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@mikepqr
Created July 22, 2015 21:39
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"x = pd.DataFrame(zip(range(10), range(0, -10, -1)), columns=['a', 'b'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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ixx4L114LQ4bUWYWISBuqt4HvAaws+3wVcEjlSWPGlPaCnD5de0GKiDRCXTcxzexjwFR3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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1067e6110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(2, 1, sharex=True)\n",
"x.a.plot(ax=ax[0])\n",
"ax[0].set_title('title a')\n",
"x.b.plot(ax=ax[1])\n",
"ax[1].set_title('title b');"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x.index = pd.date_range('2000-01-01', freq='Q', periods=10)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ktOqmZXE99M2Bl4D93f3lMsf4tGnT3vtc13IRaVxKq5bX/lou55xzTuoNfSIw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\noYuI1Ak1dBGROqGGLiJSJ9TQRUTqxP8H9r021+Ww+W0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x106922950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(2, 1, sharex=True)\n",
"x.a.plot(ax=ax[0])\n",
"ax[0].set_title('title a')\n",
"x.b.plot(ax=ax[1])\n",
"ax[1].set_title('title b');"
]
}
],
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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