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@bertomartin
Forked from germannp/GPU-Prices.ipynb
Created August 3, 2016 19:28
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Comparing GPU prices
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{
"cells": [
{
"cell_type": "code",
"execution_count": 33,
"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>Cores</th>\n",
" <th>Performance</th>\n",
" <th>Price</th>\n",
" <th>Price PSU</th>\n",
" <th>Effective Price</th>\n",
" <th>Price Dual SLI</th>\n",
" <th>Price Quad SLI</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>GTX 960</th>\n",
" <td>1024</td>\n",
" <td>100</td>\n",
" <td>250</td>\n",
" <td>0</td>\n",
" <td>250</td>\n",
" <td>1000</td>\n",
" <td>1800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GTX 970</th>\n",
" <td>1664</td>\n",
" <td>160</td>\n",
" <td>350</td>\n",
" <td>50</td>\n",
" <td>400</td>\n",
" <td>1200</td>\n",
" <td>2200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GTX 980</th>\n",
" <td>2048</td>\n",
" <td>190</td>\n",
" <td>550</td>\n",
" <td>50</td>\n",
" <td>600</td>\n",
" <td>1600</td>\n",
" <td>3000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GTX 980 Ti</th>\n",
" <td>2816</td>\n",
" <td>245</td>\n",
" <td>730</td>\n",
" <td>50</td>\n",
" <td>780</td>\n",
" <td>1960</td>\n",
" <td>3720</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Cores Performance Price Price PSU Effective Price \\\n",
"GTX 960 1024 100 250 0 250 \n",
"GTX 970 1664 160 350 50 400 \n",
"GTX 980 2048 190 550 50 600 \n",
"GTX 980 Ti 2816 245 730 50 780 \n",
"\n",
" Price Dual SLI Price Quad SLI \n",
"GTX 960 1000 1800 \n",
"GTX 970 1200 2200 \n",
"GTX 980 1600 3000 \n",
"GTX 980 Ti 1960 3720 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"gpus = pd.DataFrame({\n",
" 'Cores': [1024, 1664, 2048, 2816],\n",
" 'Performance': [100, 160, 190, 245],\n",
" 'Price': [250, 350, 550, 730], \n",
" 'Price PSU': [0, 50, 50, 50]}, \n",
" index=['GTX 960', 'GTX 970', 'GTX 980', 'GTX 980 Ti'])\n",
"gpus['Effective Price'] = gpus.apply(lambda row: row['Price'] + row['Price PSU'], axis=1)\n",
"gpus['Price Dual SLI'] = gpus.apply(lambda row: row['Price']*2 + 500, axis=1)\n",
"gpus['Price Quad SLI'] = gpus.apply(lambda row: row['Price']*4 + 800, axis=1)\n",
"\n",
"gpus"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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4kJSmmCTx9dnGHOCSyOv+hJ+m/xQYYGaNzawBMIhw1y1STX24fAf/8+cP2JGd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\niomIiJTb/wf/zEumoCfAhgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f88a070fb38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(gpus['Cores'], gpus['Performance'])\n",
"plt.xlabel('Cores')\n",
"plt.ylabel('Gaming Performance')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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lSrR68H67cNSUKkUZap2uqj4RAUBV/SJyVqPdRGQYbhHLwx3KvRaYilsMU4Dh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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f88a052b160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.title('Current Situation')\n",
"plt.xlabel('Euros')\n",
"plt.ylabel('Cores')\n",
"plt.plot(gpus['Effective Price'], gpus['Cores'], label='Single')\n",
"plt.plot(gpus['Price Dual SLI'], gpus['Cores']*2, label='Double')\n",
"plt.plot(gpus['Price Quad SLI'], gpus['Cores']*4, label='Quad')\n",
"plt.legend(loc='upper left')\n",
"sns.despine()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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1XqeUGgCMBszAV1rruUopN+BboCpgAgZprU8rpRoCswED2K+1HmnP5yBEfmcr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\ninPzMTOz4tx8zMysODcfMzMr7i94sButR7bJsAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f88a04296a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.title('Future Expectation')\n",
"plt.xlabel('Euros')\n",
"plt.ylabel('Cores')\n",
"plt.plot(gpus['Effective Price'], gpus['Cores'], color='k', label='Current')\n",
"plt.plot(gpus['Price Dual SLI'], gpus['Cores']*2, color='k')\n",
"plt.plot(gpus['Price Quad SLI'], gpus['Cores']*4, color='k')\n",
"plt.plot(gpus.loc['GTX 970', 'Price']*np.array([1, 3]) + np.array([500, 800]), \n",
" gpus.loc['GTX 980 Ti', 'Cores']*np.array([2, 4]), label='Dual/Quad GTX 980 Ti Upgrade')\n",
"plt.plot(gpus.loc['GTX 980 Ti', 'Price']*np.array([1, 2, 3]) + np.array([50, 500, 800]), \n",
" gpus.loc['GTX 980 Ti', 'Cores']*2*np.array([1, 2, 4]), label='Successor?')\n",
"plt.legend(loc='upper left')\n",
"sns.despine()\n",
"plt.show()"
]
}
],
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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