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karino2/RandomForest-interactive1.ipynb

Created January 13, 2016 15:04
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Randomforestの実装、インタラクティブに試行錯誤その1
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RandomForest-interactive1.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = [1, 2, 3]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(a)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['a b c d']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"line = \"a b c d\"\n",
"line.split(r\" +\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'b', '', '', 'c', 'd']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"line.split(\" \")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dt = []\n",
"f = open(\"iris.txt\")\n",
"for line in f:\n",
" arr = line.split(\" \")\n",
" res = [el for el in arr if el != '']\n",
" dt.append(res)\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dt = []\n",
"f = open(\"iris.txt\")\n",
"for line in f:\n",
" arr = line.split(\" \")\n",
" res = [el for el in arr if el != '']\n",
" dt.append(res)\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[['5.1', '3.5', '1.4', '0.2', '0\\n'],\n",
" ['4.9', '3', '1.4', '0.2', '0\\n'],\n",
" ['4.7', '3.2', '1.3', '0.2', '0\\n'],\n",
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" ['5', '3.6', '1.4', '0.2', '0\\n'],\n",
" ['5.4', '3.9', '1.7', '0.4', '0\\n'],\n",
" ['4.6', '3.4', '1.4', '0.3', '0\\n'],\n",
" ['5', '3.4', '1.5', '0.2', '0\\n'],\n",
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" ['5.7', '4.4', '1.5', '0.4', '0\\n'],\n",
" ['5.4', '3.9', '1.3', '0.4', '0\\n'],\n",
" ['5.1', '3.5', '1.4', '0.3', '0\\n'],\n",
" ['5.7', '3.8', '1.7', '0.3', '0\\n'],\n",
" ['5.1', '3.8', '1.5', '0.3', '0\\n'],\n",
" ['5.4', '3.4', '1.7', '0.2', '0\\n'],\n",
" ['5.1', '3.7', '1.5', '0.4', '0\\n'],\n",
" ['4.6', '3.6', '1', '0.2', '0\\n'],\n",
" ['5.1', '3.3', '1.7', '0.5', '0\\n'],\n",
" ['4.8', '3.4', '1.9', '0.2', '0\\n'],\n",
" ['5', '3', '1.6', '0.2', '0\\n'],\n",
" ['5', '3.4', '1.6', '0.4', '0\\n'],\n",
" ['5.2', '3.5', '1.5', '0.2', '0\\n'],\n",
" ['5.2', '3.4', '1.4', '0.2', '0\\n'],\n",
" ['4.7', '3.2', '1.6', '0.2', '0\\n'],\n",
" ['4.8', '3.1', '1.6', '0.2', '0\\n'],\n",
" ['5.4', '3.4', '1.5', '0.4', '0\\n'],\n",
" ['5.2', '4.1', '1.5', '0.1', '0\\n'],\n",
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" ['4.9', '3.1', '1.5', '0.2', '0\\n'],\n",
" ['5', '3.2', '1.2', '0.2', '0\\n'],\n",
" ['5.5', '3.5', '1.3', '0.2', '0\\n'],\n",
" ['4.9', '3.6', '1.4', '0.1', '0\\n'],\n",
" ['4.4', '3', '1.3', '0.2', '0\\n'],\n",
" ['5.1', '3.4', '1.5', '0.2', '0\\n'],\n",
" ['5', '3.5', '1.3', '0.3', '0\\n'],\n",
" ['4.5', '2.3', '1.3', '0.3', '0\\n'],\n",
" ['4.4', '3.2', '1.3', '0.2', '0\\n'],\n",
" ['5', '3.5', '1.6', '0.6', '0\\n'],\n",
" ['5.1', '3.8', '1.9', '0.4', '0\\n'],\n",
" ['4.8', '3', '1.4', '0.3', '0\\n'],\n",
" ['5.1', '3.8', '1.6', '0.2', '0\\n'],\n",
" ['4.6', '3.2', '1.4', '0.2', '0\\n'],\n",
" ['5.3', '3.7', '1.5', '0.2', '0\\n'],\n",
" ['5', '3.3', '1.4', '0.2', '0\\n'],\n",
" ['7', '3.2', '4.7', '1.4', '1\\n'],\n",
" ['6.4', '3.2', '4.5', '1.5', '1\\n'],\n",
" ['6.9', '3.1', '4.9', '1.5', '1\\n'],\n",
" ['5.5', '2.3', '4', '1.3', '1\\n'],\n",
" ['6.5', '2.8', '4.6', '1.5', '1\\n'],\n",
" ['5.7', '2.8', '4.5', '1.3', '1\\n'],\n",
" ['6.3', '3.3', '4.7', '1.6', '1\\n'],\n",
" ['4.9', '2.4', '3.3', '1', '1\\n'],\n",
" ['6.6', '2.9', '4.6', '1.3', '1\\n'],\n",
" ['5.2', '2.7', '3.9', '1.4', '1\\n'],\n",
" ['5', '2', '3.5', '1', '1\\n'],\n",
" ['5.9', '3', '4.2', '1.5', '1\\n'],\n",
" ['6', '2.2', '4', '1', '1\\n'],\n",
" ['6.1', '2.9', '4.7', '1.4', '1\\n'],\n",
" ['5.6', '2.9', '3.6', '1.3', '1\\n'],\n",
" ['6.7', '3.1', '4.4', '1.4', '1\\n'],\n",
" ['5.6', '3', '4.5', '1.5', '1\\n'],\n",
" ['5.8', '2.7', '4.1', '1', '1\\n'],\n",
" ['6.2', '2.2', '4.5', '1.5', '1\\n'],\n",
" ['5.6', '2.5', '3.9', '1.1', '1\\n'],\n",
" ['5.9', '3.2', '4.8', '1.8', '1\\n'],\n",
" ['6.1', '2.8', '4', '1.3', '1\\n'],\n",
" ['6.3', '2.5', '4.9', '1.5', '1\\n'],\n",
" ['6.1', '2.8', '4.7', '1.2', '1\\n'],\n",
" ['6.4', '2.9', '4.3', '1.3', '1\\n'],\n",
" ['6.6', '3', '4.4', '1.4', '1\\n'],\n",
" ['6.8', '2.8', '4.8', '1.4', '1\\n'],\n",
" ['6.7', '3', '5', '1.7', '1\\n'],\n",
" ['6', '2.9', '4.5', '1.5', '1\\n'],\n",
" ['5.7', '2.6', '3.5', '1', '1\\n'],\n",
" ['5.5', '2.4', '3.8', '1.1', '1\\n'],\n",
" ['5.5', '2.4', '3.7', '1', '1\\n'],\n",
" ['5.8', '2.7', '3.9', '1.2', '1\\n'],\n",
" ['6', '2.7', '5.1', '1.6', '1\\n'],\n",
" ['5.4', '3', '4.5', '1.5', '1\\n'],\n",
" ['6', '3.4', '4.5', '1.6', '1\\n'],\n",
" ['6.7', '3.1', '4.7', '1.5', '1\\n'],\n",
" ['6.3', '2.3', '4.4', '1.3', '1\\n'],\n",
" ['5.6', '3', '4.1', '1.3', '1\\n'],\n",
" ['5.5', '2.5', '4', '1.3', '1\\n'],\n",
" ['5.5', '2.6', '4.4', '1.2', '1\\n'],\n",
" ['6.1', '3', '4.6', '1.4', '1\\n'],\n",
" ['5.8', '2.6', '4', '1.2', '1\\n'],\n",
" ['5', '2.3', '3.3', '1', '1\\n'],\n",
" ['5.6', '2.7', '4.2', '1.3', '1\\n'],\n",
" ['5.7', '3', '4.2', '1.2', '1\\n'],\n",
" ['5.7', '2.9', '4.2', '1.3', '1\\n'],\n",
" ['6.2', '2.9', '4.3', '1.3', '1\\n'],\n",
" ['5.1', '2.5', '3', '1.1', '1\\n'],\n",
" ['5.7', '2.8', '4.1', '1.3', '1\\n'],\n",
" ['6.3', '3.3', '6', '2.5', '2\\n'],\n",
" ['5.8', '2.7', '5.1', '1.9', '2\\n'],\n",
" ['7.1', '3', '5.9', '2.1', '2\\n'],\n",
" ['6.3', '2.9', '5.6', '1.8', '2\\n'],\n",
" ['6.5', '3', '5.8', '2.2', '2\\n'],\n",
" ['7.6', '3', '6.6', '2.1', '2\\n'],\n",
" ['4.9', '2.5', '4.5', '1.7', '2\\n'],\n",
" ['7.3', '2.9', '6.3', '1.8', '2\\n'],\n",
" ['6.7', '2.5', '5.8', '1.8', '2\\n'],\n",
" ['7.2', '3.6', '6.1', '2.5', '2\\n'],\n",
" ['6.5', '3.2', '5.1', '2', '2\\n'],\n",
" ['6.4', '2.7', '5.3', '1.9', '2\\n'],\n",
" ['6.8', '3', '5.5', '2.1', '2\\n'],\n",
" ['5.7', '2.5', '5', '2', '2\\n'],\n",
" ['5.8', '2.8', '5.1', '2.4', '2\\n'],\n",
" ['6.4', '3.2', '5.3', '2.3', '2\\n'],\n",
" ['6.5', '3', '5.5', '1.8', '2\\n'],\n",
" ['7.7', '3.8', '6.7', '2.2', '2\\n'],\n",
" ['7.7', '2.6', '6.9', '2.3', '2\\n'],\n",
" ['6', '2.2', '5', '1.5', '2\\n'],\n",
" ['6.9', '3.2', '5.7', '2.3', '2\\n'],\n",
" ['5.6', '2.8', '4.9', '2', '2\\n'],\n",
" ['7.7', '2.8', '6.7', '2', '2\\n'],\n",
" ['6.3', '2.7', '4.9', '1.8', '2\\n'],\n",
" ['6.7', '3.3', '5.7', '2.1', '2\\n'],\n",
" ['7.2', '3.2', '6', '1.8', '2\\n'],\n",
" ['6.2', '2.8', '4.8', '1.8', '2\\n'],\n",
" ['6.1', '3', '4.9', '1.8', '2\\n'],\n",
" ['6.4', '2.8', '5.6', '2.1', '2\\n'],\n",
" ['7.2', '3', '5.8', '1.6', '2\\n'],\n",
" ['7.4', '2.8', '6.1', '1.9', '2\\n'],\n",
" ['7.9', '3.8', '6.4', '2', '2\\n'],\n",
" ['6.4', '2.8', '5.6', '2.2', '2\\n'],\n",
" ['6.3', '2.8', '5.1', '1.5', '2\\n'],\n",
" ['6.1', '2.6', '5.6', '1.4', '2\\n'],\n",
" ['7.7', '3', '6.1', '2.3', '2\\n'],\n",
" ['6.3', '3.4', '5.6', '2.4', '2\\n'],\n",
" ['6.4', '3.1', '5.5', '1.8', '2\\n'],\n",
" ['6', '3', '4.8', '1.8', '2\\n'],\n",
" ['6.9', '3.1', '5.4', '2.1', '2\\n'],\n",
" ['6.7', '3.1', '5.6', '2.4', '2\\n'],\n",
" ['6.9', '3.1', '5.1', '2.3', '2\\n'],\n",
" ['5.8', '2.7', '5.1', '1.9', '2\\n'],\n",
" ['6.8', '3.2', '5.9', '2.3', '2\\n'],\n",
" ['6.7', '3.3', '5.7', '2.5', '2\\n'],\n",
" ['6.7', '3', '5.2', '2.3', '2\\n'],\n",
" ['6.3', '2.5', '5', '1.9', '2\\n'],\n",
" ['6.5', '3', '5.2', '2', '2\\n'],\n",
" ['6.2', '3.4', '5.4', '2.3', '2\\n'],\n",
" ['5.9', '3', '5.1', '1.8', '2\\n']]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dt"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['0']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'0\\n'.split()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'0'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'0\\n'.strip()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dt = []\n",
"f = open(\"iris.txt\")\n",
"for line in f:\n",
" arr = line.split(\" \")\n",
" res = [el.strip() for el in arr if el != '']\n",
" dt.append(res)\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[['5.1', '3.5', '1.4', '0.2', '0'],\n",
" ['4.9', '3', '1.4', '0.2', '0'],\n",
" ['4.7', '3.2', '1.3', '0.2', '0'],\n",
" ['4.6', '3.1', '1.5', '0.2', '0'],\n",
" ['5', '3.6', '1.4', '0.2', '0'],\n",
" ['5.4', '3.9', '1.7', '0.4', '0'],\n",
" ['4.6', '3.4', '1.4', '0.3', '0'],\n",
" ['5', '3.4', '1.5', '0.2', '0'],\n",
" ['4.4', '2.9', '1.4', '0.2', '0'],\n",
" ['4.9', '3.1', '1.5', '0.1', '0'],\n",
" ['5.4', '3.7', '1.5', '0.2', '0'],\n",
" ['4.8', '3.4', '1.6', '0.2', '0'],\n",
" ['4.8', '3', '1.4', '0.1', '0'],\n",
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" ['5.8', '4', '1.2', '0.2', '0'],\n",
" ['5.7', '4.4', '1.5', '0.4', '0'],\n",
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" ['5.1', '3.5', '1.4', '0.3', '0'],\n",
" ['5.7', '3.8', '1.7', '0.3', '0'],\n",
" ['5.1', '3.8', '1.5', '0.3', '0'],\n",
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" ['5.1', '3.7', '1.5', '0.4', '0'],\n",
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" ['5.1', '3.3', '1.7', '0.5', '0'],\n",
" ['4.8', '3.4', '1.9', '0.2', '0'],\n",
" ['5', '3', '1.6', '0.2', '0'],\n",
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" ['5.2', '3.5', '1.5', '0.2', '0'],\n",
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" ['5', '3.2', '1.2', '0.2', '0'],\n",
" ['5.5', '3.5', '1.3', '0.2', '0'],\n",
" ['4.9', '3.6', '1.4', '0.1', '0'],\n",
" ['4.4', '3', '1.3', '0.2', '0'],\n",
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" ['5', '3.5', '1.3', '0.3', '0'],\n",
" ['4.5', '2.3', '1.3', '0.3', '0'],\n",
" ['4.4', '3.2', '1.3', '0.2', '0'],\n",
" ['5', '3.5', '1.6', '0.6', '0'],\n",
" ['5.1', '3.8', '1.9', '0.4', '0'],\n",
" ['4.8', '3', '1.4', '0.3', '0'],\n",
" ['5.1', '3.8', '1.6', '0.2', '0'],\n",
" ['4.6', '3.2', '1.4', '0.2', '0'],\n",
" ['5.3', '3.7', '1.5', '0.2', '0'],\n",
" ['5', '3.3', '1.4', '0.2', '0'],\n",
" ['7', '3.2', '4.7', '1.4', '1'],\n",
" ['6.4', '3.2', '4.5', '1.5', '1'],\n",
" ['6.9', '3.1', '4.9', '1.5', '1'],\n",
" ['5.5', '2.3', '4', '1.3', '1'],\n",
" ['6.5', '2.8', '4.6', '1.5', '1'],\n",
" ['5.7', '2.8', '4.5', '1.3', '1'],\n",
" ['6.3', '3.3', '4.7', '1.6', '1'],\n",
" ['4.9', '2.4', '3.3', '1', '1'],\n",
" ['6.6', '2.9', '4.6', '1.3', '1'],\n",
" ['5.2', '2.7', '3.9', '1.4', '1'],\n",
" ['5', '2', '3.5', '1', '1'],\n",
" ['5.9', '3', '4.2', '1.5', '1'],\n",
" ['6', '2.2', '4', '1', '1'],\n",
" ['6.1', '2.9', '4.7', '1.4', '1'],\n",
" ['5.6', '2.9', '3.6', '1.3', '1'],\n",
" ['6.7', '3.1', '4.4', '1.4', '1'],\n",
" ['5.6', '3', '4.5', '1.5', '1'],\n",
" ['5.8', '2.7', '4.1', '1', '1'],\n",
" ['6.2', '2.2', '4.5', '1.5', '1'],\n",
" ['5.6', '2.5', '3.9', '1.1', '1'],\n",
" ['5.9', '3.2', '4.8', '1.8', '1'],\n",
" ['6.1', '2.8', '4', '1.3', '1'],\n",
" ['6.3', '2.5', '4.9', '1.5', '1'],\n",
" ['6.1', '2.8', '4.7', '1.2', '1'],\n",
" ['6.4', '2.9', '4.3', '1.3', '1'],\n",
" ['6.6', '3', '4.4', '1.4', '1'],\n",
" ['6.8', '2.8', '4.8', '1.4', '1'],\n",
" ['6.7', '3', '5', '1.7', '1'],\n",
" ['6', '2.9', '4.5', '1.5', '1'],\n",
" ['5.7', '2.6', '3.5', '1', '1'],\n",
" ['5.5', '2.4', '3.8', '1.1', '1'],\n",
" ['5.5', '2.4', '3.7', '1', '1'],\n",
" ['5.8', '2.7', '3.9', '1.2', '1'],\n",
" ['6', '2.7', '5.1', '1.6', '1'],\n",
" ['5.4', '3', '4.5', '1.5', '1'],\n",
" ['6', '3.4', '4.5', '1.6', '1'],\n",
" ['6.7', '3.1', '4.7', '1.5', '1'],\n",
" ['6.3', '2.3', '4.4', '1.3', '1'],\n",
" ['5.6', '3', '4.1', '1.3', '1'],\n",
" ['5.5', '2.5', '4', '1.3', '1'],\n",
" ['5.5', '2.6', '4.4', '1.2', '1'],\n",
" ['6.1', '3', '4.6', '1.4', '1'],\n",
" ['5.8', '2.6', '4', '1.2', '1'],\n",
" ['5', '2.3', '3.3', '1', '1'],\n",
" ['5.6', '2.7', '4.2', '1.3', '1'],\n",
" ['5.7', '3', '4.2', '1.2', '1'],\n",
" ['5.7', '2.9', '4.2', '1.3', '1'],\n",
" ['6.2', '2.9', '4.3', '1.3', '1'],\n",
" ['5.1', '2.5', '3', '1.1', '1'],\n",
" ['5.7', '2.8', '4.1', '1.3', '1'],\n",
" ['6.3', '3.3', '6', '2.5', '2'],\n",
" ['5.8', '2.7', '5.1', '1.9', '2'],\n",
" ['7.1', '3', '5.9', '2.1', '2'],\n",
" ['6.3', '2.9', '5.6', '1.8', '2'],\n",
" ['6.5', '3', '5.8', '2.2', '2'],\n",
" ['7.6', '3', '6.6', '2.1', '2'],\n",
" ['4.9', '2.5', '4.5', '1.7', '2'],\n",
" ['7.3', '2.9', '6.3', '1.8', '2'],\n",
" ['6.7', '2.5', '5.8', '1.8', '2'],\n",
" ['7.2', '3.6', '6.1', '2.5', '2'],\n",
" ['6.5', '3.2', '5.1', '2', '2'],\n",
" ['6.4', '2.7', '5.3', '1.9', '2'],\n",
" ['6.8', '3', '5.5', '2.1', '2'],\n",
" ['5.7', '2.5', '5', '2', '2'],\n",
" ['5.8', '2.8', '5.1', '2.4', '2'],\n",
" ['6.4', '3.2', '5.3', '2.3', '2'],\n",
" ['6.5', '3', '5.5', '1.8', '2'],\n",
" ['7.7', '3.8', '6.7', '2.2', '2'],\n",
" ['7.7', '2.6', '6.9', '2.3', '2'],\n",
" ['6', '2.2', '5', '1.5', '2'],\n",
" ['6.9', '3.2', '5.7', '2.3', '2'],\n",
" ['5.6', '2.8', '4.9', '2', '2'],\n",
" ['7.7', '2.8', '6.7', '2', '2'],\n",
" ['6.3', '2.7', '4.9', '1.8', '2'],\n",
" ['6.7', '3.3', '5.7', '2.1', '2'],\n",
" ['7.2', '3.2', '6', '1.8', '2'],\n",
" ['6.2', '2.8', '4.8', '1.8', '2'],\n",
" ['6.1', '3', '4.9', '1.8', '2'],\n",
" ['6.4', '2.8', '5.6', '2.1', '2'],\n",
" ['7.2', '3', '5.8', '1.6', '2'],\n",
" ['7.4', '2.8', '6.1', '1.9', '2'],\n",
" ['7.9', '3.8', '6.4', '2', '2'],\n",
" ['6.4', '2.8', '5.6', '2.2', '2'],\n",
" ['6.3', '2.8', '5.1', '1.5', '2'],\n",
" ['6.1', '2.6', '5.6', '1.4', '2'],\n",
" ['7.7', '3', '6.1', '2.3', '2'],\n",
" ['6.3', '3.4', '5.6', '2.4', '2'],\n",
" ['6.4', '3.1', '5.5', '1.8', '2'],\n",
" ['6', '3', '4.8', '1.8', '2'],\n",
" ['6.9', '3.1', '5.4', '2.1', '2'],\n",
" ['6.7', '3.1', '5.6', '2.4', '2'],\n",
" ['6.9', '3.1', '5.1', '2.3', '2'],\n",
" ['5.8', '2.7', '5.1', '1.9', '2'],\n",
" ['6.8', '3.2', '5.9', '2.3', '2'],\n",
" ['6.7', '3.3', '5.7', '2.5', '2'],\n",
" ['6.7', '3', '5.2', '2.3', '2'],\n",
" ['6.3', '2.5', '5', '1.9', '2'],\n",
" ['6.5', '3', '5.2', '2', '2'],\n",
" ['6.2', '3.4', '5.4', '2.3', '2'],\n",
" ['5.9', '3', '5.1', '1.8', '2']]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dt"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_table('iris.txt', sep='\\s+')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td>1.5</td>\n",
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" <td>4.9</td>\n",
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" <td>6.7</td>\n",
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" <td>2</td>\n",
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" <th>122</th>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
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" <th>125</th>\n",
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" <td>4.8</td>\n",
" <td>1.8</td>\n",
" <td>2</td>\n",
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" <th>126</th>\n",
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" <td>4.9</td>\n",
" <td>1.8</td>\n",
" <td>2</td>\n",
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" <th>127</th>\n",
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" <td>2.8</td>\n",
" <td>5.6</td>\n",
" <td>2.1</td>\n",
" <td>2</td>\n",
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" <th>128</th>\n",
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" <td>5.8</td>\n",
" <td>1.6</td>\n",
" <td>2</td>\n",
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" <tr>\n",
" <th>129</th>\n",
" <td>7.4</td>\n",
" <td>2.8</td>\n",
" <td>6.1</td>\n",
" <td>1.9</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td>7.9</td>\n",
" <td>3.8</td>\n",
" <td>6.4</td>\n",
" <td>2.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>6.4</td>\n",
" <td>2.8</td>\n",
" <td>5.6</td>\n",
" <td>2.2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>6.3</td>\n",
" <td>2.8</td>\n",
" <td>5.1</td>\n",
" <td>1.5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td>6.1</td>\n",
" <td>2.6</td>\n",
" <td>5.6</td>\n",
" <td>1.4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td>7.7</td>\n",
" <td>3.0</td>\n",
" <td>6.1</td>\n",
" <td>2.3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td>6.3</td>\n",
" <td>3.4</td>\n",
" <td>5.6</td>\n",
" <td>2.4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>6.4</td>\n",
" <td>3.1</td>\n",
" <td>5.5</td>\n",
" <td>1.8</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td>6.0</td>\n",
" <td>3.0</td>\n",
" <td>4.8</td>\n",
" <td>1.8</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>138</th>\n",
" <td>6.9</td>\n",
" <td>3.1</td>\n",
" <td>5.4</td>\n",
" <td>2.1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>139</th>\n",
" <td>6.7</td>\n",
" <td>3.1</td>\n",
" <td>5.6</td>\n",
" <td>2.4</td>\n",
" <td>2</td>\n",
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" <th>140</th>\n",
" <td>6.9</td>\n",
" <td>3.1</td>\n",
" <td>5.1</td>\n",
" <td>2.3</td>\n",
" <td>2</td>\n",
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" <tr>\n",
" <th>141</th>\n",
" <td>5.8</td>\n",
" <td>2.7</td>\n",
" <td>5.1</td>\n",
" <td>1.9</td>\n",
" <td>2</td>\n",
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" <tr>\n",
" <th>142</th>\n",
" <td>6.8</td>\n",
" <td>3.2</td>\n",
" <td>5.9</td>\n",
" <td>2.3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>6.7</td>\n",
" <td>3.3</td>\n",
" <td>5.7</td>\n",
" <td>2.5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td>6.7</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>5.0</td>\n",
" <td>1.9</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>6.5</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>6.2</td>\n",
" <td>3.4</td>\n",
" <td>5.4</td>\n",
" <td>2.3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>5.9</td>\n",
" <td>3.0</td>\n",
" <td>5.1</td>\n",
" <td>1.8</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>149 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" 5.1 3.5 1.4 0.2 0\n",
"0 4.9 3.0 1.4 0.2 0\n",
"1 4.7 3.2 1.3 0.2 0\n",
"2 4.6 3.1 1.5 0.2 0\n",
"3 5.0 3.6 1.4 0.2 0\n",
"4 5.4 3.9 1.7 0.4 0\n",
"5 4.6 3.4 1.4 0.3 0\n",
"6 5.0 3.4 1.5 0.2 0\n",
"7 4.4 2.9 1.4 0.2 0\n",
"8 4.9 3.1 1.5 0.1 0\n",
"9 5.4 3.7 1.5 0.2 0\n",
"10 4.8 3.4 1.6 0.2 0\n",
"11 4.8 3.0 1.4 0.1 0\n",
"12 4.3 3.0 1.1 0.1 0\n",
"13 5.8 4.0 1.2 0.2 0\n",
"14 5.7 4.4 1.5 0.4 0\n",
"15 5.4 3.9 1.3 0.4 0\n",
"16 5.1 3.5 1.4 0.3 0\n",
"17 5.7 3.8 1.7 0.3 0\n",
"18 5.1 3.8 1.5 0.3 0\n",
"19 5.4 3.4 1.7 0.2 0\n",
"20 5.1 3.7 1.5 0.4 0\n",
"21 4.6 3.6 1.0 0.2 0\n",
"22 5.1 3.3 1.7 0.5 0\n",
"23 4.8 3.4 1.9 0.2 0\n",
"24 5.0 3.0 1.6 0.2 0\n",
"25 5.0 3.4 1.6 0.4 0\n",
"26 5.2 3.5 1.5 0.2 0\n",
"27 5.2 3.4 1.4 0.2 0\n",
"28 4.7 3.2 1.6 0.2 0\n",
"29 4.8 3.1 1.6 0.2 0\n",
".. ... ... ... ... ..\n",
"119 6.9 3.2 5.7 2.3 2\n",
"120 5.6 2.8 4.9 2.0 2\n",
"121 7.7 2.8 6.7 2.0 2\n",
"122 6.3 2.7 4.9 1.8 2\n",
"123 6.7 3.3 5.7 2.1 2\n",
"124 7.2 3.2 6.0 1.8 2\n",
"125 6.2 2.8 4.8 1.8 2\n",
"126 6.1 3.0 4.9 1.8 2\n",
"127 6.4 2.8 5.6 2.1 2\n",
"128 7.2 3.0 5.8 1.6 2\n",
"129 7.4 2.8 6.1 1.9 2\n",
"130 7.9 3.8 6.4 2.0 2\n",
"131 6.4 2.8 5.6 2.2 2\n",
"132 6.3 2.8 5.1 1.5 2\n",
"133 6.1 2.6 5.6 1.4 2\n",
"134 7.7 3.0 6.1 2.3 2\n",
"135 6.3 3.4 5.6 2.4 2\n",
"136 6.4 3.1 5.5 1.8 2\n",
"137 6.0 3.0 4.8 1.8 2\n",
"138 6.9 3.1 5.4 2.1 2\n",
"139 6.7 3.1 5.6 2.4 2\n",
"140 6.9 3.1 5.1 2.3 2\n",
"141 5.8 2.7 5.1 1.9 2\n",
"142 6.8 3.2 5.9 2.3 2\n",
"143 6.7 3.3 5.7 2.5 2\n",
"144 6.7 3.0 5.2 2.3 2\n",
"145 6.3 2.5 5.0 1.9 2\n",
"146 6.5 3.0 5.2 2.0 2\n",
"147 6.2 3.4 5.4 2.3 2\n",
"148 5.9 3.0 5.1 1.8 2\n",
"\n",
"[149 rows x 5 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_table('iris.txt', sep='\\s+', names=['x1', 'x2', 'x3','x4', 'y'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "KeyError",
"evalue": "0",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-20-9ae93f22b889>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1912\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1913\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1914\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1916\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1919\u001b[0m \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1920\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1921\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1922\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1923\u001b[0m \u001b[1;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 1088\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1089\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1090\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1091\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1092\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m 3100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3101\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3102\u001b[1;33m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3103\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3104\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 1690\u001b[0m raise ValueError('tolerance argument only valid if using pad, '\n\u001b[0;32m 1691\u001b[0m 'backfill or nearest lookups')\n\u001b[1;32m-> 1692\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1693\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1694\u001b[0m indexer = self.get_indexer([key], method=method,\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3979)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3843)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12265)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12216)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 0"
]
}
],
"source": [
"df[0]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"source": [
"import numpy as np"
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{
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"outputs": [
{
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],
"source": [
"df[:5]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
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},
"outputs": [
{
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"array([4, 0, 3, 1, 2])"
]
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"execution_count": 27,
"metadata": {},
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],
"source": [
"sampler = np.random.permutation(5)\n",
"sampler"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"1 4.9 3.0 1.4 0.2 0\n",
"2 4.7 3.2 1.3 0.2 0"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.take(sampler)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sammpler = np.random.permutation(len(df))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([4, 0, 3])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sampler[0:3]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"randomdf = df.take(sampler)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>x1</th>\n",
" <th>x2</th>\n",
" <th>x3</th>\n",
" <th>x4</th>\n",
" <th>y</th>\n",
" </tr>\n",
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" <tbody>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
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"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
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"source": [
"randomdf[:3]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sampler = np.random.permutation(len(df))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <tbody>\n",
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" <th>146</th>\n",
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" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>4.6</td>\n",
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" <td>1.4</td>\n",
" <td>0.2</td>\n",
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" <td>2.6</td>\n",
" <td>4.0</td>\n",
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" <td>1</td>\n",
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"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"randomdf = df.take(sampler)\n",
"randomdf[:3]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dfTraining = randomdf[0:125]"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"125"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dfTraining)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'randomdff' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-37-4137ab551a0f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdfTest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrandomdff\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m125\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m150\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'randomdff' is not defined"
]
}
],
"source": [
"dfTest = randomdff[125:150]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dfTest = randomdf[125:150]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[{'answerclass': 0,\n",
" 'featureidx': -1,\n",
" 'isleaf': False,\n",
" 'leftidx': -1,\n",
" 'level': -1,\n",
" 'rightidx': -1,\n",
" 'separateval': 0}]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tnodes = []\n",
"root = {\"isleaf\": False, \"level\": -1, \"featureidx\": -1, \"separateval\":0, \"answerclass\":0, \"leftidx\":-1, \"rightidx\": -1 }\n",
"tnodes.append(root)\n",
"tnodes"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"numdata = len(dfTraining)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"125"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numdata"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'module' object has no attribute 'randInt'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-42-e8a78fd5ba19>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mcur\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtnodes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mcur\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'valids'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandInt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfTraining\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnumdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mcur\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'module' object has no attribute 'randInt'"
]
}
],
"source": [
"cur = tnodes[0]\n",
"cur['valids'] = np.random.randInt(0, len(dfTraining), size=numdata)\n",
"cur"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'answerclass': 0,\n",
" 'featureidx': -1,\n",
" 'isleaf': False,\n",
" 'leftidx': -1,\n",
" 'level': -1,\n",
" 'rightidx': -1,\n",
" 'separateval': 0,\n",
" 'valids': array([115, 65, 44, 42, 67, 103, 84, 28, 120, 42, 29, 82, 99,\n",
" 72, 123, 105, 87, 37, 112, 53, 108, 31, 85, 14, 13, 106,\n",
" 67, 52, 27, 75, 103, 33, 58, 20, 35, 52, 42, 22, 64,\n",
" 45, 34, 1, 33, 18, 15, 2, 102, 34, 57, 18, 75, 69,\n",
" 12, 91, 31, 24, 2, 48, 50, 1, 40, 13, 75, 81, 108,\n",
" 116, 8, 87, 116, 35, 108, 4, 6, 62, 101, 63, 46, 82,\n",
" 43, 10, 100, 41, 62, 108, 62, 24, 44, 3, 85, 33, 78,\n",
" 78, 30, 102, 28, 34, 76, 14, 61, 33, 106, 121, 45, 29,\n",
" 102, 34, 43, 62, 97, 13, 58, 105, 92, 107, 30, 120, 50,\n",
" 61, 80, 0, 50, 68, 103, 95, 5])}"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cur['valids'] = np.random.randint(0, len(dfTraining), size=numdata)\n",
"cur"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"numradfeatures = 2\n",
"numrandpos = 5"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"curidx = 0"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'tnods' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-46-985c9ca8cc4b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcur\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtnods\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcuridx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'tnods' is not defined"
]
}
],
"source": [
"cur = tnods[curidx]"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# DT作るループの開始のつもり\n",
"cur = tnodes[curidx]"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" <th>52</th>\n",
" <td>6.9</td>\n",
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" x1 x2 x3 x4 y\n",
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"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfTraining.take(cur['valids'])[:2]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"52 1\n",
"6 0\n",
"Name: y, dtype: int64"
]
},
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfTraining.take(cur['valids'])[:2]['y']"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>1</td>\n",
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" <tr>\n",
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"</table>\n",
"</div>"
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"text/plain": [
" y\n",
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"metadata": {},
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"source": [
"dfTraining.take(cur['valids'])[:2][['y']]"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "KeyError",
"evalue": "0L",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-50-72fb654bf406>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdfTraining\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcur\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'valids'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'y'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 549\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 550\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 551\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 552\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 553\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\index.pyc\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m 1721\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1722\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1723\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1724\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1725\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'integer'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'boolean'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas\\index.c:3204)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas\\index.c:2903)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3843)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas\\hashtable.c:6525)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas\\hashtable.c:6463)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 0L"
]
}
],
"source": [
"dfTraining.take(cur['valids'])[:2]['y'][0]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'bool' object has no attribute 'any'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-51-b8f43cce2205>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdfTraining\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcur\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'valids'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'y'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\series.pyc\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indices, axis, convert, is_copy)\u001b[0m\n\u001b[0;32m 2298\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2299\u001b[0m indices = maybe_convert_indices(\n\u001b[1;32m-> 2300\u001b[1;33m indices, len(self._get_axis(axis)))\n\u001b[0m\u001b[0;32m 2301\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2302\u001b[0m \u001b[0mindices\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ensure_platform_int\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36mmaybe_convert_indices\u001b[1;34m(indices, n)\u001b[0m\n\u001b[0;32m 1715\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1716\u001b[0m \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindices\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1717\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1718\u001b[0m \u001b[0mindices\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1719\u001b[0m \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mindices\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mindices\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'bool' object has no attribute 'any'"
]
}
],
"source": [
"dfTraining.take(cur['valids'])[:2]['y'].take(0)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "len() of unsized object",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-52-ba6d626e7399>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdfTraining\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcur\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'valids'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'y'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indices, axis, convert, is_copy)\u001b[0m\n\u001b[0;32m 1368\u001b[0m new_data = self._data.take(indices,\n\u001b[0;32m 1369\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_block_manager_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1370\u001b[1;33m convert=True, verify=True)\n\u001b[0m\u001b[0;32m 1371\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1372\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indexer, axis, verify, convert)\u001b[0m\n\u001b[0;32m 3515\u001b[0m \u001b[0mnew_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3516\u001b[0m return self.reindex_indexer(new_axis=new_labels, indexer=indexer,\n\u001b[1;32m-> 3517\u001b[1;33m axis=axis, allow_dups=True)\n\u001b[0m\u001b[0;32m 3518\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3519\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlsuffix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mreindex_indexer\u001b[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy)\u001b[0m\n\u001b[0;32m 3402\u001b[0m fill_tuple=(fill_value if fill_value is not None else\n\u001b[0;32m 3403\u001b[0m blk.fill_value,))\n\u001b[1;32m-> 3404\u001b[1;33m for blk in self.blocks]\n\u001b[0m\u001b[0;32m 3405\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3406\u001b[0m \u001b[0mnew_axes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mtake_nd\u001b[1;34m(self, indexer, axis, new_mgr_locs, fill_tuple)\u001b[0m\n\u001b[0;32m 933\u001b[0m \u001b[0mfill_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfill_tuple\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 934\u001b[0m new_values = com.take_nd(values, indexer, axis=axis,\n\u001b[1;32m--> 935\u001b[1;33m allow_fill=True, fill_value=fill_value)\n\u001b[0m\u001b[0;32m 936\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 937\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnew_mgr_locs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\common.pyc\u001b[0m in \u001b[0;36mtake_nd\u001b[1;34m(arr, indexer, axis, out, fill_value, mask_info, allow_fill)\u001b[0m\n\u001b[0;32m 768\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 769\u001b[0m \u001b[0mout_shape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 770\u001b[1;33m \u001b[0mout_shape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 771\u001b[0m \u001b[0mout_shape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 772\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_contiguous\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: len() of unsized object"
]
}
],
"source": [
"dfTraining.take(cur['valids'])[:2][['y']].take(0)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfTraining.take(cur['valids'])[:2]['y'].values[0]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfTraining.take(cur['valids'])[:2]['y'].values[1]"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"curdf = dfTraining.take(cur['valids'])"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "KeyError",
"evalue": "0",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-56-a5265db84d9c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcurdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1912\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1913\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1914\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1916\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1919\u001b[0m \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1920\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1921\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1922\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1923\u001b[0m \u001b[1;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 1088\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1089\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1090\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1091\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1092\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m 3100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3101\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3102\u001b[1;33m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3103\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3104\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 1690\u001b[0m raise ValueError('tolerance argument only valid if using pad, '\n\u001b[0;32m 1691\u001b[0m 'backfill or nearest lookups')\n\u001b[1;32m-> 1692\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1693\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1694\u001b[0m indexer = self.get_indexer([key], method=method,\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3979)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3843)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12265)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12216)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 0"
]
}
],
"source": [
"curdf[0]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <tr>\n",
" <th>78</th>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>5.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>5.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td>6.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>6.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>4.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>5.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>5.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>5.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>5.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>5.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>5.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>6.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>5.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>6.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>6.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>5.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>6.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>6.7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>125 rows × 1 columns</p>\n",
"</div>"
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"30 4.8\n",
"22 4.6\n",
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"135 7.7\n",
"93 5.0\n",
"42 4.4\n",
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"39 5.1\n",
"104 6.5\n",
"7 5.0\n",
"60 5.0\n",
"15 5.7\n",
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"91 6.1\n",
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"17 5.1\n",
"130 7.4\n",
".. ...\n",
"140 6.7\n",
"78 6.0\n",
"60 5.0\n",
"66 5.6\n",
"121 5.6\n",
"137 6.4\n",
"68 6.2\n",
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"15 5.7\n",
"20 5.4\n",
"136 6.3\n",
"33 5.5\n",
"82 5.8\n",
"61 5.9\n",
"36 5.5\n",
"97 6.2\n",
"66 5.6\n",
"75 6.6\n",
"146 6.3\n",
"97 6.2\n",
"44 5.1\n",
"96 5.7\n",
"74 6.4\n",
"86 6.7\n",
"\n",
"[125 rows x 1 columns]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"curdf[[0]]"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
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},
"outputs": [
{
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"Name: y, dtype: int64"
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"metadata": {},
"output_type": "execute_result"
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"source": [
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]
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{
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"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"source": [
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]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'_IXIndexer' object has no attribute 'values'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-60-2a27a50f2214>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcurdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m: '_IXIndexer' object has no attribute 'values'"
]
}
],
"source": [
"curdf.ix.values[0]"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"52"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"curdf.index[0]"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "KeyError",
"evalue": "52",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-62-c73399d94f47>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcurdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcurdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1912\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1913\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1914\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1916\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1919\u001b[0m \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1920\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1921\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1922\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1923\u001b[0m \u001b[1;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 1088\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1089\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1090\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1091\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1092\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m 3100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3101\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3102\u001b[1;33m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3103\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3104\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 1690\u001b[0m raise ValueError('tolerance argument only valid if using pad, '\n\u001b[0;32m 1691\u001b[0m 'backfill or nearest lookups')\n\u001b[1;32m-> 1692\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1693\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1694\u001b[0m indexer = self.get_indexer([key], method=method,\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3979)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3843)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12265)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12216)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 52"
]
}
],
"source": [
"curdf[curdf.index[0]]"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IndexError",
"evalue": "indices are out-of-bounds",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-63-3d8f95ef43ca>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcurdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcurdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1906\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1907\u001b[0m \u001b[1;31m# either boolean or fancy integer index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1908\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1909\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1910\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_array\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1951\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1952\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1953\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1954\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1955\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indices, axis, convert, is_copy)\u001b[0m\n\u001b[0;32m 1368\u001b[0m new_data = self._data.take(indices,\n\u001b[0;32m 1369\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_block_manager_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1370\u001b[1;33m convert=True, verify=True)\n\u001b[0m\u001b[0;32m 1371\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1372\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indexer, axis, verify, convert)\u001b[0m\n\u001b[0;32m 3506\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3507\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3508\u001b[1;33m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaybe_convert_indices\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3509\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3510\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mverify\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36mmaybe_convert_indices\u001b[1;34m(indices, n)\u001b[0m\n\u001b[0;32m 1719\u001b[0m \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mindices\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mindices\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1720\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1721\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"indices are out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1722\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mindices\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1723\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mIndexError\u001b[0m: indices are out-of-bounds"
]
}
],
"source": [
"curdf[[curdf.index[0]]]"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"125"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(curdf)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([52, 6], dtype='int64')"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"curdf.index[0:2]"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([52, 6], dtype='int64')"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"curdf.index[:2]"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([52, 6, 87], dtype='int64')"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"curdf.index[:3]"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "KeyError",
"evalue": "52",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-68-dcb20ffcaf28>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcurdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m52\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1912\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1913\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1914\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1916\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1919\u001b[0m \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1920\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1921\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1922\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1923\u001b[0m \u001b[1;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 1088\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1089\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1090\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1091\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1092\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m 3100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3101\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3102\u001b[1;33m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3103\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3104\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 1690\u001b[0m raise ValueError('tolerance argument only valid if using pad, '\n\u001b[0;32m 1691\u001b[0m 'backfill or nearest lookups')\n\u001b[1;32m-> 1692\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1693\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1694\u001b[0m indexer = self.get_indexer([key], method=method,\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3979)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3843)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12265)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12216)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 52"
]
}
],
"source": [
"curdf[52]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'DataFrame' object has no attribute 'idx'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-69-4744a483227e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcurdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m52\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 2244\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2245\u001b[0m raise AttributeError(\"'%s' object has no attribute '%s'\" %\n\u001b[1;32m-> 2246\u001b[1;33m (type(self).__name__, name))\n\u001b[0m\u001b[0;32m 2247\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2248\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'idx'"
]
}
],
"source": [
"curdf.idx[52]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"x1 6.9\n",
"x2 3.1\n",
"x3 4.9\n",
"x4 1.5\n",
"y 1.0\n",
"Name: 52, dtype: float64"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"curdf.ix[52]"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"curdf.ix[52]['y']"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"firsty = curdf.ix[curdf.index[0]]['y']\n",
"firsty"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <tr>\n",
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],
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"84 5.4 3.0 4.5 1.5 1\n",
"50 7.0 3.2 4.7 1.4 1\n",
"92 5.8 2.6 4.0 1.2 1\n",
"67 5.8 2.7 4.1 1.0 1\n",
"97 6.2 2.9 4.3 1.3 1\n",
"51 6.4 3.2 4.5 1.5 1\n",
"79 5.7 2.6 3.5 1.0 1\n",
"51 6.4 3.2 4.5 1.5 1\n",
"72 6.3 2.5 4.9 1.5 1\n",
"59 5.2 2.7 3.9 1.4 1\n",
"58 6.6 2.9 4.6 1.3 1\n",
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"89 5.5 2.5 4.0 1.3 1\n",
"72 6.3 2.5 4.9 1.5 1\n",
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"50 7.0 3.2 4.7 1.4 1\n",
"87 6.3 2.3 4.4 1.3 1\n",
"61 5.9 3.0 4.2 1.5 1\n",
"78 6.0 2.9 4.5 1.5 1\n",
"60 5.0 2.0 3.5 1.0 1\n",
"66 5.6 3.0 4.5 1.5 1\n",
"68 6.2 2.2 4.5 1.5 1\n",
"77 6.7 3.0 5.0 1.7 1\n",
"72 6.3 2.5 4.9 1.5 1\n",
"82 5.8 2.7 3.9 1.2 1\n",
"61 5.9 3.0 4.2 1.5 1\n",
"97 6.2 2.9 4.3 1.3 1\n",
"66 5.6 3.0 4.5 1.5 1\n",
"75 6.6 3.0 4.4 1.4 1\n",
"97 6.2 2.9 4.3 1.3 1\n",
"96 5.7 2.9 4.2 1.3 1\n",
"74 6.4 2.9 4.3 1.3 1\n",
"86 6.7 3.1 4.7 1.5 1"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"curdf[curdf['y'] == firsty]"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>5.4</td>\n",
" <td>3.4</td>\n",
" <td>1.7</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>6.3</td>\n",
" <td>3.4</td>\n",
" <td>5.6</td>\n",
" <td>2.4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>5.5</td>\n",
" <td>4.2</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>5.5</td>\n",
" <td>3.5</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>5.0</td>\n",
" <td>1.9</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>5.1</td>\n",
" <td>3.8</td>\n",
" <td>1.9</td>\n",
" <td>0.4</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>75 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" x1 x2 x3 x4 y\n",
"6 4.6 3.4 1.4 0.3 0\n",
"112 6.8 3.0 5.5 2.1 2\n",
"25 5.0 3.0 1.6 0.2 0\n",
"36 5.5 3.5 1.3 0.2 0\n",
"112 6.8 3.0 5.5 2.1 2\n",
"0 5.1 3.5 1.4 0.2 0\n",
"30 4.8 3.1 1.6 0.2 0\n",
"22 4.6 3.6 1.0 0.2 0\n",
"100 6.3 3.3 6.0 2.5 2\n",
"136 6.3 3.4 5.6 2.4 2\n",
"135 7.7 3.0 6.1 2.3 2\n",
"42 4.4 3.2 1.3 0.2 0\n",
"39 5.1 3.4 1.5 0.2 0\n",
"104 6.5 3.0 5.8 2.2 2\n",
"7 5.0 3.4 1.5 0.2 0\n",
"15 5.7 4.4 1.5 0.4 0\n",
"137 6.4 3.1 5.5 1.8 2\n",
"148 6.2 3.4 5.4 2.3 2\n",
"17 5.1 3.5 1.4 0.3 0\n",
"130 7.4 2.8 6.1 1.9 2\n",
"121 5.6 2.8 4.9 2.0 2\n",
"20 5.4 3.4 1.7 0.2 0\n",
"122 7.7 2.8 6.7 2.0 2\n",
"43 5.0 3.5 1.6 0.6 0\n",
"148 6.2 3.4 5.4 2.3 2\n",
"112 6.8 3.0 5.5 2.1 2\n",
"134 6.1 2.6 5.6 1.4 2\n",
"18 5.7 3.8 1.7 0.3 0\n",
"140 6.7 3.1 5.6 2.4 2\n",
"47 4.6 3.2 1.4 0.2 0\n",
".. ... ... ... ... ..\n",
"43 5.0 3.5 1.6 0.6 0\n",
"39 5.1 3.4 1.5 0.2 0\n",
"113 5.7 2.5 5.0 2.0 2\n",
"1 4.9 3.0 1.4 0.2 0\n",
"142 5.8 2.7 5.1 1.9 2\n",
"45 4.8 3.0 1.4 0.3 0\n",
"16 5.4 3.9 1.3 0.4 0\n",
"39 5.1 3.4 1.5 0.2 0\n",
"28 5.2 3.4 1.4 0.2 0\n",
"7 5.0 3.4 1.5 0.2 0\n",
"121 5.6 2.8 4.9 2.0 2\n",
"128 6.4 2.8 5.6 2.1 2\n",
"128 6.4 2.8 5.6 2.1 2\n",
"46 5.1 3.8 1.6 0.2 0\n",
"25 5.0 3.0 1.6 0.2 0\n",
"140 6.7 3.1 5.6 2.4 2\n",
"121 5.6 2.8 4.9 2.0 2\n",
"137 6.4 3.1 5.5 1.8 2\n",
"0 5.1 3.5 1.4 0.2 0\n",
"46 5.1 3.8 1.6 0.2 0\n",
"140 6.7 3.1 5.6 2.4 2\n",
"45 4.8 3.0 1.4 0.3 0\n",
"29 4.7 3.2 1.6 0.2 0\n",
"15 5.7 4.4 1.5 0.4 0\n",
"20 5.4 3.4 1.7 0.2 0\n",
"136 6.3 3.4 5.6 2.4 2\n",
"33 5.5 4.2 1.4 0.2 0\n",
"36 5.5 3.5 1.3 0.2 0\n",
"146 6.3 2.5 5.0 1.9 2\n",
"44 5.1 3.8 1.9 0.4 0\n",
"\n",
"[75 rows x 5 columns]"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"curdf[curdf['y'] != firsty]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"75"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#これが0ならツリーの葉になる\n",
"len(curdf[curdf['y'] != firsty])"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"125"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# これがminnodesize以下なら葉になる\n",
"# あとはcur['level']がmaxlevel以上か\n",
"len(curdf)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"featurenum = 4"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 0])"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tryfeatureids = np.random.randint(0, featurenum, size=numradfeatures)\n",
"tryfeatureids"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# for tryfid in tryfeatureids:\n",
"tryfid = tryfeatureids[0]"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'numradpos' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-80-ac8dfc17ba49>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnumradpos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'numradpos' is not defined"
]
}
],
"source": [
"numradpos"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([102, 60, 79, 79, 56])"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tryposes = np.random.randint(0, len(curdf), size=numrandpos)\n",
"tryposes"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# for trypos in tryposes:\n",
"trypos = tryposes[0]"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "KeyError",
"evalue": "102",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-83-bd3077371150>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcurdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtrypos\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1912\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1913\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1914\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1916\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1919\u001b[0m \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1920\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1921\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1922\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1923\u001b[0m \u001b[1;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 1088\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1089\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1090\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1091\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1092\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m 3100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3101\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3102\u001b[1;33m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3103\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3104\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 1690\u001b[0m raise ValueError('tolerance argument only valid if using pad, '\n\u001b[0;32m 1691\u001b[0m 'backfill or nearest lookups')\n\u001b[1;32m-> 1692\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1693\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1694\u001b[0m indexer = self.get_indexer([key], method=method,\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3979)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3843)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12265)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12216)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 102"
]
}
],
"source": [
"curdf[trypos]"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "KeyError",
"evalue": "102",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-84-2e95e42025ed>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcurdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtrypos\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 71\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 936\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 937\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 938\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 939\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 940\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_iterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m 84\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'no slices here, handle elsewhere'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 86\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 88\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36mxs\u001b[1;34m(self, key, axis, level, copy, drop_level)\u001b[0m\n\u001b[0;32m 1483\u001b[0m drop_level=drop_level)\n\u001b[0;32m 1484\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1485\u001b[1;33m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1486\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1487\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 1690\u001b[0m raise ValueError('tolerance argument only valid if using pad, '\n\u001b[0;32m 1691\u001b[0m 'backfill or nearest lookups')\n\u001b[1;32m-> 1692\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1693\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1694\u001b[0m indexer = self.get_indexer([key], method=method,\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3979)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3782)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine._get_loc_duplicates (pandas\\index.c:4213)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.Int64Engine._maybe_get_bool_indexer (pandas\\index.c:7970)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 102"
]
}
],
"source": [
"curdf.ix[trypos]"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"77"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"curdf.index[trypos]"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "KeyError",
"evalue": "77",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-86-58a50645dc7a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcurdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcurdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtrypos\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1912\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1913\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1914\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1916\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1919\u001b[0m \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1920\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1921\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1922\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1923\u001b[0m \u001b[1;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 1088\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1089\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1090\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1091\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1092\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m 3100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3101\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3102\u001b[1;33m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3103\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3104\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 1690\u001b[0m raise ValueError('tolerance argument only valid if using pad, '\n\u001b[0;32m 1691\u001b[0m 'backfill or nearest lookups')\n\u001b[1;32m-> 1692\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1693\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1694\u001b[0m indexer = self.get_indexer([key], method=method,\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3979)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:3843)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12265)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:12216)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 77"
]
}
],
"source": [
"curdf[curdf.index[trypos]]"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x1</th>\n",
" <th>x2</th>\n",
" <th>x3</th>\n",
" <th>x4</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>6.7</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>1.7</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>6.7</td>\n",
" <td>3</td>\n",
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"text/plain": [
" x1 x2 x3 x4 y\n",
"77 6.7 3 5 1.7 1\n",
"77 6.7 3 5 1.7 1"
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},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"curdf.ix[curdf.index[trypos]]"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"curdf.take(77)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"102"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trypos"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "len() of unsized object",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-89-a95c29757d7a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcurdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m102\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indices, axis, convert, is_copy)\u001b[0m\n\u001b[0;32m 1368\u001b[0m new_data = self._data.take(indices,\n\u001b[0;32m 1369\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_block_manager_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1370\u001b[1;33m convert=True, verify=True)\n\u001b[0m\u001b[0;32m 1371\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1372\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indexer, axis, verify, convert)\u001b[0m\n\u001b[0;32m 3515\u001b[0m \u001b[0mnew_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3516\u001b[0m return self.reindex_indexer(new_axis=new_labels, indexer=indexer,\n\u001b[1;32m-> 3517\u001b[1;33m axis=axis, allow_dups=True)\n\u001b[0m\u001b[0;32m 3518\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3519\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlsuffix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mreindex_indexer\u001b[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy)\u001b[0m\n\u001b[0;32m 3402\u001b[0m fill_tuple=(fill_value if fill_value is not None else\n\u001b[0;32m 3403\u001b[0m blk.fill_value,))\n\u001b[1;32m-> 3404\u001b[1;33m for blk in self.blocks]\n\u001b[0m\u001b[0;32m 3405\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3406\u001b[0m \u001b[0mnew_axes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mtake_nd\u001b[1;34m(self, indexer, axis, new_mgr_locs, fill_tuple)\u001b[0m\n\u001b[0;32m 933\u001b[0m \u001b[0mfill_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfill_tuple\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 934\u001b[0m new_values = com.take_nd(values, indexer, axis=axis,\n\u001b[1;32m--> 935\u001b[1;33m allow_fill=True, fill_value=fill_value)\n\u001b[0m\u001b[0;32m 936\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 937\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnew_mgr_locs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\_\\Anaconda2\\lib\\site-packages\\pandas\\core\\common.pyc\u001b[0m in \u001b[0;36mtake_nd\u001b[1;34m(arr, indexer, axis, out, fill_value, mask_info, allow_fill)\u001b[0m\n\u001b[0;32m 768\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 769\u001b[0m \u001b[0mout_shape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 770\u001b[1;33m \u001b[0mout_shape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 771\u001b[0m \u001b[0mout_shape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 772\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_contiguous\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: len() of unsized object"
]
}
],
"source": [
"curdf.take(102)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>6.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>6.8</td>\n",
" </tr>\n",
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" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
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" <tr>\n",
" <th>76</th>\n",
" <td>6.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>5.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>6.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>4.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td>7.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>6.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>5.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td>6.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>6.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>6.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td>7.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>5.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>5.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td>6.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>6.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>4.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>5.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>5.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>5.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>5.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>5.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>5.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>6.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>5.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>6.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>6.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>5.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>6.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>6.7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>125 rows × 1 columns</p>\n",
"</div>"
],
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" x1\n",
"52 6.9\n",
"6 4.6\n",
"87 6.3\n",
"112 6.8\n",
"91 6.1\n",
"96 5.7\n",
"76 6.8\n",
"25 5.0\n",
"36 5.5\n",
"112 6.8\n",
"0 5.1\n",
"57 4.9\n",
"30 4.8\n",
"22 4.6\n",
"100 6.3\n",
"136 6.3\n",
"135 7.7\n",
"93 5.0\n",
"42 4.4\n",
"85 6.0\n",
"39 5.1\n",
"104 6.5\n",
"7 5.0\n",
"60 5.0\n",
"15 5.7\n",
"137 6.4\n",
"91 6.1\n",
"148 6.2\n",
"17 5.1\n",
"130 7.4\n",
".. ...\n",
"140 6.7\n",
"78 6.0\n",
"60 5.0\n",
"66 5.6\n",
"121 5.6\n",
"137 6.4\n",
"68 6.2\n",
"77 6.7\n",
"0 5.1\n",
"46 5.1\n",
"140 6.7\n",
"45 4.8\n",
"72 6.3\n",
"29 4.7\n",
"15 5.7\n",
"20 5.4\n",
"136 6.3\n",
"33 5.5\n",
"82 5.8\n",
"61 5.9\n",
"36 5.5\n",
"97 6.2\n",
"66 5.6\n",
"75 6.6\n",
"146 6.3\n",
"97 6.2\n",
"44 5.1\n",
"96 5.7\n",
"74 6.4\n",
"86 6.7\n",
"\n",
"[125 rows x 1 columns]"
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},
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{
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]
}
],
"source": [
":1"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
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{
"data": {
"text/plain": [
"52 1\n",
"Name: y, dtype: int64"
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},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
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},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
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{
"data": {
"text/plain": [
"52 True\n",
"Name: y, dtype: bool"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
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"source": [
"curdf[:1]['y'] == 1"
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},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
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"source": [
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{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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},
"execution_count": 97,
"metadata": {},
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{
"cell_type": "code",
"execution_count": 98,
"metadata": {
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{
"data": {
"text/plain": [
"1"
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},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
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{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
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{
"ename": "NameError",
"evalue": "name 'crdf' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-101-4fd71eb75c1b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrydf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcrdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtrypos\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrypos\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'crdf' is not defined"
]
}
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"source": [
"trydf = crdf[trypos:(trypos+1)]"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trydf = curdf[trypos:(trypos+1)]"
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{
"cell_type": "code",
"execution_count": 103,
"metadata": {
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{
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"metadata": {},
"output_type": "execute_result"
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"source": [
"trydf.columns"
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{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
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"outputs": [
{
"data": {
"text/plain": [
"'x3'"
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},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"trydf.columns[tryfid]"
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{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"tryfname = trydf.columns[tryfid]"
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{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'tryfdname' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-108-d025cce433b7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrydf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtryfdname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'tryfdname' is not defined"
]
}
],
"source": [
"trydf[tryfdname]"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"77 5\n",
"Name: x3, dtype: float64"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trydf[tryfname]"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5.0"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trydf[tryfname].values[0]"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tryfval = trydf[tryfname].values[0]"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<pandas.core.indexing._iLocIndexer at 0x77b3b10>"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"curdf.iloc(10)"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
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{
"data": {
"text/plain": [
"x1 5.1\n",
"x2 3.5\n",
"x3 1.4\n",
"x4 0.2\n",
"y 0.0\n",
"Name: 0, dtype: float64"
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"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"curdf.iloc[10]"
]
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{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tryfval"
]
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{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'tryfd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-115-a215590b001a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtryfd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtryfname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'tryfd' is not defined"
]
}
],
"source": [
"tryfd[tryfname].iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5.0"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trydf[tryfname].iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tryfval = trydf[tryfname].iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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