{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# NumPy Exercises 1\n", "### Udemy.com, PyDSML, Section 2 Numpy\n", "https://www.udemy.com/python-for-data-science-and-machine-learning-bootcamp/learn/v4/overview \n", "Answers by Jenifer Yoon \n", "Date 3/27/2019 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ***** Questions -- followed by my answers. *****" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import NumPy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create an array of 10 zeros " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.zeros(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create an array of 10 ones" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ones(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create an array of 10 fives" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ones(10)*5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create an array of the integers from 10 to 50" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n", " 44, 45, 46, 47, 48, 49, 50])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(10, 51)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create an array of all the even integers from 10 to 50" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n", " 44, 46, 48, 50])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(10, 51, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create a 3x3 matrix with values ranging from 0 to 8" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0, 1, 2],\n", " [3, 4, 5],\n", " [6, 7, 8]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat = np.arange(0, 9)\n", "mat.reshape(3, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create a 3x3 identity matrix" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1., 0., 0.],\n", " [0., 1., 0.],\n", " [0., 0., 1.]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.eye(3)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1., 0., 0.],\n", " [0., 1., 0.],\n", " [0., 0., 1.]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.eye(3, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Use NumPy to generate a random number between 0 and 1" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.27345171])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.rand(1)\n", "# np.random.rand(d0, d1, d2, ...) selects a random number from [0, 1) range. Paramters are dimensions.]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function rand:\n", "\n", "rand(...) method of mtrand.RandomState instance\n", " rand(d0, d1, ..., dn)\n", " \n", " Random values in a given shape.\n", " \n", " Create an array of the given shape and populate it with\n", " random samples from a uniform distribution\n", " over ``[0, 1)``.\n", " \n", " Parameters\n", " ----------\n", " d0, d1, ..., dn : int, optional\n", " The dimensions of the returned array, should all be positive.\n", " If no argument is given a single Python float is returned.\n", " \n", " Returns\n", " -------\n", " out : ndarray, shape ``(d0, d1, ..., dn)``\n", " Random values.\n", " \n", " See Also\n", " --------\n", " random\n", " \n", " Notes\n", " -----\n", " This is a convenience function. If you want an interface that\n", " takes a shape-tuple as the first argument, refer to\n", " np.random.random_sample .\n", " \n", " Examples\n", " --------\n", " >>> np.random.rand(3,2)\n", " array([[ 0.14022471, 0.96360618], #random\n", " [ 0.37601032, 0.25528411], #random\n", " [ 0.49313049, 0.94909878]]) #random\n", "\n" ] } ], "source": [ "help(np.random.rand)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.52566673, 0.88139993, 0.24887143, 0.2956426 , 0.17621077,\n", " 0.98663012, 0.43747115, 0.64025843, 0.02924842, 0.75702585,\n", " 0.51010062, 0.09094537, 0.1174884 , 0.53385174, 0.64869243,\n", " 0.37622658, 0.65330289, 0.41197009, 0.30165867, 0.63191869,\n", " 0.39511783, 0.81510441, 0.75499403, 0.21194784, 0.94456316])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.rand(25)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.7559873 , 0.08741562, 0.43933887, 0.13731684, 0.05901282,\n", " 0.86639925, 0.48606921, 0.33415014, 0.23369125, 0.79805061,\n", " 0.41485206, 0.6283579 , 0.84090133, 0.31386089, 0.13334902,\n", " 0.38771005, 0.18281798, 0.79785461, 0.82618869, 0.90973119,\n", " 0.28070039, 0.53574626, 0.56157786, 0.07180881, 0.8936017 ])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.rand(25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create the following matrix:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n", " [ 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n", " [ 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n", " [ 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n", " [ 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n", " [ 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n", " [ 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n", " [ 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n", " [ 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n", " [ 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n", " [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n", " [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n", " [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n", " [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n", " [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n", " [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n", " [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n", " [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n", " [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(0.01, 1.01, .01).reshape(10, 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create an array of 20 linearly spaced points between 0 and 1:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n", " 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n", " 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n", " 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linspace(0, 1, 20)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n", " 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n", " 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n", " 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linspace(0, 1, 20, endpoint=True)\n", "# linspace returns an array of evenly spaced numbers. (start, spop, numbers, default include endpoint=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numpy Indexing and Selection\n", "\n", "Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:" ] }, { "cell_type": "code", "execution_count": 28, "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": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat = np.arange(1,26).reshape(5,5)\n", "mat" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[12, 13, 14, 15],\n", " [17, 18, 19, 20],\n", " [22, 23, 24, 25]])" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n", "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n", "# BE ABLE TO SEE THE OUTPUT ANY MORE" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[12, 13, 14, 15],\n", " [17, 18, 19, 20],\n", " [22, 23, 24, 25]])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat[2:6, 1:6]\n", "# Indexing is same as list indexing." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[12, 13, 14, 15],\n", " [17, 18, 19, 20],\n", " [22, 23, 24, 25]])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Try negative indexing.\n", "mat[-3:, -4:]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1],\n", " [6]])" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Negative indexing. Always counts from top-left. [Row start:end, Col start:end]\n", "mat[:-3, :-4]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n", "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n", "# BE ABLE TO SEE THE OUTPUT ANY MORE" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat[3, -1]" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 40, "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": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n", "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n", "# BE ABLE TO SEE THE OUTPUT ANY MORE\n", "mat" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 2],\n", " [ 7],\n", " [12]])" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# mat 2nd column, 0 to 2 rows.\n", "mat[:3, 1].reshape(3, 1)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 2],\n", " [ 7],\n", " [12]])" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n", "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n", "# BE ABLE TO SEE THE OUTPUT ANY MORE" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([21, 22, 23, 24, 25])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat[-1, :]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([21, 22, 23, 24, 25])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n", "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n", "# BE ABLE TO SEE THE OUTPUT ANY MORE" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[16, 17, 18, 19, 20],\n", " [21, 22, 23, 24, 25]])" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat[-2:, :] # Last two rows, all columns." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[16, 17, 18, 19, 20],\n", " [21, 22, 23, 24, 25]])" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Now do the following" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get the sum of all the values in mat" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "325" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat.sum() # Method applied to mat object?\n", "# mat is an ndarray object, a built-in numpy class.\n", "# This class object has a method .sum() and .std()." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "325" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get the standard deviation of the values in mat" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7.211102550927978" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat.std()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7.2111025509279782" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get the sum of all the columns in mat" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([55, 60, 65, 70, 75])" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(mat)\n", "# sum() function applied to mat ndarray object produce column sums by default." ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([27, 30])" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(mat[:3, -2:])\n", "# [row 0 to 2, col 4 to 5]" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[55 60 65 70 75] [ 15 40 65 90 115]\n" ] } ], "source": [ "## mat.sum(axis=0)\n", "# Columns axis=0, not 1.\n", "x = mat.sum(axis=0)\n", "y = mat.sum(axis=1)\n", "print(x, y)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([55, 60, 65, 70, 75])" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# End" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }