{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### HW 1: Solutions
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Total: 25 pts__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start date: Tuesday Sept. 3
\n", "Due date: Tuesday Sept. 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you don't already have a version of anaconda installed, start by downloading anaconda and installing it (see for example [here](https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda/)). When working on the exercises below, keep in mind that there exists a rich python documentation online. Don't hesitate to check the documentation and examples related to the functions you want to use. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__1. (4pts) Numerical Linear Algebra: Numpy__\n", "\n", "- Start by building a 10 by 10 matrix of random Gaussian entries. Then compute the two largest eigenvalues of the matrix\n", "- Reshape the matrix that you built above into a 2 by 50 array (call it $v$) first and into a single vector then (call it 'w'). Return the vector obtained by sorting the elements of $w$ in descending order\n", "- Generate two random vectors (you can choose the distribution you use to generate the entries). Let us call those vectors $v1$ and $v2$. Stack those vectors vertically then horizontally. Store the respective results in two matrices $A$ and $B$.\n", "- Do the same with two random arrays $C_1 \\in \\mathbb{R}^{n\\times n}$ and $C_2^{n\\times n}$. Store the results in the variables $Cv$ and $Ch$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from numpy import linalg as LA\n", "\n", "M = np.random.normal(0, 1, (10, 10))\n", "w, v = LA.eig(M)\n", "\n", "\n", "# Here since I didn't specify anything, you can either return the eigenvalue with the largest real part, \n", "# the largest imaginary part, let numpy choose by just aplying sort to the vector of complex eigenvalues, \n", "# or, what I decide to do here, return the eigenvalues with the largest modulus. \n", "\n", "indices = np.argsort(np.absolute(w))\n", "indices = indices[::-1]\n", "w = w[indices]\n", "\n", "\n", "print \"first eigenvalue:\", w[0] \n", "print \"second eigenvalue:\", w[1]\n", "\n", "M_reshape_v = np.reshape(M, (2, 50))\n", "M_reshape_w = np.reshape(M, (1, 100))\n", "M_reshape_w_sorted = np.sort(M_reshape_w)\n", "M_reshape_w_sorted = M_reshape_w_sorted[:,::-1] # get the elements in descending order\n", "\n", "\n", "# to change I decide to generate random integers between 0 and n\n", "v1 = np.random.randint(10, size=(1, 10))\n", "v2 = np.random.randint(10, size=(1, 10))\n", "\n", "vertical_stack = np.vstack((v1,v2))\n", "horizontal_stack = np.hstack((v1,v2))\n", "\n", "C1 = np.random.randint(10, size = (10,10))\n", "C2 = np.random.randint(10, size = (10,10))\n", "\n", "Cv = np.vstack((C1,C2))\n", "Ch = np.hstack((C1,C2))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__2. (2pts) Towards multiclass classification: one-hot encoding__\n", "\n", "- Generate a vector (let us call it $v$) of integers taking values between 0 and 9. \n", "- Then build the vector corresponding to the one-hot encoding of each entry in $v$ (a one-hot encoding represents each categorical variable (0 to 9 digits in your vector $v$ by using binary sequences in which only one entry (for example the one corresponding to the digit that is encoded) is non zero))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# you were free to choose the size you wanted\n", "\n", "vv = np.random.randint(9, size = (10,))\n", "\n", "# there are many valid approaches. One of them is to build a matrix of zeros and then put \n", "# one at the column index corresponding to the number in vv\n", "print(vv)\n", "oneHotEncoding_vv = np.zeros((10,10))\n", "\n", "oneHotEncoding_vv[np.arange(10),vv] = 1\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__3. (6pt) Towards regression: sampling and matplolib__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__3a. (2pts) One dimensional__ In this exercise, we will successively generate points according to a function, sample pairs (t,f) from that distribution and plot the results\n", "\n", "- Using the 'linspace' function from numpy, generate $1000$ pairs $(t, f(t) = \\frac{1}{1+e^{-t}})$ for values of $t$ between $-6$ and $6$. What does the function look like? \n", "- Generate 100 random pairs $(t_i, f_i)$ from the plot. Then plot the points $(t_i,x_i)$ on top of the line $(t, f(t))$ using matplotlib (you can choose how you randomly generate the points)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# first generate te pairs\n", "\n", "t = np.linspace(-6.0, 6.0, num=1000)\n", "f = np.reciprocal(1+np.exp(-t))\n", "\n", "# Then plot the function using pyplot\n", "\n", "import matplotlib.pyplot as plt\n", "plt.plot(t, f)\n", "plt.show()\n", "\n", "# As we want to sample without replacement, we can use the function 'sample' from random\n", "\n", "from random import sample \n", "\n", "sampleData = np.random.choice(t, 100)\n", "f_sampleData = np.reciprocal(1+np.exp(-sampleData))\n", "\n", "# Then we use Scatter to plot the samples\n", "\n", "plt.scatter(sampleData, f_sampleData, c = 'red')\n", "plt.plot(t, f)\n", "plt.show()\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__3b. (4pts) The two dimensional hyperplane__\n", "\n", "- An extension of the previous case, we now want to generate triples $(x,y, t)$ according to the following hyperplane: \n", "\n", "$$t \\equiv\\pi(x, y) = x + y +1$$\n", "\n", "using _Axes3D_, _matplolib_ and _pyplot_, as well as the _meshgrid( )_ and _arrange( )_ functions from numpy and the _plot_surface( )_ and _scatter( )_ functions from pyplot,\n", "\n", "- Generate a regular grid of points $(x, y)$ covering the domain $[-20,20]\\times [-20,20]$. Let us say 200 by 200. \n", "- As in the 1D case, we now want to generate noisy samples that are lying on the plane on average. Start by generating $(50\\times 50)$ triples $(x,y,\\pi(x,y))$ covering the domain $[-20,20]\\times [-20,20]$. \n", "- Perturb the $50\\times 50$ pairs by adding to them a random gaussian noise of amplitude no larger than $0.1$\n", "- Finally using the _scatter( )_ function from pyplot, plot the noisy samples on top of the plane. \n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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CjMRHSaoJEmqMzcHtRz8MHEruhWEDh+TEpTzGLWv/nFA2hGlq2EQXPz30HaZT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ytXrsThcEwcP/300znvvPP4zne+Q29vL83NzSxcuPATX2/XOKgAGfZPYGhvsb9NwOHhYbq7uz/C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\nGoauUZ7XFhqtCSjZFsXFxWRkZMRs3RDut66oqAjzio/fSOS8i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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "x = np.linspace(-20, 20, 200)\n", "y = np.linspace(-20, 20, 200)\n", "xv, yv = np.meshgrid(x, y)\n", "\n", "tv = xv + yv + 1\n", "\n", "\n", "tv = tv+ np.random.normal(0, .1, (200,200))\n", "\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "\n", "fig = plt.figure()\n", "ax = fig.gca(projection='3d')\n", "\n", "# plot the plane\n", "ax.plot_surface(xv, yv, tv, alpha=0.2)\n", "\n", "# Although this was not part of the erxercise, I downsample the points to get a clear picture\n", "\n", "x = np.linspace(-20, 20, 20)\n", "y = np.linspace(-20, 20, 20)\n", "xv2, yv2 = np.meshgrid(x, y)\n", "\n", "\n", "# I also increased the variance a little to emphasize the noise\n", "tv2 = xv2 + yv2 + 1 + np.random.normal(0, 1, (20,20))\n", "\n", "\n", "ax.scatter(xv2.flatten() , yv2.flatten() , tv2.flatten(), color='green')\n", "\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__4. (3pts) Getting started with Pandas and Kaggle datasets__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__4a__ Download the car dataset on [Kaggle](https://www.kaggle.com/toramky/automobile-dataset/downloads/automobile-dataset.zip/2) and open this dataset with pandas. \n", "\n", "- Display a couple (5-10) of rows from the pandas data frame. \n", "- Find the brand that has the highest average price across cars\n", "- Sort the cars according to their horse power and return the corresponding panda frame. Display the first 10 lines from the frame.\n" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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audibmwchevroletdodgehondajaguarmazdamercedes-benzmitsubishinissanpeugotplymouthporschesaabsubarutoyotavolkswagenvolvo
averages18246.2518857.56007.07790.1258184.69230832250.09080.029726.47813.010415.66666715758.5714297163.33333322018.015223.3333338541.259696.6451618738.12518063.181818
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" ], "text/plain": [ " audi bmw chevrolet dodge honda jaguar \\\n", "averages 18246.25 18857.5 6007.0 7790.125 8184.692308 32250.0 \n", "\n", " mazda mercedes-benz mitsubishi nissan peugot \\\n", "averages 9080.0 29726.4 7813.0 10415.666667 15758.571429 \n", "\n", " plymouth porsche saab subaru toyota \\\n", "averages 7163.333333 22018.0 15223.333333 8541.25 9696.645161 \n", "\n", " volkswagen volvo \n", "averages 8738.125 18063.181818 " ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# There are several ways to answer the question. I give one\n", "\n", "import pandas as pd\n", "data = pd.read_csv(\"Automobile_data 3.csv\")\n", "\n", "# printing first 10 rows\n", "data.head(10)\n", "\n", "# I remove the rows that do not contain a number for the price\n", "data = data[(data.astype(str) != '?').all(axis=1)]\n", "\n", "\n", " # returning the brands\n", "Brand_names = data.make.unique()\n", "\n", "averages = np.zeros((Brand_names.size,))\n", "k=0\n", "\n", "for i in Brand_names:\n", " \n", " tmp = data.loc[data['make'] == i]\n", " tmp = tmp['price'].astype(int)\n", " averages[k] = np.mean(tmp)\n", " k += 1\n", " \n", "\n", "columns = Brand_names\n", "rows = [\"averages\"]\n", "data = np.array([averages])\n", "averageData = pd.DataFrame(data=data, index=rows, columns=columns)\n", "\n", "\n", "averageData\n", "\n", "# The last one is one line\n", "\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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202-195volvogasstdfoursedanrwdfront109.1...173mpfi3.582.878.81345500182321485
81158audigasturbofoursedanfwdfront105.8...131mpfi3.133.48.31405500172023875
1170161peugotgasturbofoursedanrwdfront108.0...134mpfi3.613.217.01425600182418150
1253186porschegasstdtwohatchbackrwdfront94.5...151mpfi3.943.119.51435500192722018
293145dodgegasturbotwohatchbackfwdfront95.9...156mfi3.63.97.01455000192412964
1020108nissangasstdfourwagonfwdfront100.4...181mpfi3.433.279.01525200172214399
1030108nissangasstdfoursedanfwdfront100.4...181mpfi3.433.279.01525200192513499
1010128nissangasstdfoursedanfwdfront100.4...181mpfi3.433.279.01525200172213499
723142mercedes-benzgasstdtwoconvertiblerwdfront96.6...234mpfi3.463.18.31554750161835056
180-190toyotagasstdfoursedanrwdfront104.5...171mpfi3.273.359.21565200202415690
1043194nissangasstdtwohatchbackrwdfront91.3...181mpfi3.433.279.01605200192517199
1061231nissangasstdtwohatchbackrwdfront99.2...181mpfi3.433.279.01605200192518399
201-195volvogasturbofoursedanrwdfront109.1...141mpfi3.783.158.71605300192519045
1372104saabgasturbofoursedanfwdfront99.1...121mpfi3.543.079.01605500192618620
1363150saabgasturbotwohatchbackfwdfront99.1...121mpfi3.543.079.01605500192618150
1793197toyotagasstdtwohatchbackrwdfront102.9...171mpfi3.273.359.31615200192415998
1783197toyotagasstdtwohatchbackrwdfront102.9...171mpfi3.273.359.31615200202416558
198-2103volvogasturbofoursedanrwdfront104.3...130mpfi3.623.157.51625100172218420
199-174volvogasturbofourwagonrwdfront104.3...130mpfi3.623.157.51625100172218950
470145jaguargasstdfoursedanrwdfront113.0...258mpfi3.634.178.11764750151932250
1053194nissangasturbotwohatchbackrwdfront91.3...181mpfi3.433.277.82005200172319699
\n", "

159 rows × 26 columns

\n", "
" ], "text/plain": [ " symboling normalized-losses make fuel-type aspiration \\\n", "18 2 121 chevrolet gas std \n", "184 2 94 volkswagen diesel std \n", "182 2 122 volkswagen diesel std \n", "90 1 128 nissan diesel std \n", "159 0 91 toyota diesel std \n", "158 0 91 toyota diesel std \n", "30 2 137 honda gas std \n", "32 1 101 honda gas std \n", "153 0 77 toyota gas std \n", "151 1 87 toyota gas std \n", "155 0 91 toyota gas std \n", "154 0 81 toyota gas std \n", "150 1 87 toyota gas std \n", "152 1 74 toyota gas std \n", "118 1 119 plymouth gas std \n", "52 1 104 mazda gas std \n", "51 1 104 mazda gas std \n", "120 1 154 plymouth gas std \n", "53 1 113 mazda gas std \n", "54 1 113 mazda gas std \n", "76 2 161 mitsubishi gas std \n", "78 2 161 mitsubishi gas std \n", "50 1 104 mazda gas std \n", "121 1 154 plymouth gas std \n", "77 2 161 mitsubishi gas std \n", "187 2 94 volkswagen diesel turbo \n", "122 1 154 plymouth gas std \n", "26 1 148 dodge gas std \n", "25 1 148 dodge gas std \n", "24 1 148 dodge gas std \n", ".. ... ... ... ... ... \n", "88 -1 137 mitsubishi gas std \n", "167 2 134 toyota gas std \n", "65 0 118 mazda gas std \n", "12 0 188 bmw gas std \n", "13 0 188 bmw gas std \n", "70 -1 93 mercedes-benz diesel turbo \n", "69 0 93 mercedes-benz diesel turbo \n", "68 -1 93 mercedes-benz diesel turbo \n", "67 -1 93 mercedes-benz diesel turbo \n", "202 -1 95 volvo gas std \n", "8 1 158 audi gas turbo \n", "117 0 161 peugot gas turbo \n", "125 3 186 porsche gas std \n", "29 3 145 dodge gas turbo \n", "102 0 108 nissan gas std \n", "103 0 108 nissan gas std \n", "101 0 128 nissan gas std \n", "72 3 142 mercedes-benz gas std \n", "180 -1 90 toyota gas std \n", "104 3 194 nissan gas std \n", "106 1 231 nissan gas std \n", "201 -1 95 volvo gas turbo \n", "137 2 104 saab gas turbo \n", "136 3 150 saab gas turbo \n", "179 3 197 toyota gas std \n", "178 3 197 toyota gas std \n", "198 -2 103 volvo gas turbo \n", "199 -1 74 volvo gas turbo \n", "47 0 145 jaguar gas std \n", "105 3 194 nissan gas turbo \n", "\n", " num-of-doors body-style drive-wheels engine-location wheel-base ... \\\n", "18 two hatchback fwd front 88.4 ... \n", "184 four sedan fwd front 97.3 ... \n", "182 two sedan fwd front 97.3 ... \n", "90 two sedan fwd front 94.5 ... \n", "159 four hatchback fwd front 95.7 ... \n", "158 four sedan fwd front 95.7 ... \n", "30 two hatchback fwd front 86.6 ... \n", "32 two hatchback fwd front 93.7 ... \n", "153 four wagon fwd front 95.7 ... \n", "151 two hatchback fwd front 95.7 ... \n", "155 four wagon 4wd front 95.7 ... \n", "154 four wagon 4wd front 95.7 ... \n", "150 two hatchback fwd front 95.7 ... \n", "152 four hatchback fwd front 95.7 ... \n", "118 two hatchback fwd front 93.7 ... \n", "52 two hatchback fwd front 93.1 ... \n", "51 two hatchback fwd front 93.1 ... \n", "120 four hatchback fwd front 93.7 ... \n", "53 four sedan fwd front 93.1 ... \n", "54 four sedan fwd front 93.1 ... \n", "76 two hatchback fwd front 93.7 ... \n", "78 two hatchback fwd front 93.7 ... \n", "50 two hatchback fwd front 93.1 ... \n", "121 four sedan fwd front 93.7 ... \n", "77 two hatchback fwd front 93.7 ... \n", "187 four sedan fwd front 97.3 ... \n", "122 four sedan fwd front 93.7 ... \n", "26 four sedan fwd front 93.7 ... \n", "25 four sedan fwd front 93.7 ... \n", "24 four hatchback fwd front 93.7 ... \n", ".. ... ... ... ... ... ... \n", "88 four sedan fwd front 96.3 ... \n", "167 two hardtop rwd front 98.4 ... \n", "65 four sedan rwd front 104.9 ... \n", "12 two sedan rwd front 101.2 ... \n", "13 four sedan rwd front 101.2 ... \n", "70 four sedan rwd front 115.6 ... \n", "69 two hardtop rwd front 106.7 ... \n", "68 four wagon rwd front 110.0 ... \n", "67 four sedan rwd front 110.0 ... \n", "202 four sedan rwd front 109.1 ... \n", "8 four sedan fwd front 105.8 ... \n", "117 four sedan rwd front 108.0 ... \n", "125 two hatchback rwd front 94.5 ... \n", "29 two hatchback fwd front 95.9 ... \n", "102 four wagon fwd front 100.4 ... \n", "103 four sedan fwd front 100.4 ... \n", "101 four sedan fwd front 100.4 ... \n", "72 two convertible rwd front 96.6 ... \n", "180 four sedan rwd front 104.5 ... \n", "104 two hatchback rwd front 91.3 ... \n", "106 two hatchback rwd front 99.2 ... \n", "201 four sedan rwd front 109.1 ... \n", "137 four sedan fwd front 99.1 ... \n", "136 two hatchback fwd front 99.1 ... \n", "179 two hatchback rwd front 102.9 ... \n", "178 two hatchback rwd front 102.9 ... \n", "198 four sedan rwd front 104.3 ... \n", "199 four wagon rwd front 104.3 ... \n", "47 four sedan rwd front 113.0 ... \n", "105 two hatchback rwd front 91.3 ... \n", "\n", " engine-size fuel-system bore stroke compression-ratio horsepower \\\n", "18 61 2bbl 2.91 3.03 9.5 48 \n", "184 97 idi 3.01 3.4 23.0 52 \n", "182 97 idi 3.01 3.4 23.0 52 \n", "90 103 idi 2.99 3.47 21.9 55 \n", "159 110 idi 3.27 3.35 22.5 56 \n", "158 110 idi 3.27 3.35 22.5 56 \n", "30 92 1bbl 2.91 3.41 9.6 58 \n", "32 79 1bbl 2.91 3.07 10.1 60 \n", "153 92 2bbl 3.05 3.03 9.0 62 \n", "151 92 2bbl 3.05 3.03 9.0 62 \n", "155 92 2bbl 3.05 3.03 9.0 62 \n", "154 92 2bbl 3.05 3.03 9.0 62 \n", "150 92 2bbl 3.05 3.03 9.0 62 \n", "152 92 2bbl 3.05 3.03 9.0 62 \n", "118 90 2bbl 2.97 3.23 9.4 68 \n", "52 91 2bbl 3.03 3.15 9.0 68 \n", "51 91 2bbl 3.03 3.15 9.0 68 \n", "120 90 2bbl 2.97 3.23 9.4 68 \n", "53 91 2bbl 3.03 3.15 9.0 68 \n", "54 91 2bbl 3.08 3.15 9.0 68 \n", "76 92 2bbl 2.97 3.23 9.4 68 \n", "78 92 2bbl 2.97 3.23 9.4 68 \n", "50 91 2bbl 3.03 3.15 9.0 68 \n", "121 90 2bbl 2.97 3.23 9.4 68 \n", "77 92 2bbl 2.97 3.23 9.4 68 \n", "187 97 idi 3.01 3.4 23.0 68 \n", "122 98 2bbl 2.97 3.23 9.4 68 \n", "26 90 2bbl 2.97 3.23 9.4 68 \n", "25 90 2bbl 2.97 3.23 9.4 68 \n", "24 90 2bbl 2.97 3.23 9.4 68 \n", ".. ... ... ... ... ... ... \n", "88 110 spdi 3.17 3.46 7.5 116 \n", "167 146 mpfi 3.62 3.5 9.3 116 \n", "65 140 mpfi 3.76 3.16 8.0 120 \n", "12 164 mpfi 3.31 3.19 9.0 121 \n", "13 164 mpfi 3.31 3.19 9.0 121 \n", "70 183 idi 3.58 3.64 21.5 123 \n", "69 183 idi 3.58 3.64 21.5 123 \n", "68 183 idi 3.58 3.64 21.5 123 \n", "67 183 idi 3.58 3.64 21.5 123 \n", "202 173 mpfi 3.58 2.87 8.8 134 \n", "8 131 mpfi 3.13 3.4 8.3 140 \n", "117 134 mpfi 3.61 3.21 7.0 142 \n", "125 151 mpfi 3.94 3.11 9.5 143 \n", "29 156 mfi 3.6 3.9 7.0 145 \n", "102 181 mpfi 3.43 3.27 9.0 152 \n", "103 181 mpfi 3.43 3.27 9.0 152 \n", "101 181 mpfi 3.43 3.27 9.0 152 \n", "72 234 mpfi 3.46 3.1 8.3 155 \n", "180 171 mpfi 3.27 3.35 9.2 156 \n", "104 181 mpfi 3.43 3.27 9.0 160 \n", "106 181 mpfi 3.43 3.27 9.0 160 \n", "201 141 mpfi 3.78 3.15 8.7 160 \n", "137 121 mpfi 3.54 3.07 9.0 160 \n", "136 121 mpfi 3.54 3.07 9.0 160 \n", "179 171 mpfi 3.27 3.35 9.3 161 \n", "178 171 mpfi 3.27 3.35 9.3 161 \n", "198 130 mpfi 3.62 3.15 7.5 162 \n", "199 130 mpfi 3.62 3.15 7.5 162 \n", "47 258 mpfi 3.63 4.17 8.1 176 \n", "105 181 mpfi 3.43 3.27 7.8 200 \n", "\n", " peak-rpm city-mpg highway-mpg price \n", "18 5100 47 53 5151 \n", "184 4800 37 46 7995 \n", "182 4800 37 46 7775 \n", "90 4800 45 50 7099 \n", "159 4500 38 47 7788 \n", "158 4500 34 36 7898 \n", "30 4800 49 54 6479 \n", "32 5500 38 42 5399 \n", "153 4800 31 37 6918 \n", "151 4800 31 38 6338 \n", "155 4800 27 32 8778 \n", "154 4800 27 32 7898 \n", "150 4800 35 39 5348 \n", "152 4800 31 38 6488 \n", "118 5500 37 41 5572 \n", "52 5000 31 38 6795 \n", "51 5000 31 38 6095 \n", "120 5500 31 38 6229 \n", "53 5000 31 38 6695 \n", "54 5000 31 38 7395 \n", "76 5500 37 41 5389 \n", "78 5500 31 38 6669 \n", "50 5000 30 31 5195 \n", "121 5500 31 38 6692 \n", "77 5500 31 38 6189 \n", "187 4500 37 42 9495 \n", "122 5500 31 38 7609 \n", "26 5500 31 38 7609 \n", "25 5500 31 38 6692 \n", "24 5500 31 38 6229 \n", ".. ... ... ... ... \n", "88 5500 23 30 9279 \n", "167 4800 24 30 8449 \n", "65 5000 19 27 18280 \n", "12 4250 21 28 20970 \n", "13 4250 21 28 21105 \n", "70 4350 22 25 31600 \n", "69 4350 22 25 28176 \n", "68 4350 22 25 28248 \n", "67 4350 22 25 25552 \n", "202 5500 18 23 21485 \n", "8 5500 17 20 23875 \n", "117 5600 18 24 18150 \n", "125 5500 19 27 22018 \n", "29 5000 19 24 12964 \n", "102 5200 17 22 14399 \n", "103 5200 19 25 13499 \n", "101 5200 17 22 13499 \n", "72 4750 16 18 35056 \n", "180 5200 20 24 15690 \n", "104 5200 19 25 17199 \n", "106 5200 19 25 18399 \n", "201 5300 19 25 19045 \n", "137 5500 19 26 18620 \n", "136 5500 19 26 18150 \n", "179 5200 19 24 15998 \n", "178 5200 20 24 16558 \n", "198 5100 17 22 18420 \n", "199 5100 17 22 18950 \n", "47 4750 15 19 32250 \n", "105 5200 17 23 19699 \n", "\n", "[159 rows x 26 columns]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# For the last point, you might want to turn the strings into number to get the proper sort. \n", "# Once you are done with that part, the answer is literally one line\n", "\n", "import pandas as pd\n", "import numpy as np\n", "data = pd.read_csv(\"Automobile_data 3.csv\")\n", "# printing first 10 rows\n", "data.head(10)\n", "# I remove the rows that do not contain a number for the price\n", "data = data[(data.astype(str) != '?').all(axis=1)]\n", "\n", "data.iloc[np.argsort(data['horsepower'].values.astype(np.float))]\n", "\n", "\n", "# returning the brands\n", "#data" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }