{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extracting data and saving it to a new file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This exercise walks you through how to read csv-data, select certain data using selection rules and saving selected data into a new file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First import the packages needed and then read and save the data from file *DoubleMuRun2011.csv* into variable *dataset*. Let's also check out the number of rows and the content of the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file has 475465 rows.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Run</th>\n",
       "      <th>Event</th>\n",
       "      <th>Type1</th>\n",
       "      <th>E1</th>\n",
       "      <th>px1</th>\n",
       "      <th>py1</th>\n",
       "      <th>pz1</th>\n",
       "      <th>pt1</th>\n",
       "      <th>eta1</th>\n",
       "      <th>phi1</th>\n",
       "      <th>...</th>\n",
       "      <th>Type2</th>\n",
       "      <th>E2</th>\n",
       "      <th>px2</th>\n",
       "      <th>py2</th>\n",
       "      <th>pz2</th>\n",
       "      <th>pt2</th>\n",
       "      <th>eta2</th>\n",
       "      <th>phi2</th>\n",
       "      <th>Q2</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165617</td>\n",
       "      <td>74601703</td>\n",
       "      <td>G</td>\n",
       "      <td>9.69873</td>\n",
       "      <td>-9.510430</td>\n",
       "      <td>0.366205</td>\n",
       "      <td>1.86329</td>\n",
       "      <td>9.51748</td>\n",
       "      <td>0.194546</td>\n",
       "      <td>3.10311</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>9.76332</td>\n",
       "      <td>7.327710</td>\n",
       "      <td>-1.152370</td>\n",
       "      <td>6.34728</td>\n",
       "      <td>7.41776</td>\n",
       "      <td>0.775564</td>\n",
       "      <td>-0.155984</td>\n",
       "      <td>1</td>\n",
       "      <td>17.49220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>165617</td>\n",
       "      <td>75100943</td>\n",
       "      <td>G</td>\n",
       "      <td>6.20385</td>\n",
       "      <td>-4.266610</td>\n",
       "      <td>0.456545</td>\n",
       "      <td>-4.47930</td>\n",
       "      <td>4.29097</td>\n",
       "      <td>-0.912070</td>\n",
       "      <td>3.03499</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>9.66898</td>\n",
       "      <td>7.273970</td>\n",
       "      <td>-2.821120</td>\n",
       "      <td>-5.71040</td>\n",
       "      <td>7.80188</td>\n",
       "      <td>-0.678625</td>\n",
       "      <td>-0.369977</td>\n",
       "      <td>1</td>\n",
       "      <td>11.55340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>165617</td>\n",
       "      <td>75587682</td>\n",
       "      <td>G</td>\n",
       "      <td>19.28920</td>\n",
       "      <td>-4.212080</td>\n",
       "      <td>-0.651623</td>\n",
       "      <td>18.81210</td>\n",
       "      <td>4.26219</td>\n",
       "      <td>2.190460</td>\n",
       "      <td>-2.98811</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>9.82439</td>\n",
       "      <td>4.343940</td>\n",
       "      <td>-0.473505</td>\n",
       "      <td>8.79849</td>\n",
       "      <td>4.36967</td>\n",
       "      <td>1.449670</td>\n",
       "      <td>-0.108575</td>\n",
       "      <td>1</td>\n",
       "      <td>9.16361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>165617</td>\n",
       "      <td>75660978</td>\n",
       "      <td>G</td>\n",
       "      <td>7.04268</td>\n",
       "      <td>-6.326780</td>\n",
       "      <td>-0.268545</td>\n",
       "      <td>3.08025</td>\n",
       "      <td>6.33248</td>\n",
       "      <td>0.469033</td>\n",
       "      <td>-3.09917</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>5.58571</td>\n",
       "      <td>4.474760</td>\n",
       "      <td>0.848935</td>\n",
       "      <td>-3.23185</td>\n",
       "      <td>4.55458</td>\n",
       "      <td>-0.660499</td>\n",
       "      <td>0.187488</td>\n",
       "      <td>1</td>\n",
       "      <td>12.47740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>165617</td>\n",
       "      <td>75947690</td>\n",
       "      <td>G</td>\n",
       "      <td>7.27507</td>\n",
       "      <td>0.102966</td>\n",
       "      <td>-5.533080</td>\n",
       "      <td>-4.72122</td>\n",
       "      <td>5.53403</td>\n",
       "      <td>-0.773616</td>\n",
       "      <td>-1.55219</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>7.31811</td>\n",
       "      <td>-0.398831</td>\n",
       "      <td>6.940810</td>\n",
       "      <td>2.28247</td>\n",
       "      <td>6.95226</td>\n",
       "      <td>0.322677</td>\n",
       "      <td>1.628190</td>\n",
       "      <td>1</td>\n",
       "      <td>14.31590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Run     Event Type1        E1       px1       py1       pz1      pt1  \\\n",
       "0  165617  74601703     G   9.69873 -9.510430  0.366205   1.86329  9.51748   \n",
       "1  165617  75100943     G   6.20385 -4.266610  0.456545  -4.47930  4.29097   \n",
       "2  165617  75587682     G  19.28920 -4.212080 -0.651623  18.81210  4.26219   \n",
       "3  165617  75660978     G   7.04268 -6.326780 -0.268545   3.08025  6.33248   \n",
       "4  165617  75947690     G   7.27507  0.102966 -5.533080  -4.72122  5.53403   \n",
       "\n",
       "       eta1     phi1    ...     Type2       E2       px2       py2      pz2  \\\n",
       "0  0.194546  3.10311    ...         G  9.76332  7.327710 -1.152370  6.34728   \n",
       "1 -0.912070  3.03499    ...         G  9.66898  7.273970 -2.821120 -5.71040   \n",
       "2  2.190460 -2.98811    ...         G  9.82439  4.343940 -0.473505  8.79849   \n",
       "3  0.469033 -3.09917    ...         G  5.58571  4.474760  0.848935 -3.23185   \n",
       "4 -0.773616 -1.55219    ...         G  7.31811 -0.398831  6.940810  2.28247   \n",
       "\n",
       "       pt2      eta2      phi2  Q2         M  \n",
       "0  7.41776  0.775564 -0.155984   1  17.49220  \n",
       "1  7.80188 -0.678625 -0.369977   1  11.55340  \n",
       "2  4.36967  1.449670 -0.108575   1   9.16361  \n",
       "3  4.55458 -0.660499  0.187488   1  12.47740  \n",
       "4  6.95226  0.322677  1.628190   1  14.31590  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('../Data/DoubleMuRun2011A.csv')\n",
    "print(\"The file has %d rows.\"% len(dataset))\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a histogram of the invariant masses using the whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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B+0inxU6MiJfzok4gXUm2BakPptIP833gB5IWki4ImNisupiZWWOaFlQi4qM1kr/fw/zT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\nNA4qZmZWmv8PiwkPcSuJKgsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a57559208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dataset['M'], bins=500, range=(0,110))\n",
    "\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0,15000])\n",
    "\n",
    "# Name the axis and set a title\n",
    "plt.xlabel('Invariant mass [GeV]')\n",
    "plt.ylabel('Number of events')\n",
    "plt.title('Histogram of invariant masses of two muons\\n') # \\n creates a new line for making the title look better\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Histogram shows multiple peaks from which we are interested in the one on the right. Let's choose the rows from the original data that hae invariant mass values between 70 < M < 110 and save these into variable *peakdata*. We can also check how many rows of data fill these conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33177"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peakdata = dataset[(dataset.M>70) & (dataset.M<110)] #Z\n",
    "len(peakdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now easily plot a histogram for invariant masses of the selected data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a5758bcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inv_mass = peakdata['M']\n",
    "plt.hist(inv_mass, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The selected conditions for data seem applicable but let's check the minimum and maximum values for invariant mass in our selected data. This way we can make sure that our conditions hold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The smallest value of invariant mass is 70.006900 and the greatest is 109.999000 in selected data.\n"
     ]
    }
   ],
   "source": [
    "minimum = min(peakdata['M'])\n",
    "maximum = max(peakdata['M'])\n",
    "print(\"The smallest value of invariant mass is %f and the greatest is %f in selected data.\" %(minimum,maximum))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that our conditions hold so let's save the data in *peakdata* into a csv-file and name it *peak.csv*. We can leave out the original indices but it's good to keep the original column titels. The created file will be saved in the same folder with the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "peakdata.to_csv('peak.csv',index=False,header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can make sure that we managed to save the data we wanted by reading the file *peak.csv* and printing the first five rows of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Run</th>\n",
       "      <th>Event</th>\n",
       "      <th>Type1</th>\n",
       "      <th>E1</th>\n",
       "      <th>px1</th>\n",
       "      <th>py1</th>\n",
       "      <th>pz1</th>\n",
       "      <th>pt1</th>\n",
       "      <th>eta1</th>\n",
       "      <th>phi1</th>\n",
       "      <th>...</th>\n",
       "      <th>Type2</th>\n",
       "      <th>E2</th>\n",
       "      <th>px2</th>\n",
       "      <th>py2</th>\n",
       "      <th>pz2</th>\n",
       "      <th>pt2</th>\n",
       "      <th>eta2</th>\n",
       "      <th>phi2</th>\n",
       "      <th>Q2</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165617</td>\n",
       "      <td>74969122</td>\n",
       "      <td>G</td>\n",
       "      <td>59.9226</td>\n",
       "      <td>-46.15160</td>\n",
       "      <td>29.4115</td>\n",
       "      <td>-24.4070</td>\n",
       "      <td>54.7266</td>\n",
       "      <td>-0.432382</td>\n",
       "      <td>2.574210</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>52.4465</td>\n",
       "      <td>30.10970</td>\n",
       "      <td>-16.39890</td>\n",
       "      <td>-39.68760</td>\n",
       "      <td>34.2859</td>\n",
       "      <td>-0.988511</td>\n",
       "      <td>-0.498717</td>\n",
       "      <td>-1</td>\n",
       "      <td>89.9557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>165617</td>\n",
       "      <td>75138253</td>\n",
       "      <td>G</td>\n",
       "      <td>97.1011</td>\n",
       "      <td>-23.61440</td>\n",
       "      <td>6.6660</td>\n",
       "      <td>-93.9497</td>\n",
       "      <td>24.5372</td>\n",
       "      <td>-2.052350</td>\n",
       "      <td>2.866470</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>30.5992</td>\n",
       "      <td>-11.61340</td>\n",
       "      <td>-25.98480</td>\n",
       "      <td>11.23470</td>\n",
       "      <td>28.4619</td>\n",
       "      <td>0.385137</td>\n",
       "      <td>-1.991090</td>\n",
       "      <td>1</td>\n",
       "      <td>88.6081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>165617</td>\n",
       "      <td>75887636</td>\n",
       "      <td>G</td>\n",
       "      <td>152.9720</td>\n",
       "      <td>7.46565</td>\n",
       "      <td>-30.7098</td>\n",
       "      <td>-149.6710</td>\n",
       "      <td>31.6042</td>\n",
       "      <td>-2.259260</td>\n",
       "      <td>-1.332320</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>33.5835</td>\n",
       "      <td>-9.28778</td>\n",
       "      <td>28.74570</td>\n",
       "      <td>-14.67190</td>\n",
       "      <td>30.2089</td>\n",
       "      <td>-0.468368</td>\n",
       "      <td>1.883310</td>\n",
       "      <td>1</td>\n",
       "      <td>88.2438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>165617</td>\n",
       "      <td>75833588</td>\n",
       "      <td>G</td>\n",
       "      <td>181.8770</td>\n",
       "      <td>44.14270</td>\n",
       "      <td>-14.9498</td>\n",
       "      <td>175.8040</td>\n",
       "      <td>46.6055</td>\n",
       "      <td>2.037920</td>\n",
       "      <td>-0.326545</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>170.0210</td>\n",
       "      <td>-34.63010</td>\n",
       "      <td>12.32480</td>\n",
       "      <td>166.00000</td>\n",
       "      <td>36.7579</td>\n",
       "      <td>2.212820</td>\n",
       "      <td>2.799670</td>\n",
       "      <td>-1</td>\n",
       "      <td>83.0943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>165617</td>\n",
       "      <td>75779415</td>\n",
       "      <td>G</td>\n",
       "      <td>50.2440</td>\n",
       "      <td>37.80720</td>\n",
       "      <td>-12.2044</td>\n",
       "      <td>-30.7590</td>\n",
       "      <td>39.7283</td>\n",
       "      <td>-0.712422</td>\n",
       "      <td>-0.312246</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>49.2396</td>\n",
       "      <td>-47.60640</td>\n",
       "      <td>8.23376</td>\n",
       "      <td>-9.50613</td>\n",
       "      <td>48.3132</td>\n",
       "      <td>-0.195513</td>\n",
       "      <td>2.970330</td>\n",
       "      <td>-1</td>\n",
       "      <td>90.3544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Run     Event Type1        E1       px1      py1       pz1      pt1  \\\n",
       "0  165617  74969122     G   59.9226 -46.15160  29.4115  -24.4070  54.7266   \n",
       "1  165617  75138253     G   97.1011 -23.61440   6.6660  -93.9497  24.5372   \n",
       "2  165617  75887636     G  152.9720   7.46565 -30.7098 -149.6710  31.6042   \n",
       "3  165617  75833588     G  181.8770  44.14270 -14.9498  175.8040  46.6055   \n",
       "4  165617  75779415     G   50.2440  37.80720 -12.2044  -30.7590  39.7283   \n",
       "\n",
       "       eta1      phi1   ...     Type2        E2       px2       py2  \\\n",
       "0 -0.432382  2.574210   ...         G   52.4465  30.10970 -16.39890   \n",
       "1 -2.052350  2.866470   ...         G   30.5992 -11.61340 -25.98480   \n",
       "2 -2.259260 -1.332320   ...         G   33.5835  -9.28778  28.74570   \n",
       "3  2.037920 -0.326545   ...         G  170.0210 -34.63010  12.32480   \n",
       "4 -0.712422 -0.312246   ...         G   49.2396 -47.60640   8.23376   \n",
       "\n",
       "         pz2      pt2      eta2      phi2  Q2        M  \n",
       "0  -39.68760  34.2859 -0.988511 -0.498717  -1  89.9557  \n",
       "1   11.23470  28.4619  0.385137 -1.991090   1  88.6081  \n",
       "2  -14.67190  30.2089 -0.468368  1.883310   1  88.2438  \n",
       "3  166.00000  36.7579  2.212820  2.799670  -1  83.0943  \n",
       "4   -9.50613  48.3132 -0.195513  2.970330  -1  90.3544  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('peak.csv')\n",
    "test.head()"
   ]
  }
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