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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"i=4\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "Missing parentheses in call to 'print'. Did you mean print(i)? (<ipython-input-2-9508ac0470ed>, line 1)",
"output_type": "error",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-2-9508ac0470ed>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m print i\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(i)?\n"
]
}
],
"source": [
"print i"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(i)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Import pandas \n",
"import pandas as pd\n",
"\n",
"# Read in white wine data \n",
"white = pd.read_csv(\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv\", sep=';')\n",
"\n",
"# Read in red wine data \n",
"red = pd.read_csv(\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\", sep=';')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "Missing parentheses in call to 'print'. Did you mean print(white)? (<ipython-input-9-718d705a5ff8>, line 1)",
"output_type": "error",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-9-718d705a5ff8>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m print white\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(white)?\n"
]
}
],
"source": [
"print white"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" fixed acidity volatile acidity citric acid residual sugar chlorides \\\n",
"0 7.0 0.270 0.36 20.70 0.045 \n",
"1 6.3 0.300 0.34 1.60 0.049 \n",
"2 8.1 0.280 0.40 6.90 0.050 \n",
"3 7.2 0.230 0.32 8.50 0.058 \n",
"4 7.2 0.230 0.32 8.50 0.058 \n",
"5 8.1 0.280 0.40 6.90 0.050 \n",
"6 6.2 0.320 0.16 7.00 0.045 \n",
"7 7.0 0.270 0.36 20.70 0.045 \n",
"8 6.3 0.300 0.34 1.60 0.049 \n",
"9 8.1 0.220 0.43 1.50 0.044 \n",
"10 8.1 0.270 0.41 1.45 0.033 \n",
"11 8.6 0.230 0.40 4.20 0.035 \n",
"12 7.9 0.180 0.37 1.20 0.040 \n",
"13 6.6 0.160 0.40 1.50 0.044 \n",
"14 8.3 0.420 0.62 19.25 0.040 \n",
"15 6.6 0.170 0.38 1.50 0.032 \n",
"16 6.3 0.480 0.04 1.10 0.046 \n",
"17 6.2 0.660 0.48 1.20 0.029 \n",
"18 7.4 0.340 0.42 1.10 0.033 \n",
"19 6.5 0.310 0.14 7.50 0.044 \n",
"20 6.2 0.660 0.48 1.20 0.029 \n",
"21 6.4 0.310 0.38 2.90 0.038 \n",
"22 6.8 0.260 0.42 1.70 0.049 \n",
"23 7.6 0.670 0.14 1.50 0.074 \n",
"24 6.6 0.270 0.41 1.30 0.052 \n",
"25 7.0 0.250 0.32 9.00 0.046 \n",
"26 6.9 0.240 0.35 1.00 0.052 \n",
"27 7.0 0.280 0.39 8.70 0.051 \n",
"28 7.4 0.270 0.48 1.10 0.047 \n",
"29 7.2 0.320 0.36 2.00 0.033 \n",
"... ... ... ... ... ... \n",
"4868 5.8 0.230 0.31 4.50 0.046 \n",
"4869 6.6 0.240 0.33 10.10 0.032 \n",
"4870 6.1 0.320 0.28 6.60 0.021 \n",
"4871 5.0 0.200 0.40 1.90 0.015 \n",
"4872 6.0 0.420 0.41 12.40 0.032 \n",
"4873 5.7 0.210 0.32 1.60 0.030 \n",
"4874 5.6 0.200 0.36 2.50 0.048 \n",
"4875 7.4 0.220 0.26 1.20 0.035 \n",
"4876 6.2 0.380 0.42 2.50 0.038 \n",
"4877 5.9 0.540 0.00 0.80 0.032 \n",
"4878 6.2 0.530 0.02 0.90 0.035 \n",
"4879 6.6 0.340 0.40 8.10 0.046 \n",
"4880 6.6 0.340 0.40 8.10 0.046 \n",
"4881 5.0 0.235 0.27 11.75 0.030 \n",
"4882 5.5 0.320 0.13 1.30 0.037 \n",
"4883 4.9 0.470 0.17 1.90 0.035 \n",
"4884 6.5 0.330 0.38 8.30 0.048 \n",
"4885 6.6 0.340 0.40 8.10 0.046 \n",
"4886 6.2 0.210 0.28 5.70 0.028 \n",
"4887 6.2 0.410 0.22 1.90 0.023 \n",
"4888 6.8 0.220 0.36 1.20 0.052 \n",
"4889 4.9 0.235 0.27 11.75 0.030 \n",
"4890 6.1 0.340 0.29 2.20 0.036 \n",
"4891 5.7 0.210 0.32 0.90 0.038 \n",
"4892 6.5 0.230 0.38 1.30 0.032 \n",
"4893 6.2 0.210 0.29 1.60 0.039 \n",
"4894 6.6 0.320 0.36 8.00 0.047 \n",
"4895 6.5 0.240 0.19 1.20 0.041 \n",
"4896 5.5 0.290 0.30 1.10 0.022 \n",
"4897 6.0 0.210 0.38 0.80 0.020 \n",
"\n",
" free sulfur dioxide total sulfur dioxide density pH sulphates \\\n",
"0 45.0 170.0 1.00100 3.00 0.45 \n",
"1 14.0 132.0 0.99400 3.30 0.49 \n",
"2 30.0 97.0 0.99510 3.26 0.44 \n",
"3 47.0 186.0 0.99560 3.19 0.40 \n",
"4 47.0 186.0 0.99560 3.19 0.40 \n",
"5 30.0 97.0 0.99510 3.26 0.44 \n",
"6 30.0 136.0 0.99490 3.18 0.47 \n",
"7 45.0 170.0 1.00100 3.00 0.45 \n",
"8 14.0 132.0 0.99400 3.30 0.49 \n",
"9 28.0 129.0 0.99380 3.22 0.45 \n",
"10 11.0 63.0 0.99080 2.99 0.56 \n",
"11 17.0 109.0 0.99470 3.14 0.53 \n",
"12 16.0 75.0 0.99200 3.18 0.63 \n",
"13 48.0 143.0 0.99120 3.54 0.52 \n",
"14 41.0 172.0 1.00020 2.98 0.67 \n",
"15 28.0 112.0 0.99140 3.25 0.55 \n",
"16 30.0 99.0 0.99280 3.24 0.36 \n",
"17 29.0 75.0 0.98920 3.33 0.39 \n",
"18 17.0 171.0 0.99170 3.12 0.53 \n",
"19 34.0 133.0 0.99550 3.22 0.50 \n",
"20 29.0 75.0 0.98920 3.33 0.39 \n",
"21 19.0 102.0 0.99120 3.17 0.35 \n",
"22 41.0 122.0 0.99300 3.47 0.48 \n",
"23 25.0 168.0 0.99370 3.05 0.51 \n",
"24 16.0 142.0 0.99510 3.42 0.47 \n",
"25 56.0 245.0 0.99550 3.25 0.50 \n",
"26 35.0 146.0 0.99300 3.45 0.44 \n",
"27 32.0 141.0 0.99610 3.38 0.53 \n",
"28 17.0 132.0 0.99140 3.19 0.49 \n",
"29 37.0 114.0 0.99060 3.10 0.71 \n",
"... ... ... ... ... ... \n",
"4868 42.0 124.0 0.99324 3.31 0.64 \n",
"4869 8.0 81.0 0.99626 3.19 0.51 \n",
"4870 29.0 132.0 0.99188 3.15 0.36 \n",
"4871 20.0 98.0 0.98970 3.37 0.55 \n",
"4872 50.0 179.0 0.99622 3.14 0.60 \n",
"4873 33.0 122.0 0.99044 3.33 0.52 \n",
"4874 16.0 125.0 0.99282 3.49 0.49 \n",
"4875 18.0 97.0 0.99245 3.12 0.41 \n",
"4876 34.0 117.0 0.99132 3.36 0.59 \n",
"4877 12.0 82.0 0.99286 3.25 0.36 \n",
"4878 6.0 81.0 0.99234 3.24 0.35 \n",
"4879 68.0 170.0 0.99494 3.15 0.50 \n",
"4880 68.0 170.0 0.99494 3.15 0.50 \n",
"4881 34.0 118.0 0.99540 3.07 0.50 \n",
"4882 45.0 156.0 0.99184 3.26 0.38 \n",
"4883 60.0 148.0 0.98964 3.27 0.35 \n",
"4884 68.0 174.0 0.99492 3.14 0.50 \n",
"4885 68.0 170.0 0.99494 3.15 0.50 \n",
"4886 45.0 121.0 0.99168 3.21 1.08 \n",
"4887 5.0 56.0 0.98928 3.04 0.79 \n",
"4888 38.0 127.0 0.99330 3.04 0.54 \n",
"4889 34.0 118.0 0.99540 3.07 0.50 \n",
"4890 25.0 100.0 0.98938 3.06 0.44 \n",
"4891 38.0 121.0 0.99074 3.24 0.46 \n",
"4892 29.0 112.0 0.99298 3.29 0.54 \n",
"4893 24.0 92.0 0.99114 3.27 0.50 \n",
"4894 57.0 168.0 0.99490 3.15 0.46 \n",
"4895 30.0 111.0 0.99254 2.99 0.46 \n",
"4896 20.0 110.0 0.98869 3.34 0.38 \n",
"4897 22.0 98.0 0.98941 3.26 0.32 \n",
"\n",
" alcohol quality \n",
"0 8.800000 6 \n",
"1 9.500000 6 \n",
"2 10.100000 6 \n",
"3 9.900000 6 \n",
"4 9.900000 6 \n",
"5 10.100000 6 \n",
"6 9.600000 6 \n",
"7 8.800000 6 \n",
"8 9.500000 6 \n",
"9 11.000000 6 \n",
"10 12.000000 5 \n",
"11 9.700000 5 \n",
"12 10.800000 5 \n",
"13 12.400000 7 \n",
"14 9.700000 5 \n",
"15 11.400000 7 \n",
"16 9.600000 6 \n",
"17 12.800000 8 \n",
"18 11.300000 6 \n",
"19 9.500000 5 \n",
"20 12.800000 8 \n",
"21 11.000000 7 \n",
"22 10.500000 8 \n",
"23 9.300000 5 \n",
"24 10.000000 6 \n",
"25 10.400000 6 \n",
"26 10.000000 6 \n",
"27 10.500000 6 \n",
"28 11.600000 6 \n",
"29 12.300000 7 \n",
"... ... ... \n",
"4868 10.800000 6 \n",
"4869 9.800000 6 \n",
"4870 11.450000 7 \n",
"4871 12.050000 6 \n",
"4872 9.700000 5 \n",
"4873 11.900000 6 \n",
"4874 10.000000 6 \n",
"4875 9.700000 6 \n",
"4876 11.600000 7 \n",
"4877 8.800000 5 \n",
"4878 9.500000 4 \n",
"4879 9.533333 6 \n",
"4880 9.533333 6 \n",
"4881 9.400000 6 \n",
"4882 10.700000 5 \n",
"4883 11.500000 6 \n",
"4884 9.600000 5 \n",
"4885 9.550000 6 \n",
"4886 12.150000 7 \n",
"4887 13.000000 7 \n",
"4888 9.200000 5 \n",
"4889 9.400000 6 \n",
"4890 11.800000 6 \n",
"4891 10.600000 6 \n",
"4892 9.700000 5 \n",
"4893 11.200000 6 \n",
"4894 9.600000 5 \n",
"4895 9.400000 6 \n",
"4896 12.800000 7 \n",
"4897 11.800000 6 \n",
"\n",
"[4898 rows x 12 columns]\n"
]
}
],
"source": [
"print(white)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" fixed acidity volatile acidity citric acid residual sugar chlorides \\\n",
"0 7.4 0.700 0.00 1.9 0.076 \n",
"1 7.8 0.880 0.00 2.6 0.098 \n",
"2 7.8 0.760 0.04 2.3 0.092 \n",
"3 11.2 0.280 0.56 1.9 0.075 \n",
"4 7.4 0.700 0.00 1.9 0.076 \n",
"5 7.4 0.660 0.00 1.8 0.075 \n",
"6 7.9 0.600 0.06 1.6 0.069 \n",
"7 7.3 0.650 0.00 1.2 0.065 \n",
"8 7.8 0.580 0.02 2.0 0.073 \n",
"9 7.5 0.500 0.36 6.1 0.071 \n",
"10 6.7 0.580 0.08 1.8 0.097 \n",
"11 7.5 0.500 0.36 6.1 0.071 \n",
"12 5.6 0.615 0.00 1.6 0.089 \n",
"13 7.8 0.610 0.29 1.6 0.114 \n",
"14 8.9 0.620 0.18 3.8 0.176 \n",
"15 8.9 0.620 0.19 3.9 0.170 \n",
"16 8.5 0.280 0.56 1.8 0.092 \n",
"17 8.1 0.560 0.28 1.7 0.368 \n",
"18 7.4 0.590 0.08 4.4 0.086 \n",
"19 7.9 0.320 0.51 1.8 0.341 \n",
"20 8.9 0.220 0.48 1.8 0.077 \n",
"21 7.6 0.390 0.31 2.3 0.082 \n",
"22 7.9 0.430 0.21 1.6 0.106 \n",
"23 8.5 0.490 0.11 2.3 0.084 \n",
"24 6.9 0.400 0.14 2.4 0.085 \n",
"25 6.3 0.390 0.16 1.4 0.080 \n",
"26 7.6 0.410 0.24 1.8 0.080 \n",
"27 7.9 0.430 0.21 1.6 0.106 \n",
"28 7.1 0.710 0.00 1.9 0.080 \n",
"29 7.8 0.645 0.00 2.0 0.082 \n",
"... ... ... ... ... ... \n",
"1569 6.2 0.510 0.14 1.9 0.056 \n",
"1570 6.4 0.360 0.53 2.2 0.230 \n",
"1571 6.4 0.380 0.14 2.2 0.038 \n",
"1572 7.3 0.690 0.32 2.2 0.069 \n",
"1573 6.0 0.580 0.20 2.4 0.075 \n",
"1574 5.6 0.310 0.78 13.9 0.074 \n",
"1575 7.5 0.520 0.40 2.2 0.060 \n",
"1576 8.0 0.300 0.63 1.6 0.081 \n",
"1577 6.2 0.700 0.15 5.1 0.076 \n",
"1578 6.8 0.670 0.15 1.8 0.118 \n",
"1579 6.2 0.560 0.09 1.7 0.053 \n",
"1580 7.4 0.350 0.33 2.4 0.068 \n",
"1581 6.2 0.560 0.09 1.7 0.053 \n",
"1582 6.1 0.715 0.10 2.6 0.053 \n",
"1583 6.2 0.460 0.29 2.1 0.074 \n",
"1584 6.7 0.320 0.44 2.4 0.061 \n",
"1585 7.2 0.390 0.44 2.6 0.066 \n",
"1586 7.5 0.310 0.41 2.4 0.065 \n",
"1587 5.8 0.610 0.11 1.8 0.066 \n",
"1588 7.2 0.660 0.33 2.5 0.068 \n",
"1589 6.6 0.725 0.20 7.8 0.073 \n",
"1590 6.3 0.550 0.15 1.8 0.077 \n",
"1591 5.4 0.740 0.09 1.7 0.089 \n",
"1592 6.3 0.510 0.13 2.3 0.076 \n",
"1593 6.8 0.620 0.08 1.9 0.068 \n",
"1594 6.2 0.600 0.08 2.0 0.090 \n",
"1595 5.9 0.550 0.10 2.2 0.062 \n",
"1596 6.3 0.510 0.13 2.3 0.076 \n",
"1597 5.9 0.645 0.12 2.0 0.075 \n",
"1598 6.0 0.310 0.47 3.6 0.067 \n",
"\n",
" free sulfur dioxide total sulfur dioxide density pH sulphates \\\n",
"0 11.0 34.0 0.99780 3.51 0.56 \n",
"1 25.0 67.0 0.99680 3.20 0.68 \n",
"2 15.0 54.0 0.99700 3.26 0.65 \n",
"3 17.0 60.0 0.99800 3.16 0.58 \n",
"4 11.0 34.0 0.99780 3.51 0.56 \n",
"5 13.0 40.0 0.99780 3.51 0.56 \n",
"6 15.0 59.0 0.99640 3.30 0.46 \n",
"7 15.0 21.0 0.99460 3.39 0.47 \n",
"8 9.0 18.0 0.99680 3.36 0.57 \n",
"9 17.0 102.0 0.99780 3.35 0.80 \n",
"10 15.0 65.0 0.99590 3.28 0.54 \n",
"11 17.0 102.0 0.99780 3.35 0.80 \n",
"12 16.0 59.0 0.99430 3.58 0.52 \n",
"13 9.0 29.0 0.99740 3.26 1.56 \n",
"14 52.0 145.0 0.99860 3.16 0.88 \n",
"15 51.0 148.0 0.99860 3.17 0.93 \n",
"16 35.0 103.0 0.99690 3.30 0.75 \n",
"17 16.0 56.0 0.99680 3.11 1.28 \n",
"18 6.0 29.0 0.99740 3.38 0.50 \n",
"19 17.0 56.0 0.99690 3.04 1.08 \n",
"20 29.0 60.0 0.99680 3.39 0.53 \n",
"21 23.0 71.0 0.99820 3.52 0.65 \n",
"22 10.0 37.0 0.99660 3.17 0.91 \n",
"23 9.0 67.0 0.99680 3.17 0.53 \n",
"24 21.0 40.0 0.99680 3.43 0.63 \n",
"25 11.0 23.0 0.99550 3.34 0.56 \n",
"26 4.0 11.0 0.99620 3.28 0.59 \n",
"27 10.0 37.0 0.99660 3.17 0.91 \n",
"28 14.0 35.0 0.99720 3.47 0.55 \n",
"29 8.0 16.0 0.99640 3.38 0.59 \n",
"... ... ... ... ... ... \n",
"1569 15.0 34.0 0.99396 3.48 0.57 \n",
"1570 19.0 35.0 0.99340 3.37 0.93 \n",
"1571 15.0 25.0 0.99514 3.44 0.65 \n",
"1572 35.0 104.0 0.99632 3.33 0.51 \n",
"1573 15.0 50.0 0.99467 3.58 0.67 \n",
"1574 23.0 92.0 0.99677 3.39 0.48 \n",
"1575 12.0 20.0 0.99474 3.26 0.64 \n",
"1576 16.0 29.0 0.99588 3.30 0.78 \n",
"1577 13.0 27.0 0.99622 3.54 0.60 \n",
"1578 13.0 20.0 0.99540 3.42 0.67 \n",
"1579 24.0 32.0 0.99402 3.54 0.60 \n",
"1580 9.0 26.0 0.99470 3.36 0.60 \n",
"1581 24.0 32.0 0.99402 3.54 0.60 \n",
"1582 13.0 27.0 0.99362 3.57 0.50 \n",
"1583 32.0 98.0 0.99578 3.33 0.62 \n",
"1584 24.0 34.0 0.99484 3.29 0.80 \n",
"1585 22.0 48.0 0.99494 3.30 0.84 \n",
"1586 34.0 60.0 0.99492 3.34 0.85 \n",
"1587 18.0 28.0 0.99483 3.55 0.66 \n",
"1588 34.0 102.0 0.99414 3.27 0.78 \n",
"1589 29.0 79.0 0.99770 3.29 0.54 \n",
"1590 26.0 35.0 0.99314 3.32 0.82 \n",
"1591 16.0 26.0 0.99402 3.67 0.56 \n",
"1592 29.0 40.0 0.99574 3.42 0.75 \n",
"1593 28.0 38.0 0.99651 3.42 0.82 \n",
"1594 32.0 44.0 0.99490 3.45 0.58 \n",
"1595 39.0 51.0 0.99512 3.52 0.76 \n",
"1596 29.0 40.0 0.99574 3.42 0.75 \n",
"1597 32.0 44.0 0.99547 3.57 0.71 \n",
"1598 18.0 42.0 0.99549 3.39 0.66 \n",
"\n",
" alcohol quality \n",
"0 9.4 5 \n",
"1 9.8 5 \n",
"2 9.8 5 \n",
"3 9.8 6 \n",
"4 9.4 5 \n",
"5 9.4 5 \n",
"6 9.4 5 \n",
"7 10.0 7 \n",
"8 9.5 7 \n",
"9 10.5 5 \n",
"10 9.2 5 \n",
"11 10.5 5 \n",
"12 9.9 5 \n",
"13 9.1 5 \n",
"14 9.2 5 \n",
"15 9.2 5 \n",
"16 10.5 7 \n",
"17 9.3 5 \n",
"18 9.0 4 \n",
"19 9.2 6 \n",
"20 9.4 6 \n",
"21 9.7 5 \n",
"22 9.5 5 \n",
"23 9.4 5 \n",
"24 9.7 6 \n",
"25 9.3 5 \n",
"26 9.5 5 \n",
"27 9.5 5 \n",
"28 9.4 5 \n",
"29 9.8 6 \n",
"... ... ... \n",
"1569 11.5 6 \n",
"1570 12.4 6 \n",
"1571 11.1 6 \n",
"1572 9.5 5 \n",
"1573 12.5 6 \n",
"1574 10.5 6 \n",
"1575 11.8 6 \n",
"1576 10.8 6 \n",
"1577 11.9 6 \n",
"1578 11.3 6 \n",
"1579 11.3 5 \n",
"1580 11.9 6 \n",
"1581 11.3 5 \n",
"1582 11.9 5 \n",
"1583 9.8 5 \n",
"1584 11.6 7 \n",
"1585 11.5 6 \n",
"1586 11.4 6 \n",
"1587 10.9 6 \n",
"1588 12.8 6 \n",
"1589 9.2 5 \n",
"1590 11.6 6 \n",
"1591 11.6 6 \n",
"1592 11.0 6 \n",
"1593 9.5 6 \n",
"1594 10.5 5 \n",
"1595 11.2 6 \n",
"1596 11.0 6 \n",
"1597 10.2 5 \n",
"1598 11.0 6 \n",
"\n",
"[1599 rows x 12 columns]\n"
]
}
],
"source": [
"print(red)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7.0</td>\n",
" <td>0.270</td>\n",
" <td>0.36</td>\n",
" <td>20.70</td>\n",
" <td>0.045</td>\n",
" <td>45.0</td>\n",
" <td>170.0</td>\n",
" <td>1.00100</td>\n",
" <td>3.00</td>\n",
" <td>0.45</td>\n",
" <td>8.800000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6.3</td>\n",
" <td>0.300</td>\n",
" <td>0.34</td>\n",
" <td>1.60</td>\n",
" <td>0.049</td>\n",
" <td>14.0</td>\n",
" <td>132.0</td>\n",
" <td>0.99400</td>\n",
" <td>3.30</td>\n",
" <td>0.49</td>\n",
" <td>9.500000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8.1</td>\n",
" <td>0.280</td>\n",
" <td>0.40</td>\n",
" <td>6.90</td>\n",
" <td>0.050</td>\n",
" <td>30.0</td>\n",
" <td>97.0</td>\n",
" <td>0.99510</td>\n",
" <td>3.26</td>\n",
" <td>0.44</td>\n",
" <td>10.100000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7.2</td>\n",
" <td>0.230</td>\n",
" <td>0.32</td>\n",
" <td>8.50</td>\n",
" <td>0.058</td>\n",
" <td>47.0</td>\n",
" <td>186.0</td>\n",
" <td>0.99560</td>\n",
" <td>3.19</td>\n",
" <td>0.40</td>\n",
" <td>9.900000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7.2</td>\n",
" <td>0.230</td>\n",
" <td>0.32</td>\n",
" <td>8.50</td>\n",
" <td>0.058</td>\n",
" <td>47.0</td>\n",
" <td>186.0</td>\n",
" <td>0.99560</td>\n",
" <td>3.19</td>\n",
" <td>0.40</td>\n",
" <td>9.900000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>8.1</td>\n",
" <td>0.280</td>\n",
" <td>0.40</td>\n",
" <td>6.90</td>\n",
" <td>0.050</td>\n",
" <td>30.0</td>\n",
" <td>97.0</td>\n",
" <td>0.99510</td>\n",
" <td>3.26</td>\n",
" <td>0.44</td>\n",
" <td>10.100000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6.2</td>\n",
" <td>0.320</td>\n",
" <td>0.16</td>\n",
" <td>7.00</td>\n",
" <td>0.045</td>\n",
" <td>30.0</td>\n",
" <td>136.0</td>\n",
" <td>0.99490</td>\n",
" <td>3.18</td>\n",
" <td>0.47</td>\n",
" <td>9.600000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7.0</td>\n",
" <td>0.270</td>\n",
" <td>0.36</td>\n",
" <td>20.70</td>\n",
" <td>0.045</td>\n",
" <td>45.0</td>\n",
" <td>170.0</td>\n",
" <td>1.00100</td>\n",
" <td>3.00</td>\n",
" <td>0.45</td>\n",
" <td>8.800000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>6.3</td>\n",
" <td>0.300</td>\n",
" <td>0.34</td>\n",
" <td>1.60</td>\n",
" <td>0.049</td>\n",
" <td>14.0</td>\n",
" <td>132.0</td>\n",
" <td>0.99400</td>\n",
" <td>3.30</td>\n",
" <td>0.49</td>\n",
" <td>9.500000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>8.1</td>\n",
" <td>0.220</td>\n",
" <td>0.43</td>\n",
" <td>1.50</td>\n",
" <td>0.044</td>\n",
" <td>28.0</td>\n",
" <td>129.0</td>\n",
" <td>0.99380</td>\n",
" <td>3.22</td>\n",
" <td>0.45</td>\n",
" <td>11.000000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>8.1</td>\n",
" <td>0.270</td>\n",
" <td>0.41</td>\n",
" <td>1.45</td>\n",
" <td>0.033</td>\n",
" <td>11.0</td>\n",
" <td>63.0</td>\n",
" <td>0.99080</td>\n",
" <td>2.99</td>\n",
" <td>0.56</td>\n",
" <td>12.000000</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>8.6</td>\n",
" <td>0.230</td>\n",
" <td>0.40</td>\n",
" <td>4.20</td>\n",
" <td>0.035</td>\n",
" <td>17.0</td>\n",
" <td>109.0</td>\n",
" <td>0.99470</td>\n",
" <td>3.14</td>\n",
" <td>0.53</td>\n",
" <td>9.700000</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>7.9</td>\n",
" <td>0.180</td>\n",
" <td>0.37</td>\n",
" <td>1.20</td>\n",
" <td>0.040</td>\n",
" <td>16.0</td>\n",
" <td>75.0</td>\n",
" <td>0.99200</td>\n",
" <td>3.18</td>\n",
" <td>0.63</td>\n",
" <td>10.800000</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>6.6</td>\n",
" <td>0.160</td>\n",
" <td>0.40</td>\n",
" <td>1.50</td>\n",
" <td>0.044</td>\n",
" <td>48.0</td>\n",
" <td>143.0</td>\n",
" <td>0.99120</td>\n",
" <td>3.54</td>\n",
" <td>0.52</td>\n",
" <td>12.400000</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>8.3</td>\n",
" <td>0.420</td>\n",
" <td>0.62</td>\n",
" <td>19.25</td>\n",
" <td>0.040</td>\n",
" <td>41.0</td>\n",
" <td>172.0</td>\n",
" <td>1.00020</td>\n",
" <td>2.98</td>\n",
" <td>0.67</td>\n",
" <td>9.700000</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>6.6</td>\n",
" <td>0.170</td>\n",
" <td>0.38</td>\n",
" <td>1.50</td>\n",
" <td>0.032</td>\n",
" <td>28.0</td>\n",
" <td>112.0</td>\n",
" <td>0.99140</td>\n",
" <td>3.25</td>\n",
" <td>0.55</td>\n",
" <td>11.400000</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>6.3</td>\n",
" <td>0.480</td>\n",
" <td>0.04</td>\n",
" <td>1.10</td>\n",
" <td>0.046</td>\n",
" <td>30.0</td>\n",
" <td>99.0</td>\n",
" <td>0.99280</td>\n",
" <td>3.24</td>\n",
" <td>0.36</td>\n",
" <td>9.600000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>6.2</td>\n",
" <td>0.660</td>\n",
" <td>0.48</td>\n",
" <td>1.20</td>\n",
" <td>0.029</td>\n",
" <td>29.0</td>\n",
" <td>75.0</td>\n",
" <td>0.98920</td>\n",
" <td>3.33</td>\n",
" <td>0.39</td>\n",
" <td>12.800000</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>7.4</td>\n",
" <td>0.340</td>\n",
" <td>0.42</td>\n",
" <td>1.10</td>\n",
" <td>0.033</td>\n",
" <td>17.0</td>\n",
" <td>171.0</td>\n",
" <td>0.99170</td>\n",
" <td>3.12</td>\n",
" <td>0.53</td>\n",
" <td>11.300000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>6.5</td>\n",
" <td>0.310</td>\n",
" <td>0.14</td>\n",
" <td>7.50</td>\n",
" <td>0.044</td>\n",
" <td>34.0</td>\n",
" <td>133.0</td>\n",
" <td>0.99550</td>\n",
" <td>3.22</td>\n",
" <td>0.50</td>\n",
" <td>9.500000</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>6.2</td>\n",
" <td>0.660</td>\n",
" <td>0.48</td>\n",
" <td>1.20</td>\n",
" <td>0.029</td>\n",
" <td>29.0</td>\n",
" <td>75.0</td>\n",
" <td>0.98920</td>\n",
" <td>3.33</td>\n",
" <td>0.39</td>\n",
" <td>12.800000</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>6.4</td>\n",
" <td>0.310</td>\n",
" <td>0.38</td>\n",
" <td>2.90</td>\n",
" <td>0.038</td>\n",
" <td>19.0</td>\n",
" <td>102.0</td>\n",
" <td>0.99120</td>\n",
" <td>3.17</td>\n",
" <td>0.35</td>\n",
" <td>11.000000</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>6.8</td>\n",
" <td>0.260</td>\n",
" <td>0.42</td>\n",
" <td>1.70</td>\n",
" <td>0.049</td>\n",
" <td>41.0</td>\n",
" <td>122.0</td>\n",
" <td>0.99300</td>\n",
" <td>3.47</td>\n",
" <td>0.48</td>\n",
" <td>10.500000</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>7.6</td>\n",
" <td>0.670</td>\n",
" <td>0.14</td>\n",
" <td>1.50</td>\n",
" <td>0.074</td>\n",
" <td>25.0</td>\n",
" <td>168.0</td>\n",
" <td>0.99370</td>\n",
" <td>3.05</td>\n",
" <td>0.51</td>\n",
" <td>9.300000</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>6.6</td>\n",
" <td>0.270</td>\n",
" <td>0.41</td>\n",
" <td>1.30</td>\n",
" <td>0.052</td>\n",
" <td>16.0</td>\n",
" <td>142.0</td>\n",
" <td>0.99510</td>\n",
" <td>3.42</td>\n",
" <td>0.47</td>\n",
" <td>10.000000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>7.0</td>\n",
" <td>0.250</td>\n",
" <td>0.32</td>\n",
" <td>9.00</td>\n",
" <td>0.046</td>\n",
" <td>56.0</td>\n",
" <td>245.0</td>\n",
" <td>0.99550</td>\n",
" <td>3.25</td>\n",
" <td>0.50</td>\n",
" <td>10.400000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>6.9</td>\n",
" <td>0.240</td>\n",
" <td>0.35</td>\n",
" <td>1.00</td>\n",
" <td>0.052</td>\n",
" <td>35.0</td>\n",
" <td>146.0</td>\n",
" <td>0.99300</td>\n",
" <td>3.45</td>\n",
" <td>0.44</td>\n",
" <td>10.000000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>7.0</td>\n",
" <td>0.280</td>\n",
" <td>0.39</td>\n",
" <td>8.70</td>\n",
" <td>0.051</td>\n",
" <td>32.0</td>\n",
" <td>141.0</td>\n",
" <td>0.99610</td>\n",
" <td>3.38</td>\n",
" <td>0.53</td>\n",
" <td>10.500000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>7.4</td>\n",
" <td>0.270</td>\n",
" <td>0.48</td>\n",
" <td>1.10</td>\n",
" <td>0.047</td>\n",
" <td>17.0</td>\n",
" <td>132.0</td>\n",
" <td>0.99140</td>\n",
" <td>3.19</td>\n",
" <td>0.49</td>\n",
" <td>11.600000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>7.2</td>\n",
" <td>0.320</td>\n",
" <td>0.36</td>\n",
" <td>2.00</td>\n",
" <td>0.033</td>\n",
" <td>37.0</td>\n",
" <td>114.0</td>\n",
" <td>0.99060</td>\n",
" <td>3.10</td>\n",
" <td>0.71</td>\n",
" <td>12.300000</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4868</th>\n",
" <td>5.8</td>\n",
" <td>0.230</td>\n",
" <td>0.31</td>\n",
" <td>4.50</td>\n",
" <td>0.046</td>\n",
" <td>42.0</td>\n",
" <td>124.0</td>\n",
" <td>0.99324</td>\n",
" <td>3.31</td>\n",
" <td>0.64</td>\n",
" <td>10.800000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4869</th>\n",
" <td>6.6</td>\n",
" <td>0.240</td>\n",
" <td>0.33</td>\n",
" <td>10.10</td>\n",
" <td>0.032</td>\n",
" <td>8.0</td>\n",
" <td>81.0</td>\n",
" <td>0.99626</td>\n",
" <td>3.19</td>\n",
" <td>0.51</td>\n",
" <td>9.800000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4870</th>\n",
" <td>6.1</td>\n",
" <td>0.320</td>\n",
" <td>0.28</td>\n",
" <td>6.60</td>\n",
" <td>0.021</td>\n",
" <td>29.0</td>\n",
" <td>132.0</td>\n",
" <td>0.99188</td>\n",
" <td>3.15</td>\n",
" <td>0.36</td>\n",
" <td>11.450000</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4871</th>\n",
" <td>5.0</td>\n",
" <td>0.200</td>\n",
" <td>0.40</td>\n",
" <td>1.90</td>\n",
" <td>0.015</td>\n",
" <td>20.0</td>\n",
" <td>98.0</td>\n",
" <td>0.98970</td>\n",
" <td>3.37</td>\n",
" <td>0.55</td>\n",
" <td>12.050000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4872</th>\n",
" <td>6.0</td>\n",
" <td>0.420</td>\n",
" <td>0.41</td>\n",
" <td>12.40</td>\n",
" <td>0.032</td>\n",
" <td>50.0</td>\n",
" <td>179.0</td>\n",
" <td>0.99622</td>\n",
" <td>3.14</td>\n",
" <td>0.60</td>\n",
" <td>9.700000</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4873</th>\n",
" <td>5.7</td>\n",
" <td>0.210</td>\n",
" <td>0.32</td>\n",
" <td>1.60</td>\n",
" <td>0.030</td>\n",
" <td>33.0</td>\n",
" <td>122.0</td>\n",
" <td>0.99044</td>\n",
" <td>3.33</td>\n",
" <td>0.52</td>\n",
" <td>11.900000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4874</th>\n",
" <td>5.6</td>\n",
" <td>0.200</td>\n",
" <td>0.36</td>\n",
" <td>2.50</td>\n",
" <td>0.048</td>\n",
" <td>16.0</td>\n",
" <td>125.0</td>\n",
" <td>0.99282</td>\n",
" <td>3.49</td>\n",
" <td>0.49</td>\n",
" <td>10.000000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4875</th>\n",
" <td>7.4</td>\n",
" <td>0.220</td>\n",
" <td>0.26</td>\n",
" <td>1.20</td>\n",
" <td>0.035</td>\n",
" <td>18.0</td>\n",
" <td>97.0</td>\n",
" <td>0.99245</td>\n",
" <td>3.12</td>\n",
" <td>0.41</td>\n",
" <td>9.700000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4876</th>\n",
" <td>6.2</td>\n",
" <td>0.380</td>\n",
" <td>0.42</td>\n",
" <td>2.50</td>\n",
" <td>0.038</td>\n",
" <td>34.0</td>\n",
" <td>117.0</td>\n",
" <td>0.99132</td>\n",
" <td>3.36</td>\n",
" <td>0.59</td>\n",
" <td>11.600000</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4877</th>\n",
" <td>5.9</td>\n",
" <td>0.540</td>\n",
" <td>0.00</td>\n",
" <td>0.80</td>\n",
" <td>0.032</td>\n",
" <td>12.0</td>\n",
" <td>82.0</td>\n",
" <td>0.99286</td>\n",
" <td>3.25</td>\n",
" <td>0.36</td>\n",
" <td>8.800000</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4878</th>\n",
" <td>6.2</td>\n",
" <td>0.530</td>\n",
" <td>0.02</td>\n",
" <td>0.90</td>\n",
" <td>0.035</td>\n",
" <td>6.0</td>\n",
" <td>81.0</td>\n",
" <td>0.99234</td>\n",
" <td>3.24</td>\n",
" <td>0.35</td>\n",
" <td>9.500000</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4879</th>\n",
" <td>6.6</td>\n",
" <td>0.340</td>\n",
" <td>0.40</td>\n",
" <td>8.10</td>\n",
" <td>0.046</td>\n",
" <td>68.0</td>\n",
" <td>170.0</td>\n",
" <td>0.99494</td>\n",
" <td>3.15</td>\n",
" <td>0.50</td>\n",
" <td>9.533333</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4880</th>\n",
" <td>6.6</td>\n",
" <td>0.340</td>\n",
" <td>0.40</td>\n",
" <td>8.10</td>\n",
" <td>0.046</td>\n",
" <td>68.0</td>\n",
" <td>170.0</td>\n",
" <td>0.99494</td>\n",
" <td>3.15</td>\n",
" <td>0.50</td>\n",
" <td>9.533333</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4881</th>\n",
" <td>5.0</td>\n",
" <td>0.235</td>\n",
" <td>0.27</td>\n",
" <td>11.75</td>\n",
" <td>0.030</td>\n",
" <td>34.0</td>\n",
" <td>118.0</td>\n",
" <td>0.99540</td>\n",
" <td>3.07</td>\n",
" <td>0.50</td>\n",
" <td>9.400000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4882</th>\n",
" <td>5.5</td>\n",
" <td>0.320</td>\n",
" <td>0.13</td>\n",
" <td>1.30</td>\n",
" <td>0.037</td>\n",
" <td>45.0</td>\n",
" <td>156.0</td>\n",
" <td>0.99184</td>\n",
" <td>3.26</td>\n",
" <td>0.38</td>\n",
" <td>10.700000</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4883</th>\n",
" <td>4.9</td>\n",
" <td>0.470</td>\n",
" <td>0.17</td>\n",
" <td>1.90</td>\n",
" <td>0.035</td>\n",
" <td>60.0</td>\n",
" <td>148.0</td>\n",
" <td>0.98964</td>\n",
" <td>3.27</td>\n",
" <td>0.35</td>\n",
" <td>11.500000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4884</th>\n",
" <td>6.5</td>\n",
" <td>0.330</td>\n",
" <td>0.38</td>\n",
" <td>8.30</td>\n",
" <td>0.048</td>\n",
" <td>68.0</td>\n",
" <td>174.0</td>\n",
" <td>0.99492</td>\n",
" <td>3.14</td>\n",
" <td>0.50</td>\n",
" <td>9.600000</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4885</th>\n",
" <td>6.6</td>\n",
" <td>0.340</td>\n",
" <td>0.40</td>\n",
" <td>8.10</td>\n",
" <td>0.046</td>\n",
" <td>68.0</td>\n",
" <td>170.0</td>\n",
" <td>0.99494</td>\n",
" <td>3.15</td>\n",
" <td>0.50</td>\n",
" <td>9.550000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4886</th>\n",
" <td>6.2</td>\n",
" <td>0.210</td>\n",
" <td>0.28</td>\n",
" <td>5.70</td>\n",
" <td>0.028</td>\n",
" <td>45.0</td>\n",
" <td>121.0</td>\n",
" <td>0.99168</td>\n",
" <td>3.21</td>\n",
" <td>1.08</td>\n",
" <td>12.150000</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4887</th>\n",
" <td>6.2</td>\n",
" <td>0.410</td>\n",
" <td>0.22</td>\n",
" <td>1.90</td>\n",
" <td>0.023</td>\n",
" <td>5.0</td>\n",
" <td>56.0</td>\n",
" <td>0.98928</td>\n",
" <td>3.04</td>\n",
" <td>0.79</td>\n",
" <td>13.000000</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4888</th>\n",
" <td>6.8</td>\n",
" <td>0.220</td>\n",
" <td>0.36</td>\n",
" <td>1.20</td>\n",
" <td>0.052</td>\n",
" <td>38.0</td>\n",
" <td>127.0</td>\n",
" <td>0.99330</td>\n",
" <td>3.04</td>\n",
" <td>0.54</td>\n",
" <td>9.200000</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4889</th>\n",
" <td>4.9</td>\n",
" <td>0.235</td>\n",
" <td>0.27</td>\n",
" <td>11.75</td>\n",
" <td>0.030</td>\n",
" <td>34.0</td>\n",
" <td>118.0</td>\n",
" <td>0.99540</td>\n",
" <td>3.07</td>\n",
" <td>0.50</td>\n",
" <td>9.400000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4890</th>\n",
" <td>6.1</td>\n",
" <td>0.340</td>\n",
" <td>0.29</td>\n",
" <td>2.20</td>\n",
" <td>0.036</td>\n",
" <td>25.0</td>\n",
" <td>100.0</td>\n",
" <td>0.98938</td>\n",
" <td>3.06</td>\n",
" <td>0.44</td>\n",
" <td>11.800000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4891</th>\n",
" <td>5.7</td>\n",
" <td>0.210</td>\n",
" <td>0.32</td>\n",
" <td>0.90</td>\n",
" <td>0.038</td>\n",
" <td>38.0</td>\n",
" <td>121.0</td>\n",
" <td>0.99074</td>\n",
" <td>3.24</td>\n",
" <td>0.46</td>\n",
" <td>10.600000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4892</th>\n",
" <td>6.5</td>\n",
" <td>0.230</td>\n",
" <td>0.38</td>\n",
" <td>1.30</td>\n",
" <td>0.032</td>\n",
" <td>29.0</td>\n",
" <td>112.0</td>\n",
" <td>0.99298</td>\n",
" <td>3.29</td>\n",
" <td>0.54</td>\n",
" <td>9.700000</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4893</th>\n",
" <td>6.2</td>\n",
" <td>0.210</td>\n",
" <td>0.29</td>\n",
" <td>1.60</td>\n",
" <td>0.039</td>\n",
" <td>24.0</td>\n",
" <td>92.0</td>\n",
" <td>0.99114</td>\n",
" <td>3.27</td>\n",
" <td>0.50</td>\n",
" <td>11.200000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4894</th>\n",
" <td>6.6</td>\n",
" <td>0.320</td>\n",
" <td>0.36</td>\n",
" <td>8.00</td>\n",
" <td>0.047</td>\n",
" <td>57.0</td>\n",
" <td>168.0</td>\n",
" <td>0.99490</td>\n",
" <td>3.15</td>\n",
" <td>0.46</td>\n",
" <td>9.600000</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4895</th>\n",
" <td>6.5</td>\n",
" <td>0.240</td>\n",
" <td>0.19</td>\n",
" <td>1.20</td>\n",
" <td>0.041</td>\n",
" <td>30.0</td>\n",
" <td>111.0</td>\n",
" <td>0.99254</td>\n",
" <td>2.99</td>\n",
" <td>0.46</td>\n",
" <td>9.400000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4896</th>\n",
" <td>5.5</td>\n",
" <td>0.290</td>\n",
" <td>0.30</td>\n",
" <td>1.10</td>\n",
" <td>0.022</td>\n",
" <td>20.0</td>\n",
" <td>110.0</td>\n",
" <td>0.98869</td>\n",
" <td>3.34</td>\n",
" <td>0.38</td>\n",
" <td>12.800000</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4897</th>\n",
" <td>6.0</td>\n",
" <td>0.210</td>\n",
" <td>0.38</td>\n",
" <td>0.80</td>\n",
" <td>0.020</td>\n",
" <td>22.0</td>\n",
" <td>98.0</td>\n",
" <td>0.98941</td>\n",
" <td>3.26</td>\n",
" <td>0.32</td>\n",
" <td>11.800000</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4898 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" fixed acidity volatile acidity citric acid residual sugar chlorides \\\n",
"0 7.0 0.270 0.36 20.70 0.045 \n",
"1 6.3 0.300 0.34 1.60 0.049 \n",
"2 8.1 0.280 0.40 6.90 0.050 \n",
"3 7.2 0.230 0.32 8.50 0.058 \n",
"4 7.2 0.230 0.32 8.50 0.058 \n",
"5 8.1 0.280 0.40 6.90 0.050 \n",
"6 6.2 0.320 0.16 7.00 0.045 \n",
"7 7.0 0.270 0.36 20.70 0.045 \n",
"8 6.3 0.300 0.34 1.60 0.049 \n",
"9 8.1 0.220 0.43 1.50 0.044 \n",
"10 8.1 0.270 0.41 1.45 0.033 \n",
"11 8.6 0.230 0.40 4.20 0.035 \n",
"12 7.9 0.180 0.37 1.20 0.040 \n",
"13 6.6 0.160 0.40 1.50 0.044 \n",
"14 8.3 0.420 0.62 19.25 0.040 \n",
"15 6.6 0.170 0.38 1.50 0.032 \n",
"16 6.3 0.480 0.04 1.10 0.046 \n",
"17 6.2 0.660 0.48 1.20 0.029 \n",
"18 7.4 0.340 0.42 1.10 0.033 \n",
"19 6.5 0.310 0.14 7.50 0.044 \n",
"20 6.2 0.660 0.48 1.20 0.029 \n",
"21 6.4 0.310 0.38 2.90 0.038 \n",
"22 6.8 0.260 0.42 1.70 0.049 \n",
"23 7.6 0.670 0.14 1.50 0.074 \n",
"24 6.6 0.270 0.41 1.30 0.052 \n",
"25 7.0 0.250 0.32 9.00 0.046 \n",
"26 6.9 0.240 0.35 1.00 0.052 \n",
"27 7.0 0.280 0.39 8.70 0.051 \n",
"28 7.4 0.270 0.48 1.10 0.047 \n",
"29 7.2 0.320 0.36 2.00 0.033 \n",
"... ... ... ... ... ... \n",
"4868 5.8 0.230 0.31 4.50 0.046 \n",
"4869 6.6 0.240 0.33 10.10 0.032 \n",
"4870 6.1 0.320 0.28 6.60 0.021 \n",
"4871 5.0 0.200 0.40 1.90 0.015 \n",
"4872 6.0 0.420 0.41 12.40 0.032 \n",
"4873 5.7 0.210 0.32 1.60 0.030 \n",
"4874 5.6 0.200 0.36 2.50 0.048 \n",
"4875 7.4 0.220 0.26 1.20 0.035 \n",
"4876 6.2 0.380 0.42 2.50 0.038 \n",
"4877 5.9 0.540 0.00 0.80 0.032 \n",
"4878 6.2 0.530 0.02 0.90 0.035 \n",
"4879 6.6 0.340 0.40 8.10 0.046 \n",
"4880 6.6 0.340 0.40 8.10 0.046 \n",
"4881 5.0 0.235 0.27 11.75 0.030 \n",
"4882 5.5 0.320 0.13 1.30 0.037 \n",
"4883 4.9 0.470 0.17 1.90 0.035 \n",
"4884 6.5 0.330 0.38 8.30 0.048 \n",
"4885 6.6 0.340 0.40 8.10 0.046 \n",
"4886 6.2 0.210 0.28 5.70 0.028 \n",
"4887 6.2 0.410 0.22 1.90 0.023 \n",
"4888 6.8 0.220 0.36 1.20 0.052 \n",
"4889 4.9 0.235 0.27 11.75 0.030 \n",
"4890 6.1 0.340 0.29 2.20 0.036 \n",
"4891 5.7 0.210 0.32 0.90 0.038 \n",
"4892 6.5 0.230 0.38 1.30 0.032 \n",
"4893 6.2 0.210 0.29 1.60 0.039 \n",
"4894 6.6 0.320 0.36 8.00 0.047 \n",
"4895 6.5 0.240 0.19 1.20 0.041 \n",
"4896 5.5 0.290 0.30 1.10 0.022 \n",
"4897 6.0 0.210 0.38 0.80 0.020 \n",
"\n",
" free sulfur dioxide total sulfur dioxide density pH sulphates \\\n",
"0 45.0 170.0 1.00100 3.00 0.45 \n",
"1 14.0 132.0 0.99400 3.30 0.49 \n",
"2 30.0 97.0 0.99510 3.26 0.44 \n",
"3 47.0 186.0 0.99560 3.19 0.40 \n",
"4 47.0 186.0 0.99560 3.19 0.40 \n",
"5 30.0 97.0 0.99510 3.26 0.44 \n",
"6 30.0 136.0 0.99490 3.18 0.47 \n",
"7 45.0 170.0 1.00100 3.00 0.45 \n",
"8 14.0 132.0 0.99400 3.30 0.49 \n",
"9 28.0 129.0 0.99380 3.22 0.45 \n",
"10 11.0 63.0 0.99080 2.99 0.56 \n",
"11 17.0 109.0 0.99470 3.14 0.53 \n",
"12 16.0 75.0 0.99200 3.18 0.63 \n",
"13 48.0 143.0 0.99120 3.54 0.52 \n",
"14 41.0 172.0 1.00020 2.98 0.67 \n",
"15 28.0 112.0 0.99140 3.25 0.55 \n",
"16 30.0 99.0 0.99280 3.24 0.36 \n",
"17 29.0 75.0 0.98920 3.33 0.39 \n",
"18 17.0 171.0 0.99170 3.12 0.53 \n",
"19 34.0 133.0 0.99550 3.22 0.50 \n",
"20 29.0 75.0 0.98920 3.33 0.39 \n",
"21 19.0 102.0 0.99120 3.17 0.35 \n",
"22 41.0 122.0 0.99300 3.47 0.48 \n",
"23 25.0 168.0 0.99370 3.05 0.51 \n",
"24 16.0 142.0 0.99510 3.42 0.47 \n",
"25 56.0 245.0 0.99550 3.25 0.50 \n",
"26 35.0 146.0 0.99300 3.45 0.44 \n",
"27 32.0 141.0 0.99610 3.38 0.53 \n",
"28 17.0 132.0 0.99140 3.19 0.49 \n",
"29 37.0 114.0 0.99060 3.10 0.71 \n",
"... ... ... ... ... ... \n",
"4868 42.0 124.0 0.99324 3.31 0.64 \n",
"4869 8.0 81.0 0.99626 3.19 0.51 \n",
"4870 29.0 132.0 0.99188 3.15 0.36 \n",
"4871 20.0 98.0 0.98970 3.37 0.55 \n",
"4872 50.0 179.0 0.99622 3.14 0.60 \n",
"4873 33.0 122.0 0.99044 3.33 0.52 \n",
"4874 16.0 125.0 0.99282 3.49 0.49 \n",
"4875 18.0 97.0 0.99245 3.12 0.41 \n",
"4876 34.0 117.0 0.99132 3.36 0.59 \n",
"4877 12.0 82.0 0.99286 3.25 0.36 \n",
"4878 6.0 81.0 0.99234 3.24 0.35 \n",
"4879 68.0 170.0 0.99494 3.15 0.50 \n",
"4880 68.0 170.0 0.99494 3.15 0.50 \n",
"4881 34.0 118.0 0.99540 3.07 0.50 \n",
"4882 45.0 156.0 0.99184 3.26 0.38 \n",
"4883 60.0 148.0 0.98964 3.27 0.35 \n",
"4884 68.0 174.0 0.99492 3.14 0.50 \n",
"4885 68.0 170.0 0.99494 3.15 0.50 \n",
"4886 45.0 121.0 0.99168 3.21 1.08 \n",
"4887 5.0 56.0 0.98928 3.04 0.79 \n",
"4888 38.0 127.0 0.99330 3.04 0.54 \n",
"4889 34.0 118.0 0.99540 3.07 0.50 \n",
"4890 25.0 100.0 0.98938 3.06 0.44 \n",
"4891 38.0 121.0 0.99074 3.24 0.46 \n",
"4892 29.0 112.0 0.99298 3.29 0.54 \n",
"4893 24.0 92.0 0.99114 3.27 0.50 \n",
"4894 57.0 168.0 0.99490 3.15 0.46 \n",
"4895 30.0 111.0 0.99254 2.99 0.46 \n",
"4896 20.0 110.0 0.98869 3.34 0.38 \n",
"4897 22.0 98.0 0.98941 3.26 0.32 \n",
"\n",
" alcohol quality \n",
"0 8.800000 6 \n",
"1 9.500000 6 \n",
"2 10.100000 6 \n",
"3 9.900000 6 \n",
"4 9.900000 6 \n",
"5 10.100000 6 \n",
"6 9.600000 6 \n",
"7 8.800000 6 \n",
"8 9.500000 6 \n",
"9 11.000000 6 \n",
"10 12.000000 5 \n",
"11 9.700000 5 \n",
"12 10.800000 5 \n",
"13 12.400000 7 \n",
"14 9.700000 5 \n",
"15 11.400000 7 \n",
"16 9.600000 6 \n",
"17 12.800000 8 \n",
"18 11.300000 6 \n",
"19 9.500000 5 \n",
"20 12.800000 8 \n",
"21 11.000000 7 \n",
"22 10.500000 8 \n",
"23 9.300000 5 \n",
"24 10.000000 6 \n",
"25 10.400000 6 \n",
"26 10.000000 6 \n",
"27 10.500000 6 \n",
"28 11.600000 6 \n",
"29 12.300000 7 \n",
"... ... ... \n",
"4868 10.800000 6 \n",
"4869 9.800000 6 \n",
"4870 11.450000 7 \n",
"4871 12.050000 6 \n",
"4872 9.700000 5 \n",
"4873 11.900000 6 \n",
"4874 10.000000 6 \n",
"4875 9.700000 6 \n",
"4876 11.600000 7 \n",
"4877 8.800000 5 \n",
"4878 9.500000 4 \n",
"4879 9.533333 6 \n",
"4880 9.533333 6 \n",
"4881 9.400000 6 \n",
"4882 10.700000 5 \n",
"4883 11.500000 6 \n",
"4884 9.600000 5 \n",
"4885 9.550000 6 \n",
"4886 12.150000 7 \n",
"4887 13.000000 7 \n",
"4888 9.200000 5 \n",
"4889 9.400000 6 \n",
"4890 11.800000 6 \n",
"4891 10.600000 6 \n",
"4892 9.700000 5 \n",
"4893 11.200000 6 \n",
"4894 9.600000 5 \n",
"4895 9.400000 6 \n",
"4896 12.800000 7 \n",
"4897 11.800000 6 \n",
"\n",
"[4898 rows x 12 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"white\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
" <th>pH</th>\n",
" <th>sulphates</th>\n",
" <th>alcohol</th>\n",
" <th>quality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7.4</td>\n",
" <td>0.700</td>\n",
" <td>0.00</td>\n",
" <td>1.9</td>\n",
" <td>0.076</td>\n",
" <td>11.0</td>\n",
" <td>34.0</td>\n",
" <td>0.99780</td>\n",
" <td>3.51</td>\n",
" <td>0.56</td>\n",
" <td>9.4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7.8</td>\n",
" <td>0.880</td>\n",
" <td>0.00</td>\n",
" <td>2.6</td>\n",
" <td>0.098</td>\n",
" <td>25.0</td>\n",
" <td>67.0</td>\n",
" <td>0.99680</td>\n",
" <td>3.20</td>\n",
" <td>0.68</td>\n",
" <td>9.8</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7.8</td>\n",
" <td>0.760</td>\n",
" <td>0.04</td>\n",
" <td>2.3</td>\n",
" <td>0.092</td>\n",
" <td>15.0</td>\n",
" <td>54.0</td>\n",
" <td>0.99700</td>\n",
" <td>3.26</td>\n",
" <td>0.65</td>\n",
" <td>9.8</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11.2</td>\n",
" <td>0.280</td>\n",
" <td>0.56</td>\n",
" <td>1.9</td>\n",
" <td>0.075</td>\n",
" <td>17.0</td>\n",
" <td>60.0</td>\n",
" <td>0.99800</td>\n",
" <td>3.16</td>\n",
" <td>0.58</td>\n",
" <td>9.8</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7.4</td>\n",
" <td>0.700</td>\n",
" <td>0.00</td>\n",
" <td>1.9</td>\n",
" <td>0.076</td>\n",
" <td>11.0</td>\n",
" <td>34.0</td>\n",
" <td>0.99780</td>\n",
" <td>3.51</td>\n",
" <td>0.56</td>\n",
" <td>9.4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>7.4</td>\n",
" <td>0.660</td>\n",
" <td>0.00</td>\n",
" <td>1.8</td>\n",
" <td>0.075</td>\n",
" <td>13.0</td>\n",
" <td>40.0</td>\n",
" <td>0.99780</td>\n",
" <td>3.51</td>\n",
" <td>0.56</td>\n",
" <td>9.4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7.9</td>\n",
" <td>0.600</td>\n",
" <td>0.06</td>\n",
" <td>1.6</td>\n",
" <td>0.069</td>\n",
" <td>15.0</td>\n",
" <td>59.0</td>\n",
" <td>0.99640</td>\n",
" <td>3.30</td>\n",
" <td>0.46</td>\n",
" <td>9.4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7.3</td>\n",
" <td>0.650</td>\n",
" <td>0.00</td>\n",
" <td>1.2</td>\n",
" <td>0.065</td>\n",
" <td>15.0</td>\n",
" <td>21.0</td>\n",
" <td>0.99460</td>\n",
" <td>3.39</td>\n",
" <td>0.47</td>\n",
" <td>10.0</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>7.8</td>\n",
" <td>0.580</td>\n",
" <td>0.02</td>\n",
" <td>2.0</td>\n",
" <td>0.073</td>\n",
" <td>9.0</td>\n",
" <td>18.0</td>\n",
" <td>0.99680</td>\n",
" <td>3.36</td>\n",
" <td>0.57</td>\n",
" <td>9.5</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>7.5</td>\n",
" <td>0.500</td>\n",
" <td>0.36</td>\n",
" <td>6.1</td>\n",
" <td>0.071</td>\n",
" <td>17.0</td>\n",
" <td>102.0</td>\n",
" <td>0.99780</td>\n",
" <td>3.35</td>\n",
" <td>0.80</td>\n",
" <td>10.5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>6.7</td>\n",
" <td>0.580</td>\n",
" <td>0.08</td>\n",
" <td>1.8</td>\n",
" <td>0.097</td>\n",
" <td>15.0</td>\n",
" <td>65.0</td>\n",
" <td>0.99590</td>\n",
" <td>3.28</td>\n",
" <td>0.54</td>\n",
" <td>9.2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>7.5</td>\n",
" <td>0.500</td>\n",
" <td>0.36</td>\n",
" <td>6.1</td>\n",
" <td>0.071</td>\n",
" <td>17.0</td>\n",
" <td>102.0</td>\n",
" <td>0.99780</td>\n",
" <td>3.35</td>\n",
" <td>0.80</td>\n",
" <td>10.5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>5.6</td>\n",
" <td>0.615</td>\n",
" <td>0.00</td>\n",
" <td>1.6</td>\n",
" <td>0.089</td>\n",
" <td>16.0</td>\n",
" <td>59.0</td>\n",
" <td>0.99430</td>\n",
" <td>3.58</td>\n",
" <td>0.52</td>\n",
" <td>9.9</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>7.8</td>\n",
" <td>0.610</td>\n",
" <td>0.29</td>\n",
" <td>1.6</td>\n",
" <td>0.114</td>\n",
" <td>9.0</td>\n",
" <td>29.0</td>\n",
" <td>0.99740</td>\n",
" <td>3.26</td>\n",
" <td>1.56</td>\n",
" <td>9.1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>8.9</td>\n",
" <td>0.620</td>\n",
" <td>0.18</td>\n",
" <td>3.8</td>\n",
" <td>0.176</td>\n",
" <td>52.0</td>\n",
" <td>145.0</td>\n",
" <td>0.99860</td>\n",
" <td>3.16</td>\n",
" <td>0.88</td>\n",
" <td>9.2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>8.9</td>\n",
" <td>0.620</td>\n",
" <td>0.19</td>\n",
" <td>3.9</td>\n",
" <td>0.170</td>\n",
" <td>51.0</td>\n",
" <td>148.0</td>\n",
" <td>0.99860</td>\n",
" <td>3.17</td>\n",
" <td>0.93</td>\n",
" <td>9.2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>8.5</td>\n",
" <td>0.280</td>\n",
" <td>0.56</td>\n",
" <td>1.8</td>\n",
" <td>0.092</td>\n",
" <td>35.0</td>\n",
" <td>103.0</td>\n",
" <td>0.99690</td>\n",
" <td>3.30</td>\n",
" <td>0.75</td>\n",
" <td>10.5</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>8.1</td>\n",
" <td>0.560</td>\n",
" <td>0.28</td>\n",
" <td>1.7</td>\n",
" <td>0.368</td>\n",
" <td>16.0</td>\n",
" <td>56.0</td>\n",
" <td>0.99680</td>\n",
" <td>3.11</td>\n",
" <td>1.28</td>\n",
" <td>9.3</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>7.4</td>\n",
" <td>0.590</td>\n",
" <td>0.08</td>\n",
" <td>4.4</td>\n",
" <td>0.086</td>\n",
" <td>6.0</td>\n",
" <td>29.0</td>\n",
" <td>0.99740</td>\n",
" <td>3.38</td>\n",
" <td>0.50</td>\n",
" <td>9.0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>7.9</td>\n",
" <td>0.320</td>\n",
" <td>0.51</td>\n",
" <td>1.8</td>\n",
" <td>0.341</td>\n",
" <td>17.0</td>\n",
" <td>56.0</td>\n",
" <td>0.99690</td>\n",
" <td>3.04</td>\n",
" <td>1.08</td>\n",
" <td>9.2</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>8.9</td>\n",
" <td>0.220</td>\n",
" <td>0.48</td>\n",
" <td>1.8</td>\n",
" <td>0.077</td>\n",
" <td>29.0</td>\n",
" <td>60.0</td>\n",
" <td>0.99680</td>\n",
" <td>3.39</td>\n",
" <td>0.53</td>\n",
" <td>9.4</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>7.6</td>\n",
" <td>0.390</td>\n",
" <td>0.31</td>\n",
" <td>2.3</td>\n",
" <td>0.082</td>\n",
" <td>23.0</td>\n",
" <td>71.0</td>\n",
" <td>0.99820</td>\n",
" <td>3.52</td>\n",
" <td>0.65</td>\n",
" <td>9.7</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>7.9</td>\n",
" <td>0.430</td>\n",
" <td>0.21</td>\n",
" <td>1.6</td>\n",
" <td>0.106</td>\n",
" <td>10.0</td>\n",
" <td>37.0</td>\n",
" <td>0.99660</td>\n",
" <td>3.17</td>\n",
" <td>0.91</td>\n",
" <td>9.5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>8.5</td>\n",
" <td>0.490</td>\n",
" <td>0.11</td>\n",
" <td>2.3</td>\n",
" <td>0.084</td>\n",
" <td>9.0</td>\n",
" <td>67.0</td>\n",
" <td>0.99680</td>\n",
" <td>3.17</td>\n",
" <td>0.53</td>\n",
" <td>9.4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>6.9</td>\n",
" <td>0.400</td>\n",
" <td>0.14</td>\n",
" <td>2.4</td>\n",
" <td>0.085</td>\n",
" <td>21.0</td>\n",
" <td>40.0</td>\n",
" <td>0.99680</td>\n",
" <td>3.43</td>\n",
" <td>0.63</td>\n",
" <td>9.7</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>6.3</td>\n",
" <td>0.390</td>\n",
" <td>0.16</td>\n",
" <td>1.4</td>\n",
" <td>0.080</td>\n",
" <td>11.0</td>\n",
" <td>23.0</td>\n",
" <td>0.99550</td>\n",
" <td>3.34</td>\n",
" <td>0.56</td>\n",
" <td>9.3</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>7.6</td>\n",
" <td>0.410</td>\n",
" <td>0.24</td>\n",
" <td>1.8</td>\n",
" <td>0.080</td>\n",
" <td>4.0</td>\n",
" <td>11.0</td>\n",
" <td>0.99620</td>\n",
" <td>3.28</td>\n",
" <td>0.59</td>\n",
" <td>9.5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>7.9</td>\n",
" <td>0.430</td>\n",
" <td>0.21</td>\n",
" <td>1.6</td>\n",
" <td>0.106</td>\n",
" <td>10.0</td>\n",
" <td>37.0</td>\n",
" <td>0.99660</td>\n",
" <td>3.17</td>\n",
" <td>0.91</td>\n",
" <td>9.5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>7.1</td>\n",
" <td>0.710</td>\n",
" <td>0.00</td>\n",
" <td>1.9</td>\n",
" <td>0.080</td>\n",
" <td>14.0</td>\n",
" <td>35.0</td>\n",
" <td>0.99720</td>\n",
" <td>3.47</td>\n",
" <td>0.55</td>\n",
" <td>9.4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>7.8</td>\n",
" <td>0.645</td>\n",
" <td>0.00</td>\n",
" <td>2.0</td>\n",
" <td>0.082</td>\n",
" <td>8.0</td>\n",
" <td>16.0</td>\n",
" <td>0.99640</td>\n",
" <td>3.38</td>\n",
" <td>0.59</td>\n",
" <td>9.8</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1569</th>\n",
" <td>6.2</td>\n",
" <td>0.510</td>\n",
" <td>0.14</td>\n",
" <td>1.9</td>\n",
" <td>0.056</td>\n",
" <td>15.0</td>\n",
" <td>34.0</td>\n",
" <td>0.99396</td>\n",
" <td>3.48</td>\n",
" <td>0.57</td>\n",
" <td>11.5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1570</th>\n",
" <td>6.4</td>\n",
" <td>0.360</td>\n",
" <td>0.53</td>\n",
" <td>2.2</td>\n",
" <td>0.230</td>\n",
" <td>19.0</td>\n",
" <td>35.0</td>\n",
" <td>0.99340</td>\n",
" <td>3.37</td>\n",
" <td>0.93</td>\n",
" <td>12.4</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1571</th>\n",
" <td>6.4</td>\n",
" <td>0.380</td>\n",
" <td>0.14</td>\n",
" <td>2.2</td>\n",
" <td>0.038</td>\n",
" <td>15.0</td>\n",
" <td>25.0</td>\n",
" <td>0.99514</td>\n",
" <td>3.44</td>\n",
" <td>0.65</td>\n",
" <td>11.1</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1572</th>\n",
" <td>7.3</td>\n",
" <td>0.690</td>\n",
" <td>0.32</td>\n",
" <td>2.2</td>\n",
" <td>0.069</td>\n",
" <td>35.0</td>\n",
" <td>104.0</td>\n",
" <td>0.99632</td>\n",
" <td>3.33</td>\n",
" <td>0.51</td>\n",
" <td>9.5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1573</th>\n",
" <td>6.0</td>\n",
" <td>0.580</td>\n",
" <td>0.20</td>\n",
" <td>2.4</td>\n",
" <td>0.075</td>\n",
" <td>15.0</td>\n",
" <td>50.0</td>\n",
" <td>0.99467</td>\n",
" <td>3.58</td>\n",
" <td>0.67</td>\n",
" <td>12.5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1574</th>\n",
" <td>5.6</td>\n",
" <td>0.310</td>\n",
" <td>0.78</td>\n",
" <td>13.9</td>\n",
" <td>0.074</td>\n",
" <td>23.0</td>\n",
" <td>92.0</td>\n",
" <td>0.99677</td>\n",
" <td>3.39</td>\n",
" <td>0.48</td>\n",
" <td>10.5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1575</th>\n",
" <td>7.5</td>\n",
" <td>0.520</td>\n",
" <td>0.40</td>\n",
" <td>2.2</td>\n",
" <td>0.060</td>\n",
" <td>12.0</td>\n",
" <td>20.0</td>\n",
" <td>0.99474</td>\n",
" <td>3.26</td>\n",
" <td>0.64</td>\n",
" <td>11.8</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1576</th>\n",
" <td>8.0</td>\n",
" <td>0.300</td>\n",
" <td>0.63</td>\n",
" <td>1.6</td>\n",
" <td>0.081</td>\n",
" <td>16.0</td>\n",
" <td>29.0</td>\n",
" <td>0.99588</td>\n",
" <td>3.30</td>\n",
" <td>0.78</td>\n",
" <td>10.8</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1577</th>\n",
" <td>6.2</td>\n",
" <td>0.700</td>\n",
" <td>0.15</td>\n",
" <td>5.1</td>\n",
" <td>0.076</td>\n",
" <td>13.0</td>\n",
" <td>27.0</td>\n",
" <td>0.99622</td>\n",
" <td>3.54</td>\n",
" <td>0.60</td>\n",
" <td>11.9</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1578</th>\n",
" <td>6.8</td>\n",
" <td>0.670</td>\n",
" <td>0.15</td>\n",
" <td>1.8</td>\n",
" <td>0.118</td>\n",
" <td>13.0</td>\n",
" <td>20.0</td>\n",
" <td>0.99540</td>\n",
" <td>3.42</td>\n",
" <td>0.67</td>\n",
" <td>11.3</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1579</th>\n",
" <td>6.2</td>\n",
" <td>0.560</td>\n",
" <td>0.09</td>\n",
" <td>1.7</td>\n",
" <td>0.053</td>\n",
" <td>24.0</td>\n",
" <td>32.0</td>\n",
" <td>0.99402</td>\n",
" <td>3.54</td>\n",
" <td>0.60</td>\n",
" <td>11.3</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1580</th>\n",
" <td>7.4</td>\n",
" <td>0.350</td>\n",
" <td>0.33</td>\n",
" <td>2.4</td>\n",
" <td>0.068</td>\n",
" <td>9.0</td>\n",
" <td>26.0</td>\n",
" <td>0.99470</td>\n",
" <td>3.36</td>\n",
" <td>0.60</td>\n",
" <td>11.9</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1581</th>\n",
" <td>6.2</td>\n",
" <td>0.560</td>\n",
" <td>0.09</td>\n",
" <td>1.7</td>\n",
" <td>0.053</td>\n",
" <td>24.0</td>\n",
" <td>32.0</td>\n",
" <td>0.99402</td>\n",
" <td>3.54</td>\n",
" <td>0.60</td>\n",
" <td>11.3</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1582</th>\n",
" <td>6.1</td>\n",
" <td>0.715</td>\n",
" <td>0.10</td>\n",
" <td>2.6</td>\n",
" <td>0.053</td>\n",
" <td>13.0</td>\n",
" <td>27.0</td>\n",
" <td>0.99362</td>\n",
" <td>3.57</td>\n",
" <td>0.50</td>\n",
" <td>11.9</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1583</th>\n",
" <td>6.2</td>\n",
" <td>0.460</td>\n",
" <td>0.29</td>\n",
" <td>2.1</td>\n",
" <td>0.074</td>\n",
" <td>32.0</td>\n",
" <td>98.0</td>\n",
" <td>0.99578</td>\n",
" <td>3.33</td>\n",
" <td>0.62</td>\n",
" <td>9.8</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1584</th>\n",
" <td>6.7</td>\n",
" <td>0.320</td>\n",
" <td>0.44</td>\n",
" <td>2.4</td>\n",
" <td>0.061</td>\n",
" <td>24.0</td>\n",
" <td>34.0</td>\n",
" <td>0.99484</td>\n",
" <td>3.29</td>\n",
" <td>0.80</td>\n",
" <td>11.6</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1585</th>\n",
" <td>7.2</td>\n",
" <td>0.390</td>\n",
" <td>0.44</td>\n",
" <td>2.6</td>\n",
" <td>0.066</td>\n",
" <td>22.0</td>\n",
" <td>48.0</td>\n",
" <td>0.99494</td>\n",
" <td>3.30</td>\n",
" <td>0.84</td>\n",
" <td>11.5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1586</th>\n",
" <td>7.5</td>\n",
" <td>0.310</td>\n",
" <td>0.41</td>\n",
" <td>2.4</td>\n",
" <td>0.065</td>\n",
" <td>34.0</td>\n",
" <td>60.0</td>\n",
" <td>0.99492</td>\n",
" <td>3.34</td>\n",
" <td>0.85</td>\n",
" <td>11.4</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587</th>\n",
" <td>5.8</td>\n",
" <td>0.610</td>\n",
" <td>0.11</td>\n",
" <td>1.8</td>\n",
" <td>0.066</td>\n",
" <td>18.0</td>\n",
" <td>28.0</td>\n",
" <td>0.99483</td>\n",
" <td>3.55</td>\n",
" <td>0.66</td>\n",
" <td>10.9</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1588</th>\n",
" <td>7.2</td>\n",
" <td>0.660</td>\n",
" <td>0.33</td>\n",
" <td>2.5</td>\n",
" <td>0.068</td>\n",
" <td>34.0</td>\n",
" <td>102.0</td>\n",
" <td>0.99414</td>\n",
" <td>3.27</td>\n",
" <td>0.78</td>\n",
" <td>12.8</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1589</th>\n",
" <td>6.6</td>\n",
" <td>0.725</td>\n",
" <td>0.20</td>\n",
" <td>7.8</td>\n",
" <td>0.073</td>\n",
" <td>29.0</td>\n",
" <td>79.0</td>\n",
" <td>0.99770</td>\n",
" <td>3.29</td>\n",
" <td>0.54</td>\n",
" <td>9.2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1590</th>\n",
" <td>6.3</td>\n",
" <td>0.550</td>\n",
" <td>0.15</td>\n",
" <td>1.8</td>\n",
" <td>0.077</td>\n",
" <td>26.0</td>\n",
" <td>35.0</td>\n",
" <td>0.99314</td>\n",
" <td>3.32</td>\n",
" <td>0.82</td>\n",
" <td>11.6</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1591</th>\n",
" <td>5.4</td>\n",
" <td>0.740</td>\n",
" <td>0.09</td>\n",
" <td>1.7</td>\n",
" <td>0.089</td>\n",
" <td>16.0</td>\n",
" <td>26.0</td>\n",
" <td>0.99402</td>\n",
" <td>3.67</td>\n",
" <td>0.56</td>\n",
" <td>11.6</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1592</th>\n",
" <td>6.3</td>\n",
" <td>0.510</td>\n",
" <td>0.13</td>\n",
" <td>2.3</td>\n",
" <td>0.076</td>\n",
" <td>29.0</td>\n",
" <td>40.0</td>\n",
" <td>0.99574</td>\n",
" <td>3.42</td>\n",
" <td>0.75</td>\n",
" <td>11.0</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1593</th>\n",
" <td>6.8</td>\n",
" <td>0.620</td>\n",
" <td>0.08</td>\n",
" <td>1.9</td>\n",
" <td>0.068</td>\n",
" <td>28.0</td>\n",
" <td>38.0</td>\n",
" <td>0.99651</td>\n",
" <td>3.42</td>\n",
" <td>0.82</td>\n",
" <td>9.5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1594</th>\n",
" <td>6.2</td>\n",
" <td>0.600</td>\n",
" <td>0.08</td>\n",
" <td>2.0</td>\n",
" <td>0.090</td>\n",
" <td>32.0</td>\n",
" <td>44.0</td>\n",
" <td>0.99490</td>\n",
" <td>3.45</td>\n",
" <td>0.58</td>\n",
" <td>10.5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1595</th>\n",
" <td>5.9</td>\n",
" <td>0.550</td>\n",
" <td>0.10</td>\n",
" <td>2.2</td>\n",
" <td>0.062</td>\n",
" <td>39.0</td>\n",
" <td>51.0</td>\n",
" <td>0.99512</td>\n",
" <td>3.52</td>\n",
" <td>0.76</td>\n",
" <td>11.2</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1596</th>\n",
" <td>6.3</td>\n",
" <td>0.510</td>\n",
" <td>0.13</td>\n",
" <td>2.3</td>\n",
" <td>0.076</td>\n",
" <td>29.0</td>\n",
" <td>40.0</td>\n",
" <td>0.99574</td>\n",
" <td>3.42</td>\n",
" <td>0.75</td>\n",
" <td>11.0</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1597</th>\n",
" <td>5.9</td>\n",
" <td>0.645</td>\n",
" <td>0.12</td>\n",
" <td>2.0</td>\n",
" <td>0.075</td>\n",
" <td>32.0</td>\n",
" <td>44.0</td>\n",
" <td>0.99547</td>\n",
" <td>3.57</td>\n",
" <td>0.71</td>\n",
" <td>10.2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1598</th>\n",
" <td>6.0</td>\n",
" <td>0.310</td>\n",
" <td>0.47</td>\n",
" <td>3.6</td>\n",
" <td>0.067</td>\n",
" <td>18.0</td>\n",
" <td>42.0</td>\n",
" <td>0.99549</td>\n",
" <td>3.39</td>\n",
" <td>0.66</td>\n",
" <td>11.0</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1599 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" fixed acidity volatile acidity citric acid residual sugar chlorides \\\n",
"0 7.4 0.700 0.00 1.9 0.076 \n",
"1 7.8 0.880 0.00 2.6 0.098 \n",
"2 7.8 0.760 0.04 2.3 0.092 \n",
"3 11.2 0.280 0.56 1.9 0.075 \n",
"4 7.4 0.700 0.00 1.9 0.076 \n",
"5 7.4 0.660 0.00 1.8 0.075 \n",
"6 7.9 0.600 0.06 1.6 0.069 \n",
"7 7.3 0.650 0.00 1.2 0.065 \n",
"8 7.8 0.580 0.02 2.0 0.073 \n",
"9 7.5 0.500 0.36 6.1 0.071 \n",
"10 6.7 0.580 0.08 1.8 0.097 \n",
"11 7.5 0.500 0.36 6.1 0.071 \n",
"12 5.6 0.615 0.00 1.6 0.089 \n",
"13 7.8 0.610 0.29 1.6 0.114 \n",
"14 8.9 0.620 0.18 3.8 0.176 \n",
"15 8.9 0.620 0.19 3.9 0.170 \n",
"16 8.5 0.280 0.56 1.8 0.092 \n",
"17 8.1 0.560 0.28 1.7 0.368 \n",
"18 7.4 0.590 0.08 4.4 0.086 \n",
"19 7.9 0.320 0.51 1.8 0.341 \n",
"20 8.9 0.220 0.48 1.8 0.077 \n",
"21 7.6 0.390 0.31 2.3 0.082 \n",
"22 7.9 0.430 0.21 1.6 0.106 \n",
"23 8.5 0.490 0.11 2.3 0.084 \n",
"24 6.9 0.400 0.14 2.4 0.085 \n",
"25 6.3 0.390 0.16 1.4 0.080 \n",
"26 7.6 0.410 0.24 1.8 0.080 \n",
"27 7.9 0.430 0.21 1.6 0.106 \n",
"28 7.1 0.710 0.00 1.9 0.080 \n",
"29 7.8 0.645 0.00 2.0 0.082 \n",
"... ... ... ... ... ... \n",
"1569 6.2 0.510 0.14 1.9 0.056 \n",
"1570 6.4 0.360 0.53 2.2 0.230 \n",
"1571 6.4 0.380 0.14 2.2 0.038 \n",
"1572 7.3 0.690 0.32 2.2 0.069 \n",
"1573 6.0 0.580 0.20 2.4 0.075 \n",
"1574 5.6 0.310 0.78 13.9 0.074 \n",
"1575 7.5 0.520 0.40 2.2 0.060 \n",
"1576 8.0 0.300 0.63 1.6 0.081 \n",
"1577 6.2 0.700 0.15 5.1 0.076 \n",
"1578 6.8 0.670 0.15 1.8 0.118 \n",
"1579 6.2 0.560 0.09 1.7 0.053 \n",
"1580 7.4 0.350 0.33 2.4 0.068 \n",
"1581 6.2 0.560 0.09 1.7 0.053 \n",
"1582 6.1 0.715 0.10 2.6 0.053 \n",
"1583 6.2 0.460 0.29 2.1 0.074 \n",
"1584 6.7 0.320 0.44 2.4 0.061 \n",
"1585 7.2 0.390 0.44 2.6 0.066 \n",
"1586 7.5 0.310 0.41 2.4 0.065 \n",
"1587 5.8 0.610 0.11 1.8 0.066 \n",
"1588 7.2 0.660 0.33 2.5 0.068 \n",
"1589 6.6 0.725 0.20 7.8 0.073 \n",
"1590 6.3 0.550 0.15 1.8 0.077 \n",
"1591 5.4 0.740 0.09 1.7 0.089 \n",
"1592 6.3 0.510 0.13 2.3 0.076 \n",
"1593 6.8 0.620 0.08 1.9 0.068 \n",
"1594 6.2 0.600 0.08 2.0 0.090 \n",
"1595 5.9 0.550 0.10 2.2 0.062 \n",
"1596 6.3 0.510 0.13 2.3 0.076 \n",
"1597 5.9 0.645 0.12 2.0 0.075 \n",
"1598 6.0 0.310 0.47 3.6 0.067 \n",
"\n",
" free sulfur dioxide total sulfur dioxide density pH sulphates \\\n",
"0 11.0 34.0 0.99780 3.51 0.56 \n",
"1 25.0 67.0 0.99680 3.20 0.68 \n",
"2 15.0 54.0 0.99700 3.26 0.65 \n",
"3 17.0 60.0 0.99800 3.16 0.58 \n",
"4 11.0 34.0 0.99780 3.51 0.56 \n",
"5 13.0 40.0 0.99780 3.51 0.56 \n",
"6 15.0 59.0 0.99640 3.30 0.46 \n",
"7 15.0 21.0 0.99460 3.39 0.47 \n",
"8 9.0 18.0 0.99680 3.36 0.57 \n",
"9 17.0 102.0 0.99780 3.35 0.80 \n",
"10 15.0 65.0 0.99590 3.28 0.54 \n",
"11 17.0 102.0 0.99780 3.35 0.80 \n",
"12 16.0 59.0 0.99430 3.58 0.52 \n",
"13 9.0 29.0 0.99740 3.26 1.56 \n",
"14 52.0 145.0 0.99860 3.16 0.88 \n",
"15 51.0 148.0 0.99860 3.17 0.93 \n",
"16 35.0 103.0 0.99690 3.30 0.75 \n",
"17 16.0 56.0 0.99680 3.11 1.28 \n",
"18 6.0 29.0 0.99740 3.38 0.50 \n",
"19 17.0 56.0 0.99690 3.04 1.08 \n",
"20 29.0 60.0 0.99680 3.39 0.53 \n",
"21 23.0 71.0 0.99820 3.52 0.65 \n",
"22 10.0 37.0 0.99660 3.17 0.91 \n",
"23 9.0 67.0 0.99680 3.17 0.53 \n",
"24 21.0 40.0 0.99680 3.43 0.63 \n",
"25 11.0 23.0 0.99550 3.34 0.56 \n",
"26 4.0 11.0 0.99620 3.28 0.59 \n",
"27 10.0 37.0 0.99660 3.17 0.91 \n",
"28 14.0 35.0 0.99720 3.47 0.55 \n",
"29 8.0 16.0 0.99640 3.38 0.59 \n",
"... ... ... ... ... ... \n",
"1569 15.0 34.0 0.99396 3.48 0.57 \n",
"1570 19.0 35.0 0.99340 3.37 0.93 \n",
"1571 15.0 25.0 0.99514 3.44 0.65 \n",
"1572 35.0 104.0 0.99632 3.33 0.51 \n",
"1573 15.0 50.0 0.99467 3.58 0.67 \n",
"1574 23.0 92.0 0.99677 3.39 0.48 \n",
"1575 12.0 20.0 0.99474 3.26 0.64 \n",
"1576 16.0 29.0 0.99588 3.30 0.78 \n",
"1577 13.0 27.0 0.99622 3.54 0.60 \n",
"1578 13.0 20.0 0.99540 3.42 0.67 \n",
"1579 24.0 32.0 0.99402 3.54 0.60 \n",
"1580 9.0 26.0 0.99470 3.36 0.60 \n",
"1581 24.0 32.0 0.99402 3.54 0.60 \n",
"1582 13.0 27.0 0.99362 3.57 0.50 \n",
"1583 32.0 98.0 0.99578 3.33 0.62 \n",
"1584 24.0 34.0 0.99484 3.29 0.80 \n",
"1585 22.0 48.0 0.99494 3.30 0.84 \n",
"1586 34.0 60.0 0.99492 3.34 0.85 \n",
"1587 18.0 28.0 0.99483 3.55 0.66 \n",
"1588 34.0 102.0 0.99414 3.27 0.78 \n",
"1589 29.0 79.0 0.99770 3.29 0.54 \n",
"1590 26.0 35.0 0.99314 3.32 0.82 \n",
"1591 16.0 26.0 0.99402 3.67 0.56 \n",
"1592 29.0 40.0 0.99574 3.42 0.75 \n",
"1593 28.0 38.0 0.99651 3.42 0.82 \n",
"1594 32.0 44.0 0.99490 3.45 0.58 \n",
"1595 39.0 51.0 0.99512 3.52 0.76 \n",
"1596 29.0 40.0 0.99574 3.42 0.75 \n",
"1597 32.0 44.0 0.99547 3.57 0.71 \n",
"1598 18.0 42.0 0.99549 3.39 0.66 \n",
"\n",
" alcohol quality \n",
"0 9.4 5 \n",
"1 9.8 5 \n",
"2 9.8 5 \n",
"3 9.8 6 \n",
"4 9.4 5 \n",
"5 9.4 5 \n",
"6 9.4 5 \n",
"7 10.0 7 \n",
"8 9.5 7 \n",
"9 10.5 5 \n",
"10 9.2 5 \n",
"11 10.5 5 \n",
"12 9.9 5 \n",
"13 9.1 5 \n",
"14 9.2 5 \n",
"15 9.2 5 \n",
"16 10.5 7 \n",
"17 9.3 5 \n",
"18 9.0 4 \n",
"19 9.2 6 \n",
"20 9.4 6 \n",
"21 9.7 5 \n",
"22 9.5 5 \n",
"23 9.4 5 \n",
"24 9.7 6 \n",
"25 9.3 5 \n",
"26 9.5 5 \n",
"27 9.5 5 \n",
"28 9.4 5 \n",
"29 9.8 6 \n",
"... ... ... \n",
"1569 11.5 6 \n",
"1570 12.4 6 \n",
"1571 11.1 6 \n",
"1572 9.5 5 \n",
"1573 12.5 6 \n",
"1574 10.5 6 \n",
"1575 11.8 6 \n",
"1576 10.8 6 \n",
"1577 11.9 6 \n",
"1578 11.3 6 \n",
"1579 11.3 5 \n",
"1580 11.9 6 \n",
"1581 11.3 5 \n",
"1582 11.9 5 \n",
"1583 9.8 5 \n",
"1584 11.6 7 \n",
"1585 11.5 6 \n",
"1586 11.4 6 \n",
"1587 10.9 6 \n",
"1588 12.8 6 \n",
"1589 9.2 5 \n",
"1590 11.6 6 \n",
"1591 11.6 6 \n",
"1592 11.0 6 \n",
"1593 9.5 6 \n",
"1594 10.5 5 \n",
"1595 11.2 6 \n",
"1596 11.0 6 \n",
"1597 10.2 5 \n",
"1598 11.0 6 \n",
"\n",
"[1599 rows x 12 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"red\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 4898 entries, 0 to 4897\n",
"Data columns (total 12 columns):\n",
"fixed acidity 4898 non-null float64\n",
"volatile acidity 4898 non-null float64\n",
"citric acid 4898 non-null float64\n",
"residual sugar 4898 non-null float64\n",
"chlorides 4898 non-null float64\n",
"free sulfur dioxide 4898 non-null float64\n",
"total sulfur dioxide 4898 non-null float64\n",
"density 4898 non-null float64\n",
"pH 4898 non-null float64\n",
"sulphates 4898 non-null float64\n",
"alcohol 4898 non-null float64\n",
"quality 4898 non-null int64\n",
"dtypes: float64(11), int64(1)\n",
"memory usage: 459.3 KB\n",
"None\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1599 entries, 0 to 1598\n",
"Data columns (total 12 columns):\n",
"fixed acidity 1599 non-null float64\n",
"volatile acidity 1599 non-null float64\n",
"citric acid 1599 non-null float64\n",
"residual sugar 1599 non-null float64\n",
"chlorides 1599 non-null float64\n",
"free sulfur dioxide 1599 non-null float64\n",
"total sulfur dioxide 1599 non-null float64\n",
"density 1599 non-null float64\n",
"pH 1599 non-null float64\n",
"sulphates 1599 non-null float64\n",
"alcohol 1599 non-null float64\n",
"quality 1599 non-null int64\n",
"dtypes: float64(11), int64(1)\n",
"memory usage: 150.0 KB\n",
"None\n"
]
}
],
"source": [
"# Print info on white wine\n",
"print(white.info())\n",
"\n",
"# Print info on red wine\n",
"print(red.info())"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
" <th>pH</th>\n",
" <th>sulphates</th>\n",
" <th>alcohol</th>\n",
" <th>quality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>4898.000000</td>\n",
" <td>4898.000000</td>\n",
" <td>4898.000000</td>\n",
" <td>4898.000000</td>\n",
" <td>4898.000000</td>\n",
" <td>4898.000000</td>\n",
" <td>4898.000000</td>\n",
" <td>4898.000000</td>\n",
" <td>4898.000000</td>\n",
" <td>4898.000000</td>\n",
" <td>4898.000000</td>\n",
" <td>4898.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>6.854788</td>\n",
" <td>0.278241</td>\n",
" <td>0.334192</td>\n",
" <td>6.391415</td>\n",
" <td>0.045772</td>\n",
" <td>35.308085</td>\n",
" <td>138.360657</td>\n",
" <td>0.994027</td>\n",
" <td>3.188267</td>\n",
" <td>0.489847</td>\n",
" <td>10.514267</td>\n",
" <td>5.877909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.843868</td>\n",
" <td>0.100795</td>\n",
" <td>0.121020</td>\n",
" <td>5.072058</td>\n",
" <td>0.021848</td>\n",
" <td>17.007137</td>\n",
" <td>42.498065</td>\n",
" <td>0.002991</td>\n",
" <td>0.151001</td>\n",
" <td>0.114126</td>\n",
" <td>1.230621</td>\n",
" <td>0.885639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>3.800000</td>\n",
" <td>0.080000</td>\n",
" <td>0.000000</td>\n",
" <td>0.600000</td>\n",
" <td>0.009000</td>\n",
" <td>2.000000</td>\n",
" <td>9.000000</td>\n",
" <td>0.987110</td>\n",
" <td>2.720000</td>\n",
" <td>0.220000</td>\n",
" <td>8.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>6.300000</td>\n",
" <td>0.210000</td>\n",
" <td>0.270000</td>\n",
" <td>1.700000</td>\n",
" <td>0.036000</td>\n",
" <td>23.000000</td>\n",
" <td>108.000000</td>\n",
" <td>0.991723</td>\n",
" <td>3.090000</td>\n",
" <td>0.410000</td>\n",
" <td>9.500000</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>6.800000</td>\n",
" <td>0.260000</td>\n",
" <td>0.320000</td>\n",
" <td>5.200000</td>\n",
" <td>0.043000</td>\n",
" <td>34.000000</td>\n",
" <td>134.000000</td>\n",
" <td>0.993740</td>\n",
" <td>3.180000</td>\n",
" <td>0.470000</td>\n",
" <td>10.400000</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>7.300000</td>\n",
" <td>0.320000</td>\n",
" <td>0.390000</td>\n",
" <td>9.900000</td>\n",
" <td>0.050000</td>\n",
" <td>46.000000</td>\n",
" <td>167.000000</td>\n",
" <td>0.996100</td>\n",
" <td>3.280000</td>\n",
" <td>0.550000</td>\n",
" <td>11.400000</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>14.200000</td>\n",
" <td>1.100000</td>\n",
" <td>1.660000</td>\n",
" <td>65.800000</td>\n",
" <td>0.346000</td>\n",
" <td>289.000000</td>\n",
" <td>440.000000</td>\n",
" <td>1.038980</td>\n",
" <td>3.820000</td>\n",
" <td>1.080000</td>\n",
" <td>14.200000</td>\n",
" <td>9.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fixed acidity volatile acidity citric acid residual sugar \\\n",
"count 4898.000000 4898.000000 4898.000000 4898.000000 \n",
"mean 6.854788 0.278241 0.334192 6.391415 \n",
"std 0.843868 0.100795 0.121020 5.072058 \n",
"min 3.800000 0.080000 0.000000 0.600000 \n",
"25% 6.300000 0.210000 0.270000 1.700000 \n",
"50% 6.800000 0.260000 0.320000 5.200000 \n",
"75% 7.300000 0.320000 0.390000 9.900000 \n",
"max 14.200000 1.100000 1.660000 65.800000 \n",
"\n",
" chlorides free sulfur dioxide total sulfur dioxide density \\\n",
"count 4898.000000 4898.000000 4898.000000 4898.000000 \n",
"mean 0.045772 35.308085 138.360657 0.994027 \n",
"std 0.021848 17.007137 42.498065 0.002991 \n",
"min 0.009000 2.000000 9.000000 0.987110 \n",
"25% 0.036000 23.000000 108.000000 0.991723 \n",
"50% 0.043000 34.000000 134.000000 0.993740 \n",
"75% 0.050000 46.000000 167.000000 0.996100 \n",
"max 0.346000 289.000000 440.000000 1.038980 \n",
"\n",
" pH sulphates alcohol quality \n",
"count 4898.000000 4898.000000 4898.000000 4898.000000 \n",
"mean 3.188267 0.489847 10.514267 5.877909 \n",
"std 0.151001 0.114126 1.230621 0.885639 \n",
"min 2.720000 0.220000 8.000000 3.000000 \n",
"25% 3.090000 0.410000 9.500000 5.000000 \n",
"50% 3.180000 0.470000 10.400000 6.000000 \n",
"75% 3.280000 0.550000 11.400000 6.000000 \n",
"max 3.820000 1.080000 14.200000 9.000000 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First rows of `red` \n",
"red.head()\n",
"\n",
"# Last rows of `white`\n",
"white.tail()\n",
"\n",
"# Take a sample of 5 rows of `red`\n",
"red.sample(5)\n",
"\n",
"# Describe `white`\n",
"white.describe()\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
" <th>pH</th>\n",
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" <th>quality</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>False</td>\n",
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" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
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" <th>2</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1569</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1570</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1571</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1572</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1573</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1574</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1575</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1576</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1577</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1578</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1579</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1580</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1581</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1582</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1583</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1584</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1585</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1586</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1588</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1589</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1590</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1591</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1592</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1593</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1594</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1595</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1596</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1597</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1598</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1599 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" fixed acidity volatile acidity citric acid residual sugar chlorides \\\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",
"5 False False False False False \n",
"6 False False False False False \n",
"7 False False False False False \n",
"8 False False False False False \n",
"9 False False False False False \n",
"10 False False False False False \n",
"11 False False False False False \n",
"12 False False False False False \n",
"13 False False False False False \n",
"14 False False False False False \n",
"15 False False False False False \n",
"16 False False False False False \n",
"17 False False False False False \n",
"18 False False False False False \n",
"19 False False False False False \n",
"20 False False False False False \n",
"21 False False False False False \n",
"22 False False False False False \n",
"23 False False False False False \n",
"24 False False False False False \n",
"25 False False False False False \n",
"26 False False False False False \n",
"27 False False False False False \n",
"28 False False False False False \n",
"29 False False False False False \n",
"... ... ... ... ... ... \n",
"1569 False False False False False \n",
"1570 False False False False False \n",
"1571 False False False False False \n",
"1572 False False False False False \n",
"1573 False False False False False \n",
"1574 False False False False False \n",
"1575 False False False False False \n",
"1576 False False False False False \n",
"1577 False False False False False \n",
"1578 False False False False False \n",
"1579 False False False False False \n",
"1580 False False False False False \n",
"1581 False False False False False \n",
"1582 False False False False False \n",
"1583 False False False False False \n",
"1584 False False False False False \n",
"1585 False False False False False \n",
"1586 False False False False False \n",
"1587 False False False False False \n",
"1588 False False False False False \n",
"1589 False False False False False \n",
"1590 False False False False False \n",
"1591 False False False False False \n",
"1592 False False False False False \n",
"1593 False False False False False \n",
"1594 False False False False False \n",
"1595 False False False False False \n",
"1596 False False False False False \n",
"1597 False False False False False \n",
"1598 False False False False False \n",
"\n",
" free sulfur dioxide total sulfur dioxide density pH sulphates \\\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",
"5 False False False False False \n",
"6 False False False False False \n",
"7 False False False False False \n",
"8 False False False False False \n",
"9 False False False False False \n",
"10 False False False False False \n",
"11 False False False False False \n",
"12 False False False False False \n",
"13 False False False False False \n",
"14 False False False False False \n",
"15 False False False False False \n",
"16 False False False False False \n",
"17 False False False False False \n",
"18 False False False False False \n",
"19 False False False False False \n",
"20 False False False False False \n",
"21 False False False False False \n",
"22 False False False False False \n",
"23 False False False False False \n",
"24 False False False False False \n",
"25 False False False False False \n",
"26 False False False False False \n",
"27 False False False False False \n",
"28 False False False False False \n",
"29 False False False False False \n",
"... ... ... ... ... ... \n",
"1569 False False False False False \n",
"1570 False False False False False \n",
"1571 False False False False False \n",
"1572 False False False False False \n",
"1573 False False False False False \n",
"1574 False False False False False \n",
"1575 False False False False False \n",
"1576 False False False False False \n",
"1577 False False False False False \n",
"1578 False False False False False \n",
"1579 False False False False False \n",
"1580 False False False False False \n",
"1581 False False False False False \n",
"1582 False False False False False \n",
"1583 False False False False False \n",
"1584 False False False False False \n",
"1585 False False False False False \n",
"1586 False False False False False \n",
"1587 False False False False False \n",
"1588 False False False False False \n",
"1589 False False False False False \n",
"1590 False False False False False \n",
"1591 False False False False False \n",
"1592 False False False False False \n",
"1593 False False False False False \n",
"1594 False False False False False \n",
"1595 False False False False False \n",
"1596 False False False False False \n",
"1597 False False False False False \n",
"1598 False False False False False \n",
"\n",
" alcohol quality \n",
"0 False False \n",
"1 False False \n",
"2 False False \n",
"3 False False \n",
"4 False False \n",
"5 False False \n",
"6 False False \n",
"7 False False \n",
"8 False False \n",
"9 False False \n",
"10 False False \n",
"11 False False \n",
"12 False False \n",
"13 False False \n",
"14 False False \n",
"15 False False \n",
"16 False False \n",
"17 False False \n",
"18 False False \n",
"19 False False \n",
"20 False False \n",
"21 False False \n",
"22 False False \n",
"23 False False \n",
"24 False False \n",
"25 False False \n",
"26 False False \n",
"27 False False \n",
"28 False False \n",
"29 False False \n",
"... ... ... \n",
"1569 False False \n",
"1570 False False \n",
"1571 False False \n",
"1572 False False \n",
"1573 False False \n",
"1574 False False \n",
"1575 False False \n",
"1576 False False \n",
"1577 False False \n",
"1578 False False \n",
"1579 False False \n",
"1580 False False \n",
"1581 False False \n",
"1582 False False \n",
"1583 False False \n",
"1584 False False \n",
"1585 False False \n",
"1586 False False \n",
"1587 False False \n",
"1588 False False \n",
"1589 False False \n",
"1590 False False \n",
"1591 False False \n",
"1592 False False \n",
"1593 False False \n",
"1594 False False \n",
"1595 False False \n",
"1596 False False \n",
"1597 False False \n",
"1598 False False \n",
"\n",
"[1599 rows x 12 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.isnull(red)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 0.98, 'Distribution of Alcohol in % Vol')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"fig, ax = plt.subplots(1, 2)\n",
"\n",
"ax[0].hist(red.alcohol, 10, facecolor='red', alpha=0.5, label=\"Red wine\")\n",
"ax[1].hist(white.alcohol, 10, facecolor='white', ec=\"black\", lw=0.5, alpha=0.5, label=\"White wine\")\n",
"\n",
"fig.subplots_adjust(left=0, right=1, bottom=0, top=0.5, hspace=0.05, wspace=1)\n",
"ax[0].set_ylim([0, 1000])\n",
"ax[0].set_xlabel(\"Alcohol in % Vol\")\n",
"ax[0].set_ylabel(\"Frequency\")\n",
"ax[1].set_xlabel(\"Alcohol in % Vol\")\n",
"ax[1].set_ylabel(\"Frequency\")\n",
"#ax[0].legend(loc='best')\n",
"#ax[1].legend(loc='best')\n",
"fig.suptitle(\"Distribution of Alcohol in % Vol\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(array([ 0, 7, 673, 452, 305, 133, 21, 8], dtype=int64), array([ 7, 8, 9, 10, 11, 12, 13, 14, 15]))\n",
"(array([ 0, 317, 1606, 1256, 906, 675, 131, 7], dtype=int64), array([ 7, 8, 9, 10, 11, 12, 13, 14, 15]))\n"
]
}
],
"source": [
"import numpy as np\n",
"print(np.histogram(red.alcohol, bins=[7,8,9,10,11,12,13,14,15]))\n",
"print(np.histogram(white.alcohol, bins=[7,8,9,10,11,12,13,14,15]))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n",
"\n",
"ax[0].scatter(red['quality'], red[\"sulphates\"], color=\"red\")\n",
"ax[1].scatter(white['quality'], white['sulphates'], color=\"white\", edgecolors=\"black\", lw=0.5)\n",
"\n",
"ax[0].set_title(\"Red Wine\")\n",
"ax[1].set_title(\"White Wine\")\n",
"ax[0].set_xlabel(\"Quality\")\n",
"ax[1].set_xlabel(\"Quality\")\n",
"ax[0].set_ylabel(\"Sulphates\")\n",
"ax[1].set_ylabel(\"Sulphates\")\n",
"ax[0].set_xlim([0,10])\n",
"ax[1].set_xlim([0,10])\n",
"ax[0].set_ylim([0,2.5])\n",
"ax[1].set_ylim([0,2.5])\n",
"fig.subplots_adjust(wspace=0.5)\n",
"fig.suptitle(\"Wine Quality by Amount of Sulphates\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n",
"\n",
"ax[0].scatter(red['quality'], red[\"density\"], color=\"red\")\n",
"ax[1].scatter(white['quality'], white['alcohol'], color=\"white\", edgecolors=\"black\", lw=0.5)\n",
"\n",
"ax[0].set_title(\"Red Wine\")\n",
"ax[1].set_title(\"White Wine\")\n",
"ax[0].set_xlabel(\"Quality\")\n",
"ax[1].set_xlabel(\"Quality\")\n",
"ax[0].set_ylabel(\"Sulphates\")\n",
"ax[1].set_ylabel(\"Sulphates\")\n",
"ax[0].set_xlim([0,10])\n",
"ax[1].set_xlim([0,10])\n",
"ax[0].set_ylim([0,2.5])\n",
"ax[1].set_ylim([0,2.5])\n",
"fig.subplots_adjust(wspace=0.5)\n",
"fig.suptitle(\"Wine Quality by Amount of Sulphates\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
"'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
"'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
"'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
"'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
"'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
"'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
"'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
"'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
"'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
"'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
"'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
"'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"np.random.seed(570)\n",
"\n",
"redlabels = np.unique(red['quality'])\n",
"whitelabels = np.unique(white['quality'])\n",
"\n",
"import matplotlib.pyplot as plt\n",
"fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n",
"redcolors = np.random.rand(6,4)\n",
"whitecolors = np.append(redcolors, np.random.rand(1,4), axis=0)\n",
"\n",
"for i in range(len(redcolors)):\n",
" redy = red['alcohol'][red.quality == redlabels[i]]\n",
" redx = red['volatile acidity'][red.quality == redlabels[i]]\n",
" ax[0].scatter(redx, redy, c=redcolors[i])\n",
"for i in range(len(whitecolors)):\n",
" whitey = white['alcohol'][white.quality == whitelabels[i]]\n",
" whitex = white['volatile acidity'][white.quality == whitelabels[i]]\n",
" ax[1].scatter(whitex, whitey, c=whitecolors[i])\n",
" \n",
"ax[0].set_title(\"Red Wine\")\n",
"ax[1].set_title(\"White Wine\")\n",
"ax[0].set_xlim([0,1.7])\n",
"ax[1].set_xlim([0,1.7])\n",
"ax[0].set_ylim([5,15.5])\n",
"ax[1].set_ylim([5,15.5])\n",
"ax[0].set_xlabel(\"Volatile Acidity\")\n",
"ax[0].set_ylabel(\"Alcohol\")\n",
"ax[1].set_xlabel(\"Volatile Acidity\")\n",
"ax[1].set_ylabel(\"Alcohol\") \n",
"#ax[0].legend(redlabels, loc='best', bbox_to_anchor=(1.3, 1))\n",
"ax[1].legend(whitelabels, loc='best', bbox_to_anchor=(1.3, 1))\n",
"#fig.suptitle(\"Alcohol - Volatile Acidity\")\n",
"fig.subplots_adjust(top=0.85, wspace=0.7)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# Add `type` column to `red` with value 1\n",
"red['type'] = 1\n",
"\n",
"# Add `type` column to `white` with value 0\n",
"white['type'] = 0\n",
"\n",
"# Append `white` to `red`\n",
"wines = red.append(white, ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x179e1886630>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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fbuuXGwRBUBZR67JtVgb2sf3NPB77fWBL229K+m/gO9lR7QisYtuSai1g6VPTrQZH2P5PXsR4g6RRth+odWIuxtxXH4rsRCoptgawBClMPzMfO9n20dnuecC2wF/ysUVsb9Zg/4MgCPqfaUNfj26w5+j+YfvOvL0+Sah0nKT7gb1JKgCvkYRNz5C0E/BW0UAdTbdG2E3SvaRCy6vle9ej1z7UYGPgEts9tp8jjTVX2ELSXZIeJOnVrVY4VksPb5a1KXe98UQftw6CICiREkuADRaD7eiK+m8Crrc9Or9Wtf3VPFe1HqkA8w7ANVU2etN0m8aszzgCQNLywCHAp2yPAq6sHKtFL32YYT8PQc5T6NNsSBoBnALsYnsN4PSq+9bSw8P2abbXsb3OJxZYsV43gyAIyqcLhi4H29EVuRPYSNJHASTNL2mlPE+3cJ7EPAgYXbyoD023ScDoPGe2LMlZASxEciqvSlqSGqreRXrpwyRg7by9PVCpr3QbsHO+75KkxY8w06m9mG3OmN8LgiDoSLogohvsOboZ2H5B0hjgQknz5ubvkwRS/5yjIQEH17i8nqbbOOBp4EHgIVLSC7YnSroPeJi0RmNcH91bsE4fTs/td5NEVisR2WXAp/I9/w7cBbxq+xVJp+f+TKLNRZBBEAT9TV/LBoYCg+bobE8CVq9q+xuwbo3T16tuaETTLaeq1lTtLurNVbVvXqPt2Tp9mMKsOnWH5/YeSYfYfkNJXfxuknPD9vdJDrzP+wZBEAw6HRypNUrHRHRdyBU5O3Me4Mc5KSUIgmBoUV4JsEEjHF0/ERFaEARdQUR0QRAEQTfjcHTBQLPXMv8u1d6IJcufaL71jmqJqfYpWyj1kHuOLtUewBHrHFGqvRd4t1R7ACesMbl0m9tPXbpUe5tsVv4o/6F3LVaqvUeHlT+cd8Hu8/R90mAQji4IgiDoaiLrMgiCIOhqIqILgiAIuhlPj4guCIIg6Ga6IKLrpBJgA0a1vE6hvWnRVklLS7q0zrEQcQ2CYGgTJcDmbLJA6jNEzcogCLqUblheMEdEdJK+nAVTJ2YNOIBNJd0u6ak60d0ISWdJelDSfZK2yO1jsijsX4DrilGgpPkkXZTvdTEwX8Few6KyQRAEHUNEdJ2PpNWAI4CNbL+YRVT/H7AUSTduFWAsUD38uD+A7TUkrUJyaivlYxsAo7Jw68jCNd8A3rI9StIochHpNkVlgyAIBg1P61wH1ihzQkT3SeBS2y8C2P5Pbr88C6M+AixZ47qNySKuth8D/gFUHN31BTtFNgXOz9c8AFQUy1sSla1QFF49f8ozjT95EARBu0RENySoJ8z6TtU5ta6rR02B1Eyte1VEZfeY7YC0HknS5wvAASTHPKtB+zTgNIBnNtyic/+agiDoPob+6oI5IqK7Adgty+WQhy4b4RayxE8eslwOeLyJa1YHRuX2lkRlgyAIBhv3uOFXp9L1EZ3thyUdA9wsaTpwX4OXngKcKulBYBowxvY7Um+BHr8FzpL0AHA/SYeuXVHZIAiCwaMLIrqud3QAts8Bzunl+AL55ySyGKztqcCYGueeDZxd2C9e8zZpCLLWPRoWlQ2CIOgUOjlSa5Q5wtEFQRAEreFpg92D9glHFwRBENQnhi6DgebwZxrNpWmMVya/0/dJTXL4sKml29xsw5dLtVe2dhzAMROOKdXetJv+UKo9gKsPeKR0mw/N2+u8ddPcdtcHS7UHsFivSdTNs/Hb5Q/nbX7uC6XbnHBc+zYcji4IgiDoasLRBUEQBN1MN0R0c8I6uiAIgqBF3NP4qy8kbS3pcUlPSjqsxvH9cn3h+yXdJmnVMp4hHF0QBEFQF09Xw6/ekDQc+A2wDakk4h41HNkfbK9hezTwc1Jd4rYJRxcEQRDUpcSIbj3gSdtP2X4XuAjYfpZ72a8Vdt9H7ZKKTdOUo5N0oKRHJV1Qxs1bRdLmkq7I2/NK+msOdXcvyf4MYVZJZ7QaPku6vS/7QRAEnYx71PCrD5YB/lXYn5zbZkHS/pL+jxTRHVjGMzSbjPJNYBvbT1d1bC570JYVrgXMnUPdhmimv7a/1mrHbG/Y6rVBEASdQDPJKJL2BfYtNJ2Wi9JD7UL5s0Vstn8D/EbSF0mlEvduvAe1aTiik3Qq8BFgrKSDJR0l6TRJ1wHnShou6XhJ47OI6H8Vrj200P6jGraH5yjnoTwReXBuv0nSOnl7MUmTqq5bgiSLMzpHdCtImpT135C0jqSb8vYs/a2yI0knZwHUK4ElCseKfdgj9+8hScfltg9LeiL3b5ikWyV9Oh97owH7a0u6WdI9kq6VtFSj70kQBEF/Y6uJl0+zvU7hdVrB1GRg2cL+h4DedMcuAnYo4xkajuhs7ydpa2CLLGB6FLA2sLHtt7Mnf9X2urlw8bjsVFbMr/VIHn2spE1t31IwPxpYxvbqAGpQgNT285K+Bhxie9t8bW+XzOhvVfuOwMrAGiRtukeAM4snSFoaOC7beJkkxLqD7cuz0zsVuAt4xPZ1jdiXNDdwErB9Lvy8O3AM8JWqe8/4lrT++9dipQWX7/0XEwRBUBI900pbbD8eWFHS8sC/SXWBv1g8QdKKtp/Iu58DnqAE2l1HN7bgND4NjCrMPS1McnCfzq+KasACub3o6J4CPiLpJOBKoNpRlMXYGk4OkmDqhbanA89I+luNc9YFbrL9AkCep9yUJOB6hqRdgf2oLbVTz/7KpILQ12cHPRx4tvrioh7d3iN3HvoVVoMgGDK4pE8c29MkHQBcS/qsOzOryxwNTLA9FjhA0pbAe6SAou1hS2jf0RUFSAV8y/a1xRMkfQb4me3f1TNi+2VJawKfAfYHdiNFNdOYObw6osE+9XZNs4KpRep+rZE0PykMh+TIX2/QvoCHbW/Qx72DIAgGhQaSTBq3lbQ3r6pqO7Kw/e3SblagzOUF1wLfyMNxKAmLvi+3f0VJZBRJy+S5tRnkObVhti8DfgB8PB+aRBoqBGg0S7F4zc4NXnML8IU8V7gUsEWNc+4CNstzccOBPYCb87HjgAuAI4HTm7D/OLC4pA0AJM0tabUG+xwEQdDvlJh1OWiUWQLsDGAkcK/SONwLwA62r5P0MeCOPDz3BrAX8Hzh2mVIgqUVx3t4/nkC8EdJXwJqDSfW4kfA7yX9D8k5NcKfgE8CDwJ/Z6YDm4HtZyUdDtxIisSusv1nSZuRhjU3sj1d0s6S9rF9Vl/2bb+bh3pPlLQw6f34FfBwg/0OgiDoV8oauhxM5G54ijmIsufoXunpB/WCafOUbnOVzcpVL/jpHeVXyA/1gnJ4U+UXVxw2BNQLfjhscuk2Jzx7a9sP/tQan274YT/y4HUdGdZFUecgCIKgLj19lPYaCoSjC4IgCOrS43B0wQDzpt8r1d5r/TB0uUCj+bFNMNcyC5Vq7wXeLdUelD/UONfmX+z7pCYZxvdLt/mcyv2bfJvppdoDGF7y0OVzc5f/R37/80+VbrMMHI4uCIIg6GY6OZuyUcLRBUEQBHXphnzFcHRBEARBXSKiC4IgCLqa6T1DX7a01yeQtIikb/ZlRNJIJUmFRs57qJkO1rFzlKRD8vYqWbngPkkrtGs72ywqINTUlGvAxjqSTuzLfhAEQSdjN/7qVPpy1YuQNOj6YiRVVagHkB2AP9tey/b/NXKBpGZUG1rSlLM9wXYpooFBEASDRY/V8KtT6cvRHQuskCOm47Ou2vGaqRu3e+G8TfJ5B+fI7VZJ9+ZXr85C0lKSbsnXPyRpk9z+RuGcXSSdXXXdZ4GDgK9JurE6YpR0SJYTqujK/VTSzcC3q+x8QNJ1OSr8HYUCzppVU262Z5e0o5LCufJz/F3SBzWrCnpv9veSdHd+9t/lOppBEAQdQTN6dJ1KX47uMOD/bI+2fSiwE0mGZk1gS+D4XKT4MODWfN4vSXUst7L9cWB3oOYQXoEvAtdmlfA1gfsb6XyuhH0q8EvbtQoxV7OI7c1s/6Kq/YfAbbbXAsYCy9W4tuaz2/4T8BxJdeF04Ie2n2vEfq4BujupTuZoYDqwZwPPEQRBMCB0w9Bls8koGzNTV21Kjo7WBV6rOm9u4GRJlQ/vlfqwO56ZQqSX227I0bXAxXXaNyU5MmxfKalWYcV6zz4W+BbwEHCn7QubsP8pktLC+Fzwej5mLXYNzCq8utb7R/GRBT7cwKMGQRC0T9cno9Sg0dj0YGAKKfpZB+i1ym9WG9+UpDp7nqQvVw4VTmukFEFRi67WNf2iR0dSX+gBliwoMDRiX8A5ORIebXtl20fNdmFBnj6cXBAEA8mcMEf3OrBgYf8WYPesq7Y4yTndXeO8hYFnbfcAXyKpydZF0oeB522fDvyemXp0UyR9LDuPHRt4ninAEnlObF5g2wauqTzXnrkv2wCL1jlntmfPiS1nkYZfHwW+04T9G4BdlPX5JL0//y6CIAg6Ajfx6lR6Hbq0/ZKkcTnB42rge8AGwETSc33P9nOSXgKmSZoInA2cAlwmaVeSfltvkRTA5sChkt4j6dVVIrrDgCuAf5GGBhfoo7/vKcmy3wU8DTzWx30r/Ai4UNK9JK24f9Y450/UfvYjSfOTt0q6nzQMeWUj9m0/Iun7wHXZmb9Hmuv7R4P9DoIg6Fc6OVJrlNCjG2Ls8uHtSn3DXpr+dpnmADhpnnlLtzlyp3KTUb91afm1Ek45Ya1S7fVHUecrVy+/qPN1I6aVam8oFHXeYFr5RZ3/6/kbS7c57d1/t/3g4z64S8OfORs9d2lHesWojBIEQRDUpXwZ3IEnHF0QBEFQl+ldMHQZji4IgiCoS0/Jw76DQTi6IcZZ25QrGPryhPIHJp781/yl21x+oblLtXfCGpNLtQdw9QGPlGqvP0RSP/fQT0q3+d4aPyjV3rpL1lrG2h4rPfpoqfbe+uC6pdoDeGH7FUu3WQYORxcEQRB0MzFHFwRBEHQ1EdEFQRAEXU25i0cGh3B0QRAEQV26IaIb+tU6+4GisGtJ9q5SErFtSMg2CIKgU+hR469OJRzdAGD7s7ZfoXEh2yAIgo6gBzX86lTC0WUkHSHpcUl/BVbObStIukbSPVlIdpXcfrakEyXdLukpSbvk9noCspMkLcbsQrbnSdq+0IcLJG034A8fBEFQh24o6hyODpC0NvAFYC2SblxlkcxpwLdsrw0cQipWXWEpkkbdtiQHBn0LyFYL2Z4B7JP7sDCwIXBVuU8XBEHQOj1NvPpC0tY5oHhS0mE1js8r6eJ8/C5JI8t4hnB0iU2AP9l+y/ZrJDHVESTHc0lWJfgdyblVuNx2j+1HgCVz23hgH0lHAWvYfr23m9q+GfholunZA7jM9mxJTpL2lTRB0oSzHvlXe08aBEHQBNOlhl+9IWk48BtgG2BVYA9Jq1ad9lXgZdsfBX4JHFfGM4Sjm0l15D0MeKUgijra9scKx98pbAt6FZDtjfNIWnX7kHTtZu9YQXh1n1WXbfBxgiAI2qfEiG494EnbT9l+F7gI2L7qnO2Bc/L2pcCnpD48aAOEo0vcAuwoaT5JCwKfB94Cns6aeiixZm9GehGQrVAtUAtJv+8gANsPt/sgQRAEZdJM1mVx9Cm/9i2YWoakLVphcm6j1jl5dOtV4APtPkOsowNs3yvpYtKc2j+AW/OhPYHfZnHUuUnfQCb2YmpzagvIVu4zi5Ct7UNtT5H0KHB5qQ8VBEFQAs1kU9o+jZTbUItahqpH0ho5p2nC0WVsHwMcU+PQ1jXOHVO1v0D+eQ4zw+7i8ZGF7VnUNCXND6wIXNhCt4MgCPqVErMpJwPFuZcPAc/UOWeypLmAhYH/tHvjGLocRCRtCTwGnGT71cHuTxAEQTUlLhgfD6woaXlJ85Ay3cdWnTMW2Dtv7wL8zXZEdEMZ238FlhvsfgRBENRjekl2bE+TdABwLTAcONP2w5KOBibYHkvKbThP0pOkSO4LZdw7HF0QBEFQlzJLe9m+iqq1wraPLGxPBXYt746JcHRDjEOuqU7abI/DF3mlVHsAp4yYWrrNP5xZrirW9lOXLtUewEPzllsC6Tm9V6o9KF8kFWCHB39cqr3H1zuwVHsANy66fqn2nn133lLtAbz7YttTUf1C6NEFQRAEXU04uiAIgqCrcefWam44Q9QrAAAgAElEQVSYcHRBEARBXUJ4NQiCIOhqOlmVoFFiHd0gIekmSesU9kfmiilBEAQdQzcIr0ZEFwRBENQlklGCPsl6StcAd5H07v5OVQ3MIAiCTqUbHF0MXQ4MKwOn2R4FvAZ8M7dfkNXG76cXwdViRfBHX39qALobBEGQCIXxoFH+ZXtc3j6fpEwOsGdF6w74bL2Li3p0H1vwI/3d1yAIghlMU+OvTiWGLgeG6i87nfzlJwiCYAbd8GEVEd3AsJykDfL2HsBtg9mZIAiCRunBDb86lXB0A8OjwN6SHgDeD/x2kPsTBEHQED1NvDqVGLocGHps71fVtnlxx/YkYPWB6lAQBEEjdG6c1jjh6IIgCIK6dHKk1ijh6PqZiNSCIBjKTNPQj+nC0Q0xfrj0i6Xae+Pl8nW19pn6vtJt3jJfufY22ey5cg0Ct931wVLtvV2atvNM1l3y5dJtlq0ft/LdJ5ZqD+CPo47s+6QmWKCn/Dhn/jUXKt1mGQx9NxeOLgiCIOiFGLoMgiAIuppOXjbQKOHogiAIgroMfTcXji4IgiDohWld4OrC0QVBEAR1GfpuLiqjIGmMpJPbPafGNQdJmr+93gVBEAwu3VAZZY53dP3IQUA4uiAIhjRu4l+n0pWOTtL7JF0paaKkhyTtLmmSpMXy8XUk3VTjurMlnSrpVkl/l7Rt4fDSkq6R9ISknxeu+W3WintY0o9y24HA0sCNkm7MbZ+WdIekeyVdImmB3H6spEckPSDphP77rQRBEDRPRHSdy9bAM7bXtL06SeG7UUYCmwGfA06VNCK3jwZ2B9YAdpe0bG4/wvY6wChgM0mjbJ8IPANsYXuL7GC/D2xp++PABOA7kt4P7AislkVZf1KrQ0Xh1fOnPNPEowRBELRHqBd0Lg8CW0o6TtImtl9t4to/2u6x/QTwFLBKbr/B9qu2pwKPAB/O7btJuhe4D1gNWLWGzfVz+7isJr53vv41YCpwhqSdgLdqdagovLrXkks38ShBEATtMR03/OpUujLr0vbfJa1NUu3+maTrgGnMdOwj6l5cXyT1nULbdGAuScsDhwDr2n5Z0tl1bAu43vYesx2Q1gM+BXwBOAD4ZG/PFgRBMJB08pBko3RlRCdpaeAt2+cDJwAfByYBa+dTdu7l8l0lDZO0AvAR4PFezl0IeBN4VdKSwDaFY68DC+btO4GNJH00929+SSvlebqFbV9FSl4Z3cRjBkEQ9DvdkIzSlREdaR7teEk9wHvAN4D5gN9L+h/grl6ufRy4GVgS2M/2VEk1T7Q9UdJ9wMOkYc5xhcOnAVdLejbP040BLpRUqaL8fZIz/HOeBxRwcEtPGwRB0E8MVESXcxYuJuVJTAJ2s/1y1TkfBv4XGA7MDZxk+9S+bHelo7N9LXBtjUMr1Tj3bODsQtM42wf3do7tbQvbY+r04STgpML+34B1a5y6Xq3rgyAIOoEBjNQOI+VCHCvpsLz/31XnPAtsaPudPCL2kKSxtnvN0uvKocsgCIKgHAZwecH2wDl5+xxgh+oTbL9ru5IvMS8N+rCujOhapV50FgRBMKcy3Y1HdJL2BfYtNJ1m+7QGL1/S9rMAtp+VtESdeywLXAl8FDi0r2gOwtENOS6evEyp9hbqhwH4eYeXb/ORnldKtXfoXYuVag9gMWrP5bbK8JLtAaz06KOl27xx0fVLtVe2SCrAbg8cXaq9tw/fr1R7AJdeUvNzvS32+VX7NppZH5edWl3HJumvQC2F4iOauMe/gFE56fBySZfantLbNeHogiAIgrqUOUdne8t6xyRNkbRUjuaWAp7vw9Yzkh4GNgEu7e3cmKMLgiAI6jKAc3RjScU0yD//XH2CpA9Jmi9vLwpsRO9LwIBwdEEQBEEvDGAJsGOBrSQ9AWyV9yu1ic/I53wMuEvSRNIysBNsP9iX4Ri6DIIgCOoyUKW9bL9EqhJV3T4B+Frevp5UV7gpIqLrg6LqQZPXnS1plybOHynpoWbvEwRB0J/YbvjVqUREFwRBENSlk1UJGiUiugKSLpd0T9aW27fG8S9n3biJks7LbR+WdENuv0HScoVLNpV0u6SnKtGdEsdnnbwHJe0+QI8XBEHQNN2gRxcR3ax8xfZ/clbPeEmXVQ5IWo201mMj2y/mumwAJwPn2j5H0leAE5m5on8pYGOS1M9YUgrsTqTizWsCi+X73DIAzxYEQdA0nVysuVEiopuVA3M2z53AssCKhWOfBC61/SKA7f/k9g2AP+Tt80iOrcLlWdvuEVKRaPLxC21Pz4scb6Z2DcwZFIVX73jjiTYeLwiCoDlCeLWLkLQ5sCWwge01SUKqRW05MbtWXS2K5xQ17FT1s2GKwqsbLLBi3xcEQRCUxHS74VenEo5uJgsDL9t+S9IqJFXwIjeQ1MQ/ADMkJQBuJ4mmAuwJ3NbHfW4Bdpc0XNLiwKbA3WU8QBAEQdmEHl13cQ2wn6QHSCvt7ywetP2wpGOAmyVNJ0V8Y4ADgTMlHQq8AOzTx33+RBrunEiK/r5n+zlJI8t7lCAIgnLo5CHJRglHl8nSD9vUODSycM45zJSRqLRNIs3fVdsbU7W/QP5p4ND8qrazegtdD4Ig6Dc6eX1co4SjC4IgCOoSEV0QBEHQ1Ux3J6+Qa4xwdEEQBEFdhn48F45uyDFXyX91d8w1tVyDwB5vl6+8usOwRUq19+iw6aXaA9j47XLfnOfmHtH3SU3y1gd7XbLZEs++O2+p9hboKT+CKFsodb6fnVqqPYAp15UvOFsGMXQZBEEQdDXh6IIgCIKuJrIugyAIgq4mIrogCIKgq+npgqzLKAHWDxRFVLMM/Il5e3NJGw5u74IgCBqnG4o6R0TXz2QZ+Al5d3PgDVJ9zCAIgo6nG+boIqKrQtIRkh6X9FdJF0o6RNJNktbJxxeTNClvj5R0q6R782u2aC1HcVfkWpb7AQdLul/SJpKeljR3Pm8hSZMq+0EQBJ1ARHRdhqS1SUoEa5F+N/cC9/RyyfPAVranSloRuBBYp9aJtidJOhV4w/YJ+X43AZ8DLs/3vcz2eyU9ThAEQdt0sipBo0RENyubAH+y/Zbt10iq4L0xN3C6pAeBS4BVm7zfGcxUO9gHOKvWSUXh1XEhvBoEwQDSYzf86lTC0c1OrXdrGjN/V8VyFQcDU4A1SZHcPE3dyB4HjJS0GTDc9kN1zpshvLpRCK8GQTCATHdPw69OJRzdrNwC7ChpPkkLAp/P7ZOAtfP2LoXzFwaetd0DfAnoq/bV68CCVW3nkoY8a0ZzQRAEg0k3CK+Goytg+17gYuB+4DLg1nzoBOAbkm4HFitccgqwt6Q7gZWAN/u4xV9IjvR+SZvktguARUnOLgiCoKPohqHLSEapwvYxwDEAko7KbY8BowqnfT+3P1HVfnhun0QWUbV9E3BT3v571fkAGwOX2n6lzOcIgiAog06O1BolHN0gIukkkqr5Zwe7L0EQBLXo5EitUcLR9YLto/rZ/rf6034QBEG79Lh8SauBJhzdEGPMzq+WbnOu1cvN5DzqJ8+Wag/gsZ5yR3Yv2L2pBNmG2PzcF8o1OB3uf+mpUk2+sH35WbvvvvifUu3Nv+ZCpdoDuPSSJco1eNWRTCn50/O79xxdrsGS6OSF4I0SyShzOGU7uaA8ynZyQXmU7eQ6GdsNv9pB0vslXS/pifxz0TrnLSfpOkmPSnokV53qlXB0QRAEQV0GsATYYcANtlcEbsj7tTgXON72x4D1SBWqeiUcXRAEQVCXgYrogO2Bc/L2OcAO1SdIWhWYy/b1uW9v2H6rL8Ph6IIgCIK6DOA6uiVtPwuQf9aaWF0JeEXS/0q6T9Lxkvoq1BHJKEEQBEF9mhFelbQvsG+h6TTbpxWO/xX4YI1Lj2jwFnORahKvBfyTVOBjDPD7vi4KWkTSIsAXbZ8y2H0JgiDoD5qZe8tO7bRejm9Z75ikKZKWsv2spKWoPfc2GbjP9lP5msuB9enD0cXQZXssAnxzsDsRBEHQXwzgHN1YYO+8vTfw5xrnjAcWlbR43v8k8EhfhsPRtcexwAq5duUlkravHJB0gaTtJI2R9GdJ12RB1x8WztlL0t35+t81MtYcBEEwkAzgHN2xwFaSngC2yvtIWkfSGQC2pwOHADdkeTQBp/dlOBxdexwG/J/t0cDJZG05SQsDGwJX5fPWA/YERgO75jfuY8DuwEb5+un5nNko6tGd9eA/+vWBgiAIigxURGf7Jdufsr1i/vmf3D7B9tcK511ve5TtNWyPsf1uX7Zjjq4kbN8s6TeSlgB2IqmFT5MEcL3tlwAk/S+pkPM0kvTP+HzOfNRZD1Ic9379oM8P/TIFQRAMGbqhMko4unI5jxSVfQH4SqG9+i/FpJD7HNuHD1DfgiAImmZ6T+cKqjZKDF22R7WQ6tnAQQC2Hy60b5XL28xHWgQ5jrTyf5ccAVbK33x4QHodBEHQIN0gvBoRXRvYfknSOEkPAVfbPlTSo8DlVafeRor2Pgr8wfYEAEnfB66TNAx4D9gfiEm4IAg6hpDpCbD9xcq2pPmBFZldLfx52wfUuPZi0oLHIAiCjqSEZQODTgxdloSkLYHHgJNsl6+lEwRBMAjE0GUwA9t/BZar0X42ae4uCIJgyNHTBcko4eiCIAiCunRunNYEzSwGjNfQegH7drK9ObWPc+pzD4U+zsnP3c2vmKPrbvbt+5RBtdcfNodCH/vDZvSxc20OhT52NeHogiAIgq4mHF0QBEHQ1YSj627q6kJ1iL3+sDkU+tgfNqOPnWtzKPSxq1Ge2AyCIAiCriQiuiAIgqCrCUcXBEEQdDXh6LoISe8v2d4ESftLWrRMu2Uhabikgwe7H4OBEntJOjLvLydpvcHuVxB0IuHouou7JF0i6bPKaq5t8gVgaZI47EWSPtOqXUkPSnqg3qsVm7anA9u3cu1AI2lJSb+XdHXeX1XSV9sweQqwAbBH3n8d+E2bfdw2K2kEbSJpeMn24otNG0QySheRndCWJNHX9UjKCGfb/nubdocB2wK/BXqAM4FfO0vdN2ijorW3f/55Xv65J/CW7aNb7NsxwMKkZ32z0m773lbsFez+HPgJ8DZwDbAmcJDt81u0dzVwFnCE7TUlzQXcZ3uNFu3da/vjku6zvVZum2h7zVbs5evPJznPy4CzbD/ahq0HqV09SoBtj2rDdinvTT/38WngUtLv8ZFW7RTsVf7vfdL2x/Ioy3W2123X9pxAOLouRdIWwPnA+4CJwGG272jBzihgH+CzwLXABcDGwJdsj27B3jjbG/XV1oS9G2s02/YnW7FXsHu/7dGSdiSJ5R4M3NiqI5E03va6VY7p/lZ+h/nau4ANgfHZ4S1O+uBbqxV7BbsLkaLEfUhO4CzgQtuvN2mn8sVGwJWkv58Z2G5Zd7Gs96YvoeM2+7ggaURkH9LI2ZnARbZfa9Fe6V9s5iSiqHMXIekDwF7Al4ApwLeAscBo4BJg+Sbt3QO8Avye5CjfyYfuktSSYwLeJ2lj27fle2xIcsYtYXuLVq/tg7nzz8+SPuj/0+Zo8Jv5/TGApPWBduScTgT+BCyRo9pdgO+300EA269JugyYDzgI2BE4VNKJtk9qws4MJyHpnXacRg1KeW+q+rgkUImO7rb9fDsdzF8MTgdOl7QpSaPyl5IuBX5s+8kmTb6Xh0Mrfz+LkyK8oAHC0XUXd5CGBHewPbnQPkHSqS3Y29X2U8UGScvbftr2Ti328avAmZIWzvuvkIZaW0bS54DVgBGVtlaHQgv8RdJjpOGxb+YPlqlt2PsO6UvHCpLGAYuTnFNL2L4gfxH5FClq2qGdoUYASduRIpAVSH9H69l+PgsKPwo07Oj6mVLfG0m7AccDN5F+lydJOtT2pW3YHA58jvT7HAn8gjQasglwFbBSkyYrX2yWLPOLzZxCDF12EZJ2s/3HqrZdbV/Sor17bX+8qu0e22u3089sZyHS319bIrXZgc8PbAGcQfoAuNt2O4keFduLAq/Zni7pfcCCtp9rw95cwMqkD9PHbb/Xgo1eM2ubmTetYftc4Azbt9Q49inbNzRhq/h3cwFpLrbYz3bnUEt7byRNBLaqRHHZcf61zfnOp4Abgd/bvr3q2Im2D2zB5iqkLzYAf2v3i82cRER03cVhwB+r2g4nDVs2TP4PtRqwsKRi5LYQhaipSZt72T5f0neq2gGw/f9asQtsaHuUpAds/0jSL4D/bdFWsV/zkxJnliNVil+a5KSuaNJOvch3JUnYbrav95CGr5T79nLeXgT4J00OT1fxbLWTk3Sc7f9uxsllflHYfg44oWKS1P+W51DLem8KDKsaqnyJ9jPSv1wZnq8gaSPb41pxcpn5gcrw5Xxt9m+OIhxdFyBpG9J8xTKSTiwcWgiY1oLJlUlZlosAny+0vw58vcVuVubhFmzx+nq8nX++JWlp0odUOx/2Fc4iOZUN8/5k0heGZj9MK7+/JbKtv+X9LUhDZU05OtvLw4xIdqztq/L+NqSM23bYCvjvqrZtarQ10s8tcr/mA75JSmAycCspe7cdynpvKlwj6VrSPBrA7qThxXY4Efh4VdtJNdoaIi8r2JWUESvgLEmX2P5JW72cQ4ihyy5A0pqkhJOjgSMLh14nZaO93KLdDVrJ1BxIJP2A9AHyKdI6MpOG337Qpt0JttcpK8tN0hXA120/m/eXAn7T6lxnrSHkSp9bsPUNkjNaASgmSSwIjLO9Vyt9zLb/CLxGGr6ElNG5iO3d2rBZ6nuTr98Z2IjkRG6x/acW7WxAcsAHAb8sHFoI2LGNv59HgbVsT8378wH32v5YK/bmNCKi6wJsTwQmSrrAdisR3CxI+p7tnwNflLRH9fE2hl6QdA7wbduv5P1FgV/YbikhxfaP8+Zl2ZmMaHfeL/Nu/jCpZLmtALzT+yW9MrLi5DJTaD4hociLkr5PWkJiUrbtSy3a+gNwNfAz0vB3hdfbmfPLrFz14X5jnhNrh7LfG2xfRoqW2mUeYAHSZ2tx9OI12kg+AiaRpg0qSTfzAv/Xhr05inB0XYCkP+ZvyPdJmi1Eb2Hha2WSe0LbnZudURUnB2D7ZUktr/2qNQcm6VXgwTZTxH9IWoy8rKQLSN/2x7Rh76bC8JhJa6xqrQFslD1yHyuRxy3MrJLSLLY9SdL+1Qckvb9NZ3efpPVt35ntfQIY14Y9gKOY/b3Zp1Vj+W/oONLwspi5YHyhZm3Zvhm4WdLZJS+peAd4WNL1pL+frYDbKlMV7Xz5nBOIocsuQNJStp9VnQWwJf+Ha4v8bX7zynBqziK82a1XCLmSVM2j4jQ2B+4kRUtH2z6vzqWN2P4AsD7pg+9O2y+2aivb24mUXg5tDI+VjaQrbG+rVM2jkuhSwbY/0obtR0lzvv/MTcuRvkj10Eb1kTLfG0lPAp8vI4tR0q9sHyTpL9SoumJ7uxbt7t3bcdvntGJ3TiEcXTAb9f6TVmj1P2u2/WVSJmhljdKuwDGtOqTc16/ZnpL3lyQlO3yN5ExWb9Jer8kC7abFt0t/fZD2F/W+fFVo5UuYpBtsf6qvtibstVyZp4attW3fI2mzWsdzxNeK3W2Bq2zHIvEWiKHLLkDS6/TumJodgqmkgu8EfJA0DwRpaGxSs/2r6su5SgudtyB9G9/J7dUCHFlxcpnngZWcqmU0vU6NmWnxI4B1SOXTBIwC7iJlDzaMpNtsb1zjPWp1eKzyheCEXs9qAaVqN/fbflPSXqQMwV/Z/mcfl9alzNEESSNIKfaL5bndSuS5EGmJQbP2KsPeEyRdDFxOYa6vhaUf2L4n/2zJofXCF4BfK1WtOSvW0DVHRHRdhKSjSWuWziN9COxJWkj78xbt3WJ7077aWrS9BLNWMmnpw1TSKaThsMpawZ1J6eaHAle4xRJhki4iRZoP5v3VgUNsj2nFXtlIWqJ6DlLSyrYfb8PmA6QCyaNIf0O/J30RqRmdDDSSvk3KZlwa+DczHd1rwOm2T27S3lm9HHYrCVKqXyi6YrSdQtGl1CGdEwlH10VIusv2J/pqa8Leo8DnnMuASVqeNHzSckqzUpmpX5A+rJ4HPgw8anu1Fu2J5NwqqeG3AZe5zT9s1Si4XKutCXtb2v5rVdverc6tSHoc+IFzJRxJ3wW+anvVVuxlG5XCwUcC/7b9e9WojjPYSPqWm6i7OZD0x1Btlf3FSBm2B5HmOj8KNFWHdE4kHF0XIel20lqyi0jf+PYA9re9Ya8X1re3NXAaUKl3ORL4L9vXttHHiaSqGH+1vZaSysIetvdt1WZ/IOlCkuxPMX1/AdstZTZKugV4GDiElH5+BvCO7ZZSzvM6vNNI6eZLkj70vmv7jVbsZZs3k7IZ9wE2BV4gDWW2lCjUn+QIe1VmHRU4t0VbHyKtxdyI9F7fRloCM7nXCwcQzV6H9BwX6pDa7tXBzvHYjleXvEiO6M/Ai6QPqctJc1jt2JyXNJy1JjBvCX2ckH9OJJVeglSbslV7r5OGrl4jfehPJ9VAbLefI0jyL3/Kr4NJa/RatSeSk3siv/YooY/7k4Zp/wlsVIK9D5KKT2+S95cjlbIa0L/jBvr5Q1KW7RTS8N1zwKVt2Lue5ETmyq8xwPVt9nF9YDzwBvBuu3+XJL3FTavajss/PzXY70mnvyKiC2ZD0idt/63WGjVobZK+YPuvJA2xnwGLkYYv13WLUWcN+zuQqu7/Txn2yiIvo/gdaRHxh0iR4nFu8T9gXk/1LHBgtncmKcv0kHJ63LnkebA1ScK1a+ZM2zNsf76PS+vZK3WYOl8/gZRAcgkpqenLwEdtH9GivVoF1h9wG3N+cxKRddkFVCqZSDqJ2innzS4m3YxUk7HWB4dpr2jy9qT6lAeTkmUWJpUuKwXbl0s6rO8za1NZfF8vqaCND5Y7gWNtn5mrehxHWjjdqoP/je3L8/YrSrp+h7doCyh34XQ/87btHknTcoLG80DLa/1IVWb2Ymatyz1ovcrMDGw/KWm47emk2pS393lRFZpZnu0jOVmowoK0v/B+jiEiui5A0udt/6XeolJ38WLSqqhzGOnb82a2N2jRXr8svpe0nKsySyVt6hqSOE3YLFUstMyF0/1JzrT9H1LE9F3S8OD9tluqjiJpOeBkUuEBA7eT5ujaURi/hVRk+wzS0OqzwBg3r4K+MLAo/VOebY4hHF1QF0k/BX7uWetSftd2xwg+VqWITyOt8zu9nQ99JdHMa223qwZQbXc7UpIHpGowf2nDVrVY6CZAu2KhpS2cHigkjQQWsv1AH6cOKPmL0vMkNfSDSSMXp7h5ZfGgBMLRdRF53mbXKsd0ke3PtGhvRnX4QlvHpZv3B5LGAl9yOQWikXQsKfoqVvGfYLul4Ub1j1jor0kJKW0vnO5vJC1DWpoyY/ql1ehYJRcaDzqPmKPrLhb37AWTl2jD3nBJ89p+ByDPLc3bTgeV1KDfdi5lJGkYKZvxrRbt/Rz4CWne7xpSksJBts/v9cK+mQo8mL88vFlpbGG+s8JngdGF5z4HuI/W59X6Qyx0IeAt4NOFtnbnZEtH0nEkzbhHSNmMkPrZ6jBwqYXGcx8rdUNnwW3UDQ1aJxxddzG9OBeUh0/aCdnPB27Iw4MGvgK0O993A2nuorLea37gOlpPyvi07e9J2pGUar8rKfW8XUd3ZX6VySJAZV5l4TZtlS4W2uoc1yCwA0n+py1pngLDJC3qWQuNt/vZWNQFHEH6u3x/mzaDFglH110cQZLuqNTZ2xRoeSF2zuR8kCRqKuDHbmOxeGaEC4uabb+RF722ytz552dJ5ZD+k4qltIftcyTNw0zNuMdtt1I7s8LPSJI1N5J+l5vSRpak7UM1q1joaW5TDUHSSqSC2EvaXl3SKGA7d56K9VOk970sR/cL4HZJsxQab8eg7eqszV9Juo1ZhZGDASLm6LqMXCKoIl9yh9uUlikbSeOAbzmrAEhaGzi5jSzJY0nf8N8G1iNFTVe4xbJnBbubk6LXSaTf5bLA3m1mSS5FmqcTcJft59rpY9nkL0iHAr/zTOXuh9ykAkR/UVg+swxpiPoGZp1LbEcQeFVSxR4BN7i9QuPVKhiVbOBvtDOHGrROOLouI0+kr8ispZFanaRfn1Qa6WMk5eThwJvtrKuStC6pRNkzuWkpYHfnqu8t2lyUVHViep4DXLBdJ6KksPBF5yLJOdq50PbaTdpZxfZjqiP/4yZlf1S+GkLR9njb6xaTkNpdOF0m9ZbPVGh2GU0eouzNXsvp+zlyr7w/lWzgE2z/vVWbQevE0GUXIelrwLdJlTLuJ0V2d5C+qbbCydSo7tBOH22Pl7QKSYxTwGNtDglSmVvJ229SSB5pg7ldUAKw/XdJc/d2QR2+C3ydmfI/RUyT743tjfPPBVvoS1+8KGmF3C8k7UJa/9URFB1ZHlZehdTXx22/24LJe5hVaLbimJS320kcuaKG7W0rw+q2/18btoMmCUfXXXybNDR2p+0tskP5UTsGy6juAL2WFVtRUiemsE+Q9Htm6r/tSfpgbArbX88/W5ILqqY/oxBS7czTgFUk/Rt4mvTcHYWkz5LKqf0fyZEsL+m/bF/djB3byxdsvp+qkZA2WZv0f/HPuY+fJ2WF/qsk+0EThKPrLqbaniqJvCzgMUkrt2HvrfzN+f6cxv8s8L4WbfVnWbH+4BukD/4DSR9UtwCnNGukhmOfhRYcfDEKWQ54OW8vQiruvHz9S+v28TuF3atIWavDSJHxzkCnRR//D9iisvg6R6FXAk05ugp1RkJuJyVhtcpiwMedteIkHQVcYvtrbdgMWiQcXXcxWdIipAW/10t6mZlzYa3wJdIH3gGk6g7Lkj74msb2D/OauaudNdTaod6cV+F+Tc191bj+HdIHarsf8hXHvgRpCcXf8v4WpKomTTm6ShQi6VRgrO2r8v42pGUbrVAZBl2ZWaOQL9H62rT+5PmqCiNPkaqQtErpIyGkLyHF4dxuIc0AAA1dSURBVNR3SeoiwSAQyShdiqTNSGu1rmlx/qJfUHkK5Tf2cti2W5qXVD8pREu6Avi67Wfz/lKkwsy9Rny92LunOjFG0gTb69S7pgGb1wE7F6KQBUlRyNat2uwPJP2WVBXlj6T3alfgcXKR42aj5EISzv3AJ2y/024SjqQjgN1IEk8GdgQutv2zVm0GrRMRXZdi++a+zxoUrpd0CElfq1hxpKm5pbLmvGqwbT/ZHVlxcpkpzFyj1wovSvo+swrDtltxf6hEISNIv7/N8v4LpMXYn6e1YfCyR0KwfYykq0k1SAH2sX1fOzaD1omILhhQcmmkatxOaSSVqDbdX0g6mZTscCHpw/gLwJO2v9WivfeTBEg3ZWb5q6PbTImf46OQTh0JCdojHF3QJ5Lel9P2y7A1wvbUvtqasPdDYHOSo7sK2Aa4zfYubfazdG22XKasMmx7S7uVTPqDPPdZiUJu6aQoRHX0Fiu0s2A86G7C0QV1URLzPANYwPZyktYE/sv2N9uwWUspuWVFBJWsNl2wOyS02eYkyl4wHsw5xBxdF1CjSsYstBGF/BL4DDA225koqaVEEkkfJJVumk+pMnxlIe1CpMLOrVK22nSFKeHkOotwZEGrhKPrAipVMiQdTVIzPo/kSPZkZup4q7b/VVUkeXq9c/vgM8AY0lqlYsr+6yS16FaZkBMJTietMXsDuLtVY4V1bxMkXUwHarMpCcMeaPuXg92XwaCqvNYMWs20DbqfGLrsIiTdVV3MuFZbE/YuJTmlk0mLaA8E1rH9hTb6uLPty1q9vg/bI2lTbVozFcuL5Zsq2C2IcWbHdI7tvVrtVw2bN9nevCx7QwmlQuAVRpDWdk6z/b1B6lLQ4URE111Ml7QnqWiySSrWrUZgAPsBvyYNOU4m6cbt34ohSXs5iaGOrKrEAbRe+6/WUKqkTVstZO2syaY6qtMt2pwuaXFJ85SYyTcuZ3JWL9Noa6H8UMCzFwAfp5nSVEEwG+HouosvkhzTr0mOblxuawkniZ+yah1WSoctUJK9CocWtkeQpHruofVC1hXKVp2eRPpAHsusjqnVyisVodqjC21NF4keilTV+6xI4HxwkLoTDAHC0XURticB27drpz/SuG3/Lv9st7RStd1ZsislLQv8vATTZatOP5Nfw2hz3hT6dcH8UKBY7/M90peIrw5mh4LOJhxdF6HyFKInlN+7RL0hwVbmvuowGShDKLSoOm3SQuqWVafLdvCSaipV2z66VnuX8d+kBd2vSfoB8HHgrUHuU9DBRDJKF6EOV4gGUEHUs7e2JuwVo89hwGhgUhmJHypRdbrsTEFJ3y3sjiCVLnu0xC8MHYukB2yPkrQx8FPSl5L/aTXpKuh+IqLrLua3fXfVcoBpzRqR9CvbB0n6C7U/nLdro49lDwkWo89pJBXwcW3Ym0F2bC07tyoOKWzPyBRs1ZjtWRJjJJ1AXu84B1BJsPoccKrtP2cZnCCoSTi67qIsheiK2OgJZXWsQNlDgkNiEfEAZArOTzkL5YcC/5b0O5Is0XGS5iVF80FQk3B03UUtheimh/AKH8qjbf+6eEzSt4GWP6BtnytpAjOHBHdqZUiwv+R0+osamYJr00amYNXzDwcWZ9YMzP/f3r3HylVVcRz//i5KgFrAR8VYgbRYjImhPA3aqhiFCCER0BaJFMtDTJGoYEDRGKMxUasxEjQINqAQg9gaCKBArBF5KQUJJZGoKEiCvKRNbaVAbfPzj32mTIe5vdyZc2emp79P0nTOzJ2VPWnvXXfvs/daTbYQ+BDwXdvrqpZHF0zwntiJ5R5dA0maBoy1+or1EadbXcqe76fVSdL+1cPWub7WLPTjwMZR25RRdW1o7RTcTPkl5Ou275xknFm2H237/FTxnrbd81JoRJMl0TWIpKuBc23/p7reH7jC9gcmGecUyvm7+cAdbS9NB7bY7rWTde0k3WV73kTPDYukBbaXS5pt+5Ea4v3J9mGSfjvZf9eInVWWLpvlTuCeqvLITMpyzue3/5au7qbc23sD21YD2QD0XF5rikyTNL81M6o6Lkyb4D2DdBGwHFhB2Qbfr7GqNdGBdVaYiWiyJLoGsX2ZpD8DvwOeBQ6x/VQPcR4DHgPeVfMQp8KZwBWS9qqu1wGjtMV+TXW0YFZVFWUbPexg/RhwAuV7t++D5xE7gyxdNoikRcBXKJ2nD6J0DDjd9uoe4x0JXAK8HdiVsunhuX6aj06VqkWPWsu2o0LSrpSZ3NXAWZ2v2+5pY4+kY23f3OfwInYKSXQNIul64Gzbz1TX7wQut31wj/Huo8wgllPqCZ4GvNX2l2sacs9aRaK7Ld/B6C3hSZph+981xOn6eVtG7XNHjIIsXTaI7RM6rldVya6fmH+XtIvtLcCVku7ua5D1ad2HG+nlu/ZD9x0H+YGeli5H+vNGjKLM6BpA0oW2l45XjLmXIsxV3Nsph3KXURq6Pgkstj23n/HuTCS9b3uv97p0GRGvXGZ0zdA6cF13MeZFlPty5wLnAftSSleNDElLgW8AzwO3AHOBz1W974ZuqhJZ1SC22y81o7QRJ2IkJNE1w8nATcDenZVM+lHtvoSSRGqtvl+jY2xfKOlESueCBZRdpyOR6FraDoxvw3avZbtuanu8G3AipQ1QRHRIomuGw6rD4WdIuopSfWMr22snE2wHK6/16urv4ygFndd2uxc2Ag5ve7wbJSG/bpyvnZDtX7ZfS7oGWNlrvIgmS6Jrhh9Rlu1mU5pStv+kN5Mv9nt8TeMahBsl/YUy6zxH0gzghSGP6WVsr+l46vuS7gS69pXrwRxgv5piRTRKNqM0iKRLbS+pOeY+wBHV5arW0YVRUjVvXW97S1Xnc3ovB+WnkqT2qihjlBnekl439kjawLaz7qeAizpnehGRRBfbIWkh8B3gNsos8T3ABbZXDHNc7STtAZwP7Gf7bElzgLfZvmmCtw5UVR2lZTPwT0r1/b8OZ0QRO48kuhiXpNXA0W0H0GcAK0fpeIGkaynLtafZfoek3YE/9HpIfkchaR7wgO3nJJ1Kqb5ycdsGooiopFlhbM9Yx1LlGkbv/8wBtpcC/wOw/Twdm3FGgaTPStpTxTJJ90s6po+QlwIbJc0FLqTUJr2qlsFGNMyo/dCK0XKLpFslLZa0GPgV8Oshj6nTpmoW16o+cgDw4nCH1NUZttcDxwBvBE4HvtVHvM0uyzEfpszkLiZVUyK6yq7LGJftCySdROlLJ0rdzOuGPKxOX6XsON1X0s+AecDioY6ou9Ys8zjgStur1d85iA2SLqJ0kH+vpF146ahFRLTJPboYl6TzgOW2Hx/2WLqpEsVbgI3AkZRk8kfbzw51YF1UlUxmArMo1Vt2AW6zfViP8d5EaY57r+07JO0HHGU7y5cRHZLoYlxVg8+FwFrg58AK208Pd1TbanXcHvY4JiJpDDgYeMT2OkmvB2baHrVGthGNk0QXE5J0EKXM2EeAx21/cMhD2krSD4Gf2L532GOJiNGUe3TxSjxDOZC8hrKRYpS8H/iUpMeA5yjLlx6xMmURMUSZ0cW4JC2hzORmACuAa20/tP13DVZV4/Nlcp4sIloyo4vt2Z/S8uaBYQ9kPDtSQpM0H5hj+8rq8P1rbD86yRjjFdzOTDZiHJnRRQxAtbHncEp5sgMlvZmyo3XeJON0ncG27EiJP2JQMqOLGIwTgUOA+wFsPyFp0ge8k8giJi+VUSIGY1NVyaRVwWVaP8EkHSnpXkn/lbRJ0hZJ62sZaUTDJNFFDMYvJF0G7C3pk5QmqT/uI94PgFOAh4HdgbOAS/oeZUQD5R5dxIBIOppS61LArbZ/00es+2wfLunB1gYUSXfbfndNw41ojNyjixicv1F2Rq6UtIek6bY39Bhro6RdgQckLQWeBPpaDo1oqixdRgxAtVy5ArisemomcH0fIRdRvn/PpRyU3xc4qZ8xRjRVEl3EYHya0llhPYDth+mvyswJtl+wvd7212yfDxxfwzgjGieJLmIwXrS9qXUh6VV0P/j9Sn2iy3OL+4gX0Vi5RxcxGL+X9CVg92pTyjnAjZMNIukUSnueWZJuaHtpT0ot0ojokF2XEQNQtek5k7Zdl8AyT/IbsKqMMgv4JvDFtpc2AA/a3lzPiCOaI4kuYopV3b9/avvUmuPuAxxRXa6y/Uyd8SOaIvfoIqaY7S3AjOo4QC0kLQBWAQsozXHvkfTRuuJHNElmdBEDUFVFORS4gXIcAADb3+sx3mrg6NYsruqGsNL23BqGG9Eo2YwSMRhPVH/GgEkXc+5irGOpcg1ZoYnoKokuYgpJutr2ImCd7YtrDH2LpFuBa6rrk4Gba4wf0RhZuoyYQpIeAo6lLFkeRdlxuZXttX3EPgmYX8W83fZ1vY80ormS6CKmkKTPAEuA2cC/2DbR2fbsHuN+2/YXJnouIpLoIgZC0qW2l9QY737bh3Y8t7WTQUS8JIkuYgciaQmlqsps4B9tL00H7qr7rF5EEyTRRexAJO0FvJYulVH6ud8X0WRJdBER0Wg5dxMREY2WRBcREY2WRBcREY2WRBcREY2WRBcREY32f/wmcfD/eYhHAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"corr = wines.corr()\n",
"sns.heatmap(corr, \n",
" xticklabels=corr.columns.values,\n",
" yticklabels=corr.columns.values)\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# Import `train_test_split` from `sklearn.model_selection`\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# Specify the data \n",
"X=wines.iloc[:,0:11]\n",
"\n",
"# Specify the target labels and flatten the array\n",
"y= np.ravel(wines.type)\n",
"\n",
"# Split the data up in train and test sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# Add `type` column to `red` with value 1\n",
"red['type'] = 1\n",
"\n",
"# Add `type` column to `white` with value 0\n",
"white['type'] = 0\n",
"\n",
"# Append `white` to `red`\n",
"wines = red.append(white, ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"# Import `StandardScaler` from `sklearn.preprocessing`\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# Define the scaler \n",
"scaler = StandardScaler().fit(X_train)\n",
"\n",
"# Scale the train set\n",
"X_train = scaler.transform(X_train)\n",
"\n",
"# Scale the test set\n",
"X_test = scaler.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"# Import `Sequential` from `keras.models`\n",
"from keras.models import Sequential\n",
"\n",
"# Import `Dense` from `keras.layers`\n",
"from keras.layers import Dense\n",
"\n",
"# Initialize the constructor\n",
"model = Sequential()\n",
"\n",
"# Add an input layer \n",
"model.add(Dense(12, activation='relu', input_shape=(11,)))\n",
"\n",
"# Add one hidden layer \n",
"model.add(Dense(8, activation='relu'))\n",
"\n",
"# Add an output layer \n",
"model.add(Dense(1, activation='sigmoid'))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting package metadata: ...working... done\n",
"Solving environment: ...working... done\n",
"\n",
"## Package Plan ##\n",
"\n",
" environment location: C:\\Users\\ITF\\Anaconda3\n",
"\n",
" added / updated specs:\n",
" - keras\n",
"\n",
"\n",
"The following NEW packages will be INSTALLED:\n",
"\n",
" _tflow_select pkgs/main/win-64::_tflow_select-2.3.0-mkl\n",
" absl-py pkgs/main/win-64::absl-py-0.7.1-py37_0\n",
" astor pkgs/main/win-64::astor-0.7.1-py37_0\n",
" gast pkgs/main/win-64::gast-0.2.2-py37_0\n",
" grpcio pkgs/main/win-64::grpcio-1.16.1-py37h351948d_1\n",
" keras pkgs/main/win-64::keras-2.2.4-0\n",
" keras-applications pkgs/main/noarch::keras-applications-1.0.8-py_0\n",
" keras-base pkgs/main/win-64::keras-base-2.2.4-py37_0\n",
" keras-preprocessi~ pkgs/main/noarch::keras-preprocessing-1.1.0-py_1\n",
" libmklml pkgs/main/win-64::libmklml-2019.0.3-0\n",
" libprotobuf pkgs/main/win-64::libprotobuf-3.8.0-h7bd577a_0\n",
" markdown pkgs/main/win-64::markdown-3.1.1-py37_0\n",
" mock pkgs/main/win-64::mock-3.0.5-py37_0\n",
" protobuf pkgs/main/win-64::protobuf-3.8.0-py37h33f27b4_0\n",
" tensorboard pkgs/main/win-64::tensorboard-1.13.1-py37h33f27b4_0\n",
" tensorflow pkgs/main/win-64::tensorflow-1.13.1-mkl_py37h9463c59_0\n",
" tensorflow-base pkgs/main/win-64::tensorflow-base-1.13.1-mkl_py37hcaf7020_0\n",
" tensorflow-estima~ pkgs/main/noarch::tensorflow-estimator-1.13.0-py_0\n",
" termcolor pkgs/main/win-64::termcolor-1.1.0-py37_1\n",
"\n",
"\n",
"Preparing transaction: ...working... done\n",
"Verifying transaction: ...working... done\n",
"Executing transaction: ...working... done\n",
"\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"conda install keras"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 5.10212787e-18, 6.12255344e-19, 1.71431496e-17, 1.79594901e-17,\n",
" 9.79608551e-18, 2.12248519e-17, -1.63268092e-18, 2.93882565e-17,\n",
" 1.30614473e-17, 1.63268092e-18, -3.18372779e-17])"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"np.mean(X_train,0)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_7 (Dense) (None, 12) 144 \n",
"_________________________________________________________________\n",
"dense_8 (Dense) (None, 8) 104 \n",
"_________________________________________________________________\n",
"dense_9 (Dense) (None, 1) 9 \n",
"=================================================================\n",
"Total params: 257\n",
"Trainable params: 257\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
},
{
"data": {
"text/plain": [
"[array([[-0.47761664, 0.04527295, 0.25246024, 0.16743088, 0.17875189,\n",
" 0.40539986, -0.29539216, 0.06351995, -0.27485362, -0.25061417,\n",
" -0.05669019, -0.15741614],\n",
" [-0.4664034 , 0.04881757, -0.40141705, -0.4950576 , 0.17823094,\n",
" -0.45020851, -0.04134798, 0.41960216, -0.02000892, 0.15490139,\n",
" -0.02947351, 0.35756052],\n",
" [ 0.03726554, -0.46101272, -0.42530382, -0.16232249, -0.30285963,\n",
" -0.28076488, 0.00744945, -0.02952513, -0.24554342, -0.19568557,\n",
" -0.44279218, 0.04747039],\n",
" [-0.25273523, 0.37722182, -0.40001422, 0.10381788, 0.12110323,\n",
" 0.10230803, 0.31522423, 0.2002148 , 0.08265883, -0.07742 ,\n",
" 0.49684757, 0.3735811 ],\n",
" [ 0.4652719 , 0.4685344 , 0.04561126, 0.05041414, -0.10340509,\n",
" 0.42024547, 0.0176838 , 0.49910992, -0.399724 , 0.30645412,\n",
" 0.15637982, 0.04318517],\n",
" [-0.46060857, -0.43991894, 0.27769083, -0.25191423, 0.49356276,\n",
" 0.04657888, 0.14086407, 0.16861522, 0.40857083, 0.2975055 ,\n",
" 0.3562942 , 0.12238503],\n",
" [ 0.00427204, -0.47762883, -0.12592983, 0.31567603, 0.06366903,\n",
" -0.44242382, -0.28439346, -0.13388771, 0.3346668 , 0.49645108,\n",
" 0.24273181, 0.08303183],\n",
" [-0.0750609 , -0.28039283, -0.11899924, 0.09538108, 0.50106674,\n",
" -0.49395043, 0.22201353, -0.0198198 , -0.32870016, -0.01852536,\n",
" 0.14476013, 0.16686988],\n",
" [-0.47166583, 0.03997791, -0.5050424 , 0.34388822, -0.40028235,\n",
" 0.01818889, 0.1276846 , -0.01236033, 0.40065998, -0.45026466,\n",
" 0.16707104, -0.39137343],\n",
" [ 0.03796256, -0.15093073, 0.0281257 , 0.39967984, 0.20432317,\n",
" 0.08440721, 0.44822007, -0.13690099, -0.14526707, 0.10798645,\n",
" 0.43049806, -0.42992598],\n",
" [ 0.02474761, 0.2092182 , -0.42367694, -0.40302664, -0.06302521,\n",
" 0.49919456, -0.24117845, -0.10386196, -0.27468777, 0.43964118,\n",
" 0.10577744, -0.2912007 ]], dtype=float32),\n",
" array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32),\n",
" array([[-0.03987873, -0.51480573, -0.15681162, -0.2525129 , 0.33307177,\n",
" 0.03280455, 0.43727058, 0.05445373],\n",
" [ 0.11702865, -0.38383704, -0.348001 , -0.25093997, 0.13669819,\n",
" 0.35098362, -0.32282692, -0.5208173 ],\n",
" [-0.39619857, -0.49622577, -0.25818235, -0.31367862, 0.53876793,\n",
" 0.24176544, -0.10865733, 0.53491783],\n",
" [-0.41648138, -0.0821732 , 0.45133954, 0.46135163, 0.00693381,\n",
" 0.11984032, 0.16685534, -0.08122173],\n",
" [-0.08952042, -0.45009387, -0.52845275, -0.44721937, 0.5067847 ,\n",
" 0.4349143 , 0.00408649, -0.26897055],\n",
" [-0.3711813 , -0.07501507, -0.5310049 , -0.24023613, 0.06136882,\n",
" -0.02146763, -0.06577274, 0.06224626],\n",
" [-0.39386913, 0.09311694, -0.54067075, 0.37520623, 0.06390077,\n",
" -0.34557718, -0.21785372, 0.52057946],\n",
" [-0.42553002, -0.0766525 , 0.08281529, -0.43404454, 0.07459301,\n",
" 0.01720893, -0.3500622 , -0.32576555],\n",
" [ 0.07368255, 0.40699548, 0.52581656, -0.24069539, 0.20755732,\n",
" -0.1970799 , -0.04386753, 0.29505962],\n",
" [ 0.13062865, -0.30224755, -0.35236692, 0.10555983, 0.36896825,\n",
" 0.12025166, 0.46803153, 0.47896826],\n",
" [-0.52543265, 0.3802758 , -0.166403 , 0.25528437, 0.03451145,\n",
" 0.24519986, -0.3066882 , -0.5435751 ],\n",
" [ 0.13834763, 0.45048797, -0.3500772 , -0.50618947, -0.43991446,\n",
" 0.38571107, 0.12666637, -0.22774363]], dtype=float32),\n",
" array([0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32),\n",
" array([[-0.67261654],\n",
" [ 0.24497032],\n",
" [-0.2709095 ],\n",
" [ 0.24476731],\n",
" [ 0.0242424 ],\n",
" [-0.6071795 ],\n",
" [ 0.43540585],\n",
" [ 0.60039186]], dtype=float32),\n",
" array([0.], dtype=float32)]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Model output shape\n",
"model.output_shape\n",
"\n",
"# Model summary\n",
"model.summary()\n",
"\n",
"# Model config\n",
"model.get_config()\n",
"\n",
"# List all weight tensors \n",
"model.get_weights()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From C:\\Users\\ITF\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n",
"Epoch 1/20\n",
"4352/4352 [==============================] - 4s 1ms/step - loss: 0.0870 - acc: 0.9704\n",
"Epoch 2/20\n",
"4352/4352 [==============================] - 3s 796us/step - loss: 0.0221 - acc: 0.9954\n",
"Epoch 3/20\n",
"4352/4352 [==============================] - 3s 794us/step - loss: 0.0190 - acc: 0.9963\n",
"Epoch 4/20\n",
"4352/4352 [==============================] - 3s 788us/step - loss: 0.0157 - acc: 0.9970\n",
"Epoch 5/20\n",
"4352/4352 [==============================] - 3s 756us/step - loss: 0.0142 - acc: 0.9970\n",
"Epoch 6/20\n",
"4352/4352 [==============================] - 3s 767us/step - loss: 0.0131 - acc: 0.9970\n",
"Epoch 7/20\n",
"4352/4352 [==============================] - 3s 757us/step - loss: 0.0115 - acc: 0.9975\n",
"Epoch 8/20\n",
"4352/4352 [==============================] - 3s 771us/step - loss: 0.0102 - acc: 0.9975\n",
"Epoch 9/20\n",
"4352/4352 [==============================] - 3s 803us/step - loss: 0.0087 - acc: 0.9977\n",
"Epoch 10/20\n",
"4352/4352 [==============================] - 3s 781us/step - loss: 0.0078 - acc: 0.9984\n",
"Epoch 11/20\n",
"4352/4352 [==============================] - 3s 758us/step - loss: 0.0082 - acc: 0.9979\n",
"Epoch 12/20\n",
"4352/4352 [==============================] - 3s 775us/step - loss: 0.0112 - acc: 0.9982\n",
"Epoch 13/20\n",
"4352/4352 [==============================] - 3s 768us/step - loss: 0.0065 - acc: 0.9984\n",
"Epoch 14/20\n",
"4352/4352 [==============================] - 3s 795us/step - loss: 0.0065 - acc: 0.9982\n",
"Epoch 15/20\n",
"4352/4352 [==============================] - 3s 757us/step - loss: 0.0053 - acc: 0.9989\n",
"Epoch 16/20\n",
"4352/4352 [==============================] - 3s 753us/step - loss: 0.0061 - acc: 0.9984\n",
"Epoch 17/20\n",
"4352/4352 [==============================] - 3s 770us/step - loss: 0.0061 - acc: 0.9982\n",
"Epoch 18/20\n",
"4352/4352 [==============================] - 3s 785us/step - loss: 0.0047 - acc: 0.9989\n",
"Epoch 19/20\n",
"4352/4352 [==============================] - 3s 782us/step - loss: 0.0053 - acc: 0.9989\n",
"Epoch 20/20\n",
"4352/4352 [==============================] - 3s 758us/step - loss: 0.0054 - acc: 0.9986\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x179e6e63160>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.compile(loss='binary_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])\n",
" \n",
"model.fit(X_train, y_train,epochs=20, batch_size=1, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_test)\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[2.6524067e-04],\n",
" [9.9331474e-01],\n",
" [1.4185905e-04],\n",
" [7.3611736e-06],\n",
" [3.8743019e-07]], dtype=float32)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred[:5]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[2.6524067e-04],\n",
" [9.9331474e-01],\n",
" [1.4185905e-04],\n",
" ...,\n",
" [4.6573281e-03],\n",
" [7.4565411e-04],\n",
" [7.4513623e-04]], dtype=float32)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'array' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-46-81380d557e05>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m array([[0],\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m [0]], dtype=int32)\n",
"\u001b[1;31mNameError\u001b[0m: name 'array' is not defined"
]
}
],
"source": [
"array([[0],\n",
" [1],\n",
" [0],\n",
" [0],\n",
" [0]], dtype=int32)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 0, 0, 0], dtype=int64)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test[:5]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'array' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-48-7c287c9bd8c2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'array' is not defined"
]
}
],
"source": [
"array([0, 1, 0, 0, 0])"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "array() argument 1 must be a unicode character, not list",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-49-ef36e9c88bb9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0marray\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m: array() argument 1 must be a unicode character, not list"
]
}
],
"source": [
"from array import array\n",
"array([0, 1, 0, 0, 0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"arraystr('u'),[0, 1, 0, 0, 0])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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