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‎NumPy_101.ipynb

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"\n",
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"1. 📚 Introduction\n",
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"2. 📚 Array Creation\n",
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"3. 📚 Basic Operation on NumPay Array\n",
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"3. 📚 Arthimetic Operations\n",
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"4. 📚 Basic Mathematic Operations\n",
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"5. 📚 Statistical Analysis\n",
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"6. 📚 Linear Algebra\n",
@@ -4163,6 +4163,379 @@
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}
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# **📚Section 6 -Linear Algebra**"
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],
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"metadata": {
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"id": "9ac_XaU4Ubv_"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"##**6.1-Basic Linear Algebra Operations with NumPy**"
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],
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"metadata": {
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"id": "jtvjoelXVINB"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# Creating Arrays and Matrices:\n",
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"import numpy as np\n",
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"# Create a 1D array\n",
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"array_1d = np.array([1, 2, 3, 4, 5])\n",
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"# Create a 2D array (matrix)\n",
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"matrix_2d = np.array([[1, 2], [3, 4]])"
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],
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"metadata": {
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"id": "H2gx6f3rVdf0"
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},
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"execution_count": 5,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Matrix Multiplication:\n",
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"result = np.dot(matrix_2d, array_1d)\n",
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"\n",
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"narray = np.array([1, 2, 3, 4]) # Define a numpy array\n",
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"print(narray * 3)\n",
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"print(alist * 3)"
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],
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"metadata": {
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"id": "A4CZm4STVh-A"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Matrix Inversion:\n",
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"inverse_matrix = np.linalg.inv(matrix_2d)"
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],
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"metadata": {
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"id": "MV0At4huVuOQ"
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},
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"execution_count": 7,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Eigenvalue decomposition\n",
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"eigenvalues, eigenvectors = np.linalg.eig(matrix_2d)"
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],
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"metadata": {
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"id": "nllQV8b2V2tL"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Singular value decomposition (SVD)\n",
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"u, s, vh = np.linalg.svd(matrix_2d)"
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],
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"metadata": {
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"id": "i09xl-xUV5Pq"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Transpose a Matrix\n",
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"matrix3x2 = np.array([[1, 2], [3, 4], [5, 6]]) # Define a 3x2 matrix\n",
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"print('Original matrix 3 x 2')\n",
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"print(matrix3x2)\n",
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"print('Transposed matrix 2 x 3')\n",
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"print(matrix3x2.T)"
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],
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"metadata": {
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"id": "OxSUYFlHV8h1"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# note that the transpose operation does not affect 1D arrays.\n",
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"nparray = np.array([1, 2, 3, 4]) # Define an array\n",
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"print('Original array')\n",
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"print(nparray)\n",
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"print('Transposed array')\n",
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"print(nparray.T)"
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],
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"metadata": {
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"id": "2hp-WgWMWHD3"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"nparray = np.array([[1, 2, 3, 4]]) # Define a 1 x 4 matrix. Note the 2 level of square brackets\n",
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"print('Original array')\n",
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"print(nparray)\n",
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"print('Transposed array')\n",
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"print(nparray.T)"
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],
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"metadata": {
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"id": "O6VUPQrgWKdh"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# dot product\n",
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"nparray1 = np.array([0, 1, 2, 3]) # Define an array\n",
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"nparray2 = np.array([4, 5, 6, 7]) # Define an array\n",
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"flavor1 = np.dot(nparray1, nparray2) # Recommended way\n",
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"print(flavor1)\n",
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"flavor2 = np.sum(nparray1 * nparray2) # Ok way\n",
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"print(flavor2)\n",
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"flavor3 = nparray1 @ nparray2 # Geeks way\n",
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"print(flavor3)\n",
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"# As you never should do: # Noobs way\n",
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"flavor4 = 0\n",
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"for a, b in zip(nparray1, nparray2):\n",
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" flavor4 += a * b\n",
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"print(flavor4)\n",
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"\n",
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"norm1 = np.dot(np.array([1, 2]), np.array([3, 4])) # Dot product on nparrays\n",
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"norm2 = np.dot([1, 2], [3, 4]) # Dot product on python lists\n",
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"print(norm1, '=', norm2 )"
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],
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"metadata": {
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"id": "I1GVbBPxWN4_"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Sums by rows and columns\n",
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"nparray2 = np.array([[1, -1], [2, -2], [3, -3]]) # Define a 3 x 2 matrix.\n",
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"sumByCols = np.sum(nparray2, axis=0) # Get the sum for each column. Returns 2 elements\n",
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"sumByRows = np.sum(nparray2, axis=1) # get the sum for each row. Returns 3 elements\n",
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"print('Matrix mean: ')\n",
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"print(mean)\n",
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"print('Mean by columns: ')\n",
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"print(meanByCols)\n",
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"print('Mean by rows:')\n",
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"print(meanByRows)"
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],
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"metadata": {
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"id": "vAnTXcBWWT0r"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"##**6.2-Advanced Linear Algebra Techniques with NumPy**"
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],
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"metadata": {
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"id": "uNk6Hc9VWZGa"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Solving Linear Systems:**"
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],
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"metadata": {
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"id": "zFoFF138XGVn"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# Solve linear equations\n",
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"coefficients = np.array([[2, 3], [1, -1]])\n",
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"constants = np.array([8, 1])\n",
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"solution = np.linalg.solve(coefficients, constants)"
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],
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"metadata": {
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"id": "Td7XmEDlXUim"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Least Squares Fitting**"
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],
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"metadata": {
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"id": "0Uy1vKIjXX-m"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# Fit a line to data using least squares\n",
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"x = np.array([0, 1, 2, 3])\n",
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"y = np.array([0, 0.9, 2.1, 2.8])\n",
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"coefficients = np.polyfit(x, y, deg=1)"
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],
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"metadata": {
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"id": "FddjOrQ_Xd6e"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"### **Princial Component Analysis (PCA)**"
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],
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"metadata": {
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"id": "EF7mojZhXsOu"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# Perform PCA\n",
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"data = np.random.rand(100, 3) # Sample data\n",
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"mean = np.mean(data, axis=0)\n",
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"centered_data = data - mean\n",
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"covariance_matrix = np.cov(centered_data, rowvar=False)\n",
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"eigenvalues, eigenvectors = np.linalg.eig(covariance_matrix)"
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],
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"metadata": {
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"id": "4ef5CfMiX1x-"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"# **📚Section 7 -Data Cleaning**"
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],
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"metadata": {
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"id": "Ph4nunOxYNLN"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## **7.1-Identifying Missing Values**"
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],
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"metadata": {
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"id": "pOWkZiDAYctQ"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# Create a NumPy array with missing values\n",
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"data = np.array([1, 2, np.nan, 4, np.nan, 6])\n",
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"# Check for missing values\n",
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"has_missing = np.isnan(data)\n",
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"print(has_missing)"
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],
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"metadata": {
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"id": "Erq0fabkYsn7",
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"outputId": "d7be023e-3def-49a7-da61-2419a7132d02",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"execution_count": 8,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"[False False True False True False]\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## **7.2-Removing Rows or Columns with Missing Values**"
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],
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"metadata": {
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"id": "QtXF8_DVYyXK"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# Create a 2D array with missing values\n",
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"data = np.array([[1, 2, 3], [4, np.nan, 6], [7, 8, 9]])\n",
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"# Remove rows with any missing values\n",
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"cleaned_data = data[~np.any(np.isnan(data), axis=1)]\n",
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"print(cleaned_data) # Result: [[1,2,3],[7,8,9]]"
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],
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"metadata": {
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"id": "KwfwO7WhY6Zj"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"## **7.3-Removing Outliers**"
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],
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"metadata": {
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"id": "CB75ZdyxY9Tz"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"data = np.array([1, 2, 3, 100, 101, 102])\n",
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"threshold = np.percentile(data, 95) # Get 95th percentile\n",
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"cleaned_data = data[data <= threshold] # Remove outliers above threshold"
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],
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"metadata": {
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"id": "eN7U5f33ZAr6"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"## **7.4-Removing Duplicates**"
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],
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"metadata": {
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"id": "P_TZjeqmZrhc"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"data = np.array([1, 2, 3, 2, 4, 5, 1])\n",
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"unique_values = np.unique(data) # Get unique values"
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],
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"metadata": {
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"id": "HidXyEwNZxIx"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [

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