{ "cells": [ { "cell_type": "code", "execution_count": 66, "id": "8fdca690", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 67, "id": "0bf6d2e0", "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": 68, "id": "f4ba0ad0", "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": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi = pd.DataFrame(proizvodi_baza)\n", "proizvodi_svi" ] }, { "cell_type": "code", "execution_count": 69, "id": "1b6879e8", "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": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_dio = pd.DataFrame(proizvodi_baza, columns=['Naziv','Cijena'])\n", "proizvodi_dio" ] }, { "cell_type": "code", "execution_count": 70, "id": "fb4af5ea", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Naziv', 'Cijena', 'Kategorija', 'Ocjena'], dtype='object')" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi.columns" ] }, { "cell_type": "code", "execution_count": 71, "id": "6f705122", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Naziv', 'Cijena'], dtype='object')" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_dio.columns" ] }, { "cell_type": "code", "execution_count": 72, "id": "9da6442e", "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": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi.values" ] }, { "cell_type": "code", "execution_count": 73, "id": "08b27c02", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Trek', 7699.0, 'Bicikl', 6.7], dtype=object)" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi.values[0]" ] }, { "cell_type": "code", "execution_count": 74, "id": "4659a30b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7699.0" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi.values[0][1]" ] }, { "cell_type": "code", "execution_count": 75, "id": "98c0c56b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RangeIndex(start=0, stop=12, step=1)" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi.index" ] }, { "cell_type": "code", "execution_count": 76, "id": "9ffe2239", "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": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi.index.name='Rbr'\n", "proizvodi_svi" ] }, { "cell_type": "code", "execution_count": 77, "id": "2fbfd0af", "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": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "proizvodi_svi['Ocjena']" ] }, { "cell_type": "code", "execution_count": 78, "id": "659e47a5", "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": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv = pd.read_csv('cars.csv', sep=';')\n", "cars_csv" ] }, { "cell_type": "code", "execution_count": 79, "id": "c7892157", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Car', 'MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n", " 'Acceleration', 'Model', 'Origin'],\n", " dtype='object')" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.columns" ] }, { "cell_type": "code", "execution_count": 80, "id": "1bf31a8b", "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": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.values" ] }, { "cell_type": "code", "execution_count": 81, "id": "46bd6b1c", "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": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecars_csv = pd.read_csv('e-cars.csv', sep=';')\n", "ecars_csv" ] }, { "cell_type": "code", "execution_count": 82, "id": "f0715dcc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RangeIndex(start=0, stop=407, step=1)" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.index" ] }, { "cell_type": "code", "execution_count": 83, "id": "9deb2066", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RangeIndex(start=0, stop=53, step=1)" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecars_csv.index" ] }, { "cell_type": "code", "execution_count": 84, "id": "a0976952", "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": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecars_csv.columns" ] }, { "cell_type": "code", "execution_count": 85, "id": "39976834", "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": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecars_csv.dtypes" ] }, { "cell_type": "code", "execution_count": 86, "id": "87c57f1c", "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": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.dtypes" ] }, { "cell_type": "code", "execution_count": 87, "id": "30c85bbc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['STRING', 'DOUBLE', 'INT', 'DOUBLE', 'DOUBLE', 'DOUBLE', 'DOUBLE',\n", " 'INT', 'CAT'], dtype=object)" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.values[0]" ] }, { "cell_type": "code", "execution_count": 88, "id": "6027573c", "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": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ecars_csv.values[0]" ] }, { "cell_type": "code", "execution_count": 91, "id": "deaa9995", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "could not convert string to float: 'DOUBLE'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[91], 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", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:6643\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m 6637\u001b[0m results \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m 6638\u001b[0m ser\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m 6639\u001b[0m ]\n\u001b[0;32m 6641\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 6642\u001b[0m \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[1;32m-> 6643\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mastype(dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m 6644\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m 6645\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:430\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m 427\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[0;32m 428\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m--> 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m 431\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 432\u001b[0m dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[0;32m 433\u001b[0m copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[0;32m 434\u001b[0m errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m 435\u001b[0m using_cow\u001b[38;5;241m=\u001b[39musing_copy_on_write(),\n\u001b[0;32m 436\u001b[0m )\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:363\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[1;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[0;32m 361\u001b[0m applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 362\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 363\u001b[0m applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(b, f)(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 364\u001b[0m result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[0;32m 366\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:758\u001b[0m, in \u001b[0;36mBlock.astype\u001b[1;34m(self, dtype, copy, errors, using_cow, squeeze)\u001b[0m\n\u001b[0;32m 755\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan not squeeze with more than one column.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 756\u001b[0m values \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m0\u001b[39m, :] \u001b[38;5;66;03m# type: ignore[call-overload]\u001b[39;00m\n\u001b[1;32m--> 758\u001b[0m new_values \u001b[38;5;241m=\u001b[39m astype_array_safe(values, dtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m 760\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[0;32m 762\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:237\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[1;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[0;32m 234\u001b[0m dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[0;32m 236\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 237\u001b[0m new_values \u001b[38;5;241m=\u001b[39m astype_array(values, dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m 238\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[0;32m 239\u001b[0m \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[0;32m 240\u001b[0m \u001b[38;5;66;03m# trying to convert to float\u001b[39;00m\n\u001b[0;32m 241\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:182\u001b[0m, in \u001b[0;36mastype_array\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m 179\u001b[0m values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m 181\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 182\u001b[0m values \u001b[38;5;241m=\u001b[39m _astype_nansafe(values, dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m 184\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[0;32m 185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:133\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m 129\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[0;32m 131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;129;01mor\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m dtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[0;32m 132\u001b[0m \u001b[38;5;66;03m# Explicit copy, or required since NumPy can't view from / to object.\u001b[39;00m\n\u001b[1;32m--> 133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n", "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'DOUBLE'" ] } ], "source": [ "cars_csv['MPG'] = cars_csv['MPG'].astype('float64')" ] }, { "cell_type": "code", "execution_count": 90, "id": "79a758e9", "metadata": {}, "outputs": [], "source": [ "cars_df = cars_csv.drop([0]) # brišemo prvi redak" ] }, { "cell_type": "code", "execution_count": 92, "id": "dbda3a45", "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": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_df.dtypes" ] }, { "cell_type": "code", "execution_count": 93, "id": "e28f33f0", "metadata": {}, "outputs": [], "source": [ "cars_df['MPG'] = cars_df['MPG'].astype('float64')" ] }, { "cell_type": "code", "execution_count": 94, "id": "925bc558", "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": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_df.dtypes" ] }, { "cell_type": "code", "execution_count": 95, "id": "41e37c95", "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": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_df['Cylinders'] = cars_df['Cylinders'].astype('int64')\n", "cars_df.dtypes" ] }, { "cell_type": "code", "execution_count": 96, "id": "e5089af1", "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
\n", "

406 rows × 9 columns

\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", ".. ... ... ... ... ... \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": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv = pd.read_csv('cars.csv', sep=';', skiprows=[1])\n", "cars_csv" ] }, { "cell_type": "code", "execution_count": 97, "id": "1a8dfa36", "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": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.dtypes" ] }, { "cell_type": "code", "execution_count": 98, "id": "d58e2a84", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.empty # ispituje ima li praznih ćelija" ] }, { "cell_type": "code", "execution_count": 99, "id": "d2b11cfa", "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": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.head(10)" ] }, { "cell_type": "code", "execution_count": 100, "id": "4d19800d", "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": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.head(10)" ] }, { "cell_type": "code", "execution_count": 101, "id": "4b4bd6f5", "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
\n", "
" ], "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": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.tail(13)" ] }, { "cell_type": "code", "execution_count": 102, "id": "c27e8bb4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(406, 9)" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.shape" ] }, { "cell_type": "code", "execution_count": 103, "id": "61ac3658", "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": 104, "id": "b22c7ab0", "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": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv.values[3]" ] }, { "cell_type": "code", "execution_count": 105, "id": "5c2fbaad", "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
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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", "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": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv[:6:] # dohvat prvih 5, kao i metoda head()" ] }, { "cell_type": "code", "execution_count": 106, "id": "052a6613", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars_csv[3:4]" ] }, { "cell_type": "markdown", "id": "75568cef", "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": 107, "id": "2a65b41c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
02595Skylit Midtown Castle2845JenniferManhattanMidtown40.753560-73.985590Entire home/apt15030482019-11-040.343341
13831Whole flr w/private bdrm, bath & kitchen(pls r...4869LisaRoxanneBrooklynBedford-Stuyvesant40.684940-73.957650Entire home/apt7514082021-06-295.091212
25121BlissArtsSpace!7356GaronBrooklynBedford-Stuyvesant40.685350-73.955120Private room6030502016-06-050.551365
35136Spacious Brooklyn Duplex, Patio + Garden7378RebeccaBrooklynSunset Park40.662650-73.994540Entire home/apt275512014-01-020.011184
45178Large Furnished Room Near B'way8967ShunichiManhattanMidtown40.764570-73.983170Private room6124852021-07-183.631255
...................................................
3671951447515Gorgeous 2 Bedroom - in Prime Midtown East51589519AsiManhattanMidtown40.755787-73.965126Entire home/apt255310NaNNaN6364
3672051449962Charming UWS 1-bdrm, 1-office, 1/2 block to Ct...2971741Dina MarieManhattanUpper West Side40.787029-73.969239Entire home/apt30650NaNNaN116
3672151450816The Hunter IIII61391963Stay With VibeManhattanUpper East Side40.768950-73.960455Entire home/apt63300NaNNaN96334
3672251451029Rockaway Beach Surf Getaway10123226ChaseQueensArverne40.599257-73.797953Private room7510NaNNaN187
3672351451368Nyc apt in the middle in nyc17770287NinaManhattanMurray Hill40.748655-73.981209Entire home/apt115300NaNNaN11364
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36724 rows × 16 columns

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" ], "text/plain": [ " id name host_id \\\n", "0 2595 Skylit Midtown Castle 2845 \n", "1 3831 Whole flr w/private bdrm, bath & kitchen(pls r... 4869 \n", "2 5121 BlissArtsSpace! 7356 \n", "3 5136 Spacious Brooklyn Duplex, Patio + Garden 7378 \n", "4 5178 Large Furnished Room Near B'way　 8967 \n", "... ... ... ... \n", "36719 51447515 Gorgeous 2 Bedroom - in Prime Midtown East 51589519 \n", "36720 51449962 Charming UWS 1-bdrm, 1-office, 1/2 block to Ct... 2971741 \n", "36721 51450816 The Hunter IIII 61391963 \n", "36722 51451029 Rockaway Beach Surf Getaway 10123226 \n", "36723 51451368 Nyc apt in the middle in nyc 17770287 \n", "\n", " host_name neighbourhood_group neighbourhood latitude \\\n", "0 Jennifer Manhattan Midtown 40.753560 \n", "1 LisaRoxanne Brooklyn Bedford-Stuyvesant 40.684940 \n", "2 Garon Brooklyn Bedford-Stuyvesant 40.685350 \n", "3 Rebecca Brooklyn Sunset Park 40.662650 \n", "4 Shunichi Manhattan Midtown 40.764570 \n", "... ... ... ... ... \n", "36719 Asi Manhattan Midtown 40.755787 \n", "36720 Dina Marie Manhattan Upper West Side 40.787029 \n", "36721 Stay With Vibe Manhattan Upper East Side 40.768950 \n", "36722 Chase Queens Arverne 40.599257 \n", "36723 Nina Manhattan Murray Hill 40.748655 \n", "\n", " longitude room_type price minimum_nights number_of_reviews \\\n", "0 -73.985590 Entire home/apt 150 30 48 \n", "1 -73.957650 Entire home/apt 75 1 408 \n", "2 -73.955120 Private room 60 30 50 \n", "3 -73.994540 Entire home/apt 275 5 1 \n", "4 -73.983170 Private room 61 2 485 \n", "... ... ... ... ... ... \n", "36719 -73.965126 Entire home/apt 255 31 0 \n", "36720 -73.969239 Entire home/apt 306 5 0 \n", "36721 -73.960455 Entire home/apt 63 30 0 \n", "36722 -73.797953 Private room 75 1 0 \n", "36723 -73.981209 Entire home/apt 115 30 0 \n", "\n", " last_review reviews_per_month calculated_host_listings_count \\\n", "0 2019-11-04 0.34 3 \n", "1 2021-06-29 5.09 1 \n", "2 2016-06-05 0.55 1 \n", "3 2014-01-02 0.01 1 \n", "4 2021-07-18 3.63 1 \n", "... ... ... ... \n", "36719 NaN NaN 6 \n", "36720 NaN NaN 1 \n", "36721 NaN NaN 96 \n", "36722 NaN NaN 1 \n", "36723 NaN NaN 11 \n", "\n", " availability_365 \n", "0 341 \n", "1 212 \n", "2 365 \n", "3 184 \n", "4 255 \n", "... ... \n", "36719 364 \n", "36720 16 \n", "36721 334 \n", "36722 87 \n", "36723 364 \n", "\n", "[36724 rows x 16 columns]" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df = pd.read_csv('airbnb_new_york_city_listings.csv')\n", "listings_df" ] }, { "cell_type": "code", "execution_count": 108, "id": "21abd487", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['id', 'name', 'host_id', 'host_name', 'neighbourhood_group',\n", " 'neighbourhood', 'latitude', 'longitude', 'room_type', 'price',\n", " 'minimum_nights', 'number_of_reviews', 'last_review',\n", " 'reviews_per_month', 'calculated_host_listings_count',\n", " 'availability_365'],\n", " dtype='object')" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df.columns" ] }, { "cell_type": "code", "execution_count": 109, "id": "eea0a3b1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id int64\n", "name object\n", "host_id int64\n", "host_name object\n", "neighbourhood_group object\n", "neighbourhood object\n", "latitude float64\n", "longitude float64\n", "room_type object\n", "price int64\n", "minimum_nights int64\n", "number_of_reviews int64\n", "last_review object\n", "reviews_per_month float64\n", "calculated_host_listings_count int64\n", "availability_365 int64\n", "dtype: object" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df.dtypes" ] }, { "cell_type": "code", "execution_count": 110, "id": "25f7365f", "metadata": {}, "outputs": [], "source": [ "listings_df['price'] = listings_df['price'].astype('float64')" ] }, { "cell_type": "code", "execution_count": 111, "id": "86105bba", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id int64\n", "name object\n", "host_id int64\n", "host_name object\n", "neighbourhood_group object\n", "neighbourhood object\n", "latitude float64\n", "longitude float64\n", "room_type object\n", "price float64\n", "minimum_nights int64\n", "number_of_reviews int64\n", "last_review object\n", "reviews_per_month float64\n", "calculated_host_listings_count int64\n", "availability_365 int64\n", "dtype: object" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df.dtypes" ] }, { "cell_type": "code", "execution_count": 112, "id": "52ef94d2", "metadata": {}, "outputs": [], "source": [ "listings_df['last_review'] = listings_df['last_review'].astype('datetime64[ns]')" ] }, { "cell_type": "code", "execution_count": 113, "id": "2d88d55d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id int64\n", "name object\n", "host_id int64\n", "host_name object\n", "neighbourhood_group object\n", "neighbourhood object\n", "latitude float64\n", "longitude float64\n", "room_type object\n", "price float64\n", "minimum_nights int64\n", "number_of_reviews int64\n", "last_review datetime64[ns]\n", "reviews_per_month float64\n", "calculated_host_listings_count int64\n", "availability_365 int64\n", "dtype: object" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df.dtypes" ] }, { "cell_type": "code", "execution_count": 114, "id": "33e159a1", "metadata": {}, "outputs": [], "source": [ "listings_df['last_review'] = pd.to_datetime(listings_df['last_review'])" ] }, { "cell_type": "code", "execution_count": 115, "id": "93c4cf28", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
02595Skylit Midtown Castle2845JenniferManhattanMidtown40.753560-73.985590Entire home/apt150.030482019-11-040.343341
13831Whole flr w/private bdrm, bath & kitchen(pls r...4869LisaRoxanneBrooklynBedford-Stuyvesant40.684940-73.957650Entire home/apt75.014082021-06-295.091212
25121BlissArtsSpace!7356GaronBrooklynBedford-Stuyvesant40.685350-73.955120Private room60.030502016-06-050.551365
35136Spacious Brooklyn Duplex, Patio + Garden7378RebeccaBrooklynSunset Park40.662650-73.994540Entire home/apt275.0512014-01-020.011184
45178Large Furnished Room Near B'way8967ShunichiManhattanMidtown40.764570-73.983170Private room61.024852021-07-183.631255
...................................................
3671951447515Gorgeous 2 Bedroom - in Prime Midtown East51589519AsiManhattanMidtown40.755787-73.965126Entire home/apt255.0310NaTNaN6364
3672051449962Charming UWS 1-bdrm, 1-office, 1/2 block to Ct...2971741Dina MarieManhattanUpper West Side40.787029-73.969239Entire home/apt306.050NaTNaN116
3672151450816The Hunter IIII61391963Stay With VibeManhattanUpper East Side40.768950-73.960455Entire home/apt63.0300NaTNaN96334
3672251451029Rockaway Beach Surf Getaway10123226ChaseQueensArverne40.599257-73.797953Private room75.010NaTNaN187
3672351451368Nyc apt in the middle in nyc17770287NinaManhattanMurray Hill40.748655-73.981209Entire home/apt115.0300NaTNaN11364
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36724 rows × 16 columns

\n", "
" ], "text/plain": [ " id name host_id \\\n", "0 2595 Skylit Midtown Castle 2845 \n", "1 3831 Whole flr w/private bdrm, bath & kitchen(pls r... 4869 \n", "2 5121 BlissArtsSpace! 7356 \n", "3 5136 Spacious Brooklyn Duplex, Patio + Garden 7378 \n", "4 5178 Large Furnished Room Near B'way　 8967 \n", "... ... ... ... \n", "36719 51447515 Gorgeous 2 Bedroom - in Prime Midtown East 51589519 \n", "36720 51449962 Charming UWS 1-bdrm, 1-office, 1/2 block to Ct... 2971741 \n", "36721 51450816 The Hunter IIII 61391963 \n", "36722 51451029 Rockaway Beach Surf Getaway 10123226 \n", "36723 51451368 Nyc apt in the middle in nyc 17770287 \n", "\n", " host_name neighbourhood_group neighbourhood latitude \\\n", "0 Jennifer Manhattan Midtown 40.753560 \n", "1 LisaRoxanne Brooklyn Bedford-Stuyvesant 40.684940 \n", "2 Garon Brooklyn Bedford-Stuyvesant 40.685350 \n", "3 Rebecca Brooklyn Sunset Park 40.662650 \n", "4 Shunichi Manhattan Midtown 40.764570 \n", "... ... ... ... ... \n", "36719 Asi Manhattan Midtown 40.755787 \n", "36720 Dina Marie Manhattan Upper West Side 40.787029 \n", "36721 Stay With Vibe Manhattan Upper East Side 40.768950 \n", "36722 Chase Queens Arverne 40.599257 \n", "36723 Nina Manhattan Murray Hill 40.748655 \n", "\n", " longitude room_type price minimum_nights number_of_reviews \\\n", "0 -73.985590 Entire home/apt 150.0 30 48 \n", "1 -73.957650 Entire home/apt 75.0 1 408 \n", "2 -73.955120 Private room 60.0 30 50 \n", "3 -73.994540 Entire home/apt 275.0 5 1 \n", "4 -73.983170 Private room 61.0 2 485 \n", "... ... ... ... ... ... \n", "36719 -73.965126 Entire home/apt 255.0 31 0 \n", "36720 -73.969239 Entire home/apt 306.0 5 0 \n", "36721 -73.960455 Entire home/apt 63.0 30 0 \n", "36722 -73.797953 Private room 75.0 1 0 \n", "36723 -73.981209 Entire home/apt 115.0 30 0 \n", "\n", " last_review reviews_per_month calculated_host_listings_count \\\n", "0 2019-11-04 0.34 3 \n", "1 2021-06-29 5.09 1 \n", "2 2016-06-05 0.55 1 \n", "3 2014-01-02 0.01 1 \n", "4 2021-07-18 3.63 1 \n", "... ... ... ... \n", "36719 NaT NaN 6 \n", "36720 NaT NaN 1 \n", "36721 NaT NaN 96 \n", "36722 NaT NaN 1 \n", "36723 NaT NaN 11 \n", "\n", " availability_365 \n", "0 341 \n", "1 212 \n", "2 365 \n", "3 184 \n", "4 255 \n", "... ... \n", "36719 364 \n", "36720 16 \n", "36721 334 \n", "36722 87 \n", "36723 364 \n", "\n", "[36724 rows x 16 columns]" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df" ] }, { "cell_type": "code", "execution_count": 116, "id": "2a46e7ae", "metadata": {}, "outputs": [], "source": [ "listings_df['last_review_year'] = listings_df['last_review'].dt.year" ] }, { "cell_type": "code", "execution_count": 117, "id": "142cbe58", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id int64\n", "name object\n", "host_id int64\n", "host_name object\n", "neighbourhood_group object\n", "neighbourhood object\n", "latitude float64\n", "longitude float64\n", "room_type object\n", "price float64\n", "minimum_nights int64\n", "number_of_reviews int64\n", "last_review datetime64[ns]\n", "reviews_per_month float64\n", "calculated_host_listings_count int64\n", "availability_365 int64\n", "last_review_year float64\n", "dtype: object" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df.dtypes" ] }, { "cell_type": "code", "execution_count": 118, "id": "845445dc", "metadata": {}, "outputs": [ { "ename": "IntCastingNaNError", "evalue": "Cannot convert non-finite values (NA or inf) to integer", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIntCastingNaNError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[118], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m listings_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlast_review_year\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39mlistings_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlast_review_year\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mint\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 2\u001b[0m listings_df\u001b[38;5;241m.\u001b[39mdtypes\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:6643\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m 6637\u001b[0m results \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m 6638\u001b[0m ser\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m 6639\u001b[0m ]\n\u001b[0;32m 6641\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 6642\u001b[0m \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[1;32m-> 6643\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mastype(dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m 6644\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m 6645\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:430\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m 427\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[0;32m 428\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m--> 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m 431\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 432\u001b[0m dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[0;32m 433\u001b[0m copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[0;32m 434\u001b[0m errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m 435\u001b[0m using_cow\u001b[38;5;241m=\u001b[39musing_copy_on_write(),\n\u001b[0;32m 436\u001b[0m )\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:363\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[1;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[0;32m 361\u001b[0m applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 362\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 363\u001b[0m applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(b, f)(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 364\u001b[0m result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[0;32m 366\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:758\u001b[0m, in \u001b[0;36mBlock.astype\u001b[1;34m(self, dtype, copy, errors, using_cow, squeeze)\u001b[0m\n\u001b[0;32m 755\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan not squeeze with more than one column.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 756\u001b[0m values \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m0\u001b[39m, :] \u001b[38;5;66;03m# type: ignore[call-overload]\u001b[39;00m\n\u001b[1;32m--> 758\u001b[0m new_values \u001b[38;5;241m=\u001b[39m astype_array_safe(values, dtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m 760\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[0;32m 762\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:237\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[1;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[0;32m 234\u001b[0m dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[0;32m 236\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 237\u001b[0m new_values \u001b[38;5;241m=\u001b[39m astype_array(values, dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m 238\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[0;32m 239\u001b[0m \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[0;32m 240\u001b[0m \u001b[38;5;66;03m# trying to convert to float\u001b[39;00m\n\u001b[0;32m 241\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:182\u001b[0m, in \u001b[0;36mastype_array\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m 179\u001b[0m values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m 181\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 182\u001b[0m values \u001b[38;5;241m=\u001b[39m _astype_nansafe(values, dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m 184\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[0;32m 185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:101\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mensure_string_array(\n\u001b[0;32m 97\u001b[0m arr, skipna\u001b[38;5;241m=\u001b[39mskipna, convert_na_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 98\u001b[0m )\u001b[38;5;241m.\u001b[39mreshape(shape)\n\u001b[0;32m 100\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(arr\u001b[38;5;241m.\u001b[39mdtype, np\u001b[38;5;241m.\u001b[39mfloating) \u001b[38;5;129;01mand\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miu\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _astype_float_to_int_nansafe(arr, dtype, copy)\n\u001b[0;32m 103\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[0;32m 104\u001b[0m \u001b[38;5;66;03m# if we have a datetime/timedelta array of objects\u001b[39;00m\n\u001b[0;32m 105\u001b[0m \u001b[38;5;66;03m# then coerce to datetime64[ns] and use DatetimeArray.astype\u001b[39;00m\n\u001b[0;32m 107\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_np_dtype(dtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\dtypes\\astype.py:145\u001b[0m, in \u001b[0;36m_astype_float_to_int_nansafe\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m 141\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 142\u001b[0m \u001b[38;5;124;03mastype with a check preventing converting NaN to an meaningless integer value.\u001b[39;00m\n\u001b[0;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(values)\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m--> 145\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m IntCastingNaNError(\n\u001b[0;32m 146\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot convert non-finite values (NA or inf) to integer\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 147\u001b[0m )\n\u001b[0;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mu\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 149\u001b[0m \u001b[38;5;66;03m# GH#45151\u001b[39;00m\n\u001b[0;32m 150\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (values \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mall():\n", "\u001b[1;31mIntCastingNaNError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer" ] } ], "source": [ "listings_df['last_review_year']=listings_df['last_review_year'].astype('int')\n", "listings_df.dtypes" ] }, { "cell_type": "code", "execution_count": 56, "id": "cefcf5e2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df.empty" ] }, { "cell_type": "code", "execution_count": 57, "id": "d742a6b3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365last_review_year
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" ], "text/plain": [ " id name host_id host_name neighbourhood_group neighbourhood \\\n", "0 False False False False False False \n", "1 False False False False False False \n", "2 False False False False False False \n", "3 False False False False False False \n", "4 False False False False False False \n", "... ... ... ... ... ... ... \n", "36719 False False False False False False \n", "36720 False False False False False False \n", "36721 False False False False False False \n", "36722 False False False False False False \n", "36723 False False False False False False \n", "\n", " latitude longitude room_type price minimum_nights \\\n", "0 False False False False False \n", "1 False False False False False \n", "2 False False False False False \n", "3 False False False False False \n", "4 False False False False False \n", "... ... ... ... ... ... \n", "36719 False False False False False \n", "36720 False False False False False \n", "36721 False False False False False \n", "36722 False False False False False \n", "36723 False False False False False \n", "\n", " number_of_reviews last_review reviews_per_month \\\n", "0 False False False \n", "1 False False False \n", "2 False False False \n", "3 False False False \n", "4 False False False \n", "... ... ... ... \n", "36719 False True True \n", "36720 False True True \n", "36721 False True True \n", "36722 False True True \n", "36723 False True True \n", "\n", " calculated_host_listings_count availability_365 last_review_year \n", "0 False False False \n", "1 False False False \n", "2 False False False \n", "3 False False False \n", "4 False False False \n", "... ... ... ... \n", "36719 False False True \n", "36720 False False True \n", "36721 False False True \n", "36722 False False True \n", "36723 False False True \n", "\n", "[36724 rows x 17 columns]" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df.isnull()" ] }, { "cell_type": "code", "execution_count": 58, "id": "ee870279", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0\n", "name 13\n", "host_id 0\n", "host_name 23\n", "neighbourhood_group 0\n", "neighbourhood 0\n", "latitude 0\n", "longitude 0\n", "room_type 0\n", "price 0\n", "minimum_nights 0\n", "number_of_reviews 0\n", "last_review 9415\n", "reviews_per_month 9415\n", "calculated_host_listings_count 0\n", "availability_365 0\n", "last_review_year 9415\n", "dtype: int64" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df.isnull().sum()" ] }, { "cell_type": "markdown", "id": "d9de51c7", "metadata": {}, "source": [ "**Popunjavanje praznih ćelija** \n", "+ 'name': 'No Name Listed'\n", "+ 'host_name': 'J. Doe'\n", "+ 'last_review': 1970-01-01 (Početak UNIX vremena)\n", "+ 'reviews_per_month': 0.0\n", "+ 'last_review_year': 0.0" ] }, { "cell_type": "code", "execution_count": 59, "id": "b5a4c3c2", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\hyperv\\AppData\\Local\\Temp\\ipykernel_3104\\239689127.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " listings_df['name'].fillna('No Name Listed', inplace=True)\n", "C:\\Users\\hyperv\\AppData\\Local\\Temp\\ipykernel_3104\\239689127.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " listings_df['host_name'].fillna('J. Doe', inplace=True)\n", "C:\\Users\\hyperv\\AppData\\Local\\Temp\\ipykernel_3104\\239689127.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " listings_df['reviews_per_month'].fillna(0.0, inplace=True)\n" ] } ], "source": [ "listings_df['name'].fillna('No Name Listed', inplace=True)\n", "listings_df['host_name'].fillna('J. Doe', inplace=True)\n", "listings_df['last_review'].fillna('1970-01-01', inplace=True)\n", "listings_df['reviews_per_month'].fillna(0.0, inplace=True)\n", "listings_df['last_review_year'].fillna(0.0, inplace=True)" ] }, { "cell_type": "code", "execution_count": 60, "id": "4d79f7a1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365last_review_year
02595Skylit Midtown Castle2845JenniferManhattanMidtown40.753560-73.985590Entire home/apt150.030482019-11-040.3433412019.0
13831Whole flr w/private bdrm, bath & kitchen(pls r...4869LisaRoxanneBrooklynBedford-Stuyvesant40.684940-73.957650Entire home/apt75.014082021-06-295.0912122021.0
25121BlissArtsSpace!7356GaronBrooklynBedford-Stuyvesant40.685350-73.955120Private room60.030502016-06-050.5513652016.0
35136Spacious Brooklyn Duplex, Patio + Garden7378RebeccaBrooklynSunset Park40.662650-73.994540Entire home/apt275.0512014-01-020.0111842014.0
45178Large Furnished Room Near B'way8967ShunichiManhattanMidtown40.764570-73.983170Private room61.024852021-07-183.6312552021.0
......................................................
3671951447515Gorgeous 2 Bedroom - in Prime Midtown East51589519AsiManhattanMidtown40.755787-73.965126Entire home/apt255.03101970-01-010.0063640.0
3672051449962Charming UWS 1-bdrm, 1-office, 1/2 block to Ct...2971741Dina MarieManhattanUpper West Side40.787029-73.969239Entire home/apt306.0501970-01-010.001160.0
3672151450816The Hunter IIII61391963Stay With VibeManhattanUpper East Side40.768950-73.960455Entire home/apt63.03001970-01-010.00963340.0
3672251451029Rockaway Beach Surf Getaway10123226ChaseQueensArverne40.599257-73.797953Private room75.0101970-01-010.001870.0
3672351451368Nyc apt in the middle in nyc17770287NinaManhattanMurray Hill40.748655-73.981209Entire home/apt115.03001970-01-010.00113640.0
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36724 rows × 17 columns

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" ], "text/plain": [ " id name host_id \\\n", "0 2595 Skylit Midtown Castle 2845 \n", "1 3831 Whole flr w/private bdrm, bath & kitchen(pls r... 4869 \n", "2 5121 BlissArtsSpace! 7356 \n", "3 5136 Spacious Brooklyn Duplex, Patio + Garden 7378 \n", "4 5178 Large Furnished Room Near B'way　 8967 \n", "... ... ... ... \n", "36719 51447515 Gorgeous 2 Bedroom - in Prime Midtown East 51589519 \n", "36720 51449962 Charming UWS 1-bdrm, 1-office, 1/2 block to Ct... 2971741 \n", "36721 51450816 The Hunter IIII 61391963 \n", "36722 51451029 Rockaway Beach Surf Getaway 10123226 \n", "36723 51451368 Nyc apt in the middle in nyc 17770287 \n", "\n", " host_name neighbourhood_group neighbourhood latitude \\\n", "0 Jennifer Manhattan Midtown 40.753560 \n", "1 LisaRoxanne Brooklyn Bedford-Stuyvesant 40.684940 \n", "2 Garon Brooklyn Bedford-Stuyvesant 40.685350 \n", "3 Rebecca Brooklyn Sunset Park 40.662650 \n", "4 Shunichi Manhattan Midtown 40.764570 \n", "... ... ... ... ... \n", "36719 Asi Manhattan Midtown 40.755787 \n", "36720 Dina Marie Manhattan Upper West Side 40.787029 \n", "36721 Stay With Vibe Manhattan Upper East Side 40.768950 \n", "36722 Chase Queens Arverne 40.599257 \n", "36723 Nina Manhattan Murray Hill 40.748655 \n", "\n", " longitude room_type price minimum_nights number_of_reviews \\\n", "0 -73.985590 Entire home/apt 150.0 30 48 \n", "1 -73.957650 Entire home/apt 75.0 1 408 \n", "2 -73.955120 Private room 60.0 30 50 \n", "3 -73.994540 Entire home/apt 275.0 5 1 \n", "4 -73.983170 Private room 61.0 2 485 \n", "... ... ... ... ... ... \n", "36719 -73.965126 Entire home/apt 255.0 31 0 \n", "36720 -73.969239 Entire home/apt 306.0 5 0 \n", "36721 -73.960455 Entire home/apt 63.0 30 0 \n", "36722 -73.797953 Private room 75.0 1 0 \n", "36723 -73.981209 Entire home/apt 115.0 30 0 \n", "\n", " last_review reviews_per_month calculated_host_listings_count \\\n", "0 2019-11-04 0.34 3 \n", "1 2021-06-29 5.09 1 \n", "2 2016-06-05 0.55 1 \n", "3 2014-01-02 0.01 1 \n", "4 2021-07-18 3.63 1 \n", "... ... ... ... \n", "36719 1970-01-01 0.00 6 \n", "36720 1970-01-01 0.00 1 \n", "36721 1970-01-01 0.00 96 \n", "36722 1970-01-01 0.00 1 \n", "36723 1970-01-01 0.00 11 \n", "\n", " availability_365 last_review_year \n", "0 341 2019.0 \n", "1 212 2021.0 \n", "2 365 2016.0 \n", "3 184 2014.0 \n", "4 255 2021.0 \n", "... ... ... \n", "36719 364 0.0 \n", "36720 16 0.0 \n", "36721 334 0.0 \n", "36722 87 0.0 \n", "36723 364 0.0 \n", "\n", "[36724 rows x 17 columns]" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df" ] }, { "cell_type": "code", "execution_count": 61, "id": "40e290cd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0\n", "name 0\n", "host_id 0\n", "host_name 0\n", "neighbourhood_group 0\n", "neighbourhood 0\n", "latitude 0\n", "longitude 0\n", "room_type 0\n", "price 0\n", "minimum_nights 0\n", "number_of_reviews 0\n", "last_review 0\n", "reviews_per_month 0\n", "calculated_host_listings_count 0\n", "availability_365 0\n", "last_review_year 0\n", "dtype: int64" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 62, "id": "58d83908", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id int64\n", "name object\n", "host_id int64\n", "host_name object\n", "neighbourhood_group object\n", "neighbourhood object\n", "latitude float64\n", "longitude float64\n", "room_type object\n", "price float64\n", "minimum_nights int64\n", "number_of_reviews int64\n", "last_review datetime64[ns]\n", "reviews_per_month float64\n", "calculated_host_listings_count int64\n", "availability_365 int64\n", "last_review_year int64\n", "dtype: object" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df['last_review_year']=listings_df['last_review_year'].astype('int64')\n", "listings_df.dtypes" ] }, { "cell_type": "code", "execution_count": 63, "id": "313250f8", "metadata": {}, "outputs": [], "source": [ "last_review_year = listings_df.pop('last_review_year')\n", "listings_df.insert(13, last_review_year.name, last_review_year)" ] }, { "cell_type": "code", "execution_count": 64, "id": "d61166b4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['id', 'name', 'host_id', 'host_name', 'neighbourhood_group',\n", " 'neighbourhood', 'latitude', 'longitude', 'room_type', 'price',\n", " 'minimum_nights', 'number_of_reviews', 'last_review',\n", " 'last_review_year', 'reviews_per_month',\n", " 'calculated_host_listings_count', 'availability_365'],\n", " dtype='object')" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df.columns" ] }, { "cell_type": "code", "execution_count": 65, "id": "8eed1f37", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewlast_review_yearreviews_per_monthcalculated_host_listings_countavailability_365
02595Skylit Midtown Castle2845JenniferManhattanMidtown40.753560-73.985590Entire home/apt150.030482019-11-0420190.343341
13831Whole flr w/private bdrm, bath & kitchen(pls r...4869LisaRoxanneBrooklynBedford-Stuyvesant40.684940-73.957650Entire home/apt75.014082021-06-2920215.091212
25121BlissArtsSpace!7356GaronBrooklynBedford-Stuyvesant40.685350-73.955120Private room60.030502016-06-0520160.551365
35136Spacious Brooklyn Duplex, Patio + Garden7378RebeccaBrooklynSunset Park40.662650-73.994540Entire home/apt275.0512014-01-0220140.011184
45178Large Furnished Room Near B'way8967ShunichiManhattanMidtown40.764570-73.983170Private room61.024852021-07-1820213.631255
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3671951447515Gorgeous 2 Bedroom - in Prime Midtown East51589519AsiManhattanMidtown40.755787-73.965126Entire home/apt255.03101970-01-0100.006364
3672051449962Charming UWS 1-bdrm, 1-office, 1/2 block to Ct...2971741Dina MarieManhattanUpper West Side40.787029-73.969239Entire home/apt306.0501970-01-0100.00116
3672151450816The Hunter IIII61391963Stay With VibeManhattanUpper East Side40.768950-73.960455Entire home/apt63.03001970-01-0100.0096334
3672251451029Rockaway Beach Surf Getaway10123226ChaseQueensArverne40.599257-73.797953Private room75.0101970-01-0100.00187
3672351451368Nyc apt in the middle in nyc17770287NinaManhattanMurray Hill40.748655-73.981209Entire home/apt115.03001970-01-0100.0011364
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36724 rows × 17 columns

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" ], "text/plain": [ " id name host_id \\\n", "0 2595 Skylit Midtown Castle 2845 \n", "1 3831 Whole flr w/private bdrm, bath & kitchen(pls r... 4869 \n", "2 5121 BlissArtsSpace! 7356 \n", "3 5136 Spacious Brooklyn Duplex, Patio + Garden 7378 \n", "4 5178 Large Furnished Room Near B'way　 8967 \n", "... ... ... ... \n", "36719 51447515 Gorgeous 2 Bedroom - in Prime Midtown East 51589519 \n", "36720 51449962 Charming UWS 1-bdrm, 1-office, 1/2 block to Ct... 2971741 \n", "36721 51450816 The Hunter IIII 61391963 \n", "36722 51451029 Rockaway Beach Surf Getaway 10123226 \n", "36723 51451368 Nyc apt in the middle in nyc 17770287 \n", "\n", " host_name neighbourhood_group neighbourhood latitude \\\n", "0 Jennifer Manhattan Midtown 40.753560 \n", "1 LisaRoxanne Brooklyn Bedford-Stuyvesant 40.684940 \n", "2 Garon Brooklyn Bedford-Stuyvesant 40.685350 \n", "3 Rebecca Brooklyn Sunset Park 40.662650 \n", "4 Shunichi Manhattan Midtown 40.764570 \n", "... ... ... ... ... \n", "36719 Asi Manhattan Midtown 40.755787 \n", "36720 Dina Marie Manhattan Upper West Side 40.787029 \n", "36721 Stay With Vibe Manhattan Upper East Side 40.768950 \n", "36722 Chase Queens Arverne 40.599257 \n", "36723 Nina Manhattan Murray Hill 40.748655 \n", "\n", " longitude room_type price minimum_nights number_of_reviews \\\n", "0 -73.985590 Entire home/apt 150.0 30 48 \n", "1 -73.957650 Entire home/apt 75.0 1 408 \n", "2 -73.955120 Private room 60.0 30 50 \n", "3 -73.994540 Entire home/apt 275.0 5 1 \n", "4 -73.983170 Private room 61.0 2 485 \n", "... ... ... ... ... ... \n", "36719 -73.965126 Entire home/apt 255.0 31 0 \n", "36720 -73.969239 Entire home/apt 306.0 5 0 \n", "36721 -73.960455 Entire home/apt 63.0 30 0 \n", "36722 -73.797953 Private room 75.0 1 0 \n", "36723 -73.981209 Entire home/apt 115.0 30 0 \n", "\n", " last_review last_review_year reviews_per_month \\\n", "0 2019-11-04 2019 0.34 \n", "1 2021-06-29 2021 5.09 \n", "2 2016-06-05 2016 0.55 \n", "3 2014-01-02 2014 0.01 \n", "4 2021-07-18 2021 3.63 \n", "... ... ... ... \n", "36719 1970-01-01 0 0.00 \n", "36720 1970-01-01 0 0.00 \n", "36721 1970-01-01 0 0.00 \n", "36722 1970-01-01 0 0.00 \n", "36723 1970-01-01 0 0.00 \n", "\n", " calculated_host_listings_count availability_365 \n", "0 3 341 \n", "1 1 212 \n", "2 1 365 \n", "3 1 184 \n", "4 1 255 \n", "... ... ... \n", "36719 6 364 \n", "36720 1 16 \n", "36721 96 334 \n", "36722 1 87 \n", "36723 11 364 \n", "\n", "[36724 rows x 17 columns]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listings_df " ] }, { "cell_type": "code", "execution_count": null, "id": "973b5e56", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "5a275e07-d903-4961-85c7-249f7960e71a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "6ab77520-deb9-4ebf-8bb9-4658dfb28294", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "a9dc2163-eb10-40b8-92ff-ed59cc90533e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "2d941ab6-2a80-4e19-9dce-bd840ddc1cb8", "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 }