dataviz_hourly_rate_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is the DVS community charging for hourly work?\n",
    "\n",
    "### An analysis of the 2020 Data Visualization Society Survey responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt # this will allow me to style the seaborn title fonts\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>How many years of professional experience do you have?</th>\n",
       "      <th>How many years of data visualization experience do you have doing professional data visualization?</th>\n",
       "      <th>Are you a Data Visualization Society member?</th>\n",
       "      <th>How did you learn data visualization?</th>\n",
       "      <th>What is the highest level of education you have completed?</th>\n",
       "      <th>If you went to college, in what area was your undergraduate major?</th>\n",
       "      <th>In what area was your graduate degree?</th>\n",
       "      <th>What country do you live in?</th>\n",
       "      <th>Do you consider yourself a member of a historically disadvantaged or underrepresented racial or ethnic group?</th>\n",
       "      <th>Which of these best describes your role as a data visualizer in the past year?</th>\n",
       "      <th>...</th>\n",
       "      <th>What methods do you find most helpful in learning new data visualization skills?</th>\n",
       "      <th>What methods do you find most helpful in learning new data visualization skills?_1</th>\n",
       "      <th>What methods do you find most helpful in learning new data visualization skills?_2</th>\n",
       "      <th>What methods do you find most helpful in learning new data visualization skills?_3</th>\n",
       "      <th>What methods do you find most helpful in learning new data visualization skills?_4</th>\n",
       "      <th>What methods do you find most helpful in learning new data visualization skills?_5</th>\n",
       "      <th>What methods do you find most helpful in learning new data visualization skills?_6</th>\n",
       "      <th>What methods do you find most helpful in learning new data visualization skills?_7</th>\n",
       "      <th>Who do you look to as a leader in data visualization?</th>\n",
       "      <th>gender_collapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5-10</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>Mostly Self-Taught</td>\n",
       "      <td>Masters Degree (or similar graduate degree)</td>\n",
       "      <td>Science, Math, and Technology</td>\n",
       "      <td>Science, Math, and Technology</td>\n",
       "      <td>NaN</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>Other:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Equal Parts School and Self-Taught</td>\n",
       "      <td>Bachelors Degree</td>\n",
       "      <td>Business</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>No</td>\n",
       "      <td>Freelance/Consultant/ Independent contractor</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Workshops</td>\n",
       "      <td>Examples</td>\n",
       "      <td>Video Tutorials</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5-10</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mostly Self-Taught</td>\n",
       "      <td>Bachelors Degree</td>\n",
       "      <td>Social Sciences</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Books</td>\n",
       "      <td>Workshops</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Video Tutorials</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Working through a project</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other:</td>\n",
       "      <td>Giorgia Lupi</td>\n",
       "      <td>Man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26-30</td>\n",
       "      <td>(16-20]</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mostly Self-Taught</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</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>Other:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5 or less</td>\n",
       "      <td>less than 1</td>\n",
       "      <td>I don't know</td>\n",
       "      <td>Mostly from School (or other formal courses)</td>\n",
       "      <td>Masters Degree (or similar graduate degree)</td>\n",
       "      <td>Science, Math, and Technology</td>\n",
       "      <td>Science, Math, and Technology</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</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>Other:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Man</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 237 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  How many years of professional experience do you have?  \\\n",
       "0                                               5-10       \n",
       "1                                                NaN       \n",
       "2                                               5-10       \n",
       "3                                              26-30       \n",
       "4                                          5 or less       \n",
       "\n",
       "  How many years of data visualization experience do you have doing professional data visualization?  \\\n",
       "0                                                  5                                                   \n",
       "1                                                NaN                                                   \n",
       "2                                                  4                                                   \n",
       "3                                            (16-20]                                                   \n",
       "4                                        less than 1                                                   \n",
       "\n",
       "  Are you a Data Visualization Society member?  \\\n",
       "0                                           No   \n",
       "1                                          Yes   \n",
       "2                                          Yes   \n",
       "3                                          Yes   \n",
       "4                                 I don't know   \n",
       "\n",
       "          How did you learn data visualization?  \\\n",
       "0                            Mostly Self-Taught   \n",
       "1            Equal Parts School and Self-Taught   \n",
       "2                            Mostly Self-Taught   \n",
       "3                            Mostly Self-Taught   \n",
       "4  Mostly from School (or other formal courses)   \n",
       "\n",
       "  What is the highest level of education you have completed?  \\\n",
       "0        Masters Degree (or similar graduate degree)           \n",
       "1                                   Bachelors Degree           \n",
       "2                                   Bachelors Degree           \n",
       "3                                                NaN           \n",
       "4        Masters Degree (or similar graduate degree)           \n",
       "\n",
       "  If you went to college, in what area was your undergraduate major?   \\\n",
       "0                      Science, Math, and Technology                    \n",
       "1                                           Business                    \n",
       "2                                    Social Sciences                    \n",
       "3                                                NaN                    \n",
       "4                      Science, Math, and Technology                    \n",
       "\n",
       "  In what area was your graduate degree? What country do you live in?  \\\n",
       "0          Science, Math, and Technology                          NaN   \n",
       "1                                    NaN                       Sweden   \n",
       "2                                    NaN               United Kingdom   \n",
       "3                                    NaN                United States   \n",
       "4          Science, Math, and Technology                United States   \n",
       "\n",
       "  Do you consider yourself a member of a historically disadvantaged or underrepresented racial or ethnic group?  \\\n",
       "0                                                NaN                                                              \n",
       "1                                                 No                                                              \n",
       "2                                                 No                                                              \n",
       "3                                                NaN                                                              \n",
       "4                                                 No                                                              \n",
       "\n",
       "  Which of these best describes your role as a data visualizer in the past year?  \\\n",
       "0                                                NaN                               \n",
       "1       Freelance/Consultant/ Independent contractor                               \n",
       "2                                                NaN                               \n",
       "3                                                NaN                               \n",
       "4                                                NaN                               \n",
       "\n",
       "   ...  \\\n",
       "0  ...   \n",
       "1  ...   \n",
       "2  ...   \n",
       "3  ...   \n",
       "4  ...   \n",
       "\n",
       "  What methods do you find most helpful in learning new data visualization skills?  \\\n",
       "0                                                NaN                                 \n",
       "1                                                NaN                                 \n",
       "2                                              Books                                 \n",
       "3                                                NaN                                 \n",
       "4                                                NaN                                 \n",
       "\n",
       "  What methods do you find most helpful in learning new data visualization skills?_1  \\\n",
       "0                                                NaN                                   \n",
       "1                                          Workshops                                   \n",
       "2                                          Workshops                                   \n",
       "3                                                NaN                                   \n",
       "4                                                NaN                                   \n",
       "\n",
       "  What methods do you find most helpful in learning new data visualization skills?_2  \\\n",
       "0                                                NaN                                   \n",
       "1                                           Examples                                   \n",
       "2                                                NaN                                   \n",
       "3                                                NaN                                   \n",
       "4                                                NaN                                   \n",
       "\n",
       "  What methods do you find most helpful in learning new data visualization skills?_3  \\\n",
       "0                                                NaN                                   \n",
       "1                                    Video Tutorials                                   \n",
       "2                                    Video Tutorials                                   \n",
       "3                                                NaN                                   \n",
       "4                                                NaN                                   \n",
       "\n",
       "  What methods do you find most helpful in learning new data visualization skills?_4  \\\n",
       "0                                                NaN                                   \n",
       "1                                                NaN                                   \n",
       "2                                                NaN                                   \n",
       "3                                                NaN                                   \n",
       "4                                                NaN                                   \n",
       "\n",
       "  What methods do you find most helpful in learning new data visualization skills?_5  \\\n",
       "0                                                NaN                                   \n",
       "1                                                NaN                                   \n",
       "2                          Working through a project                                   \n",
       "3                                                NaN                                   \n",
       "4                                                NaN                                   \n",
       "\n",
       "  What methods do you find most helpful in learning new data visualization skills?_6  \\\n",
       "0                                                NaN                                   \n",
       "1                                                NaN                                   \n",
       "2                                                NaN                                   \n",
       "3                                                NaN                                   \n",
       "4                                                NaN                                   \n",
       "\n",
       "  What methods do you find most helpful in learning new data visualization skills?_7  \\\n",
       "0                                             Other:                                   \n",
       "1                                             Other:                                   \n",
       "2                                             Other:                                   \n",
       "3                                             Other:                                   \n",
       "4                                             Other:                                   \n",
       "\n",
       "  Who do you look to as a leader in data visualization? gender_collapsed  \n",
       "0                                                NaN                 NaN  \n",
       "1                                                NaN                 Man  \n",
       "2                                       Giorgia Lupi                 Man  \n",
       "3                                                NaN                 NaN  \n",
       "4                                                NaN                 Man  \n",
       "\n",
       "[5 rows x 237 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load 2020 hourly rates\n",
    "# make a list variable of just the columns I need\n",
    "# commented this out because the characters in the technologies fields created an unhashable list error\n",
    "# col_list = [\"What is your hourly rate (converted to US dollars)?\", \n",
    "#             \"What is your yearly pay (converted to US dollars)?\", \n",
    "#             \"How many years of professional experience do you have?\", \n",
    "#             \"How many years of data visualization experience do you have doing professional data visualization?\", \n",
    "#             \"What country do you live in?\", \n",
    "#             \"&#65279;Which of these best describes the role of data visualization in your job?\", \n",
    "#             \"Are you a full time or part time student?\", \n",
    "#             \"What degree are you currently pursuing?\", \n",
    "#             \"Is data visualization formally taught within your degree program?\", \n",
    "#             \"What are you pursuing a degree in?\", \n",
    "#             \"What technologies do you use to visualize data?¬†\", \n",
    "#             \"What technologies do you use to visualize data?¬†_1\",\n",
    "#             \"What technologies do you use to visualize data?¬†_2\", \n",
    "#             \"What technologies do you use to visualize data?¬†_3\",\n",
    "#             \"What technologies do you use to visualize data?¬†_4\",\n",
    "#             \"What technologies do you use to visualize data?¬†_5\",\n",
    "#             \"What technologies do you use to visualize data?¬†_6\",\n",
    "#             \"What technologies do you use to visualize data?¬†_7\",\n",
    "#             \"What technologies do you use to visualize data?¬†_8\",\n",
    "#             \"What technologies do you use to visualize data?¬†_9\",\n",
    "#             \"What technologies do you use to visualize data?¬†_10\",\n",
    "#             \"What technologies do you use to visualize data?¬†_11\",\n",
    "#             \"What technologies do you use to visualize data?¬†_12\",\n",
    "#             \"What technologies do you use to visualize data?¬†_13\",\n",
    "#             \"What technologies do you use to visualize data?¬†_14\",\n",
    "#             \"What technologies do you use to visualize data?¬†_15\",\n",
    "#             \"What technologies do you use to visualize data?¬†_16\",\n",
    "#             \"What technologies do you use to visualize data?¬†_17\",\n",
    "#             \"What technologies do you use to visualize data?¬†_18\",\n",
    "#             \"What technologies do you use to visualize data?¬†_19\",\n",
    "#             \"What technologies do you use to visualize data?¬†_20\",\n",
    "#             \"What technologies do you use to visualize data?¬†_21\",\n",
    "#             \"What technologies do you use to visualize data?¬†_22\",\n",
    "#             \"What technologies do you use to visualize data?¬†_23\",\n",
    "#             \"What technologies do you use to visualize data?¬†_24\",\n",
    "#             \"What technologies do you use to visualize data?¬†_25\",\n",
    "#             \"What technologies do you use to visualize data?¬†_26\",\n",
    "#             \"What technologies do you use to visualize data?¬†_27\",\n",
    "#             \"What technologies do you use to visualize data?¬†_28\",\n",
    "#             \"What technologies do you use to visualize data?¬†_29\",\n",
    "#             \"What technologies do you use to visualize data?¬†_30\",\n",
    "#             \"What technologies do you use to visualize data?¬†_31\",\n",
    "#             \"What technologies do you use to visualize data?¬†_32\",\n",
    "#             \"What technologies do you use to visualize data?¬†_33\"]\n",
    "\n",
    "# read the selected columns from the csv into the notebook\n",
    "dfpayrate20 = pd.read_csv(\"data/2020/DataVizCensus2020-AnonymizedResponses.csv\") #, col_list)\n",
    "\n",
    "# view the top rows\n",
    "dfpayrate20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Leadership in your organization has a strong understanding of the value of data visualization</th>\n",
       "      <th>I work on uncompensated data visualization side projects in the hope of future professional benefit, such as building my public profile or developing a more impressive portfolio.</th>\n",
       "      <th>I work on uncompensated data visualization side projects purely for my own personal enjoyment.</th>\n",
       "      <th>Hours a week focused on creating/implementing/producing data visualizations?</th>\n",
       "      <th>Hours a week focused on data engineering?</th>\n",
       "      <th>Hours a week focused on data science?</th>\n",
       "      <th>Hours a week focused on design?</th>\n",
       "      <th>Hours a week focused on data prep work?</th>\n",
       "      <th>Hours a week focused on building your portfolio?</th>\n",
       "      <th>How much is your annual professional development fund in US dollars?</th>\n",
       "      <th>I have adequate resources to explore the data visualization options I want to use.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1076.000000</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>293.000000</td>\n",
       "      <td>1226.000000</td>\n",
       "      <td>767.000000</td>\n",
       "      <td>708.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>1056.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>1280.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.102230</td>\n",
       "      <td>5.350340</td>\n",
       "      <td>5.559727</td>\n",
       "      <td>11.099511</td>\n",
       "      <td>8.235332</td>\n",
       "      <td>6.884887</td>\n",
       "      <td>7.566571</td>\n",
       "      <td>7.477273</td>\n",
       "      <td>4.032655</td>\n",
       "      <td>5382.640878</td>\n",
       "      <td>4.903125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.537366</td>\n",
       "      <td>1.632556</td>\n",
       "      <td>1.332169</td>\n",
       "      <td>9.273832</td>\n",
       "      <td>7.643815</td>\n",
       "      <td>7.174267</td>\n",
       "      <td>8.045420</td>\n",
       "      <td>6.339260</td>\n",
       "      <td>4.788793</td>\n",
       "      <td>35288.580937</td>\n",
       "      <td>1.582248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Leadership in your organization has a strong understanding of the value of data visualization  \\\n",
       "count                                        1076.000000                                               \n",
       "mean                                            5.102230                                               \n",
       "std                                             1.537366                                               \n",
       "min                                             1.000000                                               \n",
       "25%                                             4.000000                                               \n",
       "50%                                             5.000000                                               \n",
       "75%                                             6.000000                                               \n",
       "max                                             7.000000                                               \n",
       "\n",
       "       I work on uncompensated data visualization side projects in the hope of future professional benefit, such as building my public profile or developing a more impressive portfolio.  \\\n",
       "count                                         294.000000                                                                                                                                    \n",
       "mean                                            5.350340                                                                                                                                    \n",
       "std                                             1.632556                                                                                                                                    \n",
       "min                                             1.000000                                                                                                                                    \n",
       "25%                                             5.000000                                                                                                                                    \n",
       "50%                                             6.000000                                                                                                                                    \n",
       "75%                                             7.000000                                                                                                                                    \n",
       "max                                             7.000000                                                                                                                                    \n",
       "\n",
       "       I work on uncompensated data visualization side projects purely for my own personal enjoyment.  \\\n",
       "count                                         293.000000                                                \n",
       "mean                                            5.559727                                                \n",
       "std                                             1.332169                                                \n",
       "min                                             1.000000                                                \n",
       "25%                                             5.000000                                                \n",
       "50%                                             6.000000                                                \n",
       "75%                                             7.000000                                                \n",
       "max                                             7.000000                                                \n",
       "\n",
       "       Hours a week focused on creating/implementing/producing data visualizations?  \\\n",
       "count                                        1226.000000                              \n",
       "mean                                           11.099511                              \n",
       "std                                             9.273832                              \n",
       "min                                             0.500000                              \n",
       "25%                                             5.000000                              \n",
       "50%                                            10.000000                              \n",
       "75%                                            15.000000                              \n",
       "max                                           100.000000                              \n",
       "\n",
       "       Hours a week focused on data engineering?  \\\n",
       "count                                 767.000000   \n",
       "mean                                    8.235332   \n",
       "std                                     7.643815   \n",
       "min                                     0.500000   \n",
       "25%                                     3.000000   \n",
       "50%                                     5.000000   \n",
       "75%                                    10.000000   \n",
       "max                                    80.000000   \n",
       "\n",
       "       Hours a week focused on data science?  Hours a week focused on design?  \\\n",
       "count                             708.000000                      1044.000000   \n",
       "mean                                6.884887                         7.566571   \n",
       "std                                 7.174267                         8.045420   \n",
       "min                                 0.500000                         0.500000   \n",
       "25%                                 2.000000                         2.000000   \n",
       "50%                                 5.000000                         5.000000   \n",
       "75%                                10.000000                        10.000000   \n",
       "max                                40.000000                        59.000000   \n",
       "\n",
       "       Hours a week focused on data prep work?  \\\n",
       "count                              1056.000000   \n",
       "mean                                  7.477273   \n",
       "std                                   6.339260   \n",
       "min                                   0.500000   \n",
       "25%                                   3.000000   \n",
       "50%                                   5.000000   \n",
       "75%                                  10.000000   \n",
       "max                                  63.000000   \n",
       "\n",
       "       Hours a week focused on building your portfolio?  \\\n",
       "count                                        467.000000   \n",
       "mean                                           4.032655   \n",
       "std                                            4.788793   \n",
       "min                                            0.250000   \n",
       "25%                                            1.000000   \n",
       "50%                                            2.000000   \n",
       "75%                                            5.000000   \n",
       "max                                           50.000000   \n",
       "\n",
       "       How much is your annual professional development fund in US dollars?  \\\n",
       "count                                         433.000000                      \n",
       "mean                                         5382.640878                      \n",
       "std                                         35288.580937                      \n",
       "min                                             1.000000                      \n",
       "25%                                           600.000000                      \n",
       "50%                                          1500.000000                      \n",
       "75%                                          3000.000000                      \n",
       "max                                        500000.000000                      \n",
       "\n",
       "       I have adequate resources to explore the data visualization options I want to use.  \n",
       "count                                        1280.000000                                   \n",
       "mean                                            4.903125                                   \n",
       "std                                             1.582248                                   \n",
       "min                                             1.000000                                   \n",
       "25%                                             4.000000                                   \n",
       "50%                                             5.000000                                   \n",
       "75%                                             6.000000                                   \n",
       "max                                             7.000000                                   "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get aggregate summaries for the numerical value variables\n",
    "dfpayrate20.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'How many years of professional experience do you have?': dtype('O'),\n",
       " 'How many years of data visualization experience do you have doing professional data visualization?': dtype('O'),\n",
       " 'Are you a Data Visualization Society member?': dtype('O'),\n",
       " 'How did you learn data visualization?': dtype('O'),\n",
       " 'What is the highest level of education you have completed?': dtype('O'),\n",
       " 'If you went to college, in what area was your undergraduate major?\\xa0': dtype('O'),\n",
       " 'In what area was your graduate degree?': dtype('O'),\n",
       " 'What country do you live in?': dtype('O'),\n",
       " 'Do you consider yourself a member of a historically disadvantaged or underrepresented racial or ethnic group?': dtype('O'),\n",
       " 'Which of these best describes your role as a data visualizer in the past year?': dtype('O'),\n",
       " 'Which of these best describes your role as a data visualizer in the past year?_1': dtype('O'),\n",
       " 'Which of these best describes your role as a data visualizer in the past year?_2': dtype('O'),\n",
       " 'Which of these best describes your role as a data visualizer in the past year?_3': dtype('O'),\n",
       " 'Which of these best describes your role as a data visualizer in the past year?_4': dtype('O'),\n",
       " 'Which of these best describes your role as a data visualizer in the past year?_5': dtype('O'),\n",
       " 'For the majority of your projects: were you hired to do data visualization only or is data visualization a part of your job/project description?': dtype('O'),\n",
       " 'Which one of these is the closest to describing your role when hired to freelance?': dtype('O'),\n",
       " 'For most of your projects, are you able to choose your own tools or are the choices made for you?': dtype('O'),\n",
       " 'As a freelancer, who do you make data visualizations for?\\xa0': dtype('O'),\n",
       " 'As a freelancer, who do you make data visualizations for?\\xa0_1': dtype('O'),\n",
       " 'As a freelancer, who do you make data visualizations for?\\xa0_2': dtype('O'),\n",
       " 'As a freelancer, who do you make data visualizations for?\\xa0_3': dtype('O'),\n",
       " 'As a freelancer, who do you make data visualizations for?\\xa0_4': dtype('O'),\n",
       " 'As a freelancer, who do you make data visualizations for?\\xa0_5': dtype('O'),\n",
       " 'As a freelancer, who do you make data visualizations for?\\xa0_6': dtype('O'),\n",
       " 'As a freelancer, who do you make data visualizations for?\\xa0_7': dtype('O'),\n",
       " 'As a freelancer, who do you make data visualizations for?\\xa0_8': dtype('O'),\n",
       " 'As a freelancer, who do you make data visualizations for?\\xa0_9': dtype('O'),\n",
       " 'As a freelancer, who do you make data visualizations for?\\xa0_10': dtype('O'),\n",
       " 'As a freelancer, who do you make data visualizations for?\\xa0_11': dtype('O'),\n",
       " 'As a freelancer, who do you make data visualizations for?\\xa0_12': dtype('O'),\n",
       " 'The organization you work for is in which of the following areas?': dtype('O'),\n",
       " 'The organization you work for is in which of the following areas?_1': dtype('O'),\n",
       " 'The organization you work for is in which of the following areas?_2': dtype('O'),\n",
       " 'The organization you work for is in which of the following areas?_3': dtype('O'),\n",
       " 'The organization you work for is in which of the following areas?_4': dtype('O'),\n",
       " 'The organization you work for is in which of the following areas?_5': dtype('O'),\n",
       " 'The organization you work for is in which of the following areas?_6': dtype('O'),\n",
       " 'The organization you work for is in which of the following areas?_7': dtype('O'),\n",
       " 'The organization you work for is in which of the following areas?_8': dtype('O'),\n",
       " 'The organization you work for is in which of the following areas?_9': dtype('O'),\n",
       " 'What is the size of your organization?': dtype('O'),\n",
       " '&#65279;Which of these best describes the role of data visualization in your job?': dtype('O'),\n",
       " 'Which one of these is the closest to describing your role at your organization?': dtype('O'),\n",
       " 'How is your organization using data visualization?\\xa0': dtype('O'),\n",
       " 'How is your organization using data visualization?\\xa0_1': dtype('O'),\n",
       " 'How is your organization using data visualization?\\xa0_2': dtype('O'),\n",
       " 'How is your organization using data visualization?\\xa0_3': dtype('O'),\n",
       " 'How is your organization using data visualization?\\xa0_4': dtype('O'),\n",
       " 'How is your organization using data visualization?\\xa0_5': dtype('O'),\n",
       " 'How is your organization using data visualization?\\xa0_6': dtype('O'),\n",
       " 'How is your organization using data visualization?\\xa0_7': dtype('O'),\n",
       " 'How is your organization using data visualization?\\xa0_8': dtype('O'),\n",
       " 'Is there a separate group dedicated to creating data visualization or are data visualization specialists embedded in another group?': dtype('O'),\n",
       " 'What modes of data visualization are in use at your organization?\\xa0': dtype('O'),\n",
       " 'What modes of data visualization are in use at your organization?\\xa0_1': dtype('O'),\n",
       " 'What modes of data visualization are in use at your organization?\\xa0_2': dtype('O'),\n",
       " 'What modes of data visualization are in use at your organization?\\xa0_3': dtype('O'),\n",
       " 'What modes of data visualization are in use at your organization?\\xa0_4': dtype('O'),\n",
       " 'What modes of data visualization are in use at your organization?\\xa0_5': dtype('O'),\n",
       " 'What modes of data visualization are in use at your organization?\\xa0_6': dtype('O'),\n",
       " 'Leadership in your organization has a strong understanding of the value of data visualization': dtype('float64'),\n",
       " 'Data visualization at your organization features prominently in what parts of the organization?\\xa0': dtype('O'),\n",
       " 'Data visualization at your organization features prominently in what parts of the organization?\\xa0_1': dtype('O'),\n",
       " 'Data visualization at your organization features prominently in what parts of the organization?\\xa0_2': dtype('O'),\n",
       " 'Data visualization at your organization features prominently in what parts of the organization?\\xa0_3': dtype('O'),\n",
       " 'Data visualization at your organization features prominently in what parts of the organization?\\xa0_4': dtype('O'),\n",
       " 'Data visualization at your organization features prominently in what parts of the organization?\\xa0_5': dtype('O'),\n",
       " 'Data visualization at your organization features prominently in what parts of the organization?\\xa0_6': dtype('O'),\n",
       " 'Data visualization at your organization features prominently in what parts of the organization?\\xa0_7': dtype('O'),\n",
       " 'Data visualization at your organization features prominently in what parts of the organization?\\xa0_8': dtype('O'),\n",
       " 'How would you describe your relationship with stakeholders?': dtype('O'),\n",
       " 'Are you able to choose your own tools or are the choices made for you?': dtype('O'),\n",
       " 'In your role within your organization, who do you make data visualizations for?\\xa0': dtype('O'),\n",
       " 'In your role within your organization, who do you make data visualizations for?\\xa0_1': dtype('O'),\n",
       " 'In your role within your organization, who do you make data visualizations for?\\xa0_2': dtype('O'),\n",
       " 'In your role within your organization, who do you make data visualizations for?\\xa0_3': dtype('O'),\n",
       " 'In your role within your organization, who do you make data visualizations for?\\xa0_4': dtype('O'),\n",
       " 'In your role within your organization, who do you make data visualizations for?\\xa0_5': dtype('O'),\n",
       " 'In your role within your organization, who do you make data visualizations for?\\xa0_6': dtype('O'),\n",
       " 'In your role within your organization, who do you make data visualizations for?\\xa0_7': dtype('O'),\n",
       " 'In your role within your organization, who do you make data visualizations for?\\xa0_8': dtype('O'),\n",
       " 'In your role within your organization, who do you make data visualizations for?\\xa0_9': dtype('O'),\n",
       " 'In your role within your organization, who do you make data visualizations for?\\xa0_10': dtype('O'),\n",
       " 'In your role within your organization, who do you make data visualizations for?\\xa0_11': dtype('O'),\n",
       " 'In your role within your organization, who do you make data visualizations for?\\xa0_12': dtype('O'),\n",
       " 'How many hours per week do you spend doing data visualization as a hobby or side project?': dtype('O'),\n",
       " 'I work on uncompensated data visualization side projects in the hope of future professional benefit, such as building my public profile or developing a more impressive portfolio.': dtype('float64'),\n",
       " 'I work on uncompensated data visualization side projects purely for my own personal enjoyment.': dtype('float64'),\n",
       " 'Are you a full time or part time student?': dtype('O'),\n",
       " 'What degree are you currently pursuing?': dtype('O'),\n",
       " 'Is data visualization formally taught within your degree program?': dtype('O'),\n",
       " 'What are you pursuing a degree in?': dtype('O'),\n",
       " 'In what academic area are you employed?': dtype('O'),\n",
       " 'Do you teach data visualization as a primary course topic?': dtype('O'),\n",
       " 'How do you use data visualization in your work?': dtype('O'),\n",
       " 'How do you use data visualization in your work?_1': dtype('O'),\n",
       " 'How do you use data visualization in your work?_2': dtype('O'),\n",
       " 'How do you use data visualization in your work?_3': dtype('O'),\n",
       " 'How many hours a week have you worked?': dtype('O'),\n",
       " 'Hours a week focused on creating/implementing/producing data visualizations?': dtype('float64'),\n",
       " 'Hours a week focused on data engineering?': dtype('float64'),\n",
       " 'Hours a week focused on data science?': dtype('float64'),\n",
       " 'Hours a week focused on design?': dtype('float64'),\n",
       " 'Hours a week focused on data prep work?': dtype('float64'),\n",
       " 'Hours a week focused on building your portfolio?': dtype('float64'),\n",
       " 'What technologies do you use to visualize data?\\xa0': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_1': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_2': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_3': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_4': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_5': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_6': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_7': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_8': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_9': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_10': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_11': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_12': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_13': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_14': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_15': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_16': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_17': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_18': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_19': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_20': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_21': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_22': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_23': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_24': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_25': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_26': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_27': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_28': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_29': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_30': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_31': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_32': dtype('O'),\n",
       " 'What technologies do you use to visualize data?\\xa0_33': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_1': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_2': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_3': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_4': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_5': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_6': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_7': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_8': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_9': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_10': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_11': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_12': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_13': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_14': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_15': dtype('O'),\n",
       " 'How do you present your data visualizations?\\xa0_16': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_1': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_2': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_3': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_4': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_5': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_6': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_7': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_8': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_9': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_10': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_11': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_12': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_13': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_14': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_15': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_16': dtype('O'),\n",
       " 'Which of these charts have you used in production in the last 6 months?\\xa0_17': dtype('O'),\n",
       " 'What is the general level data literacy in the audience you produce visualizations for?\\xa0': dtype('O'),\n",
       " 'What is the general level data literacy in the audience you produce visualizations for?\\xa0_1': dtype('O'),\n",
       " 'What is the general level data literacy in the audience you produce visualizations for?\\xa0_2': dtype('O'),\n",
       " 'What is your yearly pay (converted to US dollars)?': dtype('O'),\n",
       " 'What is your hourly rate (converted to US dollars)?': dtype('O'),\n",
       " '\\u200bDo you have access to professional development funds or grants to pay for professional costs such as conference registrations, memberships, software and/or training?': dtype('O'),\n",
       " 'How much is your annual professional development fund in US dollars?': dtype('float64'),\n",
       " 'Which of the following would you be likely to use professional development funds on?': dtype('O'),\n",
       " 'Which of the following would you be likely to use professional development funds on?_1': dtype('O'),\n",
       " 'Which of the following would you be likely to use professional development funds on?_2': dtype('O'),\n",
       " 'Which of the following would you be likely to use professional development funds on?_3': dtype('O'),\n",
       " 'Which of the following would you be likely to use professional development funds on?_4': dtype('O'),\n",
       " 'Which of the following would you be likely to use professional development funds on?_5': dtype('O'),\n",
       " 'Which of the following would you be likely to use professional development funds on?_6': dtype('O'),\n",
       " 'Which of the following would you be likely to use professional development funds on?_7': dtype('O'),\n",
       " 'Which of the following would you be likely to use professional development funds on?_8': dtype('O'),\n",
       " 'I have adequate resources to explore the data visualization options I want to use.': dtype('float64'),\n",
       " 'What are your biggest frustrations with doing data visualization in your job?': dtype('O'),\n",
       " 'What are your biggest frustrations with doing data visualization in your job?_1': dtype('O'),\n",
       " 'What are your biggest frustrations with doing data visualization in your job?_2': dtype('O'),\n",
       " 'What are your biggest frustrations with doing data visualization in your job?_3': dtype('O'),\n",
       " 'What are your biggest frustrations with doing data visualization in your job?_4': dtype('O'),\n",
       " 'What are your biggest frustrations with doing data visualization in your job?_5': dtype('O'),\n",
       " 'What are your biggest frustrations with doing data visualization in your job?_6': dtype('O'),\n",
       " 'What are your biggest frustrations with doing data visualization in your job?_7': dtype('O'),\n",
       " 'What are your biggest frustrations with doing data visualization in your job?_8': dtype('O'),\n",
       " 'What are your biggest frustrations with doing data visualization in your job?_9': dtype('O'),\n",
       " 'What are your biggest frustrations with doing data visualization in your job?_10': dtype('O'),\n",
       " 'What are your biggest frustrations with doing data visualization in your job?_11': dtype('O'),\n",
       " 'What are your biggest frustrations with doing data visualization in your job?_12': dtype('O'),\n",
       " 'If you could change one thing about your data visualization work what would it be?': dtype('O'),\n",
       " 'What do you think other people in your organization just don’t get about the data visualization work that you do?': dtype('O'),\n",
       " 'Have you changed employment or had a change in employment status in the last year?': dtype('O'),\n",
       " 'Was the current coronavirus pandemic and related economic crisis a factor in your change in employment or employment status?': dtype('O'),\n",
       " 'Are you currently looking for new employment opportunities?': dtype('O'),\n",
       " 'In your next role, would you like:': dtype('O'),\n",
       " 'What are the three most important issues facing data visualization?': dtype('O'),\n",
       " 'What are the three most important issues facing data visualization?_1': dtype('O'),\n",
       " 'What are the three most important issues facing data visualization?_2': dtype('O'),\n",
       " 'What are the three most important issues facing data visualization?_3': dtype('O'),\n",
       " 'What are the three most important issues facing data visualization?_4': dtype('O'),\n",
       " 'What are the three most important issues facing data visualization?_5': dtype('O'),\n",
       " 'What are the three most important issues facing data visualization?_6': dtype('O'),\n",
       " 'What are the three most important issues facing data visualization?_7': dtype('O'),\n",
       " 'What are the three most important issues facing data visualization?_8': dtype('O'),\n",
       " 'What are the three most important issues facing data visualization?_9': dtype('O'),\n",
       " 'What area is your next priority for improving your data visualization skills?': dtype('O'),\n",
       " 'What area is your next priority for improving your data visualization skills?_1': dtype('O'),\n",
       " 'What area is your next priority for improving your data visualization skills?_2': dtype('O'),\n",
       " 'What area is your next priority for improving your data visualization skills?_3': dtype('O'),\n",
       " 'What area is your next priority for improving your data visualization skills?_4': dtype('O'),\n",
       " 'What area is your next priority for improving your data visualization skills?_5': dtype('O'),\n",
       " 'What methods do you find most helpful in learning new data visualization skills?': dtype('O'),\n",
       " 'What methods do you find most helpful in learning new data visualization skills?_1': dtype('O'),\n",
       " 'What methods do you find most helpful in learning new data visualization skills?_2': dtype('O'),\n",
       " 'What methods do you find most helpful in learning new data visualization skills?_3': dtype('O'),\n",
       " 'What methods do you find most helpful in learning new data visualization skills?_4': dtype('O'),\n",
       " 'What methods do you find most helpful in learning new data visualization skills?_5': dtype('O'),\n",
       " 'What methods do you find most helpful in learning new data visualization skills?_6': dtype('O'),\n",
       " 'What methods do you find most helpful in learning new data visualization skills?_7': dtype('O'),\n",
       " 'Who do you look to as a leader in data visualization?': dtype('O'),\n",
       " 'gender_collapsed': dtype('O')}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since describe didn't include variables I expected, get the data types for all variables\n",
    "# make it a dictionary to get all of the columns, since dtypes would provide only top and bottom columns \n",
    "# for a file like this with so many columns)\n",
    "dict(dfpayrate20.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see just hourly rate data type\n",
    "dfpayrate20.dtypes[\"What is your hourly rate (converted to US dollars)?\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1766"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see the number of records i.e. the length of the index of the dataframe, includes rows where there may be NaN values\n",
    "len(dfpayrate20.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                 NaN\n",
       "1         $125 - $149\n",
       "2                 NaN\n",
       "3                 NaN\n",
       "4                 NaN\n",
       "            ...      \n",
       "1761              NaN\n",
       "1762              NaN\n",
       "1763              NaN\n",
       "1764              NaN\n",
       "1765    Less than $25\n",
       "Name: What is your hourly rate (converted to US dollars)?, Length: 1766, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take a peek at some of the hourly rates\n",
    "dfpayrate20[\"What is your hourly rate (converted to US dollars)?\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "791"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# since there are many NaN values, and I'm focused on this as the independent variable, \n",
    "# I will only work with the rows where hourly rate is not empty\n",
    "dfpayrate20 = dfpayrate20[dfpayrate20[\"What is your hourly rate (converted to US dollars)?\"].notna()]\n",
    "# see the number of records now after excluding rows with NaN hourly rate\n",
    "len(dfpayrate20.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['Less than $25', '$25 - $49', '$50 - $74', '$75 - $99',\n",
       "                  '$100 - $124', '$125 - $149', '$150 - $174', '$175 - $199',\n",
       "                  '$200 - $224', '$225 - $249', '$250 - $274', '$275 - $299',\n",
       "                  'More than $300'],\n",
       "                 ordered=True)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# treat the hourly rate scale object fields as categorical data, \n",
    "# since respondents were limited to specific value selections in the survey question format\n",
    "# and pass a specific order argument to astype\n",
    "# define the order in a variable\n",
    "catshourly = ['Less than $25', '$25 - $49', '$50 - $74', '$75 - $99', '$100 - $124', '$125 - $149', '$150 - $174', '$175 - $199', '$200 - $224', '$225 - $249', '$250 - $274','$275 - $299', 'More than $300']\n",
    "# use the updated pd.Categorical to cast the data type and order\n",
    "dfpayrate20[\"What is your hourly rate (converted to US dollars)?\"] = pd.Categorical(dfpayrate20[\"What is your hourly rate (converted to US dollars)?\"], categories=catshourly, ordered=True)\n",
    "# now check the data type of it again\n",
    "# see just hourly rate data type\n",
    "dfpayrate20.dtypes[\"What is your hourly rate (converted to US dollars)?\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['5 or less', '5-10', '11-15', '16-20', '21-25', '26-30'], ordered=True)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# repeat the type change for years of experience columns (2)\n",
    "catsyrsexp = ['5 or less', '5-10', '11-15', '16-20', '21-25', '26-30']\n",
    "dfpayrate20[\"How many years of professional experience do you have?\"] = pd.Categorical(dfpayrate20[\"How many years of professional experience do you have?\"], categories=catsyrsexp, ordered=True)\n",
    "# now check the data type of it again\n",
    "# see just hourly rate data type\n",
    "dfpayrate20.dtypes[\"How many years of professional experience do you have?\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['less than 1', '1', '2', '3', '4', '5', '(5-10]', '(11-15]',\n",
       "                  '(16-20]', '(21-25]', '(26-30]'],\n",
       "                 ordered=True)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now same for: \"How many years of data visualization experience do you have doing professional data visualization?\"\n",
    "catsyrsexpdviz = ['less than 1', '1', '2', '3', '4', '5', '(5-10]', '(11-15]', '(16-20]', '(21-25]', '(26-30]']\n",
    "dfpayrate20[\"How many years of data visualization experience do you have doing professional data visualization?\"] = pd.Categorical(dfpayrate20[\"How many years of data visualization experience do you have doing professional data visualization?\"], categories=catsyrsexpdviz, ordered=True)\n",
    "# now check the data type of it again\n",
    "# see just hourly rate data type\n",
    "dfpayrate20.dtypes[\"How many years of data visualization experience do you have doing professional data visualization?\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Hourly Rate by Years of Experience\n",
    "\n",
    "### First by general years of experience\n",
    "\n",
    "This is informative as many contract hourly projects include data visualization and other skills/tasks, and because as a relatively new field many practicioners bring extensive experience from other disciplines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set an overall seaborn style \n",
    "# with y axis grid lines that will help read the numerous categories of hourly rates\n",
    "sns.set(style=\"whitegrid\", palette=\"crest\")\n",
    "# use a discrete continuous color map\n",
    "# sns.color_palette(\"Set2\") # ColorBrewer palette with enough for our 6 categories of pricing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a categorical plot\n",
    "p = sns.catplot(x=\"How many years of professional experience do you have?\", \n",
    "                    y=\"What is your hourly rate (converted to US dollars)?\",\n",
    "#                   kind=\"swarm\", # using stripplot default format of catplot instead of swarm due to the too large # records that could not all be plotted\n",
    "#                   jitter=False, #disable the jitter to display marks all in a vertical plane overlapping each other\n",
    "                    data=dfpayrate20)\n",
    "# style the axis labels\n",
    "p.set(ylabel=None) # suppress the y label because it's too long and hard to read verticall/turning your head\n",
    "# access the axes of the FacetGrid produced by seaborn, in order to adjust font sizes of elements\n",
    "axes = p.axes.flatten()\n",
    "# Keep and clean up the x label\n",
    "axes[0].set_xlabel(\"Years of professional experience\", fontsize = 14, loc='center')\n",
    "# add a title and enlarge its font\n",
    "axes[0].set_title(\"Hourly rates of 2020 DVS Survey Respondents\\nby Years of General Experience (US $)\", fontsize = 18, fontweight='demibold')#, loc='left', ha='left', pad=12.0) # ha for horizontal alignment\n",
    "# move the title to the far left hovering over the y axis labels\n",
    "axes[0].title.set_position([0.45, 1.1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now by years of data visualization experience specifically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a categorical plot\n",
    "p = sns.catplot(x=\"How many years of data visualization experience do you have doing professional data visualization?\", \n",
    "                    y=\"What is your hourly rate (converted to US dollars)?\",\n",
    "#                   kind=\"swarm\", # using stripplot default format of catplot instead of swarm due to the too large # records that could not all be plotted\n",
    "#                   jitter=False, #disable the jitter to display marks all in a vertical plane overlapping each other\n",
    "                    data=dfpayrate20,\n",
    "                    palette=\"crest\")\n",
    "# style the axis labels\n",
    "p.set(ylabel=None) # suppress the y label because it's too long and hard to read verticall/turning your head\n",
    "# access the axes of the FacetGrid produced by seaborn, in order to adjust font sizes of elements\n",
    "axes = p.axes.flatten()\n",
    "# Keep and clean up the x label\n",
    "axes[0].set_xlabel(\"Years of data viz professional experience\", fontsize = 14, loc='center')\n",
    "# add a title and enlarge its font\n",
    "axes[0].set_title(\"Hourly rates of 2020 DVS Survey Respondents (US $)\\nby Years of Data Visualization Experience\", fontsize = 17, fontweight='demibold')#, loc='left', ha='left', pad=12.0) # ha for horizontal alignment\n",
    "# move the title to the far left hovering over the y axis labels\n",
    "axes[0].title.set_position([0.45, 1.1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot these side by side sharing a single y axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  adjustable: {'box', 'datalim'}\n",
      "  agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
      "  alpha: scalar or None\n",
      "  anchor: 2-tuple of floats or {'C', 'SW', 'S', 'SE', ...}\n",
      "  animated: bool\n",
      "  aspect: {'auto', 'equal'} or float\n",
      "  autoscale_on: bool\n",
      "  autoscalex_on: bool\n",
      "  autoscaley_on: bool\n",
      "  axes_locator: Callable[[Axes, Renderer], Bbox]\n",
      "  axisbelow: bool or 'line'\n",
      "  box_aspect: float or None\n",
      "  clip_box: `.Bbox`\n",
      "  clip_on: bool\n",
      "  clip_path: Patch or (Path, Transform) or None\n",
      "  contains: unknown\n",
      "  facecolor or fc: color\n",
      "  figure: `.Figure`\n",
      "  frame_on: bool\n",
      "  gid: str\n",
      "  in_layout: bool\n",
      "  label: object\n",
      "  navigate: bool\n",
      "  navigate_mode: unknown\n",
      "  path_effects: `.AbstractPathEffect`\n",
      "  picker: None or bool or float or callable\n",
      "  position: [left, bottom, width, height] or `~matplotlib.transforms.Bbox`\n",
      "  prop_cycle: unknown\n",
      "  rasterization_zorder: float or None\n",
      "  rasterized: bool\n",
      "  sketch_params: (scale: float, length: float, randomness: float)\n",
      "  snap: bool or None\n",
      "  subplotspec: unknown\n",
      "  title: str\n",
      "  transform: `.Transform`\n",
      "  url: str\n",
      "  visible: bool\n",
      "  xbound: unknown\n",
      "  xlabel: str\n",
      "  xlim: (bottom: float, top: float)\n",
      "  xmargin: float greater than -0.5\n",
      "  xscale: {\"linear\", \"log\", \"symlog\", \"logit\", ...} or `.ScaleBase`\n",
      "  xticklabels: unknown\n",
      "  xticks: unknown\n",
      "  ybound: unknown\n",
      "  ylabel: str\n",
      "  ylim: (bottom: float, top: float)\n",
      "  ymargin: float greater than -0.5\n",
      "  yscale: {\"linear\", \"log\", \"symlog\", \"logit\", ...} or `.ScaleBase`\n",
      "  yticklabels: unknown\n",
      "  yticks: unknown\n",
      "  zorder: float\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set up a matplotlib figure to plot these side by side\n",
    "f, axes = plt.subplots(1, 2, figsize=(16,7), sharey=True)\n",
    "sns.despine(left=True)\n",
    "\n",
    "# make a categorical plot\n",
    "p = sns.stripplot(x=\"How many years of professional experience do you have?\", \n",
    "                    y=\"What is your hourly rate (converted to US dollars)?\",\n",
    "#                   kind=\"swarm\", # using stripplot default format of catplot instead of swarm due to the too large # records that could not all be plotted\n",
    "#                   jitter=False, #disable the jitter to display marks all in a vertical plane overlapping each other\n",
    "                    data=dfpayrate20,\n",
    "                    ax=axes[0])\n",
    "# style the axis labels\n",
    "p.set(ylabel=None) # suppress the y label because it's too long and hard to read verticall/turning your head\n",
    "# access the axes of the FacetGrid produced by seaborn, in order to adjust font sizes of elements\n",
    "\n",
    "# Keep and clean up the x label\n",
    "p.set_xlabel(\"Years\", fontsize = 14, loc='center')\n",
    "# add a title and enlarge its font\n",
    "p.set_title('Hourly Rate by General Experience (US $)', fontsize = 18, fontweight='demibold', loc='left', ha='left', pad=12.0) # ha for horizontal alignment\n",
    "# move the title to the far left hovering over the y axis labels\n",
    "# p.title.set_position([0.45, 1.1])\n",
    "\n",
    "# make a categorical plot\n",
    "p1 = sns.stripplot(x=\"How many years of data visualization experience do you have doing professional data visualization?\", \n",
    "                    y=\"What is your hourly rate (converted to US dollars)?\",\n",
    "#                   kind=\"swarm\", # using stripplot default format of catplot instead of swarm due to the too large # records that could not all be plotted\n",
    "#                   jitter=False, #disable the jitter to display marks all in a vertical plane overlapping each other\n",
    "                    data=dfpayrate20,\n",
    "                    palette=\"crest\",\n",
    "                    ax=axes[1])\n",
    "# style the axis labels\n",
    "p1.set(ylabel=None) # suppress the y label because it's too long and hard to read verticall/turning your head\n",
    "# access the axes of the FacetGrid produced by seaborn, in order to adjust font sizes of elements\n",
    "\n",
    "# Keep and clean up the x label\n",
    "p1.set_xlabel(\"Years\", fontsize = 14, loc='center')\n",
    "# add a title and enlarge its font\n",
    "p1.set_title(\"by Data Visualization Experience\", fontsize = 17, fontweight='demibold', loc='left', ha='left', pad=12.0) # ha for horizontal alignment\n",
    "# move the title to the far left hovering over the y axis labels\n",
    "#p1.title.set_position([0.45, 1.1])\n",
    "\n",
    "# # add an overall group label\n",
    "# axes[0].annotate('Hourly rates of 2020 DVS Survey Respondents (converted to US dollars $)', \n",
    "#                  (0.5, 1), \n",
    "#                  xytext=(0, 30), \n",
    "#                  textcoords='offset points', \n",
    "#                  xycoords='axes fraction', \n",
    "#                  ha='center', \n",
    "#                  va='bottom', \n",
    "#                  size=24, \n",
    "#                  weight='demibold')\n",
    "\n",
    "plt.setp(axes)\n",
    "# plt.tight_layout()\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rename years of experience to shorten it\n",
    "dfpayrate20.rename(\n",
    "    columns=({ \"How many years of professional experience do you have?\": \"Years of Prof. Exp.\"}),\n",
    "    inplace=True,\n",
    ")\n",
    "\n",
    "\n",
    "# show the count for each categorical hourly rate\n",
    "p = sns.catplot(y=\"What is your hourly rate (converted to US dollars)?\",\n",
    "                col=\"Years of Prof. Exp.\",\n",
    "                kind=\"count\",\n",
    "                palette=\"crest\",\n",
    "                data=dfpayrate20,\n",
    "                col_wrap=3\n",
    "               )\n",
    "# style the axis labels\n",
    "p.set(ylabel=None) # suppress the y label because it's too long and hard to read verticall/turning your head\n",
    "# access the axes of the FacetGrid produced by seaborn, in order to adjust font sizes of elements\n",
    "\n",
    "# Keep and clean up the x label\n",
    "p.set(xlabel=\"Converted to US dollars\")\n",
    "# add a title and enlarge its font\n",
    "# p.set(title='Count of Survey Responses for Hourly Rate Ranges')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "What is your hourly rate (converted to US dollars)?\n",
       "Less than $25     133\n",
       "$25 - $49         281\n",
       "$50 - $74         141\n",
       "$75 - $99          79\n",
       "$100 - $124        50\n",
       "$125 - $149        27\n",
       "$150 - $174        26\n",
       "$175 - $199        15\n",
       "$200 - $224        13\n",
       "$225 - $249         3\n",
       "$250 - $274         5\n",
       "$275 - $299         1\n",
       "More than $300     17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the mean for each category of hourly rate by general years experience\n",
    "dfpayrate20.groupby([\"What is your hourly rate (converted to US dollars)?\"]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "What is your hourly rate (converted to US dollars)?  Years of Prof. Exp.\n",
       "Less than $25                                        5 or less               68\n",
       "                                                     5-10                    32\n",
       "                                                     11-15                   19\n",
       "                                                     16-20                    8\n",
       "                                                     21-25                    2\n",
       "                                                     26-30                    1\n",
       "$25 - $49                                            5 or less              105\n",
       "                                                     5-10                    94\n",
       "                                                     11-15                   29\n",
       "                                                     16-20                   32\n",
       "                                                     21-25                   11\n",
       "                                                     26-30                    3\n",
       "$50 - $74                                            5 or less               23\n",
       "                                                     5-10                    38\n",
       "                                                     11-15                   29\n",
       "                                                     16-20                   23\n",
       "                                                     21-25                   18\n",
       "                                                     26-30                    3\n",
       "$75 - $99                                            5 or less                9\n",
       "                                                     5-10                    17\n",
       "                                                     11-15                   13\n",
       "                                                     16-20                   20\n",
       "                                                     21-25                   11\n",
       "                                                     26-30                    3\n",
       "$100 - $124                                          5 or less                5\n",
       "                                                     5-10                    15\n",
       "                                                     11-15                   12\n",
       "                                                     16-20                    8\n",
       "                                                     21-25                    4\n",
       "                                                     26-30                    3\n",
       "$125 - $149                                          5 or less                4\n",
       "                                                     5-10                     7\n",
       "                                                     11-15                    4\n",
       "                                                     16-20                    2\n",
       "                                                     21-25                    3\n",
       "                                                     26-30                    4\n",
       "$150 - $174                                          5 or less                3\n",
       "                                                     5-10                     6\n",
       "                                                     11-15                    4\n",
       "                                                     16-20                    6\n",
       "                                                     21-25                    0\n",
       "                                                     26-30                    2\n",
       "$175 - $199                                          5 or less                1\n",
       "                                                     5-10                     4\n",
       "                                                     11-15                    6\n",
       "                                                     16-20                    2\n",
       "                                                     21-25                    1\n",
       "                                                     26-30                    1\n",
       "$200 - $224                                          5 or less                0\n",
       "                                                     5-10                     4\n",
       "                                                     11-15                    3\n",
       "                                                     16-20                    3\n",
       "                                                     21-25                    0\n",
       "                                                     26-30                    2\n",
       "$225 - $249                                          5 or less                0\n",
       "                                                     5-10                     0\n",
       "                                                     11-15                    1\n",
       "                                                     16-20                    1\n",
       "                                                     21-25                    0\n",
       "                                                     26-30                    0\n",
       "$250 - $274                                          5 or less                0\n",
       "                                                     5-10                     2\n",
       "                                                     11-15                    1\n",
       "                                                     16-20                    1\n",
       "                                                     21-25                    0\n",
       "                                                     26-30                    1\n",
       "$275 - $299                                          5 or less                0\n",
       "                                                     5-10                     0\n",
       "                                                     11-15                    0\n",
       "                                                     16-20                    1\n",
       "                                                     21-25                    0\n",
       "                                                     26-30                    0\n",
       "More than $300                                       5 or less                0\n",
       "                                                     5-10                     2\n",
       "                                                     11-15                    4\n",
       "                                                     16-20                    3\n",
       "                                                     21-25                    2\n",
       "                                                     26-30                    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "# add subgrouping for years general experience\n",
    "# calculate the mean for each category of hourly rate by general years experience\n",
    "dfpayrate20.groupby([\"What is your hourly rate (converted to US dollars)?\", \"Years of Prof. Exp.\"]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Years of Data Viz Prof. Exp.  What is your hourly rate (converted to US dollars)?\n",
       "less than 1                   Less than $25                                          32\n",
       "                              $25 - $49                                              26\n",
       "                              $50 - $74                                               8\n",
       "                              $75 - $99                                               4\n",
       "                              $100 - $124                                             2\n",
       "                              $125 - $149                                             2\n",
       "                              $150 - $174                                             1\n",
       "                              $175 - $199                                             1\n",
       "                              $200 - $224                                             0\n",
       "                              $225 - $249                                             0\n",
       "                              $250 - $274                                             1\n",
       "                              $275 - $299                                             0\n",
       "                              More than $300                                          1\n",
       "1                             Less than $25                                          16\n",
       "                              $25 - $49                                              31\n",
       "                              $50 - $74                                               5\n",
       "                              $75 - $99                                               3\n",
       "                              $100 - $124                                             3\n",
       "                              $125 - $149                                             2\n",
       "                              $150 - $174                                             1\n",
       "                              $175 - $199                                             1\n",
       "                              $200 - $224                                             0\n",
       "                              $225 - $249                                             0\n",
       "                              $250 - $274                                             0\n",
       "                              $275 - $299                                             0\n",
       "                              More than $300                                          0\n",
       "2                             Less than $25                                          22\n",
       "                              $25 - $49                                              45\n",
       "                              $50 - $74                                               6\n",
       "                              $75 - $99                                               6\n",
       "                              $100 - $124                                             2\n",
       "                              $125 - $149                                             1\n",
       "                              $150 - $174                                             1\n",
       "                              $175 - $199                                             1\n",
       "                              $200 - $224                                             1\n",
       "                              $225 - $249                                             0\n",
       "                              $250 - $274                                             0\n",
       "                              $275 - $299                                             0\n",
       "                              More than $300                                          0\n",
       "3                             Less than $25                                          23\n",
       "                              $25 - $49                                              47\n",
       "                              $50 - $74                                              20\n",
       "                              $75 - $99                                              12\n",
       "                              $100 - $124                                            10\n",
       "                              $125 - $149                                             0\n",
       "                              $150 - $174                                             2\n",
       "                              $175 - $199                                             0\n",
       "                              $200 - $224                                             1\n",
       "                              $225 - $249                                             1\n",
       "                              $250 - $274                                             0\n",
       "                              $275 - $299                                             0\n",
       "                              More than $300                                          0\n",
       "4                             Less than $25                                          13\n",
       "                              $25 - $49                                              26\n",
       "                              $50 - $74                                              11\n",
       "                              $75 - $99                                               7\n",
       "                              $100 - $124                                             5\n",
       "                              $125 - $149                                             2\n",
       "                              $150 - $174                                             0\n",
       "                              $175 - $199                                             2\n",
       "                              $200 - $224                                             2\n",
       "                              $225 - $249                                             0\n",
       "                              $250 - $274                                             0\n",
       "                              $275 - $299                                             0\n",
       "                              More than $300                                          0\n",
       "5                             Less than $25                                          11\n",
       "                              $25 - $49                                              38\n",
       "                              $50 - $74                                              28\n",
       "                              $75 - $99                                              12\n",
       "                              $100 - $124                                             6\n",
       "                              $125 - $149                                             5\n",
       "                              $150 - $174                                             4\n",
       "                              $175 - $199                                             2\n",
       "                              $200 - $224                                             2\n",
       "                              $225 - $249                                             1\n",
       "                              $250 - $274                                             1\n",
       "                              $275 - $299                                             0\n",
       "                              More than $300                                          0\n",
       "(5-10]                        Less than $25                                          13\n",
       "                              $25 - $49                                              48\n",
       "                              $50 - $74                                              46\n",
       "                              $75 - $99                                              21\n",
       "                              $100 - $124                                            17\n",
       "                              $125 - $149                                             9\n",
       "                              $150 - $174                                             9\n",
       "                              $175 - $199                                             5\n",
       "                              $200 - $224                                             7\n",
       "                              $225 - $249                                             1\n",
       "                              $250 - $274                                             2\n",
       "                              $275 - $299                                             1\n",
       "                              More than $300                                          8\n",
       "(11-15]                       Less than $25                                           0\n",
       "                              $25 - $49                                              10\n",
       "                              $50 - $74                                               6\n",
       "                              $75 - $99                                               7\n",
       "                              $100 - $124                                             4\n",
       "                              $125 - $149                                             3\n",
       "                              $150 - $174                                             3\n",
       "                              $175 - $199                                             0\n",
       "                              $200 - $224                                             0\n",
       "                              $225 - $249                                             0\n",
       "                              $250 - $274                                             0\n",
       "                              $275 - $299                                             0\n",
       "                              More than $300                                          3\n",
       "(16-20]                       Less than $25                                           1\n",
       "                              $25 - $49                                               4\n",
       "                              $50 - $74                                               6\n",
       "                              $75 - $99                                               3\n",
       "                              $100 - $124                                             1\n",
       "                              $125 - $149                                             0\n",
       "                              $150 - $174                                             2\n",
       "                              $175 - $199                                             3\n",
       "                              $200 - $224                                             0\n",
       "                              $225 - $249                                             0\n",
       "                              $250 - $274                                             0\n",
       "                              $275 - $299                                             0\n",
       "                              More than $300                                          0\n",
       "(21-25]                       Less than $25                                           0\n",
       "                              $25 - $49                                               3\n",
       "                              $50 - $74                                               2\n",
       "                              $75 - $99                                               2\n",
       "                              $100 - $124                                             0\n",
       "                              $125 - $149                                             1\n",
       "                              $150 - $174                                             1\n",
       "                              $175 - $199                                             0\n",
       "                              $200 - $224                                             0\n",
       "                              $225 - $249                                             0\n",
       "                              $250 - $274                                             1\n",
       "                              $275 - $299                                             0\n",
       "                              More than $300                                          3\n",
       "(26-30]                       Less than $25                                           0\n",
       "                              $25 - $49                                               0\n",
       "                              $50 - $74                                               1\n",
       "                              $75 - $99                                               1\n",
       "                              $100 - $124                                             0\n",
       "                              $125 - $149                                             1\n",
       "                              $150 - $174                                             0\n",
       "                              $175 - $199                                             0\n",
       "                              $200 - $224                                             0\n",
       "                              $225 - $249                                             0\n",
       "                              $250 - $274                                             0\n",
       "                              $275 - $299                                             0\n",
       "                              More than $300                                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename years of dataviz experience to shorten it\n",
    "dfpayrate20.rename(\n",
    "    columns=({ \"How many years of data visualization experience do you have doing professional data visualization?\": \"Years of Data Viz Prof. Exp.\"}),\n",
    "    inplace=True,\n",
    ")\n",
    "\n",
    "# add subgrouping for years data viz specific experience\n",
    "# calculate the mean for each category of hourly rate by general years experience\n",
    "dfpayrate20.groupby([\"Years of Data Viz Prof. Exp.\", \"What is your hourly rate (converted to US dollars)?\"]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Years of Prof. Exp.  Years of Data Viz Prof. Exp.  What is your hourly rate (converted to US dollars)?\n",
       "5 or less            less than 1                   Less than $25                                          22\n",
       "                                                   $25 - $49                                              16\n",
       "                                                   $50 - $74                                               3\n",
       "                                                   $75 - $99                                               1\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     1                             Less than $25                                          10\n",
       "                                                   $25 - $49                                              23\n",
       "                                                   $50 - $74                                               3\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             1\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             1\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     2                             Less than $25                                          14\n",
       "                                                   $25 - $49                                              27\n",
       "                                                   $50 - $74                                               3\n",
       "                                                   $75 - $99                                               1\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             1\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     3                             Less than $25                                          14\n",
       "                                                   $25 - $49                                              25\n",
       "                                                   $50 - $74                                               8\n",
       "                                                   $75 - $99                                               4\n",
       "                                                   $100 - $124                                             2\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     4                             Less than $25                                           3\n",
       "                                                   $25 - $49                                               9\n",
       "                                                   $50 - $74                                               2\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     5                             Less than $25                                           4\n",
       "                                                   $25 - $49                                               4\n",
       "                                                   $50 - $74                                               4\n",
       "                                                   $75 - $99                                               1\n",
       "                                                   $100 - $124                                             2\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             1\n",
       "                                                   $175 - $199                                             1\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (5-10]                        Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (11-15]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (16-20]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (21-25]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (26-30]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "5-10                 less than 1                   Less than $25                                           6\n",
       "                                                   $25 - $49                                               6\n",
       "                                                   $50 - $74                                               3\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             1\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          1\n",
       "                     1                             Less than $25                                           4\n",
       "                                                   $25 - $49                                               5\n",
       "                                                   $50 - $74                                               1\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             1\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             1\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     2                             Less than $25                                           0\n",
       "                                                   $25 - $49                                              11\n",
       "                                                   $50 - $74                                               1\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             1\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             1\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     3                             Less than $25                                           7\n",
       "                                                   $25 - $49                                              16\n",
       "                                                   $50 - $74                                               7\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             3\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             2\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             1\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     4                             Less than $25                                           8\n",
       "                                                   $25 - $49                                              11\n",
       "                                                   $50 - $74                                               3\n",
       "                                                   $75 - $99                                               3\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             1\n",
       "                                                   $200 - $224                                             1\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     5                             Less than $25                                           2\n",
       "                                                   $25 - $49                                              20\n",
       "                                                   $50 - $74                                              10\n",
       "                                                   $75 - $99                                               3\n",
       "                                                   $100 - $124                                             2\n",
       "                                                   $125 - $149                                             2\n",
       "                                                   $150 - $174                                             2\n",
       "                                                   $175 - $199                                             1\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             1\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (5-10]                        Less than $25                                           5\n",
       "                                                   $25 - $49                                              25\n",
       "                                                   $50 - $74                                              12\n",
       "                                                   $75 - $99                                               7\n",
       "                                                   $100 - $124                                             7\n",
       "                                                   $125 - $149                                             3\n",
       "                                                   $150 - $174                                             2\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             2\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             1\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          1\n",
       "                     (11-15]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (16-20]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (21-25]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (26-30]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "11-15                less than 1                   Less than $25                                           2\n",
       "                                                   $25 - $49                                               1\n",
       "                                                   $50 - $74                                               1\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             1\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     1                             Less than $25                                           2\n",
       "                                                   $25 - $49                                               2\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             1\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     2                             Less than $25                                           5\n",
       "                                                   $25 - $49                                               3\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             1\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     3                             Less than $25                                           2\n",
       "                                                   $25 - $49                                               3\n",
       "                                                   $50 - $74                                               4\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             3\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     4                             Less than $25                                           1\n",
       "                                                   $25 - $49                                               3\n",
       "                                                   $50 - $74                                               2\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             2\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             1\n",
       "                                                   $200 - $224                                             1\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     5                             Less than $25                                           2\n",
       "                                                   $25 - $49                                               8\n",
       "                                                   $50 - $74                                               6\n",
       "                                                   $75 - $99                                               3\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             1\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             1\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (5-10]                        Less than $25                                           5\n",
       "                                                   $25 - $49                                               4\n",
       "                                                   $50 - $74                                              14\n",
       "                                                   $75 - $99                                               3\n",
       "                                                   $100 - $124                                             3\n",
       "                                                   $125 - $149                                             2\n",
       "                                                   $150 - $174                                             2\n",
       "                                                   $175 - $199                                             4\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             1\n",
       "                                                   $250 - $274                                             1\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          4\n",
       "                     (11-15]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               5\n",
       "                                                   $50 - $74                                               2\n",
       "                                                   $75 - $99                                               3\n",
       "                                                   $100 - $124                                             3\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             1\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (16-20]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (21-25]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (26-30]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "16-20                less than 1                   Less than $25                                           0\n",
       "                                                   $25 - $49                                               1\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             1\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             1\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     1                             Less than $25                                           0\n",
       "                                                   $25 - $49                                               1\n",
       "                                                   $50 - $74                                               1\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     2                             Less than $25                                           3\n",
       "                                                   $25 - $49                                               3\n",
       "                                                   $50 - $74                                               1\n",
       "                                                   $75 - $99                                               3\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     3                             Less than $25                                           0\n",
       "                                                   $25 - $49                                               1\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               3\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     4                             Less than $25                                           1\n",
       "                                                   $25 - $49                                               3\n",
       "                                                   $50 - $74                                               2\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             2\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     5                             Less than $25                                           3\n",
       "                                                   $25 - $49                                               4\n",
       "                                                   $50 - $74                                               4\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             2\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             1\n",
       "                                                   $225 - $249                                             1\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (5-10]                        Less than $25                                           1\n",
       "                                                   $25 - $49                                              13\n",
       "                                                   $50 - $74                                              10\n",
       "                                                   $75 - $99                                               7\n",
       "                                                   $100 - $124                                             2\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             3\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             2\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             1\n",
       "                                                   More than $300                                          2\n",
       "                     (11-15]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               3\n",
       "                                                   $50 - $74                                               2\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             2\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          1\n",
       "                     (16-20]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               3\n",
       "                                                   $50 - $74                                               3\n",
       "                                                   $75 - $99                                               1\n",
       "                                                   $100 - $124                                             1\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             1\n",
       "                                                   $175 - $199                                             2\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (21-25]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (26-30]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "21-25                less than 1                   Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               1\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     1                             Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               1\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     2                             Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               1\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     3                             Less than $25                                           0\n",
       "                                                   $25 - $49                                               1\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     4                             Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               2\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     5                             Less than $25                                           0\n",
       "                                                   $25 - $49                                               1\n",
       "                                                   $50 - $74                                               4\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (5-10]                        Less than $25                                           1\n",
       "                                                   $25 - $49                                               3\n",
       "                                                   $50 - $74                                               6\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             4\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             1\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (11-15]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               2\n",
       "                                                   $50 - $74                                               2\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          1\n",
       "                     (16-20]                       Less than $25                                           1\n",
       "                                                   $25 - $49                                               1\n",
       "                                                   $50 - $74                                               1\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (21-25]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               3\n",
       "                                                   $50 - $74                                               1\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          1\n",
       "                     (26-30]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "26-30                less than 1                   Less than $25                                           1\n",
       "                                                   $25 - $49                                               1\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               1\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             1\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     1                             Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     2                             Less than $25                                           0\n",
       "                                                   $25 - $49                                               1\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     3                             Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             1\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     4                             Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             1\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     5                             Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (5-10]                        Less than $25                                           0\n",
       "                                                   $25 - $49                                               1\n",
       "                                                   $50 - $74                                               1\n",
       "                                                   $75 - $99                                               2\n",
       "                                                   $100 - $124                                             1\n",
       "                                                   $125 - $149                                             2\n",
       "                                                   $150 - $174                                             1\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             2\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (11-15]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             1\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (16-20]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               1\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             1\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "                     (21-25]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             1\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          1\n",
       "                     (26-30]                       Less than $25                                           0\n",
       "                                                   $25 - $49                                               0\n",
       "                                                   $50 - $74                                               0\n",
       "                                                   $75 - $99                                               0\n",
       "                                                   $100 - $124                                             0\n",
       "                                                   $125 - $149                                             0\n",
       "                                                   $150 - $174                                             0\n",
       "                                                   $175 - $199                                             0\n",
       "                                                   $200 - $224                                             0\n",
       "                                                   $225 - $249                                             0\n",
       "                                                   $250 - $274                                             0\n",
       "                                                   $275 - $299                                             0\n",
       "                                                   More than $300                                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 5000)\n",
    "pd.set_option('display.max_columns', 5000)\n",
    "pd.set_option('display.width', 5000)\n",
    "\n",
    "# add subgrouping for years data viz specific experience\n",
    "# calculate the mean for each category of hourly rate by general years experience\n",
    "dfpayrate20.groupby([\"Years of Prof. Exp.\", \"Years of Data Viz Prof. Exp.\", \"What is your hourly rate (converted to US dollars)?\"]).size()"
   ]
  }
 ],
 "metadata": {
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    "name": "ipython",
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   "pygments_lexer": "ipython3",
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