Demo entry 6682185

test

   

Submitted by anonymous on Dec 07, 2017 at 11:48
Language: Python 3. Code size: 73.5 kB.

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import our packages\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# connect to our data\n",
    "myexploredata = pd.read_csv (\"C:/Users/yogip/Desktop/Exercise_Files/02_03/exploratory-py.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>keyword</th>\n",
       "      <th>cpa</th>\n",
       "      <th>impressions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>low price air</td>\n",
       "      <td>5.83</td>\n",
       "      <td>118215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lowest air price</td>\n",
       "      <td>9.72</td>\n",
       "      <td>49244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>airline flight insurance</td>\n",
       "      <td>2.73</td>\n",
       "      <td>4905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lowest air prices</td>\n",
       "      <td>3.74</td>\n",
       "      <td>81605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>luggage for airlines</td>\n",
       "      <td>2.61</td>\n",
       "      <td>20435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>luggage bags for us travel</td>\n",
       "      <td>0.54</td>\n",
       "      <td>20102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>trip insurance coverage</td>\n",
       "      <td>5.70</td>\n",
       "      <td>12796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>check on luggage</td>\n",
       "      <td>3.11</td>\n",
       "      <td>8911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>flight insurance coverage</td>\n",
       "      <td>4.89</td>\n",
       "      <td>15630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      keyword   cpa  impressions\n",
       "0               low price air  5.83       118215\n",
       "1            lowest air price  9.72        49244\n",
       "2    airline flight insurance  2.73         4905\n",
       "3           lowest air prices  3.74        81605\n",
       "4        luggage for airlines  2.61        20435\n",
       "5  luggage bags for us travel  0.54        20102\n",
       "6     trip insurance coverage  5.70        12796\n",
       "7            check on luggage  3.11         8911\n",
       "8   flight insurance coverage  4.89        15630"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see a summary of our data\n",
    "myexploredata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\yogip\\anaconda3\\lib\\site-packages\n",
      "Requirement already satisfied: numpy in c:\\users\\yogip\\anaconda3\\lib\\site-packages (from seaborn)\n",
      "Requirement already satisfied: scipy in c:\\users\\yogip\\anaconda3\\lib\\site-packages (from seaborn)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\yogip\\anaconda3\\lib\\site-packages (from seaborn)\n",
      "Requirement already satisfied: pandas in c:\\users\\yogip\\anaconda3\\lib\\site-packages (from seaborn)\n",
      "Requirement already satisfied: six>=1.10 in c:\\users\\yogip\\anaconda3\\lib\\site-packages (from matplotlib->seaborn)\n",
      "Requirement already satisfied: python-dateutil>=2.0 in c:\\users\\yogip\\anaconda3\\lib\\site-packages (from matplotlib->seaborn)\n",
      "Requirement already satisfied: pytz in c:\\users\\yogip\\anaconda3\\lib\\site-packages (from matplotlib->seaborn)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\yogip\\anaconda3\\lib\\site-packages (from matplotlib->seaborn)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\yogip\\anaconda3\\lib\\site-packages (from matplotlib->seaborn)\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cf693e3518>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rBkFq0YZSquuauOOCbKejGB/57pVDONzQwpxPdjodxQQgKwZB6oXPdjOwVxwX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf67e48f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(myexploredata.cpa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cf69255908>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DEsLtNm49J8/qKCpAZSZEcdPCXKqaOnlu8zG6e11WR1IBxptisBWYLCLjRSQc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\nA6WUUmgxUEophRYDpZRSwP8HisypHs/SwtQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf693ddba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize our data with additional detail \n",
    "sns.distplot(myexploredata.cpa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pivot the data \n",
    "myetldata = myexploredata.pivot(\"keyword\", \"impressions\", \"cpa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>impressions</th>\n",
       "      <th>4905</th>\n",
       "      <th>8911</th>\n",
       "      <th>12796</th>\n",
       "      <th>15630</th>\n",
       "      <th>20102</th>\n",
       "      <th>20435</th>\n",
       "      <th>49244</th>\n",
       "      <th>81605</th>\n",
       "      <th>118215</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keyword</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>airline flight insurance</th>\n",
       "      <td>2.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>check on luggage</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flight insurance coverage</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low price air</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lowest air price</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lowest air prices</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.74</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>luggage bags for us travel</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>luggage for airlines</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip insurance coverage</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "impressions                 4905    8911    12796   15630   20102   20435   \\\n",
       "keyword                                                                      \n",
       "airline flight insurance      2.73     NaN     NaN     NaN     NaN     NaN   \n",
       "check on luggage               NaN    3.11     NaN     NaN     NaN     NaN   \n",
       "flight insurance coverage      NaN     NaN     NaN    4.89     NaN     NaN   \n",
       "low price air                  NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "lowest air price               NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "lowest air prices              NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "luggage bags for us travel     NaN     NaN     NaN     NaN    0.54     NaN   \n",
       "luggage for airlines           NaN     NaN     NaN     NaN     NaN    2.61   \n",
       "trip insurance coverage        NaN     NaN     5.7     NaN     NaN     NaN   \n",
       "\n",
       "impressions                 49244   81605   118215  \n",
       "keyword                                             \n",
       "airline flight insurance       NaN     NaN     NaN  \n",
       "check on luggage               NaN     NaN     NaN  \n",
       "flight insurance coverage      NaN     NaN     NaN  \n",
       "low price air                  NaN     NaN    5.83  \n",
       "lowest air price              9.72     NaN     NaN  \n",
       "lowest air prices              NaN    3.74     NaN  \n",
       "luggage bags for us travel     NaN     NaN     NaN  \n",
       "luggage for airlines           NaN     NaN     NaN  \n",
       "trip insurance coverage        NaN     NaN     NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see a summary of our pivot \n",
    "myetldata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cf67e5e400>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9W/9ucOoRjgR2s/2qpDHNypZilrdXQPO/SQF/sP3frbS/peNTcbntdyRNBD5J6uHeVFb+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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cf67e67dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize our data\n",
    "sns.heatmap(myetldata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The following command must be run outside of the IPython shell:\n",
      "\n",
      "    $ pip install nbconvert\n",
      "\n",
      "The Python package manager (pip) can only be used from outside of IPython.\n",
      "Please reissue the `pip` command in a separate terminal or command prompt.\n",
      "\n",
      "See the Python documentation for more informations on how to install packages:\n",
      "\n",
      "    https://docs.python.org/3/installing/\n"
     ]
    }
   ],
   "source": [
    "pip install nbconvert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}

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