Guided Project - Finding the Best Markets to Advertise.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dataquest/system/env/python3/lib/python3.4/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (17,62) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "survey_data = pd.read_csv('2017-fCC-New-Coders-Survey-Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18175, 136)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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       "      <th>AttendedBootcamp</th>\n",
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       "      <td>201.0</td>\n",
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       "      <td>27.691872</td>\n",
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       "      <td>0.699719</td>\n",
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       "      <td>0.241167</td>\n",
       "      <td>0.458594</td>\n",
       "      <td>0.460975</td>\n",
       "      <td>0.385805</td>\n",
       "      <td>0.972813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>22.000000</td>\n",
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       "                Age  AttendedBootcamp  BootcampFinish  BootcampLoanYesNo  \\\n",
       "count  15367.000000      17709.000000     1069.000000        1079.000000   \n",
       "mean      27.691872          0.062002        0.699719           0.305839   \n",
       "std        8.559239          0.241167        0.458594           0.460975   \n",
       "min        0.000000          0.000000        0.000000           0.000000   \n",
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       "75%       32.000000          0.000000        1.000000           1.000000   \n",
       "max       90.000000          1.000000        1.000000           1.000000   \n",
       "\n",
       "       BootcampRecommend  ChildrenNumber  CodeEventConferences  \\\n",
       "count        1073.000000     2314.000000                1609.0   \n",
       "mean            0.818267        1.832325                   1.0   \n",
       "std             0.385805        0.972813                   0.0   \n",
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       "max             1.000000        9.000000                   1.0   \n",
       "\n",
       "       CodeEventDjangoGirls  CodeEventFCC  CodeEventGameJam  \\\n",
       "count                 165.0        1708.0             290.0   \n",
       "mean                    1.0           1.0               1.0   \n",
       "std                     0.0           0.0               0.0   \n",
       "min                     1.0           1.0               1.0   \n",
       "25%                     1.0           1.0               1.0   \n",
       "50%                     1.0           1.0               1.0   \n",
       "75%                     1.0           1.0               1.0   \n",
       "max                     1.0           1.0               1.0   \n",
       "\n",
       "              ...           YouTubeEngineeredTruth  YouTubeFCC  \\\n",
       "count         ...                            993.0      6036.0   \n",
       "mean          ...                              1.0         1.0   \n",
       "std           ...                              0.0         0.0   \n",
       "min           ...                              1.0         1.0   \n",
       "25%           ...                              1.0         1.0   \n",
       "50%           ...                              1.0         1.0   \n",
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       "max           ...                              1.0         1.0   \n",
       "\n",
       "       YouTubeFunFunFunction  YouTubeGoogleDev  YouTubeLearnCode  \\\n",
       "count                 1261.0            3539.0            2662.0   \n",
       "mean                     1.0               1.0               1.0   \n",
       "std                      0.0               0.0               0.0   \n",
       "min                      1.0               1.0               1.0   \n",
       "25%                      1.0               1.0               1.0   \n",
       "50%                      1.0               1.0               1.0   \n",
       "75%                      1.0               1.0               1.0   \n",
       "max                      1.0               1.0               1.0   \n",
       "\n",
       "       YouTubeLevelUpTuts  YouTubeMIT  YouTubeMozillaHacks  \\\n",
       "count              1396.0      3327.0                622.0   \n",
       "mean                  1.0         1.0                  1.0   \n",
       "std                   0.0         0.0                  0.0   \n",
       "min                   1.0         1.0                  1.0   \n",
       "25%                   1.0         1.0                  1.0   \n",
       "50%                   1.0         1.0                  1.0   \n",
       "75%                   1.0         1.0                  1.0   \n",
       "max                   1.0         1.0                  1.0   \n",
       "\n",
       "       YouTubeSimplilearn  YouTubeTheNewBoston  \n",
       "count               201.0               2960.0  \n",
       "mean                  1.0                  1.0  \n",
       "std                   0.0                  0.0  \n",
       "min                   1.0                  1.0  \n",
       "25%                   1.0                  1.0  \n",
       "50%                   1.0                  1.0  \n",
       "75%                   1.0                  1.0  \n",
       "max                   1.0                  1.0  \n",
       "\n",
       "[8 rows x 105 columns]"
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     "metadata": {},
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  {
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       "       ...\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 136 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  AttendedBootcamp  BootcampFinish  BootcampLoanYesNo BootcampName  \\\n",
       "0  27.0               0.0             NaN                NaN          NaN   \n",
       "1  34.0               0.0             NaN                NaN          NaN   \n",
       "2  21.0               0.0             NaN                NaN          NaN   \n",
       "3  26.0               0.0             NaN                NaN          NaN   \n",
       "4  20.0               0.0             NaN                NaN          NaN   \n",
       "\n",
       "   BootcampRecommend  ChildrenNumber                 CityPopulation  \\\n",
       "0                NaN             NaN            more than 1 million   \n",
       "1                NaN             NaN              less than 100,000   \n",
       "2                NaN             NaN            more than 1 million   \n",
       "3                NaN             NaN  between 100,000 and 1 million   \n",
       "4                NaN             NaN  between 100,000 and 1 million   \n",
       "\n",
       "   CodeEventConferences  CodeEventDjangoGirls         ...           \\\n",
       "0                   NaN                   NaN         ...            \n",
       "1                   NaN                   NaN         ...            \n",
       "2                   NaN                   NaN         ...            \n",
       "3                   NaN                   NaN         ...            \n",
       "4                   NaN                   NaN         ...            \n",
       "\n",
       "   YouTubeFCC  YouTubeFunFunFunction  YouTubeGoogleDev  YouTubeLearnCode  \\\n",
       "0         NaN                    NaN               NaN               NaN   \n",
       "1         1.0                    NaN               NaN               NaN   \n",
       "2         NaN                    NaN               NaN               1.0   \n",
       "3         1.0                    1.0               NaN               NaN   \n",
       "4         NaN                    NaN               NaN               NaN   \n",
       "\n",
       "   YouTubeLevelUpTuts  YouTubeMIT  YouTubeMozillaHacks YouTubeOther  \\\n",
       "0                 NaN         NaN                  NaN          NaN   \n",
       "1                 NaN         NaN                  NaN          NaN   \n",
       "2                 1.0         NaN                  NaN          NaN   \n",
       "3                 1.0         NaN                  NaN          NaN   \n",
       "4                 NaN         NaN                  NaN          NaN   \n",
       "\n",
       "   YouTubeSimplilearn  YouTubeTheNewBoston  \n",
       "0                 NaN                  NaN  \n",
       "1                 NaN                  NaN  \n",
       "2                 NaN                  NaN  \n",
       "3                 NaN                  NaN  \n",
       "4                 NaN                  NaN  \n",
       "\n",
       "[5 rows x 136 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Full-Stack Web Developer                                                                                                                                                                                               11.770595\n",
       "  Front-End Web Developer                                                                                                                                                                                               6.435927\n",
       "  Data Scientist                                                                                                                                                                                                        2.173913\n",
       "Back-End Web Developer                                                                                                                                                                                                  2.030892\n",
       "  Mobile Developer                                                                                                                                                                                                      1.673341\n",
       "Game Developer                                                                                                                                                                                                          1.630435\n",
       "Information Security                                                                                                                                                                                                    1.315789\n",
       "Full-Stack Web Developer,   Front-End Web Developer                                                                                                                                                                     0.915332\n",
       "  Front-End Web Developer, Full-Stack Web Developer                                                                                                                                                                     0.800915\n",
       "  Product Manager                                                                                                                                                                                                       0.786613\n",
       "Data Engineer                                                                                                                                                                                                           0.758009\n",
       "  User Experience Designer                                                                                                                                                                                              0.743707\n",
       "  User Experience Designer,   Front-End Web Developer                                                                                                                                                                   0.614989\n",
       "  Front-End Web Developer, Back-End Web Developer, Full-Stack Web Developer                                                                                                                                             0.557780\n",
       "Back-End Web Developer,   Front-End Web Developer, Full-Stack Web Developer                                                                                                                                             0.514874\n",
       "  DevOps / SysAdmin                                                                                                                                                                                                     0.514874\n",
       "Back-End Web Developer, Full-Stack Web Developer,   Front-End Web Developer                                                                                                                                             0.514874\n",
       "Full-Stack Web Developer,   Front-End Web Developer, Back-End Web Developer                                                                                                                                             0.443364\n",
       "  Front-End Web Developer, Full-Stack Web Developer, Back-End Web Developer                                                                                                                                             0.429062\n",
       "  Front-End Web Developer,   User Experience Designer                                                                                                                                                                   0.414760\n",
       "Full-Stack Web Developer,   Mobile Developer                                                                                                                                                                            0.414760\n",
       "Back-End Web Developer, Full-Stack Web Developer                                                                                                                                                                        0.386156\n",
       "Full-Stack Web Developer, Back-End Web Developer                                                                                                                                                                        0.371854\n",
       "Back-End Web Developer,   Front-End Web Developer                                                                                                                                                                       0.286041\n",
       "Full-Stack Web Developer, Back-End Web Developer,   Front-End Web Developer                                                                                                                                             0.271739\n",
       "Data Engineer,   Data Scientist                                                                                                                                                                                         0.271739\n",
       "  Front-End Web Developer,   Mobile Developer                                                                                                                                                                           0.257437\n",
       "Full-Stack Web Developer,   Data Scientist                                                                                                                                                                              0.243135\n",
       "  Mobile Developer, Game Developer                                                                                                                                                                                      0.228833\n",
       "  Data Scientist, Data Engineer                                                                                                                                                                                         0.228833\n",
       "                                                                                                                                                                                                                         ...    \n",
       "Game Developer, support scientific resaerch                                                                                                                                                                             0.014302\n",
       "Full-Stack Web Developer, Back-End Web Developer, Data Engineer,   Quality Assurance Engineer,   Data Scientist                                                                                                         0.014302\n",
       "Data Engineer,   Mobile Developer, Back-End Web Developer, Full-Stack Web Developer, Game Developer,   Front-End Web Developer                                                                                          0.014302\n",
       "  Data Scientist, Data Engineer,   Front-End Web Developer,   Mobile Developer, Back-End Web Developer                                                                                                                  0.014302\n",
       "Full-Stack Web Developer,   Front-End Web Developer,   Mobile Developer, Game Developer,   Product Manager,   User Experience Designer                                                                                  0.014302\n",
       "  Product Manager,   Mobile Developer,   User Experience Designer,   Front-End Web Developer                                                                                                                            0.014302\n",
       "Full-Stack Web Developer,   Front-End Web Developer,   User Experience Designer, Data Engineer, Information Security,   DevOps / SysAdmin,   Mobile Developer,   Quality Assurance Engineer, Back-End Web Developer     0.014302\n",
       "  Quality Assurance Engineer,   DevOps / SysAdmin,   Front-End Web Developer, Data Engineer, Back-End Web Developer                                                                                                     0.014302\n",
       "Game Developer, Data Engineer,   Front-End Web Developer, Back-End Web Developer,   DevOps / SysAdmin                                                                                                                   0.014302\n",
       "  Product Manager, Data Engineer, Full-Stack Web Developer,   Front-End Web Developer, Back-End Web Developer, Game Developer,   Data Scientist                                                                         0.014302\n",
       "  Data Scientist, Full-Stack Web Developer, Information Security,   Quality Assurance Engineer, Data Engineer, Back-End Web Developer                                                                                   0.014302\n",
       "  Product Manager, Back-End Web Developer,   Data Scientist,   User Experience Designer,   Front-End Web Developer, Full-Stack Web Developer, Data Engineer,   Mobile Developer                                         0.014302\n",
       "Back-End Web Developer, Game Developer,   User Experience Designer,   Data Scientist, Full-Stack Web Developer,   Mobile Developer                                                                                      0.014302\n",
       "Back-End Web Developer, Full-Stack Web Developer,   User Experience Designer,   DevOps / SysAdmin                                                                                                                       0.014302\n",
       "Software Specialist                                                                                                                                                                                                     0.014302\n",
       "  Front-End Web Developer,   Mobile Developer, Game Developer,   User Experience Designer, Back-End Web Developer, Full-Stack Web Developer                                                                             0.014302\n",
       "  DevOps / SysAdmin, Information Security, Back-End Web Developer,   Mobile Developer, Full-Stack Web Developer                                                                                                         0.014302\n",
       "Back-End Web Developer,   Product Manager, Full-Stack Web Developer,   Data Scientist,   Front-End Web Developer                                                                                                        0.014302\n",
       "  Quality Assurance Engineer,   Front-End Web Developer, Back-End Web Developer                                                                                                                                         0.014302\n",
       "Information Security,   Front-End Web Developer, Game Developer,   Quality Assurance Engineer                                                                                                                           0.014302\n",
       "Back-End Web Developer, Data Engineer,   Data Scientist,   Mobile Developer, Full-Stack Web Developer, Information Security,   DevOps / SysAdmin,   Quality Assurance Engineer                                          0.014302\n",
       "  User Experience Designer,   Data Scientist,   Quality Assurance Engineer,   Front-End Web Developer, Full-Stack Web Developer, Back-End Web Developer,   Mobile Developer                                             0.014302\n",
       "  Front-End Web Developer,   User Experience Designer, Information Security, Back-End Web Developer,   DevOps / SysAdmin                                                                                                0.014302\n",
       "  Front-End Web Developer, Back-End Web Developer,   Data Scientist,   Mobile Developer, Full-Stack Web Developer, Data Engineer,   Product Manager                                                                     0.014302\n",
       "  Mobile Developer,   User Experience Designer,   Front-End Web Developer,   Data Scientist, Data Engineer, Information Security, Full-Stack Web Developer                                                              0.014302\n",
       "  User Experience Designer, Full-Stack Web Developer,   Mobile Developer, Game Developer,   Product Manager,   Front-End Web Developer                                                                                  0.014302\n",
       "Full-Stack Web Developer, Back-End Web Developer, Data Engineer,   Data Scientist                                                                                                                                       0.014302\n",
       "Information Security, Back-End Web Developer,   User Experience Designer,   Front-End Web Developer, Full-Stack Web Developer,   Mobile Developer, Game Developer                                                       0.014302\n",
       "Full-Stack Web Developer,   Mobile Developer,   Front-End Web Developer,   Quality Assurance Engineer, Game Developer                                                                                                   0.014302\n",
       "Data Engineer,   Mobile Developer, Back-End Web Developer                                                                                                                                                               0.014302\n",
       "Name: JobRoleInterest, Length: 3213, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_data['JobRoleInterest'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1                               [Full-Stack Web Developer]\n",
       "2        [  Front-End Web Developer,  Back-End Web Deve...\n",
       "3        [  Front-End Web Developer,  Full-Stack Web De...\n",
       "4        [Full-Stack Web Developer,  Information Securi...\n",
       "6                               [Full-Stack Web Developer]\n",
       "9        [Full-Stack Web Developer,    Quality Assuranc...\n",
       "11       [  DevOps / SysAdmin,    Data Scientist,  Info...\n",
       "13       [Back-End Web Developer,  Full-Stack Web Devel...\n",
       "14                              [Full-Stack Web Developer]\n",
       "15                              [Full-Stack Web Developer]\n",
       "16                              [Full-Stack Web Developer]\n",
       "18       [Full-Stack Web Developer,    Front-End Web De...\n",
       "19       [  Front-End Web Developer,    Mobile Develope...\n",
       "21                                  [Information Security]\n",
       "22                              [Full-Stack Web Developer]\n",
       "23                                [Back-End Web Developer]\n",
       "28                              [Full-Stack Web Developer]\n",
       "29       [  Front-End Web Developer,    Data Scientist,...\n",
       "30       [Back-End Web Developer,  Full-Stack Web Devel...\n",
       "31                             [  Front-End Web Developer]\n",
       "32       [  Data Scientist,  Information Security,  Dat...\n",
       "33       [Full-Stack Web Developer,    Quality Assuranc...\n",
       "34       [Back-End Web Developer,  Full-Stack Web Devel...\n",
       "35       [Back-End Web Developer,  Full-Stack Web Devel...\n",
       "37                [  Mobile Developer,    Product Manager]\n",
       "40       [  Front-End Web Developer,  Back-End Web Deve...\n",
       "41                             [  Front-End Web Developer]\n",
       "42                              [Full-Stack Web Developer]\n",
       "43       [Back-End Web Developer,    Front-End Web Deve...\n",
       "52       [  Data Scientist,  Game Developer,  Full-Stac...\n",
       "                               ...                        \n",
       "18080     [  Mobile Developer,    Front-End Web Developer]\n",
       "18081    [Full-Stack Web Developer,  Back-End Web Devel...\n",
       "18088                           [Full-Stack Web Developer]\n",
       "18089                       [  Quality Assurance Engineer]\n",
       "18090    [Game Developer,    Data Scientist,  Full-Stac...\n",
       "18093     [  Front-End Web Developer,    Mobile Developer]\n",
       "18097    [Game Developer,    Mobile Developer,  Full-St...\n",
       "18098    [  Front-End Web Developer,  Full-Stack Web De...\n",
       "18099                           [Full-Stack Web Developer]\n",
       "18107                           [Full-Stack Web Developer]\n",
       "18111                [  Mobile Developer,  Game Developer]\n",
       "18112    [  Mobile Developer,  Game Developer,  Full-St...\n",
       "18113                [  Mobile Developer,  Game Developer]\n",
       "18118    [  DevOps / SysAdmin,  Full-Stack Web Develope...\n",
       "18125    [  Front-End Web Developer,  Full-Stack Web De...\n",
       "18129                                 [  Mobile Developer]\n",
       "18130    [  Front-End Web Developer,    User Experience...\n",
       "18131    [Game Developer,    Front-End Web Developer,  ...\n",
       "18151                          [  Front-End Web Developer]\n",
       "18153    [Information Security,  Full-Stack Web Developer]\n",
       "18154                           [Full-Stack Web Developer]\n",
       "18155    [Full-Stack Web Developer,    Front-End Web De...\n",
       "18156                           [Full-Stack Web Developer]\n",
       "18157    [Back-End Web Developer,  Data Engineer,    Mo...\n",
       "18160                         [  User Experience Designer]\n",
       "18161                           [Full-Stack Web Developer]\n",
       "18162    [  Data Scientist,  Game Developer,    Quality...\n",
       "18163    [Back-End Web Developer,  Data Engineer,    Da...\n",
       "18171    [  DevOps / SysAdmin,    Mobile Developer,    ...\n",
       "18174    [Back-End Web Developer,  Data Engineer,    Da...\n",
       "Name: JobRoleInterest, Length: 6992, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_nulls =  survey_data['JobRoleInterest'].dropna()\n",
    "more_than_one_interest = no_nulls.str.split(',')\n",
    "more_than_one_interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "count_of_people_more_than_one_subject = more_than_one_interest.apply(lambda x : len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         1\n",
       "2         5\n",
       "3         3\n",
       "4         5\n",
       "6         1\n",
       "9         6\n",
       "11        4\n",
       "13        3\n",
       "14        1\n",
       "15        1\n",
       "16        1\n",
       "18        4\n",
       "19        4\n",
       "21        1\n",
       "22        1\n",
       "23        1\n",
       "28        1\n",
       "29        4\n",
       "30        2\n",
       "31        1\n",
       "32        3\n",
       "33        2\n",
       "34        2\n",
       "35        4\n",
       "37        2\n",
       "40        6\n",
       "41        1\n",
       "42        1\n",
       "43        3\n",
       "52       12\n",
       "         ..\n",
       "18080     2\n",
       "18081     3\n",
       "18088     1\n",
       "18089     1\n",
       "18090     7\n",
       "18093     2\n",
       "18097     4\n",
       "18098     2\n",
       "18099     1\n",
       "18107     1\n",
       "18111     2\n",
       "18112     5\n",
       "18113     2\n",
       "18118     3\n",
       "18125     2\n",
       "18129     1\n",
       "18130     6\n",
       "18131     3\n",
       "18151     1\n",
       "18153     2\n",
       "18154     1\n",
       "18155     2\n",
       "18156     1\n",
       "18157     6\n",
       "18160     1\n",
       "18161     1\n",
       "18162     3\n",
       "18163     4\n",
       "18171    13\n",
       "18174     3\n",
       "Name: JobRoleInterest, Length: 6992, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_of_people_more_than_one_subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     31.650458\n",
       "2     10.883867\n",
       "3     15.889588\n",
       "4     15.217391\n",
       "5     12.042334\n",
       "6      6.721968\n",
       "7      3.861556\n",
       "8      1.759153\n",
       "9      0.986842\n",
       "10     0.471968\n",
       "11     0.185927\n",
       "12     0.300343\n",
       "13     0.028604\n",
       "Name: JobRoleInterest, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_of_people_more_than_one_subject.value_counts(normalize = True).sort_index() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "web_or_mobile_development = no_nulls.str.contains('Web Developer|Mobile Developer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     86.241419\n",
       "False    13.758581\n",
       "Name: JobRoleInterest, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency_table = web_or_mobile_development.value_counts(normalize = True) * 100\n",
    "frequency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7DHfv3sWQIUPQp08fuLi4yJ4zxquViIjo4egdDL6+vvD19W3IWoiIyAg81OWq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\nBgMREckwGIiISOb/AGdkQsyNO+mTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e1bf739e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frequency_table.plot.bar()\n",
    "plt.title('Participants interested in Web or Mobile Development')\n",
    "plt.ylabel('Percentage', fontsize = 12)\n",
    "plt.xticks([0, 1], ['Web or Mobile Development', 'Other Subject'], rotation = 0)\n",
    "plt.ylim([0, 100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "country_live_data = survey_data[survey_data['JobRoleInterest'].notnull()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Absolute Frequency</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>3125</td>\n",
       "      <td>45.700497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>528</td>\n",
       "      <td>7.721556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>315</td>\n",
       "      <td>4.606610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>260</td>\n",
       "      <td>3.802281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>131</td>\n",
       "      <td>1.915765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>129</td>\n",
       "      <td>1.886517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>125</td>\n",
       "      <td>1.828020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>112</td>\n",
       "      <td>1.637906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>102</td>\n",
       "      <td>1.491664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>89</td>\n",
       "      <td>1.301550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>84</td>\n",
       "      <td>1.228429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>77</td>\n",
       "      <td>1.126060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>75</td>\n",
       "      <td>1.096812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>71</td>\n",
       "      <td>1.038315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands (Holland, Europe)</th>\n",
       "      <td>65</td>\n",
       "      <td>0.950570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>62</td>\n",
       "      <td>0.906698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serbia</th>\n",
       "      <td>52</td>\n",
       "      <td>0.760456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>52</td>\n",
       "      <td>0.760456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>46</td>\n",
       "      <td>0.672711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>43</td>\n",
       "      <td>0.628839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>39</td>\n",
       "      <td>0.570342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>37</td>\n",
       "      <td>0.541094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>36</td>\n",
       "      <td>0.526470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>34</td>\n",
       "      <td>0.497221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>34</td>\n",
       "      <td>0.497221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>33</td>\n",
       "      <td>0.482597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <td>32</td>\n",
       "      <td>0.467973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>32</td>\n",
       "      <td>0.467973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>31</td>\n",
       "      <td>0.453349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>31</td>\n",
       "      <td>0.453349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Papua New Guinea</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qatar</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jordan</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Panama</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guadeloupe</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liberia</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Myanmar</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gambia</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anguilla</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudan</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicaragua</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cameroon</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cuba</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cayman Islands</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rwanda</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mozambique</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Somalia</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel Islands</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samoa</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guatemala</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanuatu</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gibraltar</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nambia</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad &amp; Tobago</th>\n",
       "      <td>1</td>\n",
       "      <td>0.014624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>137 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Absolute Frequency  Percentage\n",
       "United States of America                     3125   45.700497\n",
       "India                                         528    7.721556\n",
       "United Kingdom                                315    4.606610\n",
       "Canada                                        260    3.802281\n",
       "Poland                                        131    1.915765\n",
       "Brazil                                        129    1.886517\n",
       "Germany                                       125    1.828020\n",
       "Australia                                     112    1.637906\n",
       "Russia                                        102    1.491664\n",
       "Ukraine                                        89    1.301550\n",
       "Nigeria                                        84    1.228429\n",
       "Spain                                          77    1.126060\n",
       "France                                         75    1.096812\n",
       "Romania                                        71    1.038315\n",
       "Netherlands (Holland, Europe)                  65    0.950570\n",
       "Italy                                          62    0.906698\n",
       "Serbia                                         52    0.760456\n",
       "Philippines                                    52    0.760456\n",
       "Greece                                         46    0.672711\n",
       "Ireland                                        43    0.628839\n",
       "South Africa                                   39    0.570342\n",
       "Mexico                                         37    0.541094\n",
       "Turkey                                         36    0.526470\n",
       "Hungary                                        34    0.497221\n",
       "Singapore                                      34    0.497221\n",
       "New Zealand                                    33    0.482597\n",
       "Croatia                                        32    0.467973\n",
       "Argentina                                      32    0.467973\n",
       "Norway                                         31    0.453349\n",
       "Sweden                                         31    0.453349\n",
       "...                                           ...         ...\n",
       "Papua New Guinea                                1    0.014624\n",
       "Turkmenistan                                    1    0.014624\n",
       "Qatar                                           1    0.014624\n",
       "Jordan                                          1    0.014624\n",
       "Panama                                          1    0.014624\n",
       "Guadeloupe                                      1    0.014624\n",
       "Liberia                                         1    0.014624\n",
       "Myanmar                                         1    0.014624\n",
       "Gambia                                          1    0.014624\n",
       "Anguilla                                        1    0.014624\n",
       "Sudan                                           1    0.014624\n",
       "Nicaragua                                       1    0.014624\n",
       "Cameroon                                        1    0.014624\n",
       "Cuba                                            1    0.014624\n",
       "Cayman Islands                                  1    0.014624\n",
       "Rwanda                                          1    0.014624\n",
       "Mozambique                                      1    0.014624\n",
       "Somalia                                         1    0.014624\n",
       "Channel Islands                                 1    0.014624\n",
       "Samoa                                           1    0.014624\n",
       "Guatemala                                       1    0.014624\n",
       "Yemen                                           1    0.014624\n",
       "Vanuatu                                         1    0.014624\n",
       "Gibraltar                                       1    0.014624\n",
       "Nambia                                          1    0.014624\n",
       "Aruba                                           1    0.014624\n",
       "Botswana                                        1    0.014624\n",
       "Angola                                          1    0.014624\n",
       "Bolivia                                         1    0.014624\n",
       "Trinidad & Tobago                               1    0.014624\n",
       "\n",
       "[137 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "absolute_country_live_freq = country_live_data['CountryLive'].value_counts()\n",
    "relative_country_live_freq = country_live_data['CountryLive'].value_counts(normalize=True)*100\n",
    "\n",
    "pd.DataFrame(data={'Absolute Frequency' : absolute_country_live_freq,\n",
    "            'Percentage' : relative_country_live_freq})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "country_live_data['MonthsProgramming'].replace(0,1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "675"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_live_data['money_per_month'] = country_live_data['MoneyForLearning'] / country_live_data['MonthsProgramming']\n",
    "country_live_data['money_per_month'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States of America    2933\n",
       "India                        463\n",
       "United Kingdom               279\n",
       "Canada                       240\n",
       "Poland                       122\n",
       "Name: CountryLive, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_live_data = country_live_data[country_live_data['money_per_month'].notnull()]\n",
    "country_live_data = country_live_data[country_live_data['CountryLive'].notnull()]\n",
    "country_live_data['CountryLive'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "country_live_data_mean = country_live_data.groupby('CountryLive').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountryLive\n",
       "United States of America    227.997996\n",
       "India                       135.100982\n",
       "United Kingdom               45.534443\n",
       "Canada                      113.510961\n",
       "Name: money_per_month, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_live_data_mean['money_per_month'][['United States of America', 'India', 'United Kingdom', 'Canada']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dataquest/system/env/python3/lib/python3.4/site-packages/seaborn/categorical.py:454: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e195a5ac8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RiIg0dZcNA2PGjOGee+7h2Wef5ZlnnjHb27VrR4cOHdzaybvvvkvnzp05deoU48ePp0eP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\nREQsTmFARETE4hQGRERELO7/A98jqSQ3JxSEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e1c29bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "top_4 = country_live_data[country_live_data['CountryLive'].str.contains('United States of America|India|United Kingdom|Canada')]\n",
    "sns.boxplot(y='money_per_month', x='CountryLive', data = top_4)\n",
    "                                                                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "country_live_data = country_live_data[country_live_data['money_per_month'] < 10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountryLive\n",
       "United States of America    155.459187\n",
       "India                       113.748387\n",
       "United Kingdom               45.534443\n",
       "Canada                      113.510961\n",
       "Name: money_per_month, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_live_data_mean = country_live_data.groupby('CountryLive').mean()\n",
    "country_live_data_mean['money_per_month'][['United States of America', 'India', 'United Kingdom', 'Canada']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dataquest/system/env/python3/lib/python3.4/site-packages/seaborn/categorical.py:454: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e1c2b3f60>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SEBwcbLHAqGnOzi64du2q2CaithMcPB3vvPO22CaSErOTfnh4ONzd3XH8+HEA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\niYiIZOL/A88W8VzVzrNIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e1863b2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_4 = country_live_data[country_live_data['CountryLive'].str.contains('United States of America|India|United Kingdom|Canada')]\n",
    "sns.boxplot(y='money_per_month', x='CountryLive', data = top_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>AttendedBootcamp</th>\n",
       "      <th>BootcampFinish</th>\n",
       "      <th>BootcampLoanYesNo</th>\n",
       "      <th>BootcampName</th>\n",
       "      <th>BootcampRecommend</th>\n",
       "      <th>ChildrenNumber</th>\n",
       "      <th>CityPopulation</th>\n",
       "      <th>CodeEventConferences</th>\n",
       "      <th>CodeEventDjangoGirls</th>\n",
       "      <th>...</th>\n",
       "      <th>YouTubeFunFunFunction</th>\n",
       "      <th>YouTubeGoogleDev</th>\n",
       "      <th>YouTubeLearnCode</th>\n",
       "      <th>YouTubeLevelUpTuts</th>\n",
       "      <th>YouTubeMIT</th>\n",
       "      <th>YouTubeMozillaHacks</th>\n",
       "      <th>YouTubeOther</th>\n",
       "      <th>YouTubeSimplilearn</th>\n",
       "      <th>YouTubeTheNewBoston</th>\n",
       "      <th>money_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3333.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7989</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8126</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15587</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4166.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 137 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age  AttendedBootcamp  BootcampFinish  BootcampLoanYesNo BootcampName  \\\n",
       "1728   24.0               0.0             NaN                NaN          NaN   \n",
       "1755   20.0               0.0             NaN                NaN          NaN   \n",
       "7989   28.0               0.0             NaN                NaN          NaN   \n",
       "8126   22.0               0.0             NaN                NaN          NaN   \n",
       "15587  27.0               0.0             NaN                NaN          NaN   \n",
       "\n",
       "       BootcampRecommend  ChildrenNumber                 CityPopulation  \\\n",
       "1728                 NaN             NaN  between 100,000 and 1 million   \n",
       "1755                 NaN             NaN            more than 1 million   \n",
       "7989                 NaN             NaN  between 100,000 and 1 million   \n",
       "8126                 NaN             NaN            more than 1 million   \n",
       "15587                NaN             NaN            more than 1 million   \n",
       "\n",
       "       CodeEventConferences  CodeEventDjangoGirls       ...         \\\n",
       "1728                    NaN                   NaN       ...          \n",
       "1755                    NaN                   NaN       ...          \n",
       "7989                    1.0                   NaN       ...          \n",
       "8126                    NaN                   NaN       ...          \n",
       "15587                   NaN                   NaN       ...          \n",
       "\n",
       "       YouTubeFunFunFunction  YouTubeGoogleDev  YouTubeLearnCode  \\\n",
       "1728                     NaN               NaN               NaN   \n",
       "1755                     NaN               NaN               1.0   \n",
       "7989                     NaN               NaN               NaN   \n",
       "8126                     NaN               1.0               NaN   \n",
       "15587                    NaN               1.0               1.0   \n",
       "\n",
       "       YouTubeLevelUpTuts  YouTubeMIT  YouTubeMozillaHacks  YouTubeOther  \\\n",
       "1728                  NaN         NaN                  NaN           NaN   \n",
       "1755                  NaN         1.0                  NaN           NaN   \n",
       "7989                  NaN         NaN                  NaN           NaN   \n",
       "8126                  NaN         1.0                  NaN           NaN   \n",
       "15587                 NaN         1.0                  NaN           NaN   \n",
       "\n",
       "      YouTubeSimplilearn  YouTubeTheNewBoston  money_per_month  \n",
       "1728                 NaN                  NaN      5000.000000  \n",
       "1755                 NaN                  NaN      3333.333333  \n",
       "7989                 NaN                  NaN      5000.000000  \n",
       "8126                 NaN                  1.0      5000.000000  \n",
       "15587                NaN                  NaN      4166.666667  \n",
       "\n",
       "[5 rows x 137 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "india_outliers = top_4[(top_4['CountryLive'] == 'India') &  (top_4['money_per_month'] >= 2500)]\n",
    "india_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "top_4 = top_4.drop(india_outliers.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "us_outliers = top_4[(top_4['CountryLive'] == 'United States of America') &  (top_4['money_per_month'] >= 6000)]\n",
    "top_4 = top_4.drop(us_outliers.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "never_attented_bootcamp = top_4[(top_4['CountryLive'] == 'United States of America') & (top_4['money_per_month'] >= 6000) &(top_4['AttendedBootcamp'] == 0)]\n",
    "top_4 = top_4.drop(never_attented_bootcamp.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "less_than_3_months = top_4[(top_4['CountryLive'] == 'United States of America') & (top_4['money_per_month'] >= 6000) & (top_4['MonthsProgramming'] <= 3)]\n",
    "top_4 = top_4.drop(less_than_3_months.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "canada_outliers = top_4[(top_4['CountryLive'] == 'Canada') & (top_4['money_per_month'] > 4500)]\n",
    "top_4 = top_4.drop(canada_outliers.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountryLive\n",
       "Canada                       93.065400\n",
       "India                        65.758763\n",
       "United Kingdom               45.534443\n",
       "United States of America    142.654608\n",
       "Name: money_per_month, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_4.groupby('CountryLive').mean()['money_per_month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dataquest/system/env/python3/lib/python3.4/site-packages/seaborn/categorical.py:454: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e1c055ac8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SEBwcbLHAqGnOzi64du2q2CaithMcPB3vvPO22CaSErOTfnh4ONzd3XH8+HEA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\niYiIZOL/A88W8VzVzrNIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e186358d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_4 = country_live_data[country_live_data['CountryLive'].str.contains('United States of America|India|United Kingdom|Canada')]\n",
    "sns.boxplot(y='money_per_month', x='CountryLive', data = top_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States of America    74.884793\n",
       "India                       11.827957\n",
       "United Kingdom               7.142857\n",
       "Canada                       6.144393\n",
       "Name: CountryLive, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_4['CountryLive'].value_counts(normalize = True) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# US is the best market to advertise in !!!!!"
   ]
  }
 ],
 "metadata": {
  "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}