diff --git a/NumPy.ipynb b/NumPy.ipynb index 7f00a7e..b3eafb8 100644 --- a/NumPy.ipynb +++ b/NumPy.ipynb @@ -2,16 +2,15 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, "source": [ "# NumPy\n", "\n", "Numpy stands for Numerical Python. Numpy(Numerical Python) is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with arrays. It contains the data structures needed for storing and accessing arrays. It has simple operations and functions for computation of arrays. In addition to numeric data types, NumPy can store other data types such as strings, boolean, and etc.\n" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## NumPy Installation\n", "It is possible to install numpy on Jupyter notebook or the command prompt using the following command.\n", @@ -19,11 +18,11 @@ "```sh\n", "pip install numpy\n", "```" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Importing NumPy\n", "Numpy can be imported as follows\n", @@ -36,23 +35,22 @@ "```py\n", "import numpy as np\n", "```" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 244, - "metadata": {}, - "outputs": [], "source": [ "# How ot import numpy\n", "import numpy as np" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 245, - "metadata": {}, - "outputs": [], "source": [ "# Let us import matplot.lib and seaborn for data visualization\n", "# installation of matplotlib and seaborn is required\n", @@ -60,47 +58,48 @@ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set()" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 246, - "metadata": {}, - "outputs": [], "source": [ "## import stats for some statistical methods\n", "from scipy import stats" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## NumPy Version\n", "Checking of the numPy version" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 247, - "metadata": {}, + "source": [ + "# How check the version of numpy package\n", + "print('NumPy:', np.__version__)\n" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "NumPy: 1.19.2\n" ] } ], - "source": [ - "# How check the version of numpy package\n", - "print('NumPy:', np.__version__)\n" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Creating of NumPy Arrays\n", "We can change Python list and tuple to NumPy array.\n", @@ -109,92 +108,96 @@ "lst = [1, 2, 3, 4,5] # list\n", "tpl = (1, 2, 3, 4, 5) # tuple\n", "```" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Creating NumPy array from Python lists" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 248, - "metadata": {}, + "source": [ + "# creating python list\n", + "lst = [1, 2, 3, 4, 5]\n", + "# Checking data types\n", + "print('Type : ', type(lst))\n", + "print(lst)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Type : \n", "[1, 2, 3, 4, 5]\n" ] } ], - "source": [ - "# creating python list\n", - "lst = [1, 2, 3, 4, 5]\n", - "# Checking data types\n", - "print('Type : ', type(lst))\n", - "print(lst)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 249, - "metadata": {}, + "source": [ + "lst_of_lsts = [[0, 1, 2], [3,4,5],[6,7,8]]\n", + "print(lst_of_lsts)\n", + "print(type(lst_of_lsts))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[0, 1, 2], [3, 4, 5], [6, 7, 8]]\n", "\n" ] } ], - "source": [ - "lst_of_lsts = [[0, 1, 2], [3,4,5],[6,7,8]]\n", - "print(lst_of_lsts)\n", - "print(type(lst_of_lsts))" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "The numPy array has a *ndarry* data type which refers to n dimensional array. " - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 250, - "metadata": {}, + "source": [ + "arr = np.array(lst)\n", + "print('Type:', type(arr))\n", + "print(arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Type: \n", "[1 2 3 4 5]\n" ] } ], - "source": [ - "arr = np.array(lst)\n", - "print('Type:', type(arr))\n", - "print(arr)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 251, - "metadata": {}, + "source": [ + "two_dim_arr = np.array(lst_of_lsts)\n", + "print(two_dim_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[0 1 2]\n", " [3 4 5]\n", @@ -202,34 +205,39 @@ ] } ], - "source": [ - "two_dim_arr = np.array(lst_of_lsts)\n", - "print(two_dim_arr)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Creating NumPy arrays with different data types\n", "It is possible to create a numpy array with different data types with dtype " - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Creating integer NumPy array" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 252, - "metadata": {}, + "source": [ + "# Python list\n", + "lst = [1,2,3,4,5]\n", + "print(lst)\n", + "\n", + "arr_int = np.array(lst, dtype='int')\n", + "print(arr_int)\n", + "print(type(arr_int))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[1, 2, 3, 4, 5]\n", "[1 2 3 4 5]\n", @@ -237,31 +245,31 @@ ] } ], - "source": [ - "# Python list\n", - "lst = [1,2,3,4,5]\n", - "print(lst)\n", - "\n", - "arr_int = np.array(lst, dtype='int')\n", - "print(arr_int)\n", - "print(type(arr_int))" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Creating floating number NumPy array" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 253, - "metadata": {}, + "source": [ + "# Python list\n", + "lst = [1,2,3,4,5]\n", + "print(lst)\n", + "\n", + "arr_float = np.array(python_list, dtype='float')\n", + "print(arr_float)\n", + "print(type(arr_float))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[1, 2, 3, 4, 5]\n", "[1. 2. 3. 4. 5.]\n", @@ -269,31 +277,31 @@ ] } ], - "source": [ - "# Python list\n", - "lst = [1,2,3,4,5]\n", - "print(lst)\n", - "\n", - "arr_float = np.array(python_list, dtype='float')\n", - "print(arr_float)\n", - "print(type(arr_float))" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Creating complex NumPy array\n" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 254, - "metadata": {}, + "source": [ + "# Python list\n", + "lst = [1,2,3,4,5]\n", + "print(lst)\n", + "\n", + "arr_complex = np.array(lst, dtype='complex')\n", + "print(arr_complex)\n", + "print(type(arr_complex))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[1, 2, 3, 4, 5]\n", "[1.+0.j 2.+0.j 3.+0.j 4.+0.j 5.+0.j]\n", @@ -301,31 +309,30 @@ ] } ], - "source": [ - "# Python list\n", - "lst = [1,2,3,4,5]\n", - "print(lst)\n", - "\n", - "arr_complex = np.array(lst, dtype='complex')\n", - "print(arr_complex)\n", - "print(type(arr_complex))" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Creating String NumPy array" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 255, - "metadata": {}, + "source": [ + "# Python list\n", + "lst = [1,2,3,4,5]\n", + "print(lst)\n", + "arr_str = np.array(lst, dtype='str')\n", + "print(arr_str)\n", + "print(type(arr_str))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[1, 2, 3, 4, 5]\n", "['1' '2' '3' '4' '5']\n", @@ -333,86 +340,65 @@ ] } ], - "source": [ - "# Python list\n", - "lst = [1,2,3,4,5]\n", - "print(lst)\n", - "arr_str = np.array(lst, dtype='str')\n", - "print(arr_str)\n", - "print(type(arr_str))" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 256, - "metadata": {}, + "source": [ + "fruits = ['Apple','Avocado', 'Banana', 'Mango', 'Orange']\n", + "fruits_arr = np.array(fruits)\n", + "print(fruits_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "['Apple' 'Avocado' 'Banana' 'Mango' 'Orange']\n" ] } ], - "source": [ - "fruits = ['Apple','Avocado', 'Banana', 'Mango', 'Orange']\n", - "fruits_arr = np.array(fruits)\n", - "print(fruits_arr)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Creating Boolean NumPy Arrays\n", "Creating a boolean a NumPy array from list" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 257, - "metadata": {}, + "source": [ + "arr_bool = np.array([0, 1, -1, 0, 0], dtype=bool)\n", + "print(type(arr_bool))\n", + "print(arr_bool)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "\n", "[False True True False False]\n" ] } ], - "source": [ - "arr_bool = np.array([0, 1, -1, 0, 0], dtype=bool)\n", - "print(type(arr_bool))\n", - "print(arr_bool)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "*Summary of creating NumPy array with different data types*" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 258, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1 2 3 4 5]\n", - "[1. 2. 3. 4. 5.]\n", - "[ True True True False True True True True]\n", - "[1.+0.j 2.+0.j 3.+0.j 4.+0.j 5.+0.j]\n", - "['1' '2' '3' '4' '5.0']\n" - ] - } - ], "source": [ "np_int_arr = np.array([1,2,3,4,5.0], dtype = 'int')\n", "np_float_arr = np.array([1,2,3,4,5.0], dtype = 'float')\n", @@ -425,23 +411,41 @@ "print(np_bool_arr)\n", "print(np_complex_arr)\n", "print(np_str_arr)" - ] + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[1 2 3 4 5]\n", + "[1. 2. 3. 4. 5.]\n", + "[ True True True False True True True True]\n", + "[1.+0.j 2.+0.j 3.+0.j 4.+0.j 5.+0.j]\n", + "['1' '2' '3' '4' '5.0']\n" + ] + } + ], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "Nested list can create multidimensional numPy arrays. It is possible to create a multidimensional array using Python list of lists" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 259, - "metadata": {}, + "source": [ + "lst_of_lsts = [[0,1,2], [3,4,5], [6,7,8]]\n", + "two_dim_arr = np.array(lst_of_lsts)\n", + "print(two_dim_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[0 1 2]\n", " [3 4 5]\n", @@ -449,15 +453,10 @@ ] } ], - "source": [ - "lst_of_lsts = [[0,1,2], [3,4,5], [6,7,8]]\n", - "two_dim_arr = np.array(lst_of_lsts)\n", - "print(two_dim_arr)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### NumPy array attributes\n", "The numPy array has different attributes that give more information about the array such as *ndim*, *shape*, *size*, *dtype*, *itemsize*\n", @@ -468,16 +467,24 @@ "- itemsize - returns the size in bytes of each elements in the numPy array\n", "\n", "The NumPy *shape* method provide the shape of the array as a tuple. The first is the row and the second is the column. If the array is just one dimensional it returns the size of the array(n,)." - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 260, - "metadata": {}, + "source": [ + "arr = np.array([1, 2, 3, 4,5])\n", + "print('NumPy array dimension: ', arr.ndim)\n", + "print('NumPy array shape: ', arr.shape)\n", + "print('NumPy array size: ', arr.size)\n", + "print('NumPy array data type: ', arr.dtype)\n", + "print('NumPy array item size: ', arr.itemsize)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "NumPy array dimension: 1\n", "NumPy array shape: 1\n", @@ -487,83 +494,82 @@ ] } ], - "source": [ - "arr = np.array([1, 2, 3, 4,5])\n", - "print('NumPy array dimension: ', arr.ndim)\n", - "print('NumPy array shape: ', arr.ndim)\n", - "print('NumPy array size: ', arr.size)\n", - "print('NumPy array data type: ', arr.dtype)\n", - "print('NumPy array item size: ', arr.itemsize)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Creating Multidimensional Array Using NumPy\n", "We can create different dimension numPy array." - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "#### NumPy array without dimension or zero deminsion" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 261, - "metadata": {}, + "source": [ + "ten = np.array(10)\n", + "print(ten)\n", + "print(f'The numPy n-d:{ten.ndim},shape:{ten.shape}, size:{ten.size}, date type:{ten.dtype}, itemsize:{ten.itemsize}') " + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "10\n", "The numPy n-d:0,shape:(), size:1, date type:int32, itemsize:4\n" ] } ], - "source": [ - "ten = np.array(10)\n", - "print(ten)\n", - "print(f'The numPy n-d:{ten.ndim},shape:{ten.shape}, size:{ten.size}, date type:{ten.dtype}, itemsize:{ten.itemsize}') " - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "We can use simple function that provides information about the numPy array" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 262, - "metadata": {}, - "outputs": [], "source": [ "def get_numpy_array_info(arr):\n", " print(f'The numPy n-d:{arr.ndim},shape:{arr.shape}, size:{arr.size}, date type:{arr.dtype}, itemsize:{arr.itemsize}')" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "#### One dimension numPy array\n", "A list can create a one dimensional numPy array.\n" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 263, - "metadata": {}, + "source": [ + "lst = [1, 2, 3, 4, 5]\n", + "arr = np.array(lst)\n", + "print(lst)\n", + "print(arr)\n", + "get_numpy_array_info(arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[1, 2, 3, 4, 5]\n", "[1 2 3 4 5]\n", @@ -571,30 +577,30 @@ ] } ], - "source": [ - "lst = [1, 2, 3, 4, 5]\n", - "arr = np.array(lst)\n", - "print(lst)\n", - "print(arr)\n", - "get_numpy_array_info(arr)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "#### Two dimensional numPy array\n", "A list of list can create a 2-d numPy array" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 271, - "metadata": {}, + "source": [ + "lst = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n", + "two_dim_arr = np.array(lst)\n", + "print(lst)\n", + "print(two_dim_arr)\n", + "get_numpy_array_info(arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n", "[[1 2 3]\n", @@ -604,22 +610,25 @@ ] } ], - "source": [ - "lst = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n", - "two_dim_arr = np.array(lst)\n", - "print(lst)\n", - "print(two_dim_arr)\n", - "get_numpy_array_info(arr)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 272, - "metadata": {}, + "source": [ + "# 2 Dimension Array\n", + "two_dim_arr = np.array([(1,2,3),(4,5,6), (7,8,9)])\n", + "print(type (two_dim_arr))\n", + "print(two_dim_arr)\n", + "print('Dim:', two_dim_arr.ndim)\n", + "print('Shape: ', two_dim_arr.shape)\n", + "print('Size:', two_dim_arr.size)\n", + "print('Data type:', two_dim_arr.dtype)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "\n", "[[1 2 3]\n", @@ -632,33 +641,30 @@ ] } ], - "source": [ - "# 2 Dimension Array\n", - "two_dim_arr = np.array([(1,2,3),(4,5,6), (7,8,9)])\n", - "print(type (two_dim_arr))\n", - "print(two_dim_arr)\n", - "print('Dim:', two_dim_arr.ndim)\n", - "print('Shape: ', two_dim_arr.shape)\n", - "print('Size:', two_dim_arr.size)\n", - "print('Data type:', two_dim_arr.dtype)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Three dimensional numPy array\n", "List of lists of list create a 3-d numPy array" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 291, - "metadata": {}, + "source": [ + "lst = [[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]\n", + "three_dim_arr = np.array(lst)\n", + "print(lst)\n", + "print(three_dim_arr)\n", + "get_numpy_array_info(two_dim_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]\n", "[[[1 2 3]\n", @@ -668,124 +674,120 @@ ] } ], - "source": [ - "lst = [[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]\n", - "three_dim_arr = np.array(lst)\n", - "print(lst)\n", - "print(three_dim_arr)\n", - "get_numpy_array_info(two_dim_arr)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Converting NumPy array to list" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 292, - "metadata": {}, + "source": [ + "lst = [1, 2, 3, 4, 5]\n", + "arr = np.array(lst)\n", + "print(arr)\n", + "lst = arr.tolist()\n", + "lst_of_lsts = two_dim_arr.tolist()\n", + "print(lst_of_lsts )" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[1 2 3 4 5]\n", "[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]\n" ] } ], - "source": [ - "lst = [1, 2, 3, 4, 5]\n", - "arr = np.array(lst)\n", - "print(arr)\n", - "lst = arr.tolist()\n", - "lst_of_lsts = two_dim_arr.tolist()\n", - "print(lst_of_lsts )" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 308, - "metadata": {}, + "source": [ + "print(lst)\n", + "print(type(lst))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[1, 2, 3, 4, 5]\n", "\n" ] } ], - "source": [ - "print(lst)\n", - "print(type(lst))" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Creating NumPy Array from Tuples" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 309, - "metadata": {}, + "source": [ + "tpl = (1,2,3,4,5)\n", + "print(type(tpl))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "\n" ] } ], - "source": [ - "tpl = (1,2,3,4,5)\n", - "print(type(tpl))" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 310, - "metadata": {}, + "source": [ + "arr = np.array(tpl)\n", + "print(type(arr))\n", + "print('numpy_array_from_tuple:',arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "\n", "numpy_array_from_tuple: [1 2 3 4 5]\n" ] } ], - "source": [ - "arr = np.array(tpl)\n", - "print(type(arr))\n", - "print('numpy_array_from_tuple:',arr)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "\n", "\n" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 311, - "metadata": {}, + "source": [ + "print(two_dim_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[[1 2 3]\n", " [4 5 6]\n", @@ -793,70 +795,70 @@ ] } ], - "source": [ - "print(two_dim_arr)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 312, - "metadata": {}, + "source": [ + "print('She of numpy_two_dimensional_array: ', two_dim_arr.shape)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "She of numpy_two_dimensional_array: (1, 3, 3)\n" ] } ], - "source": [ - "print('She of numpy_two_dimensional_array: ', two_dim_arr.shape)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Size of a NumPy array\n", "In NumPy to know the number of items in a NumPy array list we use size" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 313, - "metadata": {}, - "outputs": [], "source": [ "lst = [1, 2, 3, 4, 5]\n", "arr = np.array(lst)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 314, - "metadata": {}, + "source": [ + "print('Size: ', arr.size)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Size: 5\n" ] } ], - "source": [ - "print('Size: ', arr.size)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 315, - "metadata": {}, + "source": [ + "print(np_two_dim_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[0 1 2]\n", " [3 4 5]\n", @@ -864,43 +866,49 @@ ] } ], - "source": [ - "print(np_two_dim_arr)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 316, - "metadata": {}, + "source": [ + "print('Size: ', np_two_dim_arr.size)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Size: 9\n" ] } ], - "source": [ - "print('Size: ', np_two_dim_arr.size)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Data Type of NumPy Array\n", "Type of data types: str, int, float, complex, bool, list, None" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 317, - "metadata": {}, + "source": [ + "int_lists = [-3, -2, -1, 0, 1, 2,3]\n", + "int_arr = np.array(int_lists)\n", + "print(int_arr)\n", + "print(int_arr.dtype)\n", + "float_arr = np.array(int_lists, dtype=float)\n", + "print(float_arr)\n", + "print(float_arr.dtype)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[-3 -2 -1 0 1 2 3]\n", "int32\n", @@ -909,33 +917,31 @@ ] } ], - "source": [ - "int_lists = [-3, -2, -1, 0, 1, 2,3]\n", - "int_arr = np.array(int_lists)\n", - "print(int_arr)\n", - "print(int_arr.dtype)\n", - "float_arr = np.array(int_lists, dtype=float)\n", - "print(float_arr)\n", - "print(float_arr.dtype)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Iterating NumPy Array\n", "\n", "We can iterate through each elements of numPy array using loops. \n" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 318, - "metadata": {}, + "source": [ + "# 1-d array\n", + "arr = np.array([1, 2, 3, 4, 5])\n", + "print('Dim:', arr.ndim)\n", + "for item in arr:\n", + " print(item)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Dim: 1\n", "1\n", @@ -946,22 +952,27 @@ ] } ], - "source": [ - "# 1-d array\n", - "arr = np.array([1, 2, 3, 4, 5])\n", - "print('Dim:', arr.ndim)\n", - "for item in arr:\n", - " print(item)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 319, - "metadata": {}, + "source": [ + "# 2-d array\n", + "arr = np.array([[1, 2], [3, 4], [5,6]])\n", + "print('Dim:', arr.ndim)\n", + "for item in arr:\n", + " print(item)\n", + " \n", + "\n", + "for item in arr:\n", + " for i in item:\n", + " print(i)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Dim: 2\n", "[1 2]\n", @@ -976,27 +987,24 @@ ] } ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 320, "source": [ - "# 2-d array\n", - "arr = np.array([[1, 2], [3, 4], [5,6]])\n", + "# 3-d array\n", + "arr = np.array([[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [11, 12]]])\n", "print('Dim:', arr.ndim)\n", "for item in arr:\n", " print(item)\n", " \n", - "\n", - "for item in arr:\n", - " for i in item:\n", - " print(i)" - ] - }, - { - "cell_type": "code", - "execution_count": 320, - "metadata": {}, + "\n" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Dim: 3\n", "[[1 2]\n", @@ -1008,31 +1016,26 @@ ] } ], - "source": [ - "# 3-d array\n", - "arr = np.array([[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [11, 12]]])\n", - "print('Dim:', arr.ndim)\n", - "for item in arr:\n", - " print(item)\n", - " \n", - "\n" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "Iterating using ndenumerator method allow to access the element with the location of the specific element." - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 321, - "metadata": {}, + "source": [ + "for x in np.ndenumerate(arr):\n", + " print(x)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "((0, 0, 0), 1)\n", "((0, 0, 1), 2)\n", @@ -1049,14 +1052,10 @@ ] } ], - "source": [ - "for x in np.ndenumerate(arr):\n", - " print(x)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Mathematical Operation Using NumPy\n", "Numpy array is not like exactly like python list. To do mathematical operation in pyhton list we have to loop through the items but numpy can allow to do any mathematical operation without looping. Mathematical Operation:\n", @@ -1068,104 +1067,117 @@ "- Modules (%) \n", "- Floor Division(//) \n", "- Exponential(**) " - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Addition\n", "Adding to scalar value is easy but add some value on a python list is not an easy operation. However, numPy array made it easy to do arithemtic operation without a use of any loops." - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 323, - "metadata": {}, + "source": [ + "# Mathematical Operation\n", + "# Addition\n", + "arr = np.array([1, 2, 3, 4, 5])\n", + "print('original array: ',arr)\n", + "ten_plus_original = arr + 10\n", + "print(ten_plus_original)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "original array: [1 2 3 4 5]\n", "[11 12 13 14 15]\n" ] } ], - "source": [ - "# Mathematical Operation\n", - "# Addition\n", - "arr = np.array([1, 2, 3, 4, 5])\n", - "print('original array: ',arr)\n", - "ten_plus_original = arr + 10\n", - "print(ten_plus_original)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Subtraction" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 325, - "metadata": {}, - "outputs": [ + "source": [ + "# Subtraction\n", + "arr = np.array([1, 2, 3, 4, 5])\n", + "print('original array: ',arr)\n", + "ten_minus_original = arr - 10\n", + "print(ten_minus_original)" + ], + "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "original array: [1 2 3 4 5]\n", "[-9 -8 -7 -6 -5]\n" ] } ], - "source": [ - "# Subtraction\n", - "arr = np.array([1, 2, 3, 4, 5])\n", - "print('original array: ',arr)\n", - "ten_minus_original = arr - 10\n", - "print(ten_minus_original)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Multiplication\n" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 326, - "metadata": {}, + "source": [ + "arr = np.array([1, 2, 3, 4, 5])\n", + "print('original array: ', arr)\n", + "ten_times_original = arr * 10\n", + "print(ten_times_original)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "original array: [1 2 3 4 5]\n", "[10 20 30 40 50]\n" ] } ], - "source": [ - "arr = np.array([1, 2, 3, 4, 5])\n", - "print('original array: ', arr)\n", - "ten_times_original = arr * 10\n", - "print(ten_times_original)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 327, - "metadata": {}, + "source": [ + "# let's seee some use cases\n", + "\n", + "x = np.array(range(0,50))\n", + "y = x * 2\n", + "print(x)\n", + "print(y)\n", + "plt.scatter(x,y, c='brown')\n", + "plt.title('X vs Y')\n", + "plt.xlabel('X Axis')\n", + "plt.ylabel('Y Axis')\n", + "plt.show()" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n", @@ -1176,335 +1188,332 @@ ] }, { + "output_type": "display_data", "data": { - "image/png": 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\n", 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" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], - "source": [ - "# let's seee some use cases\n", - "\n", - "x = np.array(range(0,50))\n", - "y = x * 2\n", - "print(x)\n", - "print(y)\n", - "plt.scatter(x,y, c='brown')\n", - "plt.title('X vs Y')\n", - "plt.xlabel('X Axis')\n", - "plt.ylabel('Y Axis')\n", - "plt.show()" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Division" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 336, - "metadata": {}, + "source": [ + "# Division\n", + "arr = np.array([1, 2, 3, 4, 5])\n", + "print('original array: ', arr)\n", + "ten_times_original =arr / 10\n", + "print(ten_times_original)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "original array: [1 2 3 4 5]\n", "[0.1 0.2 0.3 0.4 0.5]\n" ] } ], - "source": [ - "# Division\n", - "arr = np.array([1, 2, 3, 4, 5])\n", - "print('original array: ', arr)\n", - "ten_times_original =arr / 10\n", - "print(ten_times_original)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 337, - "metadata": {}, + "source": [ + "### Modulus\n", + "# Modulus; Finding the remainder\n", + "arr = np.array([1, 2, 3, 4, 5])\n", + "print('original array: ',arr)\n", + "ten_times_original = arr % 3\n", + "print(ten_times_original)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "original array: [1 2 3 4 5]\n", "[1 2 0 1 2]\n" ] } ], - "source": [ - "### Modulus\n", - "# Modulus; Finding the remainder\n", - "arr = np.array([1, 2, 3, 4, 5])\n", - "print('original array: ',arr)\n", - "ten_times_original = arr % 3\n", - "print(ten_times_original)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Modulos" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 343, - "metadata": {}, + "source": [ + "#modulous: the division result without the remainder\n", + "arr = np.array([1, 2, 3, 4, 5, 11])\n", + "print('original array: ', arr)\n", + "ten_times_original = arr // 10\n", + "print(ten_times_original)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "original array: [ 1 2 3 4 5 11]\n", "[0 0 0 0 0 1]\n" ] } ], - "source": [ - "#modulous: the division result without the remainder\n", - "arr = np.array([1, 2, 3, 4, 5, 11])\n", - "print('original array: ', arr)\n", - "ten_times_original = arr // 10\n", - "print(ten_times_original)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Floor Division" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 344, - "metadata": {}, + "source": [ + "# Floor division: the division result without the remainder\n", + "arr = np.array([1, 2, 3, 4, 5, 11])\n", + "print('original array: ', arr)\n", + "ten_times_original = arr // 10\n", + "print(ten_times_original)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "original array: [ 1 2 3 4 5 11]\n", "[0 0 0 0 0 1]\n" ] } ], - "source": [ - "# Floor division: the division result without the remainder\n", - "arr = np.array([1, 2, 3, 4, 5, 11])\n", - "print('original array: ', arr)\n", - "ten_times_original = arr // 10\n", - "print(ten_times_original)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Exponential" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 359, - "metadata": {}, + "source": [ + "# Exponential is finding some number the power of another:\n", + "arr = np.array([1, 2, 3, 4, 5])\n", + "print('original array: ', arr)\n", + "ten_times_original = arr ** 2\n", + "print(ten_times_original)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "original array: [1 2 3 4 5]\n", "[ 1 4 9 16 25]\n" ] } ], - "source": [ - "# Exponential is finding some number the power of another:\n", - "arr = np.array([1, 2, 3, 4, 5])\n", - "print('original array: ', arr)\n", - "ten_times_original = arr ** 2\n", - "print(ten_times_original)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Converting Data Types\n", "We can convert the data types of numpy array" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Converting Data Types from Int to Float" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 366, - "metadata": {}, + "source": [ + "np_int_arr = np.array([1,2,3,4])\n", + "print(np_int_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[1 2 3 4]\n" ] } ], - "source": [ - "np_int_arr = np.array([1,2,3,4])\n", - "print(np_int_arr)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 367, - "metadata": {}, - "outputs": [], "source": [ "np_float_arr = np_int_arr.astype('float')" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 368, - "metadata": {}, + "source": [ + "print(np_float_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[1. 2. 3. 4.]\n" ] } ], - "source": [ - "print(np_float_arr)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Converting Data Types \n" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 369, - "metadata": {}, - "outputs": [], "source": [ "# to int\n", "np_int_arr = np_float_arr.astype('int')" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 370, - "metadata": {}, + "source": [ + "print(np_int_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[1 2 3 4]\n" ] } ], - "source": [ - "print(np_int_arr)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 371, - "metadata": {}, + "source": [ + "#to float\n", + "\n", + "np_float_arr = np_int_arr.astype('float')\n", + "print(np_float_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[1. 2. 3. 4.]\n" ] } ], - "source": [ - "#to float\n", - "\n", - "np_float_arr = np_int_arr.astype('float')\n", - "print(np_float_arr)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 372, - "metadata": {}, + "source": [ + "# Converting Data Types Int to Boolean\n", + "np_bool_arr = np.array([-3, -2, 0, 1,2,3]).astype('bool')\n", + "print(np_bool_arr)\n" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[ True True False True True True]\n" ] } ], - "source": [ - "# Converting Data Types Int to Boolean\n", - "np_bool_arr = np.array([-3, -2, 0, 1,2,3]).astype('bool')\n", - "print(np_bool_arr)\n" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 373, - "metadata": {}, + "source": [ + "# int to string\n", + "np_str_arr = np_int_arr.astype('str')\n", + "print(np_str_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "['1' '2' '3' '4']\n" ] } ], - "source": [ - "# int to string\n", - "np_str_arr = np_int_arr.astype('str')\n", - "print(np_str_arr)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Getting items from a numpy array" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 374, - "metadata": {}, + "source": [ + "# 2 Dimension Array\n", + "two_dim_arr = np.array([[1,2,3],[4,5,6], [7,8,9]])\n", + "first_row = two_dim_arr[0]\n", + "second_row = two_dim_arr[1]\n", + "third_row = two_dim_arr[2]\n", + "print('First row:', first_row)\n", + "print('Second row:', second_row)\n", + "print('Third row: ', third_row)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "First row: [1 2 3]\n", "Second row: [4 5 6]\n", @@ -1512,25 +1521,24 @@ ] } ], - "source": [ - "# 2 Dimension Array\n", - "two_dim_arr = np.array([[1,2,3],[4,5,6], [7,8,9]])\n", - "first_row = two_dim_arr[0]\n", - "second_row = two_dim_arr[1]\n", - "third_row = two_dim_arr[2]\n", - "print('First row:', first_row)\n", - "print('Second row:', second_row)\n", - "print('Third row: ', third_row)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 375, - "metadata": {}, + "source": [ + "first_column= two_dim_arr[:,0]\n", + "second_column = two_dim_arr[:,1]\n", + "third_column = two_dim_arr[:,2]\n", + "print('First column:', first_column)\n", + "print('Second column:', second_column)\n", + "print('Third column: ', third_column)\n", + "print(two_dim_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "First column: [1 4 7]\n", "Second column: [2 5 8]\n", @@ -1541,61 +1549,60 @@ ] } ], - "source": [ - "first_column= two_dim_arr[:,0]\n", - "second_column = two_dim_arr[:,1]\n", - "third_column = two_dim_arr[:,2]\n", - "print('First column:', first_column)\n", - "print('Second column:', second_column)\n", - "print('Third column: ', third_column)\n", - "print(two_dim_arr)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## NumPy array Indexing\n", "\n", "Accessing one dimensional array elements is similar to accessing list elements. The first element can be accessed by index 0. See the example below. Negative indexing is also possible. The last element is at -1." - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 376, - "metadata": {}, + "source": [ + "arr = np.array([1, 2, 3, 4, 5])\n", + "print(arr[0])\n", + "print(arr[-1])" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "1\n", "5\n" ] } ], - "source": [ - "arr = np.array([1, 2, 3, 4, 5])\n", - "print(arr[0])\n", - "print(arr[-1])" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "Accessing two or more dimensional array elements is a bit different from accessing ordinary list. In order to access a 2-D array element, we use the numbers separated by a comma.\n", "The first numbers reperesents the dimension and the second number refers to the index of the lement.Let's see an example." - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 377, - "metadata": {}, + "source": [ + "# 2-D\n", + "lst_lst =[[1,2,3],[4,5,6],[7,8,9]]\n", + "two_dim_arr = np.array(lst_lst)\n", + "print(two_dim_arr)\n", + "print(two_dim_arr[0, 2])\n", + "print('dim:', two_dim_arr.ndim)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[1 2 3]\n", " [4 5 6]\n", @@ -1605,23 +1612,24 @@ ] } ], - "source": [ - "# 2-D\n", - "lst_lst =[[1,2,3],[4,5,6],[7,8,9]]\n", - "two_dim_arr = np.array(lst_lst)\n", - "print(two_dim_arr)\n", - "print(two_dim_arr[0, 2])\n", - "print('dim:', two_dim_arr.ndim)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 378, - "metadata": {}, + "source": [ + "# 3-D\n", + "\n", + "lst_lst_lst = [[[1,2,3],[4,5,6],[7,8,9]]]\n", + "three_dim_arr = np.array(lst_lst_lst)\n", + "print(three_dim_arr)\n", + "print(three_dim_arr[0, 2, 2]) # the\n", + "print('dim:', three_dim_arr.ndim)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[[1 2 3]\n", " [4 5 6]\n", @@ -1631,32 +1639,34 @@ ] } ], - "source": [ - "# 3-D\n", - "\n", - "lst_lst_lst = [[[1,2,3],[4,5,6],[7,8,9]]]\n", - "three_dim_arr = np.array(lst_lst_lst)\n", - "print(three_dim_arr)\n", - "print(three_dim_arr[0, 2, 2]) # the\n", - "print('dim:', three_dim_arr.ndim)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Slicing some parts of Numpy array\n", "Slicing in numpy is similar to slicing in python list" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 379, - "metadata": {}, + "source": [ + "# [start:end:step]\n", + "# [:] \n", + "arr = np.array([1, 2, 3, 4, 5])\n", + "print(arr)\n", + "print(arr[:])\n", + "print(arr[::])\n", + "print(arr[::2]) # 2 is a step\n", + "print(arr[0:3])\n", + "print(arr[3:])\n" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[1 2 3 4 5]\n", "[1 2 3 4 5]\n", @@ -1667,26 +1677,20 @@ ] } ], - "source": [ - "# [start:end:step]\n", - "# [:] \n", - "arr = np.array([1, 2, 3, 4, 5])\n", - "print(arr)\n", - "print(arr[:])\n", - "print(arr[::])\n", - "print(arr[::2]) # 2 is a step\n", - "print(arr[0:3])\n", - "print(arr[3:])\n" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 380, - "metadata": {}, + "source": [ + "# Gives the same numPy array\n", + "two_dim_arr = np.array([[1,2,3],[4,5,6], [7,8,9]])\n", + "print(two_dim_arr[::])" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[1 2 3]\n", " [4 5 6]\n", @@ -1694,20 +1698,25 @@ ] } ], - "source": [ - "# Gives the same numPy array\n", - "two_dim_arr = np.array([[1,2,3],[4,5,6], [7,8,9]])\n", - "print(two_dim_arr[::])" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 381, - "metadata": {}, + "source": [ + "two_dim_arr = np.array([[1,2,3],[4,5,6], [7,8,9]])\n", + "print('First row', two_dim_arr[0:2]) # slices the first two rows\n", + "print(two_dim_arr[0:3, 0]) # slices the first item in each column\n", + "first_two_rows_and_columns = two_dim_arr[0:3, 0:2]\n", + "print(two_dim_arr[:])\n", + "print(two_dim_arr)\n", + "print(first_two_rows_and_columns)\n", + "print(type(two_dim_arr))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "First row [[1 2 3]\n", " [4 5 6]]\n", @@ -1725,43 +1734,39 @@ ] } ], - "source": [ - "two_dim_arr = np.array([[1,2,3],[4,5,6], [7,8,9]])\n", - "print('First row', two_dim_arr[0:2]) # slices the first two rows\n", - "print(two_dim_arr[0:3, 0]) # slices the first item in each column\n", - "first_two_rows_and_columns = two_dim_arr[0:3, 0:2]\n", - "print(two_dim_arr[:])\n", - "print(two_dim_arr)\n", - "print(first_two_rows_and_columns)\n", - "print(type(two_dim_arr))" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 382, - "metadata": {}, + "source": [ + "two_dim_arr[2, 1:]" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([8, 9])" ] }, - "execution_count": 382, "metadata": {}, - "output_type": "execute_result" + "execution_count": 382 } ], - "source": [ - "two_dim_arr[2, 1:]" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 383, - "metadata": {}, + "source": [ + "# Slicing out the middle four items\n", + "two_dim_arr_2= np.array([[1,2,3,7],[4,5,6, 15], [7,8,9,24], [10,11,12, 33]])\n", + "two_dim_arr_2" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[ 1, 2, 3, 7],\n", @@ -1770,44 +1775,44 @@ " [10, 11, 12, 33]])" ] }, - "execution_count": 383, "metadata": {}, - "output_type": "execute_result" + "execution_count": 383 } ], - "source": [ - "# Slicing out the middle four items\n", - "two_dim_arr_2= np.array([[1,2,3,7],[4,5,6, 15], [7,8,9,24], [10,11,12, 33]])\n", - "two_dim_arr_2" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 384, - "metadata": {}, + "source": [ + "two_dim_arr_2[1:3, 1:3]" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[5, 6],\n", " [8, 9]])" ] }, - "execution_count": 384, "metadata": {}, - "output_type": "execute_result" + "execution_count": 384 } ], - "source": [ - "two_dim_arr_2[1:3, 1:3]" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 385, - "metadata": {}, + "source": [ + "# Assigning \n", + "two_dim_arr_2[1:3, 1:3] = 25\n", + "two_dim_arr_2" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[ 1, 2, 3, 7],\n", @@ -1816,30 +1821,28 @@ " [10, 11, 12, 33]])" ] }, - "execution_count": 385, "metadata": {}, - "output_type": "execute_result" + "execution_count": 385 } ], - "source": [ - "# Assigning \n", - "two_dim_arr_2[1:3, 1:3] = 25\n", - "two_dim_arr_2" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## How to reverse the rows and the whole array?" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 386, - "metadata": {}, + "source": [ + "two_dim_arr[::]" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[1, 2, 3],\n", @@ -1847,21 +1850,21 @@ " [7, 8, 9]])" ] }, - "execution_count": 386, "metadata": {}, - "output_type": "execute_result" + "execution_count": 386 } ], - "source": [ - "two_dim_arr[::]" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 387, - "metadata": {}, + "source": [ + "two_dim_arr[::-1]" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[7, 8, 9],\n", @@ -1869,28 +1872,29 @@ " [1, 2, 3]])" ] }, - "execution_count": 387, "metadata": {}, - "output_type": "execute_result" + "execution_count": 387 } ], - "source": [ - "two_dim_arr[::-1]" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Reverse the row and column positions\n" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 388, - "metadata": {}, + "source": [ + "two_dim_arr = np.array([[1,2,3],[4,5,6], [7,8,9]])\n", + "two_dim_arr[::-1,::-1]" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[9, 8, 7],\n", @@ -1898,22 +1902,21 @@ " [3, 2, 1]])" ] }, - "execution_count": 388, "metadata": {}, - "output_type": "execute_result" + "execution_count": 388 } ], - "source": [ - "two_dim_arr = np.array([[1,2,3],[4,5,6], [7,8,9]])\n", - "two_dim_arr[::-1,::-1]" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 389, - "metadata": {}, + "source": [ + "two_dim_arr[::-1, ::-1]" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[9, 8, 7],\n", @@ -1921,32 +1924,34 @@ " [3, 2, 1]])" ] }, - "execution_count": 389, "metadata": {}, - "output_type": "execute_result" + "execution_count": 389 } ], - "source": [ - "two_dim_arr[::-1, ::-1]" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 390, - "metadata": {}, - "outputs": [], "source": [ "## How to represent missing values" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 391, - "metadata": {}, + "source": [ + "print(two_dim_arr)\n", + "two_dim_arr[1,1] = 55\n", + "two_dim_arr[1,2] = 44\n", + "print(two_dim_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[1 2 3]\n", " [4 5 6]\n", @@ -1957,26 +1962,27 @@ ] } ], - "source": [ - "print(two_dim_arr)\n", - "two_dim_arr[1,1] = 55\n", - "two_dim_arr[1,2] = 44\n", - "print(two_dim_arr)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### NumPy Zeros" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 392, - "metadata": {}, + "source": [ + "# Numpy Zeroes\n", + "# numpy.zeros(shape, dtype=float, order='C')\n", + "numpy_zeroes = np.zeros((3,3),dtype=int)\n", + "numpy_zeroes" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[0, 0, 0],\n", @@ -1984,33 +1990,31 @@ " [0, 0, 0]])" ] }, - "execution_count": 392, "metadata": {}, - "output_type": "execute_result" + "execution_count": 392 } ], - "source": [ - "# Numpy Zeroes\n", - "# numpy.zeros(shape, dtype=float, order='C')\n", - "numpy_zeroes = np.zeros((3,3),dtype=int)\n", - "numpy_zeroes" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Numpy ones\n" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 393, - "metadata": {}, + "source": [ + "# Numpy ones\n", + "numpy_ones = np.ones((3,5),dtype=int,order='C')\n", + "print(numpy_ones)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[1 1 1 1 1]\n", " [1 1 1 1 1]\n", @@ -2018,29 +2022,27 @@ ] } ], - "source": [ - "# Numpy ones\n", - "numpy_ones = np.ones((3,5),dtype=int,order='C')\n", - "print(numpy_ones)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 394, - "metadata": {}, - "outputs": [], "source": [ "twos = numpy_ones * 2" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 395, - "metadata": {}, + "source": [ + "print(twos)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[2 2 2 2 2]\n", " [2 2 2 2 2]\n", @@ -2048,25 +2050,26 @@ ] } ], - "source": [ - "print(twos)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### NumPy Full to create identical itmes\n" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 396, - "metadata": {}, + "source": [ + "np_full = np.full((3, 5), fill_value=4)\n", + "print(np_full)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[4 4 4 4 4]\n", " [4 4 4 4 4]\n", @@ -2074,28 +2077,28 @@ ] } ], - "source": [ - "np_full = np.full((3, 5), fill_value=4)\n", - "print(np_full)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### NumPy Eye\n", "\n", "The eye function creates an identity matrix, that is, a matrix with elements on the diagonal are set to one, and non-diagonal elements are set to zero:" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 397, - "metadata": {}, + "source": [ + "np_eye = np.eye(3, dtype=int)\n", + "print(np_eye)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[1 0 0]\n", " [0 1 0]\n", @@ -2103,19 +2106,19 @@ ] } ], - "source": [ - "np_eye = np.eye(3, dtype=int)\n", - "print(np_eye)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 398, - "metadata": {}, + "source": [ + "np_eye2 = np.eye(5, dtype=int)\n", + "print(np_eye2)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[1 0 0 0 0]\n", " [0 1 0 0 0]\n", @@ -2125,98 +2128,102 @@ ] } ], - "source": [ - "np_eye2 = np.eye(5, dtype=int)\n", - "print(np_eye2)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### NumPy Empty\n", "The NumPy *empty* method returns a new array of given shape and type, with random values." - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 399, - "metadata": {}, + "source": [ + "np.empty((2, 4))" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[0., 0., 1., 0.],\n", " [0., 1., 1., 1.]])" ] }, - "execution_count": 399, "metadata": {}, - "output_type": "execute_result" + "execution_count": 399 } ], - "source": [ - "np.empty((2, 4))" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 400, - "metadata": {}, + "source": [ + "np.empty((2, 2), dtype=int)" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[1, 2],\n", " [3, 4]])" ] }, - "execution_count": 400, "metadata": {}, - "output_type": "execute_result" + "execution_count": 400 } ], - "source": [ - "np.empty((2, 2), dtype=int)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Shape and Reshape a numPy array\n", "The *shape* attribute provide the dimension of the numPy array. The reshape method takes arguments and reshape the array to a different dimension." - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 401, - "metadata": {}, + "source": [ + "nums = np.arange(12)\n", + "nums.shape" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "(12,)" ] }, - "execution_count": 401, "metadata": {}, - "output_type": "execute_result" + "execution_count": 401 } ], - "source": [ - "nums = np.arange(12)\n", - "nums.shape" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 402, - "metadata": {}, + "source": [ + "nums = np.arange(12)\n", + "print(nums)\n", + "new_arr = nums.reshape(3, 4)\n", + "print(new_arr)\n", + "print(new_arr.ndim) # 2-d\n", + "print(new_arr.shape) # 3 rows and 3 columns" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[ 0 1 2 3 4 5 6 7 8 9 10 11]\n", "[[ 0 1 2 3]\n", @@ -2227,23 +2234,21 @@ ] } ], - "source": [ - "nums = np.arange(12)\n", - "print(nums)\n", - "new_arr = nums.reshape(3, 4)\n", - "print(new_arr)\n", - "print(new_arr.ndim) # 2-d\n", - "print(new_arr.shape) # 3 rows and 3 columns" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 403, - "metadata": {}, + "source": [ + "nums = np.arange(12)\n", + "print(nums)\n", + "new_arr = nums.reshape(3, 4)\n", + "print(new_arr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[ 0 1 2 3 4 5 6 7 8 9 10 11]\n", "[[ 0 1 2 3]\n", @@ -2252,28 +2257,31 @@ ] } ], - "source": [ - "nums = np.arange(12)\n", - "print(nums)\n", - "new_arr = nums.reshape(3, 4)\n", - "print(new_arr)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Reshape" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 404, - "metadata": {}, + "source": [ + "\n", + "# numpy.reshape(), numpy.flatten()\n", + "first_shape = np.array([(1,2,3), (4,5,6)])\n", + "print(first_shape)\n", + "print(first_shape.shape)\n", + "reshaped = first_shape.reshape(3,2)\n", + "print(reshaped)\n" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[1 2 3]\n", " [4 5 6]]\n", @@ -2284,52 +2292,48 @@ ] } ], - "source": [ - "\n", - "# numpy.reshape(), numpy.flatten()\n", - "first_shape = np.array([(1,2,3), (4,5,6)])\n", - "print(first_shape)\n", - "print(first_shape.shape)\n", - "reshaped = first_shape.reshape(3,2)\n", - "print(reshaped)\n" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Flatten\n", "The flaten method changes the numPy array to 1-dimensional array" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 405, - "metadata": {}, + "source": [ + "flattened = reshaped.flatten()\n", + "print(flattened)\n", + "print('Shape:', flattened.shape, 'Dim:', flattened.ndim)\n" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[1 2 3 4 5 6]\n", "Shape: (6,) Dim: 1\n" ] } ], - "source": [ - "flattened = reshaped.flatten()\n", - "print(flattened)\n", - "print('Shape:', flattened.shape, 'Dim:', flattened.ndim)\n" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 406, - "metadata": {}, + "source": [ + "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n", + "newarr = arr.reshape(3, 2, 2)\n", + "print(newarr)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[[ 1 2]\n", " [ 3 4]]\n", @@ -2342,48 +2346,47 @@ ] } ], - "source": [ - "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n", - "newarr = arr.reshape(3, 2, 2)\n", - "print(newarr)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Concatenating Numpy arrays\n", "By default the NumPy concatenate method joins the arrays along axis 0. To join the arrays horizontally, add parameter axis=1:" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 407, - "metadata": {}, + "source": [ + "x = np.arange(2)\n", + "y = np.arange(2, 5)\n", + "z = np.concatenate((x, y))\n", + "print(z)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[0 1 2 3 4]\n" ] } ], - "source": [ - "x = np.arange(2)\n", - "y = np.arange(2, 5)\n", - "z = np.concatenate((x, y))\n", - "print(z)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 408, - "metadata": {}, + "source": [ + "x = np.arange(1, 10).reshape(3,3)\n", + "print(x)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[1 2 3]\n", " [4 5 6]\n", @@ -2391,19 +2394,19 @@ ] } ], - "source": [ - "x = np.arange(1, 10).reshape(3,3)\n", - "print(x)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 412, - "metadata": {}, + "source": [ + "# concant\n", + "print(np.concatenate((x, x), axis = 0))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[1 2 3]\n", " [4 5 6]\n", @@ -2414,19 +2417,19 @@ ] } ], - "source": [ - "# concant\n", - "print(np.concatenate((x, x), axis = 0))" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 413, - "metadata": {}, + "source": [ + "# concant\n", + "print(np.concatenate((x, x), axis=1))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[1 2 3 1 2 3]\n", " [4 5 6 4 5 6]\n", @@ -2434,27 +2437,30 @@ ] } ], - "source": [ - "# concant\n", - "print(np.concatenate((x, x), axis=1))" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Joining with stack method\n", "It is possible to join horizontally or vertically by changing the value of axis to 0 or 1. The default axis value is zero and that means a horizontal join.\n" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 421, - "metadata": {}, + "source": [ + " ## Horitzontal Stack\n", + "np_list_one = np.array([1,2,3])\n", + "np_list_two = np.array([4,5,6])\n", + "print('concatenating list:', np_list_one + np_list_two)\n", + "print(np.stack((np_list_one, np_list_two))) # default axis value is zero." + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "concatenating list: [5 7 9]\n", "[[1 2 3]\n", @@ -2462,22 +2468,22 @@ ] } ], - "source": [ - " ## Horitzontal Stack\n", - "np_list_one = np.array([1,2,3])\n", - "np_list_two = np.array([4,5,6])\n", - "print('concatenating list:', np_list_one + np_list_two)\n", - "print(np.stack((np_list_one, np_list_two))) # default axis value is zero." - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 425, - "metadata": {}, + "source": [ + " ## Horitzontal Stack\n", + "np_list_one = np.array([1,2,3])\n", + "np_list_two = np.array([4,5,6])\n", + "print( np_list_one + np_list_two)\n", + "print(np.stack((np_list_one, np_list_two), axis=1))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[5 7 9]\n", "[[1 4]\n", @@ -2486,29 +2492,29 @@ ] } ], - "source": [ - " ## Horitzontal Stack\n", - "np_list_one = np.array([1,2,3])\n", - "np_list_two = np.array([4,5,6])\n", - "print( np_list_one + np_list_two)\n", - "print(np.stack((np_list_one, np_list_two), axis=1))" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Horizontal Stack" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 426, - "metadata": {}, + "source": [ + " ## Horitzontal Stack\n", + "np_list_one = np.array([1,2,3])\n", + "np_list_two = np.array([4,5,6])\n", + "print(np_list_one + np_list_two)\n", + "print('Horizontal Append:\\n', np.hstack((np_list_one, np_list_two)))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[5 7 9]\n", "Horizontal Append:\n", @@ -2516,231 +2522,230 @@ ] } ], - "source": [ - " ## Horitzontal Stack\n", - "np_list_one = np.array([1,2,3])\n", - "np_list_two = np.array([4,5,6])\n", - "print(np_list_one + np_list_two)\n", - "print('Horizontal Append:\\n', np.hstack((np_list_one, np_list_two)))" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Vertical Stack" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 427, - "metadata": {}, + "source": [ + " ## Vertical Stack\n", + "print('Vertical Append:', np.vstack((np_list_one, np_list_two)))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Vertical Append: [[1 2 3]\n", " [4 5 6]]\n" ] } ], - "source": [ - " ## Vertical Stack\n", - "print('Vertical Append:', np.vstack((np_list_one, np_list_two)))" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Generating Random Numbers" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 437, - "metadata": {}, + "source": [ + "# Generating a random integers between 0 and 10 inclusive\n", + "\n", + "random_int = np.random.randint(0, 11)\n", + "random_int" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "0" ] }, - "execution_count": 437, "metadata": {}, - "output_type": "execute_result" + "execution_count": 437 } ], - "source": [ - "# Generating a random integers between 0 and 10 inclusive\n", - "\n", - "random_int = np.random.randint(0, 11)\n", - "random_int" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 438, - "metadata": {}, + "source": [ + "# Generate a random float number between 0 and 0.999..\n", + "random_float = np.random.random()\n", + "print(random_float)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "0.4099787844112669\n" ] } ], - "source": [ - "# Generate a random float number between 0 and 0.999..\n", - "random_float = np.random.random()\n", - "print(random_float)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 439, - "metadata": {}, + "source": [ + " # Generate a random numpy array of float numbers\n", + "random_floats = np.random.random(5)\n", + "print(random_floats)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[0.22587659 0.0188218 0.1005489 0.22376462 0.86790537]\n" ] } ], - "source": [ - " # Generate a random numpy array of float numbers\n", - "random_floats = np.random.random(5)\n", - "print(random_floats)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "source": [], "outputs": [], - "source": [] + "metadata": {} }, { "cell_type": "code", "execution_count": 441, - "metadata": {}, + "source": [ + "# Random numbers between [0,1) of shape 2,3\n", + "np_random_nums = np.random.random(size=(2, 3))\n", + "print(np_random_nums)\n" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[0.16948871 0.58021159 0.4058133 ]\n", " [0.15162521 0.42037159 0.52096055]]\n" ] } ], - "source": [ - "# Random numbers between [0,1) of shape 2,3\n", - "np_random_nums = np.random.random(size=(2, 3))\n", - "print(np_random_nums)\n" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 443, - "metadata": {}, + "source": [ + "np_random_nums.shape\n", + "np_random_nums.flatten()" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([0.16948871, 0.58021159, 0.4058133 , 0.15162521, 0.42037159,\n", " 0.52096055])" ] }, - "execution_count": 443, "metadata": {}, - "output_type": "execute_result" + "execution_count": 443 } ], - "source": [ - "np_random_nums.shape\n", - "np_random_nums.flatten()" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 444, - "metadata": {}, + "source": [ + "## Random numbers between [0, 1] of shape 2, 2\n", + "rand = np.random.rand(2,2)\n", + "rand" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[0.12099852, 0.05660326],\n", " [0.69331399, 0.8406786 ]])" ] }, - "execution_count": 444, "metadata": {}, - "output_type": "execute_result" + "execution_count": 444 } ], - "source": [ - "## Random numbers between [0, 1] of shape 2, 2\n", - "rand = np.random.rand(2,2)\n", - "rand" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 449, - "metadata": {}, + "source": [ + " # Generating a random integers between 2 and 11, and creating a one row array\n", + "random_int = np.random.randint(2,10, size= 4)\n", + "random_int" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([2, 8, 2, 5])" ] }, - "execution_count": 449, "metadata": {}, - "output_type": "execute_result" + "execution_count": 449 } ], - "source": [ - " # Generating a random integers between 2 and 11, and creating a one row array\n", - "random_int = np.random.randint(2,10, size= 4)\n", - "random_int" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 451, - "metadata": {}, + "source": [ + " # Generating a random integers between 2 and 11, and creating 2-dim array\n", + "random_ints = np.random.randint(2,10, size=(2,4))\n", + "random_ints" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[9, 9, 3, 5],\n", " [3, 4, 2, 3]])" ] }, - "execution_count": 451, "metadata": {}, - "output_type": "execute_result" + "execution_count": 451 } ], - "source": [ - " # Generating a random integers between 2 and 11, and creating 2-dim array\n", - "random_ints = np.random.randint(2,10, size=(2,4))\n", - "random_ints" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 452, - "metadata": {}, + "source": [ + "# Generating a random integers between 0 and 10, [0, 10)\n", + "random_int = np.random.randint(2,10, size=(3,3))\n", + "random_int" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[7, 5, 9],\n", @@ -2748,23 +2753,23 @@ " [3, 6, 3]])" ] }, - "execution_count": 452, "metadata": {}, - "output_type": "execute_result" + "execution_count": 452 } ], - "source": [ - "# Generating a random integers between 0 and 10, [0, 10)\n", - "random_int = np.random.randint(2,10, size=(3,3))\n", - "random_int" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 453, - "metadata": {}, + "source": [ + "# Generating a random integers between 0 and 10, [0, 10)\n", + "random_int = np.random.randint(2,10,(3,3))\n", + "random_int" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[8, 2, 7],\n", @@ -2772,80 +2777,84 @@ " [3, 8, 9]])" ] }, - "execution_count": 453, "metadata": {}, - "output_type": "execute_result" + "execution_count": 453 } ], - "source": [ - "# Generating a random integers between 0 and 10, [0, 10)\n", - "random_int = np.random.randint(2,10,(3,3))\n", - "random_int" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 457, - "metadata": {}, + "source": [ + "print(np.random.choice(['a', 'e', 'i', 'o', 'u'], size=3))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "['u' 'u' 'i']\n" ] } ], - "source": [ - "print(np.random.choice(['a', 'e', 'i', 'o', 'u'], size=3))" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 459, - "metadata": {}, + "source": [ + "print(np.random.choice(list('Python'), size=5))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "['h' 't' 'y' 'o' 'y']\n" ] } ], - "source": [ - "print(np.random.choice(list('Python'), size=5))" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 462, - "metadata": {}, + "source": [ + "random_id = np.random.choice(list('abcdefghijklmnopkrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'), size=24).tolist()\n", + "random_id = ''.join(random_id)\n", + "print(random_id)\n", + "print(len(random_id))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "nNa3RMvCtyIebVkbmEy7lkKG\n", "24\n" ] } ], - "source": [ - "random_id = np.random.choice(list('abcdefghijklmnopkrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'), size=24).tolist()\n", - "random_id = ''.join(random_id)\n", - "print(random_id)\n", - "print(len(random_id))" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 468, - "metadata": {}, + "source": [ + "def generate_random_id(n = 12):\n", + " random_id = np.random.choice(list('abcdefghijklmnopkrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'), size = n).tolist()\n", + " random_id = ''.join(random_id)\n", + " return random_id\n", + "\n", + "print(generate_random_id())\n", + "print(generate_random_id(6))\n", + "print(generate_random_id(24))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "CJGmWahaGYgC\n", "Stg3zm\n", @@ -2853,32 +2862,28 @@ ] } ], - "source": [ - "def generate_random_id(n = 12):\n", - " random_id = np.random.choice(list('abcdefghijklmnopkrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'), size = n).tolist()\n", - " random_id = ''.join(random_id)\n", - " return random_id\n", - "\n", - "print(generate_random_id())\n", - "print(generate_random_id(6))\n", - "print(generate_random_id(24))" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Generating random data" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 472, - "metadata": {}, + "source": [ + "# first argument min, second argument max and third argument is size\n", + "s = np.random.uniform(-1, 0, 1000)\n", + "print(np.all(s <= 0))\n", + "print(s)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "True\n", "[-9.09125352e-03 -8.40358293e-01 -8.79495448e-01 -9.44144541e-02\n", @@ -3134,121 +3139,119 @@ ] } ], - "source": [ - "# first argument min, second argument max and third argument is size\n", - "s = np.random.uniform(-1, 0, 1000)\n", - "print(np.all(s <= 0))\n", - "print(s)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 475, - "metadata": {}, + "source": [ + "count, bins, ignored = plt.hist(s, 10, density=True)\n", + "plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n", + "plt.show()" + ], "outputs": [ { + "output_type": "display_data", "data": { - "image/png": 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" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], - "source": [ - "count, bins, ignored = plt.hist(s, 10, density=True)\n", - "plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n", - "plt.show()" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 476, - "metadata": {}, + "source": [ + "# np.random.normal(mu, sigma, size)\n", + "# mu - mean, sigma - sigma -standard deviation, size - number of obsevations\n", + "normal_array = np.random.normal(79, 15, 10000)\n", + "normal_array" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([68.80039957, 57.06427274, 90.00446115, ..., 84.40814004,\n", " 57.12822484, 87.91751196])" ] }, - "execution_count": 476, "metadata": {}, - "output_type": "execute_result" + "execution_count": 476 } ], - "source": [ - "# np.random.normal(mu, sigma, size)\n", - "# mu - mean, sigma - sigma -standard deviation, size - number of obsevations\n", - "normal_array = np.random.normal(79, 15, 10000)\n", - "normal_array" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 480, - "metadata": {}, + "source": [ + "plt.hist(normal_array,bins=50, color='lightgreen', density=True)\n", + "plt.show()" + ], "outputs": [ { + "output_type": "display_data", "data": { - "image/png": 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" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], - "source": [ - "plt.hist(normal_array,bins=50, color='lightgreen', density=True)\n", - "plt.show()" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Maintaining the same generated data" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "Sometimes we would not like to generate a new set of data instead we would like to keep what we created. To keep the created data we can use the *seed* method." - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 110, - "metadata": {}, + "source": [ + "np.random.seed(0)\n", + "integers = np.random.randint(0, 10, 21)\n", + "print(integers)\n" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[5 0 3 3 7 9 3 5 2 4 7 6 8 8 1 6 7 7 8 1 5]\n" ] } ], - "source": [ - "np.random.seed(0)\n", - "integers = np.random.randint(0, 10, 21)\n", - "print(integers)\n" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 111, - "metadata": {}, + "source": [ + "data = np.random.normal(0, 2, 30)\n", + "print(data)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[-0.94154201 1.94603199 -2.55629824 2.87474136 -0.15540913 2.17926033\n", " 0.19308534 2.83733422 2.33654627 1.89437189 2.17097407 4.7644489\n", @@ -3258,54 +3261,54 @@ ] } ], - "source": [ - "data = np.random.normal(0, 2, 30)\n", - "print(data)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 112, - "metadata": {}, + "source": [ + "generator = np.random.RandomState(seed=123) # RandomState is a class, so we give the seed to its constructor\n", + "generator.randint(0, 10, 21)" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([2, 2, 6, 1, 3, 9, 6, 1, 0, 1, 9, 0, 0, 9, 3, 4, 0, 0, 4, 1, 7])" ] }, - "execution_count": 112, "metadata": {}, - "output_type": "execute_result" + "execution_count": 112 } ], - "source": [ - "generator = np.random.RandomState(seed=123) # RandomState is a class, so we give the seed to its constructor\n", - "generator.randint(0, 10, 21)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Matrix in NumPy" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 113, - "metadata": {}, - "outputs": [], "source": [ "four_by_four_matrix = np.matrix(np.ones((4,4), dtype=float))" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 114, - "metadata": {}, + "source": [ + "four_by_four_matrix" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "matrix([[1., 1., 1., 1.],\n", @@ -3314,21 +3317,22 @@ " [1., 1., 1., 1.]])" ] }, - "execution_count": 114, "metadata": {}, - "output_type": "execute_result" + "execution_count": 114 } ], - "source": [ - "four_by_four_matrix" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 115, - "metadata": {}, + "source": [ + "np.asarray(four_by_four_matrix)[2] = 2\n", + "four_by_four_matrix" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "matrix([[1., 1., 1., 1.],\n", @@ -3337,24 +3341,25 @@ " [1., 1., 1., 1.]])" ] }, - "execution_count": 115, "metadata": {}, - "output_type": "execute_result" + "execution_count": 115 } ], - "source": [ - "np.asarray(four_by_four_matrix)[2] = 2\n", - "four_by_four_matrix" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 116, - "metadata": {}, + "source": [ + "A = four_by_four_matrix * 2\n", + "B = four_by_four_matrix * 4\n", + "print(A)\n", + "print(B)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[2. 2. 2. 2.]\n", " [2. 2. 2. 2.]\n", @@ -3367,21 +3372,19 @@ ] } ], - "source": [ - "A = four_by_four_matrix * 2\n", - "B = four_by_four_matrix * 4\n", - "print(A)\n", - "print(B)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 117, - "metadata": {}, + "source": [ + "A_plus_B = A + B\n", + "print(A_plus_B)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[ 6. 6. 6. 6.]\n", " [ 6. 6. 6. 6.]\n", @@ -3390,19 +3393,19 @@ ] } ], - "source": [ - "A_plus_B = A + B\n", - "print(A_plus_B)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 118, - "metadata": {}, + "source": [ + "A_minus_B = A - B\n", + "print(A_minus_B)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[-2. -2. -2. -2.]\n", " [-2. -2. -2. -2.]\n", @@ -3411,19 +3414,19 @@ ] } ], - "source": [ - "A_minus_B = A - B\n", - "print(A_minus_B)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 119, - "metadata": {}, + "source": [ + "A_times_B = A - B\n", + "print(A_times_B)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[-2. -2. -2. -2.]\n", " [-2. -2. -2. -2.]\n", @@ -3432,17 +3435,17 @@ ] } ], - "source": [ - "A_times_B = A - B\n", - "print(A_times_B)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 120, - "metadata": {}, + "source": [ + "np.transpose(A_times_B)" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "matrix([[-2., -2., -4., -2.],\n", @@ -3451,55 +3454,55 @@ " [-2., -2., -4., -2.]])" ] }, - "execution_count": 120, "metadata": {}, - "output_type": "execute_result" + "execution_count": 120 } ], - "source": [ - "np.transpose(A_times_B)" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Numpy numpy.arange()\n", "\n", "#### What is arrange?\n", "Sometimes, you want to create values that are evenly spaced within a defined interval. For instance, you want to create values from 1 to 10; you can use numpy.arange() function" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 121, - "metadata": {}, + "source": [ + "# creating list using range(starting, stop, step)\n", + "lst = range(0, 11, 2)\n", + "lst" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "range(0, 11, 2)" ] }, - "execution_count": 121, "metadata": {}, - "output_type": "execute_result" + "execution_count": 121 } ], - "source": [ - "# creating list using range(starting, stop, step)\n", - "lst = range(0, 11, 2)\n", - "lst" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 122, - "metadata": {}, + "source": [ + "for l in lst:\n", + " print(l)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "0\n", "2\n", @@ -3510,336 +3513,332 @@ ] } ], - "source": [ - "for l in lst:\n", - " print(l)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 123, - "metadata": {}, + "source": [ + "# Similar to range arange numpy.arange(start, stop, step)\n", + "whole_numbers = np.arange(0, 20, 1)\n", + "whole_numbers" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", " 17, 18, 19])" ] }, - "execution_count": 123, "metadata": {}, - "output_type": "execute_result" + "execution_count": 123 } ], - "source": [ - "# Similar to range arange numpy.arange(start, stop, step)\n", - "whole_numbers = np.arange(0, 20, 1)\n", - "whole_numbers" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 124, - "metadata": {}, + "source": [ + "natural_numbers = np.arange(1, 20, 1)\n", + "natural_numbers" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n", " 18, 19])" ] }, - "execution_count": 124, "metadata": {}, - "output_type": "execute_result" + "execution_count": 124 } ], - "source": [ - "natural_numbers = np.arange(1, 20, 1)\n", - "natural_numbers" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 125, - "metadata": {}, + "source": [ + "odd_numbers = np.arange(1, 20, 2)\n", + "odd_numbers" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([ 1, 3, 5, 7, 9, 11, 13, 15, 17, 19])" ] }, - "execution_count": 125, "metadata": {}, - "output_type": "execute_result" + "execution_count": 125 } ], - "source": [ - "odd_numbers = np.arange(1, 20, 2)\n", - "odd_numbers" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 126, - "metadata": {}, + "source": [ + "even_numbers = np.arange(2, 20, 2)\n", + "even_numbers" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([ 2, 4, 6, 8, 10, 12, 14, 16, 18])" ] }, - "execution_count": 126, "metadata": {}, - "output_type": "execute_result" + "execution_count": 126 } ], - "source": [ - "even_numbers = np.arange(2, 20, 2)\n", - "even_numbers" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Creating sequence of numbers using linspace" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 127, - "metadata": {}, + "source": [ + "# numpy.linspace(start, end, size, endpoint) the end is included by default\n", + "# numpy.logspace() in Python with Example\n", + "# For instance, it can be used to create 10 values from 1 to 5 evenly spaced.\n", + "np.linspace(1.0, 5.0, num=10)" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([1. , 1.44444444, 1.88888889, 2.33333333, 2.77777778,\n", " 3.22222222, 3.66666667, 4.11111111, 4.55555556, 5. ])" ] }, - "execution_count": 127, "metadata": {}, - "output_type": "execute_result" + "execution_count": 127 } ], - "source": [ - "# numpy.linspace(start, end, size, endpoint) the end is included by default\n", - "# numpy.logspace() in Python with Example\n", - "# For instance, it can be used to create 10 values from 1 to 5 evenly spaced.\n", - "np.linspace(1.0, 5.0, num=10)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 128, - "metadata": {}, + "source": [ + "# not to include the last value in the interval\n", + "np.linspace(1.0, 5.0, num=5, endpoint=False)" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([1. , 1.8, 2.6, 3.4, 4.2])" ] }, - "execution_count": 128, "metadata": {}, - "output_type": "execute_result" + "execution_count": 128 } ], - "source": [ - "# not to include the last value in the interval\n", - "np.linspace(1.0, 5.0, num=5, endpoint=False)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 129, - "metadata": {}, + "source": [ + "x = np.linspace(-10, 10, num=50)\n", + "y = 2 * x + 2\n", + "plt.scatter(x, y)\n", + "plt.show()" + ], "outputs": [ { + "output_type": "display_data", "data": { - "image/png": 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\n", 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" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], - "source": [ - "x = np.linspace(-10, 10, num=50)\n", - "y = 2 * x + 2\n", - "plt.scatter(x, y)\n", - "plt.show()" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 130, - "metadata": {}, + "source": [ + "x = np.linspace(-10, 10, num=50)\n", + "y = x ** 2\n", + "plt.scatter(x, y)\n", + "plt.show()" + ], "outputs": [ { + "output_type": "display_data", "data": { - "image/png": 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" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], - "source": [ - "x = np.linspace(-10, 10, num=50)\n", - "y = x ** 2\n", - "plt.scatter(x, y)\n", - "plt.show()" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 131, - "metadata": {}, + "source": [ + "x = np.linspace(-10, 10, num=50)\n", + "y = x ** 2 - 25\n", + "plt.scatter(x, y)\n", + "plt.show()" + ], "outputs": [ { + "output_type": "display_data", "data": { - "image/png": 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\n", 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\n", 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 ���z<�7ػw/���7!!<��bu��u�0j�(���!>>��sB�^O<��;���fttt�СC�:u�s��>������ ����_	ׯ_�ʕ+QZZ�� 0x�`���+hhh��v����*���[�n��j��۷QYY����h/�.`���2d�۾p��ĉ���_�ʕ+���Ɓ��$..8x�  �����v��rm�����.�kZZZD~~�����<�hhhBq��a�a�!��Y %%%"33Sdff��?�X��&L� �ܹ�m��ݢ��Lqwǒ%KDVV��;w�sv��N�8!�̙#fϞ-V�X!Z[[��s�(,,���bΜ9�̙3��t��Y��}Odff������#�-[�VW��k߾}"++K̜9S���B!�-[&N�>-�����b޼yb֬Yⷿ���Z��ֳy�f1i�$g���ݻw��T]]�����H�zz�����ٳ�̙3EEE�"������C���.���fӧOwfS]]�0�Lb�̙b˖-�n�!�ذa�8|��B�o��V<���h4��K�����?~�/���HbR��gy""�1䉈$Ɛ'"�C��Hby""�1䉈$Ɛ'"���x��ռ��    IEND�B`�", "text/plain": [ "
" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], - "source": [ - "x = np.linspace(-10, 10, num=50)\n", - "y = -x ** 2 + 25\n", - "plt.scatter(x, y)\n", - "plt.show()" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "source": [], "outputs": [], - "source": [] + "metadata": {} }, { "cell_type": "code", "execution_count": 133, - "metadata": {}, - "outputs": [], "source": [ "# LogSpace\n", "# LogSpace returns even spaced numbers on a log scale. Logspace has the same parameters as np.linspace.\n" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 134, - "metadata": {}, + "source": [ + "# Syntax:\n", + "\n", + "# numpy.logspace(start, stop, num, endpoint)\n", + "\n", + "np.logspace(2, 4.0, num=4)" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([ 100. , 464.15888336, 2154.43469003, 10000. ])" ] }, - "execution_count": 134, "metadata": {}, - "output_type": "execute_result" + "execution_count": 134 } ], - "source": [ - "# Syntax:\n", - "\n", - "# numpy.logspace(start, stop, num, endpoint)\n", - "\n", - "np.logspace(2, 4.0, num=4)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 135, - "metadata": {}, - "outputs": [], "source": [ "# to check the size of an array\n", "x = np.array([1,2,3], dtype=np.complex128)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 136, - "metadata": {}, + "source": [ + "x" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([1.+0.j, 2.+0.j, 3.+0.j])" ] }, - "execution_count": 136, "metadata": {}, - "output_type": "execute_result" + "execution_count": 136 } ], - "source": [ - "x" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 137, - "metadata": {}, + "source": [ + "x.itemsize" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "16" ] }, - "execution_count": 137, "metadata": {}, - "output_type": "execute_result" + "execution_count": 137 } ], - "source": [ - "x.itemsize" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## NumPy Statistical Functions with Example\n", "NumPy has quite useful statistical functions for finding minimum, maximum, mean, median, percentile,standard deviation and variance, etc from the given elements in the array. The functions are explained as follows − Statistical function Numpy is equipped with the robust statistical function as listed below\n", @@ -3861,54 +3860,58 @@ "# A set of age of students in python class\n", "\n", "data = [19, 20, 22, 33, 45, 60, 25, 24, 40, 20, 15, 22, 24, 26, 26, 28, 27, 24, 35, 40, 40, 24, 38, 40, 28, 23] " - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 138, - "metadata": {}, + "source": [ + "np.random.random()" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "0.7506861412184562" ] }, - "execution_count": 138, "metadata": {}, - "output_type": "execute_result" + "execution_count": 138 } ], - "source": [ - "np.random.random()" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 139, - "metadata": {}, + "source": [ + "np.random.random(3)" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([0.60783067, 0.32504723, 0.03842543])" ] }, - "execution_count": 139, "metadata": {}, - "output_type": "execute_result" + "execution_count": 139 } ], - "source": [ - "np.random.random(3)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 140, - "metadata": {}, + "source": [ + "np.random.randint(5, 10, (3, 4)) # does not inlcued the last value [5, 10)" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[8, 9, 9, 9],\n", @@ -3916,41 +3919,41 @@ " [5, 9, 8, 7]])" ] }, - "execution_count": 140, "metadata": {}, - "output_type": "execute_result" + "execution_count": 140 } ], - "source": [ - "np.random.randint(5, 10, (3, 4)) # does not inlcued the last value [5, 10)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 141, - "metadata": {}, + "source": [ + "np.random.normal(0, 2, 5)" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([-1.79093312, 0.773805 , -1.02161028, -2.36126437, -0.05636446])" ] }, - "execution_count": 141, "metadata": {}, - "output_type": "execute_result" + "execution_count": 141 } ], - "source": [ - "np.random.normal(0, 2, 5)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 142, - "metadata": {}, + "source": [ + "np.random.normal(0, 2, (3, 4)) # mean = 0, stdev = 2, 3 x 4 array" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[ 0.85666374, 0.13303444, 0.6049438 , -1.26864419],\n", @@ -3958,23 +3961,34 @@ " [-3.4525652 , 0.35485228, -0.80356187, -3.26039669]])" ] }, - "execution_count": 142, "metadata": {}, - "output_type": "execute_result" + "execution_count": 142 } ], - "source": [ - "np.random.normal(0, 2, (3, 4)) # mean = 0, stdev = 2, 3 x 4 array" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 143, - "metadata": {}, + "source": [ + "from scipy import stats\n", + "np_normal_dis = np.random.normal(5, 0.5, 100)\n", + "np_normal_dis\n", + "## min, max, mean, median, sd\n", + "print('min: ', two_dim_arr.min())\n", + "print('max: ', two_dim_arr.max())\n", + "print('25th percentile', np.percentile(two_dim_arr, 25))\n", + "print('50th percentile', np.percentile(two_dim_arr, 50))\n", + "print('75th percentile', np.percentile(two_dim_arr, 75))\n", + "print('mean: ',round(two_dim_arr.mean(), 2))\n", + "print('mode: ', stats.mode(two_dim_arr)) # numpy has no mode method\n", + "print('median: ', np.median(two_dim_arr))\n", + "print('sd: ', round(two_dim_arr.std(), 2))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "min: 1\n", "max: 55\n", @@ -3988,30 +4002,23 @@ ] } ], - "source": [ - "from scipy import stats\n", - "np_normal_dis = np.random.normal(5, 0.5, 100)\n", - "np_normal_dis\n", - "## min, max, mean, median, sd\n", - "print('min: ', two_dim_arr.min())\n", - "print('max: ', two_dim_arr.max())\n", - "print('25th percentile', np.percentile(two_dim_arr, 25))\n", - "print('50th percentile', np.percentile(two_dim_arr, 50))\n", - "print('75th percentile', np.percentile(two_dim_arr, 75))\n", - "print('mean: ',round(two_dim_arr.mean(), 2))\n", - "print('mode: ', stats.mode(two_dim_arr)) # numpy has no mode method\n", - "print('median: ', np.median(two_dim_arr))\n", - "print('sd: ', round(two_dim_arr.std(), 2))" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 144, - "metadata": {}, + "source": [ + "print(two_dim_arr)\n", + "print('Column with minimum: ', np.amin(two_dim_arr, axis=0))\n", + "print('Column with maximum: ', np.amax(two_dim_arr, axis=0))\n", + "print('=== Row ==')\n", + "print('Row with minimum: ', np.amin(two_dim_arr, axis=1))\n", + "print('Row with maximum: ', np.amax(two_dim_arr, axis=1))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[ 1 2 3]\n", " [ 4 55 44]\n", @@ -4024,36 +4031,18 @@ ] } ], - "source": [ - "print(two_dim_arr)\n", - "print('Column with minimum: ', np.amin(two_dim_arr, axis=0))\n", - "print('Column with maximum: ', np.amax(two_dim_arr, axis=0))\n", - "print('=== Row ==')\n", - "print('Row with minimum: ', np.amin(two_dim_arr, axis=1))\n", - "print('Row with maximum: ', np.amax(two_dim_arr, axis=1))" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## How to create repeating sequences?" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 145, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tile: [1 2 3 1 2 3 1 2 3]\n", - "Repeat: [1 1 1 2 2 2 3 3 3]\n" - ] - } - ], "source": [ "a = [1,2,3]\n", "\n", @@ -4062,26 +4051,22 @@ "\n", "# Repeat each element of 'a' two times\n", "print('Repeat: ', np.repeat(a, 3))" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "metadata": {}, + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "min: 3.4769284726000365\n", - "max: 6.58548738664509\n", - "mean: 4.9757236255377535\n", - "median: 4.974828091188378\n", - "mode: ModeResult(mode=array([3.47692847]), count=array([1]))\n", - "sd: 0.4859817987330746\n" + "Tile: [1 2 3 1 2 3 1 2 3]\n", + "Repeat: [1 1 1 2 2 2 3 3 3]\n" ] } ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 146, "source": [ "from scipy import stats\n", "np_normal_dis = np.random.normal(5, 0.5, 1000) # mean, standard deviation, number of samples\n", @@ -4093,64 +4078,77 @@ "print('median: ', np.median(np_normal_dis))\n", "print('mode: ', stats.mode(np_normal_dis))\n", "print('sd: ', np.std(np_normal_dis))" - ] + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "min: 3.4769284726000365\n", + "max: 6.58548738664509\n", + "mean: 4.9757236255377535\n", + "median: 4.974828091188378\n", + "mode: ModeResult(mode=array([3.47692847]), count=array([1]))\n", + "sd: 0.4859817987330746\n" + ] + } + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 147, - "metadata": {}, + "source": [ + "plt.hist(np_normal_dis, color=\"grey\", bins=30)\n", + "plt.show()" + ], "outputs": [ { + "output_type": "display_data", "data": { - "image/png": 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\n", 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" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], - "source": [ - "plt.hist(np_normal_dis, color=\"grey\", bins=30)\n", - "plt.show()" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 148, - "metadata": {}, + "source": [ + "x = np.random.normal(28, 15, 100000)\n", + "ax = sns.histplot(x, kde=True, linewidth=0)\n", + "ax.set(xlabel='x', ylabel='y', title='X vs Y')\n", + "plt.show()" + ], "outputs": [ { + "output_type": "display_data", "data": { - "image/png": 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" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], - "source": [ - "x = np.random.normal(28, 15, 100000)\n", - "ax = sns.histplot(x, kde=True, linewidth=0)\n", - "ax.set(xlabel='x', ylabel='y', title='X vs Y')\n", - "plt.show()" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "## Linear Algebra" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 149, - "metadata": {}, - "outputs": [], "source": [ "# numpy.dot(): Dot Product in Python using Numpy\n", "# Dot Product\n", @@ -4159,55 +4157,65 @@ "# Syntax\n", "\n", "# numpy.dot(x, y, out=None)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Dot Product" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 150, - "metadata": {}, + "source": [ + "## Linear algebra\n", + "### Dot product: product of two arrays\n", + "f = np.array([1,2,3])\n", + "g = np.array([4,5,3])\n", + "### 1*4+2*5 + 3*3\n", + "np.dot(f, g) # 23" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "23" ] }, - "execution_count": 150, "metadata": {}, - "output_type": "execute_result" + "execution_count": 150 } ], - "source": [ - "## Linear algebra\n", - "### Dot product: product of two arrays\n", - "f = np.array([1,2,3])\n", - "g = np.array([4,5,3])\n", - "### 1*4+2*5 + 3*3\n", - "np.dot(f, g) # 23" - ] + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### NumPy Matrix Multiplication with np.matmul()" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 151, - "metadata": {}, + "source": [ + "### Matmul: matruc product of two arrays\n", + "h = np.array([[1,2],[3,4]])\n", + "i = np.array([[5,6],[7,8]])\n", + "### 1*5+2*7 = 19\n", + "print(h)\n", + "print(i)\n", + "np.matmul(h, i)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[[1 2]\n", " [3 4]]\n", @@ -4216,74 +4224,69 @@ ] }, { + "output_type": "execute_result", "data": { "text/plain": [ "array([[19, 22],\n", " [43, 50]])" ] }, - "execution_count": 151, "metadata": {}, - "output_type": "execute_result" + "execution_count": 151 } ], - "source": [ - "### Matmul: matruc product of two arrays\n", - "h = np.array([[1,2],[3,4]])\n", - "i = np.array([[5,6],[7,8]])\n", - "### 1*5+2*7 = 19\n", - "print(h)\n", - "print(i)\n", - "np.matmul(h, i)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 152, - "metadata": {}, - "outputs": [], "source": [ "## Determinant 2*2 matrix\n", "### 5*8-7*6np.linalg.det(i)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 153, - "metadata": {}, + "source": [ + "np.linalg.det(i)" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "-1.999999999999999" ] }, - "execution_count": 153, "metadata": {}, - "output_type": "execute_result" + "execution_count": 153 } ], - "source": [ - "np.linalg.det(i)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 154, - "metadata": {}, - "outputs": [], "source": [ "Z = np.zeros((8,8))\n", "Z[1::2,::2] = 1\n", "Z[::2,1::2] = 1" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 155, - "metadata": {}, + "source": [ + "Z" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "array([[0., 1., 0., 1., 0., 1., 0., 1.],\n", @@ -4296,21 +4299,18 @@ " [1., 0., 1., 0., 1., 0., 1., 0.]])" ] }, - "execution_count": 155, "metadata": {}, - "output_type": "execute_result" + "execution_count": 155 } ], - "source": [ - "Z" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "source": [], "outputs": [], - "source": [] + "metadata": {} } ], "metadata": { @@ -4334,4 +4334,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file