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jiffyclub/scientific_python_demo.ipynb

Created February 19, 2015 04:38
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Demo from SF Python Project Night 2015-02-18
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scientific_python_demo.ipynb
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"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>col1</th>\n",
" <th>col2</th>\n",
" <th>col3</th>\n",
" <th>col4</th>\n",
" <th>col5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>c</th>\n",
" <td> 10</td>\n",
" <td> 11</td>\n",
" <td> 12</td>\n",
" <td> 13</td>\n",
" <td> 14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td> 15</td>\n",
" <td> 16</td>\n",
" <td> 17</td>\n",
" <td> 18</td>\n",
" <td> 19</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 47,
"text": [
" col1 col2 col3 col4 col5\n",
"c 10 11 12 13 14\n",
"d 15 16 17 18 19"
]
}
],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.loc[df.col1 >= 10].sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 48,
"text": [
"col1 25\n",
"col2 27\n",
"col3 29\n",
"col4 31\n",
"col5 33\n",
"dtype: int64"
]
}
],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>col1</th>\n",
" <th>col2</th>\n",
" <th>col3</th>\n",
" <th>col4</th>\n",
" <th>col5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td> 5</td>\n",
" <td> 6</td>\n",
" <td> 7</td>\n",
" <td> 8</td>\n",
" <td> 9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td> 10</td>\n",
" <td> 11</td>\n",
" <td> 12</td>\n",
" <td> 13</td>\n",
" <td> 14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td> 15</td>\n",
" <td> 16</td>\n",
" <td> 17</td>\n",
" <td> 18</td>\n",
" <td> 19</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 49,
"text": [
" col1 col2 col3 col4 col5\n",
"a 0 1 2 3 4\n",
"b 5 6 7 8 9\n",
"c 10 11 12 13 14\n",
"d 15 16 17 18 19"
]
}
],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.col4.isin([8, 18])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 50,
"text": [
"a False\n",
"b True\n",
"c False\n",
"d True\n",
"Name: col4, dtype: bool"
]
}
],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.loc[df.col4.isin([8, 18])]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>col1</th>\n",
" <th>col2</th>\n",
" <th>col3</th>\n",
" <th>col4</th>\n",
" <th>col5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>b</th>\n",
" <td> 5</td>\n",
" <td> 6</td>\n",
" <td> 7</td>\n",
" <td> 8</td>\n",
" <td> 9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td> 15</td>\n",
" <td> 16</td>\n",
" <td> 17</td>\n",
" <td> 18</td>\n",
" <td> 19</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 51,
"text": [
" col1 col2 col3 col4 col5\n",
"b 5 6 7 8 9\n",
"d 15 16 17 18 19"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.col3.plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 52,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10ac22978>"
]
},
{
"metadata": {},
"output_type": "display_data",
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"text": [
"<matplotlib.figure.Figure at 0x10ac1c6a0>"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.DataFrame(np.random.random(100).reshape(10, 10))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10b070cc0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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A/wIMI/O47ATuTCjD1cAKM1tuZheZ2acTmjffncCpwKVmtgj4e+D/kPmjekcS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\nKCIipal4i4ikkIq3iEgKqXiLiKTQ/wcw5c5xHFcx4wAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10af47748>"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df[8].plot(kind='hist')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 57,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10af47940>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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gYWGBHTt2MBqNgOPVeyPHv/Vbv82tt/4NW7acynPP/V8ANm/+ZwCNHx8+/A8z/3dZ8d9R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\n",
"text": [
"<matplotlib.figure.Figure at 0x10b62a278>"
]
}
],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(figsize=(10, 12))\n",
"ax.hist(df[8])\n",
"fig.savefig('myplot.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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DADhRloZVd38yybcO2eTGJO9bbHtbkour6pKZ8QAATo4LB67j0iT3nXb+/iSXJXlg4LqP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\nCCsAgCHCCgBgiLACABjy/wEtfpbvWH9UkQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10bb562e8>"
]
}
],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!open /tmp/myplot.pdf"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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