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Ipython notebook exploring nikeplus data
{
"metadata": {
"name": "NikePlus data exploration"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": "# CSV file for demonstration\n# Backup your own NikePlus data to CSV by using the following:\n# API wrapper: https://github.com/durden/nikeplus\n# Video explaining concept behind API wrapper: http://www.youtube.com/watch?v=jA0dwPtiu7c",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 102
},
{
"cell_type": "code",
"collapsed": false,
"input": "cat nikeplus.csv | head",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "device,miles,steps,pace,fuel,duration,kilometers,calories,start_time,distance\r\nFUELBAND,2.83188048382,5788,(23'47/mi),3032,12:27:00,4.55747127533,938,2013-08-15T05:00:00Z,4.55747127533\r\r\nFUELBAND,3.88869817064,7948,(7'43/mi),3469,12:10:00,6.25825500488,1074,2013-08-14T05:00:00Z,6.25825500488\r\r\nFUELBAND,2.64008749951,5396,(51'39/mi),2797,12:50:00,4.24881029129,865,2013-08-13T05:00:00Z,4.24881029129\r\r\nFUELBAND,3.94300685979,8059,(2'21/mi),3097,11:59:00,6.34565639496,960,2013-08-12T05:00:00Z,6.34565639496\r\r\nFUELBAND,4.18121737721,6502,(52'35/mi),2935,9:09:00,5.11967468262,908,2013-08-11T05:00:00Z,5.11967468262\r\r\nFUELBAND,1.86949115843,3821,(41'22/mi),2186,8:46:00,3.00865530968,675,2013-08-10T05:00:00Z,3.00865530968\r\r\nFUELBAND,4.3549702696,8901,(23'58/mi),3484,10:27:00,7.00864744186,1085,2013-08-09T05:00:00Z,7.00864744186\r\r\nFUELBAND,3.81432957128,7796,(5'53/mi),3159,11:49:00,6.13857030869,979,2013-08-08T05:00:00Z,6.13857030869\r\r\nFUELBAND,3.30010959661,6745,(57'34/mi),3252,13:04:00,5.31101322174,1006,2013-08-07T05:00:00Z,5.31101322174\r\r\n"
}
],
"prompt_number": 103
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Lets use the pandas library to explore this data in memory\n# in a data structure that called a DataFrame, which you can\n# think of as similar to an Excel spreadsheet.\nimport pandas as pd",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 104
},
{
"cell_type": "code",
"collapsed": false,
"input": "nike = pd.read_csv('nikeplus.csv')\nnike",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nInt64Index: 528 entries, 0 to 527\nData columns (total 10 columns):\ndevice 528 non-null values\nmiles 528 non-null values\nsteps 528 non-null values\npace 528 non-null values\nfuel 528 non-null values\nduration 528 non-null values\nkilometers 528 non-null values\ncalories 528 non-null values\nstart_time 528 non-null values\ndistance 528 non-null values\ndtypes: float64(3), int64(3), object(4)\n</pre>",
"output_type": "pyout",
"prompt_number": 105,
"text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 528 entries, 0 to 527\nData columns (total 10 columns):\ndevice 528 non-null values\nmiles 528 non-null values\nsteps 528 non-null values\npace 528 non-null values\nfuel 528 non-null values\nduration 528 non-null values\nkilometers 528 non-null values\ncalories 528 non-null values\nstart_time 528 non-null values\ndistance 528 non-null values\ndtypes: float64(3), int64(3), object(4)"
}
],
"prompt_number": 105
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Use date column as index instead of a normal column so we can plot with it and\n# anchor all data based on a date since our data only has 1 entry per day.\nnike = pd.read_csv('nikeplus.csv', index_col=8)\nnike",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nIndex: 528 entries, 2013-08-15T05:00:00Z to 2012-02-28T06:00:00Z\nData columns (total 9 columns):\ndevice 528 non-null values\nmiles 528 non-null values\nsteps 528 non-null values\npace 528 non-null values\nfuel 528 non-null values\nduration 528 non-null values\nkilometers 528 non-null values\ncalories 528 non-null values\ndistance 528 non-null values\ndtypes: float64(3), int64(3), object(3)\n</pre>",
"output_type": "pyout",
"prompt_number": 106,
"text": "<class 'pandas.core.frame.DataFrame'>\nIndex: 528 entries, 2013-08-15T05:00:00Z to 2012-02-28T06:00:00Z\nData columns (total 9 columns):\ndevice 528 non-null values\nmiles 528 non-null values\nsteps 528 non-null values\npace 528 non-null values\nfuel 528 non-null values\nduration 528 non-null values\nkilometers 528 non-null values\ncalories 528 non-null values\ndistance 528 non-null values\ndtypes: float64(3), int64(3), object(3)"
}
],
"prompt_number": 106
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Pandas DataFrame's support easy indexing by column names and traditional Python\n# list style slicing. We'll use this trick a lot in this demonstration just\n# to prevent showing lots of data at once.\nnike['miles'][:5]",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 107,
"text": "start_time\n2013-08-15T05:00:00Z 2.831880\n2013-08-14T05:00:00Z 3.888698\n2013-08-13T05:00:00Z 2.640087\n2013-08-12T05:00:00Z 3.943007\n2013-08-11T05:00:00Z 3.181217\nName: miles, dtype: float64"
}
],
"prompt_number": 107
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Pandas can turn our DataFrame into html, but displaying is not as nice in\n# this notebook interface by default.\nnike[:5].to_html()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 108,
"text": "u'<table border=\"1\" class=\"dataframe\">\\n <thead>\\n <tr style=\"text-align: right;\">\\n <th></th>\\n <th>device</th>\\n <th>miles</th>\\n <th>steps</th>\\n <th>pace</th>\\n <th>fuel</th>\\n <th>duration</th>\\n <th>kilometers</th>\\n <th>calories</th>\\n <th>distance</th>\\n </tr>\\n <tr>\\n <th>start_time</th>\\n <th></th>\\n <th></th>\\n <th></th>\\n <th></th>\\n <th></th>\\n <th></th>\\n <th></th>\\n <th></th>\\n <th></th>\\n </tr>\\n </thead>\\n <tbody>\\n <tr>\\n <th>2013-08-15T05:00:00Z</th>\\n <td> FUELBAND</td>\\n <td> 2.831880</td>\\n <td> 5788</td>\\n <td> (23\\'47/mi)</td>\\n <td> 3032</td>\\n <td> 12:27:00</td>\\n <td> 4.557471</td>\\n <td> 938</td>\\n <td> 4.557471</td>\\n </tr>\\n <tr>\\n <th>2013-08-14T05:00:00Z</th>\\n <td> FUELBAND</td>\\n <td> 3.888698</td>\\n <td> 7948</td>\\n <td> (7\\'43/mi)</td>\\n <td> 3469</td>\\n <td> 12:10:00</td>\\n <td> 6.258255</td>\\n <td> 1074</td>\\n <td> 6.258255</td>\\n </tr>\\n <tr>\\n <th>2013-08-13T05:00:00Z</th>\\n <td> FUELBAND</td>\\n <td> 2.640087</td>\\n <td> 5396</td>\\n <td> (51\\'39/mi)</td>\\n <td> 2797</td>\\n <td> 12:50:00</td>\\n <td> 4.248810</td>\\n <td> 865</td>\\n <td> 4.248810</td>\\n </tr>\\n <tr>\\n <th>2013-08-12T05:00:00Z</th>\\n <td> FUELBAND</td>\\n <td> 3.943007</td>\\n <td> 8059</td>\\n <td> (2\\'21/mi)</td>\\n <td> 3097</td>\\n <td> 11:59:00</td>\\n <td> 6.345656</td>\\n <td> 960</td>\\n <td> 6.345656</td>\\n </tr>\\n <tr>\\n <th>2013-08-11T05:00:00Z</th>\\n <td> FUELBAND</td>\\n <td> 3.181217</td>\\n <td> 6502</td>\\n <td> (52\\'35/mi)</td>\\n <td> 2935</td>\\n <td> 9:09:00</td>\\n <td> 5.119675</td>\\n <td> 908</td>\\n <td> 5.119675</td>\\n </tr>\\n </tbody>\\n</table>'"
}
],
"prompt_number": 108
},
{
"cell_type": "code",
"collapsed": false,
"input": "# IPython is several things all rolled into one including, but not limited to:\n# 1. Python library that can do all sorts of things including displaying html\n# 2. Provides this notebook interface to execute code and display results\nfrom IPython.core.display import display_html\ndisplay_html(nike[:5].to_html(), raw=True)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>device</th>\n <th>miles</th>\n <th>steps</th>\n <th>pace</th>\n <th>fuel</th>\n <th>duration</th>\n <th>kilometers</th>\n <th>calories</th>\n <th>distance</th>\n </tr>\n <tr>\n <th>start_time</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2013-08-15T05:00:00Z</th>\n <td> FUELBAND</td>\n <td> 2.831880</td>\n <td> 5788</td>\n <td> (23'47/mi)</td>\n <td> 3032</td>\n <td> 12:27:00</td>\n <td> 4.557471</td>\n <td> 938</td>\n <td> 4.557471</td>\n </tr>\n <tr>\n <th>2013-08-14T05:00:00Z</th>\n <td> FUELBAND</td>\n <td> 3.888698</td>\n <td> 7948</td>\n <td> (7'43/mi)</td>\n <td> 3469</td>\n <td> 12:10:00</td>\n <td> 6.258255</td>\n <td> 1074</td>\n <td> 6.258255</td>\n </tr>\n <tr>\n <th>2013-08-13T05:00:00Z</th>\n <td> FUELBAND</td>\n <td> 2.640087</td>\n <td> 5396</td>\n <td> (51'39/mi)</td>\n <td> 2797</td>\n <td> 12:50:00</td>\n <td> 4.248810</td>\n <td> 865</td>\n <td> 4.248810</td>\n </tr>\n <tr>\n <th>2013-08-12T05:00:00Z</th>\n <td> FUELBAND</td>\n <td> 3.943007</td>\n <td> 8059</td>\n <td> (2'21/mi)</td>\n <td> 3097</td>\n <td> 11:59:00</td>\n <td> 6.345656</td>\n <td> 960</td>\n <td> 6.345656</td>\n </tr>\n <tr>\n <th>2013-08-11T05:00:00Z</th>\n <td> FUELBAND</td>\n <td> 3.181217</td>\n <td> 6502</td>\n <td> (52'35/mi)</td>\n <td> 2935</td>\n <td> 9:09:00</td>\n <td> 5.119675</td>\n <td> 908</td>\n <td> 5.119675</td>\n </tr>\n </tbody>\n</table>",
"output_type": "display_data"
}
],
"prompt_number": 109
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Typically you could just plot a DataFrame like this, which by default\n# plots all columns as a separate line on our graph. However, our\n# data contains some non-numeric columns like 'device' and 'start_time'.\n\n# Uncomment this line and run this cell with shift-enter to see\n# the associated error.\n#nike.plot()",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 110
},
{
"cell_type": "code",
"collapsed": false,
"input": "# However, we can index by column names and plot only a single column,\n# a Pandas.DataSeries data structure, to focus on a single column\n# of data. This will work because we now the miles column is all numerical\n# data. Again, still could be prettier, maybe there's a problem with our\n# importing?\nnike['miles'].plot()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 111,
"text": "<matplotlib.axes.AxesSubplot at 0x72c7150>"
},
{
"output_type": "display_data",
"png": 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P/p8UDE1HYGciExWMeH3o+n/0UdREMG5NZEFRMJpgbOCVYM4/Hzj9dGOZMWDW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\nFynfXbpzXgs0AAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x72c7250>"
}
],
"prompt_number": 111
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Pandas has A LOT of arguments to tweak how CSV files are imported\n# including the ability to parse dates into Python datetime objects\n# instead of strings\nnike = pd.read_csv('nikeplus.csv', index_col=8, parse_dates=[8])\nnike",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nDatetimeIndex: 528 entries, 2013-08-15 05:00:00 to 2012-02-28 06:00:00\nData columns (total 9 columns):\ndevice 528 non-null values\nmiles 528 non-null values\nsteps 528 non-null values\npace 528 non-null values\nfuel 528 non-null values\nduration 528 non-null values\nkilometers 528 non-null values\ncalories 528 non-null values\ndistance 528 non-null values\ndtypes: float64(3), int64(3), object(3)\n</pre>",
"output_type": "pyout",
"prompt_number": 112,
"text": "<class 'pandas.core.frame.DataFrame'>\nDatetimeIndex: 528 entries, 2013-08-15 05:00:00 to 2012-02-28 06:00:00\nData columns (total 9 columns):\ndevice 528 non-null values\nmiles 528 non-null values\nsteps 528 non-null values\npace 528 non-null values\nfuel 528 non-null values\nduration 528 non-null values\nkilometers 528 non-null values\ncalories 528 non-null values\ndistance 528 non-null values\ndtypes: float64(3), int64(3), object(3)"
}
],
"prompt_number": 112
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Note that IPython code cells keep a global state, so we don't\n# need to import this function again if we run this cell after\n# the cell above with the import in it.\ndisplay_html(nike[:5].to_html(), raw=True)",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>device</th>\n <th>miles</th>\n <th>steps</th>\n <th>pace</th>\n <th>fuel</th>\n <th>duration</th>\n <th>kilometers</th>\n <th>calories</th>\n <th>distance</th>\n </tr>\n <tr>\n <th>start_time</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2013-08-15 05:00:00</th>\n <td> FUELBAND</td>\n <td> 2.831880</td>\n <td> 5788</td>\n <td> (23'47/mi)</td>\n <td> 3032</td>\n <td> 12:27:00</td>\n <td> 4.557471</td>\n <td> 938</td>\n <td> 4.557471</td>\n </tr>\n <tr>\n <th>2013-08-14 05:00:00</th>\n <td> FUELBAND</td>\n <td> 3.888698</td>\n <td> 7948</td>\n <td> (7'43/mi)</td>\n <td> 3469</td>\n <td> 12:10:00</td>\n <td> 6.258255</td>\n <td> 1074</td>\n <td> 6.258255</td>\n </tr>\n <tr>\n <th>2013-08-13 05:00:00</th>\n <td> FUELBAND</td>\n <td> 2.640087</td>\n <td> 5396</td>\n <td> (51'39/mi)</td>\n <td> 2797</td>\n <td> 12:50:00</td>\n <td> 4.248810</td>\n <td> 865</td>\n <td> 4.248810</td>\n </tr>\n <tr>\n <th>2013-08-12 05:00:00</th>\n <td> FUELBAND</td>\n <td> 3.943007</td>\n <td> 8059</td>\n <td> (2'21/mi)</td>\n <td> 3097</td>\n <td> 11:59:00</td>\n <td> 6.345656</td>\n <td> 960</td>\n <td> 6.345656</td>\n </tr>\n <tr>\n <th>2013-08-11 05:00:00</th>\n <td> FUELBAND</td>\n <td> 3.181217</td>\n <td> 6502</td>\n <td> (52'35/mi)</td>\n <td> 2935</td>\n <td> 9:09:00</td>\n <td> 5.119675</td>\n <td> 908</td>\n <td> 5.119675</td>\n </tr>\n </tbody>\n</table>",
"output_type": "display_data"
}
],
"prompt_number": 113
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Now our plot will look much better because Pandas and matplotlib\n# know our index column is actually a datetime object, not a string.\n\n# Notice this is just too much data to show in the default\n# width, but IPython provides the ability to drag the bottom\n# right corner to increase the size of a plot.\nnike['miles'].plot()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 114,
"text": "<matplotlib.axes.AxesSubplot at 0x73e8f90>"
},
{
"output_type": "display_data",
"png": 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eEO7ipk1mG/yEn1lMfoZ83IjUfl7PaZRVNwThNsRkFWtO1YPYt8/c8vfqQagh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\n3EHIyMjIyDTI/wGPhxjTlEcWxgAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x74e0d50>"
}
],
"prompt_number": 114
},
{
"cell_type": "code",
"collapsed": false,
"input": "# However, showing less data looks even better in this window.\nnike['miles'][:20].plot()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 115,
"text": "<matplotlib.axes.AxesSubplot at 0x74dad50>"
},
{
"output_type": "display_data",
"png": 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fBuvDw8Opb9++BR6jKPozZz55wwgPDze5nVZL5OVl9mEtIitLZDT999+CXfKS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\nQmCC\n",
"text": "<matplotlib.figure.Figure at 0x74cee50>"
}
],
"prompt_number": 115
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Again, the plot function passes information directly to matplotlib\n# so there are lots of arguments to tweak the display. For example,\n# we can add a title.\nnike['miles'][:30].plot(title='Miles')",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 116,
"text": "<matplotlib.axes.AxesSubplot at 0x72a73d0>"
},
{
"output_type": "display_data",
"png": 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VwWMSto9cDlSrBjx+DDjomew+Lw9o0gTIzQUcHU2jz9r47Tfgq6+Ao0fZ8pMnQFAQi/m8\n955ltXHMh6jnk1CyZs0aeHt74+HDhxq3BwcHIyoqysyqOGLj/n2gTh39DQTA9mvWDLh0CfDzE16b\nNbJzJ3MxKalWDdixAwgMBDp1Anr0sJw2juURjbspLS0N0dHReOutt7S2BIRqIdiy/9BQxKIDqFyL\noa4mJbq6nKzpmhjKkycsaP10zi8VzZsDkZHAq68CmZnm0aIvYtEB2LYW0RiJWbNm4YsvvoCdnWZJ\nEokEJ0+ehJ+fH0JCQnDp0iUzK+SIBXMZiarAkSNAu3as+2tZXn6ZTQ87dixQUmJ+bRxxIAp30759\n+9CoUSMEBARotYLt27dHamoqnJ2dceDAAQwbNgxXr17VWHbixInw9PQEALi6usLf3x9SqRRAeSur\nXC673VzLynWWOr5Yl5Vo2n78ONCwoeH129sDp05VXl4qlYrmephqef16Gdq1AwDN24OCZNi/H/jo\nIymWLxfX76cq3B9T/H6UyzKZDJGRkQCgel9qQhSB648++gg//vgjHBwc8PjxY+Tl5WHkyJHYsmWL\n1n1atGiBM2fOoF69emrreeDa9omIYC2Bb781bP+iIjbG4sYNoH59YbVZE0VFLIh/7hzg7q69XE4O\na32tXMkSJHJsE1EPpluyZAlSU1Nx48YNbN++Hb169SpnILKyslQnEB8fDyIqZyB0pay1tSRi0SIW\nHYDpYxJOTkD37kBMjHE6zIkptMTEAC+8ULGBAJhB3bEDmDoVuHpVPNdFLDoA29YiCiNRFsnT6cYi\nIiIQEREBANi5cyd8fX3h7++P8PBwbNcnRzTHpsjJMc5IAGwsgLLLZ1Vl506W3kQXOnUCFi1i5R8/\nNq0ujrgQhbtJSLi7yfZ57TUWVDUmx9D588x1UlUnISopYa6m+HigRQvd9iECJkxgU8b+8IPuU8dy\nrANRu5s4HH0w1t0EAL6+LKndjRvCaLI2jh9n3Vx1NRAAMwobN7JMut98YzptHHFRJY2ELfsPy7Jv\nHwv0WlqHPpg6JgGwF15lLidruib6smuX7q6m0tSsCXz4oQxz5wIJCYJK0htbvj/GUCViEhzhiI0F\n5sxhX822ghAxCaDqxiXkcmD3bvVR1vrg4QGsXcvGUCgUwmrjiA8ek7Bxxo1jI2rnzgXef9/SaoyH\nCKhRA7h3D3B2Nq6u1FQgIAC4cwews5LPpdxcwNXVuDr+/BOYPp11fTUUIhbMnjPHcGPDERc8JlFF\nycgA5s9nfdxtoVdKfj5gb2+8gQDYF3GDBsa9LM3JypWsBXXmjHH17Npl/ItdImG9nebP5zP92TpV\n0kjYsv+wLOnpwMCB7Iv56eBKi+jQh4q0CBGPKE2fPiw1hb46zIlCAYwdK8N337EX85QphqfJUCgM\nj0coUV6XAQOA2rWBX381vC5jEMv9AWxbS5U0ElUFItaSeO454KOPgGXLrD8Hj1DxCCVij0sUFwOh\noWy2uL/+AmbPBurWZTEBQ0hIYC92b2/jtUkkwKefAgsWWP9zJXaKi9mARkvAYxI2TG4u6+aonGUs\nOBiYPJlNLGOt7N8PrF8PHDggTH25ucztlJ0NVK8uTJ1CkZ/P5vB2cAB++eWZiy05GejalbmdmjfX\nr84PPmDn+emnwmgkAqRSNklRaKgwdXLUIQKmTWO9FNPT2UefKeAxiSpI2Qfq44+Bzz+37h4pQrub\nXF2Btm2BkyeFq1MIsrOBXr3YgLffflOPwbRuzeai/t//9Js9jki/Uda6oGxNLFrEvnY5wrN6NXs+\ng4Is0+24ShoJW/YfliYjA2ja9Nly376sZ5CmeZus5ZoIbSQA7XEJS12TmzdZbqm+fYFNm1hLoqyW\n999n5fRxQSQmsqA/y/pqOGW19OgBtGxZcczLFFjLM2sMe/YAX37Jxjv17KmbkeAxCY7OlG1JSCQs\nNrFkifXOXyx0TAIQV1zi/HlmIKZPBz77THvqCycnNup51izmMtMFZSvCFOk0Pv0UWLyYTWLEEYaz\nZ4G33gJ+/53Npti5M0ujYm54TMKGWbKEDaL7/PNn6xQKwMcHWLeOvRytjUmTgG7d2I9HKJ48YYbn\n5k3AwMTCghAby2IQ69cDo0frts+0aczgb9xYcTki4MUXgZ9+YuMbTMHAgUBICHODcYwjLY3FnVav\nftZd+c4ddg/v3jXNuB4ek6iCaApy2dmxAVBLllhGk7GYwt1UrZpuqcNNye7dzEBs26a7gQDYB8De\nvaznU0VcvMiMYceOxumsiEWL2HNVWGi6Y1QF8vOBwYNZa7L0eJZGjVgMzdxJKaukkagKvkygfExC\nyauvAtevA3//bR4d+mLumASg2eVkrmuyYQPwzjvAoUNA796ay2jT4uoKrFkDhIWxSYS0IaSrSZuW\nDh2YS2TDBuOPYYwOSyCUFrmcZUno0AH48MPy23VxOdl0TEIulyMgIACDBw/WuH3GjBlo3bo1/Pz8\nkJiYaGZ11oe27nKOjuwBLO2GshZyctgoaaGpaFCdKYmIYCOp//yTDXg0hJEjWeB4+XLtZYQYZa0L\nCxcyHfn5pj+WLfL++8CjR8DXX2s26BaJS5CIWLFiBY0bN44GDx5cbtv+/ftpwIABREQUFxdHgYGB\nGusQ2SlZlKZNiW7d0rytoIDIzY3o3DnzajKWOnWI7t0Tvl65nKhRI6Lr14Wvu6JjenoSJSQYX9d/\n/xE1aED077/lt12+TPTcc+x45mDMGKLPPzfPsWyJr74ievHFip/v48eJtLz6jEbbu1M0LYm0tDRE\nR0fjrbfe0hg8iYqKQujT0TqBgYHIzc1FVlaWuWVaDXI5C3S5uWneXqMG6xmzdKl5dRnDkydAQYHx\nCe40YWfHWhN//CF83do4dgxwcWGuBWNp1oyNg5k6tXzPtV272ARL5kpiuGABax3l5ZnneLbAwYMs\nprN/PxtRr4327dno+4pci0IjGiMxa9YsfPHFF7DT8iSnp6fDw8NDtezu7o60tDSDjmWLvsyy3LnD\neuo4OmovM3UqcPgwC4RZwzW5e5e5mkw1I1rZuISpr8mmTayXli7no4uWd95hL+YfflBfL/QAusq0\ntGnDZg5cvVq4Yxqiw5wYo+XCBTbj386dwPPPV1y2Zk2gVSvWVdoUWjThIGhtBrJv3z40atQIAQEB\nFZ5g2RaGRMuva+LEifD09AQAuLq6wt/fH1KpFAC7gElJSWrLACy2nJSUZJL6a9WSomnTisvXqQOE\nhMgQHv4sjbilr0dF9yc7G6hRQwaZzDTH790bmDVLhmPHgF69THu+vr5SHDgAjBun2/koqah+e3tg\nyhQZZs0CBg6UomFD4OefZU8H5wmnX5ffz7x5UnTpAgQEyFC7tmWfJ3MsK9F3/927ZXj7bWD1aim6\nd9dtfw8PID5eio4dDb8/UqkUMpkMkU9HQCrflxoxjXdLP+bMmUPu7u7k6elJbm5u5OzsTK+//rpa\nmbCwMNq2bZtq+cUXX6Tbt2+Xq0sMp6RQEJ05Y1kNv/9ONGhQ5eWys4nq1iVKSzO9JmM5coSoZ0/T\nHuOFF4jOnjXtMYiI1qwhGjfONHW/9x7R+PHs/8uWEYWFmeY4lfHmm0Qff2yZY2ujqIjo9GlLq2A8\nekTUuTPRggX67ffNN0ShocLr0fbuFIW7acmSJUhNTcWNGzewfft29OrVC1u2bFErM2TIENW6uLg4\nuLq6onHjxpaQWyn//AMEBjL3iKXQ1v21LA0aABMnAitWmFyS0Ziq+2tp+vY1/ehrIuC774A33zRN\n/QsXst5SR44I72rSh08+Yd1hc3Isc3xNHD0K9O8vjjkwPviAuZfmzdNvP3P3cBKFkSiL0o0UERGB\niKcTNIeEhKBly5Zo1aoVwsLC8PXXXxtcf9kmotCcPctSJ//2m+W06JMt8r33gG+/lYnmx6ztmpgi\nJUdZSsclTHVvTp9m3RyfegB0Qh8tNWuyLpRvvMHGwwQH6y1REC3NmwNjxlTcNdccOkoTH88+3oR+\nyeqr5cYNltl33Tr9Y2w+PsCtW8+yOxurpTJEZySCg4MR9TQDXVhYGMLCwlTb1q9fj5SUFJw7dw7t\n27c36jgpKWyEqyk4c4ZZe0tNxgLo3pIAWDmp1PA5CsxFdrZpxkiURiplGTdNOYvfpk0svYidCX99\nISEsa+iIERV3XjA1H3/Mzvf2bctpKE1CAuDlxXoRWZJPPwXefhuoX1//fR0cAH9/42co1BnhPVuW\nRddTWreOyN6ejRcQmpdeItq7l/Xpv3NH+Pp1oV8/ouho3cunpBDVr0/04IHpNBnL1KlE69eb/jiB\ngUR//GGauvPzzRcDKi4mKiw0/XEqY+ZM9mdpFAqihg2Jtm0j8ve3nI6rV9lv7f59w+uYNUv4sSja\n3p2ia0mYi+PHmV/yaeciwZDL2ZzJ3buz6R137xa2fl3RpyUBMN9o//6VJ4qzJOaISQCmjUvs3MkS\nFOpzbwzFwUEcEynNng38+CPw33+W1fHff6xVNWoUc9ekp1tGx8KFwMyZxo33MWdcokoaiZgYGY4f\nZy9FoSfxuHr1WSKuMWOY37EixBCTUOqYPRtYtcryCdoqikmY2t0EPItLmOLeGBqwNnUcTR/01eLm\nxgZu6jtJktA64uPZy9XBAejXD4iONr+Wy5fZ2KSZM407XkVGwuZjEuYgLY1l/hw1SngjcebMsxG0\nL7/Mgtjm9scWFrKRyfr6O319gS5d2DwFYsRcLYkuXYArV1iadSH591829eigQcLWaw18+CFLxW7J\nOJ3SSAAsrbkl4hILFrCOInXqGFdPixYsbpaRIYisihHWq2V5dDmlb75h/ciTkojatBH2+LNmES1d\n+mz5tdfM40cvTUoKywlkCImJRE2amCZWYywNGxJlZprnWC+/TLRzp7B1fvgh0QcfCFunNXHyJMsX\ndveuZY4fFER09Cj7f3Y2ixk+fmy+4587x84/P1+Y+gYMYOOhhELbu7NKtiRiY1m3QB8fIDVV2Bwz\nZ86w/CpKdHE5CU16uuE+b39/9iX9tOexaFAogHv3DOsNYghCxyWKi1m6DFONjbAGunZlmWg/+MD8\nxy4pYdO3KufTaNCA/f5jY82nYf581qKqWVOY+swVl6hyRoIIOHxYhh49mG/Sz0+4rmQKBXsQSxuJ\nfv3YhC/agmSm8DVnZOgXjyirY9481rfdUrEJTdfk/n2gdm3zdefs0wfYu7e8DkPZvx9o3ZrNLGYI\n1hyTKM2SJcwnL8QET/rouHSJfTi5uDxbJ6TLqTItZ86wF/rUqcIcD9BuJHhMwkhu3mQ9kFq3Zsud\nOgkXl0hJYV+6pb92q1UDhgxhvVrMhTEtCUCcrQlzxSOUtG3LBrzdvClMfd99J+yUq9ZKnTrAV18B\nU6aY9yOkdDxCidJImGO243nz2IyQNWoIV6fy3aVQCFenJqqckTh+HOjTR6oa5dixo3BGoqyrScno\n0dpdTlJ9ht3qiL7dXzXpmD+ftSYKCoTTZagWwPxGws4OGDBAKojLKT2dDdAzJj2GKZ4TQzFWy5Ah\n7ENk8WLz6dBkJPz8mKFKTjZOR2Va4uJYptfJk40/TmkaNmSZnsvqF/pZqZJGonSaAiFbEmfPap4b\noE8f1rPl1i1hjlMZ+nZ/1YSfH/Mhi6U1YW4jAQgXl/jhB/ahIJQv2hZYuxb49tuKU14LiSYjIZGw\nkemm7uU0bx4wdy7zKgiNOeISVc5IxMaydNNKWrcGcnPZS8hYtLUknJyA4cOBHTvKbzNVTELfloQm\nHcrYhLlbE5q0mGuMRGmcnWX44w/jmvMKBUtLYWzA2lZiEkqaNAE++4x9XRuabE9XHQUFbPySn1/5\nbULFJbRp+fNP9qU/caLxx9CEJiPBYxJGkJ7ODELz5s/W2dmxr//Tp42rm0h7SwJgX5Lm6iMuREsC\nYD+qbt3E0ZqwREuicWPWnDfmazc2FqhV61mvGs4z3nyTjQj/6ivTHicxkcWYNH3J9+kDnDol/JgY\ngL0TPvmEfWw5OQlfP8A8ISbv4SRcL1txUNEp/fwz0bBh5df/3/8RLVxo3HFTUog8PLRvLy5mcxCb\neg5lhYKoenXh+mIr+3Y/eiRMfYYycybRihXmP+7bbxMtX274/uPGsbkjOJq5fJnlMfrvP9MdY+VK\nov/9T/uQSt0JAAAgAElEQVT2vn2Jdu0S/rhHjxK1bs1++6YiP5+oRg3jx3t89ZXIx0k8fvwYgYGB\n8Pf3h7e3N+bMmVOujEwmg4uLCwICAhAQEIDFBkS9jh8HevQov16IuIQ2V5MSBweWkVOTy0lI7t9n\nX0xC+b/btQNeesnyOZ3MkSZcE8bEJe7fZ66M114TVpMt0aYNS1Hx9tum62WkKR5RGlOMviZiLYj5\n89lv31TUrMlc5sa0dpOSWNdkrRhnf4Tj0dNP1eLiYgoMDKQ///xTbXtMTAwNHjy40noqOiVvbzZj\nXExMjNr6mzeJGjdmX+GG8uGHRIsWVVzmjz+I2rdXX1dWi7FcuMDOU18q0mHu1oQmLfpmtRVKx/37\nRLVqGZZNdd06orFjhdMiFoTW8uQJkY8P0S+/mEZHy5asxaKN5GT2fMvl+h2/Ii0HDhB5eRGVlBhe\np6689ZZ6Vgd978/rr7MsEdrenaJoSQCAs7MzAKCoqAhyuRz16tUrV4aM+NTIzma+ek3Bq2bNWIAx\nLc3g6iuMRygJDmYaUlIMP05lCBWPKI2yNbFhg7D16oMlYhIAS9To48N6KOn7+AkRsK4KODmxnk7h\n4az1JSQ5OWySoRde0F6mVSs2fiMxUZhjKmMRCxYA9vbC1FkRxvRwSk8H9u1j41a0Yrj9Eha5XE5+\nfn5Uq1Yt+kBDghuZTEb16tWjdu3a0YABA+iff/7RWI+2U9q1i+U60UZICNHu3QZJJ4WCzRGgS16h\nt98m+uwzw46jC5s2mWb+2/PnWWtLqFiHvri7sxafJTh+nMjPj8jXl81FoMvX4ZkzLH+WMV+nVY3/\n/Y/Niy0k0dFEvXpVXm7WLOPjkkr27CFq1858996YHHSzZxPNmMH+r+3daUJvmX7Y2dkhKSkJDx48\nQP/+/SGTydQGhbRv3x6pqalwdnbGgQMHMGzYMFy9elVjXRMnToSnpycAwNXVFf7+/oiNlSI4+Fn3\nMGXdyuVOnaRISADq1tW8vaLlzEygRg0p3NwqL//CCzKsXQt89JHu9euzfOKEDMXFACB8/UFBwAcf\nyDB6tHB6dVkmAnJypGjQwDzH07ScmChFdDTw4YcyvP8+sGiRFOPHAydPai7/669STJoEHD9uGb3W\nuLxkCdCqlQyrVwPh4cLU/+uvMri5AZX9HgYOlOKjj4AePYw73rFjMrz7LvDll1LY2Znn+snlQGqq\nFA8eAImJuu9/4IAMq1ZFYuBAYMECT2jFQONlUhYtWkRffPFFhWU8PT3proZ0ktpOyd+fZaEk0uyz\n27ePqE8fvaUSEcsWOmiQbmVLSliWVaWPVGj/rqGzt+miw1ytibJaHj5kPTjMjaZrolAQHTtG1Ls3\nUbNmLO5QNmPuo0dE9eoR3bplWi2WwpRa9uxhPYJ0yUKsiw5dPQRPnhC5uBBlZVVetiItO3cSdehg\nXHzTELp3f5bhVtf7s3Yt0ciRz5a1vTtFEZPIyclBbm4uAKCwsBBHjhxBQECAWpmsrCxVTCI+Ph5E\npDFuoYn791kcoKKYQadObKyEIWGP0nNIVIa9PZtb21RjJgxJ7qcrvr5s3mRzxyYsFY/QhEQC9OzJ\nejz9+itw5AjQsiUbdKjsa79rFxAYCHh4WFarNTJkCIuBff658XURVd6zSYmTE9C7N3DggOHHk8tZ\nb6ZFi6BK+2Mu9I1LyOXA6tVsbotKMcp8CcT58+cpICCA/Pz8yNfXl5Y/7Zi+ceNG2rhxIxERrV+/\nnnx8fMjPz4+6du1Kf//9t8a6NJ3S3r3sy68ymjVj88/qS79+RFFRupc/ccKwHki60KED0alTpqmb\niPWeMnds4tQpdl5i5dw51oupQQOi+fOJunY1Tb/7qsK1a+xaPnliXD3XrxM995zu5TdtIho92vDj\nLVtGJJWavxVBRLR9u+YxYNrYtYuoSxf1ddrMgSiMhJBoOtEPPqi8eyoR0YgRbMCdPigU7IHWZ2J7\nuZwFYi9e1O9YutCkiX5aDOGVV4gq8QYKyv79RP37m+94hnL1KtGkSUQvvmj8C66q0707cz0Zg74v\nzowMIldXoqIi/Y919iybFMtSnSv0NYjduhHt2KG+TpuREIW7ydTExqoPolMGb8piyKC61FTmQtLH\nxWNn9ywzrDYthlBSwrr8NW6s/7766Jg3D/jyS5ZKuzKUc2x8/jkbtJSaqr8WS7mb9L03rVuzbq9X\nrgifhkHI58RYzKHl9deBrVuN05GQoJurSUmTJsx1ePKk7vsALJPssGEyrFqlnvLHnHh6AkVFrEtr\nZdclLg7IzGT55HTB5o1Efj7wzz+6PSyGGAnl+Ah9fZDKXE5CjjLNymJJ8Ew5whNgeXCCg4Gvv9a8\nPScH2LYNCA1lxnPMGPZQEhk2S5+YYhIc8/DKK8ChQ8CDB4bXoWs8ojSGjL7+v/8Dnn8eGDdOv/2E\nRCJh56rL+2vlSjbKXecxHEa0cERJ2VM6dIjNbasLytG1+uRamTuX/emLQsH60Scl6b+vNszpuy8d\nmygpYT3H5s0j6tyZzR08ZAjR118z/7KSQ4fK+0F14cMPiZYsEU47xzoYPpzou+8M27e4mP2Wc3P1\n2y8uTr944cGDLGfbvXv6HccUzJ9PNGdOxWWuX2e5svLyym/TZg5sviWhLV+TJlxd2Zfv5cu6169P\nz6bSSCTCZ4Y1JEW4oShbE1Ip0KgRm5bx8WNg6VLgzh1gzx5g2jTWfFcilbKUzfqObLdEmnCO5dHF\n5aSNS5cAd3f16Up1oVMn1nLVZUbCnBxg0iQgMhKoW9cQlcKiSw+ntWtZFoDatXWvt0oYidKTDAEV\n++z0cTkRVZ7YryJGjwYiI2WCuZyMSclhiJ/5yy+Bd95hs26dOwcsW8a6h2qbXMXJCRg0CPj9d/20\nWEtMwpRURS0hISxxnbbJuirSYYirCVDOSFi5y4kICAsDXn0V6NVLHPdH2Y3/2DHNWnJzWXqZd97R\nr16bNhKFhSxm0LWr7vt07Kj73BIZGSwwa2h/eKVxOXvWsP3LYuzc1vri4QFMmKCfYRo5ko0j0Ace\nk6iaVKvGpnz9+Wf9942PZy9NQ9AlLhEZySYT+uwzw45hCpTTmWprqX/7LTO87u56Vmy8J0xclD4l\nmYwoMFC//f/6i6hjR93K7tljfNfMOXOYz10IQkNZX28xU1jIRrbeuaP7Ps8/T3Tliuk0ccTLn3+y\nGIG+Yw/8/AwfL3T/PlHt2tqzHivHcZw/b1j9pmTMGKIffii/vqiIdbs/c0b7vtrMgU23JMp2fdWF\ngADmz3zypPKyZ88a7mpSMmaMcL2czN2SMITq1YH+/VnMQlcsNZcEx/J068amHz13Tvd9Hj3SPl2p\nLri6st/1sWPlt5WUsFjJRx+xDARiQ1tcYscOlu3WkPeVTRsJbUHrivyHzs7sYuoyiYehQevS3Lsn\nQ7Vqxk96BBiXksOcPtXKXE6ltRQVsR+9q6vpdVWkw9JUVS12dmzSph9/1F1HRdOV6oo2l9PSpUCN\nGqwLqS5azE3nzsDRozK1dUTAihU6puDQgM0aiaIiNndt9+7676tr8FoIIyGRAMOGCTMzljW0JADm\nFz1xggXSKiMnB6hfn70sOFWT8ePZuBu5XLfyhgatS6M0EqVb+AkJwLp1LB4h1ucxIAC4cUPdE3L8\nOPvQCgkxsFKBXGGiQXlKJ0+yzK+GsHEj0cSJFZfJyGCZPoXI07Jrl+5ZZLWRn8/mtrZE3hhDGDKE\naMuWysudO8dmLeNUbTp2JDp8WLey2vzy+qAcx3ThAlvOzyd64QX9Z8+zBGXjMYMHs3daZWgzByK1\nh8ajqeurrujSw0kZjxAi22P79qxVYgxKV5O5s08aysiRwO7dlZfjPZs4AGtNaHI5aUKIloREou5y\nev99ltl39Gjj6jUHpeMS//7LPCoTJhhen80aiYqC1pX5D319gevXK85NZMz4iLJamjdnzcPMTMPr\nMXYgnbl9qoMHs8Bgfn7FWixpJMTiZwa4lrFjgago9d+kJh05OcC9exVPV6orSiOxbx9w8CBzNWlD\nTPfHxUWmMhKrV7OBrjVqGF6fTRoJuZwl6QoKMmx/JycW+Kpo/IIuc1rrikTCDI4xc+yaYm5rU1K3\nLhu/Uln+ft6S4AAsaWW3bpX3iktIYJ4AIWIGUimQlARMngxs2aL/6G1L0aYNa0nk5LBcaW+/bVx9\nojASjx8/RmBgIPz9/eHt7Y05c+ZoLDdjxgy0bt0afn5+SKzgjZqUxL6qtb1clFP5VURlwWshgtal\ntbRvb9ygOmOD1rpcE6EZMUJzL6fSWiyZksMS10QbXEt5l5MmHUK4mpTUqAH068fSWFT2wSmm+xMa\nKkVaGsu8PGKEYVmhSyMKI1G9enXExMQgKSkJ58+fR0xMDP766y+1MtHR0UhJSUFycjK++eYbTJs2\nTWt9+uRr0kZFRuLOHTYLWem8RMZibFzClDPSmYphw1gz/vFj7WV4S4KjZNgwluY6K0t7GWNGWmti\n+3Zg8WLh6jMHDg6sl9OaNcCsWcbXJwojAQDOzs4AgKKiIsjl8nJTk0ZFRSE0NBQAEBgYiNzcXGRp\neVpiYysOWuviP6zISAgZtFZq6dDBsi0JS/hUGzUC/P3ZFKDatPCYBINrYWOYhgxhL25NOvSZrlRX\ndE27L7b7ExgI9O0L+PgYX5+JZx7QHYVCgfbt2+PatWuYNm0avL291banp6fDo1SSJHd3d6SlpaGx\nhrbUgQMT8fzznrhyBXB1dYW/v7+qOSiTyZCUlKS2DKDcclCQFHfuAHv3ylC7tvr2nTuBDh0q3l/X\n5aSkJABAjx5S5OYCe/bI4OKif30ZGVI895zhepQYez76Lvv6yvDVV8Dgwc+2l74/yckypKcDgHn0\niHVZiRj06PL7MeXz8vXXwMyZ5bffvAkQyZCcDDRtal59SsRyf+bOlUIur7i8TCZDZGQkAMDT0xNa\nEahrrmDk5uZSYGAgxcTEqK0fNGgQ/fXXX6rl3r170xkNiUgAUKtWwmjp0UNz32xDpjnVheBg3fuC\nl8XTkyglRVA5ZiE1lY030TZlpI8PGyvB4RCxuUuaNCG6dKn8Nn2nK+Woo80ciMbdpMTFxQUDBw7E\n6TIDFZo2bYrUUnNfpqWloakW/4qx8Qgl2lxOQgWty2JoXIKIdZ+1tpgEwDJStm4NaGut85gEpzT2\n9mwGuJ9+Kr9NaFcThyEKI5GTk4PcpzkaCgsLceTIEQQEBKiVGTJkCLZs2QIAiIuLg6urq0ZXE1C5\nkSjbRNSGJiNx9y7rh92qlU5VVEppLYbGJe7eZf5aY/pC63pNTEHZXk5KLQoFOzdL9W6y5DUpC9fy\njPHj2WREZedNsKSRsPQ1KY3QWkRhJDIzM9GrVy/4+/sjMDAQgwcPRu/evREREYGIiAgAQEhICFq2\nbIlWrVohLCwMX2ubYBmGj7QuiyYjcfYs6zlgZ4IrZ2g3WGvs2VSakSPZRERlc/Pcvw/UqgU4OlpG\nF0ec+PmxmdUuXny2rqSEjTPq2NFyumwVyVNflM0gkUigUJAgPY+ImKvjwgWgSRO2Tjk958qVxtdf\nFrmcZTtNTdUv6+mBA2xk5aFDwmsyF/7+bERr6f7o//7LZrJLTracLo44WbaMZUV4+g2Jc+fYqGx9\nph7mqCORSKDJHIiiJSE0QuUvkkjK53ESKh2HJuzt2VeSviOvrb0lAWhOH87jERxtjBsH7Nz5bIwN\nj0eYDps0EpWhj8+uY0d1l5OQ6Tg0aTHE5SREinBL+1RHjGAJ/4ieabG0kbD0NSkN16KOhwfQrJkM\n0dFsOSHBskZCDNdEiU3GJMRM6bjE/fvM1SRE8jBtWMpIWBpvbxZ8L91qy862XNCaI3769mUBbIC3\nJEyJTcYkhDyljAygXTv2wjp2DJg/HyiTMURQzp9n6YivXNF9n8GDgbfeAoYONZ0uc/Dxxywus3Qp\nW16yBMjLe7bM4ZTmwQOgeXMWwH7hBfYRZ8xsdFWdKhWTEJLnnmMP3s2bwruaNOHtzQLXDx/qvo8t\ntCSAZ11hlc+ppd1NHHHj4sLmS58zx/jpSjnaqZJGQl+fXadOzA1iikF0ZbU4OLAHXp+J34UIXIvB\np9q+PevK+P33TIuljYQYrokSrqU8MplMNWbC0q4msVwTgMckLIIyLqFM7Gdq9Bl5XVzMBvcZmw5Y\nDEgkrDXx559s2ZJpwjnWQf/+bA50SxsJW4bHJHTg8GHWpL1yhflBdc0MaSjffcdelD/8UHnZ1FSg\nSxc8TYJn/Zw8CYSFsbEpHToAGzcKm/qZY3scO8aeFWuZFEis8JiEESjTZbRrZ3oDAejXw8lW4hFK\nunRhqTiuXrW8u4ljHfTqxQ2EKamSRkJfn139+myCIVMErTVp8fEBrl0DCgoq31+ogXRi8ana2QGd\nO8uwa5flu8CK5ZoAXIsmxKIDsG0tVdJIGELPnobPma0v1aoBXl6sO2xl2FpLAmAJGpXTVNasaVkt\nHE5Vh8ckRMrkySyRYGWTmM+Zw5LgffyxeXSZg5ISwM2NDa67dcvSajicqgGPSVgZusYlbLEl4eDA\nBgbyeASHY3lEYSRSU1PRs2dP+Pj4oG3btli7dm25MjKZDC4uLggICEBAQAAWGzE7uTX4D3XtBpue\nblsxCYBpmTDBPN2NK9MhFriW8ohFB2DbWkQxx7WjoyNWrVoFf39/5Ofno0OHDujbty+8vLzUygUH\nByMqKspCKs1Lu3YsVfaTJxWPJM3IsL2WBMDmBBFqXhAOh2M4ooxJDBs2DO+88w569+6tWieTybBi\nxQrs3bu3wn1tJSYBMEOxeXPFvarq1GF+e33mn+BwOJyyWE1M4ubNm0hMTERgYKDaeolEgpMnT8LP\nzw8hISG4dOmShRSaj8riEg8fsiAv7yPO4XBMhSjcTUry8/MxatQorFmzBrVq1VLb1r59e6SmpsLZ\n2RkHDhzAsGHDcPXqVY31TJw4EZ6engAAV1dX+Pv7QyqVAmAtkqSkJISHh6uWAahtN+fy6tWry+lT\nbu/QAdi7V4bWrTXvn5EB1KsnQ2ys8XqU6yx9PcR0f8peG0vqKauJ3x9+f7Qt63p/ZDIZIiMjAUD1\nvtQIiYSioiLq168frVq1Sqfynp6edPfu3XLrdTmlmJgYfeWZjIq0/PUXUadO2vc9doyoRw/T6zA3\nYtEiFh1EXIsmxKKDyDa0aHt3iiImQUQIDQ1F/fr1sWrVKo1lsrKy0KhRI0gkEsTHx2P06NG4efNm\nuXK2FJPIzwcaNWL5ohwdy2/fuhWIjgZ+/tn82jgcjm2h7d0pCnfTiRMnsHXrVrRr1w4BAQEAgCVL\nluDW05FUYWFh2LlzJzZs2AAHBwc4Oztj+/btlpRsFmrVYpOqXLrE5r4uiy3Mbc3hcESOYQ0a8aLL\nKVlT0/C114i+/17zthkziFauNI8OcyIWLWLRQcS1aEIsOohsQ4u2d6foejdx1KmohxNvSXA4HFMj\nipiEkNhSTAIAZDKWl+nEifLbunUDli8Hunc3uywOh2NjWM04CY46AQFsKlO5vPw2oVJycDgcjjaq\npJEo3bfZ0lSmxcUFaNKEpegojUIBZGYKZySs6ZqYC7HoALgWTYhFB2DbWqqkkbA2NMUlcnKA2rWB\n6tUto4nD4VQNeEzCCli+HLh9G1i58tm6pCRgwgTdJibicDicyuAxCStGU9pwW5xHgsPhiI8qaSSs\nzX8YEAAkJrI4hBKhu79a2zUxB2LRAXAtmhCLDsC2tVRJI2Ft1K8P1KsHXLv2bB1vSXA4HHPAYxJW\nwsiRwCuvAGPHsuUpU5gbaupUy+ricDi2AY9JWDllezjxlgSHwzEHVdJIWKP/UJOR4DEJ0yIWHQDX\nogmx6ABsW0uVNBLWiNJIKFuDtjq3NYfDEReiiEmkpqZiwoQJuHPnDiQSCaZMmYIZM2aUKzdjxgwc\nOHAAzs7OiIyMVKUVL42txiQAwN0d+PNP1oKoXRsoLATs7S2tisPh2AKink/C0dERq1atgr+/P/Lz\n89GhQwf07dsXXl5eqjLR0dFISUlBcnIyTp06hWnTpiEuLs6Cqs2PsjVhZwc0bswNBIfDMT2icDe5\nubnB398fAFCrVi14eXkhIyNDrUxUVBRCQ0MBAIGBgcjNzUVWVpZBx7NW/6HSSJgisZ+1XhNTIhYd\nANeiCbHoAGxbiyiMRGlu3ryJxMREBAYGqq1PT0+Hh4eHatnd3R1paWnmlmdROnRgI695PILD4ZgL\nUbiblOTn52PUqFFYs2YNatWqVW57WX+ZRCLRWM/EiRPh6ekJAHB1dYW/vz+kUimA8lZWuVx2u7mW\nlet0Kd++PRAXJ0Pr1sBzz1lGr7mWlVhSj1QqFc31ENuyEn5/xLmspKLyMpkMkZGRAKB6X2pCFIFr\nACguLsagQYMwYMAAhIeHl9s+depUSKVSjH06mqxNmzaIjY1F48aN1crZcuCaCHBzA/r0Adq2BebM\nsbQiDodjK4h6MB0R4c0334S3t7dGAwEAQ4YMwZYtWwAAcXFxcHV1LWcgdKWstbUk+miRSJjL6cAB\n4d1N1npNTIlYdABciybEogOwbS2icDedOHECW7duRbt27VTdWpcsWYJbt24BAMLCwhASEoLo6Gi0\natUKNWvWxObNmy0p2WK0b8+MBJ+RjsPhmAPRuJuEwpbdTQCwezfL43TpElCqhzCHw+EYhajdTRzd\n6dCB/ctbEhwOxxxUSSNhzf7DZs2AiAigTh3L6jAlYtEiFh0A16IJsegAbFuLKGISHN2RSFiacA6H\nwzEHPCbB4XA4HB6T4HA4HI7+VEkjYcv+Q0MRiw5APFrEogPgWjQhFh2AbWupkkaCw+FwOLrBYxIc\nDofD4TEJDofD4ehPlTQStuw/NBSx6ADEo0UsOgCuRRNi0QHYtpYqaSQ4HA6Hoxs8JsHhcDgcHpPg\ncDgcjv5USSNhy/5DQxGLDkA8WsSiA+BaNCEWHYBtaxGNkZg0aRIaN24MX19fjdtlMhlcXFwQEBCA\ngIAALF682OBjJSUlGbyv0IhFi1h0AOLRIhYdANeiCbHoAGxbi2gS/L3xxht45513MGHCBK1lgoOD\nERUVZfSxcnNzja5DKMSiRSw6APFoEYsOgGvRhFh0ALatRTQtiaCgINStW7fCMjwgzeFwOOZFNEai\nMiQSCU6ePAk/Pz+EhITg0qVLBtd18+ZN4YQZiVi0iEUHIB4tYtEBcC2aEIsOwMa1kIi4ceMGtW3b\nVuO2vLw8evToERERRUdHU+vWrTWWA8D/+B//43/8z4A/TYgmJlEZtWvXVv1/wIABePvtt3Hv3j3U\nq1dPrRxxlxSHw+EIhtW4m7KyslQGID4+HkRUzkBwOBwOR1hE05J49dVXERsbi5ycHHh4eGDhwoUo\nLi4GAISFhWHnzp3YsGEDHBwc4OzsjO3bt1tYMYfD4dg+NpeWg8OxRq5evYrk5GR4eXmhZcuWUCgU\nsLOzTEOfaxGvDktosRp3k66kpqYiPDwckZGRRvWAsiUtYtHBtZSnuLgYs2bNwogRI7Bv3z5069YN\nRUVFFnkBcS3i1WFJLTZlJJYvX46QkBA4OTnh7NmziIiIQF5eXpXWIhYdXEt5iAiHDx/G48ePERsb\niw0bNiAgIACHDx82qw6uRdw6LK1FNDEJYyksLAQRYf/+/WjWrBkOHjyI2NhY1KlTp8pqEYsOrqX8\n8WvUqAGJRAKpVIqBAwcCAKKjo3Hjxg3cunULN2/ehKenJ9diAS1i0SEWLfYLFixYYLLaTUxycjKq\nVasGJycnODo6onv37nBxccH58+cxffp0XL9+Hfn5+ahZsybc3NxARJBIJDatRSw6uJbyZGZmYujQ\noTh9+jSkUimcnJzg5OQEALh+/TpWrlyJ4cOHIyUlBdu2bYNUKlXr+s21mFaLWHSITYuoBtPpysOH\nD+nVV1+lBg0a0CeffEJERAqFQrV948aNtG3bNkpKSqIFCxbQuHHjbF6LWHRwLZrJzc2lhQsX0rBh\nw6hnz54UGxurtr2oqEhtuWPHjvT7779zLWbSIhYdYtNCRGSVMYmMjAwAwLfffouLFy/i3LlzkEgk\nKCoqAgBMmTIFY8eOhZ+fH/r37w9HR0dkZ2ebZKCdWLSIRQfXok52djYAwMXFBSNHjsRvv/2Gfv36\nYfPmzbh7966qnKOjo9p+nTp1EvzLkGsRrw6xaSmN1RiJkydPIi8vDyUlJXjhhRewevVq9OzZEwEB\nAfj6668BQNUcK+0mOH36NKpXr46GDRsK5j4Qixax6OBaypOQkICuXbti8uTJWLt2LR49egQfHx8A\nQHh4ONLT03H48GHVWKCSkhIUFBTg77//xsCBA3H9+nX4+fkZpYFrEb8OsWnRiMnaKAKRnp5OgwYN\nIi8vL5oyZYrKZaAkKSmJRowYoWpuKRQKksvltHfvXho0aBAFBQXRqVOnVNtsQYtYdHAtmikqKqI3\n3niDvv/+e7p06RKNHTuW5s2bR9nZ2aoyP/30Ew0ePJjS09NV606fPk3Dhw+nTZs2GXxsrsV6dIhN\nizZEbyT27NlDr7zyChGxBIBubm509OhR1fZHjx7Rxo0bVWWUfPXVV7R27Vqb1CIWHVyLZvLy8qh1\n69aUlpZGRER///03vffee7Ru3Tq1cpMmTaJVq1bR3LlzacOGDUSkbpxKSkq4FhNoEYsOsWnRhiiN\nRGmL+dNPP9GcOXOosLCQiIi+/fZbCg4OJrlcriqTn59PkydPpuDgYHr55ZfV9icy7gKKRYtYdHAt\n5dm9ezcNHTqU1q1bR//88w8REU2bNo2WLFlCRERPnjyh7du309SpU+natWuq/ebMmUMSiYQGDx5M\nN2/eVK0vrZdrMV6LWHSITYuuiComceDAAXTv3h2TJ0/GmjVrkJ+fj/r16+PChQuqQOJbb72Fx48f\n47vvvlPtd+zYMezfvx/Vq1fHsmXL8NxzzwFgA1CICPb29larRSw6uJby5OXl4Y033sCKFSswevRo\n3Hqye/8AABggSURBVLp1C2+++SYAoH///khOTsbly5fh5OSENm3aQKFQqALmJ06cQEJCAqKjoxEV\nFYXmzZurdBsygpZrEa8OsWnRG5ObIR3ZuHEj+fv70/Hjx+nvv/+m8ePH065du4iIqEePHvTtt9+q\nyu7Zs4ekUqlq+f/+7/9ox44dqmVjratYtIhFB9eimVu3bqma/kRExcXFFBQURJcuXaJbt27RvHnz\n6N1331Vt7969Ox0/fpyIWItGiUKhMNpdwLWIV4fYtOiLxY2E0q92+fJlOnHihGr99OnTafbs2URE\ndPDgQXrppZdUQcUTJ07Q7NmzNf7Ai4uLrV6LWHRwLdo1KFG6qxQKBd24cYP69eun6sf+zz//UFBQ\nEC1YsIDWrl1LXbp0oUuXLqntL+QP3pJaxHpd+DUxHou7m5RdDV988UV07doVJSUlAIBmzZqhXr16\nICL0798fw4YNw4YNGzBp0iS8/vrrqFu3rlpTi542vxwcDM80IhYtYtFBpUYdW1oLYNnrkp6erqZB\nWYfSXSWRSFCjRg0UFxejsLAQAODt7Y1vvvkGNWvWxPHjxxEREQEvLy+1eg1xtcXExCArK6vcekto\nOXfunOoYAKBQKCymBSg/6ZgldCQkJEChUEAikaiuh6W0CIK5rdJff/1F+/bto8zMTCIqbx2VX3yj\nRo2in3/+WbW+uLiYUlNTafny5XT69GlBtGRkZKiOr6nLo7m0yGQy2rVrFz1+/FijFnNekxMnTpCX\nlxf98ccfRGTZ+xMXF0dXr15VTVtrCS3Jyck0ZMgQ6t27N73//vt08eLFcmWU92vr1q00evRoIiJK\nSUmhu3fvqm1Xaja0e+21a9fotddeIx8fH0pKStJYxlxabty4QcOHDyd/f3+aNm0abd261SJakpOT\nKTg4WOVi1NQ6NNc1ISLavn07SSQS+vzzz4mo/DNrTi1CYTYjkZubS5MnT6Z27dpRaGgoeXl5aS37\n4MEDGjJkCBUUFFBGRgZ9//33KqOiRC6XG+xPzsnJoQEDBtBLL72kehlquxmm1kJE1LVrV+rXr5/K\nRWIJHZmZmTRhwgQKDg6mrl270nvvvWcxLQUFBfT2229T8+bNadKkSTRkyBCLaImLiyNfX19au3Yt\n3b17lyZNmkTLly9XGfOyLFy4kL7++mtasmQJNWvWjKKjo9W2G+MuOHv2LDVv3pw++uijcts0Pbum\n1FJQUEBTp06lL774goqLi2n+/Pn0xRdfEJHmGI+ptNy4cYOGDRtGISEh1KBBA5XrRtu9NuU1UR4z\nJiaGRo4cSX5+fnT9+nWt9ZpSi9CYzd105swZZGVl4dy5c4iMjET16tXx77//AoBakwwAcnNzUVBQ\ngNmzZ6NXr17Iz8+Hm5ubartykg1DIvtyuRwymQy1atVCjx49EB8fj7t370IikWhMxWAqLcpjFRUV\nwcHBAQ0bNkRCQgLu3LmjqtccOgDmSpk3bx6ef/55yGQyvPfee6hZsyYUCkU5HabWotRz5coV3Lx5\nE5s2bUJubi5WrlyJJ0+emEWL8px9fHzw2Wef4Z133kG9evUwYMAAHDx4ENWqVdNY/tSpU3j33Xdx\n+/ZtJCYmYsCAAWrlDHEXKOsOCAhAmzZt0LNnTwDA3r17cezYMTx8+FBtdLg5tDg5OeHo0aPo3Lkz\nHBwckJ2djby8POTl5aldZ1NqAQBPT0+Eh4dj//796N69O95++20A5V1OptZBRKrzTkpKQkhICMaP\nH4+PPvoIwLMeSERkci0mwVzWKDIykkJDQykhIYG+//576tSpE0VGRqp9lSm/iA4ePEgSiYRmzZpV\nrh+7ofz777+q/+fm5lJRURH98ccfNH36dNq5c6fW/YTWUloHEVFhYSGtWLGCtm7dSuPGjaOEhAS1\n7ea6JsqxBUREW7ZsIT8/P637mUJLYmKi6v/Xrl2jMWPG0NWrV4mI6NSpUzRgwIBy10ZoLTExMapW\nlPLLrvRXaUJCAo0fP17tWimRy+W0bNkyOn/+vGpdSUmJwe6C0lqioqKIiGjv3r3UqVMn6tKlCw0Y\nMIBCQkJo6tSp9Oeff6ppNaWWvXv3EhHR+vXraejQoeTh4UFdunShSZMm0dixY1W9zEyhJSYmhj75\n5BO1+/zkyRMiYi3hWrVqqZ7psm4nU1yT0lqUrZhff/2VvvrqK8rNzaXOnTvTihUrKCYmxqRaTI1J\njMTp06fJx8dHzU98/fp12rx5M/Xs2ZPat29PUVFRNGjQIJo+fTrl5eWp7f/kyRM1X7IxFzA+Pp76\n9OlDQUFB9MEHH6i6lRGxF/CyZctozpw5qhdS2aaqUFrK6lBmdszMzKSgoCAiIlq+fDmFhoZSeHg4\n/ffffybRoUmLTCYjomcPemFhIXXr1k2r+0tILad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CeEJTYAmRkJaWBpvNJt6Cdy58Ph/0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\n",
"text": "<matplotlib.figure.Figure at 0x746a350>"
}
],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Maybe we don't want to deal with all those columns and are only\n# interested in a DataFrame with a few columns.\nnike2 = nike.reindex(columns=['calories', 'fuel'])\nnike2",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nDatetimeIndex: 528 entries, 2013-08-15 05:00:00 to 2012-02-28 06:00:00\nData columns (total 2 columns):\ncalories 528 non-null values\nfuel 528 non-null values\ndtypes: int64(2)\n</pre>",
"output_type": "pyout",
"prompt_number": 117,
"text": "<class 'pandas.core.frame.DataFrame'>\nDatetimeIndex: 528 entries, 2013-08-15 05:00:00 to 2012-02-28 06:00:00\nData columns (total 2 columns):\ncalories 528 non-null values\nfuel 528 non-null values\ndtypes: int64(2)"
}
],
"prompt_number": 117
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Our new DataFrame, nike2, only has numerical columns now. So, the default\n# Pandas plotting will work and can automatically make a new line for\n# each of our columns.\nnike2.plot()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 118,
"text": "<matplotlib.axes.AxesSubplot at 0x7447b50>"
},
{
"output_type": "display_data",
"png": 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zAqgHIJ0OAycH0NbdBsEkqI5HKKZ9e/bh4uUXa56PfF54+UIAQN2hOjjzJPtI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\ndXXFjRs38O233zK/dMOKppzDX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"text": "<matplotlib.figure.Figure at 0x70b3910>"
}
],
"prompt_number": 118
},
{
"cell_type": "code",
"collapsed": false,
"input": "# We can further control what data is actually shown on the x/y axes.\n# So, this plot effectively shows that calories and Nike's proprietary\n# fuel measurement have a linear relationship.\nnike2[:30].plot(x='calories', y='fuel')",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 119,
"text": "<matplotlib.axes.AxesSubplot at 0x7cca9d0>"
},
{
"output_type": "display_data",
"png": 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Wg8W9esHGjfDaa3DFFfbkEkJYS8YURJta++t/6lSYNw8apqcIIWwkl6QKn/jw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\nhxQFIYQQHlIUhBBCePx/kVWLroe/pUEAAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x7cbacd0>"
}
],
"prompt_number": 119
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Of course, we aren't limited to just line plots, there are\n# all sorts provided by matplotlib.\nnike2[:30].plot(kind='bar')",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 120,
"text": "<matplotlib.axes.AxesSubplot at 0x7d5cb50>"
},
{
"output_type": "display_data",
"png": 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NyV/UbGs99fOqHneCMp7cSXvc6yd/4bbmXY/bkyci6knY5ImIdIxNnohIx9jk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\nAAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x7d6a7d0>"
}
],
"prompt_number": 120
},
{
"cell_type": "code",
"collapsed": false,
"input": "# Finally, we could remove the above reindexing step and save memory and\n# time up front if we know we're only interested in a few columns at the\n# time of reading the data from the CSV. Remember, Pandas read_csv has\n# ALOT of arguments...\n\n# Note that with usecols the index_col is RELATIVE to the columns in usecols argument!\nnike = pd.read_csv('nikeplus.csv', usecols=['miles', 'steps', 'fuel', 'calories', 'start_time'], index_col=4)\nnike",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nIndex: 528 entries, 2013-08-15T05:00:00Z to 2012-02-28T06:00:00Z\nData columns (total 4 columns):\nmiles 528 non-null values\nsteps 528 non-null values\nfuel 528 non-null values\ncalories 528 non-null values\ndtypes: float64(1), int64(3)\n</pre>",
"output_type": "pyout",
"prompt_number": 121,
"text": "<class 'pandas.core.frame.DataFrame'>\nIndex: 528 entries, 2013-08-15T05:00:00Z to 2012-02-28T06:00:00Z\nData columns (total 4 columns):\nmiles 528 non-null values\nsteps 528 non-null values\nfuel 528 non-null values\ncalories 528 non-null values\ndtypes: float64(1), int64(3)"
}
],
"prompt_number": 121
}
],
"metadata": {}
}
]
}
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