Created
June 2, 2014 21:04
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seaborn and incomplete dataset
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{ | |
"metadata": { | |
"name": "", | |
"signature": "sha256:02df2567c602a3c2ec5d8b8c21298297f531d7bf67a2a444ce4c621682c295f5" | |
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"nbformat": 3, | |
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"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "heading", | |
"level": 1, | |
"metadata": {}, | |
"source": [ | |
"Linear models with categorical data" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"import numpy as np\n", | |
"import pandas as pd\n", | |
"import seaborn as sns\n", | |
"import matplotlib as mpl\n", | |
"import matplotlib.pyplot as plt\n", | |
"\n", | |
"%matplotlib inline" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 1 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"sns.set(style=\"whitegrid\")\n", | |
"np.random.seed(sum(map(ord, \"linear_categorical\")))" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 2 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"exercise = sns.load_dataset(\"exercise\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 3 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"exercise2 = pd.DataFrame(exercise)\n", | |
"to_be_dropped = (exercise2.kind == 'running') & (exercise2.diet == 'no fat')\n", | |
"exercise2 = exercise2[~to_be_dropped]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 4 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"I have to force ci=None .. does not handle empty set for some subset." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"sns.factorplot(\"kind\", \"pulse\", \"diet\", exercise2, kind=\"point\", ci=None);" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stderr", | |
"text": [ | |
"/home/deheeger/Python/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['Arial'] not found. Falling back to Bitstream Vera Sans\n", | |
" (prop.get_family(), self.defaultFamily[fontext]))\n" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
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XgeeueaChQyfEKkgREYkPP8q3twIjzSzXzALAbcBmYJuZ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\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x42ff690>" | |
] | |
} | |
], | |
"prompt_number": 5 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Drop one value in not forces x_order:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"sns.factorplot(\"kind\", \"pulse\", row=\"diet\",data= exercise2, hue='kind',\n", | |
" kind=\"point\", size=2, aspect=3, ci=None, sharex=True);" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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cd8S7cqzOzKz78poksUXUkaSpwAmkJPO/AGdGxMo86qsn0+56arPGqd2qtc3c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\nMzMzMzMzMzMzMzMzMzMzMzMzK+L/A2H2Gyxt0PqxAAAAAElFTkSuQmCC\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x455e210>" | |
] | |
} | |
], | |
"prompt_number": 6 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"sns.factorplot(\"kind\", \"pulse\", row=\"diet\",data=exercise2, hue='kind',\n", | |
" kind=\"point\", size=2, aspect=3, ci=None, x_order=['rest','running','walking',], \n", | |
" sharex=False);" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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| |
"text": [ | |
"<matplotlib.figure.Figure at 0x4a61550>" | |
] | |
} | |
], | |
"prompt_number": 15 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"modify color order if not hue_order:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"sns.factorplot(\"kind\", \"pulse\", row=\"diet\",data=exercise2, hue='kind',\n", | |
" kind=\"point\", size=2, aspect=3, ci=None, x_order=['rest','running','walking',], \n", | |
" hue_order=['rest','running','walking'],\n", | |
" sharex=False);" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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| |
"text": [ | |
"<matplotlib.figure.Figure at 0x4565ed0>" | |
] | |
} | |
], | |
"prompt_number": 17 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"modify witdh and position if hue is x or y (which is sometimes useful)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"sns.factorplot(\"kind\", \"pulse\", row=\"diet\",data= exercise2, hue='kind',\n", | |
" kind=\"bar\", size=2, aspect=3, ci=None);" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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| |
"text": [ | |
"<matplotlib.figure.Figure at 0x18bbad0>" | |
] | |
} | |
], | |
"prompt_number": 7 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"sns.factorplot(\"kind\", \"pulse\", row=\"diet\",data= exercise2, hue='kind',\n", | |
" kind=\"bar\", size=2, aspect=3, ci=None, x_order=['rest','walking','running',], sharex=False);" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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| |
"text": [ | |
"<matplotlib.figure.Figure at 0x4a44310>" | |
] | |
} | |
], | |
"prompt_number": 8 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"sns.factorplot(\"kind\", \"pulse\", row=\"diet\",data= exercise2, hue='kind',\n", | |
" kind=\"box\", size=2, aspect=3, );" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stderr", | |
"text": [ | |
"/home/deheeger/Python/anaconda/lib/python2.7/site-packages/numpy/core/_methods.py:55: RuntimeWarning: Mean of empty slice.\n", | |
" warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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X3et5wOeAPwWOzswXAScDsyPiFcCrgPsz86RqJfpvZ+ZnKWsqnmpxUgf9O2X5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\nJEmSJEmSJEmSJEmSJEkN/H+FX3LNc+1a6gAAAABJRU5ErkJggg==\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x47f4850>" | |
] | |
} | |
], | |
"prompt_number": 9 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 9 | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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