From 66c15ef0f1e66c8acb462573b667b18467262e4a Mon Sep 17 00:00:00 2001 From: shaswatsingh19 <45266507+shaswatsingh19@users.noreply.github.com> Date: Mon, 24 Aug 2020 14:12:44 +0530 Subject: [PATCH 1/3] Update Assignment.md --- Assignment/Assignment.md | 551 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 551 insertions(+) diff --git a/Assignment/Assignment.md b/Assignment/Assignment.md index 8b13789..cce6916 100644 --- a/Assignment/Assignment.md +++ b/Assignment/Assignment.md @@ -1 +1,552 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Make a python list => [1,2,3,4,5]\n", + "\n", + "### Convert it into numpy array and print it" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "l = [1,2,3,4,5]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "nu_arr = np.array(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nu_arr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make a python matrix (3 x 3) => [[1,2,3],[4,5,6],[7,8,9]]\n", + "\n", + "### Convert it into numpy array and print it" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "mat = [[1,2,3],[4,5,6],[7,8,9]]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "mat_numpy = np.array(mat)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make a matrix (3 x 3) using built-in methods (like arange(), reshape() etc.):\n", + "\n", + "#### [ [1,3,5],\n", + "\n", + "#### [7,9,11],\n", + "\n", + "#### [13,15,17] ]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 3],\n", + " [4, 5, 6],\n", + " [7, 8, 9]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mat_numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(mat_numpy)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "new_mat = np.arange(1,18,2)\n", + "new_mat = new_mat.reshape(3,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1, 3, 5],\n", + " [ 7, 9, 11],\n", + " [13, 15, 17]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_mat" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a numpy array with 10 random numbers from 0 to 10 (there should be few numbers greater than 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.92122993, 0.50568944, 0.08677143, 0.57494363, 0.14618692,\n", + " 0.29914527, 0.52833599, 0.71395998, 0.26991673, 0.33347513]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.rand(1,10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create numpy array => [1,2,3,4,5] and convert it to 2D array with 5 rows" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "arr_2d = np.array([1,2,3,4,5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Print the shape of the above created array" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "arr_2d = arr_2d.reshape(5,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1],\n", + " [2],\n", + " [3],\n", + " [4],\n", + " [5]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr_2d" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5, 1)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr_2d.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Create a numpy array with 10 elements in it. Access and print its 3rd, 4th and 9th element." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "numpy_10 = np.arange(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy_10" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy_10[3]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy_10[4]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy_10[9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Change last 3 elements into 100 using broadcasting and print" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 4, 5, 6, 100, 100, 100])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy_10[7:10] = 100\n", + "numpy_10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Print alternate elements of that array" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "2\n", + "4\n", + "6\n", + "8\n", + "10\n" + ] + } + ], + "source": [ + "for i in range(0,len(numpy_10),2):\n", + " print(numpy_10[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Create a 5 x 5 matrix (fill it with any element you like), print it.\n", + "\n", + "##### Then print the middle (3 x 3) matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "arr_5 =np.arange(1,26).reshape(5,5)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1, 2, 3, 4, 5],\n", + " [ 6, 7, 8, 9, 10],\n", + " [11, 12, 13, 14, 15],\n", + " [16, 17, 18, 19, 20],\n", + " [21, 22, 23, 24, 25]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr_5" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "mid_3 = arr_5[1:4,1:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 7, 8, 9],\n", + " [12, 13, 14],\n", + " [17, 18, 19]])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mid_3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.4 64-bit ('base': conda)", + "language": "python", + "name": "python37464bitbaseconda231219d898d74bfab37e16b07919cc99" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From a6b6dd6388a31212f216211b95dda3124f041bfc Mon Sep 17 00:00:00 2001 From: shaswatsingh19 <45266507+shaswatsingh19@users.noreply.github.com> Date: Mon, 24 Aug 2020 14:12:57 +0530 Subject: [PATCH 2/3] Delete Assignment.md --- Assignment/Assignment.md | 552 --------------------------------------- 1 file changed, 552 deletions(-) delete mode 100644 Assignment/Assignment.md diff --git a/Assignment/Assignment.md b/Assignment/Assignment.md deleted file mode 100644 index cce6916..0000000 --- a/Assignment/Assignment.md +++ /dev/null @@ -1,552 +0,0 @@ - -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Assignment" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Make a python list => [1,2,3,4,5]\n", - "\n", - "### Convert it into numpy array and print it" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "l = [1,2,3,4,5]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "nu_arr = np.array(l)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3, 4, 5])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nu_arr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make a python matrix (3 x 3) => [[1,2,3],[4,5,6],[7,8,9]]\n", - "\n", - "### Convert it into numpy array and print it" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "mat = [[1,2,3],[4,5,6],[7,8,9]]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "mat_numpy = np.array(mat)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make a matrix (3 x 3) using built-in methods (like arange(), reshape() etc.):\n", - "\n", - "#### [ [1,3,5],\n", - "\n", - "#### [7,9,11],\n", - "\n", - "#### [13,15,17] ]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1, 2, 3],\n", - " [4, 5, 6],\n", - " [7, 8, 9]])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mat_numpy" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(mat_numpy)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "new_mat = np.arange(1,18,2)\n", - "new_mat = new_mat.reshape(3,3)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1, 3, 5],\n", - " [ 7, 9, 11],\n", - " [13, 15, 17]])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_mat" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "import random" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create a numpy array with 10 random numbers from 0 to 10 (there should be few numbers greater than 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.92122993, 0.50568944, 0.08677143, 0.57494363, 0.14618692,\n", - " 0.29914527, 0.52833599, 0.71395998, 0.26991673, 0.33347513]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.rand(1,10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create numpy array => [1,2,3,4,5] and convert it to 2D array with 5 rows" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "arr_2d = np.array([1,2,3,4,5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Print the shape of the above created array" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "arr_2d = arr_2d.reshape(5,1)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1],\n", - " [2],\n", - " [3],\n", - " [4],\n", - " [5]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr_2d" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5, 1)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr_2d.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Create a numpy array with 10 elements in it. Access and print its 3rd, 4th and 9th element." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "numpy_10 = np.arange(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numpy_10" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numpy_10[3]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numpy_10[4]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "9" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numpy_10[9]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Change last 3 elements into 100 using broadcasting and print" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 1, 2, 3, 4, 5, 6, 100, 100, 100])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numpy_10[7:10] = 100\n", - "numpy_10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Print alternate elements of that array" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "2\n", - "4\n", - "6\n", - "8\n", - "10\n" - ] - } - ], - "source": [ - "for i in range(0,len(numpy_10),2):\n", - " print(numpy_10[i])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "##### Create a 5 x 5 matrix (fill it with any element you like), print it.\n", - "\n", - "##### Then print the middle (3 x 3) matrix." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "arr_5 =np.arange(1,26).reshape(5,5)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1, 2, 3, 4, 5],\n", - " [ 6, 7, 8, 9, 10],\n", - " [11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20],\n", - " [21, 22, 23, 24, 25]])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr_5" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "mid_3 = arr_5[1:4,1:4]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 7, 8, 9],\n", - " [12, 13, 14],\n", - " [17, 18, 19]])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mid_3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.7.4 64-bit ('base': conda)", - "language": "python", - "name": "python37464bitbaseconda231219d898d74bfab37e16b07919cc99" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From ff174fd8983c8bfbf7f5d6233cdc5ef42d7e9852 Mon Sep 17 00:00:00 2001 From: shaswatsingh19 <45266507+shaswatsingh19@users.noreply.github.com> Date: Mon, 24 Aug 2020 14:13:23 +0530 Subject: [PATCH 3/3] Assignment 1 done --- Assignment/Assignment 1.ipynb | 551 ++++++++++++++++++++++++++++++++++ 1 file changed, 551 insertions(+) create mode 100644 Assignment/Assignment 1.ipynb diff --git a/Assignment/Assignment 1.ipynb b/Assignment/Assignment 1.ipynb new file mode 100644 index 0000000..83f9b8e --- /dev/null +++ b/Assignment/Assignment 1.ipynb @@ -0,0 +1,551 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Make a python list => [1,2,3,4,5]\n", + "\n", + "### Convert it into numpy array and print it" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "l = [1,2,3,4,5]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "nu_arr = np.array(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nu_arr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make a python matrix (3 x 3) => [[1,2,3],[4,5,6],[7,8,9]]\n", + "\n", + "### Convert it into numpy array and print it" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "mat = [[1,2,3],[4,5,6],[7,8,9]]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "mat_numpy = np.array(mat)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make a matrix (3 x 3) using built-in methods (like arange(), reshape() etc.):\n", + "\n", + "#### [ [1,3,5],\n", + "\n", + "#### [7,9,11],\n", + "\n", + "#### [13,15,17] ]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 3],\n", + " [4, 5, 6],\n", + " [7, 8, 9]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mat_numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(mat_numpy)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "new_mat = np.arange(1,18,2)\n", + "new_mat = new_mat.reshape(3,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1, 3, 5],\n", + " [ 7, 9, 11],\n", + " [13, 15, 17]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_mat" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a numpy array with 10 random numbers from 0 to 10 (there should be few numbers greater than 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.92122993, 0.50568944, 0.08677143, 0.57494363, 0.14618692,\n", + " 0.29914527, 0.52833599, 0.71395998, 0.26991673, 0.33347513]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.rand(1,10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create numpy array => [1,2,3,4,5] and convert it to 2D array with 5 rows" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "arr_2d = np.array([1,2,3,4,5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Print the shape of the above created array" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "arr_2d = arr_2d.reshape(5,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1],\n", + " [2],\n", + " [3],\n", + " [4],\n", + " [5]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr_2d" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5, 1)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr_2d.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Create a numpy array with 10 elements in it. Access and print its 3rd, 4th and 9th element." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "numpy_10 = np.arange(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy_10" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy_10[3]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy_10[4]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy_10[9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Change last 3 elements into 100 using broadcasting and print" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 4, 5, 6, 100, 100, 100])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy_10[7:10] = 100\n", + "numpy_10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Print alternate elements of that array" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "2\n", + "4\n", + "6\n", + "8\n", + "10\n" + ] + } + ], + "source": [ + "for i in range(0,len(numpy_10),2):\n", + " print(numpy_10[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##### Create a 5 x 5 matrix (fill it with any element you like), print it.\n", + "\n", + "##### Then print the middle (3 x 3) matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "arr_5 =np.arange(1,26).reshape(5,5)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1, 2, 3, 4, 5],\n", + " [ 6, 7, 8, 9, 10],\n", + " [11, 12, 13, 14, 15],\n", + " [16, 17, 18, 19, 20],\n", + " [21, 22, 23, 24, 25]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr_5" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "mid_3 = arr_5[1:4,1:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 7, 8, 9],\n", + " [12, 13, 14],\n", + " [17, 18, 19]])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mid_3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.4 64-bit ('base': conda)", + "language": "python", + "name": "python37464bitbaseconda231219d898d74bfab37e16b07919cc99" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}