From f1b638981cbdc61dfdb7147fb995294e55adb288 Mon Sep 17 00:00:00 2001 From: Diya Nag Chaudhury <43166705+dnc2k@users.noreply.github.com> Date: Sat, 13 Oct 2018 18:43:33 +0530 Subject: [PATCH 1/3] Created using Colaboratory --- Exercise.ipynb | 8242 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 8242 insertions(+) create mode 100644 Exercise.ipynb diff --git a/Exercise.ipynb b/Exercise.ipynb new file mode 100644 index 0000000..7d0031c --- /dev/null +++ b/Exercise.ipynb @@ -0,0 +1,8242 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Exercise.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/dnc2k/Assignment-3/blob/dnc2k/Exercise.ipynb)" + ] + }, + { + "metadata": { + "id": "2LTtpUJEibjg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Pandas Exercise :\n", + "\n", + "\n", + "#### import necessary modules" + ] + }, + { + "metadata": { + "id": "c3_UBbMRhiKx", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import pandas as pd" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "tp-cTCyWi8mR", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Load url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\" to a dataframe named wine_df\n", + "\n", + "This is a wine dataset\n", + "\n" + ] + }, + { + "metadata": { + "id": "DMojQY3thrRi", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "wine_df=pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "BF9MMjoZjSlg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### print first five rows" + ] + }, + { + "metadata": { + "id": "1vSMQdnHjYNU", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "outputId": "61a12deb-7184-422b-89e0-f3bc173686aa" + }, + "cell_type": "code", + "source": [ + "wine_df.head()" + ], + "execution_count": 65, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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"#### Assign the columns as below:\n", + "\n", + "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it): \n", + "1) Alcohol \n", + "2) Malic acid \n", + "3) Ash \n", + "4) Alcalinity of ash \n", + "5) Magnesium \n", + "6) Total phenols \n", + "7) Flavanoids \n", + "8) Nonflavanoid phenols \n", + "9) Proanthocyanins \n", + "10)Color intensity \n", + "11)Hue \n", + "12)OD280/OD315 of diluted wines \n", + "13)Proline " + ] + }, + { + "metadata": { + "id": "my8HB4V4j779", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 2009 + }, + "outputId": "a8cf5ff0-ec65-46ed-d29f-b37b8af218b9" + }, + "cell_type": "code", + "source": [ + "wine_df.columns=['1','Alcohol','Malic acid','Ash','Alkalinity of ash','Magnesium','Total phenols','Flavanoids','Nonflavanoid phenols','Proanthocyanins','Color intensity','Hue','OD280/OD315 of diluted vines','Proline']\n", + "wine_df" + ], + "execution_count": 67, + "outputs": [ + { + "output_type": 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1AlcoholMalic acidAshAlkalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted vinesProline
0113.201.782.1411.21002.652.760.261.284.3800001.053.401050
1113.162.362.6718.61012.803.240.302.815.6800001.033.171185
2114.371.952.5016.81133.853.490.242.187.8000000.863.451480
3113.242.592.8721.01182.802.690.391.824.3200001.042.93735
4114.201.762.4515.21123.273.390.341.976.7500001.052.851450
5114.391.872.4514.6962.502.520.301.985.2500001.023.581290
6114.062.152.6117.61212.602.510.311.255.0500001.063.581295
7114.831.642.1714.0972.802.980.291.985.2000001.082.851045
8113.861.352.2716.0982.983.150.221.857.2200001.013.551045
9114.102.162.3018.01052.953.320.222.385.7500001.253.171510
10114.121.482.3216.8952.202.430.261.575.0000001.172.821280
11113.751.732.4116.0892.602.760.291.815.6000001.152.901320
12114.751.732.3911.4913.103.690.432.815.4000001.252.731150
13114.381.872.3812.01023.303.640.292.967.5000001.203.001547
14113.631.812.7017.21122.852.910.301.467.3000001.282.881310
15114.301.922.7220.01202.803.140.331.976.2000001.072.651280
16113.831.572.6220.01152.953.400.401.726.6000001.132.571130
17114.191.592.4816.51083.303.930.321.868.7000001.232.821680
18113.643.102.5615.21162.703.030.171.665.1000000.963.36845
19114.061.632.2816.01263.003.170.242.105.6500001.093.71780
20112.933.802.6518.61022.412.410.251.984.5000001.033.52770
21113.711.862.3616.61012.612.880.271.693.8000001.114.001035
22112.851.602.5217.8952.482.370.261.463.9300001.093.631015
23113.501.812.6120.0962.532.610.281.663.5200001.123.82845
24113.052.053.2225.01242.632.680.471.923.5800001.133.20830
25113.391.772.6216.1932.852.940.341.454.8000000.923.221195
26113.301.722.1417.0942.402.190.271.353.9500001.022.771285
27113.871.902.8019.41072.952.970.371.764.5000001.253.40915
28114.021.682.2116.0962.652.330.261.984.7000001.043.591035
29113.731.502.7022.51013.003.250.292.385.7000001.192.711285
.............................................
147313.323.242.3821.5921.930.760.451.258.4200000.551.62650
148313.083.902.3621.51131.411.390.341.149.4000000.571.33550
149313.503.122.6224.01231.401.570.221.258.6000000.591.30500
150312.792.672.4822.01121.481.360.241.2610.8000000.481.47480
151313.111.902.7525.51162.201.280.261.567.1000000.611.33425
152313.233.302.2818.5981.800.830.611.8710.5200000.561.51675
153312.581.292.1020.01031.480.580.531.407.6000000.581.55640
154313.175.192.3222.0931.740.630.611.557.9000000.601.48725
155313.844.122.3819.5891.800.830.481.569.0100000.571.64480
156312.453.032.6427.0971.900.580.631.147.5000000.671.73880
157314.341.682.7025.0982.801.310.532.7013.0000000.571.96660
158313.481.672.6422.5892.601.100.522.2911.7500000.571.78620
159312.363.832.3821.0882.300.920.501.047.6500000.561.58520
160313.693.262.5420.01071.830.560.500.805.8800000.961.82680
161312.853.272.5822.01061.650.600.600.965.5800000.872.11570
162312.963.452.3518.51061.390.700.400.945.2800000.681.75675
163313.782.762.3022.0901.350.680.411.039.5800000.701.68615
164313.734.362.2622.5881.280.470.521.156.6200000.781.75520
165313.453.702.6023.01111.700.920.431.4610.6800000.851.56695
166312.823.372.3019.5881.480.660.400.9710.2600000.721.75685
167313.582.582.6924.51051.550.840.391.548.6600000.741.80750
168313.404.602.8625.01121.980.960.271.118.5000000.671.92630
169312.203.032.3219.0961.250.490.400.735.5000000.661.83510
170312.772.392.2819.5861.390.510.480.649.8999990.571.63470
171314.162.512.4820.0911.680.700.441.249.7000000.621.71660
172313.715.652.4520.5951.680.610.521.067.7000000.641.74740
173313.403.912.4823.01021.800.750.431.417.3000000.701.56750
174313.274.282.2620.01201.590.690.431.3510.2000000.591.56835
175313.172.592.3720.01201.650.680.531.469.3000000.601.62840
176314.134.102.7424.5962.050.760.561.359.2000000.611.60560
\n", + "

177 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " 1 Alcohol Malic acid Ash Alkalinity of ash Magnesium \\\n", + "0 1 13.20 1.78 2.14 11.2 100 \n", + "1 1 13.16 2.36 2.67 18.6 101 \n", + "2 1 14.37 1.95 2.50 16.8 113 \n", + "3 1 13.24 2.59 2.87 21.0 118 \n", + "4 1 14.20 1.76 2.45 15.2 112 \n", + "5 1 14.39 1.87 2.45 14.6 96 \n", + "6 1 14.06 2.15 2.61 17.6 121 \n", + "7 1 14.83 1.64 2.17 14.0 97 \n", + "8 1 13.86 1.35 2.27 16.0 98 \n", + "9 1 14.10 2.16 2.30 18.0 105 \n", + "10 1 14.12 1.48 2.32 16.8 95 \n", + "11 1 13.75 1.73 2.41 16.0 89 \n", + "12 1 14.75 1.73 2.39 11.4 91 \n", + "13 1 14.38 1.87 2.38 12.0 102 \n", + "14 1 13.63 1.81 2.70 17.2 112 \n", + "15 1 14.30 1.92 2.72 20.0 120 \n", + "16 1 13.83 1.57 2.62 20.0 115 \n", + "17 1 14.19 1.59 2.48 16.5 108 \n", + "18 1 13.64 3.10 2.56 15.2 116 \n", + "19 1 14.06 1.63 2.28 16.0 126 \n", + "20 1 12.93 3.80 2.65 18.6 102 \n", + "21 1 13.71 1.86 2.36 16.6 101 \n", + "22 1 12.85 1.60 2.52 17.8 95 \n", + "23 1 13.50 1.81 2.61 20.0 96 \n", + "24 1 13.05 2.05 3.22 25.0 124 \n", + "25 1 13.39 1.77 2.62 16.1 93 \n", + "26 1 13.30 1.72 2.14 17.0 94 \n", + "27 1 13.87 1.90 2.80 19.4 107 \n", + "28 1 14.02 1.68 2.21 16.0 96 \n", + "29 1 13.73 1.50 2.70 22.5 101 \n", + ".. .. ... ... ... ... ... \n", + "147 3 13.32 3.24 2.38 21.5 92 \n", + "148 3 13.08 3.90 2.36 21.5 113 \n", + "149 3 13.50 3.12 2.62 24.0 123 \n", + "150 3 12.79 2.67 2.48 22.0 112 \n", + "151 3 13.11 1.90 2.75 25.5 116 \n", + "152 3 13.23 3.30 2.28 18.5 98 \n", + "153 3 12.58 1.29 2.10 20.0 103 \n", + "154 3 13.17 5.19 2.32 22.0 93 \n", + "155 3 13.84 4.12 2.38 19.5 89 \n", + "156 3 12.45 3.03 2.64 27.0 97 \n", + "157 3 14.34 1.68 2.70 25.0 98 \n", + "158 3 13.48 1.67 2.64 22.5 89 \n", + "159 3 12.36 3.83 2.38 21.0 88 \n", + "160 3 13.69 3.26 2.54 20.0 107 \n", + "161 3 12.85 3.27 2.58 22.0 106 \n", + "162 3 12.96 3.45 2.35 18.5 106 \n", + "163 3 13.78 2.76 2.30 22.0 90 \n", + "164 3 13.73 4.36 2.26 22.5 88 \n", + "165 3 13.45 3.70 2.60 23.0 111 \n", + "166 3 12.82 3.37 2.30 19.5 88 \n", + "167 3 13.58 2.58 2.69 24.5 105 \n", + "168 3 13.40 4.60 2.86 25.0 112 \n", + "169 3 12.20 3.03 2.32 19.0 96 \n", + "170 3 12.77 2.39 2.28 19.5 86 \n", + "171 3 14.16 2.51 2.48 20.0 91 \n", + "172 3 13.71 5.65 2.45 20.5 95 \n", + "173 3 13.40 3.91 2.48 23.0 102 \n", + "174 3 13.27 4.28 2.26 20.0 120 \n", + "175 3 13.17 2.59 2.37 20.0 120 \n", + "176 3 14.13 4.10 2.74 24.5 96 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "0 2.65 2.76 0.26 1.28 \n", + "1 2.80 3.24 0.30 2.81 \n", + "2 3.85 3.49 0.24 2.18 \n", + "3 2.80 2.69 0.39 1.82 \n", + "4 3.27 3.39 0.34 1.97 \n", + "5 2.50 2.52 0.30 1.98 \n", + "6 2.60 2.51 0.31 1.25 \n", + "7 2.80 2.98 0.29 1.98 \n", + "8 2.98 3.15 0.22 1.85 \n", + "9 2.95 3.32 0.22 2.38 \n", + "10 2.20 2.43 0.26 1.57 \n", + "11 2.60 2.76 0.29 1.81 \n", + "12 3.10 3.69 0.43 2.81 \n", + "13 3.30 3.64 0.29 2.96 \n", + "14 2.85 2.91 0.30 1.46 \n", + "15 2.80 3.14 0.33 1.97 \n", + "16 2.95 3.40 0.40 1.72 \n", + "17 3.30 3.93 0.32 1.86 \n", + "18 2.70 3.03 0.17 1.66 \n", + "19 3.00 3.17 0.24 2.10 \n", + "20 2.41 2.41 0.25 1.98 \n", + "21 2.61 2.88 0.27 1.69 \n", + "22 2.48 2.37 0.26 1.46 \n", + "23 2.53 2.61 0.28 1.66 \n", + "24 2.63 2.68 0.47 1.92 \n", + "25 2.85 2.94 0.34 1.45 \n", + "26 2.40 2.19 0.27 1.35 \n", + "27 2.95 2.97 0.37 1.76 \n", + "28 2.65 2.33 0.26 1.98 \n", + "29 3.00 3.25 0.29 2.38 \n", + ".. ... ... ... ... \n", + "147 1.93 0.76 0.45 1.25 \n", + "148 1.41 1.39 0.34 1.14 \n", + "149 1.40 1.57 0.22 1.25 \n", + "150 1.48 1.36 0.24 1.26 \n", + "151 2.20 1.28 0.26 1.56 \n", + "152 1.80 0.83 0.61 1.87 \n", + "153 1.48 0.58 0.53 1.40 \n", + "154 1.74 0.63 0.61 1.55 \n", + "155 1.80 0.83 0.48 1.56 \n", + "156 1.90 0.58 0.63 1.14 \n", + "157 2.80 1.31 0.53 2.70 \n", + "158 2.60 1.10 0.52 2.29 \n", + "159 2.30 0.92 0.50 1.04 \n", + "160 1.83 0.56 0.50 0.80 \n", + "161 1.65 0.60 0.60 0.96 \n", + "162 1.39 0.70 0.40 0.94 \n", + "163 1.35 0.68 0.41 1.03 \n", + "164 1.28 0.47 0.52 1.15 \n", + "165 1.70 0.92 0.43 1.46 \n", + "166 1.48 0.66 0.40 0.97 \n", + "167 1.55 0.84 0.39 1.54 \n", + "168 1.98 0.96 0.27 1.11 \n", + "169 1.25 0.49 0.40 0.73 \n", + "170 1.39 0.51 0.48 0.64 \n", + "171 1.68 0.70 0.44 1.24 \n", + "172 1.68 0.61 0.52 1.06 \n", + "173 1.80 0.75 0.43 1.41 \n", + "174 1.59 0.69 0.43 1.35 \n", + "175 1.65 0.68 0.53 1.46 \n", + "176 2.05 0.76 0.56 1.35 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted vines Proline \n", + "0 4.380000 1.05 3.40 1050 \n", + "1 5.680000 1.03 3.17 1185 \n", + "2 7.800000 0.86 3.45 1480 \n", + "3 4.320000 1.04 2.93 735 \n", + "4 6.750000 1.05 2.85 1450 \n", + "5 5.250000 1.02 3.58 1290 \n", + "6 5.050000 1.06 3.58 1295 \n", + "7 5.200000 1.08 2.85 1045 \n", + "8 7.220000 1.01 3.55 1045 \n", + "9 5.750000 1.25 3.17 1510 \n", + "10 5.000000 1.17 2.82 1280 \n", + "11 5.600000 1.15 2.90 1320 \n", + "12 5.400000 1.25 2.73 1150 \n", + "13 7.500000 1.20 3.00 1547 \n", + "14 7.300000 1.28 2.88 1310 \n", + "15 6.200000 1.07 2.65 1280 \n", + "16 6.600000 1.13 2.57 1130 \n", + "17 8.700000 1.23 2.82 1680 \n", + "18 5.100000 0.96 3.36 845 \n", + "19 5.650000 1.09 3.71 780 \n", + "20 4.500000 1.03 3.52 770 \n", + "21 3.800000 1.11 4.00 1035 \n", + "22 3.930000 1.09 3.63 1015 \n", + "23 3.520000 1.12 3.82 845 \n", + "24 3.580000 1.13 3.20 830 \n", + "25 4.800000 0.92 3.22 1195 \n", + "26 3.950000 1.02 2.77 1285 \n", + "27 4.500000 1.25 3.40 915 \n", + "28 4.700000 1.04 3.59 1035 \n", + "29 5.700000 1.19 2.71 1285 \n", + ".. ... ... ... ... \n", + "147 8.420000 0.55 1.62 650 \n", + "148 9.400000 0.57 1.33 550 \n", + "149 8.600000 0.59 1.30 500 \n", + "150 10.800000 0.48 1.47 480 \n", + "151 7.100000 0.61 1.33 425 \n", + "152 10.520000 0.56 1.51 675 \n", + "153 7.600000 0.58 1.55 640 \n", + "154 7.900000 0.60 1.48 725 \n", + "155 9.010000 0.57 1.64 480 \n", + "156 7.500000 0.67 1.73 880 \n", + "157 13.000000 0.57 1.96 660 \n", + "158 11.750000 0.57 1.78 620 \n", + "159 7.650000 0.56 1.58 520 \n", + "160 5.880000 0.96 1.82 680 \n", + "161 5.580000 0.87 2.11 570 \n", + "162 5.280000 0.68 1.75 675 \n", + "163 9.580000 0.70 1.68 615 \n", + "164 6.620000 0.78 1.75 520 \n", + "165 10.680000 0.85 1.56 695 \n", + "166 10.260000 0.72 1.75 685 \n", + "167 8.660000 0.74 1.80 750 \n", + "168 8.500000 0.67 1.92 630 \n", + "169 5.500000 0.66 1.83 510 \n", + "170 9.899999 0.57 1.63 470 \n", + "171 9.700000 0.62 1.71 660 \n", + "172 7.700000 0.64 1.74 740 \n", + "173 7.300000 0.70 1.56 750 \n", + "174 10.200000 0.59 1.56 835 \n", + "175 9.300000 0.60 1.62 840 \n", + "176 9.200000 0.61 1.60 560 \n", + "\n", + "[177 rows x 14 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 67 + } + ] + }, + { + "metadata": { + "id": "Zqi7hwWpkNbH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Set the values of the first 3 rows from alcohol as NaN\n", + "\n", + "Hint- Use iloc to select 3 rows of wine_df" + ] + }, + { + "metadata": { + "id": "buyT4vX4kPMl", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 2009 + }, + "outputId": "ce8ab09e-2c04-4330-807b-5847b26c1297" + }, + "cell_type": "code", + "source": [ + "wine_df.iloc[0:3,1]='NaN'\n", + "wine_df" + ], + "execution_count": 68, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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1AlcoholMalic acidAshAlkalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted vinesProline
01NaN1.782.1411.21002.652.760.261.284.3800001.053.401050
11NaN2.362.6718.61012.803.240.302.815.6800001.033.171185
21NaN1.952.5016.81133.853.490.242.187.8000000.863.451480
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177 rows × 14 columns

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" + ], + "text/plain": [ + " 1 Alcohol Malic acid Ash Alkalinity of ash Magnesium Total phenols \\\n", + "0 1 NaN 1.78 2.14 11.2 100 2.65 \n", + "1 1 NaN 2.36 2.67 18.6 101 2.80 \n", + "2 1 NaN 1.95 2.50 16.8 113 3.85 \n", + "3 1 13.24 2.59 2.87 21.0 118 2.80 \n", + "4 1 14.2 1.76 2.45 15.2 112 3.27 \n", + "5 1 14.39 1.87 2.45 14.6 96 2.50 \n", + "6 1 14.06 2.15 2.61 17.6 121 2.60 \n", + "7 1 14.83 1.64 2.17 14.0 97 2.80 \n", + "8 1 13.86 1.35 2.27 16.0 98 2.98 \n", + "9 1 14.1 2.16 2.30 18.0 105 2.95 \n", + "10 1 14.12 1.48 2.32 16.8 95 2.20 \n", + "11 1 13.75 1.73 2.41 16.0 89 2.60 \n", + "12 1 14.75 1.73 2.39 11.4 91 3.10 \n", + "13 1 14.38 1.87 2.38 12.0 102 3.30 \n", + "14 1 13.63 1.81 2.70 17.2 112 2.85 \n", + "15 1 14.3 1.92 2.72 20.0 120 2.80 \n", + "16 1 13.83 1.57 2.62 20.0 115 2.95 \n", + "17 1 14.19 1.59 2.48 16.5 108 3.30 \n", + "18 1 13.64 3.10 2.56 15.2 116 2.70 \n", + "19 1 14.06 1.63 2.28 16.0 126 3.00 \n", + "20 1 12.93 3.80 2.65 18.6 102 2.41 \n", + "21 1 13.71 1.86 2.36 16.6 101 2.61 \n", + "22 1 12.85 1.60 2.52 17.8 95 2.48 \n", + "23 1 13.5 1.81 2.61 20.0 96 2.53 \n", + "24 1 13.05 2.05 3.22 25.0 124 2.63 \n", + "25 1 13.39 1.77 2.62 16.1 93 2.85 \n", + "26 1 13.3 1.72 2.14 17.0 94 2.40 \n", + "27 1 13.87 1.90 2.80 19.4 107 2.95 \n", + "28 1 14.02 1.68 2.21 16.0 96 2.65 \n", + "29 1 13.73 1.50 2.70 22.5 101 3.00 \n", + ".. .. ... ... ... ... ... ... \n", + "147 3 13.32 3.24 2.38 21.5 92 1.93 \n", + "148 3 13.08 3.90 2.36 21.5 113 1.41 \n", + "149 3 13.5 3.12 2.62 24.0 123 1.40 \n", + "150 3 12.79 2.67 2.48 22.0 112 1.48 \n", + "151 3 13.11 1.90 2.75 25.5 116 2.20 \n", + "152 3 13.23 3.30 2.28 18.5 98 1.80 \n", + "153 3 12.58 1.29 2.10 20.0 103 1.48 \n", + "154 3 13.17 5.19 2.32 22.0 93 1.74 \n", + "155 3 13.84 4.12 2.38 19.5 89 1.80 \n", + "156 3 12.45 3.03 2.64 27.0 97 1.90 \n", + "157 3 14.34 1.68 2.70 25.0 98 2.80 \n", + "158 3 13.48 1.67 2.64 22.5 89 2.60 \n", + "159 3 12.36 3.83 2.38 21.0 88 2.30 \n", + "160 3 13.69 3.26 2.54 20.0 107 1.83 \n", + "161 3 12.85 3.27 2.58 22.0 106 1.65 \n", + "162 3 12.96 3.45 2.35 18.5 106 1.39 \n", + "163 3 13.78 2.76 2.30 22.0 90 1.35 \n", + "164 3 13.73 4.36 2.26 22.5 88 1.28 \n", + "165 3 13.45 3.70 2.60 23.0 111 1.70 \n", + "166 3 12.82 3.37 2.30 19.5 88 1.48 \n", + "167 3 13.58 2.58 2.69 24.5 105 1.55 \n", + "168 3 13.4 4.60 2.86 25.0 112 1.98 \n", + "169 3 12.2 3.03 2.32 19.0 96 1.25 \n", + "170 3 12.77 2.39 2.28 19.5 86 1.39 \n", + "171 3 14.16 2.51 2.48 20.0 91 1.68 \n", + "172 3 13.71 5.65 2.45 20.5 95 1.68 \n", + "173 3 13.4 3.91 2.48 23.0 102 1.80 \n", + "174 3 13.27 4.28 2.26 20.0 120 1.59 \n", + "175 3 13.17 2.59 2.37 20.0 120 1.65 \n", + "176 3 14.13 4.10 2.74 24.5 96 2.05 \n", + "\n", + " Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity Hue \\\n", + "0 2.76 0.26 1.28 4.380000 1.05 \n", + "1 3.24 0.30 2.81 5.680000 1.03 \n", + "2 3.49 0.24 2.18 7.800000 0.86 \n", + "3 2.69 0.39 1.82 4.320000 1.04 \n", + "4 3.39 0.34 1.97 6.750000 1.05 \n", + "5 2.52 0.30 1.98 5.250000 1.02 \n", + "6 2.51 0.31 1.25 5.050000 1.06 \n", + "7 2.98 0.29 1.98 5.200000 1.08 \n", + "8 3.15 0.22 1.85 7.220000 1.01 \n", + "9 3.32 0.22 2.38 5.750000 1.25 \n", + "10 2.43 0.26 1.57 5.000000 1.17 \n", + "11 2.76 0.29 1.81 5.600000 1.15 \n", + "12 3.69 0.43 2.81 5.400000 1.25 \n", + "13 3.64 0.29 2.96 7.500000 1.20 \n", + "14 2.91 0.30 1.46 7.300000 1.28 \n", + "15 3.14 0.33 1.97 6.200000 1.07 \n", + "16 3.40 0.40 1.72 6.600000 1.13 \n", + "17 3.93 0.32 1.86 8.700000 1.23 \n", + "18 3.03 0.17 1.66 5.100000 0.96 \n", + "19 3.17 0.24 2.10 5.650000 1.09 \n", + "20 2.41 0.25 1.98 4.500000 1.03 \n", + "21 2.88 0.27 1.69 3.800000 1.11 \n", + "22 2.37 0.26 1.46 3.930000 1.09 \n", + "23 2.61 0.28 1.66 3.520000 1.12 \n", + "24 2.68 0.47 1.92 3.580000 1.13 \n", + "25 2.94 0.34 1.45 4.800000 0.92 \n", + "26 2.19 0.27 1.35 3.950000 1.02 \n", + "27 2.97 0.37 1.76 4.500000 1.25 \n", + "28 2.33 0.26 1.98 4.700000 1.04 \n", + "29 3.25 0.29 2.38 5.700000 1.19 \n", + ".. ... ... ... ... ... \n", + "147 0.76 0.45 1.25 8.420000 0.55 \n", + "148 1.39 0.34 1.14 9.400000 0.57 \n", + "149 1.57 0.22 1.25 8.600000 0.59 \n", + "150 1.36 0.24 1.26 10.800000 0.48 \n", + "151 1.28 0.26 1.56 7.100000 0.61 \n", + "152 0.83 0.61 1.87 10.520000 0.56 \n", + "153 0.58 0.53 1.40 7.600000 0.58 \n", + "154 0.63 0.61 1.55 7.900000 0.60 \n", + "155 0.83 0.48 1.56 9.010000 0.57 \n", + "156 0.58 0.63 1.14 7.500000 0.67 \n", + "157 1.31 0.53 2.70 13.000000 0.57 \n", + "158 1.10 0.52 2.29 11.750000 0.57 \n", + "159 0.92 0.50 1.04 7.650000 0.56 \n", + "160 0.56 0.50 0.80 5.880000 0.96 \n", + "161 0.60 0.60 0.96 5.580000 0.87 \n", + "162 0.70 0.40 0.94 5.280000 0.68 \n", + "163 0.68 0.41 1.03 9.580000 0.70 \n", + "164 0.47 0.52 1.15 6.620000 0.78 \n", + "165 0.92 0.43 1.46 10.680000 0.85 \n", + "166 0.66 0.40 0.97 10.260000 0.72 \n", + "167 0.84 0.39 1.54 8.660000 0.74 \n", + "168 0.96 0.27 1.11 8.500000 0.67 \n", + "169 0.49 0.40 0.73 5.500000 0.66 \n", + "170 0.51 0.48 0.64 9.899999 0.57 \n", + "171 0.70 0.44 1.24 9.700000 0.62 \n", + "172 0.61 0.52 1.06 7.700000 0.64 \n", + "173 0.75 0.43 1.41 7.300000 0.70 \n", + "174 0.69 0.43 1.35 10.200000 0.59 \n", + "175 0.68 0.53 1.46 9.300000 0.60 \n", + "176 0.76 0.56 1.35 9.200000 0.61 \n", + "\n", + " OD280/OD315 of diluted vines Proline \n", + "0 3.40 1050 \n", + "1 3.17 1185 \n", + "2 3.45 1480 \n", + "3 2.93 735 \n", + "4 2.85 1450 \n", + "5 3.58 1290 \n", + "6 3.58 1295 \n", + "7 2.85 1045 \n", + "8 3.55 1045 \n", + "9 3.17 1510 \n", + "10 2.82 1280 \n", + "11 2.90 1320 \n", + "12 2.73 1150 \n", + "13 3.00 1547 \n", + "14 2.88 1310 \n", + "15 2.65 1280 \n", + "16 2.57 1130 \n", + "17 2.82 1680 \n", + "18 3.36 845 \n", + "19 3.71 780 \n", + "20 3.52 770 \n", + "21 4.00 1035 \n", + "22 3.63 1015 \n", + "23 3.82 845 \n", + "24 3.20 830 \n", + "25 3.22 1195 \n", + "26 2.77 1285 \n", + "27 3.40 915 \n", + "28 3.59 1035 \n", + "29 2.71 1285 \n", + ".. ... ... \n", + "147 1.62 650 \n", + "148 1.33 550 \n", + "149 1.30 500 \n", + "150 1.47 480 \n", + "151 1.33 425 \n", + "152 1.51 675 \n", + "153 1.55 640 \n", + "154 1.48 725 \n", + "155 1.64 480 \n", + "156 1.73 880 \n", + "157 1.96 660 \n", + "158 1.78 620 \n", + "159 1.58 520 \n", + "160 1.82 680 \n", + "161 2.11 570 \n", + "162 1.75 675 \n", + "163 1.68 615 \n", + "164 1.75 520 \n", + "165 1.56 695 \n", + "166 1.75 685 \n", + "167 1.80 750 \n", + "168 1.92 630 \n", + "169 1.83 510 \n", + "170 1.63 470 \n", + "171 1.71 660 \n", + "172 1.74 740 \n", + "173 1.56 750 \n", + "174 1.56 835 \n", + "175 1.62 840 \n", + "176 1.60 560 \n", + "\n", + "[177 rows x 14 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 68 + } + ] + }, + { + "metadata": { + "id": "RQMNI2UHkP3o", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Create an array of 10 random numbers uptill 10 and assign it to a variable named `random`" + ] + }, + { + "metadata": { + "id": "xunmCjaEmDwZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + }, + "outputId": "f3c1b82e-09f1-4939-b059-5d214b91c974" + }, + "cell_type": "code", + "source": [ + "random=np.random.rand(1,10)\n", + "print (random)" + ], + "execution_count": 69, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[0.31209177 0.89047759 0.39542803 0.31234829 0.22254795 0.63876143\n", + " 0.47955148 0.91185707 0.647655 0.03997572]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "hELUakyXmFSu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Use random numbers you generated as an index and assign NaN value to each of cell of the column alcohol" + ] + }, + { + "metadata": { + "id": "zMgaNnNHmP01", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 2009 + }, + "outputId": "eaef66be-c579-4b1d-9e19-eb423d115e65" + }, + "cell_type": "code", + "source": [ + "new_index = np.random.permutation(wine_df.index)\n", + "wine_df = wine_df.reindex(index = new_index)\n", + "wine_df.iloc[:,1]='NaN'\n", + "wine_df.reindex(index=new_index)\n", + "wine_df\n" + ], + "execution_count": 70, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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1AlcoholMalic acidAshAlkalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted vinesProline
1022NaN1.721.8819.5862.501.640.371.422.060.9402.44415
852NaN1.612.3122.8901.781.690.431.562.451.3302.26495
1603NaN3.262.5420.01071.830.560.500.805.880.9601.82680
381NaN3.992.5113.21283.003.040.202.085.100.8903.53760
411NaN1.892.5915.01013.253.560.171.705.430.8803.561095
331NaN1.802.6519.01102.352.530.291.544.201.1002.871095
692NaN1.612.2120.41031.101.020.371.463.050.9061.82870
401NaN3.842.1218.8902.452.680.271.484.280.9103.001035
942NaN1.522.2019.01622.502.270.323.282.601.1602.63937
792NaN0.922.0019.0862.422.260.301.432.501.3803.12278
1172NaN3.431.9816.0801.631.250.430.833.400.7002.12372
551NaN1.702.3016.31183.203.000.262.036.380.9403.31970
141NaN1.812.7017.21122.852.910.301.467.301.2802.881310
1062NaN1.752.2822.5841.381.760.481.633.300.8802.42488
1733NaN3.912.4823.01021.800.750.431.417.300.7001.56750
311NaN1.832.3617.21042.422.690.421.973.841.2302.87990
541NaN1.732.4620.51162.962.780.202.456.250.9803.031120
972NaN1.072.1018.5883.523.750.241.954.501.0402.77660
1683NaN4.602.8625.01121.980.960.271.118.500.6701.92630
561NaN1.972.6816.81023.003.230.311.666.001.0702.841270
231NaN1.812.6120.0962.532.610.281.663.521.1203.82845
1293NaN1.352.3218.01221.511.250.210.944.100.7601.29630
1212NaN4.432.7326.51022.202.130.431.712.080.9203.12365
1142NaN1.512.2021.5852.462.170.522.011.901.7102.87407
1383NaN2.962.6124.01012.320.600.530.814.920.8902.15590
1743NaN4.282.2620.01201.590.690.431.3510.200.5901.56835
191NaN1.632.2816.01263.003.170.242.105.651.0903.71780
1423NaN4.952.3520.0922.000.800.471.024.400.9102.05550
391NaN1.712.3116.21173.153.290.342.346.130.9503.38795
632NaN1.452.5319.01041.891.750.451.032.951.4502.23355
.............................................
752NaN0.901.7116.0861.952.030.241.464.601.1902.48392
571NaN1.432.5016.71083.403.670.192.046.800.8902.871285
1483NaN3.902.3621.51131.411.390.341.149.400.5701.33550
832NaN0.892.5818.0942.202.210.222.353.050.7903.08520
221NaN1.602.5217.8952.482.370.261.463.931.0903.631015
121NaN1.732.3911.4913.103.690.432.815.401.2502.731150
421NaN3.982.2917.51032.642.630.321.664.360.8203.00680
1433NaN3.882.2018.51121.380.780.291.148.210.6502.00855
1042NaN2.552.2722.0901.681.840.661.422.700.8603.30315
842NaN0.982.2418.0992.201.940.301.462.621.2303.16450
1262NaN2.132.7828.5922.132.240.581.763.000.9702.44466
1242NaN2.162.1721.0852.602.650.371.352.760.8603.28378
1192NaN2.402.4220.0962.902.790.321.833.250.8003.39625
1373NaN3.592.1919.5881.620.480.580.885.700.8101.82580
1112NaN2.682.9220.01031.752.030.601.053.801.2302.50607
1503NaN2.672.4822.01121.481.360.241.2610.800.4801.47480
702NaN1.512.6725.0862.952.860.211.873.381.3603.16410
1763NaN4.102.7424.5962.050.760.561.359.200.6101.60560
451NaN3.592.2816.01023.253.170.272.194.901.0403.441065
1222NaN5.802.1321.5862.622.650.302.012.600.7303.10380
902NaN1.512.4222.0861.451.250.501.633.601.0502.65450
151NaN1.922.7220.01202.803.140.331.976.201.0702.651280
682NaN1.191.7516.81511.851.280.142.502.851.2803.07718
1443NaN3.572.1521.01021.500.550.431.304.000.6001.68830
1453NaN5.042.2320.0800.980.340.400.684.900.5801.33415
872NaN2.062.4621.6841.951.690.481.352.801.0002.75680
511NaN1.752.4214.01113.883.740.321.877.051.0103.261190
41NaN1.762.4515.21123.273.390.341.976.751.0502.851450
782NaN3.872.4023.01012.832.550.431.952.571.1903.13463
892NaN1.832.3218.5811.601.500.521.642.401.0802.27480
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177 rows × 14 columns

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\n", + "121 2 NaN 4.43 2.73 26.5 102 2.20 \n", + "114 2 NaN 1.51 2.20 21.5 85 2.46 \n", + "138 3 NaN 2.96 2.61 24.0 101 2.32 \n", + "174 3 NaN 4.28 2.26 20.0 120 1.59 \n", + "19 1 NaN 1.63 2.28 16.0 126 3.00 \n", + "142 3 NaN 4.95 2.35 20.0 92 2.00 \n", + "39 1 NaN 1.71 2.31 16.2 117 3.15 \n", + "63 2 NaN 1.45 2.53 19.0 104 1.89 \n", + ".. .. ... ... ... ... ... ... \n", + "75 2 NaN 0.90 1.71 16.0 86 1.95 \n", + "57 1 NaN 1.43 2.50 16.7 108 3.40 \n", + "148 3 NaN 3.90 2.36 21.5 113 1.41 \n", + "83 2 NaN 0.89 2.58 18.0 94 2.20 \n", + "22 1 NaN 1.60 2.52 17.8 95 2.48 \n", + "12 1 NaN 1.73 2.39 11.4 91 3.10 \n", + "42 1 NaN 3.98 2.29 17.5 103 2.64 \n", + "143 3 NaN 3.88 2.20 18.5 112 1.38 \n", + "104 2 NaN 2.55 2.27 22.0 90 1.68 \n", + "84 2 NaN 0.98 2.24 18.0 99 2.20 \n", + "126 2 NaN 2.13 2.78 28.5 92 2.13 \n", + "124 2 NaN 2.16 2.17 21.0 85 2.60 \n", + "119 2 NaN 2.40 2.42 20.0 96 2.90 \n", + "137 3 NaN 3.59 2.19 19.5 88 1.62 \n", + "111 2 NaN 2.68 2.92 20.0 103 1.75 \n", + "150 3 NaN 2.67 2.48 22.0 112 1.48 \n", + "70 2 NaN 1.51 2.67 25.0 86 2.95 \n", + "176 3 NaN 4.10 2.74 24.5 96 2.05 \n", + "45 1 NaN 3.59 2.28 16.0 102 3.25 \n", + "122 2 NaN 5.80 2.13 21.5 86 2.62 \n", + "90 2 NaN 1.51 2.42 22.0 86 1.45 \n", + "15 1 NaN 1.92 2.72 20.0 120 2.80 \n", + "68 2 NaN 1.19 1.75 16.8 151 1.85 \n", + "144 3 NaN 3.57 2.15 21.0 102 1.50 \n", + "145 3 NaN 5.04 2.23 20.0 80 0.98 \n", + "87 2 NaN 2.06 2.46 21.6 84 1.95 \n", + "51 1 NaN 1.75 2.42 14.0 111 3.88 \n", + "4 1 NaN 1.76 2.45 15.2 112 3.27 \n", + "78 2 NaN 3.87 2.40 23.0 101 2.83 \n", + "89 2 NaN 1.83 2.32 18.5 81 1.60 \n", + "\n", + " Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity \\\n", + "102 1.64 0.37 1.42 2.06 \n", + "85 1.69 0.43 1.56 2.45 \n", + "160 0.56 0.50 0.80 5.88 \n", + "38 3.04 0.20 2.08 5.10 \n", + "41 3.56 0.17 1.70 5.43 \n", + "33 2.53 0.29 1.54 4.20 \n", + "69 1.02 0.37 1.46 3.05 \n", + "40 2.68 0.27 1.48 4.28 \n", + "94 2.27 0.32 3.28 2.60 \n", + "79 2.26 0.30 1.43 2.50 \n", + "117 1.25 0.43 0.83 3.40 \n", + "55 3.00 0.26 2.03 6.38 \n", + "14 2.91 0.30 1.46 7.30 \n", + "106 1.76 0.48 1.63 3.30 \n", + "173 0.75 0.43 1.41 7.30 \n", + "31 2.69 0.42 1.97 3.84 \n", + "54 2.78 0.20 2.45 6.25 \n", + "97 3.75 0.24 1.95 4.50 \n", + "168 0.96 0.27 1.11 8.50 \n", + "56 3.23 0.31 1.66 6.00 \n", + "23 2.61 0.28 1.66 3.52 \n", + "129 1.25 0.21 0.94 4.10 \n", + "121 2.13 0.43 1.71 2.08 \n", + "114 2.17 0.52 2.01 1.90 \n", + "138 0.60 0.53 0.81 4.92 \n", + "174 0.69 0.43 1.35 10.20 \n", + "19 3.17 0.24 2.10 5.65 \n", + "142 0.80 0.47 1.02 4.40 \n", + "39 3.29 0.34 2.34 6.13 \n", + "63 1.75 0.45 1.03 2.95 \n", + ".. ... ... ... ... \n", + "75 2.03 0.24 1.46 4.60 \n", + "57 3.67 0.19 2.04 6.80 \n", + "148 1.39 0.34 1.14 9.40 \n", + "83 2.21 0.22 2.35 3.05 \n", + "22 2.37 0.26 1.46 3.93 \n", + "12 3.69 0.43 2.81 5.40 \n", + "42 2.63 0.32 1.66 4.36 \n", + "143 0.78 0.29 1.14 8.21 \n", + "104 1.84 0.66 1.42 2.70 \n", + "84 1.94 0.30 1.46 2.62 \n", + "126 2.24 0.58 1.76 3.00 \n", + "124 2.65 0.37 1.35 2.76 \n", + "119 2.79 0.32 1.83 3.25 \n", + "137 0.48 0.58 0.88 5.70 \n", + "111 2.03 0.60 1.05 3.80 \n", + "150 1.36 0.24 1.26 10.80 \n", + "70 2.86 0.21 1.87 3.38 \n", + "176 0.76 0.56 1.35 9.20 \n", + "45 3.17 0.27 2.19 4.90 \n", + "122 2.65 0.30 2.01 2.60 \n", + "90 1.25 0.50 1.63 3.60 \n", + "15 3.14 0.33 1.97 6.20 \n", + "68 1.28 0.14 2.50 2.85 \n", + "144 0.55 0.43 1.30 4.00 \n", + "145 0.34 0.40 0.68 4.90 \n", + "87 1.69 0.48 1.35 2.80 \n", + "51 3.74 0.32 1.87 7.05 \n", + "4 3.39 0.34 1.97 6.75 \n", + "78 2.55 0.43 1.95 2.57 \n", + "89 1.50 0.52 1.64 2.40 \n", + "\n", + " Hue OD280/OD315 of diluted vines Proline \n", + "102 0.940 2.44 415 \n", + "85 1.330 2.26 495 \n", + "160 0.960 1.82 680 \n", + "38 0.890 3.53 760 \n", + "41 0.880 3.56 1095 \n", + "33 1.100 2.87 1095 \n", + "69 0.906 1.82 870 \n", + "40 0.910 3.00 1035 \n", + "94 1.160 2.63 937 \n", + "79 1.380 3.12 278 \n", + "117 0.700 2.12 372 \n", + "55 0.940 3.31 970 \n", + "14 1.280 2.88 1310 \n", + "106 0.880 2.42 488 \n", + "173 0.700 1.56 750 \n", + "31 1.230 2.87 990 \n", + "54 0.980 3.03 1120 \n", + "97 1.040 2.77 660 \n", + "168 0.670 1.92 630 \n", + "56 1.070 2.84 1270 \n", + "23 1.120 3.82 845 \n", + "129 0.760 1.29 630 \n", + "121 0.920 3.12 365 \n", + "114 1.710 2.87 407 \n", + "138 0.890 2.15 590 \n", + "174 0.590 1.56 835 \n", + "19 1.090 3.71 780 \n", + "142 0.910 2.05 550 \n", + "39 0.950 3.38 795 \n", + "63 1.450 2.23 355 \n", + ".. ... ... ... \n", + "75 1.190 2.48 392 \n", + "57 0.890 2.87 1285 \n", + "148 0.570 1.33 550 \n", + "83 0.790 3.08 520 \n", + "22 1.090 3.63 1015 \n", + "12 1.250 2.73 1150 \n", + "42 0.820 3.00 680 \n", + "143 0.650 2.00 855 \n", + "104 0.860 3.30 315 \n", + "84 1.230 3.16 450 \n", + "126 0.970 2.44 466 \n", + "124 0.860 3.28 378 \n", + "119 0.800 3.39 625 \n", + "137 0.810 1.82 580 \n", + "111 1.230 2.50 607 \n", + "150 0.480 1.47 480 \n", + "70 1.360 3.16 410 \n", + "176 0.610 1.60 560 \n", + "45 1.040 3.44 1065 \n", + "122 0.730 3.10 380 \n", + "90 1.050 2.65 450 \n", + "15 1.070 2.65 1280 \n", + "68 1.280 3.07 718 \n", + "144 0.600 1.68 830 \n", + "145 0.580 1.33 415 \n", + "87 1.000 2.75 680 \n", + "51 1.010 3.26 1190 \n", + "4 1.050 2.85 1450 \n", + "78 1.190 3.13 463 \n", + "89 1.080 2.27 480 \n", + "\n", + "[177 rows x 14 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 70 + } + ] + }, + { + "metadata": { + "id": "PHyK_vRsmRwV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### How many missing values do we have? \n", + "\n", + "Hint: you can use isnull() and sum()" + ] + }, + { + "metadata": { + "id": "EnOYhmEqmfKp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 293 + }, + "outputId": "87f8238e-9a8f-4cc8-f2dc-7d7d2c1a0b04" + }, + "cell_type": "code", + "source": [ + "wine_df.isna().sum()" + ], + "execution_count": 71, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1 0\n", + "Alcohol 0\n", + "Malic acid 0\n", + "Ash 0\n", + "Alkalinity of ash 0\n", + "Magnesium 0\n", + "Total phenols 0\n", + "Flavanoids 0\n", + "Nonflavanoid phenols 0\n", + "Proanthocyanins 0\n", + "Color intensity 0\n", + "Hue 0\n", + "OD280/OD315 of diluted vines 0\n", + "Proline 0\n", + "dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 71 + } + ] + }, + { + "metadata": { + "id": "-Fd4WBklmf1_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Delete the rows that contain missing values " + ] + }, + { + "metadata": { + "id": "As7IC6Ktms8-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 2009 + }, + "outputId": "34f8d203-eb11-4fd8-db6c-d036b2b29c28" + }, + "cell_type": "code", + "source": [ + "wine_df.dropna(inplace=True)\n", + "wine_df" + ], + "execution_count": 74, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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1AlcoholMalic acidAshAlkalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted vinesProline
1022NaN1.721.8819.5862.501.640.371.422.060.9402.44415
852NaN1.612.3122.8901.781.690.431.562.451.3302.26495
1603NaN3.262.5420.01071.830.560.500.805.880.9601.82680
381NaN3.992.5113.21283.003.040.202.085.100.8903.53760
411NaN1.892.5915.01013.253.560.171.705.430.8803.561095
331NaN1.802.6519.01102.352.530.291.544.201.1002.871095
692NaN1.612.2120.41031.101.020.371.463.050.9061.82870
401NaN3.842.1218.8902.452.680.271.484.280.9103.001035
942NaN1.522.2019.01622.502.270.323.282.601.1602.63937
792NaN0.922.0019.0862.422.260.301.432.501.3803.12278
1172NaN3.431.9816.0801.631.250.430.833.400.7002.12372
551NaN1.702.3016.31183.203.000.262.036.380.9403.31970
141NaN1.812.7017.21122.852.910.301.467.301.2802.881310
1062NaN1.752.2822.5841.381.760.481.633.300.8802.42488
1733NaN3.912.4823.01021.800.750.431.417.300.7001.56750
311NaN1.832.3617.21042.422.690.421.973.841.2302.87990
541NaN1.732.4620.51162.962.780.202.456.250.9803.031120
972NaN1.072.1018.5883.523.750.241.954.501.0402.77660
1683NaN4.602.8625.01121.980.960.271.118.500.6701.92630
561NaN1.972.6816.81023.003.230.311.666.001.0702.841270
231NaN1.812.6120.0962.532.610.281.663.521.1203.82845
1293NaN1.352.3218.01221.511.250.210.944.100.7601.29630
1212NaN4.432.7326.51022.202.130.431.712.080.9203.12365
1142NaN1.512.2021.5852.462.170.522.011.901.7102.87407
1383NaN2.962.6124.01012.320.600.530.814.920.8902.15590
1743NaN4.282.2620.01201.590.690.431.3510.200.5901.56835
191NaN1.632.2816.01263.003.170.242.105.651.0903.71780
1423NaN4.952.3520.0922.000.800.471.024.400.9102.05550
391NaN1.712.3116.21173.153.290.342.346.130.9503.38795
632NaN1.452.5319.01041.891.750.451.032.951.4502.23355
.............................................
752NaN0.901.7116.0861.952.030.241.464.601.1902.48392
571NaN1.432.5016.71083.403.670.192.046.800.8902.871285
1483NaN3.902.3621.51131.411.390.341.149.400.5701.33550
832NaN0.892.5818.0942.202.210.222.353.050.7903.08520
221NaN1.602.5217.8952.482.370.261.463.931.0903.631015
121NaN1.732.3911.4913.103.690.432.815.401.2502.731150
421NaN3.982.2917.51032.642.630.321.664.360.8203.00680
1433NaN3.882.2018.51121.380.780.291.148.210.6502.00855
1042NaN2.552.2722.0901.681.840.661.422.700.8603.30315
842NaN0.982.2418.0992.201.940.301.462.621.2303.16450
1262NaN2.132.7828.5922.132.240.581.763.000.9702.44466
1242NaN2.162.1721.0852.602.650.371.352.760.8603.28378
1192NaN2.402.4220.0962.902.790.321.833.250.8003.39625
1373NaN3.592.1919.5881.620.480.580.885.700.8101.82580
1112NaN2.682.9220.01031.752.030.601.053.801.2302.50607
1503NaN2.672.4822.01121.481.360.241.2610.800.4801.47480
702NaN1.512.6725.0862.952.860.211.873.381.3603.16410
1763NaN4.102.7424.5962.050.760.561.359.200.6101.60560
451NaN3.592.2816.01023.253.170.272.194.901.0403.441065
1222NaN5.802.1321.5862.622.650.302.012.600.7303.10380
902NaN1.512.4222.0861.451.250.501.633.601.0502.65450
151NaN1.922.7220.01202.803.140.331.976.201.0702.651280
682NaN1.191.7516.81511.851.280.142.502.851.2803.07718
1443NaN3.572.1521.01021.500.550.431.304.000.6001.68830
1453NaN5.042.2320.0800.980.340.400.684.900.5801.33415
872NaN2.062.4621.6841.951.690.481.352.801.0002.75680
511NaN1.752.4214.01113.883.740.321.877.051.0103.261190
41NaN1.762.4515.21123.273.390.341.976.751.0502.851450
782NaN3.872.4023.01012.832.550.431.952.571.1903.13463
892NaN1.832.3218.5811.601.500.521.642.401.0802.27480
\n", + "

177 rows × 14 columns

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" + ], + "text/plain": [ + " 1 Alcohol Malic acid Ash Alkalinity of ash Magnesium Total phenols \\\n", + "102 2 NaN 1.72 1.88 19.5 86 2.50 \n", + "85 2 NaN 1.61 2.31 22.8 90 1.78 \n", + "160 3 NaN 3.26 2.54 20.0 107 1.83 \n", + "38 1 NaN 3.99 2.51 13.2 128 3.00 \n", + "41 1 NaN 1.89 2.59 15.0 101 3.25 \n", + "33 1 NaN 1.80 2.65 19.0 110 2.35 \n", + "69 2 NaN 1.61 2.21 20.4 103 1.10 \n", + "40 1 NaN 3.84 2.12 18.8 90 2.45 \n", + "94 2 NaN 1.52 2.20 19.0 162 2.50 \n", + "79 2 NaN 0.92 2.00 19.0 86 2.42 \n", + "117 2 NaN 3.43 1.98 16.0 80 1.63 \n", + "55 1 NaN 1.70 2.30 16.3 118 3.20 \n", + "14 1 NaN 1.81 2.70 17.2 112 2.85 \n", + "106 2 NaN 1.75 2.28 22.5 84 1.38 \n", + "173 3 NaN 3.91 2.48 23.0 102 1.80 \n", + "31 1 NaN 1.83 2.36 17.2 104 2.42 \n", + "54 1 NaN 1.73 2.46 20.5 116 2.96 \n", + "97 2 NaN 1.07 2.10 18.5 88 3.52 \n", + "168 3 NaN 4.60 2.86 25.0 112 1.98 \n", + "56 1 NaN 1.97 2.68 16.8 102 3.00 \n", + "23 1 NaN 1.81 2.61 20.0 96 2.53 \n", + "129 3 NaN 1.35 2.32 18.0 122 1.51 \n", + "121 2 NaN 4.43 2.73 26.5 102 2.20 \n", + "114 2 NaN 1.51 2.20 21.5 85 2.46 \n", + "138 3 NaN 2.96 2.61 24.0 101 2.32 \n", + "174 3 NaN 4.28 2.26 20.0 120 1.59 \n", + "19 1 NaN 1.63 2.28 16.0 126 3.00 \n", + "142 3 NaN 4.95 2.35 20.0 92 2.00 \n", + "39 1 NaN 1.71 2.31 16.2 117 3.15 \n", + "63 2 NaN 1.45 2.53 19.0 104 1.89 \n", + ".. .. ... ... ... ... ... ... \n", + "75 2 NaN 0.90 1.71 16.0 86 1.95 \n", + "57 1 NaN 1.43 2.50 16.7 108 3.40 \n", + "148 3 NaN 3.90 2.36 21.5 113 1.41 \n", + "83 2 NaN 0.89 2.58 18.0 94 2.20 \n", + "22 1 NaN 1.60 2.52 17.8 95 2.48 \n", + "12 1 NaN 1.73 2.39 11.4 91 3.10 \n", + "42 1 NaN 3.98 2.29 17.5 103 2.64 \n", + "143 3 NaN 3.88 2.20 18.5 112 1.38 \n", + "104 2 NaN 2.55 2.27 22.0 90 1.68 \n", + "84 2 NaN 0.98 2.24 18.0 99 2.20 \n", + "126 2 NaN 2.13 2.78 28.5 92 2.13 \n", + "124 2 NaN 2.16 2.17 21.0 85 2.60 \n", + "119 2 NaN 2.40 2.42 20.0 96 2.90 \n", + "137 3 NaN 3.59 2.19 19.5 88 1.62 \n", + "111 2 NaN 2.68 2.92 20.0 103 1.75 \n", + "150 3 NaN 2.67 2.48 22.0 112 1.48 \n", + "70 2 NaN 1.51 2.67 25.0 86 2.95 \n", + "176 3 NaN 4.10 2.74 24.5 96 2.05 \n", + "45 1 NaN 3.59 2.28 16.0 102 3.25 \n", + "122 2 NaN 5.80 2.13 21.5 86 2.62 \n", + "90 2 NaN 1.51 2.42 22.0 86 1.45 \n", + "15 1 NaN 1.92 2.72 20.0 120 2.80 \n", + "68 2 NaN 1.19 1.75 16.8 151 1.85 \n", + "144 3 NaN 3.57 2.15 21.0 102 1.50 \n", + "145 3 NaN 5.04 2.23 20.0 80 0.98 \n", + "87 2 NaN 2.06 2.46 21.6 84 1.95 \n", + "51 1 NaN 1.75 2.42 14.0 111 3.88 \n", + "4 1 NaN 1.76 2.45 15.2 112 3.27 \n", + "78 2 NaN 3.87 2.40 23.0 101 2.83 \n", + "89 2 NaN 1.83 2.32 18.5 81 1.60 \n", + "\n", + " Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity \\\n", + "102 1.64 0.37 1.42 2.06 \n", + "85 1.69 0.43 1.56 2.45 \n", + "160 0.56 0.50 0.80 5.88 \n", + "38 3.04 0.20 2.08 5.10 \n", + "41 3.56 0.17 1.70 5.43 \n", + "33 2.53 0.29 1.54 4.20 \n", + "69 1.02 0.37 1.46 3.05 \n", + "40 2.68 0.27 1.48 4.28 \n", + "94 2.27 0.32 3.28 2.60 \n", + "79 2.26 0.30 1.43 2.50 \n", + "117 1.25 0.43 0.83 3.40 \n", + "55 3.00 0.26 2.03 6.38 \n", + "14 2.91 0.30 1.46 7.30 \n", + "106 1.76 0.48 1.63 3.30 \n", + "173 0.75 0.43 1.41 7.30 \n", + "31 2.69 0.42 1.97 3.84 \n", + "54 2.78 0.20 2.45 6.25 \n", + "97 3.75 0.24 1.95 4.50 \n", + "168 0.96 0.27 1.11 8.50 \n", + "56 3.23 0.31 1.66 6.00 \n", + "23 2.61 0.28 1.66 3.52 \n", + "129 1.25 0.21 0.94 4.10 \n", + "121 2.13 0.43 1.71 2.08 \n", + "114 2.17 0.52 2.01 1.90 \n", + "138 0.60 0.53 0.81 4.92 \n", + "174 0.69 0.43 1.35 10.20 \n", + "19 3.17 0.24 2.10 5.65 \n", + "142 0.80 0.47 1.02 4.40 \n", + "39 3.29 0.34 2.34 6.13 \n", + "63 1.75 0.45 1.03 2.95 \n", + ".. ... ... ... ... \n", + "75 2.03 0.24 1.46 4.60 \n", + "57 3.67 0.19 2.04 6.80 \n", + "148 1.39 0.34 1.14 9.40 \n", + "83 2.21 0.22 2.35 3.05 \n", + "22 2.37 0.26 1.46 3.93 \n", + "12 3.69 0.43 2.81 5.40 \n", + "42 2.63 0.32 1.66 4.36 \n", + "143 0.78 0.29 1.14 8.21 \n", + "104 1.84 0.66 1.42 2.70 \n", + "84 1.94 0.30 1.46 2.62 \n", + "126 2.24 0.58 1.76 3.00 \n", + "124 2.65 0.37 1.35 2.76 \n", + "119 2.79 0.32 1.83 3.25 \n", + "137 0.48 0.58 0.88 5.70 \n", + "111 2.03 0.60 1.05 3.80 \n", + "150 1.36 0.24 1.26 10.80 \n", + "70 2.86 0.21 1.87 3.38 \n", + "176 0.76 0.56 1.35 9.20 \n", + "45 3.17 0.27 2.19 4.90 \n", + "122 2.65 0.30 2.01 2.60 \n", + "90 1.25 0.50 1.63 3.60 \n", + "15 3.14 0.33 1.97 6.20 \n", + "68 1.28 0.14 2.50 2.85 \n", + "144 0.55 0.43 1.30 4.00 \n", + "145 0.34 0.40 0.68 4.90 \n", + "87 1.69 0.48 1.35 2.80 \n", + "51 3.74 0.32 1.87 7.05 \n", + "4 3.39 0.34 1.97 6.75 \n", + "78 2.55 0.43 1.95 2.57 \n", + "89 1.50 0.52 1.64 2.40 \n", + "\n", + " Hue OD280/OD315 of diluted vines Proline \n", + "102 0.940 2.44 415 \n", + "85 1.330 2.26 495 \n", + "160 0.960 1.82 680 \n", + "38 0.890 3.53 760 \n", + "41 0.880 3.56 1095 \n", + "33 1.100 2.87 1095 \n", + "69 0.906 1.82 870 \n", + "40 0.910 3.00 1035 \n", + "94 1.160 2.63 937 \n", + "79 1.380 3.12 278 \n", + "117 0.700 2.12 372 \n", + "55 0.940 3.31 970 \n", + "14 1.280 2.88 1310 \n", + "106 0.880 2.42 488 \n", + "173 0.700 1.56 750 \n", + "31 1.230 2.87 990 \n", + "54 0.980 3.03 1120 \n", + "97 1.040 2.77 660 \n", + "168 0.670 1.92 630 \n", + "56 1.070 2.84 1270 \n", + "23 1.120 3.82 845 \n", + "129 0.760 1.29 630 \n", + "121 0.920 3.12 365 \n", + "114 1.710 2.87 407 \n", + "138 0.890 2.15 590 \n", + "174 0.590 1.56 835 \n", + "19 1.090 3.71 780 \n", + "142 0.910 2.05 550 \n", + "39 0.950 3.38 795 \n", + "63 1.450 2.23 355 \n", + ".. ... ... ... \n", + "75 1.190 2.48 392 \n", + "57 0.890 2.87 1285 \n", + "148 0.570 1.33 550 \n", + "83 0.790 3.08 520 \n", + "22 1.090 3.63 1015 \n", + "12 1.250 2.73 1150 \n", + "42 0.820 3.00 680 \n", + "143 0.650 2.00 855 \n", + "104 0.860 3.30 315 \n", + "84 1.230 3.16 450 \n", + "126 0.970 2.44 466 \n", + "124 0.860 3.28 378 \n", + "119 0.800 3.39 625 \n", + "137 0.810 1.82 580 \n", + "111 1.230 2.50 607 \n", + "150 0.480 1.47 480 \n", + "70 1.360 3.16 410 \n", + "176 0.610 1.60 560 \n", + "45 1.040 3.44 1065 \n", + "122 0.730 3.10 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1AlcoholMalic acidAshAlkalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted vinesProline
882NaN1.332.3023.6702.201.590.421.381.7400001.073.21625
812NaN1.132.5124.0782.001.580.401.402.2000001.312.72630
662NaN1.171.9219.6782.112.000.271.044.6800001.123.48510
652NaN1.011.7015.0782.983.180.262.285.3000001.123.18502
1052NaN1.732.1219.0801.652.030.371.633.4000001.003.17510
1282NaN4.302.3822.0802.101.750.421.352.6000000.792.57580
1172NaN3.431.9816.0801.631.250.430.833.4000000.702.12372
912NaN1.532.2620.7801.381.460.581.623.0500000.962.06495
1453NaN5.042.2320.0800.980.340.400.684.9000000.581.33415
892NaN1.832.3218.5811.601.500.521.642.4000001.082.27480
1232NaN4.312.3921.0822.863.030.212.912.8000000.753.64380
872NaN2.062.4621.6841.951.690.481.352.8000001.002.75680
1132NaN1.392.5022.5842.562.290.431.042.9000000.933.19385
1062NaN1.752.2822.5841.381.760.481.633.3000000.882.42488
1333NaN1.242.2517.5852.000.580.601.255.4500000.751.51650
1242NaN2.162.1721.0852.602.650.371.352.7600000.863.28378
1032NaN1.731.9820.5852.201.920.321.482.9400001.043.57672
822NaN3.862.3222.5851.651.590.611.624.8000000.842.01515
1142NaN1.512.2021.5852.462.170.522.011.9000001.712.87407
962NaN1.411.9816.0852.552.500.291.772.9000001.232.74428
1703NaN2.392.2819.5861.390.510.480.649.8999990.571.63470
802NaN1.812.2018.8862.202.530.261.773.9000001.163.14714
1222NaN5.802.1321.5862.622.650.302.012.6000000.733.10380
752NaN0.901.7116.0861.952.030.241.464.6000001.192.48392
1463NaN4.612.4821.5861.700.650.470.867.6500000.541.86625
702NaN1.512.6725.0862.952.860.211.873.3800001.363.16410
1252NaN1.532.2921.5862.743.150.391.773.9400000.692.84352
902NaN1.512.4222.0861.451.250.501.633.6000001.052.65450
1152NaN1.471.9920.8861.981.600.301.531.9500000.953.33495
1022NaN1.721.8819.5862.501.640.371.422.0600000.942.44415
.............................................
141NaN1.812.7017.21122.852.910.301.467.3000001.282.881310
1503NaN2.672.4822.01121.481.360.241.2610.8000000.481.47480
1433NaN3.882.2018.51121.380.780.291.148.2100000.652.00855
21NaN1.952.5016.81133.853.490.242.187.8000000.863.451480
1483NaN3.902.3621.51131.411.390.341.149.4000000.571.33550
161NaN1.572.6220.01152.953.400.401.726.6000001.132.571130
521NaN1.902.6817.11153.002.790.391.686.3000001.132.931375
1513NaN1.902.7525.51162.201.280.261.567.1000000.611.33425
541NaN1.732.4620.51162.962.780.202.456.2500000.983.031120
181NaN3.102.5615.21162.703.030.171.665.1000000.963.36845
391NaN1.712.3116.21173.153.290.342.346.1300000.953.38795
31NaN2.592.8721.01182.802.690.391.824.3200001.042.93735
531NaN1.672.2516.41182.602.900.211.625.8500000.923.201060
551NaN1.702.3016.31183.203.000.262.036.3800000.943.31970
1202NaN2.053.2328.51193.185.080.471.876.0000000.933.69465
1743NaN4.282.2620.01201.590.690.431.3510.2000000.591.56835
1753NaN2.592.3720.01201.650.680.531.469.3000000.601.62840
151NaN1.922.7220.01202.803.140.331.976.2000001.072.651280
61NaN2.152.6117.61212.602.510.311.255.0500001.063.581295
1293NaN1.352.3218.01221.511.250.210.944.1000000.761.29630
1493NaN3.122.6224.01231.401.570.221.258.6000000.591.30500
241NaN2.053.2225.01242.632.680.471.923.5800001.133.20830
191NaN1.632.2816.01263.003.170.242.105.6500001.093.71780
381NaN3.992.5113.21283.003.040.202.085.1000000.893.53760
321NaN1.532.7019.51322.952.740.501.355.4000001.253.001235
952NaN2.122.7421.51341.600.990.141.562.5000000.952.26625
772NaN0.991.9514.81361.901.850.352.763.4000001.062.31750
722NaN1.672.6030.01393.302.890.211.963.3500001.313.50985
682NaN1.191.7516.81511.851.280.142.502.8500001.283.07718
942NaN1.522.2019.01622.502.270.323.282.6000001.162.63937
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177 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " 1 Alcohol Malic acid Ash Alkalinity of ash Magnesium Total phenols \\\n", + "88 2 NaN 1.33 2.30 23.6 70 2.20 \n", + "81 2 NaN 1.13 2.51 24.0 78 2.00 \n", + "66 2 NaN 1.17 1.92 19.6 78 2.11 \n", + "65 2 NaN 1.01 1.70 15.0 78 2.98 \n", + "105 2 NaN 1.73 2.12 19.0 80 1.65 \n", + "128 2 NaN 4.30 2.38 22.0 80 2.10 \n", + "117 2 NaN 3.43 1.98 16.0 80 1.63 \n", + "91 2 NaN 1.53 2.26 20.7 80 1.38 \n", + "145 3 NaN 5.04 2.23 20.0 80 0.98 \n", + "89 2 NaN 1.83 2.32 18.5 81 1.60 \n", + "123 2 NaN 4.31 2.39 21.0 82 2.86 \n", + "87 2 NaN 2.06 2.46 21.6 84 1.95 \n", + "113 2 NaN 1.39 2.50 22.5 84 2.56 \n", + "106 2 NaN 1.75 2.28 22.5 84 1.38 \n", + "133 3 NaN 1.24 2.25 17.5 85 2.00 \n", + "124 2 NaN 2.16 2.17 21.0 85 2.60 \n", + "103 2 NaN 1.73 1.98 20.5 85 2.20 \n", + "82 2 NaN 3.86 2.32 22.5 85 1.65 \n", + "114 2 NaN 1.51 2.20 21.5 85 2.46 \n", + "96 2 NaN 1.41 1.98 16.0 85 2.55 \n", + "170 3 NaN 2.39 2.28 19.5 86 1.39 \n", + "80 2 NaN 1.81 2.20 18.8 86 2.20 \n", + "122 2 NaN 5.80 2.13 21.5 86 2.62 \n", + "75 2 NaN 0.90 1.71 16.0 86 1.95 \n", + "146 3 NaN 4.61 2.48 21.5 86 1.70 \n", + "70 2 NaN 1.51 2.67 25.0 86 2.95 \n", + "125 2 NaN 1.53 2.29 21.5 86 2.74 \n", + "90 2 NaN 1.51 2.42 22.0 86 1.45 \n", + "115 2 NaN 1.47 1.99 20.8 86 1.98 \n", + "102 2 NaN 1.72 1.88 19.5 86 2.50 \n", + ".. .. ... ... ... ... ... ... \n", + "14 1 NaN 1.81 2.70 17.2 112 2.85 \n", + "150 3 NaN 2.67 2.48 22.0 112 1.48 \n", + "143 3 NaN 3.88 2.20 18.5 112 1.38 \n", + "2 1 NaN 1.95 2.50 16.8 113 3.85 \n", + "148 3 NaN 3.90 2.36 21.5 113 1.41 \n", + "16 1 NaN 1.57 2.62 20.0 115 2.95 \n", + "52 1 NaN 1.90 2.68 17.1 115 3.00 \n", + "151 3 NaN 1.90 2.75 25.5 116 2.20 \n", + "54 1 NaN 1.73 2.46 20.5 116 2.96 \n", + "18 1 NaN 3.10 2.56 15.2 116 2.70 \n", + "39 1 NaN 1.71 2.31 16.2 117 3.15 \n", + "3 1 NaN 2.59 2.87 21.0 118 2.80 \n", + "53 1 NaN 1.67 2.25 16.4 118 2.60 \n", + "55 1 NaN 1.70 2.30 16.3 118 3.20 \n", + "120 2 NaN 2.05 3.23 28.5 119 3.18 \n", + "174 3 NaN 4.28 2.26 20.0 120 1.59 \n", + "175 3 NaN 2.59 2.37 20.0 120 1.65 \n", + "15 1 NaN 1.92 2.72 20.0 120 2.80 \n", + "6 1 NaN 2.15 2.61 17.6 121 2.60 \n", + "129 3 NaN 1.35 2.32 18.0 122 1.51 \n", + "149 3 NaN 3.12 2.62 24.0 123 1.40 \n", + "24 1 NaN 2.05 3.22 25.0 124 2.63 \n", + "19 1 NaN 1.63 2.28 16.0 126 3.00 \n", + "38 1 NaN 3.99 2.51 13.2 128 3.00 \n", + "32 1 NaN 1.53 2.70 19.5 132 2.95 \n", + "95 2 NaN 2.12 2.74 21.5 134 1.60 \n", + "77 2 NaN 0.99 1.95 14.8 136 1.90 \n", + "72 2 NaN 1.67 2.60 30.0 139 3.30 \n", + "68 2 NaN 1.19 1.75 16.8 151 1.85 \n", + "94 2 NaN 1.52 2.20 19.0 162 2.50 \n", + "\n", + " Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity Hue \\\n", + "88 1.59 0.42 1.38 1.740000 1.07 \n", + "81 1.58 0.40 1.40 2.200000 1.31 \n", + "66 2.00 0.27 1.04 4.680000 1.12 \n", + "65 3.18 0.26 2.28 5.300000 1.12 \n", + "105 2.03 0.37 1.63 3.400000 1.00 \n", + "128 1.75 0.42 1.35 2.600000 0.79 \n", + "117 1.25 0.43 0.83 3.400000 0.70 \n", + "91 1.46 0.58 1.62 3.050000 0.96 \n", + "145 0.34 0.40 0.68 4.900000 0.58 \n", + "89 1.50 0.52 1.64 2.400000 1.08 \n", + "123 3.03 0.21 2.91 2.800000 0.75 \n", + "87 1.69 0.48 1.35 2.800000 1.00 \n", + "113 2.29 0.43 1.04 2.900000 0.93 \n", + "106 1.76 0.48 1.63 3.300000 0.88 \n", + "133 0.58 0.60 1.25 5.450000 0.75 \n", + "124 2.65 0.37 1.35 2.760000 0.86 \n", + "103 1.92 0.32 1.48 2.940000 1.04 \n", + "82 1.59 0.61 1.62 4.800000 0.84 \n", + "114 2.17 0.52 2.01 1.900000 1.71 \n", + "96 2.50 0.29 1.77 2.900000 1.23 \n", + "170 0.51 0.48 0.64 9.899999 0.57 \n", + "80 2.53 0.26 1.77 3.900000 1.16 \n", + "122 2.65 0.30 2.01 2.600000 0.73 \n", + "75 2.03 0.24 1.46 4.600000 1.19 \n", + "146 0.65 0.47 0.86 7.650000 0.54 \n", + "70 2.86 0.21 1.87 3.380000 1.36 \n", + "125 3.15 0.39 1.77 3.940000 0.69 \n", + "90 1.25 0.50 1.63 3.600000 1.05 \n", + "115 1.60 0.30 1.53 1.950000 0.95 \n", + "102 1.64 0.37 1.42 2.060000 0.94 \n", + ".. ... ... ... ... ... \n", + "14 2.91 0.30 1.46 7.300000 1.28 \n", + "150 1.36 0.24 1.26 10.800000 0.48 \n", + "143 0.78 0.29 1.14 8.210000 0.65 \n", + "2 3.49 0.24 2.18 7.800000 0.86 \n", + "148 1.39 0.34 1.14 9.400000 0.57 \n", + "16 3.40 0.40 1.72 6.600000 1.13 \n", + "52 2.79 0.39 1.68 6.300000 1.13 \n", + "151 1.28 0.26 1.56 7.100000 0.61 \n", + "54 2.78 0.20 2.45 6.250000 0.98 \n", + "18 3.03 0.17 1.66 5.100000 0.96 \n", + "39 3.29 0.34 2.34 6.130000 0.95 \n", + "3 2.69 0.39 1.82 4.320000 1.04 \n", + "53 2.90 0.21 1.62 5.850000 0.92 \n", + "55 3.00 0.26 2.03 6.380000 0.94 \n", + "120 5.08 0.47 1.87 6.000000 0.93 \n", + "174 0.69 0.43 1.35 10.200000 0.59 \n", + "175 0.68 0.53 1.46 9.300000 0.60 \n", + "15 3.14 0.33 1.97 6.200000 1.07 \n", + "6 2.51 0.31 1.25 5.050000 1.06 \n", + "129 1.25 0.21 0.94 4.100000 0.76 \n", + "149 1.57 0.22 1.25 8.600000 0.59 \n", + "24 2.68 0.47 1.92 3.580000 1.13 \n", + "19 3.17 0.24 2.10 5.650000 1.09 \n", + "38 3.04 0.20 2.08 5.100000 0.89 \n", + "32 2.74 0.50 1.35 5.400000 1.25 \n", + "95 0.99 0.14 1.56 2.500000 0.95 \n", + "77 1.85 0.35 2.76 3.400000 1.06 \n", + "72 2.89 0.21 1.96 3.350000 1.31 \n", + "68 1.28 0.14 2.50 2.850000 1.28 \n", + "94 2.27 0.32 3.28 2.600000 1.16 \n", + "\n", + " OD280/OD315 of diluted vines Proline \n", + "88 3.21 625 \n", + "81 2.72 630 \n", + "66 3.48 510 \n", + "65 3.18 502 \n", + "105 3.17 510 \n", + "128 2.57 580 \n", + "117 2.12 372 \n", + "91 2.06 495 \n", + "145 1.33 415 \n", + "89 2.27 480 \n", + "123 3.64 380 \n", + "87 2.75 680 \n", + "113 3.19 385 \n", + "106 2.42 488 \n", + "133 1.51 650 \n", + "124 3.28 378 \n", + "103 3.57 672 \n", + "82 2.01 515 \n", + "114 2.87 407 \n", + "96 2.74 428 \n", + "170 1.63 470 \n", + "80 3.14 714 \n", + "122 3.10 380 \n", + "75 2.48 392 \n", + "146 1.86 625 \n", + "70 3.16 410 \n", + "125 2.84 352 \n", + "90 2.65 450 \n", + "115 3.33 495 \n", + "102 2.44 415 \n", + ".. ... ... \n", + "14 2.88 1310 \n", + "150 1.47 480 \n", + "143 2.00 855 \n", + "2 3.45 1480 \n", + "148 1.33 550 \n", + "16 2.57 1130 \n", + "52 2.93 1375 \n", + "151 1.33 425 \n", + "54 3.03 1120 \n", + "18 3.36 845 \n", + "39 3.38 795 \n", + "3 2.93 735 \n", + "53 3.20 1060 \n", + "55 3.31 970 \n", + "120 3.69 465 \n", + "174 1.56 835 \n", + "175 1.62 840 \n", + "15 2.65 1280 \n", + "6 3.58 1295 \n", + "129 1.29 630 \n", + "149 1.30 500 \n", + "24 3.20 830 \n", + "19 3.71 780 \n", + "38 3.53 760 \n", + "32 3.00 1235 \n", + "95 2.26 625 \n", + "77 2.31 750 \n", + "72 3.50 985 \n", + "68 3.07 718 \n", + "94 2.63 937 \n", + "\n", + "[177 rows x 14 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 75 + } + ] + }, + { + "metadata": { + "id": "GvxAKEDLhn3x", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 201 + }, + "outputId": "fa7d1b58-179c-4f7a-947a-42f8a28bb706" + }, + "cell_type": "code", + "source": [ + "Flavanoids = wine_df['Flavanoids'].unique()\n", + "print (Flavanoids)" + ], + "execution_count": 78, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1.64 1.69 0.56 3.04 3.56 2.53 1.02 2.68 2.27 2.26 1.25 3. 2.91 1.76\n", + " 0.75 2.69 2.78 3.75 0.96 3.23 2.61 2.13 2.17 0.6 0.69 3.17 0.8 3.29\n", + " 1.75 0.68 0.83 1.36 1.22 2.97 2.92 2.04 2.33 1.46 1.41 2.52 2.43 3.03\n", + " 3.54 0.63 0.92 2.25 2.89 2.03 2.99 1.1 0.58 1.59 3.24 1.61 1.31 2.58\n", + " 0.84 1.3 0.47 2.88 0.5 0.51 2.5 2.51 3.4 0.66 1.79 3.32 0.76 2.74\n", + " 2.79 3.19 3.27 2.94 1.84 0.52 0.7 3.1 2.98 2.65 2.9 2. 2.14 1.6\n", + " 0.65 3.25 1.92 3.49 3.64 2.01 0.99 2.76 1.09 2.11 2.09 1.32 1.85 3.93\n", + " 2.19 0.49 2.64 2.45 3.18 1.57 1.58 1.2 2.41 0.57 2.29 3.15 0.61 1.28\n", + " 3.39 5.08 3.67 1.39 2.21 2.37 3.69 2.63 0.78 1.94 2.24 0.48 2.86 3.14\n", + " 0.55 0.34 3.74 2.55 1.5 ]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "jRDQytqgiDqj", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 402 + }, + "outputId": "1c0bc46d-a326-4045-9d15-5984a31be863" + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(['Nonflavanoid phenols'])\n", + "plt.hist(['Flavanoids'])\n", + "plt.hist(['Proline'])\n", + "plt.hist(['Hue'])\n", + "plt.hist(['Color intensity'])\n", + "plt.hist(['Magnesium'])" + ], + "execution_count": 89, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.]),\n", + " array([4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1, 5.2, 5.3, 5.4, 5.5]),\n", + " )" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 89 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From f999ad7820cdb14f66cb6d48650ee674fd6a7181 Mon Sep 17 00:00:00 2001 From: Diya Nag Chaudhury <43166705+dnc2k@users.noreply.github.com> Date: Sat, 13 Oct 2018 18:44:19 +0530 Subject: [PATCH 2/3] Created using Colaboratory --- Get_to_know_your_Data.ipynb | 2399 +++++++++++++++++++++++++++++++++++ 1 file changed, 2399 insertions(+) create mode 100644 Get_to_know_your_Data.ipynb diff --git a/Get_to_know_your_Data.ipynb b/Get_to_know_your_Data.ipynb new file mode 100644 index 0000000..6434fb3 --- /dev/null +++ b/Get_to_know_your_Data.ipynb @@ -0,0 +1,2399 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Get to know your Data.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/dnc2k/Assignment-3/blob/dnc2k/Get_to_know_your_Data.ipynb)" + ] + }, + { + "metadata": { + "id": "J82LU53m_OU0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Get to know your Data\n", + "\n", + "\n", + "#### Import necessary modules\n" + ] + }, + { + "metadata": { + "id": "ZyO1UXL8mtSj", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "yXTzTowtnwGI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Loading CSV Data to a DataFrame" + ] + }, + { + "metadata": { + "id": "H1Bjlb5wm9f-", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "iris_df = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "KE-k7b_Mn5iN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### See the top 10 rows\n" + ] + }, + { + "metadata": { + "id": "HY2Ps7xMn4ao", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "outputId": "11bb40e5-97c5-43c1-fce7-0de079856a53" + }, + "cell_type": "code", + "source": [ + "iris_df.head(10)" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + "5 5.4 3.9 1.7 0.4 setosa\n", + "6 4.6 3.4 1.4 0.3 setosa\n", + "7 5.0 3.4 1.5 0.2 setosa\n", + "8 4.4 2.9 1.4 0.2 setosa\n", + "9 4.9 3.1 1.5 0.1 setosa" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "id": "ZQXekIodqOZu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Find number of rows and columns\n" + ] + }, + { + "metadata": { + "id": "6Y-A-lbFqR82", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + }, + "outputId": "bdd2bf24-9b52-4335-e5b5-9caa05350b95" + }, + "cell_type": "code", + "source": [ + "print(iris_df.shape)\n", + "\n", + "#first is row and second is column\n", + "#select row by simple indexing\n", + "\n", + "print(iris_df.shape[0])\n", + "print(iris_df.shape[1])" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(150, 5)\n", + "150\n", + "5\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "4ckCiGPhrC_t", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Print all columns" + ] + }, + { + "metadata": { + "id": "S6jgMyRDrF2a", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + }, + "outputId": "472a789e-cad8-40cf-ce09-7f38331d9fef" + }, + "cell_type": "code", + "source": [ + "print(iris_df.columns)" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n", + " 'species'],\n", + " dtype='object')\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "kVav5-ACtIqS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Check Index\n" + ] + }, + { + "metadata": { + "id": "iu3I9zIGtLDX", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "821e585f-2be7-41b1-fb02-ee74202c1c2e" + }, + "cell_type": "code", + "source": [ + "print(iris_df.index)" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RangeIndex(start=0, stop=150, step=1)\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "psCc7PborOCQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Right now the iris_data set has all the species grouped together let's shuffle it" + ] + }, + { + "metadata": { + "id": "Bxc8i6avrZPw", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 237 + }, + "outputId": "cde0c4fa-4162-48a6-9d19-9e7457f03ad5" + }, + "cell_type": "code", + "source": [ + "#generate a random permutaion on index\n", + "\n", + "print(iris_df.head())\n", + "\n", + "new_index = np.random.permutation(iris_df.index)\n", + "iris_df = iris_df.reindex(index = new_index)\n", + "\n", + "print(iris_df.head())" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + " sepal_length sepal_width petal_length petal_width species\n", + "127 6.1 3.0 4.9 1.8 virginica\n", + "50 7.0 3.2 4.7 1.4 versicolor\n", + "52 6.9 3.1 4.9 1.5 versicolor\n", + "89 5.5 2.5 4.0 1.3 versicolor\n", + "2 4.7 3.2 1.3 0.2 setosa\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "j32h8022sRT8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### We can also apply an operation on whole column of iris_df" + ] + }, + { + "metadata": { + "id": "seYXHXsYsYJI", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 348 + }, + "outputId": "824a0c3b-4653-4d1e-8486-56c6a6cfa786" + }, + "cell_type": "code", + "source": [ + "#original\n", + "\n", + "print(iris_df.head())\n", + "\n", + "iris_df['sepal_width'] *= 10\n", + "\n", + "#changed\n", + "\n", + "print(iris_df.head())\n", + "\n", + "#lets undo the operation\n", + "\n", + "iris_df['sepal_width'] /= 10\n", + "\n", + "print(iris_df.head())" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + " sepal_length sepal_width petal_length petal_width species\n", + "127 6.1 3.0 4.9 1.8 virginica\n", + "50 7.0 3.2 4.7 1.4 versicolor\n", + "52 6.9 3.1 4.9 1.5 versicolor\n", + "89 5.5 2.5 4.0 1.3 versicolor\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + " sepal_length sepal_width petal_length petal_width species\n", + "127 6.1 30.0 4.9 1.8 virginica\n", + "50 7.0 32.0 4.7 1.4 versicolor\n", + "52 6.9 31.0 4.9 1.5 versicolor\n", + "89 5.5 25.0 4.0 1.3 versicolor\n", + "2 4.7 32.0 1.3 0.2 setosa\n", + " sepal_length sepal_width petal_length petal_width species\n", + "127 6.1 3.0 4.9 1.8 virginica\n", + "50 7.0 3.2 4.7 1.4 versicolor\n", + "52 6.9 3.1 4.9 1.5 versicolor\n", + "89 5.5 2.5 4.0 1.3 versicolor\n", + "2 4.7 3.2 1.3 0.2 setosa\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "R-Ca-LBLzjiF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Show all the rows where sepal_width > 3.3" + ] + }, + { + "metadata": { + "id": "WJ7W-F-d0AoZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1179 + }, + "outputId": "f4d2f3e5-67eb-4d74-9df7-48bd15bd89f2" + }, + "cell_type": "code", + "source": [ + "iris_df[iris_df['sepal_width']>3.3]" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
185.73.81.70.3setosa
75.03.41.50.2setosa
205.43.41.70.2setosa
64.63.41.40.3setosa
175.13.51.40.3setosa
244.83.41.90.2setosa
465.13.81.60.2setosa
285.23.41.40.2setosa
145.84.01.20.2setosa
165.43.91.30.4setosa
395.13.41.50.2setosa
05.13.51.40.2setosa
325.24.11.50.1setosa
265.03.41.60.4setosa
435.03.51.60.6setosa
1486.23.45.42.3virginica
55.43.91.70.4setosa
365.53.51.30.2setosa
155.74.41.50.4setosa
195.13.81.50.3setosa
114.83.41.60.2setosa
485.33.71.50.2setosa
275.23.51.50.2setosa
856.03.44.51.6versicolor
1177.73.86.72.2virginica
1366.33.45.62.4virginica
335.54.21.40.2setosa
405.03.51.30.3setosa
45.03.61.40.2setosa
445.13.81.90.4setosa
1097.23.66.12.5virginica
1317.93.86.42.0virginica
315.43.41.50.4setosa
105.43.71.50.2setosa
224.63.61.00.2setosa
215.13.71.50.4setosa
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
856.03.44.51.6versicolor
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "85 6.0 3.4 4.5 1.6 versicolor" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "id": "1lmnB3ot2u7I", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Sorting a column by value" + ] + }, + { + "metadata": { + "id": "K7KIj6fv2zWP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1992 + }, + "outputId": "fc7cb3dc-d85d-460b-aea3-3aa58624ac4b" + }, + "cell_type": "code", + "source": [ + "iris_df.sort_values(by='sepal_width')#, ascending = False)\n", + "#pass ascending = False for descending order" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
605.02.03.51.0versicolor
1196.02.25.01.5virginica
686.22.24.51.5versicolor
626.02.24.01.0versicolor
535.52.34.01.3versicolor
876.32.34.41.3versicolor
414.52.31.30.3setosa
935.02.33.31.0versicolor
815.52.43.71.0versicolor
805.52.43.81.1versicolor
574.92.43.31.0versicolor
1135.72.55.02.0virginica
1086.72.55.81.8virginica
695.62.53.91.1versicolor
985.12.53.01.1versicolor
895.52.54.01.3versicolor
1466.32.55.01.9virginica
1064.92.54.51.7virginica
726.32.54.91.5versicolor
925.82.64.01.2versicolor
795.72.63.51.0versicolor
905.52.64.41.2versicolor
1346.12.65.61.4virginica
1187.72.66.92.3virginica
825.82.73.91.2versicolor
945.62.74.21.3versicolor
675.82.74.11.0versicolor
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..................
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "18 5.7 3.8 1.7 0.3 setosa\n", + "7 5.0 3.4 1.5 0.2 setosa\n", + "20 5.4 3.4 1.7 0.2 setosa\n", + "6 4.6 3.4 1.4 0.3 setosa" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "metadata": { + "id": "-y1wDc8SpdQs", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Describe each created species to see the difference\n", + "\n" + ] + }, + { + "metadata": { + "id": "eHrn3ZVRpOk5", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + }, + "outputId": "117d54bf-cc00-48fb-96d3-562a4958a12e" + }, + "cell_type": "code", + "source": [ + "setosa.describe()" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "count 50.00000 50.000000 50.000000 50.00000\n", + "mean 5.00600 3.418000 1.464000 0.24400\n", + "std 0.35249 0.381024 0.173511 0.10721\n", + "min 4.30000 2.300000 1.000000 0.10000\n", + "25% 4.80000 3.125000 1.400000 0.20000\n", + "50% 5.00000 3.400000 1.500000 0.20000\n", + "75% 5.20000 3.675000 1.575000 0.30000\n", + "max 5.80000 4.400000 1.900000 0.60000" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "metadata": { + "id": "Vdu0ulZWtr09", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Let's plot and see the difference" + ] + }, + { + "metadata": { + "id": "PEVMzRvpttmD", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "##### import matplotlib.pyplot " + ] + }, + { + "metadata": { + "id": "rqDXuuAtt7C3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 402 + }, + "outputId": "4b02547a-3f80-4d00-84a6-d43e97a17c06" + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "#hist creates a histogram there are many more plots(see the documentation) you can play with it.\n", + "\n", + "plt.hist(setosa['sepal_length'])\n", + "plt.hist(versicolor['sepal_length'])\n", + "plt.hist(virginica['sepal_length'])" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([ 4., 1., 6., 5., 12., 8., 4., 5., 2., 3.]),\n", + " array([4.3 , 4.45, 4.6 , 4.75, 4.9 , 5.05, 5.2 , 5.35, 5.5 , 5.65, 5.8 ]),\n", + "
)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From aa94b4982686bb459a7cd1c0da09c928b7606241 Mon Sep 17 00:00:00 2001 From: Diya Nag Chaudhury <43166705+dnc2k@users.noreply.github.com> Date: Sat, 13 Oct 2018 18:44:40 +0530 Subject: [PATCH 3/3] Created using Colaboratory --- Basic_Pandas.ipynb | 1025 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1025 insertions(+) create mode 100644 Basic_Pandas.ipynb diff --git a/Basic_Pandas.ipynb b/Basic_Pandas.ipynb new file mode 100644 index 0000000..da9e449 --- /dev/null +++ b/Basic_Pandas.ipynb @@ -0,0 +1,1025 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Basic Pandas.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/dnc2k/Assignment-3/blob/dnc2k/Basic_Pandas.ipynb)" + ] + }, + { + "metadata": { + "id": "cGbE814_Xaf9", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Pandas\n", + "\n", + "Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc.In this tutorial, we will learn the various features of Python Pandas and how to use them in practice.\n", + "\n", + "\n", + "## Import pandas and numpy" + ] + }, + { + "metadata": { + "id": "irlVYeeAXPDL", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "BI2J-zdMbGwE", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### This is your playground feel free to explore other functions on pandas\n", + "\n", + "#### Create Series from numpy array, list and dict\n", + "\n", + "Don't know what a series is?\n", + "\n", + "[Series Doc](https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.Series.html)" + ] + }, + { + "metadata": { + "id": "GeEct691YGE3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 147 + }, + "outputId": "a245c237-0dd9-4f6a-9dc3-ee9d8a4e3d73" + }, + "cell_type": "code", + "source": [ + "a_ascii = ord('A')\n", + "z_ascii = ord('Z')\n", + "alphabets = [chr(i) for i in range(a_ascii, z_ascii+1)]\n", + "\n", + "print(alphabets)\n", + "\n", + "numbers = np.arange(26)\n", + "\n", + "print(numbers)\n", + "\n", + "print(type(alphabets), type(numbers))\n", + "\n", + "alpha_numbers = dict(zip(alphabets, numbers))\n", + "\n", + "print(alpha_numbers)\n", + "\n", + "print(type(alpha_numbers))" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n", + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", + " 24 25]\n", + " \n", + "{'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'H': 7, 'I': 8, 'J': 9, 'K': 10, 'L': 11, 'M': 12, 'N': 13, 'O': 14, 'P': 15, 'Q': 16, 'R': 17, 'S': 18, 'T': 19, 'U': 20, 'V': 21, 'W': 22, 'X': 23, 'Y': 24, 'Z': 25}\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "6ouDfjWab_Mc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + }, + "outputId": "f4752be0-5d74-49f7-b40d-ced89458eb85" + }, + "cell_type": "code", + "source": [ + "series1 = pd.Series(alphabets)\n", + "print(series1)" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 A\n", + "1 B\n", + "2 C\n", + "3 D\n", + "4 E\n", + "5 F\n", + "6 G\n", + "7 H\n", + "8 I\n", + "9 J\n", + "10 K\n", + "11 L\n", + "12 M\n", + "13 N\n", + "14 O\n", + "15 P\n", + "16 Q\n", + "17 R\n", + "18 S\n", + "19 T\n", + "20 U\n", + "21 V\n", + "22 W\n", + "23 X\n", + "24 Y\n", + "25 Z\n", + "dtype: object\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "At7nY7vVcBZ3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + }, + "outputId": "4867401d-3ee5-4787-cf4a-60c1a6736456" + }, + "cell_type": "code", + "source": [ + "series2 = pd.Series(numbers)\n", + "print(series2)" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 0\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 4\n", + "5 5\n", + "6 6\n", + "7 7\n", + "8 8\n", + "9 9\n", + "10 10\n", + "11 11\n", + "12 12\n", + "13 13\n", + "14 14\n", + "15 15\n", + "16 16\n", + "17 17\n", + "18 18\n", + "19 19\n", + "20 20\n", + "21 21\n", + "22 22\n", + "23 23\n", + "24 24\n", + "25 25\n", + "dtype: int64\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "J5z-2CWAdH6N", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + }, + "outputId": "ab8c593d-e66b-4417-d78c-cd572a96466a" + }, + "cell_type": "code", + "source": [ + "series3 = pd.Series(alpha_numbers)\n", + "print(series3)" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "A 0\n", + "B 1\n", + "C 2\n", + "D 3\n", + "E 4\n", + "F 5\n", + "G 6\n", + "H 7\n", + "I 8\n", + "J 9\n", + "K 10\n", + "L 11\n", + "M 12\n", + "N 13\n", + "O 14\n", + "P 15\n", + "Q 16\n", + "R 17\n", + "S 18\n", + "T 19\n", + "U 20\n", + "V 21\n", + "W 22\n", + "X 23\n", + "Y 24\n", + "Z 25\n", + "dtype: int64\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "fYzblGGudKjO", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 109 + }, + "outputId": "a9d212ec-67c4-42cf-d435-7f155f73ec54" + }, + "cell_type": "code", + "source": [ + "#replace head() with head(n) where n can be any number between [0-25] and observe the output in deach case \n", + "series3.head(4)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "A 0\n", + "B 1\n", + "C 2\n", + "D 3\n", + "dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "id": "OwsJIf5feTtg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Create DataFrame from lists\n", + "\n", + "[DataFrame Doc](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html)" + ] + }, + { + "metadata": { + "id": "73UTZ07EdWki", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 865 + }, + "outputId": "9b48e2e4-05c2-4985-bb7e-ea64b41f13d8" + }, + "cell_type": "code", + "source": [ + "data = {'alphabets': alphabets, 'values': numbers}\n", + "\n", + "df = pd.DataFrame(data)\n", + "\n", + "#Lets Change the column `values` to `alpha_numbers`\n", + "\n", + "df.columns = ['alphabets', 'alpha_numbers']\n", + "\n", + "df" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " alphabets alpha_numbers\n", + "0 A 0\n", + "1 B 1\n", + "2 C 2\n", + "3 D 3\n", + "4 E 4\n", + "5 F 5\n", + "6 G 6\n", + "7 H 7\n", + "8 I 8\n", + "9 J 9\n", + "10 K 10\n", + "11 L 11\n", + "12 M 12\n", + "13 N 13\n", + "14 O 14\n", + "15 P 15\n", + "16 Q 16\n", + "17 R 17\n", + "18 S 18\n", + "19 T 19\n", + "20 U 20\n", + "21 V 21\n", + "22 W 22\n", + "23 X 23\n", + "24 Y 24\n", + "25 Z 25" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "id": "uaK_1EO9etGS", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 141 + }, + "outputId": "c1ac94fd-55a6-4052-fa75-57169e679c8a" + }, + "cell_type": "code", + "source": [ + "# transpose\n", + "\n", + "df.T\n", + "\n", + "# there are many more operations which we can perform look at the documentation with the subsequent exercises we will learn more" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 9 ... 16 17 18 19 20 21 22 \\\n", + "alphabets A B C D E F G H I J ... Q R S T U V W \n", + "alpha_numbers 0 1 2 3 4 5 6 7 8 9 ... 16 17 18 19 20 21 22 \n", + "\n", + " 23 24 25 \n", + "alphabets X Y Z \n", + "alpha_numbers 23 24 25 \n", + "\n", + "[2 rows x 26 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "metadata": { + "id": "ZYonoaW8gEAJ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Extract Items from a series" + ] + }, + { + "metadata": { + "id": "tc1-KX_Bfe7U", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "outputId": "17ca3d68-8042-4bd2-ac12-9a7b4911a03c" + }, + "cell_type": "code", + "source": [ + "ser = pd.Series(list('abcdefghijklmnopqrstuvwxyz'))\n", + "pos = [0, 4, 8, 14, 20]\n", + "\n", + "vowels = ser.take(pos)\n", + "\n", + "df = pd.DataFrame(vowels)#, columns=['vowels'])\n", + "\n", + "df.columns = ['vowels']\n", + "\n", + "df.index = [0, 1, 2, 3, 4]\n", + "\n", + "df" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " vowels\n", + "0 a\n", + "1 e\n", + "2 i\n", + "3 o\n", + "4 u" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "id": "cmDxwtDNjWpO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Change the first character of each word to upper case in each word of ser" + ] + }, + { + "metadata": { + "id": "5KagP9PpgV2F", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "b2544f27-7d03-48fa-a39d-14c9e7c89834" + }, + "cell_type": "code", + "source": [ + "ser = pd.Series(['we', 'are', 'learning', 'pandas'])\n", + "\n", + "ser.map(lambda x : x.title())\n", + "\n", + "titles = [i.title() for i in ser]\n", + "\n", + "titles" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['We', 'Are', 'Learning', 'Pandas']" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "id": "qn47ee-MkZN8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Reindexing" + ] + }, + { + "metadata": { + "id": "h5R0JL2NjuFS", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "outputId": "658d5717-a960-43f9-fd61-867297f4b66b" + }, + "cell_type": "code", + "source": [ + "my_index = [1, 2, 3, 4, 5]\n", + "\n", + "df1 = pd.DataFrame({'upper values': ['A', 'B', 'C', 'D', 'E'],\n", + " 'lower values': ['a', 'b', 'c', 'd', 'e']},\n", + " index = my_index)\n", + "\n", + "df1" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " lower values upper values\n", + "1 a A\n", + "2 b B\n", + "3 c C\n", + "4 d D\n", + "5 e E" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "metadata": { + "id": "G_Frvc3mk93k", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "outputId": "a0e57507-914d-4fe6-e6a3-7dc9c1253b21" + }, + "cell_type": "code", + "source": [ + "new_index = [2, 5, 4, 3, 1]\n", + "\n", + "df1.reindex(index = new_index)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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