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plottest.ipynb
{
"metadata": {
"name": "",
"signature": "sha256:9299994bc33725ce2fb54720efc2a7b1a7fb1e955d3914b9e8ba9de6a50bd456"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ts = pd.Series(np.random.randn(1000))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ts = ts.cumsum()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ts.plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10f5640f0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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MCMgmnQBYt86ZuZEnqmzfTi/Sddc545oTCWeuE1ariyEAKskHMGKEc0RtwwIAMBqfbd4o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\nirAEgLQNg9SF/4QuAGqSizvbzr2//90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3n0rufxLAfVrrhNZ6A+gffERRCx0gSqkpAE4F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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10f545da0>"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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