{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MPG Cars\n", "\n", "Check out [Cars Exercises Video Tutorial](https://www.youtube.com/watch?v=avzLRBxoguU&list=PLgJhDSE2ZLxaY_DigHeiIDC1cD09rXgJv&index=3) to watch a data scientist go through the exercises" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Introduction:\n", "\n", "The following exercise utilizes data from [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Auto+MPG)\n", "\n", "### Step 1. Import the necessary libraries" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2. Import the first dataset [cars1](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars1.csv) and [cars2](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars2.csv). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " ### Step 3. Assign each to a to a variable called cars1 and cars2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " mpg cylinders displacement horsepower weight acceleration model \\\n", "0 18.0 8 307 130 3504 12.0 70 \n", "1 15.0 8 350 165 3693 11.5 70 \n", "2 18.0 8 318 150 3436 11.0 70 \n", "3 16.0 8 304 150 3433 12.0 70 \n", "4 17.0 8 302 140 3449 10.5 70 \n", "\n", " origin car Unnamed: 9 Unnamed: 10 Unnamed: 11 \\\n", "0 1 chevrolet chevelle malibu NaN NaN NaN \n", "1 1 buick skylark 320 NaN NaN NaN \n", "2 1 plymouth satellite NaN NaN NaN \n", "3 1 amc rebel sst NaN NaN NaN \n", "4 1 ford torino NaN NaN NaN \n", "\n", " Unnamed: 12 Unnamed: 13 \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", " mpg cylinders displacement horsepower weight acceleration model \\\n", "0 33.0 4 91 53 1795 17.4 76 \n", "1 20.0 6 225 100 3651 17.7 76 \n", "2 18.0 6 250 78 3574 21.0 76 \n", "3 18.5 6 250 110 3645 16.2 76 \n", "4 17.5 6 258 95 3193 17.8 76 \n", "\n", " origin car \n", "0 3 honda civic \n", "1 1 dodge aspen se \n", "2 1 ford granada ghia \n", "3 1 pontiac ventura sj \n", "4 1 amc pacer d/l \n" ] } ], "source": [ "cars1 = pd.read_csv(\"https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars1.csv\")\n", "cars2 = pd.read_csv(\"https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars2.csv\")\n", "\n", "print(cars1.head())\n", "print(cars2.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 4. Oops, it seems our first dataset has some unnamed blank columns, fix cars1" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodelorigincar
018.08307130350412.0701chevrolet chevelle malibu
115.08350165369311.5701buick skylark 320
218.08318150343611.0701plymouth satellite
316.08304150343312.0701amc rebel sst
417.08302140344910.5701ford torino
\n", "
" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration model \\\n", "0 18.0 8 307 130 3504 12.0 70 \n", "1 15.0 8 350 165 3693 11.5 70 \n", "2 18.0 8 318 150 3436 11.0 70 \n", "3 16.0 8 304 150 3433 12.0 70 \n", "4 17.0 8 302 140 3449 10.5 70 \n", "\n", " origin car \n", "0 1 chevrolet chevelle malibu \n", "1 1 buick skylark 320 \n", "2 1 plymouth satellite \n", "3 1 amc rebel sst \n", "4 1 ford torino " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars1 = cars1.loc[:, \"mpg\":\"car\"]\n", "cars1.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 5. What is the number of observations in each dataset?" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(198, 9)\n", "(200, 9)\n" ] } ], "source": [ "print(cars1.shape)\n", "print(cars2.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6. Join cars1 and cars2 into a single DataFrame called cars" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodelorigincar
018.08307130350412.0701chevrolet chevelle malibu
115.08350165369311.5701buick skylark 320
218.08318150343611.0701plymouth satellite
316.08304150343312.0701amc rebel sst
417.08302140344910.5701ford torino
515.08429198434110.0701ford galaxie 500
614.0845422043549.0701chevrolet impala
714.0844021543128.5701plymouth fury iii
814.08455225442510.0701pontiac catalina
915.0839019038508.5701amc ambassador dpl
1015.08383170356310.0701dodge challenger se
1114.0834016036098.0701plymouth 'cuda 340
1215.0840015037619.5701chevrolet monte carlo
1314.08455225308610.0701buick estate wagon (sw)
1424.0411395237215.0703toyota corona mark ii
1522.0619895283315.5701plymouth duster
1618.0619997277415.5701amc hornet
1721.0620085258716.0701ford maverick
1827.049788213014.5703datsun pl510
1926.049746183520.5702volkswagen 1131 deluxe sedan
2025.0411087267217.5702peugeot 504
2124.0410790243014.5702audi 100 ls
2225.0410495237517.5702saab 99e
2326.04121113223412.5702bmw 2002
2421.0619990264815.0701amc gremlin
2510.08360215461514.0701ford f250
2610.08307200437615.0701chevy c20
2711.08318210438213.5701dodge d200
289.08304193473218.5701hi 1200d
2927.049788213014.5713datsun pl510
..............................
17027.0411288264018.6821chevrolet cavalier wagon
17134.0411288239518.0821chevrolet cavalier 2-door
17231.0411285257516.2821pontiac j2000 se hatchback
17329.0413584252516.0821dodge aries se
17427.0415190273518.0821pontiac phoenix
17524.0414092286516.4821ford fairmont futura
17623.04151?303520.5821amc concord dl
17736.0410574198015.3822volkswagen rabbit l
17837.049168202518.2823mazda glc custom l
17931.049168197017.6823mazda glc custom
18038.0410563212514.7821plymouth horizon miser
18136.049870212517.3821mercury lynx l
18236.0412088216014.5823nissan stanza xe
18336.0410775220514.5823honda accord
18434.0410870224516.9823toyota corolla
18538.049167196515.0823honda civic
18632.049167196515.7823honda civic (auto)
18738.049167199516.2823datsun 310 gx
18825.06181110294516.4821buick century limited
18938.0626285301517.0821oldsmobile cutlass ciera (diesel)
19026.0415692258514.5821chrysler lebaron medallion
19122.06232112283514.7821ford granada l
19232.0414496266513.9823toyota celica gt
19336.0413584237013.0821dodge charger 2.2
19427.0415190295017.3821chevrolet camaro
19527.0414086279015.6821ford mustang gl
19644.049752213024.6822vw pickup
19732.0413584229511.6821dodge rampage
19828.0412079262518.6821ford ranger
19931.0411982272019.4821chevy s-10
\n", "

398 rows × 9 columns

\n", "
" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration model \\\n", "0 18.0 8 307 130 3504 12.0 70 \n", "1 15.0 8 350 165 3693 11.5 70 \n", "2 18.0 8 318 150 3436 11.0 70 \n", "3 16.0 8 304 150 3433 12.0 70 \n", "4 17.0 8 302 140 3449 10.5 70 \n", "5 15.0 8 429 198 4341 10.0 70 \n", "6 14.0 8 454 220 4354 9.0 70 \n", "7 14.0 8 440 215 4312 8.5 70 \n", "8 14.0 8 455 225 4425 10.0 70 \n", "9 15.0 8 390 190 3850 8.5 70 \n", "10 15.0 8 383 170 3563 10.0 70 \n", "11 14.0 8 340 160 3609 8.0 70 \n", "12 15.0 8 400 150 3761 9.5 70 \n", "13 14.0 8 455 225 3086 10.0 70 \n", "14 24.0 4 113 95 2372 15.0 70 \n", "15 22.0 6 198 95 2833 15.5 70 \n", "16 18.0 6 199 97 2774 15.5 70 \n", "17 21.0 6 200 85 2587 16.0 70 \n", "18 27.0 4 97 88 2130 14.5 70 \n", "19 26.0 4 97 46 1835 20.5 70 \n", "20 25.0 4 110 87 2672 17.5 70 \n", "21 24.0 4 107 90 2430 14.5 70 \n", "22 25.0 4 104 95 2375 17.5 70 \n", "23 26.0 4 121 113 2234 12.5 70 \n", "24 21.0 6 199 90 2648 15.0 70 \n", "25 10.0 8 360 215 4615 14.0 70 \n", "26 10.0 8 307 200 4376 15.0 70 \n", "27 11.0 8 318 210 4382 13.5 70 \n", "28 9.0 8 304 193 4732 18.5 70 \n", "29 27.0 4 97 88 2130 14.5 71 \n", ".. ... ... ... ... ... ... ... \n", "170 27.0 4 112 88 2640 18.6 82 \n", "171 34.0 4 112 88 2395 18.0 82 \n", "172 31.0 4 112 85 2575 16.2 82 \n", "173 29.0 4 135 84 2525 16.0 82 \n", "174 27.0 4 151 90 2735 18.0 82 \n", "175 24.0 4 140 92 2865 16.4 82 \n", "176 23.0 4 151 ? 3035 20.5 82 \n", "177 36.0 4 105 74 1980 15.3 82 \n", "178 37.0 4 91 68 2025 18.2 82 \n", "179 31.0 4 91 68 1970 17.6 82 \n", "180 38.0 4 105 63 2125 14.7 82 \n", "181 36.0 4 98 70 2125 17.3 82 \n", "182 36.0 4 120 88 2160 14.5 82 \n", "183 36.0 4 107 75 2205 14.5 82 \n", "184 34.0 4 108 70 2245 16.9 82 \n", "185 38.0 4 91 67 1965 15.0 82 \n", "186 32.0 4 91 67 1965 15.7 82 \n", "187 38.0 4 91 67 1995 16.2 82 \n", "188 25.0 6 181 110 2945 16.4 82 \n", "189 38.0 6 262 85 3015 17.0 82 \n", "190 26.0 4 156 92 2585 14.5 82 \n", "191 22.0 6 232 112 2835 14.7 82 \n", "192 32.0 4 144 96 2665 13.9 82 \n", "193 36.0 4 135 84 2370 13.0 82 \n", "194 27.0 4 151 90 2950 17.3 82 \n", "195 27.0 4 140 86 2790 15.6 82 \n", "196 44.0 4 97 52 2130 24.6 82 \n", "197 32.0 4 135 84 2295 11.6 82 \n", "198 28.0 4 120 79 2625 18.6 82 \n", "199 31.0 4 119 82 2720 19.4 82 \n", "\n", " origin car \n", "0 1 chevrolet chevelle malibu \n", "1 1 buick skylark 320 \n", "2 1 plymouth satellite \n", "3 1 amc rebel sst \n", "4 1 ford torino \n", "5 1 ford galaxie 500 \n", "6 1 chevrolet impala \n", "7 1 plymouth fury iii \n", "8 1 pontiac catalina \n", "9 1 amc ambassador dpl \n", "10 1 dodge challenger se \n", "11 1 plymouth 'cuda 340 \n", "12 1 chevrolet monte carlo \n", "13 1 buick estate wagon (sw) \n", "14 3 toyota corona mark ii \n", "15 1 plymouth duster \n", "16 1 amc hornet \n", "17 1 ford maverick \n", "18 3 datsun pl510 \n", "19 2 volkswagen 1131 deluxe sedan \n", "20 2 peugeot 504 \n", "21 2 audi 100 ls \n", "22 2 saab 99e \n", "23 2 bmw 2002 \n", "24 1 amc gremlin \n", "25 1 ford f250 \n", "26 1 chevy c20 \n", "27 1 dodge d200 \n", "28 1 hi 1200d \n", "29 3 datsun pl510 \n", ".. ... ... \n", "170 1 chevrolet cavalier wagon \n", "171 1 chevrolet cavalier 2-door \n", "172 1 pontiac j2000 se hatchback \n", "173 1 dodge aries se \n", "174 1 pontiac phoenix \n", "175 1 ford fairmont futura \n", "176 1 amc concord dl \n", "177 2 volkswagen rabbit l \n", "178 3 mazda glc custom l \n", "179 3 mazda glc custom \n", "180 1 plymouth horizon miser \n", "181 1 mercury lynx l \n", "182 3 nissan stanza xe \n", "183 3 honda accord \n", "184 3 toyota corolla \n", "185 3 honda civic \n", "186 3 honda civic (auto) \n", "187 3 datsun 310 gx \n", "188 1 buick century limited \n", "189 1 oldsmobile cutlass ciera (diesel) \n", "190 1 chrysler lebaron medallion \n", "191 1 ford granada l \n", "192 3 toyota celica gt \n", "193 1 dodge charger 2.2 \n", "194 1 chevrolet camaro \n", "195 1 ford mustang gl \n", "196 2 vw pickup \n", "197 1 dodge rampage \n", "198 1 ford ranger \n", "199 1 chevy s-10 \n", "\n", "[398 rows x 9 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars = cars1.append(cars2)\n", "cars" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 7. Oops, there is a column missing, called owners. Create a random number Series from 15,000 to 73,000." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([29487, 25680, 65268, 31827, 69215, 72602, 52693, 58440, 16183,\n", " 45014, 32318, 72942, 62163, 35951, 57625, 59355, 36533, 67048,\n", " 58159, 69743, 25146, 22755, 44966, 46792, 56553, 65013, 55908,\n", " 69563, 22030, 59561, 15593, 52998, 54795, 16169, 24809, 35580,\n", " 46590, 38792, 43099, 37166, 21390, 56496, 68606, 21110, 56334,\n", " 45477, 51961, 27625, 51176, 30796, 61809, 65450, 67375, 23342,\n", " 27499, 50585, 57302, 56191, 60281, 32865, 58605, 66374, 15315,\n", " 31791, 28670, 38796, 69214, 41055, 32353, 31574, 65799, 42998,\n", " 72785, 18415, 31977, 29812, 65439, 21161, 60871, 67151, 22179,\n", " 32821, 55392, 34586, 67937, 31646, 66397, 35258, 63815, 71291,\n", " 51130, 27684, 49648, 52691, 50681, 68185, 32635, 51553, 28970,\n", " 19112, 26035, 67666, 55471, 51477, 62055, 53003, 41265, 18565,\n", " 48851, 48673, 45832, 67891, 57638, 29240, 41236, 16950, 31449,\n", " 50528, 22397, 15876, 26414, 16736, 23896, 46104, 17583, 65951,\n", " 38538, 31443, 19299, 46095, 31239, 19290, 38051, 68575, 61755,\n", " 22560, 34460, 35395, 34608, 56906, 44895, 48429, 20900, 49770,\n", " 50513, 59402, 26893, 37233, 19036, 20523, 18765, 46333, 42831,\n", " 53698, 25218, 63106, 16928, 34901, 43674, 65453, 54428, 68502,\n", " 19043, 20325, 45039, 29466, 49672, 67972, 30547, 22522, 69354,\n", " 40489, 72887, 15724, 51442, 65182, 64555, 42138, 72988, 20861,\n", " 67898, 20768, 36415, 47480, 16820, 48739, 62610, 43473, 23002,\n", " 43488, 62581, 37724, 63019, 44912, 35595, 59188, 51814, 65283,\n", " 53479, 27660, 38237, 22957, 47870, 15533, 41944, 51830, 56676,\n", " 57481, 48529, 72220, 66675, 50099, 30585, 25436, 49195, 26050,\n", " 24899, 37213, 25870, 67447, 23808, 71275, 67572, 18545, 43553,\n", " 54858, 23077, 33705, 31282, 26298, 23742, 36110, 51491, 18019,\n", " 60655, 27453, 35563, 63627, 35315, 56717, 59281, 55634, 18415,\n", " 59570, 47320, 20110, 18425, 19352, 18032, 31816, 28573, 66030,\n", " 54723, 21592, 37160, 59518, 35629, 47619, 52359, 34566, 64932,\n", " 24072, 39445, 31203, 63975, 62041, 70175, 51029, 32058, 19428,\n", " 65553, 50799, 48190, 68061, 68201, 53389, 15901, 44585, 54723,\n", " 30446, 63716, 57488, 67134, 22033, 53694, 40002, 24854, 59747,\n", " 59827, 53378, 53196, 68686, 20784, 28181, 33044, 41694, 39857,\n", " 57296, 69021, 17359, 29794, 22515, 55877, 22806, 50027, 56787,\n", " 50844, 17420, 65259, 19141, 40204, 19530, 30116, 34973, 15641,\n", " 53492, 59574, 59082, 64400, 70163, 43058, 69696, 67996, 26158,\n", " 32936, 45461, 47390, 32368, 15400, 40895, 16572, 31776, 62121,\n", " 56704, 39335, 27716, 52565, 50831, 45049, 25173, 25018, 18606,\n", " 71177, 66288, 46754, 68175, 35829, 24959, 54792, 19059, 29092,\n", " 58736, 62938, 44733, 17884, 33905, 33965, 24641, 52257, 28178,\n", " 29515, 37703, 56036, 51556, 23590, 61888, 70224, 53730, 41328,\n", " 16501, 30360, 54106, 29101, 35631, 56173, 30424, 46887, 23657,\n", " 17723, 71709, 45270, 30380, 27779, 33774, 36379, 47127, 63625,\n", " 16750, 65740, 53802, 40995, 37487, 42791, 21825, 69344, 63210,\n", " 15982, 20259])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nr_owners = np.random.randint(15000, high=73001, size=398, dtype='l')\n", "nr_owners" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 8. Add the column owners to cars" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodelorigincarowners
19527.0414086279015.6821ford mustang gl21825
19644.049752213024.6822vw pickup69344
19732.0413584229511.6821dodge rampage63210
19828.0412079262518.6821ford ranger15982
19931.0411982272019.4821chevy s-1020259
\n", "
" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration model \\\n", "195 27.0 4 140 86 2790 15.6 82 \n", "196 44.0 4 97 52 2130 24.6 82 \n", "197 32.0 4 135 84 2295 11.6 82 \n", "198 28.0 4 120 79 2625 18.6 82 \n", "199 31.0 4 119 82 2720 19.4 82 \n", "\n", " origin car owners \n", "195 1 ford mustang gl 21825 \n", "196 2 vw pickup 69344 \n", "197 1 dodge rampage 63210 \n", "198 1 ford ranger 15982 \n", "199 1 chevy s-10 20259 " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars['owners'] = nr_owners\n", "cars.tail()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "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": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }