{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "1a9ee5d3", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "id": "c915de97", "metadata": {}, "outputs": [], "source": [ "proizvodi_baza = {\n", " 'Naziv': [\n", " 'Trek', 'Kona', 'Giant', \n", " 'Bianchi', 'Cosmo Ride', 'Neon', \n", " 'Zeeclo', 'Atomic', 'Head', \n", " 'Elan', 'Salomon', 'Rossignol'\n", " ],\n", " 'Cijena': [\n", " 7699.00, 4699.00, 5999.00, \n", " 22499.00, 2599.00, 1999.00, \n", " 2799.00, 1499.00, 1359.00, \n", " 1499.00, 1699.00, 999.00\n", " ],\n", " 'Kategorija': [\n", " 'Bicikl', 'Bicikl', 'Bicikl', \n", " 'Bicikl', 'E-Romobil', 'E-Romobil',\n", " 'E-Romobil', 'Skije', 'Skije', \n", " 'Skije', 'Skije', 'Skije'\n", " ],\n", " 'Ocjena': [\n", " 6.70, 6.50, 6.65, \n", " 7.20, 8.64, 8.61, \n", " 8.59, 7.99, 8.15,\n", " 8.05, 7.91, 6.10\n", " ]\n", "}" ] }, { "cell_type": "code", "execution_count": 3, "id": "6978b6c3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NazivCijenaKategorijaOcjena
0Trek7699.0Bicikl6.70
1Kona4699.0Bicikl6.50
2Giant5999.0Bicikl6.65
3Bianchi22499.0Bicikl7.20
4Cosmo Ride2599.0E-Romobil8.64
5Neon1999.0E-Romobil8.61
6Zeeclo2799.0E-Romobil8.59
7Atomic1499.0Skije7.99
8Head1359.0Skije8.15
9Elan1499.0Skije8.05
10Salomon1699.0Skije7.91
11Rossignol999.0Skije6.10
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" ], "text/plain": [ " Naziv Cijena Kategorija Ocjena\n", "0 Trek 7699.0 Bicikl 6.70\n", "1 Kona 4699.0 Bicikl 6.50\n", "2 Giant 5999.0 Bicikl 6.65\n", "3 Bianchi 22499.0 Bicikl 7.20\n", "4 Cosmo Ride 2599.0 E-Romobil 8.64\n", "5 Neon 1999.0 E-Romobil 8.61\n", "6 Zeeclo 2799.0 E-Romobil 8.59\n", "7 Atomic 1499.0 Skije 7.99\n", "8 Head 1359.0 Skije 8.15\n", "9 Elan 1499.0 Skije 8.05\n", "10 Salomon 1699.0 Skije 7.91\n", "11 Rossignol 999.0 Skije 6.10" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi = pd.DataFrame(proizvodi_baza)\n", "proizvodi_svi" ] }, { "cell_type": "code", "execution_count": 4, "id": "b260cbea", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NazivCijena
0Trek7699.0
1Kona4699.0
2Giant5999.0
3Bianchi22499.0
4Cosmo Ride2599.0
5Neon1999.0
6Zeeclo2799.0
7Atomic1499.0
8Head1359.0
9Elan1499.0
10Salomon1699.0
11Rossignol999.0
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" ], "text/plain": [ " Naziv Cijena\n", "0 Trek 7699.0\n", "1 Kona 4699.0\n", "2 Giant 5999.0\n", "3 Bianchi 22499.0\n", "4 Cosmo Ride 2599.0\n", "5 Neon 1999.0\n", "6 Zeeclo 2799.0\n", "7 Atomic 1499.0\n", "8 Head 1359.0\n", "9 Elan 1499.0\n", "10 Salomon 1699.0\n", "11 Rossignol 999.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_dio = pd.DataFrame(proizvodi_baza, columns=['Naziv','Cijena'])\n", "proizvodi_dio" ] }, { "cell_type": "code", "execution_count": 5, "id": "dec50ea5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Naziv', 'Cijena', 'Kategorija', 'Ocjena'], dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi.columns" ] }, { "cell_type": "code", "execution_count": 6, "id": "6372a0fe", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Naziv', 'Cijena'], dtype='object')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_dio.columns" ] }, { "cell_type": "code", "execution_count": 9, "id": "1ab2b783", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([['Trek', 7699.0, 'Bicikl', 6.7],\n", " ['Kona', 4699.0, 'Bicikl', 6.5],\n", " ['Giant', 5999.0, 'Bicikl', 6.65],\n", " ['Bianchi', 22499.0, 'Bicikl', 7.2],\n", " ['Cosmo Ride', 2599.0, 'E-Romobil', 8.64],\n", " ['Neon', 1999.0, 'E-Romobil', 8.61],\n", " ['Zeeclo', 2799.0, 'E-Romobil', 8.59],\n", " ['Atomic', 1499.0, 'Skije', 7.99],\n", " ['Head', 1359.0, 'Skije', 8.15],\n", " ['Elan', 1499.0, 'Skije', 8.05],\n", " ['Salomon', 1699.0, 'Skije', 7.91],\n", " ['Rossignol', 999.0, 'Skije', 6.1]], dtype=object)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi.values" ] }, { "cell_type": "code", "execution_count": 10, "id": "c6b136ba", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Trek', 7699.0, 'Bicikl', 6.7], dtype=object)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi.values[0]" ] }, { "cell_type": "code", "execution_count": 11, "id": "d2974a74", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7699.0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi.values[0][1]" ] }, { "cell_type": "code", "execution_count": 12, "id": "2080cb08", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RangeIndex(start=0, stop=12, step=1)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi.index" ] }, { "cell_type": "code", "execution_count": 13, "id": "fed4e246", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NazivCijenaKategorijaOcjena
Rbr
0Trek7699.0Bicikl6.70
1Kona4699.0Bicikl6.50
2Giant5999.0Bicikl6.65
3Bianchi22499.0Bicikl7.20
4Cosmo Ride2599.0E-Romobil8.64
5Neon1999.0E-Romobil8.61
6Zeeclo2799.0E-Romobil8.59
7Atomic1499.0Skije7.99
8Head1359.0Skije8.15
9Elan1499.0Skije8.05
10Salomon1699.0Skije7.91
11Rossignol999.0Skije6.10
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" ], "text/plain": [ " Naziv Cijena Kategorija Ocjena\n", "Rbr \n", "0 Trek 7699.0 Bicikl 6.70\n", "1 Kona 4699.0 Bicikl 6.50\n", "2 Giant 5999.0 Bicikl 6.65\n", "3 Bianchi 22499.0 Bicikl 7.20\n", "4 Cosmo Ride 2599.0 E-Romobil 8.64\n", "5 Neon 1999.0 E-Romobil 8.61\n", "6 Zeeclo 2799.0 E-Romobil 8.59\n", "7 Atomic 1499.0 Skije 7.99\n", "8 Head 1359.0 Skije 8.15\n", "9 Elan 1499.0 Skije 8.05\n", "10 Salomon 1699.0 Skije 7.91\n", "11 Rossignol 999.0 Skije 6.10" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi.index.name='Rbr'\n", "proizvodi_svi" ] }, { "cell_type": "code", "execution_count": 14, "id": "4dc17eb5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Rbr\n", "0 6.70\n", "1 6.50\n", "2 6.65\n", "3 7.20\n", "4 8.64\n", "5 8.61\n", "6 8.59\n", "7 7.99\n", "8 8.15\n", "9 8.05\n", "10 7.91\n", "11 6.10\n", "Name: Ocjena, dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi['Ocjena']" ] }, { "cell_type": "code", "execution_count": 15, "id": "98d9ddb9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CarMPGCylindersDisplacementHorsepowerWeightAccelerationModelOrigin
0STRINGDOUBLEINTDOUBLEDOUBLEDOUBLEDOUBLEINTCAT
1Chevrolet Chevelle Malibu18.08307.0130.03504.12.070US
2Buick Skylark 32015.08350.0165.03693.11.570US
3Plymouth Satellite18.08318.0150.03436.11.070US
4AMC Rebel SST16.08304.0150.03433.12.070US
..............................
402Ford Mustang GL27.04140.086.002790.15.682US
403Volkswagen Pickup44.0497.0052.002130.24.682Europe
404Dodge Rampage32.04135.084.002295.11.682US
405Ford Ranger28.04120.079.002625.18.682US
406Chevy S-1031.04119.082.002720.19.482US
\n", "

407 rows × 9 columns

\n", "
" ], "text/plain": [ " Car MPG Cylinders Displacement Horsepower \\\n", "0 STRING DOUBLE INT DOUBLE DOUBLE \n", "1 Chevrolet Chevelle Malibu 18.0 8 307.0 130.0 \n", "2 Buick Skylark 320 15.0 8 350.0 165.0 \n", "3 Plymouth Satellite 18.0 8 318.0 150.0 \n", "4 AMC Rebel SST 16.0 8 304.0 150.0 \n", ".. ... ... ... ... ... \n", "402 Ford Mustang GL 27.0 4 140.0 86.00 \n", "403 Volkswagen Pickup 44.0 4 97.00 52.00 \n", "404 Dodge Rampage 32.0 4 135.0 84.00 \n", "405 Ford Ranger 28.0 4 120.0 79.00 \n", "406 Chevy S-10 31.0 4 119.0 82.00 \n", "\n", " Weight Acceleration Model Origin \n", "0 DOUBLE DOUBLE INT CAT \n", "1 3504. 12.0 70 US \n", "2 3693. 11.5 70 US \n", "3 3436. 11.0 70 US \n", "4 3433. 12.0 70 US \n", ".. ... ... ... ... \n", "402 2790. 15.6 82 US \n", "403 2130. 24.6 82 Europe \n", "404 2295. 11.6 82 US \n", "405 2625. 18.6 82 US \n", "406 2720. 19.4 82 US \n", "\n", "[407 rows x 9 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv = pd.read_csv('cars.csv', sep=';')\n", "cars_csv" ] }, { "cell_type": "code", "execution_count": 16, "id": "327d4a66", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Car', 'MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n", " 'Acceleration', 'Model', 'Origin'],\n", " dtype='object')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.columns" ] }, { "cell_type": "code", "execution_count": 17, "id": "35c95e6f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([['STRING', 'DOUBLE', 'INT', ..., 'DOUBLE', 'INT', 'CAT'],\n", " ['Chevrolet Chevelle Malibu', '18.0', '8', ..., '12.0', '70',\n", " 'US'],\n", " ['Buick Skylark 320', '15.0', '8', ..., '11.5', '70', 'US'],\n", " ...,\n", " ['Dodge Rampage', '32.0', '4', ..., '11.6', '82', 'US'],\n", " ['Ford Ranger', '28.0', '4', ..., '18.6', '82', 'US'],\n", " ['Chevy S-10', '31.0', '4', ..., '19.4', '82', 'US']], dtype=object)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.values" ] }, { "cell_type": "code", "execution_count": 18, "id": "841d6832", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YEARMakeModelSize(kW)Unnamed: 5TYPECITY (kWh/100 km)HWY (kWh/100 km)COMB (kWh/100 km)CITY (Le/100 km)HWY (Le/100 km)COMB (Le/100 km)(g/km)RATING(km)TIME (h)
02012MITSUBISHIi-MiEVSUBCOMPACT49A1B16.921.418.71.92.42.10NaN1007
12012NISSANLEAFMID-SIZE80A1B19.323.021.12.22.62.40NaN1177
22013FORDFOCUS ELECTRICCOMPACT107A1B19.021.120.02.12.42.20NaN1224
32013MITSUBISHIi-MiEVSUBCOMPACT49A1B16.921.418.71.92.42.10NaN1007
42013NISSANLEAFMID-SIZE80A1B19.323.021.12.22.62.40NaN1177
52013SMARTFORTWO ELECTRIC DRIVE CABRIOLETTWO-SEATER35A1B17.222.519.61.92.52.20NaN1098
62013SMARTFORTWO ELECTRIC DRIVE COUPETWO-SEATER35A1B17.222.519.61.92.52.20NaN1098
72013TESLAMODEL S (40 kWh battery)FULL-SIZE270A1B22.421.922.22.52.52.50NaN2246
82013TESLAMODEL S (60 kWh battery)FULL-SIZE270A1B22.221.721.92.52.42.50NaN33510
92013TESLAMODEL S (85 kWh battery)FULL-SIZE270A1B23.823.223.62.72.62.60NaN42612
102013TESLAMODEL S PERFORMANCEFULL-SIZE310A1B23.923.223.62.72.62.60NaN42612
112014CHEVROLETSPARK EVSUBCOMPACT104A1B16.019.617.81.82.22.00NaN1317
122014FORDFOCUS ELECTRICCOMPACT107A1B19.021.120.02.12.42.20NaN1224
132014MITSUBISHIi-MiEVSUBCOMPACT49A1B16.921.418.71.92.42.10NaN1007
142014NISSANLEAFMID-SIZE80A1B16.520.818.41.92.32.10NaN1355
152014SMARTFORTWO ELECTRIC DRIVE CABRIOLETTWO-SEATER35A1B17.222.519.61.92.52.20NaN1098
162014SMARTFORTWO ELECTRIC DRIVE COUPETWO-SEATER35A1B17.222.519.61.92.52.20NaN1098
172014TESLAMODEL S (60 kWh battery)FULL-SIZE225A1B22.221.721.92.52.42.50NaN33510
182014TESLAMODEL S (85 kWh battery)FULL-SIZE270A1B23.823.223.62.72.62.60NaN42612
192014TESLAMODEL S PERFORMANCEFULL-SIZE310A1B23.923.223.62.72.62.60NaN42612
202015BMWi3SUBCOMPACT125A1B15.218.816.81.72.11.90NaN1304
212015CHEVROLETSPARK EVSUBCOMPACT104A1B16.019.617.81.82.22.00NaN1317
222015FORDFOCUS ELECTRICCOMPACT107A1B19.021.120.02.12.42.20NaN1224
232015KIASOUL EVSTATION WAGON - SMALL81A1B17.522.719.92.02.62.20NaN1494
242015MITSUBISHIi-MiEVSUBCOMPACT49A1B16.921.418.71.92.42.10NaN1007
252015NISSANLEAFMID-SIZE80A1B16.520.818.41.92.32.10NaN1355
262015SMARTFORTWO ELECTRIC DRIVE CABRIOLETTWO-SEATER35A1B17.222.519.61.92.52.20NaN1098
272015SMARTFORTWO ELECTRIC DRIVE COUPETWO-SEATER35A1B17.222.519.61.92.52.20NaN1098
282015TESLAMODEL S (60 kWh battery)FULL-SIZE283A1B22.221.721.92.52.42.50NaN33510
292015TESLAMODEL S (70 kWh battery)FULL-SIZE283A1B23.823.223.62.72.62.60NaN37712
302015TESLAMODEL S (85/90 kWh battery)FULL-SIZE283A1B23.823.223.62.72.62.60NaN42612
312015TESLAMODEL S 70DFULL-SIZE280A1B20.820.620.72.32.32.30NaN38612
322015TESLAMODEL S 85D/90DFULL-SIZE280A1B22.019.821.02.52.22.40NaN43512
332015TESLAMODEL S P85D/P90DFULL-SIZE515A1B23.421.522.52.62.42.50NaN40712
342016BMWi3SUBCOMPACT125A1B15.218.816.81.72.11.9010.01304
352016CHEVROLETSPARK EVSUBCOMPACT104A1B16.019.617.81.82.22.0010.01317
362016FORDFOCUS ELECTRICCOMPACT107A1B19.021.120.02.12.42.2010.01224
372016KIASOUL EVSTATION WAGON - SMALL81A1B17.522.719.92.02.62.2010.01494
382016MITSUBISHIi-MiEVSUBCOMPACT49A1B16.921.418.71.92.42.1010.01007
392016NISSANLEAF (24 kWh battery)MID-SIZE80A1B16.520.818.41.92.32.1010.01355
402016NISSANLEAF (30 kWh battery)MID-SIZE80A1B17.020.718.61.92.32.1010.01726
412016SMARTFORTWO ELECTRIC DRIVE CABRIOLETTWO-SEATER35A1B17.222.519.61.92.52.2010.01098
422016SMARTFORTWO ELECTRIC DRIVE COUPETWO-SEATER35A1B17.222.519.61.92.52.2010.01098
432016TESLAMODEL S (60 kWh battery)FULL-SIZE283A1B22.221.721.92.52.42.5010.033510
442016TESLAMODEL S (70 kWh battery)FULL-SIZE283A1B23.823.223.62.72.62.6010.037712
452016TESLAMODEL S (85/90 kWh battery)FULL-SIZE283A1B23.823.223.62.72.62.6010.042612
462016TESLAMODEL S 70DFULL-SIZE386A1B20.820.620.72.32.32.3010.038612
472016TESLAMODEL S 85D/90DFULL-SIZE386A1B22.019.821.02.52.22.4010.043512
482016TESLAMODEL S 90D (Refresh)FULL-SIZE386A1B20.819.720.32.32.22.3010.047312
492016TESLAMODEL S P85D/P90DFULL-SIZE568A1B23.421.522.52.62.42.5010.040712
502016TESLAMODEL S P90D (Refresh)FULL-SIZE568A1B22.921.022.12.62.42.5010.043512
512016TESLAMODEL X 90DSUV - STANDARD386A1B23.222.222.72.62.52.6010.041412
522016TESLAMODEL X P90DSUV - STANDARD568A1B23.623.323.52.72.62.6010.040212
\n", "
" ], "text/plain": [ " YEAR Make Model Size \\\n", "0 2012 MITSUBISHI i-MiEV SUBCOMPACT \n", "1 2012 NISSAN LEAF MID-SIZE \n", "2 2013 FORD FOCUS ELECTRIC COMPACT \n", "3 2013 MITSUBISHI i-MiEV SUBCOMPACT \n", "4 2013 NISSAN LEAF MID-SIZE \n", "5 2013 SMART FORTWO ELECTRIC DRIVE CABRIOLET TWO-SEATER \n", "6 2013 SMART FORTWO ELECTRIC DRIVE COUPE TWO-SEATER \n", "7 2013 TESLA MODEL S (40 kWh battery) FULL-SIZE \n", "8 2013 TESLA MODEL S (60 kWh battery) FULL-SIZE \n", "9 2013 TESLA MODEL S (85 kWh battery) FULL-SIZE \n", "10 2013 TESLA MODEL S PERFORMANCE FULL-SIZE \n", "11 2014 CHEVROLET SPARK EV SUBCOMPACT \n", "12 2014 FORD FOCUS ELECTRIC COMPACT \n", "13 2014 MITSUBISHI i-MiEV SUBCOMPACT \n", "14 2014 NISSAN LEAF MID-SIZE \n", "15 2014 SMART FORTWO ELECTRIC DRIVE CABRIOLET TWO-SEATER \n", "16 2014 SMART FORTWO ELECTRIC DRIVE COUPE TWO-SEATER \n", "17 2014 TESLA MODEL S (60 kWh battery) FULL-SIZE \n", "18 2014 TESLA MODEL S (85 kWh battery) FULL-SIZE \n", "19 2014 TESLA MODEL S PERFORMANCE FULL-SIZE \n", "20 2015 BMW i3 SUBCOMPACT \n", "21 2015 CHEVROLET SPARK EV SUBCOMPACT \n", "22 2015 FORD FOCUS ELECTRIC COMPACT \n", "23 2015 KIA SOUL EV STATION WAGON - SMALL \n", "24 2015 MITSUBISHI i-MiEV SUBCOMPACT \n", "25 2015 NISSAN LEAF MID-SIZE \n", "26 2015 SMART FORTWO ELECTRIC DRIVE CABRIOLET TWO-SEATER \n", "27 2015 SMART FORTWO ELECTRIC DRIVE COUPE TWO-SEATER \n", "28 2015 TESLA MODEL S (60 kWh battery) FULL-SIZE \n", "29 2015 TESLA MODEL S (70 kWh battery) FULL-SIZE \n", "30 2015 TESLA MODEL S (85/90 kWh battery) FULL-SIZE \n", "31 2015 TESLA MODEL S 70D FULL-SIZE \n", "32 2015 TESLA MODEL S 85D/90D FULL-SIZE \n", "33 2015 TESLA MODEL S P85D/P90D FULL-SIZE \n", "34 2016 BMW i3 SUBCOMPACT \n", "35 2016 CHEVROLET SPARK EV SUBCOMPACT \n", "36 2016 FORD FOCUS ELECTRIC COMPACT \n", "37 2016 KIA SOUL EV STATION WAGON - SMALL \n", "38 2016 MITSUBISHI i-MiEV SUBCOMPACT \n", "39 2016 NISSAN LEAF (24 kWh battery) MID-SIZE \n", "40 2016 NISSAN LEAF (30 kWh battery) MID-SIZE \n", "41 2016 SMART FORTWO ELECTRIC DRIVE CABRIOLET TWO-SEATER \n", "42 2016 SMART FORTWO ELECTRIC DRIVE COUPE TWO-SEATER \n", "43 2016 TESLA MODEL S (60 kWh battery) FULL-SIZE \n", "44 2016 TESLA MODEL S (70 kWh battery) FULL-SIZE \n", "45 2016 TESLA MODEL S (85/90 kWh battery) FULL-SIZE \n", "46 2016 TESLA MODEL S 70D FULL-SIZE \n", "47 2016 TESLA MODEL S 85D/90D FULL-SIZE \n", "48 2016 TESLA MODEL S 90D (Refresh) FULL-SIZE \n", "49 2016 TESLA MODEL S P85D/P90D FULL-SIZE \n", "50 2016 TESLA MODEL S P90D (Refresh) FULL-SIZE \n", "51 2016 TESLA MODEL X 90D SUV - STANDARD \n", "52 2016 TESLA MODEL X P90D SUV - STANDARD \n", "\n", " (kW) Unnamed: 5 TYPE CITY (kWh/100 km) HWY (kWh/100 km) \\\n", "0 49 A1 B 16.9 21.4 \n", "1 80 A1 B 19.3 23.0 \n", "2 107 A1 B 19.0 21.1 \n", "3 49 A1 B 16.9 21.4 \n", "4 80 A1 B 19.3 23.0 \n", "5 35 A1 B 17.2 22.5 \n", "6 35 A1 B 17.2 22.5 \n", "7 270 A1 B 22.4 21.9 \n", "8 270 A1 B 22.2 21.7 \n", "9 270 A1 B 23.8 23.2 \n", "10 310 A1 B 23.9 23.2 \n", "11 104 A1 B 16.0 19.6 \n", "12 107 A1 B 19.0 21.1 \n", "13 49 A1 B 16.9 21.4 \n", "14 80 A1 B 16.5 20.8 \n", "15 35 A1 B 17.2 22.5 \n", "16 35 A1 B 17.2 22.5 \n", "17 225 A1 B 22.2 21.7 \n", "18 270 A1 B 23.8 23.2 \n", "19 310 A1 B 23.9 23.2 \n", "20 125 A1 B 15.2 18.8 \n", "21 104 A1 B 16.0 19.6 \n", "22 107 A1 B 19.0 21.1 \n", "23 81 A1 B 17.5 22.7 \n", "24 49 A1 B 16.9 21.4 \n", "25 80 A1 B 16.5 20.8 \n", "26 35 A1 B 17.2 22.5 \n", "27 35 A1 B 17.2 22.5 \n", "28 283 A1 B 22.2 21.7 \n", "29 283 A1 B 23.8 23.2 \n", "30 283 A1 B 23.8 23.2 \n", "31 280 A1 B 20.8 20.6 \n", "32 280 A1 B 22.0 19.8 \n", "33 515 A1 B 23.4 21.5 \n", "34 125 A1 B 15.2 18.8 \n", "35 104 A1 B 16.0 19.6 \n", "36 107 A1 B 19.0 21.1 \n", "37 81 A1 B 17.5 22.7 \n", "38 49 A1 B 16.9 21.4 \n", "39 80 A1 B 16.5 20.8 \n", "40 80 A1 B 17.0 20.7 \n", "41 35 A1 B 17.2 22.5 \n", "42 35 A1 B 17.2 22.5 \n", "43 283 A1 B 22.2 21.7 \n", "44 283 A1 B 23.8 23.2 \n", "45 283 A1 B 23.8 23.2 \n", "46 386 A1 B 20.8 20.6 \n", "47 386 A1 B 22.0 19.8 \n", "48 386 A1 B 20.8 19.7 \n", "49 568 A1 B 23.4 21.5 \n", "50 568 A1 B 22.9 21.0 \n", "51 386 A1 B 23.2 22.2 \n", "52 568 A1 B 23.6 23.3 \n", "\n", " COMB (kWh/100 km) CITY (Le/100 km) HWY (Le/100 km) COMB (Le/100 km) \\\n", "0 18.7 1.9 2.4 2.1 \n", "1 21.1 2.2 2.6 2.4 \n", "2 20.0 2.1 2.4 2.2 \n", "3 18.7 1.9 2.4 2.1 \n", "4 21.1 2.2 2.6 2.4 \n", "5 19.6 1.9 2.5 2.2 \n", "6 19.6 1.9 2.5 2.2 \n", "7 22.2 2.5 2.5 2.5 \n", "8 21.9 2.5 2.4 2.5 \n", "9 23.6 2.7 2.6 2.6 \n", "10 23.6 2.7 2.6 2.6 \n", "11 17.8 1.8 2.2 2.0 \n", "12 20.0 2.1 2.4 2.2 \n", "13 18.7 1.9 2.4 2.1 \n", "14 18.4 1.9 2.3 2.1 \n", "15 19.6 1.9 2.5 2.2 \n", "16 19.6 1.9 2.5 2.2 \n", "17 21.9 2.5 2.4 2.5 \n", "18 23.6 2.7 2.6 2.6 \n", "19 23.6 2.7 2.6 2.6 \n", "20 16.8 1.7 2.1 1.9 \n", "21 17.8 1.8 2.2 2.0 \n", "22 20.0 2.1 2.4 2.2 \n", "23 19.9 2.0 2.6 2.2 \n", "24 18.7 1.9 2.4 2.1 \n", "25 18.4 1.9 2.3 2.1 \n", "26 19.6 1.9 2.5 2.2 \n", "27 19.6 1.9 2.5 2.2 \n", "28 21.9 2.5 2.4 2.5 \n", "29 23.6 2.7 2.6 2.6 \n", "30 23.6 2.7 2.6 2.6 \n", "31 20.7 2.3 2.3 2.3 \n", "32 21.0 2.5 2.2 2.4 \n", "33 22.5 2.6 2.4 2.5 \n", "34 16.8 1.7 2.1 1.9 \n", "35 17.8 1.8 2.2 2.0 \n", "36 20.0 2.1 2.4 2.2 \n", "37 19.9 2.0 2.6 2.2 \n", "38 18.7 1.9 2.4 2.1 \n", "39 18.4 1.9 2.3 2.1 \n", "40 18.6 1.9 2.3 2.1 \n", "41 19.6 1.9 2.5 2.2 \n", "42 19.6 1.9 2.5 2.2 \n", "43 21.9 2.5 2.4 2.5 \n", "44 23.6 2.7 2.6 2.6 \n", "45 23.6 2.7 2.6 2.6 \n", "46 20.7 2.3 2.3 2.3 \n", "47 21.0 2.5 2.2 2.4 \n", "48 20.3 2.3 2.2 2.3 \n", "49 22.5 2.6 2.4 2.5 \n", "50 22.1 2.6 2.4 2.5 \n", "51 22.7 2.6 2.5 2.6 \n", "52 23.5 2.7 2.6 2.6 \n", "\n", " (g/km) RATING (km) TIME (h) \n", "0 0 NaN 100 7 \n", "1 0 NaN 117 7 \n", "2 0 NaN 122 4 \n", "3 0 NaN 100 7 \n", "4 0 NaN 117 7 \n", "5 0 NaN 109 8 \n", "6 0 NaN 109 8 \n", "7 0 NaN 224 6 \n", "8 0 NaN 335 10 \n", "9 0 NaN 426 12 \n", "10 0 NaN 426 12 \n", "11 0 NaN 131 7 \n", "12 0 NaN 122 4 \n", "13 0 NaN 100 7 \n", "14 0 NaN 135 5 \n", "15 0 NaN 109 8 \n", "16 0 NaN 109 8 \n", "17 0 NaN 335 10 \n", "18 0 NaN 426 12 \n", "19 0 NaN 426 12 \n", "20 0 NaN 130 4 \n", "21 0 NaN 131 7 \n", "22 0 NaN 122 4 \n", "23 0 NaN 149 4 \n", "24 0 NaN 100 7 \n", "25 0 NaN 135 5 \n", "26 0 NaN 109 8 \n", "27 0 NaN 109 8 \n", "28 0 NaN 335 10 \n", "29 0 NaN 377 12 \n", "30 0 NaN 426 12 \n", "31 0 NaN 386 12 \n", "32 0 NaN 435 12 \n", "33 0 NaN 407 12 \n", "34 0 10.0 130 4 \n", "35 0 10.0 131 7 \n", "36 0 10.0 122 4 \n", "37 0 10.0 149 4 \n", "38 0 10.0 100 7 \n", "39 0 10.0 135 5 \n", "40 0 10.0 172 6 \n", "41 0 10.0 109 8 \n", "42 0 10.0 109 8 \n", "43 0 10.0 335 10 \n", "44 0 10.0 377 12 \n", "45 0 10.0 426 12 \n", "46 0 10.0 386 12 \n", "47 0 10.0 435 12 \n", "48 0 10.0 473 12 \n", "49 0 10.0 407 12 \n", "50 0 10.0 435 12 \n", "51 0 10.0 414 12 \n", "52 0 10.0 402 12 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecars_csv = pd.read_csv('e-cars.csv', sep=';')\n", "ecars_csv" ] }, { "cell_type": "code", "execution_count": 19, "id": "24a1293b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RangeIndex(start=0, stop=407, step=1)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.index" ] }, { "cell_type": "code", "execution_count": 20, "id": "d959c70c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RangeIndex(start=0, stop=53, step=1)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecars_csv.index" ] }, { "cell_type": "code", "execution_count": 21, "id": "af659547", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['YEAR', 'Make', 'Model', 'Size', '(kW)', 'Unnamed: 5', 'TYPE',\n", " 'CITY (kWh/100 km)', 'HWY (kWh/100 km)', 'COMB (kWh/100 km)',\n", " 'CITY (Le/100 km)', 'HWY (Le/100 km)', 'COMB (Le/100 km)', '(g/km)',\n", " 'RATING', '(km)', 'TIME (h)'],\n", " dtype='object')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecars_csv.columns" ] }, { "cell_type": "code", "execution_count": 22, "id": "a174ef19", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "YEAR int64\n", "Make object\n", "Model object\n", "Size object\n", "(kW) int64\n", "Unnamed: 5 object\n", "TYPE object\n", "CITY (kWh/100 km) float64\n", "HWY (kWh/100 km) float64\n", "COMB (kWh/100 km) float64\n", "CITY (Le/100 km) float64\n", "HWY (Le/100 km) float64\n", "COMB (Le/100 km) float64\n", "(g/km) int64\n", "RATING float64\n", "(km) int64\n", "TIME (h) int64\n", "dtype: object" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecars_csv.dtypes" ] }, { "cell_type": "code", "execution_count": 23, "id": "144f86d2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Car object\n", "MPG object\n", "Cylinders object\n", "Displacement object\n", "Horsepower object\n", "Weight object\n", "Acceleration object\n", "Model object\n", "Origin object\n", "dtype: object" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.dtypes" ] }, { "cell_type": "code", "execution_count": 24, "id": "66e16236", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['STRING', 'DOUBLE', 'INT', 'DOUBLE', 'DOUBLE', 'DOUBLE', 'DOUBLE',\n", " 'INT', 'CAT'], dtype=object)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.values[0]" ] }, { "cell_type": "code", "execution_count": 25, "id": "f31f14b0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([2012, 'MITSUBISHI', 'i-MiEV', 'SUBCOMPACT', 49, 'A1', 'B', 16.9,\n", " 21.4, 18.7, 1.9, 2.4, 2.1, 0, nan, 100, 7], dtype=object)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecars_csv.values[0]" ] }, { "cell_type": "code", "execution_count": 2, "id": "82060cc7", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'cars_csv' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m cars_csv[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMPG\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m cars_csv[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMPG\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfloat64\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "\u001b[1;31mNameError\u001b[0m: name 'cars_csv' is not defined" ] } ], "source": [ "cars_csv['MPG'] = cars_csv['MPG'].astype('float64')" ] }, { "cell_type": "code", "execution_count": 28, "id": "30cc6309", "metadata": {}, "outputs": [], "source": [ "cars_df = cars_csv.drop([0]) # brišemo prvi redak" ] }, { "cell_type": "code", "execution_count": 29, "id": "47174dd1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Car object\n", "MPG object\n", "Cylinders object\n", "Displacement object\n", "Horsepower object\n", "Weight object\n", "Acceleration object\n", "Model object\n", "Origin object\n", "dtype: object" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_df.dtypes" ] }, { "cell_type": "code", "execution_count": 30, "id": "cbeea332", "metadata": {}, "outputs": [], "source": [ "cars_df['MPG'] = cars_df['MPG'].astype('float64')" ] }, { "cell_type": "code", "execution_count": 32, "id": "793e7845", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Car object\n", "MPG float64\n", "Cylinders object\n", "Displacement object\n", "Horsepower object\n", "Weight object\n", "Acceleration object\n", "Model object\n", "Origin object\n", "dtype: object" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_df.dtypes" ] }, { "cell_type": "code", "execution_count": 33, "id": "6892cd33", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Car object\n", "MPG float64\n", "Cylinders int64\n", "Displacement object\n", "Horsepower object\n", "Weight object\n", "Acceleration object\n", "Model object\n", "Origin object\n", "dtype: object" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_df['Cylinders'] = cars_df['Cylinders'].astype('int64')\n", "cars_df.dtypes" ] }, { "cell_type": "code", "execution_count": 34, "id": "43034cdd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CarMPGCylindersDisplacementHorsepowerWeightAccelerationModelOrigin
0Chevrolet Chevelle Malibu18.08307.0130.03504.012.070US
1Buick Skylark 32015.08350.0165.03693.011.570US
2Plymouth Satellite18.08318.0150.03436.011.070US
3AMC Rebel SST16.08304.0150.03433.012.070US
4Ford Torino17.08302.0140.03449.010.570US
..............................
401Ford Mustang GL27.04140.086.02790.015.682US
402Volkswagen Pickup44.0497.052.02130.024.682Europe
403Dodge Rampage32.04135.084.02295.011.682US
404Ford Ranger28.04120.079.02625.018.682US
405Chevy S-1031.04119.082.02720.019.482US
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406 rows × 9 columns

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" ], "text/plain": [ " Car MPG Cylinders Displacement Horsepower \\\n", "0 Chevrolet Chevelle Malibu 18.0 8 307.0 130.0 \n", "1 Buick Skylark 320 15.0 8 350.0 165.0 \n", "2 Plymouth Satellite 18.0 8 318.0 150.0 \n", "3 AMC Rebel SST 16.0 8 304.0 150.0 \n", "4 Ford Torino 17.0 8 302.0 140.0 \n", ".. ... ... ... ... ... \n", "401 Ford Mustang GL 27.0 4 140.0 86.0 \n", "402 Volkswagen Pickup 44.0 4 97.0 52.0 \n", "403 Dodge Rampage 32.0 4 135.0 84.0 \n", "404 Ford Ranger 28.0 4 120.0 79.0 \n", "405 Chevy S-10 31.0 4 119.0 82.0 \n", "\n", " Weight Acceleration Model Origin \n", "0 3504.0 12.0 70 US \n", "1 3693.0 11.5 70 US \n", "2 3436.0 11.0 70 US \n", "3 3433.0 12.0 70 US \n", "4 3449.0 10.5 70 US \n", ".. ... ... ... ... \n", "401 2790.0 15.6 82 US \n", "402 2130.0 24.6 82 Europe \n", "403 2295.0 11.6 82 US \n", "404 2625.0 18.6 82 US \n", "405 2720.0 19.4 82 US \n", "\n", "[406 rows x 9 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv = pd.read_csv('cars.csv', sep=';', skiprows=[1])\n", "cars_csv" ] }, { "cell_type": "code", "execution_count": 35, "id": "004afa87", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Car object\n", "MPG float64\n", "Cylinders int64\n", "Displacement float64\n", "Horsepower float64\n", "Weight float64\n", "Acceleration float64\n", "Model int64\n", "Origin object\n", "dtype: object" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.dtypes" ] }, { "cell_type": "code", "execution_count": 36, "id": "1a5b9726", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.empty # ispituje ima li praznih ćelija" ] }, { "cell_type": "code", "execution_count": 38, "id": "56fa4c04", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CarMPGCylindersDisplacementHorsepowerWeightAccelerationModelOrigin
0Chevrolet Chevelle Malibu18.08307.0130.03504.012.070US
1Buick Skylark 32015.08350.0165.03693.011.570US
2Plymouth Satellite18.08318.0150.03436.011.070US
3AMC Rebel SST16.08304.0150.03433.012.070US
4Ford Torino17.08302.0140.03449.010.570US
5Ford Galaxie 50015.08429.0198.04341.010.070US
6Chevrolet Impala14.08454.0220.04354.09.070US
7Plymouth Fury iii14.08440.0215.04312.08.570US
8Pontiac Catalina14.08455.0225.04425.010.070US
9AMC Ambassador DPL15.08390.0190.03850.08.570US
\n", "
" ], "text/plain": [ " Car MPG Cylinders Displacement Horsepower \\\n", "0 Chevrolet Chevelle Malibu 18.0 8 307.0 130.0 \n", "1 Buick Skylark 320 15.0 8 350.0 165.0 \n", "2 Plymouth Satellite 18.0 8 318.0 150.0 \n", "3 AMC Rebel SST 16.0 8 304.0 150.0 \n", "4 Ford Torino 17.0 8 302.0 140.0 \n", "5 Ford Galaxie 500 15.0 8 429.0 198.0 \n", "6 Chevrolet Impala 14.0 8 454.0 220.0 \n", "7 Plymouth Fury iii 14.0 8 440.0 215.0 \n", "8 Pontiac Catalina 14.0 8 455.0 225.0 \n", "9 AMC Ambassador DPL 15.0 8 390.0 190.0 \n", "\n", " Weight Acceleration Model Origin \n", "0 3504.0 12.0 70 US \n", "1 3693.0 11.5 70 US \n", "2 3436.0 11.0 70 US \n", "3 3433.0 12.0 70 US \n", "4 3449.0 10.5 70 US \n", "5 4341.0 10.0 70 US \n", "6 4354.0 9.0 70 US \n", "7 4312.0 8.5 70 US \n", "8 4425.0 10.0 70 US \n", "9 3850.0 8.5 70 US " ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.head(10)" ] }, { "cell_type": "code", "execution_count": 38, "id": "04d8f478", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CarMPGCylindersDisplacementHorsepowerWeightAccelerationModelOrigin
0Chevrolet Chevelle Malibu18.08307.0130.03504.012.070US
1Buick Skylark 32015.08350.0165.03693.011.570US
2Plymouth Satellite18.08318.0150.03436.011.070US
3AMC Rebel SST16.08304.0150.03433.012.070US
4Ford Torino17.08302.0140.03449.010.570US
5Ford Galaxie 50015.08429.0198.04341.010.070US
6Chevrolet Impala14.08454.0220.04354.09.070US
7Plymouth Fury iii14.08440.0215.04312.08.570US
8Pontiac Catalina14.08455.0225.04425.010.070US
9AMC Ambassador DPL15.08390.0190.03850.08.570US
\n", "
" ], "text/plain": [ " Car MPG Cylinders Displacement Horsepower \\\n", "0 Chevrolet Chevelle Malibu 18.0 8 307.0 130.0 \n", "1 Buick Skylark 320 15.0 8 350.0 165.0 \n", "2 Plymouth Satellite 18.0 8 318.0 150.0 \n", "3 AMC Rebel SST 16.0 8 304.0 150.0 \n", "4 Ford Torino 17.0 8 302.0 140.0 \n", "5 Ford Galaxie 500 15.0 8 429.0 198.0 \n", "6 Chevrolet Impala 14.0 8 454.0 220.0 \n", "7 Plymouth Fury iii 14.0 8 440.0 215.0 \n", "8 Pontiac Catalina 14.0 8 455.0 225.0 \n", "9 AMC Ambassador DPL 15.0 8 390.0 190.0 \n", "\n", " Weight Acceleration Model Origin \n", "0 3504.0 12.0 70 US \n", "1 3693.0 11.5 70 US \n", "2 3436.0 11.0 70 US \n", "3 3433.0 12.0 70 US \n", "4 3449.0 10.5 70 US \n", "5 4341.0 10.0 70 US \n", "6 4354.0 9.0 70 US \n", "7 4312.0 8.5 70 US \n", "8 4425.0 10.0 70 US \n", "9 3850.0 8.5 70 US " ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.head(10)" ] }, { "cell_type": "code", "execution_count": 40, "id": "11d000f5", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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CarMPGCylindersDisplacementHorsepowerWeightAccelerationModelOrigin
393Datsun 310 GX38.0491.067.01995.016.282Japan
394Buick Century Limited25.06181.0110.02945.016.482US
395Oldsmobile Cutlass Ciera (diesel)38.06262.085.03015.017.082US
396Chrysler Lebaron Medallion26.04156.092.02585.014.582US
397Ford Grenada l22.06232.0112.02835.014.782US
398Toyota Celica GT32.04144.096.02665.013.982Japan
399Dodge Charger 2.236.04135.084.02370.013.082US
400Chevrolet Camaro27.04151.090.02950.017.382US
401Ford Mustang GL27.04140.086.02790.015.682US
402Volkswagen Pickup44.0497.052.02130.024.682Europe
403Dodge Rampage32.04135.084.02295.011.682US
404Ford Ranger28.04120.079.02625.018.682US
405Chevy S-1031.04119.082.02720.019.482US
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" ], "text/plain": [ " Car MPG Cylinders Displacement \\\n", "393 Datsun 310 GX 38.0 4 91.0 \n", "394 Buick Century Limited 25.0 6 181.0 \n", "395 Oldsmobile Cutlass Ciera (diesel) 38.0 6 262.0 \n", "396 Chrysler Lebaron Medallion 26.0 4 156.0 \n", "397 Ford Grenada l 22.0 6 232.0 \n", "398 Toyota Celica GT 32.0 4 144.0 \n", "399 Dodge Charger 2.2 36.0 4 135.0 \n", "400 Chevrolet Camaro 27.0 4 151.0 \n", "401 Ford Mustang GL 27.0 4 140.0 \n", "402 Volkswagen Pickup 44.0 4 97.0 \n", "403 Dodge Rampage 32.0 4 135.0 \n", "404 Ford Ranger 28.0 4 120.0 \n", "405 Chevy S-10 31.0 4 119.0 \n", "\n", " Horsepower Weight Acceleration Model Origin \n", "393 67.0 1995.0 16.2 82 Japan \n", "394 110.0 2945.0 16.4 82 US \n", "395 85.0 3015.0 17.0 82 US \n", "396 92.0 2585.0 14.5 82 US \n", "397 112.0 2835.0 14.7 82 US \n", "398 96.0 2665.0 13.9 82 Japan \n", "399 84.0 2370.0 13.0 82 US \n", "400 90.0 2950.0 17.3 82 US \n", "401 86.0 2790.0 15.6 82 US \n", "402 52.0 2130.0 24.6 82 Europe \n", "403 84.0 2295.0 11.6 82 US \n", "404 79.0 2625.0 18.6 82 US \n", "405 82.0 2720.0 19.4 82 US " ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.tail(13)" ] }, { "cell_type": "code", "execution_count": 41, "id": "b50484cd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(406, 9)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.shape" ] }, { "cell_type": "code", "execution_count": 42, "id": "093098e1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 406 entries, 0 to 405\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Car 406 non-null object \n", " 1 MPG 406 non-null float64\n", " 2 Cylinders 406 non-null int64 \n", " 3 Displacement 406 non-null float64\n", " 4 Horsepower 406 non-null float64\n", " 5 Weight 406 non-null float64\n", " 6 Acceleration 406 non-null float64\n", " 7 Model 406 non-null int64 \n", " 8 Origin 406 non-null object \n", "dtypes: float64(5), int64(2), object(2)\n", "memory usage: 28.7+ KB\n" ] } ], "source": [ "cars_csv.info()" ] }, { "cell_type": "code", "execution_count": 44, "id": "f7fcfa7d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['AMC Rebel SST', 16.0, 8, 304.0, 150.0, 3433.0, 12.0, 70, 'US'],\n", " dtype=object)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.values[3]" ] }, { "cell_type": "code", "execution_count": 45, "id": "85525192", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CarMPGCylindersDisplacementHorsepowerWeightAccelerationModelOrigin
0Chevrolet Chevelle Malibu18.08307.0130.03504.012.070US
1Buick Skylark 32015.08350.0165.03693.011.570US
2Plymouth Satellite18.08318.0150.03436.011.070US
3AMC Rebel SST16.08304.0150.03433.012.070US
4Ford Torino17.08302.0140.03449.010.570US
5Ford Galaxie 50015.08429.0198.04341.010.070US
\n", "
" ], "text/plain": [ " Car MPG Cylinders Displacement Horsepower \\\n", "0 Chevrolet Chevelle Malibu 18.0 8 307.0 130.0 \n", "1 Buick Skylark 320 15.0 8 350.0 165.0 \n", "2 Plymouth Satellite 18.0 8 318.0 150.0 \n", "3 AMC Rebel SST 16.0 8 304.0 150.0 \n", "4 Ford Torino 17.0 8 302.0 140.0 \n", "5 Ford Galaxie 500 15.0 8 429.0 198.0 \n", "\n", " Weight Acceleration Model Origin \n", "0 3504.0 12.0 70 US \n", "1 3693.0 11.5 70 US \n", "2 3436.0 11.0 70 US \n", "3 3433.0 12.0 70 US \n", "4 3449.0 10.5 70 US \n", "5 4341.0 10.0 70 US " ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv[:6:] # dohvat prvih 5, kao i metoda head()" ] }, { "cell_type": "code", "execution_count": 47, "id": "69e0c6b1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CarMPGCylindersDisplacementHorsepowerWeightAccelerationModelOrigin
3AMC Rebel SST16.08304.0150.03433.012.070US
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" ], "text/plain": [ " Car MPG Cylinders Displacement Horsepower Weight \\\n", "3 AMC Rebel SST 16.0 8 304.0 150.0 3433.0 \n", "\n", " Acceleration Model Origin \n", "3 12.0 70 US " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv[3:4]" ] }, { "cell_type": "markdown", "id": "775e4867", "metadata": {}, "source": [ "Datoteku _airbnb_new_york_city_listings.csv_ pročitati kao DataFrame \n", "ispisati stupce, tipove podataka\n", "+ pretvoriti podatke u stupcu _price_ u float64\n", "+ izdvojiti retke od 5000 do 10000\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "9a9cc0a0", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 7, "id": "9d75156c", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'airbnb_new_york_city_listings' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m listings_df\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mread_csv(airbnb_new_york_city_listings\u001b[38;5;241m.\u001b[39mcsv)\n\u001b[0;32m 2\u001b[0m listings_df\n", "\u001b[1;31mNameError\u001b[0m: name 'airbnb_new_york_city_listings' is not defined" ] } ], "source": [ "listings_df=pd.read_csv(airbnb_new_york_city_listings.csv)\n", "listings_df" ] }, { "cell_type": "code", "execution_count": 5, "id": "19cdddd9-f23d-49ac-9b4d-42a5ded82f9d", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'listings_df' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m listings_df\u001b[38;5;241m.\u001b[39mdtypes()\n", "\u001b[1;31mNameError\u001b[0m: name 'listings_df' is not defined" ] } ], "source": [ "listings_df.dtypes()" ] }, { "cell_type": "code", "execution_count": null, "id": "e5033655-fbd6-4b61-84df-865d2cdb1998", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 }