jakebrinkmann/Calculate_working_mean.ipynb

## Calculate_working_mean.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE_ACQU</th>\n",
       "      <th>DSL</th>\n",
       "      <th>YSL</th>\n",
       "      <th>LAMP_PAIR_ID</th>\n",
       "      <th>LAMP_COLLECTION_TYPE</th>\n",
       "      <th>BAND_ID</th>\n",
       "      <th>MEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-16</td>\n",
       "      <td>32.954129</td>\n",
       "      <td>0.090223</td>\n",
       "      <td>2</td>\n",
       "      <td>WORKING</td>\n",
       "      <td>1</td>\n",
       "      <td>48.051590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-16</td>\n",
       "      <td>32.954129</td>\n",
       "      <td>0.090223</td>\n",
       "      <td>2</td>\n",
       "      <td>WORKING</td>\n",
       "      <td>1</td>\n",
       "      <td>48.137718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-16</td>\n",
       "      <td>32.954129</td>\n",
       "      <td>0.090223</td>\n",
       "      <td>2</td>\n",
       "      <td>WORKING</td>\n",
       "      <td>2</td>\n",
       "      <td>131.953857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-16</td>\n",
       "      <td>32.954129</td>\n",
       "      <td>0.090223</td>\n",
       "      <td>2</td>\n",
       "      <td>WORKING</td>\n",
       "      <td>2</td>\n",
       "      <td>132.076828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-16</td>\n",
       "      <td>32.954129</td>\n",
       "      <td>0.090223</td>\n",
       "      <td>2</td>\n",
       "      <td>WORKING</td>\n",
       "      <td>3</td>\n",
       "      <td>325.504059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DATE_ACQU         DSL       YSL  LAMP_PAIR_ID LAMP_COLLECTION_TYPE  \\\n",
       "0 2013-03-16   32.954129  0.090223             2             WORKING    \n",
       "1 2013-03-16   32.954129  0.090223             2             WORKING    \n",
       "2 2013-03-16   32.954129  0.090223             2             WORKING    \n",
       "3 2013-03-16   32.954129  0.090223             2             WORKING    \n",
       "4 2013-03-16   32.954129  0.090223             2             WORKING    \n",
       "\n",
       "   BAND_ID        MEAN  \n",
       "0        1   48.051590  \n",
       "1        1   48.137718  \n",
       "2        2  131.953857  \n",
       "3        2  132.076828  \n",
       "4        3  325.504059  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe = pd.read_excel(\"C:\\Users\\Jake.Brinkmann\\Desktop\\FOR_JAKE.xlsx\", parse_dates=['DATE_ACQU'])\n",
    "dframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DSL</th>\n",
       "      <th>LAMP_PAIR_ID</th>\n",
       "      <th>BAND_ID</th>\n",
       "      <th>MEAN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YSL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.090223</th>\n",
       "      <td>32.954129</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>48.094654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.097834</th>\n",
       "      <td>35.733816</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>47.917873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DSL  LAMP_PAIR_ID  BAND_ID       MEAN\n",
       "YSL                                                   \n",
       "0.090223   32.954129             2        1  48.094654\n",
       "0.097834   35.733816             2        1  47.917873"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "band = 1\n",
    "wmean = dframe[(dframe.BAND_ID == band) & (dframe.LAMP_COLLECTION_TYPE == 'WORKING ')].groupby('YSL').mean()\n",
    "wmean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb3cd7f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3417f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wmean['MEAN'].plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"Populating the interactive namespace from numpy and matplotlib\n"
	]
	}
	],
	"source": [
	"%pylab inline\n",
	"import pandas as pd"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/html": [
	"<div>\n",
	"<table border=\"1\" class=\"dataframe\">\n",
	" <thead>\n",
	" <tr style=\"text-align: right;\">\n",
	" <th></th>\n",
	" <th>DATE_ACQU</th>\n",
	" <th>DSL</th>\n",
	" <th>YSL</th>\n",
	" <th>LAMP_PAIR_ID</th>\n",
	" <th>LAMP_COLLECTION_TYPE</th>\n",
	" <th>BAND_ID</th>\n",
	" <th>MEAN</th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th>0</th>\n",
	" <td>2013-03-16</td>\n",
	" <td>32.954129</td>\n",
	" <td>0.090223</td>\n",
	" <td>2</td>\n",
	" <td>WORKING</td>\n",
	" <td>1</td>\n",
	" <td>48.051590</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>1</th>\n",
	" <td>2013-03-16</td>\n",
	" <td>32.954129</td>\n",
	" <td>0.090223</td>\n",
	" <td>2</td>\n",
	" <td>WORKING</td>\n",
	" <td>1</td>\n",
	" <td>48.137718</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>2</th>\n",
	" <td>2013-03-16</td>\n",
	" <td>32.954129</td>\n",
	" <td>0.090223</td>\n",
	" <td>2</td>\n",
	" <td>WORKING</td>\n",
	" <td>2</td>\n",
	" <td>131.953857</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>3</th>\n",
	" <td>2013-03-16</td>\n",
	" <td>32.954129</td>\n",
	" <td>0.090223</td>\n",
	" <td>2</td>\n",
	" <td>WORKING</td>\n",
	" <td>2</td>\n",
	" <td>132.076828</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>4</th>\n",
	" <td>2013-03-16</td>\n",
	" <td>32.954129</td>\n",
	" <td>0.090223</td>\n",
	" <td>2</td>\n",
	" <td>WORKING</td>\n",
	" <td>3</td>\n",
	" <td>325.504059</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" DATE_ACQU DSL YSL LAMP_PAIR_ID LAMP_COLLECTION_TYPE \\\n",
	"0 2013-03-16 32.954129 0.090223 2 WORKING \n",
	"1 2013-03-16 32.954129 0.090223 2 WORKING \n",
	"2 2013-03-16 32.954129 0.090223 2 WORKING \n",
	"3 2013-03-16 32.954129 0.090223 2 WORKING \n",
	"4 2013-03-16 32.954129 0.090223 2 WORKING \n",
	"\n",
	" BAND_ID MEAN \n",
	"0 1 48.051590 \n",
	"1 1 48.137718 \n",
	"2 2 131.953857 \n",
	"3 2 132.076828 \n",
	"4 3 325.504059 "
	]
	},
	"execution_count": 2,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"dframe = pd.read_excel(\"C:\\Users\\Jake.Brinkmann\\Desktop\\FOR_JAKE.xlsx\", parse_dates=['DATE_ACQU'])\n",
	"dframe.head()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/html": [
	"<div>\n",
	"<table border=\"1\" class=\"dataframe\">\n",
	" <thead>\n",
	" <tr style=\"text-align: right;\">\n",
	" <th></th>\n",
	" <th>DSL</th>\n",
	" <th>LAMP_PAIR_ID</th>\n",
	" <th>BAND_ID</th>\n",
	" <th>MEAN</th>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>YSL</th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th>0.090223</th>\n",
	" <td>32.954129</td>\n",
	" <td>2</td>\n",
	" <td>1</td>\n",
	" <td>48.094654</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>0.097834</th>\n",
	" <td>35.733816</td>\n",
	" <td>2</td>\n",
	" <td>1</td>\n",
	" <td>47.917873</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" DSL LAMP_PAIR_ID BAND_ID MEAN\n",
	"YSL \n",
	"0.090223 32.954129 2 1 48.094654\n",
	"0.097834 35.733816 2 1 47.917873"
	]
	},
	"execution_count": 3,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"band = 1\n",
	"wmean = dframe[(dframe.BAND_ID == band) & (dframe.LAMP_COLLECTION_TYPE == 'WORKING ')].groupby('YSL').mean()\n",
	"wmean"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.axes._subplots.AxesSubplot at 0xb3cd7f0>"
	]
	},
	"execution_count": 4,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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	"text/plain": [
	"<matplotlib.figure.Figure at 0xb3417f0>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"wmean['MEAN'].plot()"
	]
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 2",
	"language": "python",
	"name": "python2"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 2
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython2",
	"version": "2.7.10"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}