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@joelotz
Created October 21, 2014 18:30
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"metadata": {
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"signature": "sha256:f8edf9c1663e0e67e6fbbbfbfc78f83f1f702f53194b9792e22bda26e5b094e5"
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"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": 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uOe+8ZNCgWtcDeigBDgB7aOPG5NZbk5tvTt54o8zGjCnfEr/yymTEiHr3A3oW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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": 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0oHH06NGBTz755LKSkpKRHh4eFxITE7cuXLjwOY1GY5k6deqatWvXpjqOX7Zs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\np6evSklJWa/T6cw6nc6clJS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"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": 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JZtasWWPatGmze+vWrR29vLxStFqtcvjw4eaFhYWmv/32W2dDFgwAAADAOJQa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\n/Pns2bP1VSqVWlEU7cqVK99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"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": 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XnunevfsK7UoGAAAAEEg+Owlr165t/8QTT7yqKIpbROTzzz//h9vtVvr06bPY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\nXBWB5fjx43W1rAsAAAAaY2WjoKZpJwE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cExNz3tvb+75EIlkukUiWi8Xi29HR0ZVtbW3z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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": {}
}
]
}
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