggregation, element-wise comparison, sorting, concatenation, splitting, broadcasting, vectorization, and Fourier transforms, among others. Understanding and leveraging these operations is essential for working effectively with NumPy arrays.

Arithmetic Operations link

NumPy supports standard arithmetic operations such as addition, subtraction, multiplication, and division, both element-wise and matrix-wise. These operations can be performed using NumPy’s array broadcasting and ufuncs.

  import numpy as np

# Create arrays
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([5, 6, 7, 8])

# Element-wise addition
result_add = arr1 + arr2

# Element-wise multiplication
result_mult = arr1 * arr2

print("Element-wise addition:", result_add)
print("Element-wise multiplication:", result_mult)
  

Statistical Operations link

NumPy provides functions for calculating various statistical measures, such as mean, median, standard deviation, variance, and percentile.

  import numpy as np

# Create array
data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

# Calculate mean
mean = np.mean(data)

# Calculate standard deviation
std_dev = np.std(data)

print("Mean:", mean)
print("Standard deviation:", std_dev)
  

Logical Operations link

NumPy supports logical operations such as AND, OR, NOT, and XOR on boolean arrays. These operations are useful for filtering data based on conditions.

  import numpy as np

# Create boolean arrays
arr1 = np.array([True, False, True, False])
arr2 = np.array([False, False, True, True])

# Logical AND
result_and = np.logical_and(arr1, arr2)

# Logical OR
result_or = np.logical_or(arr1, arr2)

print("Logical AND:", result_and)
print("Logical OR:", result_or)
  

Aggregation Operations link

NumPy provides functions for aggregating data along specified axes, such as sum, mean, min, max, and cumulative sum.

  import numpy as np

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

# Sum along rows
row_sum = np.sum(arr, axis=1)

# Mean along columns
col_mean = np.mean(arr, axis=0)

print("Sum along rows:", row_sum)
print("Mean along columns:", col_mean)
  

Additional Useful NumPy Operations link

1. Element-wise Comparison link

NumPy provides functions for element-wise comparison between arrays, such as np.equal, np.not_equal, np.greater, np.less, np.greater_equal, and np.less_equal.

  import numpy as np

# Create arrays
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([3, 2, 1, 4])

# Element-wise comparison
equal_arr = np.equal(arr1, arr2)
greater_arr = np.greater(arr1, arr2)

print("Equal:", equal_arr)
print("Greater than:", greater_arr)
  

2. Sorting link

NumPy allows sorting arrays along specified axes using functions like np.sort and np.argsort.

  import numpy as np

# Create array
arr = np.array([3, 1, 2, 4])

# Sort array
sorted_arr = np.sort(arr)

print("Sorted array:", sorted_arr)
  

3. Concatenation and Splitting link

NumPy provides functions for concatenating and splitting arrays, such as np.concatenate, np.vstack, np.hstack, np.split, and np.vsplit.

  import numpy as np

# Create arrays
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])

# Concatenate arrays vertically
concatenated_arr_v = np.vstack((arr1, arr2))

print("Concatenated array (vertical):", concatenated_arr_v)
  

4. Broadcasting link

Broadcasting is a powerful mechanism in NumPy that enables arithmetic operations between arrays of different shapes.

  import numpy as np

# Create arrays
arr1 = np.array([[1, 2, 3], [4, 5, 6]])
scalar = 10

# Broadcasting scalar with array
result = arr1 + scalar

print("Result of broadcasting:", result)
  

5. Vectorization link

NumPy encourages vectorized operations, which are faster than traditional looping constructs.

  import numpy as np

# Create arrays
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([5, 6, 7, 8])

# Vectorized operation
result = arr1 * arr2

print("Result of vectorized operation:", result)
  

6. Fourier Transforms link

NumPy provides functions for computing Fourier transforms and inverse Fourier transforms.

  import numpy as np

# Create array
arr = np.array([1, 2, 3, 4])

# Compute Fourier transform
fourier_transform = np.fft.fft(arr)

print("Fourier transform:", fourier_transform)
  

These additional operations expand the capabilities of NumPy and are commonly used in scientific computing, data analysis, and machine learning tasks.

Conclusion link

NumPy offers a vast array of operations for manipulating and analyzing data efficiently. By leveraging these operations, users can perform a wide range of tasks in scientific computing, data analysis, and machine learning. Understanding and mastering NumPy operations is essential for effectively utilizing the library’s capabilities and achieving optimal performance in computational tasks.

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