Skip to content

Instantly share code, notes, and snippets.

@joelotz

joelotz/SWS

Created October 21, 2014 18:30
Show Gist options
  • Save joelotz/001d03dc2900c9529625 to your computer and use it in GitHub Desktop.
Save joelotz/001d03dc2900c9529625 to your computer and use it in GitHub Desktop.
Download ZIP
Raw
SWS
{
"metadata": {
"name": "",
"signature": "sha256:f8edf9c1663e0e67e6fbbbfbfc78f83f1f702f53194b9792e22bda26e5b094e5"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
"Author : Joe Lotz\n",
"Date : Sept 2014"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import glob\n",
"import brewer2mpl\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import linear_model"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/usr/local/lib/python2.7/dist-packages/pytz/__init__.py:29: UserWarning: Module numpy was already imported from /usr/lib/python2.7/dist-packages/numpy/__init__.pyc, but /usr/local/lib/python2.7/dist-packages is being added to sys.path\n",
" from pkg_resources import resource_stream\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%reload_ext version_information\n",
"%version_information pandas, numpy, sklearn, matplotlib, brewer2mpl"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>2.7.5+ (default, Sep 19 2013, 13:49:51) [GCC 4.8.1]</td></tr><tr><td>IPython</td><td>2.0.0-dev</td></tr><tr><td>OS</td><td>posix [linux2]</td></tr><tr><td>pandas</td><td>0.13.1</td></tr><tr><td>numpy</td><td>1.7.1</td></tr><tr><td>sklearn</td><td>0.13</td></tr><tr><td>matplotlib</td><td>1.2.1</td></tr><tr><td>brewer2mpl</td><td>1.4.dev</td></tr><tr><td colspan='2'>Thu Sep 25 21:58:01 2014 PDT</td></tr></table>"
],
"json": [
"{\"Software versions\": [{\"version\": \"2.7.5+ (default, Sep 19 2013, 13:49:51) [GCC 4.8.1]\", \"module\": \"Python\"}, {\"version\": \"2.0.0-dev\", \"module\": \"IPython\"}, {\"version\": \"posix [linux2]\", \"module\": \"OS\"}, {\"version\": \"0.13.1\", \"module\": \"pandas\"}, {\"version\": \"1.7.1\", \"module\": \"numpy\"}, {\"version\": \"0.13\", \"module\": \"sklearn\"}, {\"version\": \"1.2.1\", \"module\": \"matplotlib\"}, {\"version\": \"1.4.dev\", \"module\": \"brewer2mpl\"}]}"
],
"latex": [
"\\begin{tabular}{|l|l|}\\hline\n",
"{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
"Python & 2.7.5+ (default, Sep 19 2013, 13:49:51) [GCC 4.8.1] \\\\ \\hline\n",
"IPython & 2.0.0-dev \\\\ \\hline\n",
"OS & posix [linux2] \\\\ \\hline\n",
"pandas & 0.13.1 \\\\ \\hline\n",
"numpy & 1.7.1 \\\\ \\hline\n",
"sklearn & 0.13 \\\\ \\hline\n",
"matplotlib & 1.2.1 \\\\ \\hline\n",
"brewer2mpl & 1.4.dev \\\\ \\hline\n",
"\\hline \\multicolumn{2}{|l|}{Thu Sep 25 21:58:01 2014 PDT} \\\\ \\hline\n",
"\\end{tabular}\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"Software versions\n",
"Python 2.7.5+ (default, Sep 19 2013, 13:49:51) [GCC 4.8.1]\n",
"IPython 2.0.0-dev\n",
"OS posix [linux2]\n",
"pandas 0.13.1\n",
"numpy 1.7.1\n",
"sklearn 0.13\n",
"matplotlib 1.2.1\n",
"brewer2mpl 1.4.dev\n",
"<tr><td colspan='2'>Thu Sep 25 21:58:01 2014 PDT</td></tr>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Set up some better defaults for matplotlib\n",
"from matplotlib import rcParams\n",
"\n",
"#colorbrewer2 Dark2 qualitative color table\n",
"my_colors = brewer2mpl.get_map('Set2', 'Qualitative', 3).mpl_colors\n",
"\n",
"PHI = 1.6180339887498948482\n",
"HEIGHT = 8\n",
"rcParams['figure.figsize'] = (HEIGHT*PHI, HEIGHT)\n",
"rcParams['figure.dpi'] = 300\n",
"rcParams['axes.color_cycle'] = my_colors\n",
"rcParams['lines.linewidth'] = 2\n",
"rcParams['axes.facecolor'] = 'white'\n",
"rcParams['font.size'] = 14\n",
"rcParams['patch.edgecolor'] = 'white'\n",
"rcParams['patch.facecolor'] = my_colors[0]\n",
"rcParams['font.family'] = 'Arial'\n",
"\n",
"\n",
"def remove_border(axes=None, top=False, right=False, left=True, bottom=True):\n",
" \"\"\"\n",
" Minimize chartjunk by stripping out unnecesasry plot borders and axis ticks\n",
" \n",
" The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn\n",
" \"\"\"\n",
" ax = axes or plt.gca()\n",
" ax.spines['top'].set_visible(top)\n",
" ax.spines['right'].set_visible(right)\n",
" ax.spines['left'].set_visible(left)\n",
" ax.spines['bottom'].set_visible(bottom)\n",
" \n",
" #turn off all ticks\n",
" ax.yaxis.set_ticks_position('none')\n",
" ax.xaxis.set_ticks_position('none')\n",
" ax.tick_params(axis='x', labelsize=16)\n",
" \n",
" #now re-enable visibles\n",
" if top:\n",
" ax.xaxis.tick_top()\n",
" if bottom:\n",
" ax.xaxis.tick_bottom()\n",
" if left:\n",
" ax.yaxis.tick_left()\n",
" if right:\n",
" ax.yaxis.tick_right()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# define location\n",
"#myDir = \"C:\\User\\Joe Lotz\\MyPython\\SWS\\DataStore\"\n",
"myDir = \"/home/joe/Python/SWS/DataStore/\"\n",
"\n",
"# build a list\n",
"fileList = []\n",
"os.chdir(myDir)\n",
"for mfile in glob.glob(\"*.csv\"):\n",
" fileList.append(mfile)\n",
"\n",
"# check it \n",
"print fileList\n",
"\n",
"# hand built from fileList\n",
"mux = ['S91-1012-200.csv', 'J91-6002-220.csv']\n",
"non = ['J91-6000-100.csv', 'J91-6000-200.csv', 'J91-6002-100.csv', 'V4206AS.csv']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['S91-1012-200.csv', 'V4206AS.csv', 'J91-6002-220.csv', 'J91-6000-200.csv', 'J91-6002-100.csv', 'J91-6000-100.csv']\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# define cols based on excel view\n",
"cols = ['ChassisNumber','OrderProcessingDivisionCode','ItemNumber','ChassisBuildDate','VehicleIdentificationNumber','ChassisModelName','Covered']\n",
"\n",
"# build empty df\n",
"df_mux = pd.DataFrame(columns=cols)\n",
"df_non = pd.DataFrame(columns=cols)\n",
"\n",
"# loop and merge\n",
"for mfile in mux:\n",
" temp = pd.read_csv(os.path.join(myDir, mfile), header=0, index_col=False)\n",
" df_mux=df_mux.append(temp)\n",
" \n",
"# loop and merge \n",
"for mfile in non:\n",
" temp = pd.read_csv(os.path.join(myDir, mfile), header=0, index_col=False)\n",
" df_non=df_non.append(temp)\n",
"\n",
"# sanity check \n",
"print 'mux=',df_mux.shape\n",
"print 'non=',df_non.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"mux= (13056, 7)\n",
"non= (201956, 7)\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_mux.drop(['OrderProcessingDivisionCode','VehicleIdentificationNumber','Covered'],axis=1, inplace=True)\n",
"df_non.drop(['OrderProcessingDivisionCode','VehicleIdentificationNumber','Covered'],axis=1, inplace=True)\n",
"df_mux.head()"
],
"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>ChassisNumber</th>\n",
" <th>ItemNumber</th>\n",
" <th>ChassisBuildDate</th>\n",
" <th>ChassisModelName</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 2812</td>\n",
" <td> S91-1012-200</td>\n",
" <td> 12/24/2007</td>\n",
" <td> T2000 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2817</td>\n",
" <td> S91-1012-200</td>\n",
" <td> 12/24/2007</td>\n",
" <td> W900B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3104</td>\n",
" <td> S91-1012-200</td>\n",
" <td> 12/10/2007</td>\n",
" <td> W900B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 3136</td>\n",
" <td> S91-1012-200</td>\n",
" <td> 12/3/2007</td>\n",
" <td> T2000 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 3137</td>\n",
" <td> S91-1012-200</td>\n",
" <td> 12/3/2007</td>\n",
" <td> T2000 </td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 4 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
" ChassisNumber ItemNumber ChassisBuildDate ChassisModelName\n",
"0 2812 S91-1012-200 12/24/2007 T2000 \n",
"1 2817 S91-1012-200 12/24/2007 W900B\n",
"2 3104 S91-1012-200 12/10/2007 W900B\n",
"3 3136 S91-1012-200 12/3/2007 T2000 \n",
"4 3137 S91-1012-200 12/3/2007 T2000 \n",
"\n",
"[5 rows x 4 columns]"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df_mux['ChassisBuildDate'] = pd.to_datetime(df_mux['ChassisBuildDate'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://stackoverflow.com/questions/11391969/how-to-group-pandas-dataframe-entries-by-date-in-a-non-unique-column\n",
"df_mux['ChassisBuildDate'] = pd.to_datetime(df_mux['ChassisBuildDate'])\n",
"df_non['ChassisBuildDate'] = pd.to_datetime(df_non['ChassisBuildDate'])\n",
"\n",
"muxYear = df_mux['ChassisNumber'].groupby([df_mux['ChassisBuildDate'].map(lambda x: x.year),df_mux['ItemNumber']]).count().unstack()\n",
"nonYear = df_non['ChassisNumber'].groupby([df_non['ChassisBuildDate'].map(lambda x: x.year),df_non['ItemNumber']]).count().unstack()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nonYear"
],
"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>ItemNumber</th>\n",
" <th>J91-6000-100</th>\n",
" <th>J91-6000-200</th>\n",
" <th>J91-6002-100</th>\n",
" <th>V4206AS</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ChassisBuildDate</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2007</th>\n",
" <td> 22114</td>\n",
" <td> 2545</td>\n",
" <td> NaN</td>\n",
" <td> 707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008</th>\n",
" <td> 21405</td>\n",
" <td> 1902</td>\n",
" <td> NaN</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009</th>\n",
" <td> 14203</td>\n",
" <td> 1562</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010</th>\n",
" <td> 15652</td>\n",
" <td> 1765</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td> 29445</td>\n",
" <td> 4130</td>\n",
" <td> 2</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td> 27590</td>\n",
" <td> 4179</td>\n",
" <td> 827</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td> 17327</td>\n",
" <td> 2625</td>\n",
" <td> 8463</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014</th>\n",
" <td> 10538</td>\n",
" <td> 1709</td>\n",
" <td> 13259</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows \u00d7 4 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"ItemNumber J91-6000-100 J91-6000-200 J91-6002-100 V4206AS\n",
"ChassisBuildDate \n",
"2007 22114 2545 NaN 707\n",
"2008 21405 1902 NaN 7\n",
"2009 14203 1562 NaN NaN\n",
"2010 15652 1765 NaN NaN\n",
"2011 29445 4130 2 NaN\n",
"2012 27590 4179 827 NaN\n",
"2013 17327 2625 8463 NaN\n",
"2014 10538 1709 13259 NaN\n",
"\n",
"[8 rows x 4 columns]"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"non = list(nonYear.sum(axis=1))\n",
"mux = list(muxYear.sum(axis=1))\n",
" \n",
"#normalizing the data\n",
"fm = []\n",
"fn = []\n",
"for r in range(0,8):\n",
" fm.append(mux[r]/(mux[r]+non[r]))\n",
" fn.append(non[r]/(mux[r]+non[r]))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"yr = muxYear.index.values\n",
"x = np.array(yr)\n",
"y = np.array(fn)\n",
"\n",
"clf = linear_model.LinearRegression()\n",
"xx = x.reshape((x.shape[0],-1))\n",
"yy = y.reshape((y.shape[0],-1))\n",
"clf.fit(xx,yy)\n",
"\n",
"\n",
"px = np.append(xx,[2015,2016,2017])\n",
"px = px.reshape((px.shape[0],-1))\n",
"\n",
"\n",
"# Plot outputs\n",
"plt.scatter(xx, yy, color='black')\n",
"plt.plot(xx, clf.predict(xx), color='blue',\n",
" linewidth=2)\n",
"\n",
"plt.xticks(())\n",
"plt.yticks(())\n",
"\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAHMCAYAAACUWB3ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngAoJcAAAqJAABwCACv1/3QeRUDHW2h4AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0xbea346c>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://nbviewer.ipython.org/github/cs109/content/blob/master/lec_03_statistical_graphs.ipynb\n",
"plt.subplots(1,1,figsize=(HEIGHT*PHI,HEIGHT))\n",
"plt.bar(range(0,8), non, color=my_colors[2], label='Non')\n",
"plt.bar(range(0,8), mux, color=my_colors[1], bottom=non, label='Mux')\n",
"plt.xticks(np.arange(0.4,8.4,1), muxYear.index.values, rotation='horizontal')\n",
"plt.ylabel(\"EAU\")\n",
"#plt.xlabel(\"Years\")\n",
"plt.legend(loc='upper right')\n",
"remove_border()\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAxoAAAHiCAYAAABvHroPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naAAAAAAAAONQaAAAAAAAAOP+HysG/L9o3UywAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xc0b960c>"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"yr = list(muxYear.index.values)\n",
"fn.extend([0,0,0])\n",
"fm.extend([0,0,0])\n",
"yr.extend([2015,2016,2017])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.subplots(1,1,figsize=(HEIGHT*PHI,HEIGHT))\n",
"plt.bar(range(0,11), fn, color=my_colors[2], label='Non')\n",
"plt.bar(range(0,11), fm, color=my_colors[1], bottom=fn, label='Mux')\n",
"#plt.plot(np.arange(0.5,11.5,1), clf.predict(px), color='k', marker='o',linewidth=3)\n",
"plt.xticks(np.arange(0.4,11.4,1), yr, rotation='horizontal')\n",
"plt.ylabel(\"Percentage of Total Sales\")\n",
"#plt.xlabel(\"Years\")\n",
"plt.legend(loc='upper right')\n",
"remove_border()\n",
"\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAHiCAYAAACA3NExAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAACgBiw0AAAAAAAAAJf4D5IoDIvPUtqwAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0xc0ae7cc>"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.subplots(1,1,figsize=(HEIGHT*PHI,HEIGHT))\n",
"plt.bar(range(0,11), fn, color=my_colors[2], label='Non')\n",
"plt.bar(range(0,11), fm, color=my_colors[1], bottom=fn, label='Mux')\n",
"plt.plot(np.arange(0.4,11.4,1), clf.predict(px), color='k',linewidth=1.5)\n",
"plt.plot(np.ones(11)*10.4,np.arange(0,1.1,0.1), linestyle='--', color='r', alpha=0.5 )\n",
"plt.plot(10.4, clf.predict(2017), marker='o', markerfacecolor='r')\n",
"\n",
"\n",
"\n",
"plt.xticks(np.arange(0.4,11.4,1), yr, rotation='horizontal', )\n",
"plt.ylabel(\"Percentage of Total Sales\")\n",
"#plt.xlabel(\"Years\")\n",
"#plt.legend(loc='lower right')\n",
"remove_border()\n",
"\n",
"plt.show()\n",
"\n",
"1-clf.predict(2017)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAwkAAAHiCAYAAACjh4DkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nI42OEb1tAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xc87130c>"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"array([[ 0.12241262]])"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#savefig('foo.png', bbox_inches='tight')"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment