Skip to content

Instantly share code, notes, and snippets.

@JonasHeylen
Created February 26, 2016 09:22
Show Gist options
  • Save JonasHeylen/f052b3898971d32fb695 to your computer and use it in GitHub Desktop.
Save JonasHeylen/f052b3898971d32fb695 to your computer and use it in GitHub Desktop.
Display the source blob
Display the rendered blob
Raw
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEZCAYAAACNebLAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x676e198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, axes = plt.subplots(2)\n",
"axes[0].plot(np.random.normal(size=100))\n",
"axes[1].plot(np.random.normal(size=100))\n",
"for i, ax in enumerate(axes):\n",
" ax.set_xlabel('x-as')\n",
" ax.set_ylabel('y-as')\n",
" ax.set_ylim([-5, 5])\n",
" ax.set_title('random #{}'.format(i+1))\n",
"f.subplots_adjust(hspace=0.7)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2 (relays_in_shelters)",
"language": "python",
"name": "relays_in_shelters"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
@Mustapha1992
Copy link

Cool code. An ardent R-user but am falling in love with Python (Pandas).

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment