{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 데이터 다루기 (Data Handling) - Iris Data Set"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Iris(붓꽃) Data Set 개요"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 참고: https://archive.ics.uci.edu/ml/datasets/Iris\n",
"- 데이터 원본: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\n",
"- 데이터 제목: Iris Plants Database\n",
"- 원작자 정보\n",
" - Creator: R.A. Fisher\n",
" - Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
" - Date: July, 1988\n",
"- 사례 개수: 150 \n",
" - 각 붓꽃 종류당 50개씩\n",
"- 4개의 독립 변수(Feature) \n",
" - a: 꽃받침 길이 (sepal length in cm)\n",
" - b: 꽃받침 넓이 (sepal width in cm)\n",
" - c: 꽃잎 길이 (petal length in cm)\n",
" - d: 꽃이 넓이 (petal width in cm)\n",
"- 3부류의 백합 종류 (Image Source: wikipedia) \n",
"
\n",
"\n",
"Iris Setosa | Iris Versicolor | Iris Virginica | \n",
"
\n",
"\n",
" | \n",
" | \n",
" | \n",
"
\n",
"
\n",
"- 참고 (Image Source: wikipedia)\n",
" - Sepal(꽃받침), Petal(꽃잎)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. 데이터 로딩하기"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/yhhan/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
]
}
],
"source": [
"import urllib2\n",
"from scipy import stats\n",
"from pandas import Series, DataFrame\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"%matplotlib inline\n",
"\n",
"path = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
"raw_csv = urllib2.urlopen(path)\n",
"feature_names = ('sepal length', 'sepal width', 'petal length', 'petal width')\n",
"all_names = feature_names + ('class',)\n",
"df = pd.read_csv(raw_csv, names=all_names)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"\n",
"
\n",
" \n",
" \n",
" | \n",
" sepal length | \n",
" sepal width | \n",
" petal length | \n",
" petal width | \n",
" class | \n",
"
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" \n",
" \n",
" \n",
" 0 | \n",
" 5.1 | \n",
" 3.5 | \n",
" 1.4 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
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" 1 | \n",
" 4.9 | \n",
" 3.0 | \n",
" 1.4 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
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" 2 | \n",
" 4.7 | \n",
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" 1.3 | \n",
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" 3 | \n",
" 4.6 | \n",
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" 1.5 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
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" 4 | \n",
" 5.0 | \n",
" 3.6 | \n",
" 1.4 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
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" 5 | \n",
" 5.4 | \n",
" 3.9 | \n",
" 1.7 | \n",
" 0.4 | \n",
" Iris-setosa | \n",
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" 6 | \n",
" 4.6 | \n",
" 3.4 | \n",
" 1.4 | \n",
" 0.3 | \n",
" Iris-setosa | \n",
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" 5.0 | \n",
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" 0.2 | \n",
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" 4.4 | \n",
" 2.9 | \n",
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" 4.9 | \n",
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" 5.4 | \n",
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" 1.5 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
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" 11 | \n",
" 4.8 | \n",
" 3.4 | \n",
" 1.6 | \n",
" 0.2 | \n",
" Iris-setosa | \n",
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" 12 | \n",
" 4.8 | \n",
" 3.0 | \n",
" 1.4 | \n",
" 0.1 | \n",
" Iris-setosa | \n",
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" 13 | \n",
" 4.3 | \n",
" 3.0 | \n",
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" 0.1 | \n",
" Iris-setosa | \n",
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" 27 | \n",
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" 3.5 | \n",
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" 0.2 | \n",
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" 28 | \n",
" 5.2 | \n",
" 3.4 | \n",
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" 0.2 | \n",
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" 29 | \n",
" 4.7 | \n",
" 3.2 | \n",
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"
\n",
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" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
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" ... | \n",
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" \n",
" 120 | \n",
" 6.9 | \n",
" 3.2 | \n",
" 5.7 | \n",
" 2.3 | \n",
" Iris-virginica | \n",
"
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" \n",
" 121 | \n",
" 5.6 | \n",
" 2.8 | \n",
" 4.9 | \n",
" 2.0 | \n",
" Iris-virginica | \n",
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" 122 | \n",
" 7.7 | \n",
" 2.8 | \n",
" 6.7 | \n",
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" 123 | \n",
" 6.3 | \n",
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" 124 | \n",
" 6.7 | \n",
" 3.3 | \n",
" 5.7 | \n",
" 2.1 | \n",
" Iris-virginica | \n",
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" 125 | \n",
" 7.2 | \n",
" 3.2 | \n",
" 6.0 | \n",
" 1.8 | \n",
" Iris-virginica | \n",
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" 126 | \n",
" 6.2 | \n",
" 2.8 | \n",
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" 1.8 | \n",
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" 127 | \n",
" 6.1 | \n",
" 3.0 | \n",
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" 1.8 | \n",
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" 128 | \n",
" 6.4 | \n",
" 2.8 | \n",
" 5.6 | \n",
" 2.1 | \n",
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" 7.2 | \n",
" 3.0 | \n",
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" 1.6 | \n",
" Iris-virginica | \n",
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" 130 | \n",
" 7.4 | \n",
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" 7.9 | \n",
" 3.8 | \n",
" 6.4 | \n",
" 2.0 | \n",
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" 132 | \n",
" 6.4 | \n",
" 2.8 | \n",
" 5.6 | \n",
" 2.2 | \n",
" Iris-virginica | \n",
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" 133 | \n",
" 6.3 | \n",
" 2.8 | \n",
" 5.1 | \n",
" 1.5 | \n",
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" 1.4 | \n",
" Iris-virginica | \n",
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" 135 | \n",
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" 3.0 | \n",
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" 5.6 | \n",
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" 137 | \n",
" 6.4 | \n",
" 3.1 | \n",
" 5.5 | \n",
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" Iris-virginica | \n",
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" \n",
" 138 | \n",
" 6.0 | \n",
" 3.0 | \n",
" 4.8 | \n",
" 1.8 | \n",
" Iris-virginica | \n",
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" \n",
" 139 | \n",
" 6.9 | \n",
" 3.1 | \n",
" 5.4 | \n",
" 2.1 | \n",
" Iris-virginica | \n",
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" \n",
" 140 | \n",
" 6.7 | \n",
" 3.1 | \n",
" 5.6 | \n",
" 2.4 | \n",
" Iris-virginica | \n",
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" 141 | \n",
" 6.9 | \n",
" 3.1 | \n",
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" 2.3 | \n",
" Iris-virginica | \n",
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" 142 | \n",
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" 2.7 | \n",
" 5.1 | \n",
" 1.9 | \n",
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" 3.2 | \n",
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" 3.3 | \n",
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" 2.5 | \n",
" Iris-virginica | \n",
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" 145 | \n",
" 6.7 | \n",
" 3.0 | \n",
" 5.2 | \n",
" 2.3 | \n",
" Iris-virginica | \n",
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" 146 | \n",
" 6.3 | \n",
" 2.5 | \n",
" 5.0 | \n",
" 1.9 | \n",
" Iris-virginica | \n",
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" 147 | \n",
" 6.5 | \n",
" 3.0 | \n",
" 5.2 | \n",
" 2.0 | \n",
" Iris-virginica | \n",
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" 148 | \n",
" 6.2 | \n",
" 3.4 | \n",
" 5.4 | \n",
" 2.3 | \n",
" Iris-virginica | \n",
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" \n",
" 149 | \n",
" 5.9 | \n",
" 3.0 | \n",
" 5.1 | \n",
" 1.8 | \n",
" Iris-virginica | \n",
"
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"
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"
150 rows × 5 columns
\n",
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"
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"text/plain": [
" sepal length sepal width petal length petal width class\n",
"0 5.1 3.5 1.4 0.2 Iris-setosa\n",
"1 4.9 3.0 1.4 0.2 Iris-setosa\n",
"2 4.7 3.2 1.3 0.2 Iris-setosa\n",
"3 4.6 3.1 1.5 0.2 Iris-setosa\n",
"4 5.0 3.6 1.4 0.2 Iris-setosa\n",
"5 5.4 3.9 1.7 0.4 Iris-setosa\n",
"6 4.6 3.4 1.4 0.3 Iris-setosa\n",
"7 5.0 3.4 1.5 0.2 Iris-setosa\n",
"8 4.4 2.9 1.4 0.2 Iris-setosa\n",
"9 4.9 3.1 1.5 0.1 Iris-setosa\n",
"10 5.4 3.7 1.5 0.2 Iris-setosa\n",
"11 4.8 3.4 1.6 0.2 Iris-setosa\n",
"12 4.8 3.0 1.4 0.1 Iris-setosa\n",
"13 4.3 3.0 1.1 0.1 Iris-setosa\n",
"14 5.8 4.0 1.2 0.2 Iris-setosa\n",
"15 5.7 4.4 1.5 0.4 Iris-setosa\n",
"16 5.4 3.9 1.3 0.4 Iris-setosa\n",
"17 5.1 3.5 1.4 0.3 Iris-setosa\n",
"18 5.7 3.8 1.7 0.3 Iris-setosa\n",
"19 5.1 3.8 1.5 0.3 Iris-setosa\n",
"20 5.4 3.4 1.7 0.2 Iris-setosa\n",
"21 5.1 3.7 1.5 0.4 Iris-setosa\n",
"22 4.6 3.6 1.0 0.2 Iris-setosa\n",
"23 5.1 3.3 1.7 0.5 Iris-setosa\n",
"24 4.8 3.4 1.9 0.2 Iris-setosa\n",
"25 5.0 3.0 1.6 0.2 Iris-setosa\n",
"26 5.0 3.4 1.6 0.4 Iris-setosa\n",
"27 5.2 3.5 1.5 0.2 Iris-setosa\n",
"28 5.2 3.4 1.4 0.2 Iris-setosa\n",
"29 4.7 3.2 1.6 0.2 Iris-setosa\n",
".. ... ... ... ... ...\n",
"120 6.9 3.2 5.7 2.3 Iris-virginica\n",
"121 5.6 2.8 4.9 2.0 Iris-virginica\n",
"122 7.7 2.8 6.7 2.0 Iris-virginica\n",
"123 6.3 2.7 4.9 1.8 Iris-virginica\n",
"124 6.7 3.3 5.7 2.1 Iris-virginica\n",
"125 7.2 3.2 6.0 1.8 Iris-virginica\n",
"126 6.2 2.8 4.8 1.8 Iris-virginica\n",
"127 6.1 3.0 4.9 1.8 Iris-virginica\n",
"128 6.4 2.8 5.6 2.1 Iris-virginica\n",
"129 7.2 3.0 5.8 1.6 Iris-virginica\n",
"130 7.4 2.8 6.1 1.9 Iris-virginica\n",
"131 7.9 3.8 6.4 2.0 Iris-virginica\n",
"132 6.4 2.8 5.6 2.2 Iris-virginica\n",
"133 6.3 2.8 5.1 1.5 Iris-virginica\n",
"134 6.1 2.6 5.6 1.4 Iris-virginica\n",
"135 7.7 3.0 6.1 2.3 Iris-virginica\n",
"136 6.3 3.4 5.6 2.4 Iris-virginica\n",
"137 6.4 3.1 5.5 1.8 Iris-virginica\n",
"138 6.0 3.0 4.8 1.8 Iris-virginica\n",
"139 6.9 3.1 5.4 2.1 Iris-virginica\n",
"140 6.7 3.1 5.6 2.4 Iris-virginica\n",
"141 6.9 3.1 5.1 2.3 Iris-virginica\n",
"142 5.8 2.7 5.1 1.9 Iris-virginica\n",
"143 6.8 3.2 5.9 2.3 Iris-virginica\n",
"144 6.7 3.3 5.7 2.5 Iris-virginica\n",
"145 6.7 3.0 5.2 2.3 Iris-virginica\n",
"146 6.3 2.5 5.0 1.9 Iris-virginica\n",
"147 6.5 3.0 5.2 2.0 Iris-virginica\n",
"148 6.2 3.4 5.4 2.3 Iris-virginica\n",
"149 5.9 3.0 5.1 1.8 Iris-virginica\n",
"\n",
"[150 rows x 5 columns]"
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},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- data frame의 describe()를 통해 기본적인 통계치 알아보기"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
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{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sepal length | \n",
" sepal width | \n",
" petal length | \n",
" petal width | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 150.000000 | \n",
" 150.000000 | \n",
" 150.000000 | \n",
" 150.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 5.843333 | \n",
" 3.054000 | \n",
" 3.758667 | \n",
" 1.198667 | \n",
"
\n",
" \n",
" std | \n",
" 0.828066 | \n",
" 0.433594 | \n",
" 1.764420 | \n",
" 0.763161 | \n",
"
\n",
" \n",
" min | \n",
" 4.300000 | \n",
" 2.000000 | \n",
" 1.000000 | \n",
" 0.100000 | \n",
"
\n",
" \n",
" 25% | \n",
" 5.100000 | \n",
" 2.800000 | \n",
" 1.600000 | \n",
" 0.300000 | \n",
"
\n",
" \n",
" 50% | \n",
" 5.800000 | \n",
" 3.000000 | \n",
" 4.350000 | \n",
" 1.300000 | \n",
"
\n",
" \n",
" 75% | \n",
" 6.400000 | \n",
" 3.300000 | \n",
" 5.100000 | \n",
" 1.800000 | \n",
"
\n",
" \n",
" max | \n",
" 7.900000 | \n",
" 4.400000 | \n",
" 6.900000 | \n",
" 2.500000 | \n",
"
\n",
" \n",
"
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"
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],
"text/plain": [
" sepal length sepal width petal length petal width\n",
"count 150.000000 150.000000 150.000000 150.000000\n",
"mean 5.843333 3.054000 3.758667 1.198667\n",
"std 0.828066 0.433594 1.764420 0.763161\n",
"min 4.300000 2.000000 1.000000 0.100000\n",
"25% 5.100000 2.800000 1.600000 0.300000\n",
"50% 5.800000 3.000000 4.350000 1.300000\n",
"75% 6.400000 3.300000 5.100000 1.800000\n",
"max 7.900000 4.400000 6.900000 2.500000"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"class\n",
"Iris-setosa 50\n",
"Iris-versicolor 50\n",
"Iris-virginica 50\n",
"Name: class, dtype: int64\n",
"\n",
" sepal length sepal width petal length petal width class\n",
"0 5.1 3.5 1.4 0.2 Iris-setosa\n",
"1 4.9 3.0 1.4 0.2 Iris-setosa\n",
"2 4.7 3.2 1.3 0.2 Iris-setosa\n",
"3 4.6 3.1 1.5 0.2 Iris-setosa\n",
"4 5.0 3.6 1.4 0.2 Iris-setosa\n",
"5 5.4 3.9 1.7 0.4 Iris-setosa\n",
"6 4.6 3.4 1.4 0.3 Iris-setosa\n",
"7 5.0 3.4 1.5 0.2 Iris-setosa\n",
"8 4.4 2.9 1.4 0.2 Iris-setosa\n",
"9 4.9 3.1 1.5 0.1 Iris-setosa\n",
"10 5.4 3.7 1.5 0.2 Iris-setosa\n",
"11 4.8 3.4 1.6 0.2 Iris-setosa\n",
"12 4.8 3.0 1.4 0.1 Iris-setosa\n",
"13 4.3 3.0 1.1 0.1 Iris-setosa\n",
"14 5.8 4.0 1.2 0.2 Iris-setosa\n",
"15 5.7 4.4 1.5 0.4 Iris-setosa\n",
"16 5.4 3.9 1.3 0.4 Iris-setosa\n",
"17 5.1 3.5 1.4 0.3 Iris-setosa\n",
"18 5.7 3.8 1.7 0.3 Iris-setosa\n",
"19 5.1 3.8 1.5 0.3 Iris-setosa\n",
"20 5.4 3.4 1.7 0.2 Iris-setosa\n",
"21 5.1 3.7 1.5 0.4 Iris-setosa\n",
"22 4.6 3.6 1.0 0.2 Iris-setosa\n",
"23 5.1 3.3 1.7 0.5 Iris-setosa\n",
"24 4.8 3.4 1.9 0.2 Iris-setosa\n",
"25 5.0 3.0 1.6 0.2 Iris-setosa\n",
"26 5.0 3.4 1.6 0.4 Iris-setosa\n",
"27 5.2 3.5 1.5 0.2 Iris-setosa\n",
"28 5.2 3.4 1.4 0.2 Iris-setosa\n",
"29 4.7 3.2 1.6 0.2 Iris-setosa\n",
"30 4.8 3.1 1.6 0.2 Iris-setosa\n",
"31 5.4 3.4 1.5 0.4 Iris-setosa\n",
"32 5.2 4.1 1.5 0.1 Iris-setosa\n",
"33 5.5 4.2 1.4 0.2 Iris-setosa\n",
"34 4.9 3.1 1.5 0.1 Iris-setosa\n",
"35 5.0 3.2 1.2 0.2 Iris-setosa\n",
"36 5.5 3.5 1.3 0.2 Iris-setosa\n",
"37 4.9 3.1 1.5 0.1 Iris-setosa\n",
"38 4.4 3.0 1.3 0.2 Iris-setosa\n",
"39 5.1 3.4 1.5 0.2 Iris-setosa\n",
"40 5.0 3.5 1.3 0.3 Iris-setosa\n",
"41 4.5 2.3 1.3 0.3 Iris-setosa\n",
"42 4.4 3.2 1.3 0.2 Iris-setosa\n",
"43 5.0 3.5 1.6 0.6 Iris-setosa\n",
"44 5.1 3.8 1.9 0.4 Iris-setosa\n",
"45 4.8 3.0 1.4 0.3 Iris-setosa\n",
"46 5.1 3.8 1.6 0.2 Iris-setosa\n",
"47 4.6 3.2 1.4 0.2 Iris-setosa\n",
"48 5.3 3.7 1.5 0.2 Iris-setosa\n",
"49 5.0 3.3 1.4 0.2 Iris-setosa\n"
]
}
],
"source": [
"iris_names = ('Iris-setosa', 'Iris-versicolor', 'Iris-virginica')\n",
"df_group = df.groupby('class')['class']\n",
"print df_group.count()\n",
"\n",
"Iris_Se_Sub_Df = df[df['class'] == iris_names[0]]\n",
"Iris_Ve_Sub_Df = df[df['class'] == iris_names[1]]\n",
"Iris_Vi_Sub_Df = df[df['class'] == iris_names[2]]\n",
"print\n",
"print Iris_Se_Sub_Df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. 탐색적 자료 분석 (Exploratory data analysis)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Sepal length (a) and Sepal width (b)\n",
"- Sepal length (a) and Petal length (c)\n",
"- Sepal length (a) and Petal width (d)\n",
"- Sepal width (b) and Petal length (c)\n",
"- Sepal width (b) and Petal width (d)\n",
"- Petal length (c) and Petal width (d)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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GDGDcuHFUVVUFzKuwsJCnn366+bihoYGDDz6Y1atXs379etLS0jhw4AAAU6dO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\n7aUuPdLj7pxLA1YBm8zsjAD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TubmSorwixubGx3QD6bpYb2nknHvbzL7e6rgX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\nnRYDjwDXFWq4m9mFQJ+7v9TMng98zN3PjsirYWNnVGN4dDSYW97dndx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E0pDF2GlmFwAf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\nfMCMGTMYO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m+uvJk129fQ1NZEc1szyz9czv2v39/rOuXl5TF//nySkpIYMGCA17rU1FT2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\ngf1/iDldrp6MFUgAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"unit_str = ' (cm)'\n",
"options = {\n",
" 0: {\n",
" 'data_x': feature_names[0],\n",
" 'data_y': feature_names[1],\n",
" 'label_x': feature_names[0] + unit_str,\n",
" 'label_y': feature_names[1] + unit_str,\n",
" 'ylim_min': 1.5,\n",
" 'ylim_max': 5.0\n",
" },\n",
" 1: {\n",
" 'data_x': feature_names[0],\n",
" 'data_y': feature_names[2],\n",
" 'label_x': feature_names[0] + unit_str,\n",
" 'label_y': feature_names[2] + unit_str,\n",
" 'ylim_min': 0.0,\n",
" 'ylim_max': 9.0\n",
" },\n",
" 2: {\n",
" 'data_x': feature_names[0],\n",
" 'data_y': feature_names[3],\n",
" 'label_x': feature_names[0] + unit_str,\n",
" 'label_y': feature_names[3] + unit_str,\n",
" 'ylim_min': -0.5,\n",
" 'ylim_max': 3.5\n",
" },\n",
" 3: {\n",
" 'data_x': feature_names[1],\n",
" 'data_y': feature_names[2],\n",
" 'label_x': feature_names[1] + unit_str,\n",
" 'label_y': feature_names[2] + unit_str,\n",
" 'ylim_min': 0.0,\n",
" 'ylim_max': 9.0\n",
" },\n",
" 4: {\n",
" 'data_x': feature_names[1],\n",
" 'data_y': feature_names[3],\n",
" 'label_x': feature_names[1] + unit_str,\n",
" 'label_y': feature_names[3] + unit_str,\n",
" 'ylim_min': 0.0,\n",
" 'ylim_max': 3.5\n",
" },\n",
" 5: {\n",
" 'data_x': feature_names[2],\n",
" 'data_y': feature_names[3],\n",
" 'label_x': feature_names[2] + unit_str,\n",
" 'label_y': feature_names[3] + unit_str,\n",
" 'ylim_min': 0.0,\n",
" 'ylim_max': 3.5\n",
" }\n",
"}\n",
"ax = []\n",
"fig = plt.figure(figsize=(17, 12))\n",
"for i in range(0,6):\n",
" ax.append(fig.add_subplot(230 + (i+1)))\n",
"\n",
"for i in range(0,6):\n",
" se = ax[i].scatter(Iris_Se_Sub_Df[options[i]['data_x']], Iris_Se_Sub_Df[options[i]['data_y']], color='red')\n",
" ve = ax[i].scatter(Iris_Ve_Sub_Df[options[i]['data_x']], Iris_Ve_Sub_Df[options[i]['data_y']], color='blue')\n",
" vi = ax[i].scatter(Iris_Vi_Sub_Df[options[i]['data_x']], Iris_Vi_Sub_Df[options[i]['data_y']], color='green')\n",
" ax[i].set_xlabel(options[i]['label_x'])\n",
" ax[i].set_ylabel(options[i]['label_y'])\n",
" ax[i].set_ylim([options[i]['ylim_min'], options[i]['ylim_max']])\n",
" ax[i].legend((se, ve, vi), iris_names)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sepal length | \n",
" sepal width | \n",
" petal length | \n",
" petal width | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 5.1 | \n",
" 3.5 | \n",
" 1.4 | \n",
" 0.2 | \n",
"
\n",
" \n",
" 1 | \n",
" 4.9 | \n",
" 3.0 | \n",
" 1.4 | \n",
" 0.2 | \n",
"
\n",
" \n",
" 2 | \n",
" 4.7 | \n",
" 3.2 | \n",
" 1.3 | \n",
" 0.2 | \n",
"
\n",
" \n",
" 3 | \n",
" 4.6 | \n",
" 3.1 | \n",
" 1.5 | \n",
" 0.2 | \n",
"
\n",
" \n",
" 4 | \n",
" 5.0 | \n",
" 3.6 | \n",
" 1.4 | \n",
" 0.2 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" sepal length sepal width petal length petal width\n",
"0 5.1 3.5 1.4 0.2\n",
"1 4.9 3.0 1.4 0.2\n",
"2 4.7 3.2 1.3 0.2\n",
"3 4.6 3.1 1.5 0.2\n",
"4 5.0 3.6 1.4 0.2"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = df.ix[:,0:4]\n",
"df2[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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HRwDfEpGTXZPCSbgkMrru2AEnnAC7dkHXP6Q5zY4dMHq0dQZQ//5eS+NMOjM8JhNyp/ts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\np67uR1RXj+4OHa6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PJT8CJ5x8SqxMrRexbt1HjB3bn8bGJSilepl0AK3DNlPdvsn2i1zq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\nCPwdGI21KMmKo8EyxekLEnewzSVHMLc4cABefBE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V40xinHnmmQwdug+f78sMHbqPM888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\nI9x7YhY9j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ZMvzj8fDjwQrrsufz5A8k5JAfQxi4NMxaixorEglxhPl0IWSuYiKqvFkmui\n7XrlyrdoaVnDe+8NpaFhHA88cCt33/3rtNtstq/1tjZ47z2z8J5fjjsOnn02/J2SV14xTyfy2SEB\nuP56OOIIuPhiqK7Ory+JCN9z/iIlUzFqNpcYT5dCFkrmIiqrxZJrom1zzJiFtLTsparqDtas2Upr\na2tGbTbbbT46OySVB8jHHJPdcO1BsXSp0ZPkmwMOMILX//t/8+1JYmynJEdkKkbN5hLj6VLIQslc\nRGW1WHJNtG1u334nNTWD2br1Murrq6iurs6ozWa7zaczZXX2bFizBjo7k++bT557znSgwsB3vgOr\nVpnzFlaspiSHWE1JcqympDSxmpLgKERNyRFHwD339F/vJhnTpsEdd4R3CEfVxFJ56SWj5wgD99xj\ngs/99a/5Kd8uyBciysrKGDduHGVlZX0isMZGRIwXFTbdqKiZRu1M5F8uoo7mG6+orF54RdXt7u5m\n3bp1dHd396Z51UeiiLwWSyJ6enrYuHEjXV1dbNmyBVX1FaE12uZ6enriXt/u70Cf/EFFA1aFr37V\ndDBSJexDOM3NZiHBsHRIwITn37zZ6HHCiK8nJU68kSpcwlhVfTuLfkXLLaonJVHcotejjqpkzpyj\nWL36bQ8B676osDNmTGTZsrWsXdtOfX1VjHAtfvTYTAVquRbY5uofcybRVoF+aaraT8gciUQ4+OA5\ntLUNYsKETjZuXEZ5ebmvvGGc1p1N7JOS9IgKsTds2MugQVsYNaqGGTOSC1vd96D999/FoYcexezZ\nn+h3fce73oMSvmZa7488YhadC2vMkjvvhBdegJ//PN+e9OU3v4GbbzbDOLn+P5nxkxIRuRzYCjwD\nPOm8ngjMwxLELXptbNzC4sUbPAWs7qiwS5a00Nj4916hrFu4lih6bKYCtTAKbIMgk2irXmleQubm\n5mba2gYxZMgTtLUNorm52Xdei8UPUSH2fvs9REdHBfvvf5MvYWu0zVVW3ktLy17Gjj3f8/qOd72H\nRew9axY0Npppt2EkTHoSN1/5CnR1mU5d2PDzd+ybwKGqepiqTnFeR2TbsWLGLXptaBjHvHmTPQWs\n7qiwxx5bQ0PDAb1CWbdwLVH02EwFamEU2AZBJtFWvdK8hMy1tbVMmNDJnj0nMmFCJ7W1tb7zWix+\niAqxP/jgLCoqOnj//e/4ErZG21x7+0XU1Axm27b7PK/veNd7WMTelZXwsY/B66/npfiEqIa3U1JW\nZqYI/+hH4Qs/n3T4RkSWAPNVtTvhjlmgWIdvoK/oVUTiClhhX1RYVe0jlPUbPTZogVs2RZ5hFLp6\n7eeV5iVk7u7uprm5mdraWgY4oSf95i0l7PBN+kSF2BMnTuS9997zLWyNtrmxY8eye/fuuNd3PDtB\n3AeCqPczz4TjjzfBwcLEa6+ZWCqbNuXbE29UzQymb3wDTj89d+WmHdFVRL7lfDwMOBQzbLM3ul1V\nbwnQz3g+ZKVTUmozJwrpeMPYKQl7GcWA7ZQkJ+xLFqRDEPV+222mA3DXXQE5FRB3321EuA88kG9P\n4vOnP8H3vw8vv5xajJhMyERTMtJ5vY3RkwxypY0I0slcEhVoLVx4O4sWPVT0N8JSO16/5OK82HNv\nCQq/bakU21x9fTjjbqxaZTQvYWbBArN20BMhUonG7ZSo6rWqei3QHP3sStuQOxeDJSwCrVxRasfr\nl1ycF3vuLUGRiTC72Jk2zTwp2bUr3570ZfVqmDEj314kRgR++EOzHk5Y+q9+Hthc5TOtIAiLQCtX\nlNrx+iUX58Wee0tQZCLMLnaGDDFr5jQ15duTfbz/vlnP5/DD8+1Jck49FT78MDxxSxJpShYAXwBO\nA37j2rQfUKuqKcbeS8M5qykJhEI6XqspKU2spiQ5VlMSn8svh0mTTBj1MPDUU3DTTfmLmpoqDzxg\nYqksWZL9sjLRlLQBTcAe5z36egz4fJBO5pqwRSH1G80z3QiKmR5vUJEbw4bf85Kv4/cqt1jrwpI6\n8dpCtF0DabWVQmxjRx0Fa9fm24t9rF4NDQ359sI/Z5wBb75pwuHnGz9TggeqaleO/Iktu2inBEdx\nR1ZMFM0zqAiKqZLrcsP2jzmT4w86L/SPJBuWjnWmhK3ew0aqkZXTbXuFer03N8NJJ5kf1jAwf75Z\nkffEE/PtiX/+67/MebzvvuyWk/aTEhFZLyLrgCYRWRf7yoq3JYjfaJ75ErCVonDOTSbHH3TeUq+L\nUibVyMrptpVCbWOHHgpbtkBHR749McHI1q4trCclABdfDI89Zs5jPkk0fHMi8EXgaed1tvN6CviT\nH+MiMl5EmkRkl4iUxWy7WkReFpHFInJFeu4XPn6jeeZLwFaKwjk3mRx/0HlLvS5KmVQjK6fbVgq1\njZWXw9Sp4Rh+aG6GqioYMybfnqTG/vubQHR33plfP/wM37ykqtNi0l5U1elJjYsMAoYCjwCfU9WI\na9vVwHJVXZwgf9EP34D/aJ75ErDlstwwPsbP5PiDzltIIsZUCGO9h41UIyun21YK9Xq//HKoroZv\nfzsQc2nzs5/BypXZHwbJBi0tJiz+pk1mVlM2yHhBPmNDZrm+zPSZD1XtVNUdQDwHbhCRv4jIVD/2\nwkIiIZhfkVg6+8XmSbTNT1l+fQibMNiNX5GwF5kcv1e5XvYikQhbt27ts5/fvF7lhrkuLMEQr112\nd3fzyiuv0NXV5bk92q56enoyEqoWahurq4MXX8y3F2aBwEIbuolSU2PO469/nT8f/DwpqQN+DozC\ndC7+AVyoqr6rX0QW0/9JSYWqdojIJ4Gfq+ocj3yhe1KSSAjmVyTm3q+hYSIrVqxlzZr2fkLXRPu5\nlyafMWMSIrBq1SbfPuVLOJuMVP45+RUJe5HJ8XuVKyL97EUiERoaTqGlZS81NYNpbPwjIuIrbxjq\nIpfYJyWGeO2yq6uLqqo6OjoqGDJkKyeffAYi/2DAgEpmzjyIiy8+g3PPvZLGxi2MHr2bQw89ikhk\nW+/2sLapIOt93To47TTzbz+fTJ4MDz8MRx6ZXz/S5bHH4D//00SkzQYZPylR1SZVnQpMBY5Q1SNT\n6ZAksNvhvL8JxG2V11xzTe9r6dKlmRabMYmEYOlEXVy8+FVWr/YWuibaz700+bJlr7Ns2Zsp+RQW\nQdvSpUv71HEq+BUJe5HJ8XuV62Uvuqx8RcUjtLTspbW11XdeS2kSry00NTXR0VFBefmT7N5dSXn5\nMU47uoJVq1ppbW1lzZqtVFXdQUvLXkaNOr/P9lJoU5Mnw+bNJhBYvnj/ffj73wsjaFo8vvAFM3yz\nfn1+yh8Qb4OInKOqD7oW5oumAykvyCfEDOGIyEhV/VBExiTyI9UfqmwTFYKtWtVfCJZoWzwb8+Yd\nxqBBO1mzpr/QNdF+0aXJV626kTlzDnGelPj3ya+v2Wbu3LnMnTu39/u1117rO29UJOx17pKRyfF7\nlSsi/exFl5VvaTmVmprBVFdXIyK+8lpKk3jtsq6ujoqKDjo6TmDo0HZ6ep5zBPK3MXPmQVRXV1Nf\nX0Vj42XU1Axmx477+mwvhTY1cKDpDPztb2b123ywZg18+tNmPZlCZcAAuOgis6DgT36S+/ITRXS9\nRFUXOYLUfjhr4CQ2LjIAM1tnOvAi8EPgbFX9poj8FDgc01n5vqou98gfuuEbSCwESyfqoqrGFbom\n2s+9DUjZpzCKJlN9nOtXJOxFJsfvVa6Xveiy8tXV1ZSXl6eUt5Swwzf7iNcWurq6aGpqYvr06XR2\ndjJs2DB27drVu1+0XY0dO5bdu3f32x5Ggq73Sy81uohvfjMwkynxL/9ipgRfd11+yg+Kt982awpt\n3gzDhgVrO9nwjR9NyRBV3ROsW/7Id6ckXz8UXj9kqZBIpQ+pd15ySb5/nPzOePGb5rcDEoZzn0/y\nXe/5wG+dx7ahaL6hQ4eybdu23k5Ium0nn20v6Hq/+25YsSJ/M18KMWhaPE44AU4/Hc47L1i7QXRK\n3gS2Asud1wpnRk3WyWenJF9C0J6enn7iyFQ6JrF+uyM/piOIzTX5/HHyOgfQP4qq3zRV9SVq9cpb\nah2TUuuU+L3eYkXVUYH7ypVv0dKyhu3bhzBmzB5qauqZNSt1QWu+r/ug672pCS64ID96iJ4eE+vj\nzTdh7Njclx80jz4KN95oOnlBEoTQ9ZPAmcB64ATgbyLycnAuhpN8iQ+9xJGpkCjyYzqC2FLCbxRV\nv2l+Ra323Jcefus8tg1FBe5jxiykpWUvY8feQEvLXsaMWZhW2ym2tnf44bBxI+zalfuyN2yAysri\n6JCAEby+8UbuQ/cn7ZSIyIHALOBoYBrwKn1XDS5K8hXZMCqO7OjYJ45MhUSRH+fMOYQ5cz7peUyF\nGskxSPxGUfWb5hWt10ZqtYD/6y22DUUF7tu330lNzWC2bfseNTWD2b79zrTaTrG1vcGDjaZkXR4W\nQlm9GmbMyH252WLgQLNQ34MP5rZcP8M3EeB54HpVfTQnXu0r22pKrKYkp1hNSX7Id73nA6spyU69\n//M/G5HmZZcFajYpF14In/mMEdsWCy+8YDomb7wBQTWNICK6TgPuB84SkdUicr+IXBSMe+HGb2RD\nd4TOnp4eNm7cSE9PT5/PqVBWVkZVVVXvDchPRFe/x5HomAo1kmMsfqO8+o2iGq+M2Eit0Yib3d3d\nCe3ZSK2lRbzrNFrnkUiEjRs30tXVxZYtW+ju7u7zrqqMGDECoI+dsrIyxo0bR3l5eUZtp9jaXl2d\n0ZbkmtWrCzeSazzq6swTk9Wrc1dm0iclACIyApiNGcI5B0BVJ2XXtfw/KfGDW4j2mc+MZePGVlpa\nOjnkkAGIlPHaa50pCVbdwrO+wtRJqMLq1ZsSLl2eb+FapmT6z8lvlNd0ou8mitQaiUR6I25WVHSw\ndWsTAwYMKOi6yCXF+qQkWTuLCtubm/cwePBWRo2qIRJ5B5EDUP07ZWUHMmbMvgitZWVjCyJSq1+y\nUe9r18LXvgYv51D5+I9/wMSJ5r2QY5R4cf31ZmrwXXcFYy/jJyUi8gKwGjgV2ADMyUWHpFBwC9FW\nrnyb5uY9VFQ8woYNH7Fhw56UBatu4ZlbmLps2ZssW/Z60qXLi024lip+o7ymE303UaTWfRE3/0RH\nRwVNTU0lXxeW5O0s2pb22+/ndHRUMHLkDbS1DaKi4jra2gax//639onQOnZs6UVqTZUpU+C112BP\nDgNZFEPQtHicfTb87newd29uyvMzfLNAVaeo6iWq+qCqbsq6VwWEW4g2a9ZEamuH0NFxKpMnj2Dy\n5CEpC1bdwjO3MHXOnE8yZ84hSZcuLzbhWqp4iUu98HuevPbzEiNHI2729HyBiooO6urqSr4uLMnb\nWbQtffDBhVRUdPDhh99jwoROOjp+yIQJnbz//pV9IrRu2+aO1GrblBdDh8Ihh+RW7FrIi/AlY9Ik\nM6vpqadyU56v4Zt8UQjDN9BXiKaqvSJVIC3BajxhqvtzrIA17JFa/RLE41y/UV7Tib6bKFJrNOJm\nXV0dAwcOTKmMUqdYh28geRuItqWJEyfy3nvvMWbMGLZv3977HhuhtRAitfolW/V+4YVw1FHw9a8H\nbtqTz3/eCGtPOik35eWau+6C5cvhoYcyt5Vx8LR8UiidEr+dg6B/LIuRfP84BT1bppTrMhXyXe/5\nINo2YjsZiWbPFcufjyjZqvc77jCakrvvDtx0PyIREzTt9ddNnJJiZOtWOPRQePdd8yQqE5J1Sopw\nBCy3xBOyxaZHlxYPSoBpCR4vkWwmEVhtXVriEW0bK1e+RU9Pe69wNVbAXqyC9mwzfTrce29uynrt\nNRg9ung7JABVVWaa9Z//DKeckt2y4v5dF5EvJXpl163CIZ6QLTY9urR4UAJMS/AEHYHV1qUlHtG2\nMWHCwj7C1VgBuxW0p8fUqdDSkhtxZjFOBfbiK18xgtdsk0jo+sUEryJYbigY4gnZYtOrq6sDFWBa\ngifoCKy2Li3xiLaNtrY7+whXYwXsVtCeHsOGwcEHwyuvZL+sxsbiiuQajy99CZ58MvuzmqymJACs\npiQ48q0tsJqS/JDves8HVlOS3Xq/4AKYOdPELMkmU6bAL35hpgQXO3PnwpVXwsknp28jEKGriJwA\nHAYMiaap6r+m75Y/8tEpSedC9xvG3ZKcbN2k/IaAt+SHQuqUZNoZiNcZKUWyWe8/+Qm8+iosWpQV\n8wB88AFMmGCCpjkT7oqaO+4ww1WZrIeTsdBVRH4KDAOOBe4B/glYm75L4SUd8VjfPH2jrlrxWTjw\nqldV9RX51WJxk6nAdF/+t+jubqe8vJJZswo/MmsYqauDBx7Ibhlr1xoBaCl0SAC+/GX40Y+MVmfw\n4OyU4ecuPFNVzwP+oarXAjOAQ7LjTn5JRzzWNwJr36irVnwWDrzq1W/kV4vFTaYC02j+8eNNh3jC\nhIX2XpEljjzSPCnp7MxeGaWiJ4kybhxMngzPPZe9Mvx0SnY777tEZALQBYz3Y1xExotIk4jsEpEy\nj21/FZEVIjIvNbezQzrisb4RWPtGXbXis3DgVa9+I79aLG4yFZhG87/77m3U11fR1nanvVdkieHD\nobradEyyRanMvHFz0knw2GPZs59UUyIiPwb+G/gscAegwD2q+uOkxkUGAUOBR4DPqWrEte124GFg\nHfCkqh7rkd9qSkoMqykpTaympDTvFdmu93PPNeLMi7Kwrr0qjBkD69cbXUmp0NwMxx8PmzZBOs02\n4wX5gBtUtUNV/xeYBNQA/+6ncFXtVNUdgJcDU1S1UVV3AR84KxHnBPdS4rHL3Ge6jLc7f+yS5fGW\nMI+XXsr4PSde+/lNiy797u6Q+M1rKV3i3SNi7yWx36PtCGDEiBGUlZUxYoS57X300UdEIhHbzgKm\nrg6amrJj+7XXYOTI0uqQgBm+GTw4e6sw+4nouhqYDqCqe4G9IvJiNM0nXleZu0P0AVABfJSCzbRw\nC9UaGiayYsVa1qxpT1vs6Deiq43M6B+/58RrP+gfbdWvqNWvvVKvH0t/YqMB33//LZx33rd6vz/w\nwK3cffev417/sZFdbTsLhro6ePjh7NhevhyOPjo7tsOMyL4hnGnTgrefKKLrOBGpA4aKyDQRme68\n5mJm42RKxPV5P6DDa6drrrmm97V06dKMC3UL1RYvfpXVqzMTO/qN6GojM3qzdOnSPnUM/s+J32ir\nfkWtmURvtZQ2sW2subm5z/fW1taE139sZFfbzoLhyCNNALWuruBtl2qnJbTHQwAAHlVJREFUBLKr\nK0n0pOTzwAXAgcAtrvQPgB+kWI7QfwhnnYg0AOuBkarq+ZQk+kMVFFGh2apVNzJv3mEMGrSTNWvS\nFzu67XlFXIymRyMzJtuv1ARvc+fOZe7cub3fr732Wt/nJN5+sWnDhg2jvr4qaT37tWexxBIVTkfb\nWG1tbZ/v1dXVCa//lSvdkV0Psu0sIEaOhI9/3Oggpk4N1vby5XDVVcHaLBRmzTKaks2bzfkNEj9C\n1y87epLUjYsMAJ7CDPW8CPwQOFtVvykiBwD3YwKyXa2qz3rkz4rQ1S1UU9WMxY5+Iy4Wc2TGoIie\np0wipmYias0keqslfQpJ6BqP2DYW+z3Z9V+Kwtdc1PsFF5hpu5dcEpzNd94xQxft7emJPYuB884z\nM48WLkwtX8YRXUVkHHAdMEFVF4hILTBDVbO+BmOhhJm3BEcx/DhZUsfWe2mSi3pftAhWrYL77gvO\n5sMPw29/C488EpzNQuP3v4d77oGnn04tXxCzb34B/BmIaoxfB65IzQ2LxWKxWHLPzJkmnkiQlLKe\nJMr8+bByJezaFaxdP52SMar6Wxxhqqp2Az3BumGxWCwWS/DU1sLWrbB9e3A2bacERo0yQ1gBzD/p\ng59OyU4RGY0zrdcRp+4I1g2LxWKxWIKnvBzq64N7WvL++0bkmY3psIXGggXw1FPB2vTTKfkW8Bhw\nsIisxIhTLw/WDYvFYrFYssOMGcF1SpYtM/YG+InyVeTkpVOiqi8CxwAzgUuAw1R1XbBuWCwWi8WS\nHYLslDz7LHzuc8HYKnSmTjWakjffDM5m0k6JiAwBvgH8G3AtcJmTZrFYLBZL6GlogBdegO7uzG3Z\nTsk+RMw6OEE+LfEzfHM/cBhmUb7/cT4/EJwLFovFYrFkj4oKOOigzNfB2bzZCGaDDsRWyAQ9hONn\nVOxwVa11fV8iIs3BuWCxWCwWS3aZNw+WLDGi13T561/hs58Fu6j4Pj73ObjwQti9G4YOzdyen1P7\nojPjBgARqQdeyLxoi8VisVhyw7x5plORCXbopj8f+5h5cvTcc8HY8xPRdQNwKPC2kzQReA3oBlRV\njwjGFc+ybUTXEsNG9ixNbL2XJrms944Os07L9u0weHDq+SMRmDDBCGYPOih4/wqZ666DbdvgttuS\n75ssoquf4ZvjU/DNYrFYLJbQUVFhAqk1NsIxx6Se//nnYfRo2yHxYsECOPNMf52SZCTtlKjqpsyL\nsVgsFoslv8ybB4sXp9cpefxx+OIXg/epGDjySPMkqrUVqqszs2XlOhaLxWIpCebPT3+myGOPwUkn\nBetPsVBWZgTAzzwTgK3MTVgsFovFEn6OPtoE+mprSy1fayts2ZLZzJ1iZ/582ykJBFXlo48+siI7\nS8FSym24lI/dkjoDB5pgX088kVq+P/wBTjzRrKNj8Wb+fDO7qSfD5XpLulOiqixa9BALF97OokUP\n2RubpeAo5TZcysduSZ+TToJHH00tz69+BWedlR1/ioUDD4TKSnjppczslHSnZOfOnaxa1cqkSd9l\n1apWdu7cmW+XLJaUKOU2XMrHbkmfBQtgxQojzPRDSwu8+y4ce2x2/SoGghjCyXqnRERuEZFlInJr\nTPrVIvKyiCwWkSuy7YcXw4cPZ+bMajZtupGZM6sZPnx4PtywWNKmlNtwKR+7JX1GjTIB0H73O3/7\n/+pXcMYZdujGD0F0SpIGT8vIuMg04OuqeomI3Ancq6pNzrargeWqujhB/qwHT1NVdu7cyfDhwxGJ\nG8/FkiNsEK3UKYY2nG69F8OxlzL5ut4fewxuuME8MUlEV5eZ4vr00zBlSk5cK2g+/BDGj4f2dhg2\nzHufZMHTsv2kpAGI9pueBWbEbL9BRP4iInlb3khEGDFiRO8NzQrnLGHBb1uMbcOlRPTYAXvdWnyz\nYAG88YYZmknEI4/Apz5lOyR+GTkSpk2D5cvTt5HtTkkF8IHzeYfzPcrtqvppYCFmBeK8Y4VzlrBg\n26J/7LmypMrAgfD1r8PNN8ffR9Vs/8Y3cudXMZDpEI6fMPOZsAPYz/m8H9ArLVLVDuf9TRGJexe5\n5pprej/PnTuXuXPnZsNPIFY4dyPnnLOz91+YJTssXbqUpUuX5tuN0GHbon/subKkw+WXwyGHwDXX\nwAEH9N/++ONm5dtTTsm5awXN/Pmmw5cuudCUfE1VLxWRO4BfqOoLzraRqvqhiIwBHlXVWR75c7og\nX/Qf16pVrcycWc0ll5xVko/E84nVlBhKrS1mUu+ldq6KiXxf7z/4gQmM9tBDfdN37TKh02+9FU44\nIS+uFSzd3TB2rBkaq6rqvz2ZpiSrnRLHgduA6cCLqnqFiNyuqt8UkZ8ChwMCfF9V+41C5WOVYCuc\nyy/5vkmFiVJqi5nWeymdq2Ii39f7rl1GL3L99XD66SZNFS6+GHbuhIcfzptrBc2pp8I//ROcfXb/\nbfkWuqKqV6jqHFW9wvn+Tef966o6W1VneXVIckWsmNCPuLCnp4eNGzfSkyR0nRXNWjLBS8AaiUTY\nsmULkUgkZXte7dErLZMyMvEllf2j12B3d7enHXvtWfwwbJiJ1nr55XDLLWYl4K9+1QQAW7Qo394V\nLpnoSrL+pCQTsv2kJPax78UXn8G5517JmjVbqa+v4sEHb6OsrG+/raenh4aGU2hp2UtNzWAaG/9I\nuccEdvtIOT3y/c8pzEQiEc4554qE7TMeXu0R6JemqmmX4RcvX8rKyuLWe+z+//zPpzNjxqls2LCX\n0aM/4uijjycS2U55eSWzZh3E1752Jj/72cP22isAwnK9t7TAj34Er78Oxx1ndCZWlpQ+b7wBc+fC\nO+9A7KWX9yclYSY2ImRraytr1mxl/PhfsmbNVtrb2/vlaW1tpaVlLxUVj9DSspfW1lZftm20SUum\ntLe3J22f8fBqj15pmZSRiS+p7N/c3ExLy15GjfoNbW2DGDbsbNas2cqECQtZtaqV9vZ2e+1ZUqKm\nBn7/e1i3Dm66yXZIMuWTnzQznDZsSD1vSXdKYiNCVldXU19fxbvvXkB9fRWVlZX98lRXV1NTM5iO\njlOpqRlMdXW1L9s22qQlUyorK5O2z3h4tUevtEzKyMSXVPavra2lpmYwO3aczoQJneza9Svq66to\na7uTmTOrqaystNeexZJHRMwTp3SGcEp6+Ab6C+QikQjt7e1UVlbGfWzd09NDa2sr1dXVnkM38Wxb\nkhOWx7lhxU/7jIdXe/RKy6SMdH1JVu+x+0evwUmTJrFnzx6GDRvGrl27erfba68wsNd78fK738F9\n9/Vfkbnoh2/cgrZE4rYghW/l5eUcfPDBCTsklvwRJpFj0L5kIn71yuvlX1lZGePGjctahySeL4mI\n+tnd3c3GjRtRVaqqqigvL/e0U8pRbi2WMDBvHixbBp2dqeXLdvC0rNJXADcJVVi9elM/cVs80Wk6\nQtf0fLNiu1wRpvMetC9e9jIRpmYinM0lUT9Xr97Cjh0tdHZWMXr0To4+egGzZ3/CClstlhAyerQJ\nTtfYCHPm+M8XvjtQCrgFcMuWvcmyZa97itviCevSEbqm45sV2+WOMJ33oH0JWpiaC1FrEET93H//\nW+noqGDEiF/Q1jaIUaPOt8JWiyXEzJ8Pzz6bWp6C7pS4BXBz5nySOXMO8RS3xRPWpSN0Tcc3K7bL\nHWE670H7ErQwNRei1iCI+vn++1dSUdHBRx99lQkTOtmx4z4rbLVYQkw68UoKXujqFrQBccVt8YRv\n6Qhd/WLFdqkThPAtTOc9aF+CFqbmQtTqh2T1HvVz9OjRvP32270CVytsLWys0LW42bPHhJzfvBkq\nnOV4i07oGhtN1S1oi0QibN26lUgk0k/8597mFvd1d3fzyiuv0N3dDey76atqHxvuPF62vYSGsWK7\nMAkwi5l8iRzjRUeNtjt3Wmx76erqorGxka6uroT2urq6WLt2bb/9om02URleaX7Pld+2m2kbj80f\n9bm7u5v29nb27t3LSy+9xK5du1i7di27du3qd96C8MNisWTOkCEwcyYsWeI/T0EJXRNFU+27bRAH\nH1zN889vo76+ivvuu5mZM7/Uu+2CC05jzZrNfOYzB3D11bfQ0VFBRUUHbW1rOfror7hsTOL557dz\n1FGVzJlzFKtXv82MGRNZtmwta9e2U19fxf3338J5530rqVgwTAJMS/B41W8kEunXXkWkn7i0p6eH\nqqq63na4dWsTAwYM6Gevq6uLioop7N5dydCh7XR0rKe8vNxXGUC/NBHx1Sb9tt1M23g84fmqVW20\nt69nz54qVN8CJgFXAhOBt4FJDB26jVNOOZM5cz5lha8WS4iI6kpOPdXf/gX1pCRRNFX3tubmPaxc\n+XavgK+pqal324YNnTzzzMtMmvRdnnxyFR0dFZSX/4mOjgqeeOKJGBubGT/+lzQ2/p0lS1qYNOm7\nLFnSQmPj33ttNzc3+xILhkmAaQker/r1aq9e4tKmpqY+7bCpqcnT3ooVK9i9uxJ4it27K1mxYoXv\nMrzS/LbJoPfzew6jwvOKin9h9+5KBgy4E6gGfuW8/w9QjcgD7N5dyeDBx1vhq8USMk4/Hc480//+\nBdUpSRRN1b2ttnYIs2ZN7BXw1dXV9W6bPHkQ8+cfyaZNN3LCCTOpqOigp+cLVFR0cOKJJ8bY+Djv\nvnsBDQ0HcOyxNWzadCPHHltDQ8MBvbZra2t9iQXDJMC0BI9X/Xq1Vy9xaV1dXZ92WFdX52lv9uzZ\nDB3aDixg6NB2Zs+e7bsMrzS/bTLo/fyew6jwvKPjXxk6tJ3u7oVAK3C28/5/gFZUz2Xo0Hb27n3a\nCl8tlpDx8Y/D7Nn+9y84oWuiaKrubSLSR8Dn3lZWVtYriuvu7qapqYm6ujoGDhwY14aI9OZR1T62\n/YoFrRgvOYUsfPOqX6/26tVeurq6+rTDePY6OztZsWIFs2fPZtCgQSmV4ZXmt00GvV8s0XqPJzzf\nf//9aWlp4ROf+ARPP/00xx13HC+88AL19fWsX7+e6dOn09nZaYWvBUYhX++W9EgmdC2ITkkQNxi3\njUgkYsPEh5Riu0l5dQT8LlMQdoK8PmLrPdlsucGDB7NhwwZqa2sZMKCgpHEWF8V2vVuSU/Czb6Li\nt4ULb2fRooc8G/DSpUt927jrrgdoaDiZI4+8lIaGU+jp6fHM76fcVHxIRqb5w2IjCB+yYSsfZUQj\nkU6ffibnnHMFkUikV5Dtbn9BkOtzler1kQpLlizxtB0t85JLbqaqqo7p07/BwQfP6Z0558fvoAja\nZqnZy5bNYrJfqmWEvlPiRzyX7IDdNp55Zh0bNvQVBnrlT1W0F4Yf8zDYsJ2SfUTFpQMGzOwVlyYS\na2dCrs9VNoXbzzzzTMIIzMOHn0pHRwVDhjxOW9sgmpubffsdFGH/0Q+7vWzZLCb7pVpG6DslQQhE\n3Tbmzz+CyZO9xbJBl2spbaLi0g8//GOvuDSRWLuQyOb1MXDgwIQRmHfufISKig727PkiEyZ0Ultb\nG1jZFoslv4R+MFZEuOSSszjnnPTHrmNtXHLJ2UnH9IMo11LalJWV8eCDt/G9732PG264oVdT0tj4\nx4LXlGTz+ohn250+ePAPrKbEYilCQi90zbcPFovFYrFYgqNgZ99YLBaLxWIpHUKvKbFYLBaLxVIa\n2E6JxWKxWCyWUGA7JRaLxWKxWEKB7ZRYLBaLxWIJBSXbKRGRESJyoIiMKGUfwuKH24cw+JMpxXAM\nkL/jEJHPZJD3MBGpiUmrz9CfOhGpFJFyETlZRI7LxJ6H/csCsnO4iJyR4fkb77yLiJwiIlc5NtOe\ney0iJ4nIsHTzh4liuLbDfAwFNftGRK5Q1dtEZCrw34BiYq18X1WX+7QxD/gx8IHz2g8YCVyvqs/m\nwo8w+BCEH1nwYSgwzdn0MrArFX98lpexz0nsZ1y3PsrI6jE4ZWT9OJxyvP4YCfC0qs5Pw97NQBXQ\nBYwBLlTVbSKyWFXnpenjvY5Pe4FK4O+Yc1Kpql9Lw95yTJ3h2AU4DHhFVeekYe9pVT1eRK4APgs8\nCcwC3lHVq9Kwt1hV54nI7cBuYDFwJPBpVT0tVXuOzTZgE7AVeAR4TFX/kY6tOPaL4pqw9yfoXZmz\nEF7AYuf9L8Annc9jgJUp2FgBDItJG56ijYz8CIMPQfgRtA/Rz24fUj0vufA523Wb72PI1XE4Nndh\nfvSWOO/Rz++laW+Z6/MRwFLg09FzlqbN51yf17s+L0nT3pXAL4G5rrSnAmgPzwFl7jpM096z7vdM\nj9edFzgI+LZTL38GFgbUjorimrD3Jy244Zv9nZ7e/qr6JoCqbmffvw4/7MXcrNxMAfbk0I8w+BCE\nH0H7EP3s9iHV85KMIHxORBB1m4xsHwPk5jgANgCnquqxqjrPeR0LvJimvXIRGQSgquuAU4FrMU8i\n0sU9bPED1+e0wtiq6q3A14DJIvJrETkpA98AakXkfuBgYLArfUia9u4TkXuAzSLyoIhcLCL/DbyQ\noZ+o6luqerOqzgXOAxKvpuifYrkmSv7+VGjxmR8BjgYeF5EKVe0QkZHAKynYOAf4vohcj9HURIB1\nmAskV36EwYcg/AjahyHA75z0zSKyOEV/cuVzIoKo22Rk+xggN8cBcCJmiCCWBWnauxKoANoBVPUf\nzo/+V9K0B/A1ESlX1R5VfRzA6fjckq5BVe0E7hKRu4Fzgb9l4F9UL/NjnB95Ryvw4zR9e0BE/gp8\nHjMUNgC4R1Uz8fE/PcrZCvwsA5tuiuWaKPn7U0FpSiwWi8VisRQvhTZ844kjyMrUxk/y7UcYfAjC\nRtD1EYQ9H+VltYwg6tZHGbk4T1k/DoslKIrh3pGLMsJ0DAX/pERELlPVO9LMezhwOLBRVZ/PlQ0R\nGa+q74qIACcDk4G3gN+rqq8x1oBsnIQRs+3ys3/Q+R0bA4HjMcLGVSLyfYzY9RZV7XD2qVfVNemW\n4VHmYUCPqra40hpUtTEg+3XAZuA9nOEJVf1LELZzWYZHmWlfaxZLtnGuiRmY4bsOoFFVM9bB+Cj3\nKFVdWyhlxLn/heYeW1CdkiCm0iWYPrdZVX+QOHcwNoKYcheQjYym6QUxzU9EHgGex9xITge2AzuB\nGuAwzXA6p0d5gU8ZjbEf6PTRPJYR6LRViyWbiMitGJHvs8AOzFTXzwFdqnpFQGUEOn09H2Vk+/4X\nSBlBTQPKxYsAptIRwPS5TG0QwJS7gGwscd7TmqaXaf5Yf4Gdrs9rCWA6p0d5gU8ZjbEf6PTRPJYR\n6LTVYn0B5wPjfOz3C+BLftMD8Osq1+dJ7nZSjC/3de0nPc0yAp2+no8ysn3/C6KMgpp9o6q3Oqr3\ni0Tk68BDaZiJnT4XVf6nMn0uUxt9ptxhOjdHkNqUu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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pandas.tools.plotting import scatter_matrix\n",
"\n",
"_ = scatter_matrix(df2, figsize=(9,9), diagonal='kde')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- KDE: Kernel Density Estimattion (위 그래프의 대각선에 있는 Line 그래프 4개)\n",
" - 해당 변수가 가지는 값의 범위와 그 값을 가지는 정도 (히스토그램에 대한 Smoothing 그래프)\n",
" - 자세한 설명: http://darkpgmr.tistory.com/147"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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JpPdlLeqIY7p20XXAtYzbRsBHDuZWsVmxFi0/J5DuTyu1CLbwaPkkV7Gff6cm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\nZmbWIN4MMjMzMzMzMzNrkP8CiEIi5NpWsnwAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stats = {}\n",
"for i in range(0,4):\n",
" stats[i] = {}\n",
" stats[i]['mean'] = (Iris_Se_Sub_Df[feature_names[i]].mean(), \n",
" Iris_Ve_Sub_Df[feature_names[i]].mean(), \n",
" Iris_Vi_Sub_Df[feature_names[i]].mean())\n",
" stats[i]['std'] = (Iris_Se_Sub_Df[feature_names[i]].std(), \n",
" Iris_Ve_Sub_Df[feature_names[i]].std(), \n",
" Iris_Vi_Sub_Df[feature_names[i]].std())\n",
" \n",
"ind = Series([0.5, 1.5, 2.5])\n",
"width = 0.5\n",
"fig = plt.figure(figsize=(20, 5))\n",
"ay = []\n",
"for i in range(0,4):\n",
" ay.append(fig.add_subplot(140 + (i+1)))\n",
" \n",
"for i in range(0,4):\n",
" ay[i].bar(ind, stats[i]['mean'], 0.5, color='magenta', yerr=stats[i]['std'])\n",
" ay[i].set_xlim([0, 3.5])\n",
" ay[i].set_ylabel('Mean of ' + feature_names[i])\n",
" ay[i].set_xticks(ind + width/2)\n",
" ay[i].set_xticklabels(iris_names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- box plot\n",
" - 참고: https://goo.gl/ghxcx2"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/yhhan/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: \n",
"The default value for 'return_type' will change to 'axes' in a future release.\n",
" To use the future behavior now, set return_type='axes'.\n",
" To keep the previous behavior and silence this warning, set return_type='dict'.\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_ = df2.boxplot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. scikit 활용한 데이터 탐색"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"from sklearn.datasets import load_iris\n",
"from sklearn import tree\n",
"iris = load_iris()\n",
"print type(iris)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['target_names', 'data', 'target', 'DESCR', 'feature_names']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.keys()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['setosa', 'versicolor', 'virginica'], \n",
" dtype='|S10')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.target_names"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['sepal length (cm)',\n",
" 'sepal width (cm)',\n",
" 'petal length (cm)',\n",
" 'petal width (cm)']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.feature_names"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iris Plants Database\n",
"\n",
"Notes\n",
"-----\n",
"Data Set Characteristics:\n",
" :Number of Instances: 150 (50 in each of three classes)\n",
" :Number of Attributes: 4 numeric, predictive attributes and the class\n",
" :Attribute Information:\n",
" - sepal length in cm\n",
" - sepal width in cm\n",
" - petal length in cm\n",
" - petal width in cm\n",
" - class:\n",
" - Iris-Setosa\n",
" - Iris-Versicolour\n",
" - Iris-Virginica\n",
" :Summary Statistics:\n",
"\n",
" ============== ==== ==== ======= ===== ====================\n",
" Min Max Mean SD Class Correlation\n",
" ============== ==== ==== ======= ===== ====================\n",
" sepal length: 4.3 7.9 5.84 0.83 0.7826\n",
" sepal width: 2.0 4.4 3.05 0.43 -0.4194\n",
" petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n",
" petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n",
" ============== ==== ==== ======= ===== ====================\n",
"\n",
" :Missing Attribute Values: None\n",
" :Class Distribution: 33.3% for each of 3 classes.\n",
" :Creator: R.A. Fisher\n",
" :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
" :Date: July, 1988\n",
"\n",
"This is a copy of UCI ML iris datasets.\n",
"http://archive.ics.uci.edu/ml/datasets/Iris\n",
"\n",
"The famous Iris database, first used by Sir R.A Fisher\n",
"\n",
"This is perhaps the best known database to be found in the\n",
"pattern recognition literature. Fisher's paper is a classic in the field and\n",
"is referenced frequently to this day. (See Duda & Hart, for example.) The\n",
"data set contains 3 classes of 50 instances each, where each class refers to a\n",
"type of iris plant. One class is linearly separable from the other 2; the\n",
"latter are NOT linearly separable from each other.\n",
"\n",
"References\n",
"----------\n",
" - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
" Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
" Mathematical Statistics\" (John Wiley, NY, 1950).\n",
" - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
" (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n",
" - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
" Structure and Classification Rule for Recognition in Partially Exposed\n",
" Environments\". IEEE Transactions on Pattern Analysis and Machine\n",
" Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
" - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n",
" on Information Theory, May 1972, 431-433.\n",
" - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n",
" conceptual clustering system finds 3 classes in the data.\n",
" - Many, many more ...\n",
"\n"
]
}
],
"source": [
"print iris.DESCR"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 5.1, 3.5, 1.4, 0.2],\n",
" [ 4.9, 3. , 1.4, 0.2],\n",
" [ 4.7, 3.2, 1.3, 0.2],\n",
" [ 4.6, 3.1, 1.5, 0.2],\n",
" [ 5. , 3.6, 1.4, 0.2]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.data[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.target[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 7. , 3.2, 4.7, 1.4],\n",
" [ 6.4, 3.2, 4.5, 1.5],\n",
" [ 6.9, 3.1, 4.9, 1.5],\n",
" [ 5.5, 2.3, 4. , 1.3],\n",
" [ 6.5, 2.8, 4.6, 1.5]])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.data[50:55]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, 1, 1])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.target[50:55]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Spark을 활용한 데이터 탐색\n",
"- 참고: https://github.com/jadianes/spark-py-notebooks/blob/master/nb7-mllib-statistics/nb7-mllib-statistics.ipynb"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"151\n",
"150\n",
"[u'5.1,3.5,1.4,0.2,Iris-setosa', u'4.9,3.0,1.4,0.2,Iris-setosa', u'4.7,3.2,1.3,0.2,Iris-setosa', u'4.6,3.1,1.5,0.2,Iris-setosa', u'5.0,3.6,1.4,0.2,Iris-setosa']\n"
]
}
],
"source": [
"import findspark \n",
" \n",
"findspark.init() \n",
"from pyspark import SparkContext, SparkFiles, SQLContext\n",
"\n",
"if not 'sc' in locals(): \n",
" sc = SparkContext() \n",
" \n",
"import urllib\n",
"_ = urllib.urlretrieve (\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\", \"iris.data\")\n",
"data_file = \"./iris.data\"\n",
"raw_data = sc.textFile(data_file)\n",
"\n",
"# num of parallel cores\n",
"print sc.defaultParallelism\n",
"\n",
"print raw_data.count()\n",
"\n",
"raw_data = sc.textFile(data_file).filter(lambda x: x != '')\n",
"print raw_data.count()\n",
"\n",
"print raw_data.take(5)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[array([ 5.1, 3.5, 1.4, 0.2]), array([ 4.9, 3. , 1.4, 0.2]), array([ 4.7, 3.2, 1.3, 0.2]), array([ 4.6, 3.1, 1.5, 0.2]), array([ 5. , 3.6, 1.4, 0.2])]\n"
]
}
],
"source": [
"def parse_raw_data(line):\n",
" line_split = line.split(\",\")[0:4]\n",
" return np.array([float(x) for x in line_split])\n",
"\n",
"vector_data = raw_data.map(parse_raw_data)\n",
"print vector_data.take(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Summary statistics using Spark ML"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Statistics:\n",
" Mean - sepal length: 5.843\n",
" St. Dev - sepal length: 0.828\n",
" Max value - sepal length: 7.9\n",
" Min value - sepal length: 4.3\n",
" Number of non-zero values - sepal length: 150.0\n",
"\n",
" Mean - sepal width: 3.054\n",
" St. Dev - sepal width: 0.434\n",
" Max value - sepal width: 4.4\n",
" Min value - sepal width: 2.0\n",
" Number of non-zero values - sepal width: 150.0\n",
"\n",
" Mean - petal length: 3.759\n",
" St. Dev - petal length: 1.764\n",
" Max value - petal length: 6.9\n",
" Min value - petal length: 1.0\n",
" Number of non-zero values - petal length: 150.0\n",
"\n",
" Mean - petal width: 1.199\n",
" St. Dev - petal width: 0.763\n",
" Max value - petal width: 2.5\n",
" Min value - petal width: 0.1\n",
" Number of non-zero values - petal width: 150.0\n",
"\n"
]
}
],
"source": [
"from pyspark.mllib.stat import Statistics \n",
"from math import sqrt \n",
"\n",
"# Compute column summary statistics.\n",
"summary = Statistics.colStats(vector_data)\n",
"\n",
"print \"Statistics:\"\n",
"for i in range(4):\n",
" print \" Mean - {}: {}\".format(feature_names[i], round(summary.mean()[i],3))\n",
" print \" St. Dev - {}: {}\".format(feature_names[i], round(sqrt(summary.variance()[i]),3))\n",
" print \" Max value - {}: {}\".format(feature_names[i], round(summary.max()[i],3)) \n",
" print \" Min value - {}: {}\".format(feature_names[i], round(summary.min()[i],3))\n",
" print \" Number of non-zero values - {}: {}\".format(feature_names[i], summary.numNonzeros()[i]) \n",
" print"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Correlation"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pyspark.mllib.stat import Statistics \n",
"correlation_matrix = Statistics.corr(vector_data, method=\"spearman\")"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"print type(correlation_matrix)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1 2 3\n",
"0 1.000000 -0.159457 0.881386 0.834421\n",
"1 -0.159457 1.000000 -0.303421 -0.277511\n",
"2 0.881386 -0.303421 1.000000 0.936003\n",
"3 0.834421 -0.277511 0.936003 1.000000\n"
]
}
],
"source": [
"print pd.DataFrame(correlation_matrix)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}