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@fdeheeger
Created June 2, 2014 21:04
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seaborn and incomplete dataset
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{
"metadata": {
"name": "",
"signature": "sha256:02df2567c602a3c2ec5d8b8c21298297f531d7bf67a2a444ce4c621682c295f5"
},
"nbformat": 3,
"nbformat_minor": 0,
"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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19/pmZtY3lNNA/Qa4CNgK2Br4KnA7aV2o7qwNVSpJAuAa4LxuXNPMzPqYcob4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\nAABJRU5ErkJggg==\n",
"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": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHA9JREFUeJzt3XuYXXV56PHvJCSQcAs0gBJ4xEd9FXIQIfEC6BMKcqTc\nvFaF0nKrrWIR7TFFVDAW5XCpBeqlnlRBUFFQsUIrKDc5USiYIRFKkBfUWBJPCBCDkJCSzMz5Y61J\ndobZyc7M7L1WZr6f55ln1vqttfd6h4e93/x+67d+L0iSJEmSJEmSJEmSJEmSJEmSJKkVEdEbEZPL\n7QURsW0Lr5kTERNGMIZTIuI7I/V+A977bRGxKCK6IyI2cd5LIuJ97YhBamabqgOQthaZeWCLp54H\nXAKsHaFL943Q+wzmr4FzM/N7mznvpcBfAf/SxlikjZigpAYR8Q7gs8Aa4PoBx3qBHTJzdUS8ErgU\nmApMBC7LzK9FxBfL0+8qzz8sM58eZlhdA+I4Gzip3P05cGZmroqIJcBrMvPJiPgh0JuZx0bE7kB3\nZu494H0uBd5YbMYZmXlERHwTCGBb4FHgtMxcCXwR2CciFgCPZOa7h/k3SZtlgpJKEbEHMBc4ODMf\niYjZTc7bBrgGODEzH46IHYH5EXFXZn4wIj5QvsfqJq//GTB5kEMrMvOIzcT4JxTJ6eDMfDYirgLO\nBT4G3AEcERHfo+jx9JSxHgHcPvC9MvMjEfEa4JLM/GHZ/KHMfKq81meAs4FzgDOAf8jM124qPmkk\nmaCkDV4P3JeZj5T7c4GLBjkvgFcB3264bTMB2BfIzV0kMw8dRoxvBr6Vmc82xHh5uX1beXwpcHfZ\n9nqaJKgGjT20kyPiRIpe4fbAw4OcI3WECUraYOC9nmZfyl3Ak1twT2ojEXEXMGmQQ7/PzMM38/K+\nAXE1bt9Bcf9rCUWy6qJIWEcAczbznkTEm4D3U/TOnioTlRMjVBkTlLTBPcAVEfHyzHwU+Msm5z0M\nrI6IkzLzGwAR8SpgaWY+AzwDTAEGHeLLzEOGEeOtwMURcTmwqozxx+X7/ra873UycDAwDjgfeD4z\nl7Tw3lOAp4EV5WzF0xqO/QHYeRhxS1tsXNUBSHWRmcspZqrdGBH3UUwUaOxV9ZXnrQOOA94bEb+I\niP8EvkAxzAfwOeD2iLgvIkbiS72v4do3A9+gGMK7H+gFPtNw7q3Aqsx8PDP/H0WS3NTwXqObgF9R\nDFP+BOhmw9//C+DhiHggIq4b1l8jtaht48oRcQVwDLA8M/cv2y4BjgWep/ggnNo/wykizqH4F1sP\nxY3aH7crNklS/bWzB3UlcNSAth8D0zPzAIp/pZ0DEBH7Ae8B9itf86WIsHcnSWNY25JAZs4Dfj+g\n7ZbM7C137wH2KrffSjEzaW1mLqZ4/uJ17YpNklR/VfZSTgP6n73Yk2LmUb8lwLSORyRJqo1KZvFF\nxCcoZhZds4nTNrm8y4IFC/p6enpGNjBJUsfNnDlz0PkQHU9QEXEKcDTFsxn9lgKNy7DsVbY11dPT\nw4wZM0Y8PklSPXQ0QUXEUcBsYFZmrmk4dANwTUT8I8XQ3iuAezsZmySpXtqWoCLiW8AsYGpEPAZ8\nimLW3kTglnKJmLsz84zMXFQ+W7EIWAeckZntXMFZklRzW+36WvPnz+9ziE+Stn5dXV2D5iKfNZIk\n1ZIJSpJUSyYoSVItmaAkSbVkgpIk1ZIJSpJUSxYslLRV6Ovro7e3d/MnbgXGjRtHk5nVatDOB3UH\nqwe1K3At8BJgMfDuzFxZHrMelKSment7Of+Oy3nyuRVVhzIsUyftyrl/fBbjx4+vOpTaa2cP6krg\n88DVDW0fA27JzIsj4uxy/2MD6kFNA26NiGgozSFJPPncCpavfqrqMNQhHa0HBRwPXFVuXwW8rdy2\nHpQkaSOdniSxR2Y+Xm4/DuxRblsPSpK0kcomSWRmX0RsakHYzS4W293dPYIRSaqzCRMmVB3CiHnw\nwQdZu3Zt1WHUXqcT1OMR8aLMXBYRLwaWl+1bXA8KsB6UNIb09PTAb6uOYmRMnz7dSRIt6PQQ3w3A\nyeX2ycC/NrS/NyImRsRLsR6UJI15nawHdR5wIXBdRJxOOc0cwHpQkqSB2pagMvOEJofe3OT8C4AL\n2hWPJGnr4lJHkqRaMkFJkmrJBCVJqqUxv1jsaFmA0sUnJY02Yz5B9fb2cu6Xf8YTK9dUHcqQ7TZl\nO85//6E+VyFpVBnzCQrgiZVrWLbiuarDkCQ18B6UJKmWKulBlbWfTgJ6gQeAU4HtaVIrSpI09nS8\nBxUR+wDvAw4qCxmOB97LhlpRAdxW7kuSxqgqhvj+AKwFJkfENsBk4Hc0rxUlSRqDOp6gMnMF8Dng\nvygS08rMvIXmtaIkSWNQFUN8LwM+DOxDUahwh4g4qfGccqFYF4uVpDGsikkSM4G7MvMpgIi4HjgY\nWNakVlRTI1GwcLQUQbMAmka70fJZBT+vraoiQf0SODciJgFrKFY3vxdYRVEj6iI2rhXV1EgULOzp\n6YEblw37fapmATSNdhYsHHuquAf1C+BqYD5wf9k8l6JW1JERkcDh5b4kaYxqpQcVwBUUZdj3AWYA\nxwFzhnrRzLwYuHhA8wqa1IqSJI09rfSg/hn4LND/0OxCykq4kiS1SysJamfgJjbMqusBnm9bRJIk\n0VqCWgdMbNifRpGkJElqm1aH+K4HpgKfBn5K8aCtJElt08okiauAX1NMjJgE/AUwr51BSZLU6nNQ\n88qfbYFd2heOJEmFVob4rqWYKDGJojTGQ8DsdgYlSVIrPahXAk8D7wJuB/4WuAe4ZKgXjYgpwFeA\n6RSzA08FHsF6UJKkUis9qP4FsA6jmG6+muHP4rsc+GFm7gu8mmL5I+tBSZLWayVBLQJuppgkcStF\n/aYhi4idgTdl5hUAmbkuM5/GelCSpAatDPGdDLyFYgWJVRTPQZ0zjGu+FHgiIq4EDgC6KcpvWA9K\nkrReKz2o1cD3gd+U+0sphvqGahvgIOBLmXkQRdLbaDjPelCSpE31oJ7YxLE+YPchXnMJsCQzf17u\nf5eiR2Y9qGGwvoxGu9HyWQU/r63aVIJ6bTsuWCagxyIiMjMpVjB/sPyxHtQQWV9Go531oMaeTSWo\nxW287pnANyNiIvArimnm44HrIuL08tqumC5JY1grkyQGG+obzhBff9HCwXpo1oOSJAGtJajGRLId\ncCLFCueSJLVNK7P4Fjf8/BI4DzimbRFJkkRrCWqglwG7jXQgkiQ12tJ7UOMolj46qz3hSJJU2NJ7\nUOuAZXgPSpLUZq3eg3oW2B94DTClnQFJkgStJah3UEyOOBP4EEU9qLe3MyhJkloZ4rsAOATIcv8V\nwI0U6/MNWUSMB+ZTLHt0XETsivWgJEmlVnpQz7EhOUFRWHD1CFz7LIpSHv2LwloPSpK0XisJ6gfA\nJ4EXA3sCn6BYJ28yQ6wNFRF7AUdTVNXtKputByVJWq+VIb7zyt9/P6B9DkXvZygrHl4KzAZ2amiz\nHpQkab1WelDjNvGzxckpIo4FlmfmAjb0njZiPShJUis9qJF2CHB8RBxNsbbfThHxdeBx60ENnfVl\nNNqNls8q+HltVccTVGZ+HPg4QETMAj6amX8eERdjPaghs76MRjvrQY09Q1mLb6T1D+VdCBwZEQkc\nXu5LksaoKob41svMO4E7y+0VWA9KklSqQw9KkqQXMEFJkmrJBCVJqiUTlCSplkxQkqRaMkFJkmrJ\nBCVJqqWOPwcVEXsDVwO7UzykOzcz/8l6UJKkRlX0oNYCH8nM6cAbgA9GxL5YD0qS1KDjCSozl2Xm\nwnL7WYoS8tOwHpQkqUGl96AiYh/gQOAerAclSWpQWYKKiB2A7wFnZeYzjcesByVJqmSx2IiYQJGc\nvp6Z/WU1rAc1DNaX0Wg3Wj6r4Oe1VVXM4usCvgosyszLGg7dgPWghsz6MhrtrAc19lTRgzoUOAm4\nPyIWlG3nUNR/ui4iTqecZl5BbJKkmqiiou5PaX7vy3pQkiTAlSQkSTVlgpIk1ZIJSpJUSyYoSVIt\nmaAkSbVkgpIk1ZIJSpJUS5UsddRMRBwFXAaMB76SmRdVHJIkqSK16UFFxHjgC8BRwH7ACWWdKEnS\nGFSbBAW8Dng0Mxdn5lrg28BbK45JklSROiWoacBjDftLyjZJ0hhUp3tQldV/2m3KdlVdekRs7fFL\nrZo6adeqQxi20fA3dEqdEtRSYO+G/b0pelHN3Nnd3T1rJC78zjdsD2w/Em9VmYULF1YdgtR2x+0y\nC3apOorh8/O6sfnz58+ZOXPmnIHtXRXEMqiI2AZ4GDgC+B1wL3BCZj5UaWCSpErU5h5UZq4D/gb4\nEbAIuNbkJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmqt4jojYjJ5faCiNi2hdfM\niYgJ7Y9uy0TEByLioYjojogdNnHeARHxp52MTWpUp4KF0lYhMw9s8dTzgEuAtW0MZyjOBE7KzO7N\nnHcgcAzwnfaHJL1QbQoWSnUSEe8APgusAa4HPg3skJmrI6K3YfuVwKXAVGAicFlmfi0ivgh8AHgA\n6AUOy8ynhxnTKcCJwArgfwArgXdm5uMRMR64CHhLefrNwNmZ2TvgPa4F3gb8GpgPnAL8O/BHwCSK\nQqF/DewE3Ff+XgzcmZkfHk780payByUNEBF7AHOBgzPzkYiY3eS8bYBrgBMz8+GI2BGYHxF3ZeYH\nI+ID5XusbvL6nwGTBzm0IjOPaBLeTGD/zFwaEXMpekOfBP4KOICi19MF3FS2fbnxxZn5noj4DUVi\nW1TGcWJmroiILuBrwGmZ+X8i4jzg2Mx0mE+VMEFJL/R64L7MfKTcn0vROxkogFcB346I/rYJwL5A\nbu4imXn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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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