From 77ec91f48e4af715ee985d4a61fb79e8601090a9 Mon Sep 17 00:00:00 2001 From: MONA7584908095 <43202653+MONA7584908095@users.noreply.github.com> Date: Fri, 12 Oct 2018 20:45:49 +0530 Subject: [PATCH] i completed assignment 3 --- MONA7584908095.ipynb | 7133 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 7103 insertions(+), 30 deletions(-) diff --git a/MONA7584908095.ipynb b/MONA7584908095.ipynb index 9e2543a..10cf189 100644 --- a/MONA7584908095.ipynb +++ b/MONA7584908095.ipynb @@ -1,32 +1,7105 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "MONA7584908095.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "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": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/MONA7584908095/Assignment-3/blob/MONA7584908095/MONA7584908095.ipynb)" + ] + }, + { + "metadata": { + "id": "YOJQVOsdxqya", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "uSEYujUOxtlU", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "outputId": "090ff9e1-b10c-4e96-80b5-f691e7c1cf99" + }, + "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": "-nzFHlBJx-GU", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + }, + "outputId": "61ab6562-2a38-4f2f-82ed-39fa3a861c3f" + }, + "cell_type": "code", + "source": [ + "series1 = pd.Series(alphabets)\n", + "print(series1)\n" + ], + "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": "aEo7tuhMxtnm", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + }, + "outputId": "a468591b-1edd-40fd-d900-60d174d33155" + }, + "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": "XAp1Pvz2xtpn", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + }, + "outputId": "0396ab0c-77ab-47b3-d85b-cf596452cebf" + }, + "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": "jSO4X7sqxtrA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 136 + }, + "outputId": "44f3d911-3a12-4551-edd6-301d8b40cf54" + }, + "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(6)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "A 0\n", + "B 1\n", + "C 2\n", + "D 3\n", + "E 4\n", + "F 5\n", + "dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "id": "RPHTMAvnxttv", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 855 + }, + "outputId": "7ca92b69-9390-40ce-8fec-6e81902f79a4" + }, + "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": "MAig0mqfxtvJ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 140 + }, + "outputId": "19ea20e9-1fd5-44ac-d54d-edec330d86b9" + }, + "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": "grOlNd65xtx4", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "3e9fabf1-96c3-4cc7-cfef-90aab0ec84b2" + }, + "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": [ + " 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": "HQnqjfWixt6G", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "9b7a232e-22a5-4ee0-ffd0-e22b247fa0ba" + }, + "cell_type": "code", + "source": [ + "\n", + "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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45.03.61.40.2setosa
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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" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "metadata": { + "id": "wTq8voN8xuB6", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "079c2389-1629-4bd4-9fee-40b66804a502" + }, + "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": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(150, 5)\n", + "150\n", + "5\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "FHa09nbNxuD7", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "e004079b-521c-45d9-ce7d-9d5886407172" + }, + "cell_type": "code", + "source": [ + "print(iris_df.columns)\n" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n", + " 'species'],\n", + " dtype='object')\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "8s3y7XDWxuHv", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "ff97a932-bb61-412b-902c-636f7ae27d9d" + }, + "cell_type": "code", + "source": [ + "\n", + "print(iris_df.index)" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RangeIndex(start=0, stop=150, step=1)\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "-vNTOl9SxuKd", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "2754602b-5fe9-4104-e82b-9caa608a0a6d" + }, + "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": 20, + "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", + "125 7.2 3.2 6.0 1.8 virginica\n", + "76 6.8 2.8 4.8 1.4 versicolor\n", + "119 6.0 2.2 5.0 1.5 virginica\n", + "89 5.5 2.5 4.0 1.3 versicolor\n", + "88 5.6 3.0 4.1 1.3 versicolor\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "xa3wJ-zzxuMI", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "outputId": "716a496a-cf36-4a5c-ec96-97f6a5c1b41a" + }, + "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": 21, + "outputs": [ + { + "output_type": "stream", + "text": [ + " sepal_length sepal_width petal_length petal_width species\n", + "125 7.2 3.2 6.0 1.8 virginica\n", + "76 6.8 2.8 4.8 1.4 versicolor\n", + "119 6.0 2.2 5.0 1.5 virginica\n", + "89 5.5 2.5 4.0 1.3 versicolor\n", + "88 5.6 3.0 4.1 1.3 versicolor\n", + " sepal_length sepal_width petal_length petal_width species\n", + "125 7.2 32.0 6.0 1.8 virginica\n", + "76 6.8 28.0 4.8 1.4 versicolor\n", + "119 6.0 22.0 5.0 1.5 virginica\n", + "89 5.5 25.0 4.0 1.3 versicolor\n", + "88 5.6 30.0 4.1 1.3 versicolor\n", + " sepal_length sepal_width petal_length petal_width species\n", + "125 7.2 3.2 6.0 1.8 virginica\n", + "76 6.8 2.8 4.8 1.4 versicolor\n", + "119 6.0 2.2 5.0 1.5 virginica\n", + "89 5.5 2.5 4.0 1.3 versicolor\n", + "88 5.6 3.0 4.1 1.3 versicolor\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "ZTdXsrNxxuO8", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1165 + }, + "outputId": "befca5ab-1ff8-43fd-f818-0cae5ec8ff73" + }, + "cell_type": "code", + "source": [ + "iris_df[iris_df['sepal_width']>3.3]" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
365.53.51.30.2setosa
55.43.91.70.4setosa
465.13.81.60.2setosa
75.03.41.50.2setosa
244.83.41.90.2setosa
265.03.41.60.4setosa
395.13.41.50.2setosa
165.43.91.30.4setosa
315.43.41.50.4setosa
1097.23.66.12.5virginica
1366.33.45.62.4virginica
195.13.81.50.3setosa
856.03.44.51.6versicolor
335.54.21.40.2setosa
205.43.41.70.2setosa
1317.93.86.42.0virginica
45.03.61.40.2setosa
1177.73.86.72.2virginica
405.03.51.30.3setosa
224.63.61.00.2setosa
215.13.71.50.4setosa
485.33.71.50.2setosa
175.13.51.40.3setosa
64.63.41.40.3setosa
155.74.41.50.4setosa
185.73.81.70.3setosa
435.03.51.60.6setosa
145.84.01.20.2setosa
114.83.41.60.2setosa
275.23.51.50.2setosa
325.24.11.50.1setosa
05.13.51.40.2setosa
105.43.71.50.2setosa
1486.23.45.42.3virginica
445.13.81.90.4setosa
285.23.41.40.2setosa
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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": 23 + } + ] + }, + { + "metadata": { + "id": "GqO9bZu-xuGb", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1969 + }, + "outputId": "34e539d0-5280-4a6c-e19e-4947b0bf3166" + }, + "cell_type": "code", + "source": [ + "iris_df.sort_values(by='sepal_width', ascending = False)\n", + "#pass ascending = False for descending order" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
155.74.41.50.4setosa
335.54.21.40.2setosa
325.24.11.50.1setosa
145.84.01.20.2setosa
165.43.91.30.4setosa
55.43.91.70.4setosa
445.13.81.90.4setosa
195.13.81.50.3setosa
1317.93.86.42.0virginica
465.13.81.60.2setosa
1177.73.86.72.2virginica
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105.43.71.50.2setosa
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856.03.44.51.6versicolor
1486.23.45.42.3virginica
1366.33.45.62.4virginica
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..................
945.62.74.21.3versicolor
1015.82.75.11.9virginica
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1236.32.74.91.8virginica
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1187.72.66.92.3virginica
1346.12.65.61.4virginica
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1466.32.55.01.9virginica
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1064.92.54.51.7virginica
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1446.73.35.72.5virginica
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "125 7.2 3.2 6.0 1.8 virginica\n", + "119 6.0 2.2 5.0 1.5 virginica\n", + "115 6.4 3.2 5.3 2.3 virginica\n", + "137 6.4 3.1 5.5 1.8 virginica\n", + "144 6.7 3.3 5.7 2.5 virginica" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 26 + } + ] + }, + { + "metadata": { + "id": "ZdpWbCQKzk9J", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "cd457826-c474-4a23-fed6-24920493d489" + }, + "cell_type": "code", + "source": [ + "# do the same for other 2 species \n", + "versicolor = iris_df[iris_df['species'] == species[1]]\n", + "\n", + "versicolor.head()" + ], + "execution_count": 27, + "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": 31 + } + ] + }, + { + "metadata": { + "id": "SGacl2Dez7fY", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 398 + }, + "outputId": "f89dfe44-0a3c-4aed-f941-05d08d2c8d0f" + }, + "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": 32, + "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": 32 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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61NaN1.872.4514.6962.502.520.301.985.251.023.581290
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targetAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
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7114.062.152.6117.61212.602.510.311.255.0500001.063.581295
9113.861.352.2716.0982.983.150.221.857.2200001.013.551045
10114.102.162.3018.01052.953.320.222.385.7500001.253.171510
11114.121.482.3216.8952.202.430.261.575.0000001.172.821280
12113.751.732.4116.0892.602.760.291.815.6000001.152.901320
13114.751.732.3911.4913.103.690.432.815.4000001.252.731150
14114.381.872.3812.01023.303.640.292.967.5000001.203.001547
15113.631.812.7017.21122.852.910.301.467.3000001.282.881310
16114.301.922.7220.01202.803.140.331.976.2000001.072.651280
17113.831.572.6220.01152.953.400.401.726.6000001.132.571130
18114.191.592.4816.51083.303.930.321.868.7000001.232.821680
19113.643.102.5615.21162.703.030.171.665.1000000.963.36845
20114.061.632.2816.01263.003.170.242.105.6500001.093.71780
21112.933.802.6518.61022.412.410.251.984.5000001.033.52770
22113.711.862.3616.61012.612.880.271.693.8000001.114.001035
23112.851.602.5217.8952.482.370.261.463.9300001.093.631015
24113.501.812.6120.0962.532.610.281.663.5200001.123.82845
25113.052.053.2225.01242.632.680.471.923.5800001.133.20830
26113.391.772.6216.1932.852.940.341.454.8000000.923.221195
27113.301.722.1417.0942.402.190.271.353.9500001.022.771285
28113.871.902.8019.41072.952.970.371.764.5000001.253.40915
29114.021.682.2116.0962.652.330.261.984.7000001.043.591035
30113.731.502.7022.51013.003.250.292.385.7000001.192.711285
31113.581.662.3619.11062.863.190.221.956.9000001.092.881515
32113.681.832.3617.21042.422.690.421.973.8400001.232.87990
33113.761.532.7019.51322.952.740.501.355.4000001.253.001235
34113.511.802.6519.01102.352.530.291.544.2000001.102.871095
35113.481.812.4120.51002.702.980.261.865.1000001.043.47920
36113.281.642.8415.51102.602.680.341.364.6000001.092.78880
.............................................
148313.323.242.3821.5921.930.760.451.258.4200000.551.62650
149313.083.902.3621.51131.411.390.341.149.4000000.571.33550
150313.503.122.6224.01231.401.570.221.258.6000000.591.30500
151312.792.672.4822.01121.481.360.241.2610.8000000.481.47480
152313.111.902.7525.51162.201.280.261.567.1000000.611.33425
153313.233.302.2818.5981.800.830.611.8710.5200000.561.51675
154312.581.292.1020.01031.480.580.531.407.6000000.581.55640
155313.175.192.3222.0931.740.630.611.557.9000000.601.48725
156313.844.122.3819.5891.800.830.481.569.0100000.571.64480
157312.453.032.6427.0971.900.580.631.147.5000000.671.73880
158314.341.682.7025.0982.801.310.532.7013.0000000.571.96660
159313.481.672.6422.5892.601.100.522.2911.7500000.571.78620
160312.363.832.3821.0882.300.920.501.047.6500000.561.58520
161313.693.262.5420.01071.830.560.500.805.8800000.961.82680
162312.853.272.5822.01061.650.600.600.965.5800000.872.11570
163312.963.452.3518.51061.390.700.400.945.2800000.681.75675
164313.782.762.3022.0901.350.680.411.039.5800000.701.68615
165313.734.362.2622.5881.280.470.521.156.6200000.781.75520
166313.453.702.6023.01111.700.920.431.4610.6800000.851.56695
167312.823.372.3019.5881.480.660.400.9710.2600000.721.75685
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171 rows × 14 columns

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" + ], + "text/plain": [ + " target Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "5 1 14.20 1.76 2.45 15.2 112 \n", + "7 1 14.06 2.15 2.61 17.6 121 \n", + "9 1 13.86 1.35 2.27 16.0 98 \n", + "10 1 14.10 2.16 2.30 18.0 105 \n", + "11 1 14.12 1.48 2.32 16.8 95 \n", + "12 1 13.75 1.73 2.41 16.0 89 \n", + "13 1 14.75 1.73 2.39 11.4 91 \n", + "14 1 14.38 1.87 2.38 12.0 102 \n", + "15 1 13.63 1.81 2.70 17.2 112 \n", + "16 1 14.30 1.92 2.72 20.0 120 \n", + "17 1 13.83 1.57 2.62 20.0 115 \n", + "18 1 14.19 1.59 2.48 16.5 108 \n", + "19 1 13.64 3.10 2.56 15.2 116 \n", + "20 1 14.06 1.63 2.28 16.0 126 \n", + "21 1 12.93 3.80 2.65 18.6 102 \n", + "22 1 13.71 1.86 2.36 16.6 101 \n", + "23 1 12.85 1.60 2.52 17.8 95 \n", + "24 1 13.50 1.81 2.61 20.0 96 \n", + "25 1 13.05 2.05 3.22 25.0 124 \n", + "26 1 13.39 1.77 2.62 16.1 93 \n", + "27 1 13.30 1.72 2.14 17.0 94 \n", + "28 1 13.87 1.90 2.80 19.4 107 \n", + "29 1 14.02 1.68 2.21 16.0 96 \n", + "30 1 13.73 1.50 2.70 22.5 101 \n", + "31 1 13.58 1.66 2.36 19.1 106 \n", + "32 1 13.68 1.83 2.36 17.2 104 \n", + "33 1 13.76 1.53 2.70 19.5 132 \n", + "34 1 13.51 1.80 2.65 19.0 110 \n", + "35 1 13.48 1.81 2.41 20.5 100 \n", + "36 1 13.28 1.64 2.84 15.5 110 \n", + ".. ... ... ... ... ... ... \n", + "148 3 13.32 3.24 2.38 21.5 92 \n", + "149 3 13.08 3.90 2.36 21.5 113 \n", + "150 3 13.50 3.12 2.62 24.0 123 \n", + "151 3 12.79 2.67 2.48 22.0 112 \n", + "152 3 13.11 1.90 2.75 25.5 116 \n", + "153 3 13.23 3.30 2.28 18.5 98 \n", + "154 3 12.58 1.29 2.10 20.0 103 \n", + "155 3 13.17 5.19 2.32 22.0 93 \n", + "156 3 13.84 4.12 2.38 19.5 89 \n", + "157 3 12.45 3.03 2.64 27.0 97 \n", + "158 3 14.34 1.68 2.70 25.0 98 \n", + "159 3 13.48 1.67 2.64 22.5 89 \n", + "160 3 12.36 3.83 2.38 21.0 88 \n", + "161 3 13.69 3.26 2.54 20.0 107 \n", + "162 3 12.85 3.27 2.58 22.0 106 \n", + "163 3 12.96 3.45 2.35 18.5 106 \n", + "164 3 13.78 2.76 2.30 22.0 90 \n", + "165 3 13.73 4.36 2.26 22.5 88 \n", + "166 3 13.45 3.70 2.60 23.0 111 \n", + "167 3 12.82 3.37 2.30 19.5 88 \n", + "168 3 13.58 2.58 2.69 24.5 105 \n", + "169 3 13.40 4.60 2.86 25.0 112 \n", + "170 3 12.20 3.03 2.32 19.0 96 \n", + "171 3 12.77 2.39 2.28 19.5 86 \n", + "172 3 14.16 2.51 2.48 20.0 91 \n", + "173 3 13.71 5.65 2.45 20.5 95 \n", + "174 3 13.40 3.91 2.48 23.0 102 \n", + "175 3 13.27 4.28 2.26 20.0 120 \n", + "176 3 13.17 2.59 2.37 20.0 120 \n", + "177 3 14.13 4.10 2.74 24.5 96 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "5 3.27 3.39 0.34 1.97 \n", + "7 2.60 2.51 0.31 1.25 \n", + "9 2.98 3.15 0.22 1.85 \n", + "10 2.95 3.32 0.22 2.38 \n", + "11 2.20 2.43 0.26 1.57 \n", + "12 2.60 2.76 0.29 1.81 \n", + "13 3.10 3.69 0.43 2.81 \n", + "14 3.30 3.64 0.29 2.96 \n", + "15 2.85 2.91 0.30 1.46 \n", + "16 2.80 3.14 0.33 1.97 \n", + "17 2.95 3.40 0.40 1.72 \n", + "18 3.30 3.93 0.32 1.86 \n", + "19 2.70 3.03 0.17 1.66 \n", + "20 3.00 3.17 0.24 2.10 \n", + "21 2.41 2.41 0.25 1.98 \n", + "22 2.61 2.88 0.27 1.69 \n", + "23 2.48 2.37 0.26 1.46 \n", + "24 2.53 2.61 0.28 1.66 \n", + "25 2.63 2.68 0.47 1.92 \n", + "26 2.85 2.94 0.34 1.45 \n", + "27 2.40 2.19 0.27 1.35 \n", + "28 2.95 2.97 0.37 1.76 \n", + "29 2.65 2.33 0.26 1.98 \n", + "30 3.00 3.25 0.29 2.38 \n", + "31 2.86 3.19 0.22 1.95 \n", + "32 2.42 2.69 0.42 1.97 \n", + "33 2.95 2.74 0.50 1.35 \n", + "34 2.35 2.53 0.29 1.54 \n", + "35 2.70 2.98 0.26 1.86 \n", + "36 2.60 2.68 0.34 1.36 \n", + ".. ... ... ... ... \n", + "148 1.93 0.76 0.45 1.25 \n", + "149 1.41 1.39 0.34 1.14 \n", + "150 1.40 1.57 0.22 1.25 \n", + "151 1.48 1.36 0.24 1.26 \n", + "152 2.20 1.28 0.26 1.56 \n", + "153 1.80 0.83 0.61 1.87 \n", + "154 1.48 0.58 0.53 1.40 \n", + "155 1.74 0.63 0.61 1.55 \n", + "156 1.80 0.83 0.48 1.56 \n", + "157 1.90 0.58 0.63 1.14 \n", + "158 2.80 1.31 0.53 2.70 \n", + "159 2.60 1.10 0.52 2.29 \n", + "160 2.30 0.92 0.50 1.04 \n", + "161 1.83 0.56 0.50 0.80 \n", + "162 1.65 0.60 0.60 0.96 \n", + "163 1.39 0.70 0.40 0.94 \n", + "164 1.35 0.68 0.41 1.03 \n", + "165 1.28 0.47 0.52 1.15 \n", + "166 1.70 0.92 0.43 1.46 \n", + "167 1.48 0.66 0.40 0.97 \n", + "168 1.55 0.84 0.39 1.54 \n", + "169 1.98 0.96 0.27 1.11 \n", + "170 1.25 0.49 0.40 0.73 \n", + "171 1.39 0.51 0.48 0.64 \n", + "172 1.68 0.70 0.44 1.24 \n", + "173 1.68 0.61 0.52 1.06 \n", + "174 1.80 0.75 0.43 1.41 \n", + "175 1.59 0.69 0.43 1.35 \n", + "176 1.65 0.68 0.53 1.46 \n", + "177 2.05 0.76 0.56 1.35 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted wines Proline \n", + "5 6.750000 1.05 2.85 1450 \n", + "7 5.050000 1.06 3.58 1295 \n", + "9 7.220000 1.01 3.55 1045 \n", + "10 5.750000 1.25 3.17 1510 \n", + "11 5.000000 1.17 2.82 1280 \n", + "12 5.600000 1.15 2.90 1320 \n", + "13 5.400000 1.25 2.73 1150 \n", + "14 7.500000 1.20 3.00 1547 \n", + "15 7.300000 1.28 2.88 1310 \n", + "16 6.200000 1.07 2.65 1280 \n", + "17 6.600000 1.13 2.57 1130 \n", + "18 8.700000 1.23 2.82 1680 \n", + "19 5.100000 0.96 3.36 845 \n", + "20 5.650000 1.09 3.71 780 \n", + "21 4.500000 1.03 3.52 770 \n", + "22 3.800000 1.11 4.00 1035 \n", + "23 3.930000 1.09 3.63 1015 \n", + "24 3.520000 1.12 3.82 845 \n", + "25 3.580000 1.13 3.20 830 \n", + "26 4.800000 0.92 3.22 1195 \n", + "27 3.950000 1.02 2.77 1285 \n", + "28 4.500000 1.25 3.40 915 \n", + "29 4.700000 1.04 3.59 1035 \n", + "30 5.700000 1.19 2.71 1285 \n", + "31 6.900000 1.09 2.88 1515 \n", + "32 3.840000 1.23 2.87 990 \n", + "33 5.400000 1.25 3.00 1235 \n", + "34 4.200000 1.10 2.87 1095 \n", + "35 5.100000 1.04 3.47 920 \n", + "36 4.600000 1.09 2.78 880 \n", + ".. ... ... ... ... \n", + "148 8.420000 0.55 1.62 650 \n", + "149 9.400000 0.57 1.33 550 \n", + "150 8.600000 0.59 1.30 500 \n", + "151 10.800000 0.48 1.47 480 \n", + "152 7.100000 0.61 1.33 425 \n", + "153 10.520000 0.56 1.51 675 \n", + "154 7.600000 0.58 1.55 640 \n", + "155 7.900000 0.60 1.48 725 \n", + "156 9.010000 0.57 1.64 480 \n", + "157 7.500000 0.67 1.73 880 \n", + "158 13.000000 0.57 1.96 660 \n", + "159 11.750000 0.57 1.78 620 \n", + "160 7.650000 0.56 1.58 520 \n", + "161 5.880000 0.96 1.82 680 \n", + "162 5.580000 0.87 2.11 570 \n", + "163 5.280000 0.68 1.75 675 \n", + "164 9.580000 0.70 1.68 615 \n", + "165 6.620000 0.78 1.75 520 \n", + "166 10.680000 0.85 1.56 695 \n", + "167 10.260000 0.72 1.75 685 \n", + "168 8.660000 0.74 1.80 750 \n", + "169 8.500000 0.67 1.92 630 \n", + "170 5.500000 0.66 1.83 510 \n", + "171 9.899999 0.57 1.63 470 \n", + "172 9.700000 0.62 1.71 660 \n", + "173 7.700000 0.64 1.74 740 \n", + "174 7.300000 0.70 1.56 750 \n", + "175 10.200000 0.59 1.56 835 \n", + "176 9.300000 0.60 1.62 840 \n", + "177 9.200000 0.61 1.60 560 \n", + "\n", + "[171 rows x 14 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 49 + } + ] + }, + { + "metadata": { + "id": "Kq5mbLlr0-Ge", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "WtT0HFp7095Y", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', header=None)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zWPnZuKV094L", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "e87e0101-d534-4621-9faf-424c398ec698" + }, + "cell_type": "code", + "source": [ + "df.columns = ['target'] + features_lst\n", + "df.head()" + ], + "execution_count": 51, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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targetAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
0114.231.712.4315.61272.803.060.282.295.641.043.921065
1113.201.782.1411.21002.652.760.261.284.381.053.401050
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" + ], + "text/plain": [ + " target Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "0 1 14.23 1.71 2.43 15.6 127 \n", + "1 1 13.20 1.78 2.14 11.2 100 \n", + "2 1 13.16 2.36 2.67 18.6 101 \n", + "3 1 14.37 1.95 2.50 16.8 113 \n", + "4 1 13.24 2.59 2.87 21.0 118 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "0 2.80 3.06 0.28 2.29 \n", + "1 2.65 2.76 0.26 1.28 \n", + "2 2.80 3.24 0.30 2.81 \n", + "3 3.85 3.49 0.24 2.18 \n", + "4 2.80 2.69 0.39 1.82 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted wines Proline \n", + "0 5.64 1.04 3.92 1065 \n", + "1 4.38 1.05 3.40 1050 \n", + "2 5.68 1.03 3.17 1185 \n", + "3 7.80 0.86 3.45 1480 \n", + "4 4.32 1.04 2.93 735 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 51 + } + ] + }, + { + "metadata": { + "id": "Jf36qMZE09zi", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "854cdebd-2670-4fd1-d1cc-c498ddd1b0c9" + }, + "cell_type": "code", + "source": [ + "\n", + "df.shape #178 examples, 13 attributes" + ], + "execution_count": 52, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(178, 14)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 52 + } + ] + }, + { + "metadata": { + "id": "on5PyeEN1m8U", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 351 + }, + "outputId": "01de9515-58b8-41ff-9f31-214768c96a31" + }, + "cell_type": "code", + "source": [ + "df.describe()\n" + ], + "execution_count": 53, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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targetAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
count178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000178.000000
mean1.93820213.0006182.3363482.36651719.49494499.7415732.2951122.0292700.3618541.5908995.0580900.9574492.611685746.893258
std0.7750350.8118271.1171460.2743443.33956414.2824840.6258510.9988590.1244530.5723592.3182860.2285720.709990314.907474
min1.00000011.0300000.7400001.36000010.60000070.0000000.9800000.3400000.1300000.4100001.2800000.4800001.270000278.000000
25%1.00000012.3625001.6025002.21000017.20000088.0000001.7425001.2050000.2700001.2500003.2200000.7825001.937500500.500000
50%2.00000013.0500001.8650002.36000019.50000098.0000002.3550002.1350000.3400001.5550004.6900000.9650002.780000673.500000
75%3.00000013.6775003.0825002.55750021.500000107.0000002.8000002.8750000.4375001.9500006.2000001.1200003.170000985.000000
max3.00000014.8300005.8000003.23000030.000000162.0000003.8800005.0800000.6600003.58000013.0000001.7100004.0000001680.000000
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" + ], + "text/plain": [ + " target Alcohol Malic acid Ash Alcalinity of ash \\\n", + "count 178.000000 178.000000 178.000000 178.000000 178.000000 \n", + "mean 1.938202 13.000618 2.336348 2.366517 19.494944 \n", + "std 0.775035 0.811827 1.117146 0.274344 3.339564 \n", + "min 1.000000 11.030000 0.740000 1.360000 10.600000 \n", + "25% 1.000000 12.362500 1.602500 2.210000 17.200000 \n", + "50% 2.000000 13.050000 1.865000 2.360000 19.500000 \n", + "75% 3.000000 13.677500 3.082500 2.557500 21.500000 \n", + "max 3.000000 14.830000 5.800000 3.230000 30.000000 \n", + "\n", + " Magnesium Total phenols Flavanoids Nonflavanoid phenols \\\n", + "count 178.000000 178.000000 178.000000 178.000000 \n", + "mean 99.741573 2.295112 2.029270 0.361854 \n", + "std 14.282484 0.625851 0.998859 0.124453 \n", + "min 70.000000 0.980000 0.340000 0.130000 \n", + "25% 88.000000 1.742500 1.205000 0.270000 \n", + "50% 98.000000 2.355000 2.135000 0.340000 \n", + "75% 107.000000 2.800000 2.875000 0.437500 \n", + "max 162.000000 3.880000 5.080000 0.660000 \n", + "\n", + " Proanthocyanins Color intensity Hue \\\n", + "count 178.000000 178.000000 178.000000 \n", + "mean 1.590899 5.058090 0.957449 \n", + "std 0.572359 2.318286 0.228572 \n", + "min 0.410000 1.280000 0.480000 \n", + "25% 1.250000 3.220000 0.782500 \n", + "50% 1.555000 4.690000 0.965000 \n", + "75% 1.950000 6.200000 1.120000 \n", + "max 3.580000 13.000000 1.710000 \n", + "\n", + " OD280/OD315 of diluted wines Proline \n", + "count 178.000000 178.000000 \n", + "mean 2.611685 746.893258 \n", + "std 0.709990 314.907474 \n", + "min 1.270000 278.000000 \n", + "25% 1.937500 500.500000 \n", + "50% 2.780000 673.500000 \n", + "75% 3.170000 985.000000 \n", + "max 4.000000 1680.000000 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 53 + } + ] + }, + { + "metadata": { + "id": "WVIbctv31ozd", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "cultivar1 = df[df['target']==1]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "YQDtWqWt1uYT", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 351 + }, + "outputId": "e2d2c192-d427-4745-d5d0-d2a3fb0af990" + }, + "cell_type": "code", + "source": [ + "cultivar1.describe()" + ], + "execution_count": 55, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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targetAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
count59.059.00000059.00000059.00000059.00000059.00000059.00000059.00000059.00000059.00000059.00000059.00000059.00000059.000000
mean1.013.7447462.0106782.45559317.037288106.3389832.8401692.9823730.2900001.8993225.5283051.0620343.1577971115.711864
std0.00.4621250.6885490.2271662.54632210.4989490.3389610.3974940.0700490.4121091.2385730.1164830.357077221.520767
min1.012.8500001.3500002.04000011.20000089.0000002.2000002.1900000.1700001.2500003.5200000.8200002.510000680.000000
25%1.013.4000001.6650002.29500016.00000098.0000002.6000002.6800000.2550001.6400004.5500000.9950002.870000987.500000
50%1.013.7500001.7700002.44000016.800000104.0000002.8000002.9800000.2900001.8700005.4000001.0700003.1700001095.000000
75%1.014.1000001.9350002.61500018.700000114.0000003.0000003.2450000.3200002.0900006.2250001.1300003.4200001280.000000
max1.014.8300004.0400003.22000025.000000132.0000003.8800003.9300000.5000002.9600008.9000001.2800004.0000001680.000000
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" + ], + "text/plain": [ + " target Alcohol Malic acid Ash Alcalinity of ash \\\n", + "count 59.0 59.000000 59.000000 59.000000 59.000000 \n", + "mean 1.0 13.744746 2.010678 2.455593 17.037288 \n", + "std 0.0 0.462125 0.688549 0.227166 2.546322 \n", + "min 1.0 12.850000 1.350000 2.040000 11.200000 \n", + "25% 1.0 13.400000 1.665000 2.295000 16.000000 \n", + "50% 1.0 13.750000 1.770000 2.440000 16.800000 \n", + "75% 1.0 14.100000 1.935000 2.615000 18.700000 \n", + "max 1.0 14.830000 4.040000 3.220000 25.000000 \n", + "\n", + " Magnesium Total phenols Flavanoids Nonflavanoid phenols \\\n", + "count 59.000000 59.000000 59.000000 59.000000 \n", + "mean 106.338983 2.840169 2.982373 0.290000 \n", + "std 10.498949 0.338961 0.397494 0.070049 \n", + "min 89.000000 2.200000 2.190000 0.170000 \n", + "25% 98.000000 2.600000 2.680000 0.255000 \n", + "50% 104.000000 2.800000 2.980000 0.290000 \n", + "75% 114.000000 3.000000 3.245000 0.320000 \n", + "max 132.000000 3.880000 3.930000 0.500000 \n", + "\n", + " Proanthocyanins Color intensity Hue \\\n", + "count 59.000000 59.000000 59.000000 \n", + "mean 1.899322 5.528305 1.062034 \n", + "std 0.412109 1.238573 0.116483 \n", + "min 1.250000 3.520000 0.820000 \n", + "25% 1.640000 4.550000 0.995000 \n", + "50% 1.870000 5.400000 1.070000 \n", + "75% 2.090000 6.225000 1.130000 \n", + "max 2.960000 8.900000 1.280000 \n", + "\n", + " OD280/OD315 of diluted wines Proline \n", + "count 59.000000 59.000000 \n", + "mean 3.157797 1115.711864 \n", + "std 0.357077 221.520767 \n", + "min 2.510000 680.000000 \n", + "25% 2.870000 987.500000 \n", + "50% 3.170000 1095.000000 \n", + "75% 3.420000 1280.000000 \n", + "max 4.000000 1680.000000 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 55 + } + ] + }, + { + "metadata": { + "id": "E3Eg55nG1ueI", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "cultivar2 = df[df['target']==2]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "gxz1faMt1ubs", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 351 + }, + "outputId": "dd68ee00-6941-4407-c14f-58fb8935bac3" + }, + "cell_type": "code", + "source": [ + "cultivar2.describe()" + ], + "execution_count": 57, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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targetAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
count71.071.00000071.00000071.00000071.00000071.00000071.00000071.00000071.00000071.00000071.00000071.00000071.00000071.000000
mean2.012.2787321.9326762.24478920.23802894.5492962.2588732.0808450.3636621.6302823.0866201.0562822.785352519.507042
std0.00.5379641.0155690.3154673.34977016.7534970.5453610.7057010.1239610.6020680.9249290.2029370.496573157.211220
min2.011.0300000.7400001.36000010.60000070.0000001.1000000.5700000.1300000.4100001.2800000.6900001.590000278.000000
25%2.011.9150001.2700002.00000018.00000085.5000001.8950001.6050000.2700001.3500002.5350000.9250002.440000406.500000
50%2.012.2900001.6100002.24000020.00000088.0000002.2000002.0300000.3700001.6100002.9000001.0400002.830000495.000000
75%2.012.5150002.1450002.42000022.00000099.5000002.5600002.4750000.4300001.8850003.4000001.2050003.160000625.000000
max2.013.8600005.8000003.23000030.000000162.0000003.5200005.0800000.6600003.5800006.0000001.7100003.690000985.000000
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" + ], + "text/plain": [ + " target Alcohol Malic acid Ash Alcalinity of ash \\\n", + "count 71.0 71.000000 71.000000 71.000000 71.000000 \n", + "mean 2.0 12.278732 1.932676 2.244789 20.238028 \n", + "std 0.0 0.537964 1.015569 0.315467 3.349770 \n", + "min 2.0 11.030000 0.740000 1.360000 10.600000 \n", + "25% 2.0 11.915000 1.270000 2.000000 18.000000 \n", + "50% 2.0 12.290000 1.610000 2.240000 20.000000 \n", + "75% 2.0 12.515000 2.145000 2.420000 22.000000 \n", + "max 2.0 13.860000 5.800000 3.230000 30.000000 \n", + "\n", + " Magnesium Total phenols Flavanoids Nonflavanoid phenols \\\n", + "count 71.000000 71.000000 71.000000 71.000000 \n", + "mean 94.549296 2.258873 2.080845 0.363662 \n", + "std 16.753497 0.545361 0.705701 0.123961 \n", + "min 70.000000 1.100000 0.570000 0.130000 \n", + "25% 85.500000 1.895000 1.605000 0.270000 \n", + "50% 88.000000 2.200000 2.030000 0.370000 \n", + "75% 99.500000 2.560000 2.475000 0.430000 \n", + "max 162.000000 3.520000 5.080000 0.660000 \n", + "\n", + " Proanthocyanins Color intensity Hue \\\n", + "count 71.000000 71.000000 71.000000 \n", + "mean 1.630282 3.086620 1.056282 \n", + "std 0.602068 0.924929 0.202937 \n", + "min 0.410000 1.280000 0.690000 \n", + "25% 1.350000 2.535000 0.925000 \n", + "50% 1.610000 2.900000 1.040000 \n", + "75% 1.885000 3.400000 1.205000 \n", + "max 3.580000 6.000000 1.710000 \n", + "\n", + " OD280/OD315 of diluted wines Proline \n", + "count 71.000000 71.000000 \n", + "mean 2.785352 519.507042 \n", + "std 0.496573 157.211220 \n", + "min 1.590000 278.000000 \n", + "25% 2.440000 406.500000 \n", + "50% 2.830000 495.000000 \n", + "75% 3.160000 625.000000 \n", + "max 3.690000 985.000000 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 57 + } + ] + }, + { + "metadata": { + "id": "RR1f0VIa1uWD", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "cultivar3 = df[df['target']==3]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Z0TMbK942BLr", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 351 + }, + "outputId": "6e0d1443-b4b4-4e3b-bf10-81fc38513d09" + }, + "cell_type": "code", + "source": [ + "\n", + "cultivar3.describe()" + ], + "execution_count": 59, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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targetAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
count48.048.00000048.00000048.00000048.00000048.00000048.00000048.00000048.0000048.00000048.00000048.00000048.00000048.000000
mean3.013.1537503.3337502.43708321.41666799.3125001.6787500.7814580.447501.1535427.3962500.6827081.683542629.895833
std0.00.5302411.0879060.1846902.25816110.8904730.3569710.2935040.124140.4088362.3109420.1144410.272111115.097043
min3.012.2000001.2400002.10000017.50000080.0000000.9800000.3400000.170000.5500003.8500000.4800001.270000415.000000
25%3.012.8050002.5875002.30000020.00000089.7500001.4075000.5800000.397500.8550005.4375000.5875001.510000545.000000
50%3.013.1650003.2650002.38000021.00000097.0000001.6350000.6850000.470001.1050007.5500000.6650001.660000627.500000
75%3.013.5050003.9575002.60250023.000000106.0000001.8075000.9200000.530001.3500009.2250000.7525001.820000695.000000
max3.014.3400005.6500002.86000027.000000123.0000002.8000001.5700000.630002.70000013.0000000.9600002.470000880.000000
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" + ], + "text/plain": [ + " target Alcohol Malic acid Ash Alcalinity of ash \\\n", + "count 48.0 48.000000 48.000000 48.000000 48.000000 \n", + "mean 3.0 13.153750 3.333750 2.437083 21.416667 \n", + "std 0.0 0.530241 1.087906 0.184690 2.258161 \n", + "min 3.0 12.200000 1.240000 2.100000 17.500000 \n", + "25% 3.0 12.805000 2.587500 2.300000 20.000000 \n", + "50% 3.0 13.165000 3.265000 2.380000 21.000000 \n", + "75% 3.0 13.505000 3.957500 2.602500 23.000000 \n", + "max 3.0 14.340000 5.650000 2.860000 27.000000 \n", + "\n", + " Magnesium Total phenols Flavanoids Nonflavanoid phenols \\\n", + "count 48.000000 48.000000 48.000000 48.00000 \n", + "mean 99.312500 1.678750 0.781458 0.44750 \n", + "std 10.890473 0.356971 0.293504 0.12414 \n", + "min 80.000000 0.980000 0.340000 0.17000 \n", + "25% 89.750000 1.407500 0.580000 0.39750 \n", + "50% 97.000000 1.635000 0.685000 0.47000 \n", + "75% 106.000000 1.807500 0.920000 0.53000 \n", + "max 123.000000 2.800000 1.570000 0.63000 \n", + "\n", + " Proanthocyanins Color intensity Hue \\\n", + "count 48.000000 48.000000 48.000000 \n", + "mean 1.153542 7.396250 0.682708 \n", + "std 0.408836 2.310942 0.114441 \n", + "min 0.550000 3.850000 0.480000 \n", + "25% 0.855000 5.437500 0.587500 \n", + "50% 1.105000 7.550000 0.665000 \n", + "75% 1.350000 9.225000 0.752500 \n", + "max 2.700000 13.000000 0.960000 \n", + "\n", + " OD280/OD315 of diluted wines Proline \n", + "count 48.000000 48.000000 \n", + "mean 1.683542 629.895833 \n", + "std 0.272111 115.097043 \n", + "min 1.270000 415.000000 \n", + "25% 1.510000 545.000000 \n", + "50% 1.660000 627.500000 \n", + "75% 1.820000 695.000000 \n", + "max 2.470000 880.000000 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 59 + } + ] + }, + { + "metadata": { + "id": "yJDJ5zYp2BPG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 415 + }, + "outputId": "b8603236-68a7-4817-ad3a-7fcf406732e1" + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.hist(cultivar1['Alcohol'])\n", + "plt.hist(cultivar2['Alcohol'])\n", + "plt.hist(cultivar3['Alcohol'])" + ], + "execution_count": 60, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([4., 5., 6., 6., 6., 7., 6., 5., 0., 3.]),\n", + " array([12.2 , 12.414, 12.628, 12.842, 13.056, 13.27 , 13.484, 13.698,\n", + " 13.912, 14.126, 14.34 ]),\n", + " )" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 60 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "79Xe2mAb2BJ6", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file