Skip to content

Instantly share code, notes, and snippets.

@ajasja
Last active June 9, 2016 09:41
Show Gist options
  • Save ajasja/40378cb2514e510c48d66ae330137776 to your computer and use it in GitHub Desktop.
Save ajasja/40378cb2514e510c48d66ae330137776 to your computer and use it in GitHub Desktop.
Display the source blob
Display the rendered blob
Raw
{
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "UnicodeEncodeError",
"evalue": "'ascii' codec can't encode characters in position 0-4: ordinal not in range(128)",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mUnicodeEncodeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-20-b3c09f3d3c0d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'dataset_unicode'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34mu'ÁéÍóÚ {0}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFacetGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'dataset_unicode'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'x'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'y'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\bin\\python\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.pyc\u001b[0m in \u001b[0;36mmap\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m 728\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 729\u001b[0m \u001b[1;31m# Finalize the annotations and layout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 730\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_finalize_grid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[0;32m 731\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 732\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\bin\\python\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.pyc\u001b[0m in \u001b[0;36m_finalize_grid\u001b[1;34m(self, axlabels)\u001b[0m\n\u001b[0;32m 818\u001b[0m \u001b[1;34m\"\"\"Finalize the annotations and layout.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 819\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_axis_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0maxlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 820\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_titles\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 821\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 822\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\bin\\python\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.pyc\u001b[0m in \u001b[0;36mset_titles\u001b[1;34m(self, template, row_template, col_template, **kwargs)\u001b[0m\n\u001b[0;32m 960\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol_name\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol_names\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 961\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 962\u001b[1;33m \u001b[0mtitle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtemplate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 963\u001b[0m \u001b[1;31m# Index the flat array so col_wrap works\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 964\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mUnicodeEncodeError\u001b[0m: 'ascii' codec can't encode characters in position 0-4: ordinal not in range(128)"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA2MAAADfCAYAAAB70QuaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDmjGAAAAOvD/AeZguIEYHX01AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xe8d0e90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"data = sns.load_dataset('anscombe')\n",
"data['dataset_unicode'] = data.dataset.map(lambda x: u'ÁéÍóÚ {0}'.format(x))\n",
"g = sns.FacetGrid(data, col='dataset_unicode')\n",
"g.map(plt.scatter, 'x', 'y')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"data = sns.load_dataset('anscombe')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.columns = ['data',u'Č', u'Ž']"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "UnicodeEncodeError",
"evalue": "'ascii' codec can't encode character u'\\u017d' in position 0: ordinal not in range(128)",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mUnicodeEncodeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-14-9f82b08d047b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFacetGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34mu'Ž'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu'Č'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu'Ž'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\bin\\python\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.pyc\u001b[0m in \u001b[0;36mmap\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m 728\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 729\u001b[0m \u001b[1;31m# Finalize the annotations and layout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 730\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_finalize_grid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[0;32m 731\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 732\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\bin\\python\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.pyc\u001b[0m in \u001b[0;36m_finalize_grid\u001b[1;34m(self, axlabels)\u001b[0m\n\u001b[0;32m 818\u001b[0m \u001b[1;34m\"\"\"Finalize the annotations and layout.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 819\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_axis_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0maxlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 820\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_titles\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 821\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 822\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\bin\\python\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.pyc\u001b[0m in \u001b[0;36mset_titles\u001b[1;34m(self, template, row_template, col_template, **kwargs)\u001b[0m\n\u001b[0;32m 960\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol_name\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcol_names\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 961\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 962\u001b[1;33m \u001b[0mtitle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtemplate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 963\u001b[0m \u001b[1;31m# Index the flat array so col_wrap works\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 964\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mUnicodeEncodeError\u001b[0m: 'ascii' codec can't encode character u'\\u017d' in position 0: ordinal not in range(128)"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAJEsAAADgCAYAAAB90GeSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xbb3d8f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.FacetGrid(data, col=u'Ž')\n",
"g.map(plt.scatter, u'Č', u'Ž')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ImportError",
"evalue": "Import by filename is not supported.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-15-10af9cfa7209>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'load_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark/watermark.py'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\bin\\python\\anaconda\\lib\\site-packages\\IPython\\core\\interactiveshell.pyc\u001b[0m in \u001b[0;36mmagic\u001b[1;34m(self, arg_s)\u001b[0m\n\u001b[0;32m 2161\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2162\u001b[0m \u001b[0mmagic_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2163\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2165\u001b[0m \u001b[1;31m#-------------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\bin\\python\\anaconda\\lib\\site-packages\\IPython\\core\\interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_line_magic\u001b[1;34m(self, magic_name, line)\u001b[0m\n\u001b[0;32m 2082\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'local_ns'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2083\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2084\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2085\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2086\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<decorator-gen-65>\u001b[0m in \u001b[0;36mload_ext\u001b[1;34m(self, module_str)\u001b[0m\n",
"\u001b[1;32mc:\\bin\\python\\anaconda\\lib\\site-packages\\IPython\\core\\magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(f, *a, **k)\u001b[0m\n\u001b[0;32m 191\u001b[0m \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 192\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\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 194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 195\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\bin\\python\\anaconda\\lib\\site-packages\\IPython\\core\\magics\\extension.pyc\u001b[0m in \u001b[0;36mload_ext\u001b[1;34m(self, module_str)\u001b[0m\n\u001b[0;32m 64\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mUsageError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Missing module name.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'already loaded'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\bin\\python\\anaconda\\lib\\site-packages\\IPython\\core\\extensions.pyc\u001b[0m in \u001b[0;36mload_extension\u001b[1;34m(self, module_str)\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmodule_str\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 83\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mprepended_to_syspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m \u001b[0m__import__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 85\u001b[0m \u001b[0mmod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 86\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_load_ipython_extension\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mImportError\u001b[0m: Import by filename is not supported."
]
}
],
"source": [
"%load_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark/watermark.py"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Installed watermark.py. To use it, type:\n",
" %load_ext watermark\n",
"The watermark extension is already loaded. To reload it, use:\n",
" %reload_ext watermark\n",
"CPython 2.7.11\n",
"IPython 4.1.1\n",
"\n",
"matplotlib 1.5.1\n",
"seaborn 0.7.0\n",
"\n",
"compiler : MSC v.1500 32 bit (Intel)\n",
"system : Windows\n",
"release : 8.1\n",
"machine : AMD64\n",
"processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel\n",
"CPU cores : 8\n",
"interpreter: 32bit\n"
]
}
],
"source": [
"%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark/watermark.py\n",
"%load_ext watermark\n",
"%watermark -vm -p matplotlib,seaborn"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"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": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
Display the source blob
Display the rendered blob
Raw
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment