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@wildmichael
Last active January 22, 2017 16:03
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"from ggplot import *"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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nnihHz56Kf/WV4hkZquzeXabXa3VZACzWv39/rVq1SmPGjJEkrVy5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"text/plain": [
"<matplotlib.figure.Figure at 0xc48a400>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<ggplot: (12836395)>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zMin = 1e1\n",
"zMax = 2e1\n",
"n = 100\n",
"x, z, z = np.random.rand(n), np.random.rand(n), np.random.rand(n)*(zMax - zMin) + zMin\n",
"xyz = pd.DataFrame(dict(x=XX, y=YY, z=ZZ))\n",
"ggplot(xyz, aes(x='x', y='y', color='z')) \\\n",
" + geom_point() \\\n",
" + scale_color_gradient(low=\"blue\", mid=\"white\", high=\"red\")"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:data]",
"language": "python",
"name": "conda-env-data-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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