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numpy.py

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+# -*- coding: utf-8 -*-
+"""numpy.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1DFKFWggXRc3u0GkmvMy0ssb7fTtxATi2
+"""
+
+import numpy as np
+
+"""1. Creating Arrays"""
+
+# Creating NumPy arrays
+arr = np.array([1, 2, 3, 4, 5])
+zeros = np.zeros((3, 3))  # Creating an array filled with zeros
+ones = np.ones((2, 4))  # Creating an array filled with ones
+
+# Printing the arrays
+print("Array 'arr':", arr)
+print("Array 'zeros':\n", zeros)
+print("Array 'ones':\n", ones)
+
+"""2. Array Indexing and Slicing"""
+
+# Array Indexing and Slicing
+print(arr[0])  # Accessing elements
+print(arr[1:4])  # Slicing elements
+
+"""3. Array Reshaping"""
+
+# Array Reshaping
+reshaped = arr.reshape(5, 1)  # Reshaping arrays
+print(reshaped)
+
+"""4. Mathematical Operations"""
+
+arr = np.array([1, 2, 3, 4, 5])
+arr2 = np.array([6, 7, 8, 9, 10])
+
+add = arr + arr2  # Element-wise addition
+sub = arr - arr2  # Element-wise subtraction
+mul = arr * arr2  # Element-wise multiplication
+div = arr / arr2  # Element-wise division
+mod = arr % arr2  # Element-wise modulo
+
+# Printing the results
+print("Addition:", add)
+print("Subtraction:", sub)
+print("Multiplication:", mul)
+print("Division:", div)
+print("Modulo:", mod)
+
+"""5. Universal Functions (ufunc)"""
+
+arr = np.array([1, 2, 3, 4, 5])
+
+# Square root of each element
+sqrt_arr = np.sqrt(arr)
+
+# Exponential function (e^x) for each element
+exp_arr = np.exp(arr)
+
+# Sine of each element
+sin_arr = np.sin(arr)
+
+# Logarithm (base 10) for each element
+log_arr = np.log10(arr)
+
+# Printing the results
+print("Square root:", sqrt_arr)
+print("Exponential function:", exp_arr)
+print("Sine:", sin_arr)
+print("Logarithm (base 10):", log_arr)
+
+"""6. Statistical Operations"""
+
+arr = np.array([3, 5, 1, 7, 9, 2, 6, 8, 4, 10])
+
+# Calculate mean
+mean = np.mean(arr)
+
+# Calculate median
+median = np.median(arr)
+
+# Calculate standard deviation
+std_dev = np.std(arr)
+
+# Calculate variance
+variance = np.var(arr)
+
+# Calculate minimum and maximum values
+min_val = np.min(arr)
+max_val = np.max(arr)
+
+# Printing the statistical results
+print("Mean:", mean)
+print("Median:", median)
+print("Standard Deviation:", std_dev)
+print("Variance:", variance)
+print("Minimum value:", min_val)
+print("Maximum value:", max_val)
+
+"""7. Array Concatenation and Splitting"""
+
+arr1 = np.array([[1, 2, 3], [4, 5, 6]])
+arr2 = np.array([[7, 8, 9], [10, 11, 12]])
+
+# Concatenating arrays vertically
+concatenated_vertical = np.vstack((arr1, arr2))
+
+# Concatenating arrays horizontally
+concatenated_horizontal = np.hstack((arr1, arr2))
+
+# Printing the concatenated arrays
+print("Concatenated Vertically:")
+print(concatenated_vertical)
+print("\nConcatenated Horizontally:")
+print(concatenated_horizontal)
+
+# Splitting arrays vertically
+split_vertical = np.vsplit(concatenated_vertical, 2)
+
+# Splitting arrays horizontally
+split_horizontal = np.hsplit(concatenated_horizontal, 2)
+
+# Printing the split arrays
+print("\nSplit Vertically:")
+print(split_vertical)
+print("\nSplit Horizontally:")
+print(split_horizontal)
+
+"""8. Array Stacking"""
+
+# Create sample arrays
+arr1 = np.array([1, 2, 3])
+arr2 = np.array([4, 5, 6])
+
+# Horizontal stacking
+horizontal_stack = np.hstack((arr1, arr2))
+
+# Vertical stacking
+vertical_stack = np.vstack((arr1, arr2))
+
+# Depth-wise stacking
+depth_stack = np.dstack((arr1, arr2))
+
+# Print the stacked arrays
+print("Horizontal Stack:")
+print(horizontal_stack)
+
+print("\nVertical Stack:")
+print(vertical_stack)
+
+print("\nDepth-wise Stack:")
+print(depth_stack)
+
+"""9. Broadcasting"""
+
+# Create a sample array
+arr = np.array([[1, 2, 3], [4, 5, 6]])
+
+# Add a scalar to the array using broadcasting
+result = arr + 10
+
+# Print the result
+print("Array after adding 10:\n", result)
+
+"""11. Random Number Generation"""
+
+# Generating random integers
+random_integers = np.random.randint(1, 100, size=(3, 3))  # Generates a 3x3 array of random integers from 1 to 100
+print("Random Integers:\n", random_integers)
+
+# Generating random floating-point numbers from a uniform distribution
+random_floats = np.random.rand(2, 4)  # Generates a 2x4 array of random floats in the half-open interval [0.0, 1.0)
+print("Random Floats:\n", random_floats)
+
+# Generating random numbers from a standard normal distribution (mean 0, variance 1)
+random_normal = np.random.randn(2, 3)  # Generates a 2x3 array from the standard normal distribution
+print("Random Normal Distribution:\n", random_normal)
+
+"""12. Array Manipulation"""
+
+arr = np.arange(6)  # Create an array from 0 to 5
+reshaped_arr = arr.reshape(2, 3)  # Reshape to a 2x3 array
+print(reshaped_arr)
+
+flattened_arr = reshaped_arr.flatten()
+print(flattened_arr)
+
+transposed_arr = reshaped_arr.T  # Transpose the 2x3 array
+print(transposed_arr)
+
+arr1 = np.array([[1, 2], [3, 4]])
+arr2 = np.array([[5, 6], [7, 8]])
+
+concatenated_axis0 = np.concatenate((arr1, arr2), axis=0)  # Concatenate along axis 0
+concatenated_axis1 = np.concatenate((arr1, arr2), axis=1)  # Concatenate along axis 1
+
+print("Concatenated along axis 0:\n", concatenated_axis0)
+print("Concatenated along axis 1:\n", concatenated_axis1)
+
+arr_to_split = np.arange(9)  # Create an array from 0 to 8
+split_arr = np.split(arr_to_split, 3)  # Split the array into 3 equal parts
+print(split_arr)
+
+"""13. Finding Unique Values"""
+
+arr = np.array([1, 2, 3, 2, 4, 1, 5])
+
+# Using np.unique to find the unique values
+unique_values = np.unique(arr)
+print(unique_values)
+
+unique, counts = np.unique(arr, return_counts=True)
+unique_with_counts = np.asarray((unique, counts)).T
+print(unique_with_counts)
+
+arr_2d = np.array([[1, 2], [3, 2], [4, 3], [1, 2]])
+
+# Checking for unique rows
+unique_rows = np.unique(arr_2d, axis=0)
+print(unique_rows)
+
+"""14. Linear Algebra Operations"""
+
+matrix_a = np.array([[1, 2], [3, 4]])
+matrix_b = np.array([[2, 0], [1, 2]])
+# Matrix multiplication using np.dot
+result = np.dot(matrix_a, matrix_b)
+print(result)
+
+matrix = np.array([[1, 2], [3, 4]])
+
+# Transposing a matrix
+transposed_matrix = matrix.T
+print(transposed_matrix)
+
+matrix = np.array([[1, 2], [3, 4]])
+
+# Calculating determinant
+det = np.linalg.det(matrix)
+print(det)
+
+matrix = np.array([[1, 2], [3, 4]])
+
+# Finding the inverse of a matrix
+inverse_matrix = np.linalg.inv(matrix)
+print(inverse_matrix)
+
+matrix = np.array([[1, 2], [3, 4]])
+
+# Singular Value Decomposition
+u, s, vh = np.linalg.svd(matrix)
+print("U:", u)
+print("S:", s)
+print("VH:", vh)
+
+"""15. Save and Load NumPy Arrays"""
+
+arr = np.array([[1, 2, 3], [4, 5, 6]])
+
+# Save the array to a file
+np.save('my_array.npy', arr)
+
+# Load the array from the saved file
+loaded_array = np.load('my_array.npy')
+print(loaded_array)