HowToBreakIntoTheField.ipynb

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    "### Screencast\n",
    "\n",
    "In the previous video, I brought a few questions we will be exploring throughout this lesson. First, let's take a look at the data, and see how we might answer the first question about how to break into the field of becoming a software developoer according to the survey results.\n",
    "\n",
    "To get started, let's read in the necessary libraries we will need to wrangle our data: pandas and numpy.  If we decided to build some basic plots, matplotlib might prove useful as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "      <th>Professional</th>\n",
       "      <th>ProgramHobby</th>\n",
       "      <th>Country</th>\n",
       "      <th>University</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>FormalEducation</th>\n",
       "      <th>MajorUndergrad</th>\n",
       "      <th>HomeRemote</th>\n",
       "      <th>CompanySize</th>\n",
       "      <th>...</th>\n",
       "      <th>StackOverflowMakeMoney</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HighestEducationParents</th>\n",
       "      <th>Race</th>\n",
       "      <th>SurveyLong</th>\n",
       "      <th>QuestionsInteresting</th>\n",
       "      <th>QuestionsConfusing</th>\n",
       "      <th>InterestedAnswers</th>\n",
       "      <th>Salary</th>\n",
       "      <th>ExpectedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Student</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Not employed, and not looking for work</td>\n",
       "      <td>Secondary school</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>High school</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Student</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Employed part-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>More than half, but not all, the time</td>\n",
       "      <td>20 to 99 employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A master's degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Professional developer</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor's degree</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>Less than half the time, but at least one day ...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A professional degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>113750.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Professional non-developer who sometimes write...</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Doctoral degree</td>\n",
       "      <td>A non-computer-focused engineering discipline</td>\n",
       "      <td>Less than half the time, but at least one day ...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A doctoral degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Professional developer</td>\n",
       "      <td>Yes, I program as a hobby</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Master's degree</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>Never</td>\n",
       "      <td>10 to 19 employees</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 × 154 columns</p>\n",
       "</div>"
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      "text/plain": [
       "   Respondent                                       Professional  \\\n",
       "0           1                                            Student   \n",
       "1           2                                            Student   \n",
       "2           3                             Professional developer   \n",
       "3           4  Professional non-developer who sometimes write...   \n",
       "4           5                             Professional developer   \n",
       "\n",
       "                ProgramHobby         Country      University  \\\n",
       "0                  Yes, both   United States              No   \n",
       "1                  Yes, both  United Kingdom  Yes, full-time   \n",
       "2                  Yes, both  United Kingdom              No   \n",
       "3                  Yes, both   United States              No   \n",
       "4  Yes, I program as a hobby     Switzerland              No   \n",
       "\n",
       "                         EmploymentStatus  \\\n",
       "0  Not employed, and not looking for work   \n",
       "1                      Employed part-time   \n",
       "2                      Employed full-time   \n",
       "3                      Employed full-time   \n",
       "4                      Employed full-time   \n",
       "\n",
       "                                     FormalEducation  \\\n",
       "0                                   Secondary school   \n",
       "1  Some college/university study without earning ...   \n",
       "2                                  Bachelor's degree   \n",
       "3                                    Doctoral degree   \n",
       "4                                    Master's degree   \n",
       "\n",
       "                                  MajorUndergrad  \\\n",
       "0                                            NaN   \n",
       "1       Computer science or software engineering   \n",
       "2       Computer science or software engineering   \n",
       "3  A non-computer-focused engineering discipline   \n",
       "4       Computer science or software engineering   \n",
       "\n",
       "                                          HomeRemote  \\\n",
       "0                                                NaN   \n",
       "1              More than half, but not all, the time   \n",
       "2  Less than half the time, but at least one day ...   \n",
       "3  Less than half the time, but at least one day ...   \n",
       "4                                              Never   \n",
       "\n",
       "                CompanySize       ...       StackOverflowMakeMoney Gender  \\\n",
       "0                       NaN       ...            Strongly disagree   Male   \n",
       "1        20 to 99 employees       ...            Strongly disagree   Male   \n",
       "2  10,000 or more employees       ...                     Disagree   Male   \n",
       "3  10,000 or more employees       ...                     Disagree   Male   \n",
       "4        10 to 19 employees       ...                          NaN    NaN   \n",
       "\n",
       "  HighestEducationParents                          Race         SurveyLong  \\\n",
       "0             High school  White or of European descent  Strongly disagree   \n",
       "1       A master's degree  White or of European descent     Somewhat agree   \n",
       "2   A professional degree  White or of European descent     Somewhat agree   \n",
       "3       A doctoral degree  White or of European descent              Agree   \n",
       "4                     NaN                           NaN                NaN   \n",
       "\n",
       "  QuestionsInteresting QuestionsConfusing InterestedAnswers    Salary  \\\n",
       "0       Strongly agree           Disagree    Strongly agree       NaN   \n",
       "1       Somewhat agree           Disagree    Strongly agree       NaN   \n",
       "2                Agree           Disagree             Agree  113750.0   \n",
       "3                Agree     Somewhat agree    Strongly agree       NaN   \n",
       "4                  NaN                NaN               NaN       NaN   \n",
       "\n",
       "   ExpectedSalary  \n",
       "0             NaN  \n",
       "1         37500.0  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "\n",
       "[5 rows x 154 columns]"
      ]
     },
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     "metadata": {},
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   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import defaultdict\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv('./Part I/stackoverflow/survey_results_public.csv')\n",
    "df.head()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to look at our first question of interest: What do those employed in the industry suggest to help others enter the field?  Looking at the `CousinEducation` field, you can see what these individuals would suggest to help others break into their field.  Below you can take a look at the full field that survey participants would see."
   ]
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   "cell_type": "code",
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   "metadata": {},
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     "data": {
      "text/plain": [
       "[\"Let's pretend you have a distant cousin. They are 24 years old, have a college degree in a field not related to computer programming, and have been working a non-coding job for the last two years. They want your advice on how to switch to a career as a software developer. Which of the following options would you most strongly recommend to your cousin?\\nLet's pretend you have a distant cousin named Robert. He is 24 years old, has a college degree in a field not related to computer programming, and has been working a non-coding job for the last two years. He wants your advice on how to switch to a career as a software developer. Which of the following options would you most strongly recommend to Robert?\\nLet's pretend you have a distant cousin named Alice. She is 24 years old, has a college degree in a field not related to computer programming, and has been working a non-coding job for the last two years. She wants your advice on how to switch to a career as a software developer. Which of the following options would you most strongly recommend to Alice?\"]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('./Part I/stackoverflow/survey_results_schema.csv')\n",
    "list(df2[df2.Column == 'CousinEducation']['Question'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Take online courses; Buy books and work throug...</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Take online courses</td>\n",
       "      <td>551</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None of these</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Take online courses; Part-time/evening courses...</td>\n",
       "      <td>479</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Take online courses; Bootcamp; Part-time/eveni...</td>\n",
       "      <td>465</td>\n",
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       "                                               index  CousinEducation\n",
       "0  Take online courses; Buy books and work throug...              711\n",
       "1                                Take online courses              551\n",
       "2                                      None of these              523\n",
       "3  Take online courses; Part-time/evening courses...              479\n",
       "4  Take online courses; Bootcamp; Part-time/eveni...              465"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's have a look at what the participants say\n",
    "study = df['CousinEducation'].value_counts().reset_index()\n",
    "study.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Take online courses; Buy books and work throug...</td>\n",
       "      <td>711</td>\n",
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       "      <td>Take online courses</td>\n",
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       "      <th>2</th>\n",
       "      <td>None of these</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Take online courses; Part-time/evening courses...</td>\n",
       "      <td>479</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Take online courses; Bootcamp; Part-time/eveni...</td>\n",
       "      <td>465</td>\n",
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      "text/plain": [
       "                                              method  count\n",
       "0  Take online courses; Buy books and work throug...    711\n",
       "1                                Take online courses    551\n",
       "2                                      None of these    523\n",
       "3  Take online courses; Part-time/evening courses...    479\n",
       "4  Take online courses; Bootcamp; Part-time/eveni...    465"
      ]
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   ],
   "source": [
    "# Oh this isn't what I was expecting, it is grouping items together if a participant provided \n",
    "# more than just one answer.  Let's see if we can clean this up.\n",
    "# first to change this index column to a more appropriate name\n",
    "study.rename(columns={'index': 'method', 'CousinEducation': 'count'}, inplace=True)\n",
    "study.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick look through data, allows us to create a list of all of the individual methods marked by a user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here is a list of the different answers provided\n",
    "possible_vals = [\"Take online courses\", \"Buy books and work through the exercises\", \n",
    "                 \"None of these\", \"Part-time/evening courses\", \"Return to college\",\n",
    "                 \"Contribute to open source\", \"Conferences/meet-ups\", \"Bootcamp\",\n",
    "                 \"Get a job as a QA tester\", \"Participate in online coding competitions\",\n",
    "                 \"Master's degree\", \"Participate in hackathons\", \"Other\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Now we want to see how often each of these individual values appears - I wrote \n",
    "# this function to assist with process - it isn't the best solution, but it gets\n",
    "# the job done and our dataset isn't large enough to computationally hurt us too much.\n",
    "\n",
    "def total_count(df, col1, col2, look_for):\n",
    "    '''\n",
    "    INPUT:\n",
    "    df - the pandas dataframe you want to search\n",
    "    col1 - the column name you want to look through\n",
    "    col2 - the column you want to count values from\n",
    "    look_for - a list of strings you want to search for in each row of df[col]\n",
    "    \n",
    "    OUTPUT:\n",
    "    new_df - a dataframe of each look_for with the count of how often it shows up \n",
    "    '''\n",
    "    new_df = defaultdict(int)\n",
    "    for val in look_for:\n",
    "        for idx in range(df.shape[0]):\n",
    "            if val in df[col1][idx]:\n",
    "                new_df[val] += int(df[col2][idx])   \n",
    "    new_df = pd.DataFrame(pd.Series(new_df)).reset_index()\n",
    "    new_df.columns = [col1, col2]\n",
    "    new_df.sort_values('count', ascending=False, inplace=True)\n",
    "    return new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>count</th>\n",
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       "      <th>12</th>\n",
       "      <td>Take online courses</td>\n",
       "      <td>15246</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>Buy books and work through the exercises</td>\n",
       "      <td>11750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Part-time/evening courses</td>\n",
       "      <td>7517</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Contribute to open source</td>\n",
       "      <td>7423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bootcamp</td>\n",
       "      <td>5276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conferences/meet-ups</td>\n",
       "      <td>5244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Return to college</td>\n",
       "      <td>5017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Participate in online coding competitions</td>\n",
       "      <td>3610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Get a job as a QA tester</td>\n",
       "      <td>3376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Participate in hackathons</td>\n",
       "      <td>2747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Master's degree</td>\n",
       "      <td>2639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Other</td>\n",
       "      <td>2348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None of these</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       method  count\n",
       "12                        Take online courses  15246\n",
       "1    Buy books and work through the exercises  11750\n",
       "8                   Part-time/evening courses   7517\n",
       "3                   Contribute to open source   7423\n",
       "0                                    Bootcamp   5276\n",
       "2                        Conferences/meet-ups   5244\n",
       "11                          Return to college   5017\n",
       "10  Participate in online coding competitions   3610\n",
       "4                    Get a job as a QA tester   3376\n",
       "9                   Participate in hackathons   2747\n",
       "5                             Master's degree   2639\n",
       "7                                       Other   2348\n",
       "6                               None of these    604"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we can use our function and take a look at the results\n",
    "# Looks like good news for Udacity - most individuals think that you \n",
    "# should take online courses\n",
    "\n",
    "\n",
    "study_df = total_count(study, 'method', 'count', possible_vals)\n",
    "study_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>method</th>\n",
       "      <th>count</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Take online courses</td>\n",
       "      <td>15246</td>\n",
       "      <td>0.209432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Buy books and work through the exercises</td>\n",
       "      <td>11750</td>\n",
       "      <td>0.161408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Part-time/evening courses</td>\n",
       "      <td>7517</td>\n",
       "      <td>0.103260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Contribute to open source</td>\n",
       "      <td>7423</td>\n",
       "      <td>0.101968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bootcamp</td>\n",
       "      <td>5276</td>\n",
       "      <td>0.072476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conferences/meet-ups</td>\n",
       "      <td>5244</td>\n",
       "      <td>0.072036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Return to college</td>\n",
       "      <td>5017</td>\n",
       "      <td>0.068918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Participate in online coding competitions</td>\n",
       "      <td>3610</td>\n",
       "      <td>0.049590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Get a job as a QA tester</td>\n",
       "      <td>3376</td>\n",
       "      <td>0.046376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Participate in hackathons</td>\n",
       "      <td>2747</td>\n",
       "      <td>0.037735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Master's degree</td>\n",
       "      <td>2639</td>\n",
       "      <td>0.036251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Other</td>\n",
       "      <td>2348</td>\n",
       "      <td>0.032254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None of these</td>\n",
       "      <td>604</td>\n",
       "      <td>0.008297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       method  count      perc\n",
       "12                        Take online courses  15246  0.209432\n",
       "1    Buy books and work through the exercises  11750  0.161408\n",
       "8                   Part-time/evening courses   7517  0.103260\n",
       "3                   Contribute to open source   7423  0.101968\n",
       "0                                    Bootcamp   5276  0.072476\n",
       "2                        Conferences/meet-ups   5244  0.072036\n",
       "11                          Return to college   5017  0.068918\n",
       "10  Participate in online coding competitions   3610  0.049590\n",
       "4                    Get a job as a QA tester   3376  0.046376\n",
       "9                   Participate in hackathons   2747  0.037735\n",
       "5                             Master's degree   2639  0.036251\n",
       "7                                       Other   2348  0.032254\n",
       "6                               None of these    604  0.008297"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We might also look at the percent\n",
    "\n",
    "study_df['perc'] = study_df['count']/np.sum(study_df['count'])\n",
    "study_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We might want to take this one step further and say we care more about the methods that are suggested by those who earn more, or those who have higher job satisfaction.  Let's take a stab at incorporating that into this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# let's rewrite part of this function to get the mean salary for each method\n",
    "\n",
    "def mean_amt(df, col_name, col_mean, look_for):\n",
    "    '''\n",
    "    INPUT:\n",
    "    df - the pandas dataframe you want to search\n",
    "    col_name - the column name you want to look through\n",
    "    col_count - the column you want to count values from\n",
    "    col_mean - the column you want the mean amount for\n",
    "    look_for - a list of strings you want to search for in each row of df[col]\n",
    "    \n",
    "    OUTPUT:\n",
    "    df_all - holds sum, square, total, mean, variance, and standard deviation for the col_mean\n",
    "    '''\n",
    "    new_df = defaultdict(int)\n",
    "    squares_df = defaultdict(int)\n",
    "    denoms = dict()\n",
    "    for val in look_for:\n",
    "        denoms[val] = 0\n",
    "        for idx in range(df.shape[0]):\n",
    "            if df[col_name].isnull()[idx] == False:\n",
    "                if val in df[col_name][idx] and df[col_mean][idx] > 0:\n",
    "                    new_df[val] += df[col_mean][idx]\n",
    "                    squares_df[val] += df[col_mean][idx]**2 #Needed to understand the spread\n",
    "                    denoms[val] += 1 \n",
    "    \n",
    "    # Turn into dataframes\n",
    "    new_df = pd.DataFrame(pd.Series(new_df)).reset_index()\n",
    "    squares_df = pd.DataFrame(pd.Series(squares_df)).reset_index()\n",
    "    denoms = pd.DataFrame(pd.Series(denoms)).reset_index()\n",
    "    \n",
    "    # Change the column names\n",
    "    new_df.columns = [col_name, 'col_sum']\n",
    "    squares_df.columns = [col_name, 'col_squares']\n",
    "    denoms.columns = [col_name, 'col_total']\n",
    "    \n",
    "    # Merge dataframes\n",
    "    df_means = pd.merge(new_df, denoms)\n",
    "    df_all = pd.merge(df_means, squares_df)\n",
    "    \n",
    "    # Additional columns needed for analysis\n",
    "    df_all['mean_col'] = df_means['col_sum']/df_means['col_total']\n",
    "    df_all['var_col'] = df_all['col_squares']/df_all['col_total'] - df_all['mean_col']**2\n",
    "    df_all['std_col'] = np.sqrt(df_all['var_col'])\n",
    "    df_all['lower_95'] = df_all['mean_col'] - 1.96*df_all['std_col']/np.sqrt(df_all['col_total'])\n",
    "    df_all['upper_95'] = df_all['mean_col'] + 1.96*df_all['std_col']/np.sqrt(df_all['col_total'])\n",
    "    return df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>CousinEducation</th>\n",
       "      <th>col_sum</th>\n",
       "      <th>col_total</th>\n",
       "      <th>col_squares</th>\n",
       "      <th>mean_col</th>\n",
       "      <th>var_col</th>\n",
       "      <th>std_col</th>\n",
       "      <th>lower_95</th>\n",
       "      <th>upper_95</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Contribute to open source</td>\n",
       "      <td>1.392267e+08</td>\n",
       "      <td>2253</td>\n",
       "      <td>1.239062e+13</td>\n",
       "      <td>61796.145495</td>\n",
       "      <td>1.680845e+09</td>\n",
       "      <td>40998.103671</td>\n",
       "      <td>60103.214545</td>\n",
       "      <td>63489.076445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Other</td>\n",
       "      <td>4.491415e+07</td>\n",
       "      <td>738</td>\n",
       "      <td>3.851360e+12</td>\n",
       "      <td>60859.281694</td>\n",
       "      <td>1.514792e+09</td>\n",
       "      <td>38920.331073</td>\n",
       "      <td>58051.234411</td>\n",
       "      <td>63667.328977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Master's degree</td>\n",
       "      <td>4.284612e+07</td>\n",
       "      <td>721</td>\n",
       "      <td>3.771773e+12</td>\n",
       "      <td>59425.969277</td>\n",
       "      <td>1.699862e+09</td>\n",
       "      <td>41229.387609</td>\n",
       "      <td>56416.462517</td>\n",
       "      <td>62435.476037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Return to college</td>\n",
       "      <td>8.733691e+07</td>\n",
       "      <td>1474</td>\n",
       "      <td>7.243713e+12</td>\n",
       "      <td>59251.636145</td>\n",
       "      <td>1.403567e+09</td>\n",
       "      <td>37464.208781</td>\n",
       "      <td>57339.037276</td>\n",
       "      <td>61164.235014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bootcamp</td>\n",
       "      <td>9.583229e+07</td>\n",
       "      <td>1622</td>\n",
       "      <td>8.502989e+12</td>\n",
       "      <td>59082.794998</td>\n",
       "      <td>1.751510e+09</td>\n",
       "      <td>41851.043917</td>\n",
       "      <td>57046.048671</td>\n",
       "      <td>61119.541325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Participate in hackathons</td>\n",
       "      <td>4.641498e+07</td>\n",
       "      <td>796</td>\n",
       "      <td>4.044473e+12</td>\n",
       "      <td>58310.277060</td>\n",
       "      <td>1.680908e+09</td>\n",
       "      <td>40998.879264</td>\n",
       "      <td>55462.070236</td>\n",
       "      <td>61158.483884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conferences/meet-ups</td>\n",
       "      <td>9.699603e+07</td>\n",
       "      <td>1677</td>\n",
       "      <td>8.366275e+12</td>\n",
       "      <td>57839.015307</td>\n",
       "      <td>1.643482e+09</td>\n",
       "      <td>40539.886446</td>\n",
       "      <td>55898.701080</td>\n",
       "      <td>59779.329533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Get a job as a QA tester</td>\n",
       "      <td>5.852363e+07</td>\n",
       "      <td>1032</td>\n",
       "      <td>5.017250e+12</td>\n",
       "      <td>56708.940771</td>\n",
       "      <td>1.645773e+09</td>\n",
       "      <td>40568.122771</td>\n",
       "      <td>54233.792987</td>\n",
       "      <td>59184.088555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Buy books and work through the exercises</td>\n",
       "      <td>1.909928e+08</td>\n",
       "      <td>3393</td>\n",
       "      <td>1.624985e+13</td>\n",
       "      <td>56290.232211</td>\n",
       "      <td>1.620639e+09</td>\n",
       "      <td>40257.160560</td>\n",
       "      <td>54935.644033</td>\n",
       "      <td>57644.820390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Take online courses</td>\n",
       "      <td>2.415638e+08</td>\n",
       "      <td>4493</td>\n",
       "      <td>2.011544e+13</td>\n",
       "      <td>53764.488012</td>\n",
       "      <td>1.586442e+09</td>\n",
       "      <td>39830.167883</td>\n",
       "      <td>52599.825762</td>\n",
       "      <td>54929.150262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Part-time/evening courses</td>\n",
       "      <td>1.124542e+08</td>\n",
       "      <td>2117</td>\n",
       "      <td>9.290785e+12</td>\n",
       "      <td>53119.613118</td>\n",
       "      <td>1.566963e+09</td>\n",
       "      <td>39584.879140</td>\n",
       "      <td>51433.351458</td>\n",
       "      <td>54805.874778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None of these</td>\n",
       "      <td>5.377087e+06</td>\n",
       "      <td>112</td>\n",
       "      <td>4.273189e+11</td>\n",
       "      <td>48009.706357</td>\n",
       "      <td>1.510415e+09</td>\n",
       "      <td>38864.063653</td>\n",
       "      <td>40811.981041</td>\n",
       "      <td>55207.431672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Participate in online coding competitions</td>\n",
       "      <td>4.332660e+07</td>\n",
       "      <td>931</td>\n",
       "      <td>3.326921e+12</td>\n",
       "      <td>46537.703161</td>\n",
       "      <td>1.407734e+09</td>\n",
       "      <td>37519.785181</td>\n",
       "      <td>44127.567026</td>\n",
       "      <td>48947.839295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              CousinEducation       col_sum  col_total  \\\n",
       "3                   Contribute to open source  1.392267e+08       2253   \n",
       "7                                       Other  4.491415e+07        738   \n",
       "5                             Master's degree  4.284612e+07        721   \n",
       "11                          Return to college  8.733691e+07       1474   \n",
       "0                                    Bootcamp  9.583229e+07       1622   \n",
       "9                   Participate in hackathons  4.641498e+07        796   \n",
       "2                        Conferences/meet-ups  9.699603e+07       1677   \n",
       "4                    Get a job as a QA tester  5.852363e+07       1032   \n",
       "1    Buy books and work through the exercises  1.909928e+08       3393   \n",
       "12                        Take online courses  2.415638e+08       4493   \n",
       "8                   Part-time/evening courses  1.124542e+08       2117   \n",
       "6                               None of these  5.377087e+06        112   \n",
       "10  Participate in online coding competitions  4.332660e+07        931   \n",
       "\n",
       "     col_squares      mean_col       var_col       std_col      lower_95  \\\n",
       "3   1.239062e+13  61796.145495  1.680845e+09  40998.103671  60103.214545   \n",
       "7   3.851360e+12  60859.281694  1.514792e+09  38920.331073  58051.234411   \n",
       "5   3.771773e+12  59425.969277  1.699862e+09  41229.387609  56416.462517   \n",
       "11  7.243713e+12  59251.636145  1.403567e+09  37464.208781  57339.037276   \n",
       "0   8.502989e+12  59082.794998  1.751510e+09  41851.043917  57046.048671   \n",
       "9   4.044473e+12  58310.277060  1.680908e+09  40998.879264  55462.070236   \n",
       "2   8.366275e+12  57839.015307  1.643482e+09  40539.886446  55898.701080   \n",
       "4   5.017250e+12  56708.940771  1.645773e+09  40568.122771  54233.792987   \n",
       "1   1.624985e+13  56290.232211  1.620639e+09  40257.160560  54935.644033   \n",
       "12  2.011544e+13  53764.488012  1.586442e+09  39830.167883  52599.825762   \n",
       "8   9.290785e+12  53119.613118  1.566963e+09  39584.879140  51433.351458   \n",
       "6   4.273189e+11  48009.706357  1.510415e+09  38864.063653  40811.981041   \n",
       "10  3.326921e+12  46537.703161  1.407734e+09  37519.785181  44127.567026   \n",
       "\n",
       "        upper_95  \n",
       "3   63489.076445  \n",
       "7   63667.328977  \n",
       "5   62435.476037  \n",
       "11  61164.235014  \n",
       "0   61119.541325  \n",
       "9   61158.483884  \n",
       "2   59779.329533  \n",
       "4   59184.088555  \n",
       "1   57644.820390  \n",
       "12  54929.150262  \n",
       "8   54805.874778  \n",
       "6   55207.431672  \n",
       "10  48947.839295  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = mean_amt(df, 'CousinEducation', 'Salary', possible_vals)\n",
    "\n",
    "# To get a simple answer to our questions - see these two tables.\n",
    "\n",
    "df_all.sort_values('mean_col', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>count</th>\n",
       "      <th>perc</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Take online courses</td>\n",
       "      <td>15246</td>\n",
       "      <td>0.209432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Buy books and work through the exercises</td>\n",
       "      <td>11750</td>\n",
       "      <td>0.161408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Part-time/evening courses</td>\n",
       "      <td>7517</td>\n",
       "      <td>0.103260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Contribute to open source</td>\n",
       "      <td>7423</td>\n",
       "      <td>0.101968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bootcamp</td>\n",
       "      <td>5276</td>\n",
       "      <td>0.072476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conferences/meet-ups</td>\n",
       "      <td>5244</td>\n",
       "      <td>0.072036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Return to college</td>\n",
       "      <td>5017</td>\n",
       "      <td>0.068918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Participate in online coding competitions</td>\n",
       "      <td>3610</td>\n",
       "      <td>0.049590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Get a job as a QA tester</td>\n",
       "      <td>3376</td>\n",
       "      <td>0.046376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Participate in hackathons</td>\n",
       "      <td>2747</td>\n",
       "      <td>0.037735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Master's degree</td>\n",
       "      <td>2639</td>\n",
       "      <td>0.036251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Other</td>\n",
       "      <td>2348</td>\n",
       "      <td>0.032254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None of these</td>\n",
       "      <td>604</td>\n",
       "      <td>0.008297</td>\n",
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      "text/plain": [
       "                                       method  count      perc\n",
       "12                        Take online courses  15246  0.209432\n",
       "1    Buy books and work through the exercises  11750  0.161408\n",
       "8                   Part-time/evening courses   7517  0.103260\n",
       "3                   Contribute to open source   7423  0.101968\n",
       "0                                    Bootcamp   5276  0.072476\n",
       "2                        Conferences/meet-ups   5244  0.072036\n",
       "11                          Return to college   5017  0.068918\n",
       "10  Participate in online coding competitions   3610  0.049590\n",
       "4                    Get a job as a QA tester   3376  0.046376\n",
       "9                   Participate in hackathons   2747  0.037735\n",
       "5                             Master's degree   2639  0.036251\n",
       "7                                       Other   2348  0.032254\n",
       "6                               None of these    604  0.008297"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "study_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although we can see the mean salary is highest for the individuals who say that you should contribute to open source, you might be asking - is that really a significant difference?  The salary differences don't see that large...\n",
    "\n",
    "By the Central Limit Theorem, we know that the mean of any set of data will follow a normal distribution with a standard deviation equal to the standard deviation of the original data divided by the square root of the sample size, as long as we collect a large enough sample size.  With that in mind, we can consider two salaries significantly different if a second salary is two standard deviations or more away from the other.\n",
    "\n",
    "Using the lower and upper bound components, we can get an idea of the salaries that are significantly different from one another.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Quiz - perform a similar analysis looking at career and job satisfaction for this individuals\n",
    "# to determine which you want to be like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_jobsat = mean_amt(df, 'CousinEducation', 'JobSatisfaction', possible_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3904</td>\n",
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       "      <td>3000</td>\n",
       "      <td>162716.0</td>\n",
       "      <td>7.098000</td>\n",
       "      <td>3.857063</td>\n",
       "      <td>1.963941</td>\n",
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       "      <td>7.168279</td>\n",
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       "      <td>Contribute to open source</td>\n",
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       "      <td>7.001645</td>\n",
       "      <td>7.122354</td>\n",
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       "      <th>0</th>\n",
       "      <td>Bootcamp</td>\n",
       "      <td>30404.0</td>\n",
       "      <td>4307</td>\n",
       "      <td>231670.0</td>\n",
       "      <td>7.059206</td>\n",
       "      <td>3.956792</td>\n",
       "      <td>1.989169</td>\n",
       "      <td>6.999799</td>\n",
       "      <td>7.118613</td>\n",
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       "      <th>1</th>\n",
       "      <td>Buy books and work through the exercises</td>\n",
       "      <td>66788.0</td>\n",
       "      <td>9492</td>\n",
       "      <td>508944.0</td>\n",
       "      <td>7.036241</td>\n",
       "      <td>4.109517</td>\n",
       "      <td>2.027194</td>\n",
       "      <td>6.995459</td>\n",
       "      <td>7.077023</td>\n",
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       "      <th>8</th>\n",
       "      <td>Part-time/evening courses</td>\n",
       "      <td>42797.0</td>\n",
       "      <td>6100</td>\n",
       "      <td>324829.0</td>\n",
       "      <td>7.015902</td>\n",
       "      <td>4.027780</td>\n",
       "      <td>2.006933</td>\n",
       "      <td>6.965537</td>\n",
       "      <td>7.066266</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Take online courses</td>\n",
       "      <td>85746.0</td>\n",
       "      <td>12222</td>\n",
       "      <td>651056.0</td>\n",
       "      <td>7.015709</td>\n",
       "      <td>4.049009</td>\n",
       "      <td>2.012215</td>\n",
       "      <td>6.980035</td>\n",
       "      <td>7.051384</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None of these</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>433</td>\n",
       "      <td>22898.0</td>\n",
       "      <td>6.928406</td>\n",
       "      <td>4.879401</td>\n",
       "      <td>2.208937</td>\n",
       "      <td>6.720343</td>\n",
       "      <td>7.136470</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Master's degree</td>\n",
       "      <td>14459.0</td>\n",
       "      <td>2091</td>\n",
       "      <td>108711.0</td>\n",
       "      <td>6.914873</td>\n",
       "      <td>4.174485</td>\n",
       "      <td>2.043156</td>\n",
       "      <td>6.827298</td>\n",
       "      <td>7.002448</td>\n",
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       "      <th>10</th>\n",
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       "      <td>18184.0</td>\n",
       "      <td>2675</td>\n",
       "      <td>135660.0</td>\n",
       "      <td>6.797757</td>\n",
       "      <td>4.504518</td>\n",
       "      <td>2.122385</td>\n",
       "      <td>6.717327</td>\n",
       "      <td>6.878187</td>\n",
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       "                              CousinEducation  col_sum  col_total  \\\n",
       "9                   Participate in hackathons  14884.0       2073   \n",
       "7                                       Other  13797.0       1937   \n",
       "11                          Return to college  27767.0       3904   \n",
       "4                    Get a job as a QA tester  21294.0       3000   \n",
       "3                   Contribute to open source  42374.0       5999   \n",
       "2                        Conferences/meet-ups  30868.0       4371   \n",
       "0                                    Bootcamp  30404.0       4307   \n",
       "1    Buy books and work through the exercises  66788.0       9492   \n",
       "8                   Part-time/evening courses  42797.0       6100   \n",
       "12                        Take online courses  85746.0      12222   \n",
       "6                               None of these   3000.0        433   \n",
       "5                             Master's degree  14459.0       2091   \n",
       "10  Participate in online coding competitions  18184.0       2675   \n",
       "\n",
       "    col_squares  mean_col   var_col   std_col  lower_95  upper_95  \n",
       "9      115166.0  7.179932  4.003804  2.000951  7.093795  7.266070  \n",
       "7      106521.0  7.122870  4.257489  2.063368  7.030980  7.214760  \n",
       "11     212971.0  7.112449  3.965070  1.991248  7.049985  7.174912  \n",
       "4      162716.0  7.098000  3.857063  1.963941  7.027721  7.168279  \n",
       "3      324340.0  7.063511  4.172496  2.042669  7.011820  7.115202  \n",
       "2      236106.0  7.062000  4.144635  2.035838  7.001645  7.122354  \n",
       "0      231670.0  7.059206  3.956792  1.989169  6.999799  7.118613  \n",
       "1      508944.0  7.036241  4.109517  2.027194  6.995459  7.077023  \n",
       "8      324829.0  7.015902  4.027780  2.006933  6.965537  7.066266  \n",
       "12     651056.0  7.015709  4.049009  2.012215  6.980035  7.051384  \n",
       "6       22898.0  6.928406  4.879401  2.208937  6.720343  7.136470  \n",
       "5      108711.0  6.914873  4.174485  2.043156  6.827298  7.002448  \n",
       "10     135660.0  6.797757  4.504518  2.122385  6.717327  6.878187  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_jobsat.sort_values('mean_col', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "      <td>56290.2</td>\n",
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       "      <td>40257.2</td>\n",
       "      <td>54935.6</td>\n",
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       "      <td>2.04267</td>\n",
       "      <td>7.01182</td>\n",
       "      <td>7.1152</td>\n",
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       "      <td>3000</td>\n",
       "      <td>162716</td>\n",
       "      <td>7.098</td>\n",
       "      <td>3.85706</td>\n",
       "      <td>1.96394</td>\n",
       "      <td>7.02772</td>\n",
       "      <td>7.16828</td>\n",
       "      <td>Get a job as a QA tester</td>\n",
       "      <td>5.85236e+07</td>\n",
       "      <td>1032</td>\n",
       "      <td>5.01725e+12</td>\n",
       "      <td>56708.9</td>\n",
       "      <td>1.64577e+09</td>\n",
       "      <td>40568.1</td>\n",
       "      <td>54233.8</td>\n",
       "      <td>59184.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Master's degree</td>\n",
       "      <td>14459</td>\n",
       "      <td>2091</td>\n",
       "      <td>108711</td>\n",
       "      <td>6.91487</td>\n",
       "      <td>4.17448</td>\n",
       "      <td>2.04316</td>\n",
       "      <td>6.8273</td>\n",
       "      <td>7.00245</td>\n",
       "      <td>Master's degree</td>\n",
       "      <td>4.28461e+07</td>\n",
       "      <td>721</td>\n",
       "      <td>3.77177e+12</td>\n",
       "      <td>59426</td>\n",
       "      <td>1.69986e+09</td>\n",
       "      <td>41229.4</td>\n",
       "      <td>56416.5</td>\n",
       "      <td>62435.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None of these</td>\n",
       "      <td>3000</td>\n",
       "      <td>433</td>\n",
       "      <td>22898</td>\n",
       "      <td>6.92841</td>\n",
       "      <td>4.8794</td>\n",
       "      <td>2.20894</td>\n",
       "      <td>6.72034</td>\n",
       "      <td>7.13647</td>\n",
       "      <td>None of these</td>\n",
       "      <td>5.37709e+06</td>\n",
       "      <td>112</td>\n",
       "      <td>4.27319e+11</td>\n",
       "      <td>48009.7</td>\n",
       "      <td>1.51042e+09</td>\n",
       "      <td>38864.1</td>\n",
       "      <td>40812</td>\n",
       "      <td>55207.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Other</td>\n",
       "      <td>13797</td>\n",
       "      <td>1937</td>\n",
       "      <td>106521</td>\n",
       "      <td>7.12287</td>\n",
       "      <td>4.25749</td>\n",
       "      <td>2.06337</td>\n",
       "      <td>7.03098</td>\n",
       "      <td>7.21476</td>\n",
       "      <td>Other</td>\n",
       "      <td>4.49141e+07</td>\n",
       "      <td>738</td>\n",
       "      <td>3.85136e+12</td>\n",
       "      <td>60859.3</td>\n",
       "      <td>1.51479e+09</td>\n",
       "      <td>38920.3</td>\n",
       "      <td>58051.2</td>\n",
       "      <td>63667.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Part-time/evening courses</td>\n",
       "      <td>42797</td>\n",
       "      <td>6100</td>\n",
       "      <td>324829</td>\n",
       "      <td>7.0159</td>\n",
       "      <td>4.02778</td>\n",
       "      <td>2.00693</td>\n",
       "      <td>6.96554</td>\n",
       "      <td>7.06627</td>\n",
       "      <td>Part-time/evening courses</td>\n",
       "      <td>1.12454e+08</td>\n",
       "      <td>2117</td>\n",
       "      <td>9.29078e+12</td>\n",
       "      <td>53119.6</td>\n",
       "      <td>1.56696e+09</td>\n",
       "      <td>39584.9</td>\n",
       "      <td>51433.4</td>\n",
       "      <td>54805.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Participate in hackathons</td>\n",
       "      <td>14884</td>\n",
       "      <td>2073</td>\n",
       "      <td>115166</td>\n",
       "      <td>7.17993</td>\n",
       "      <td>4.0038</td>\n",
       "      <td>2.00095</td>\n",
       "      <td>7.09379</td>\n",
       "      <td>7.26607</td>\n",
       "      <td>Participate in hackathons</td>\n",
       "      <td>4.6415e+07</td>\n",
       "      <td>796</td>\n",
       "      <td>4.04447e+12</td>\n",
       "      <td>58310.3</td>\n",
       "      <td>1.68091e+09</td>\n",
       "      <td>40998.9</td>\n",
       "      <td>55462.1</td>\n",
       "      <td>61158.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Participate in online coding competitions</td>\n",
       "      <td>18184</td>\n",
       "      <td>2675</td>\n",
       "      <td>135660</td>\n",
       "      <td>6.79776</td>\n",
       "      <td>4.50452</td>\n",
       "      <td>2.12239</td>\n",
       "      <td>6.71733</td>\n",
       "      <td>6.87819</td>\n",
       "      <td>Participate in online coding competitions</td>\n",
       "      <td>4.33266e+07</td>\n",
       "      <td>931</td>\n",
       "      <td>3.32692e+12</td>\n",
       "      <td>46537.7</td>\n",
       "      <td>1.40773e+09</td>\n",
       "      <td>37519.8</td>\n",
       "      <td>44127.6</td>\n",
       "      <td>48947.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Return to college</td>\n",
       "      <td>27767</td>\n",
       "      <td>3904</td>\n",
       "      <td>212971</td>\n",
       "      <td>7.11245</td>\n",
       "      <td>3.96507</td>\n",
       "      <td>1.99125</td>\n",
       "      <td>7.04999</td>\n",
       "      <td>7.17491</td>\n",
       "      <td>Return to college</td>\n",
       "      <td>8.73369e+07</td>\n",
       "      <td>1474</td>\n",
       "      <td>7.24371e+12</td>\n",
       "      <td>59251.6</td>\n",
       "      <td>1.40357e+09</td>\n",
       "      <td>37464.2</td>\n",
       "      <td>57339</td>\n",
       "      <td>61164.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Take online courses</td>\n",
       "      <td>85746</td>\n",
       "      <td>12222</td>\n",
       "      <td>651056</td>\n",
       "      <td>7.01571</td>\n",
       "      <td>4.04901</td>\n",
       "      <td>2.01221</td>\n",
       "      <td>6.98003</td>\n",
       "      <td>7.05138</td>\n",
       "      <td>Take online courses</td>\n",
       "      <td>2.41564e+08</td>\n",
       "      <td>4493</td>\n",
       "      <td>2.01154e+13</td>\n",
       "      <td>53764.5</td>\n",
       "      <td>1.58644e+09</td>\n",
       "      <td>39830.2</td>\n",
       "      <td>52599.8</td>\n",
       "      <td>54929.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           0      1      2       3        4   \\\n",
       "0                                    Bootcamp  30404   4307  231670  7.05921   \n",
       "1    Buy books and work through the exercises  66788   9492  508944  7.03624   \n",
       "2                        Conferences/meet-ups  30868   4371  236106    7.062   \n",
       "3                   Contribute to open source  42374   5999  324340  7.06351   \n",
       "4                    Get a job as a QA tester  21294   3000  162716    7.098   \n",
       "5                             Master's degree  14459   2091  108711  6.91487   \n",
       "6                               None of these   3000    433   22898  6.92841   \n",
       "7                                       Other  13797   1937  106521  7.12287   \n",
       "8                   Part-time/evening courses  42797   6100  324829   7.0159   \n",
       "9                   Participate in hackathons  14884   2073  115166  7.17993   \n",
       "10  Participate in online coding competitions  18184   2675  135660  6.79776   \n",
       "11                          Return to college  27767   3904  212971  7.11245   \n",
       "12                        Take online courses  85746  12222  651056  7.01571   \n",
       "\n",
       "         5        6        7        8   \\\n",
       "0   3.95679  1.98917   6.9998  7.11861   \n",
       "1   4.10952  2.02719  6.99546  7.07702   \n",
       "2   4.14463  2.03584  7.00165  7.12235   \n",
       "3    4.1725  2.04267  7.01182   7.1152   \n",
       "4   3.85706  1.96394  7.02772  7.16828   \n",
       "5   4.17448  2.04316   6.8273  7.00245   \n",
       "6    4.8794  2.20894  6.72034  7.13647   \n",
       "7   4.25749  2.06337  7.03098  7.21476   \n",
       "8   4.02778  2.00693  6.96554  7.06627   \n",
       "9    4.0038  2.00095  7.09379  7.26607   \n",
       "10  4.50452  2.12239  6.71733  6.87819   \n",
       "11  3.96507  1.99125  7.04999  7.17491   \n",
       "12  4.04901  2.01221  6.98003  7.05138   \n",
       "\n",
       "                                           9            10    11           12  \\\n",
       "0                                    Bootcamp  9.58323e+07  1622  8.50299e+12   \n",
       "1    Buy books and work through the exercises  1.90993e+08  3393  1.62499e+13   \n",
       "2                        Conferences/meet-ups   9.6996e+07  1677  8.36627e+12   \n",
       "3                   Contribute to open source  1.39227e+08  2253  1.23906e+13   \n",
       "4                    Get a job as a QA tester  5.85236e+07  1032  5.01725e+12   \n",
       "5                             Master's degree  4.28461e+07   721  3.77177e+12   \n",
       "6                               None of these  5.37709e+06   112  4.27319e+11   \n",
       "7                                       Other  4.49141e+07   738  3.85136e+12   \n",
       "8                   Part-time/evening courses  1.12454e+08  2117  9.29078e+12   \n",
       "9                   Participate in hackathons   4.6415e+07   796  4.04447e+12   \n",
       "10  Participate in online coding competitions  4.33266e+07   931  3.32692e+12   \n",
       "11                          Return to college  8.73369e+07  1474  7.24371e+12   \n",
       "12                        Take online courses  2.41564e+08  4493  2.01154e+13   \n",
       "\n",
       "         13           14       15       16       17  \n",
       "0   59082.8  1.75151e+09    41851    57046  61119.5  \n",
       "1   56290.2  1.62064e+09  40257.2  54935.6  57644.8  \n",
       "2     57839  1.64348e+09  40539.9  55898.7  59779.3  \n",
       "3   61796.1  1.68084e+09  40998.1  60103.2  63489.1  \n",
       "4   56708.9  1.64577e+09  40568.1  54233.8  59184.1  \n",
       "5     59426  1.69986e+09  41229.4  56416.5  62435.5  \n",
       "6   48009.7  1.51042e+09  38864.1    40812  55207.4  \n",
       "7   60859.3  1.51479e+09  38920.3  58051.2  63667.3  \n",
       "8   53119.6  1.56696e+09  39584.9  51433.4  54805.9  \n",
       "9   58310.3  1.68091e+09  40998.9  55462.1  61158.5  \n",
       "10  46537.7  1.40773e+09  37519.8  44127.6  48947.8  \n",
       "11  59251.6  1.40357e+09  37464.2    57339  61164.2  \n",
       "12  53764.5  1.58644e+09  39830.2  52599.8  54929.2  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(np.hstack([df_jobsat, df_all]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      4307\n",
       "1      9492\n",
       "2      4371\n",
       "3      5999\n",
       "4      3000\n",
       "5      2091\n",
       "6       433\n",
       "7      1937\n",
       "8      6100\n",
       "9      2073\n",
       "10     2675\n",
       "11     3904\n",
       "12    12222\n",
       "Name: col_total, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jobsat.col_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dotplot = pd.DataFrame(np.array(['Method', \"At least Master's\", \"Less Than Master's\", \n",
    "\"Master's Degree\",0.0589517,0.0293459,\n",
    "\"Bootcamp\",0.0746172,0.071824,\n",
    "\"Become QA Tester\",0.0484688,0.0457388,\n",
    "\"Buy Books\",0.162073,0.161205,\n",
    "\"None of these\",0.00836278,0.00827705,\n",
    "\"Part Time Courses\",0.103298,0.103248,\n",
    "\"Return to College\",0.0687279,0.0689754,\n",
    "\"Online Courses\",0.207892,0.2099,\n",
    "\"Contribute to Opensource\",0.097821,0.10323,\n",
    "\"Coding Competitions\",0.0453475,0.0508806,\n",
    "\"Other\",0.0269729,0.0338607,\n",
    "\"Hackathons\", 0.0316254,0.0395937,\n",
    "\"Conferences\", 0.0658422, 0.0739201]).reshape((14, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dotplot.columns = df_dotplot.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dotplot.drop(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_dotplot.prop = df_dotplot.prop.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dotplot = df_dotplot.melt(id_vars='Method', value_name='prop', var_name='status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/N2/9ikZzC7XaEXf3JlTwbIFaLY9uniT59dQlqIsSYTCkc+Toh+h0iXmWcXCo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\n97sud/y2JQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116f16a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(data=df_dotplot, x='status', y='prop', hue='Method');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rVi1++eUXPB4PzZo1Y/To0cTGxjJ16lTGjh1LnTp1eOihhzh48CDr16/n8ccf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\nIBAIKglC2AkEAoFAIBBUEv4fsCwcmM5O6lcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138f6d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx, row in df_dotplot.iterrows():\n",
    "    plt.plot([0,1],[row[\"At least Master's\"], row[\"Less Than Master's\"]]);\n",
    "    plt.text(1.05, row[\"Less Than Master's\"], row['Method']);\n",
    "plt.xticks([0,1], [\"At least Master's\", \"Less Than Master's\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method\n",
      "At least Master's\n",
      "Less Than Master's\n"
     ]
    }
   ],
   "source": [
    "for row in df_dotplot:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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