146 lines
34 KiB
Plaintext
146 lines
34 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pylab\n",
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"import scipy.stats as stats\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plot"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"data = np.mat([[1,200,105,3,False],[2,165,80,2,False],[3,184.5,120,2,False],[4,116,70.8,1,False],[5,270,150,4,True]])\n",
|
|
"col1 = []\n",
|
|
"for row in data:\n",
|
|
" col1.append(row[0,1])\n",
|
|
"stats.probplot(col1,plot=pylab)\n",
|
|
"pylab.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0 2\n",
|
|
"1 165\n",
|
|
"2 80\n",
|
|
"Name: 1, dtype: object\n",
|
|
"0 3\n",
|
|
"1 184.5\n",
|
|
"2 120\n",
|
|
"Name: 2, dtype: object\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0 4\n",
|
|
"1 116\n",
|
|
"2 70.8\n",
|
|
"Name: 3, dtype: object\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rocksVMines = pd.DataFrame([[1,200,105,3,False],[2,165,80,2,False],[3,184.5,120,2,False],[4,116,70.8,1,False],[5,270,150,4,True]])\n",
|
|
"dataRow1 = rocksVMines.iloc[1,0:3]\n",
|
|
"dataRow2 = rocksVMines.iloc[2,0:3]\n",
|
|
"print(dataRow1)\n",
|
|
"print(dataRow2)\n",
|
|
"plot.scatter(dataRow1,dataRow2)\n",
|
|
"plot.xlabel(\"Attribute1\")\n",
|
|
"plot.ylabel(\"Attribute2\")\n",
|
|
"plot.show()\n",
|
|
"\n",
|
|
"dataRow3 = rocksVMines.iloc[3,0:3]\n",
|
|
"print(dataRow3)\n",
|
|
"plot.scatter(dataRow2,dataRow3)\n",
|
|
"plot.xlabel(\"Attribute2\")\n",
|
|
"plot.ylabel(\"Attribute3\")\n",
|
|
"plot.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.6.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|