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Music & Gender.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Music & Gender"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading packages and data"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"The rpy2.ipython extension is already loaded. To reload it, use:\n",
" %reload_ext rpy2.ipython\n"
]
}
],
"source": [
"# Show graphs in Notebook\n",
"%matplotlib inline\n",
"\n",
"%pylab inline\n",
"figsize(15, 15);\n",
"\n",
"import rpy2\n",
"%load_ext rpy2.ipython\n",
"\n",
"# importing packages and loading data\n",
"%run -i Music_Gender_Plots.py"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/paulakaanders/anaconda/lib/python2.7/site-packages/rpy2/rinterface/__init__.py:186: RRuntimeWarning: Loading required package: Matrix\n",
"\n",
" warnings.warn(x, RRuntimeWarning)\n"
]
}
],
"source": [
"%%R\n",
"library(lme4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In de dataset die je me hebt gestuurd heb ik de volgende dingen aangepast om de analyse simpeler te maken:\n",
" \n",
"- Ik heb alleen de data gebruikt waarin de artiest specifiek als 'man' of 'vrouw' is geclassificeerd.\n",
"- Ik heb ieder radiostation een nummer gegeven.\n",
"\n",
"De dataset ziet er als volgt uit:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <td>6.36</td>\n",
" <td>harlea|you don't get it</td>\n",
" <td>harlea</td>\n",
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" <td>1</td>\n",
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" <td>harlea|you don't get it</td>\n",
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" <td>42.0</td>\n",
" <td>1</td>\n",
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" <td>18.49</td>\n",
" <td>andr_ hazes|het laatste rondje (2005 digital r...</td>\n",
" <td>andre hazes</td>\n",
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" <td>skyradio</td>\n",
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" <td>8.32</td>\n",
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" <td>ariana grande|dangerous woman</td>\n",
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" <th>23</th>\n",
" <td>538</td>\n",
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" <td>12.43</td>\n",
" <td>enrique iglesias|bailando</td>\n",
" <td>enrique iglesias</td>\n",
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" <td>2</td>\n",
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" <td>538</td>\n",
" <td>10/04/17</td>\n",
" <td>13.44</td>\n",
" <td>enrique iglesias|bailando</td>\n",
" <td>enrique iglesias</td>\n",
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" <td>61.0</td>\n",
" <td>71.0</td>\n",
" <td>2</td>\n",
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" <td>538</td>\n",
" <td>06/04/17</td>\n",
" <td>5.51</td>\n",
" <td>enrique iglesias|bailando</td>\n",
" <td>enrique iglesias</td>\n",
" <td>bailando</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5.0</td>\n",
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" <th>27</th>\n",
" <td>538</td>\n",
" <td>14/03/17</td>\n",
" <td>0.51</td>\n",
" <td>enrique iglesias|bailando</td>\n",
" <td>enrique iglesias</td>\n",
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" <td>05/03/17</td>\n",
" <td>14.43</td>\n",
" <td>enrique iglesias|bailando</td>\n",
" <td>enrique iglesias</td>\n",
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" <td>0</td>\n",
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" <td>5.0</td>\n",
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" <tr>\n",
" <th>29</th>\n",
" <td>538</td>\n",
" <td>15/02/17</td>\n",
" <td>10.17</td>\n",
" <td>enrique iglesias|bailando</td>\n",
" <td>enrique iglesias</td>\n",
" <td>bailando</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5.0</td>\n",
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" <td>71.0</td>\n",
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" <tr>\n",
" <th>495384</th>\n",
" <td>3fm</td>\n",
" <td>13/04/17</td>\n",
" <td>0.55</td>\n",
" <td>sue the night|mind dear</td>\n",
" <td>sue the night</td>\n",
" <td>mind dear</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495385</th>\n",
" <td>3fm</td>\n",
" <td>13/04/17</td>\n",
" <td>0.50</td>\n",
" <td>anne-marie|ciao adios</td>\n",
" <td>anne-marie</td>\n",
" <td>ciao adios</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
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" <tr>\n",
" <th>495386</th>\n",
" <td>3fm</td>\n",
" <td>13/04/17</td>\n",
" <td>0.41</td>\n",
" <td>rilan &amp; the bombardiers|supernatural</td>\n",
" <td>rilan &amp; the bombardiers</td>\n",
" <td>supernatural</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>495387</th>\n",
" <td>3fm</td>\n",
" <td>13/04/17</td>\n",
" <td>0.37</td>\n",
" <td>kensington|bridges</td>\n",
" <td>kensington</td>\n",
" <td>bridges</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>13.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495388</th>\n",
" <td>3fm</td>\n",
" <td>13/04/17</td>\n",
" <td>0.32</td>\n",
" <td>macklemore ft. ryan lewis &amp; wanz|thrift shop</td>\n",
" <td>macklemore ft. ryan lewis &amp; wanz</td>\n",
" <td>thrift shop</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495389</th>\n",
" <td>3fm</td>\n",
" <td>13/04/17</td>\n",
" <td>0.29</td>\n",
" <td>beth ditto|fire</td>\n",
" <td>beth ditto</td>\n",
" <td>fire</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495390</th>\n",
" <td>3fm</td>\n",
" <td>13/04/17</td>\n",
" <td>0.25</td>\n",
" <td>the chainsmokers|paris</td>\n",
" <td>the chainsmokers</td>\n",
" <td>paris</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>33.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495391</th>\n",
" <td>3fm</td>\n",
" <td>13/04/17</td>\n",
" <td>0.22</td>\n",
" <td>mumford &amp; sons|little lion man</td>\n",
" <td>mumford &amp; sons</td>\n",
" <td>little lion man</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495392</th>\n",
" <td>3fm</td>\n",
" <td>13/04/17</td>\n",
" <td>0.11</td>\n",
" <td>lorde|green light</td>\n",
" <td>lorde</td>\n",
" <td>green light</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495393</th>\n",
" <td>3fm</td>\n",
" <td>13/04/17</td>\n",
" <td>0.05</td>\n",
" <td>vant|parking lot</td>\n",
" <td>vant</td>\n",
" <td>parking lot</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495394</th>\n",
" <td>3fm</td>\n",
" <td>13/04/17</td>\n",
" <td>0.02</td>\n",
" <td>rag'n'bone man|skin</td>\n",
" <td>rag'n'bone man</td>\n",
" <td>skin</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>40.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495395</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>1.56</td>\n",
" <td>nathaniel rateliff|son of a bitch</td>\n",
" <td>nathaniel rateliff</td>\n",
" <td>son of a bitch</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495396</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>1.48</td>\n",
" <td>boef|habiba</td>\n",
" <td>boef</td>\n",
" <td>habiba</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>10.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495397</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>1.45</td>\n",
" <td>candi staton|you got the love</td>\n",
" <td>candi staton</td>\n",
" <td>you got the love</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495398</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>1.36</td>\n",
" <td>the chainsmokers ft coldplay|something just li...</td>\n",
" <td>the chainsmokers ft coldplay</td>\n",
" <td>something just like this</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>11.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495399</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>1.31</td>\n",
" <td>starley|call on me (ryan riback remix)</td>\n",
" <td>starley</td>\n",
" <td>call on me (ryan riback remix)</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>24.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495400</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>1.22</td>\n",
" <td>mark ronson ft. bruno mars|uptown funk</td>\n",
" <td>mark ronson ft. bruno mars</td>\n",
" <td>uptown funk</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495401</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>1.14</td>\n",
" <td>dua lipa|blow your mind (mwah)</td>\n",
" <td>dua lipa</td>\n",
" <td>blow your mind (mwah)</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495402</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>1.10</td>\n",
" <td>mattanja joy bradley|stand by your man</td>\n",
" <td>mattanja joy bradley</td>\n",
" <td>stand by your man</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495403</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>1.05</td>\n",
" <td>matt simons|catch &amp; release</td>\n",
" <td>matt simons</td>\n",
" <td>catch &amp; release</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>11.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495404</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>1.02</td>\n",
" <td>ed sheeran|galway girl</td>\n",
" <td>ed sheeran</td>\n",
" <td>galway girl</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495405</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>0.51</td>\n",
" <td>the weeknd|in the night</td>\n",
" <td>the weeknd</td>\n",
" <td>in the night</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>17.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495406</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>0.48</td>\n",
" <td>rag'n'bone man|skin (wilkinson remix)</td>\n",
" <td>rag'n'bone man</td>\n",
" <td>skin (wilkinson remix)</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495407</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>0.43</td>\n",
" <td>halsey|now or never (nieuw op 3fm)</td>\n",
" <td>halsey</td>\n",
" <td>now or never (nieuw op 3fm)</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495408</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>0.40</td>\n",
" <td>lenny kravitz|fly away</td>\n",
" <td>lenny kravitz</td>\n",
" <td>fly away</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495409</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>0.40</td>\n",
" <td>lenny kravitz|fly away</td>\n",
" <td>lenny kravitz</td>\n",
" <td>fly away</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495410</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>0.38</td>\n",
" <td>julia michaels|issues</td>\n",
" <td>julia michaels</td>\n",
" <td>issues</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495411</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>0.16</td>\n",
" <td>calvin harris ft frank ocean &amp; migos|slide</td>\n",
" <td>calvin harris ft frank ocean &amp; migos</td>\n",
" <td>slide</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495412</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>0.07</td>\n",
" <td>jax jones ft. raye|you don't know me</td>\n",
" <td>jax jones ft. raye</td>\n",
" <td>you don't know me</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>13.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495413</th>\n",
" <td>3fm</td>\n",
" <td>17/04/17</td>\n",
" <td>0.04</td>\n",
" <td>the shins|no way down</td>\n",
" <td>the shins</td>\n",
" <td>no way down</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>495414 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" station date time \\\n",
"0 radio2 18/07/16 9.23 \n",
"1 538 31/01/17 9.28 \n",
"2 538 11/10/16 9.24 \n",
"3 538 09/09/16 17.51 \n",
"4 538 10/08/16 9.15 \n",
"5 538 06/07/16 9.23 \n",
"6 538 20/05/16 9.11 \n",
"7 538 29/01/17 18.56 \n",
"8 538 01/01/17 14.28 \n",
"9 538 27/11/16 16.48 \n",
"10 radio2 21/03/17 18.10 \n",
"11 radio2 20/03/17 6.19 \n",
"12 radio2 12/02/17 15.54 \n",
"13 radio2 09/02/17 8.41 \n",
"14 radio2 08/02/17 6.36 \n",
"15 radio2 07/02/17 8.41 \n",
"16 538 21/03/17 18.49 \n",
"17 538 17/03/17 18.56 \n",
"18 skyradio 27/04/16 9.35 \n",
"19 skyradio 06/09/16 8.32 \n",
"20 skyradio 05/09/16 4.44 \n",
"21 radio2 18/10/16 1.25 \n",
"22 radio2 04/09/16 18.54 \n",
"23 538 27/04/17 12.43 \n",
"24 538 19/04/17 0.59 \n",
"25 538 10/04/17 13.44 \n",
"26 538 06/04/17 5.51 \n",
"27 538 14/03/17 0.51 \n",
"28 538 05/03/17 14.43 \n",
"29 538 15/02/17 10.17 \n",
"... ... ... ... \n",
"495384 3fm 13/04/17 0.55 \n",
"495385 3fm 13/04/17 0.50 \n",
"495386 3fm 13/04/17 0.41 \n",
"495387 3fm 13/04/17 0.37 \n",
"495388 3fm 13/04/17 0.32 \n",
"495389 3fm 13/04/17 0.29 \n",
"495390 3fm 13/04/17 0.25 \n",
"495391 3fm 13/04/17 0.22 \n",
"495392 3fm 13/04/17 0.11 \n",
"495393 3fm 13/04/17 0.05 \n",
"495394 3fm 13/04/17 0.02 \n",
"495395 3fm 17/04/17 1.56 \n",
"495396 3fm 17/04/17 1.48 \n",
"495397 3fm 17/04/17 1.45 \n",
"495398 3fm 17/04/17 1.36 \n",
"495399 3fm 17/04/17 1.31 \n",
"495400 3fm 17/04/17 1.22 \n",
"495401 3fm 17/04/17 1.14 \n",
"495402 3fm 17/04/17 1.10 \n",
"495403 3fm 17/04/17 1.05 \n",
"495404 3fm 17/04/17 1.02 \n",
"495405 3fm 17/04/17 0.51 \n",
"495406 3fm 17/04/17 0.48 \n",
"495407 3fm 17/04/17 0.43 \n",
"495408 3fm 17/04/17 0.40 \n",
"495409 3fm 17/04/17 0.40 \n",
"495410 3fm 17/04/17 0.38 \n",
"495411 3fm 17/04/17 0.16 \n",
"495412 3fm 17/04/17 0.07 \n",
"495413 3fm 17/04/17 0.04 \n",
"\n",
" artist_song \\\n",
"0 psy|gangnam style \n",
"1 psy|gangnam style \n",
"2 psy|gangnam style \n",
"3 psy|gangnam style \n",
"4 psy|gangnam style \n",
"5 psy|gangnam style \n",
"6 psy|gangnam style \n",
"7 psy|gangnam style \n",
"8 rihanna|work \n",
"9 rihanna|work \n",
"10 harlea|you don't get it \n",
"11 harlea|you don't get it \n",
"12 harlea|you don't get it \n",
"13 harlea|you don't get it \n",
"14 harlea|you don't get it \n",
"15 harlea|you don't get it \n",
"16 andr_ hazes|het laatste rondje (2005 digital r... \n",
"17 andre hazes|kleine jongen \n",
"18 armin van buuren ft kensington|heading up high \n",
"19 avicii ft aloe blacc|wake me up \n",
"20 avicii ft aloe blacc|wake me up \n",
"21 ariana grande|dangerous woman \n",
"22 ariana grande|dangerous woman \n",
"23 enrique iglesias|bailando \n",
"24 enrique iglesias|bailando \n",
"25 enrique iglesias|bailando \n",
"26 enrique iglesias|bailando \n",
"27 enrique iglesias|bailando \n",
"28 enrique iglesias|bailando \n",
"29 enrique iglesias|bailando \n",
"... ... \n",
"495384 sue the night|mind dear \n",
"495385 anne-marie|ciao adios \n",
"495386 rilan & the bombardiers|supernatural \n",
"495387 kensington|bridges \n",
"495388 macklemore ft. ryan lewis & wanz|thrift shop \n",
"495389 beth ditto|fire \n",
"495390 the chainsmokers|paris \n",
"495391 mumford & sons|little lion man \n",
"495392 lorde|green light \n",
"495393 vant|parking lot \n",
"495394 rag'n'bone man|skin \n",
"495395 nathaniel rateliff|son of a bitch \n",
"495396 boef|habiba \n",
"495397 candi staton|you got the love \n",
"495398 the chainsmokers ft coldplay|something just li... \n",
"495399 starley|call on me (ryan riback remix) \n",
"495400 mark ronson ft. bruno mars|uptown funk \n",
"495401 dua lipa|blow your mind (mwah) \n",
"495402 mattanja joy bradley|stand by your man \n",
"495403 matt simons|catch & release \n",
"495404 ed sheeran|galway girl \n",
"495405 the weeknd|in the night \n",
"495406 rag'n'bone man|skin (wilkinson remix) \n",
"495407 halsey|now or never (nieuw op 3fm) \n",
"495408 lenny kravitz|fly away \n",
"495409 lenny kravitz|fly away \n",
"495410 julia michaels|issues \n",
"495411 calvin harris ft frank ocean & migos|slide \n",
"495412 jax jones ft. raye|you don't know me \n",
"495413 the shins|no way down \n",
"\n",
" artist \\\n",
"0 psy \n",
"1 psy \n",
"2 psy \n",
"3 psy \n",
"4 psy \n",
"5 psy \n",
"6 psy \n",
"7 psy \n",
"8 rihanna \n",
"9 rihanna \n",
"10 harlea \n",
"11 harlea \n",
"12 harlea \n",
"13 harlea \n",
"14 harlea \n",
"15 harlea \n",
"16 andre hazes \n",
"17 andre hazes \n",
"18 armin van buuren ft kensington \n",
"19 avicii ft aloe blacc \n",
"20 avicii ft aloe blacc \n",
"21 ariana grande \n",
"22 ariana grande \n",
"23 enrique iglesias \n",
"24 enrique iglesias \n",
"25 enrique iglesias \n",
"26 enrique iglesias \n",
"27 enrique iglesias \n",
"28 enrique iglesias \n",
"29 enrique iglesias \n",
"... ... \n",
"495384 sue the night \n",
"495385 anne-marie \n",
"495386 rilan & the bombardiers \n",
"495387 kensington \n",
"495388 macklemore ft. ryan lewis & wanz \n",
"495389 beth ditto \n",
"495390 the chainsmokers \n",
"495391 mumford & sons \n",
"495392 lorde \n",
"495393 vant \n",
"495394 rag'n'bone man \n",
"495395 nathaniel rateliff \n",
"495396 boef \n",
"495397 candi staton \n",
"495398 the chainsmokers ft coldplay \n",
"495399 starley \n",
"495400 mark ronson ft. bruno mars \n",
"495401 dua lipa \n",
"495402 mattanja joy bradley \n",
"495403 matt simons \n",
"495404 ed sheeran \n",
"495405 the weeknd \n",
"495406 rag'n'bone man \n",
"495407 halsey \n",
"495408 lenny kravitz \n",
"495409 lenny kravitz \n",
"495410 julia michaels \n",
"495411 calvin harris ft frank ocean & migos \n",
"495412 jax jones ft. raye \n",
"495413 the shins \n",
"\n",
" song female male \\\n",
"0 gangnam style 0 1 \n",
"1 gangnam style 0 1 \n",
"2 gangnam style 0 1 \n",
"3 gangnam style 0 1 \n",
"4 gangnam style 0 1 \n",
"5 gangnam style 0 1 \n",
"6 gangnam style 0 1 \n",
"7 gangnam style 0 1 \n",
"8 work 1 0 \n",
"9 work 1 0 \n",
"10 you don't get it 1 0 \n",
"11 you don't get it 1 0 \n",
"12 you don't get it 1 0 \n",
"13 you don't get it 1 0 \n",
"14 you don't get it 1 0 \n",
"15 you don't get it 1 0 \n",
"16 het laatste rondje (2005 digital remaster) (2005 0 1 \n",
"17 kleine jongen 0 1 \n",
"18 heading up high 0 1 \n",
"19 wake me up 0 1 \n",
"20 wake me up 0 1 \n",
"21 dangerous woman 1 0 \n",
"22 dangerous woman 1 0 \n",
"23 bailando 0 1 \n",
"24 bailando 0 1 \n",
"25 bailando 0 1 \n",
"26 bailando 0 1 \n",
"27 bailando 0 1 \n",
"28 bailando 0 1 \n",
"29 bailando 0 1 \n",
"... ... ... ... \n",
"495384 mind dear 1 0 \n",
"495385 ciao adios 1 0 \n",
"495386 supernatural 0 1 \n",
"495387 bridges 0 1 \n",
"495388 thrift shop 0 1 \n",
"495389 fire 1 0 \n",
"495390 paris 0 1 \n",
"495391 little lion man 0 1 \n",
"495392 green light 1 0 \n",
"495393 parking lot 0 1 \n",
"495394 skin 0 1 \n",
"495395 son of a bitch 0 1 \n",
"495396 habiba 0 1 \n",
"495397 you got the love 1 0 \n",
"495398 something just like this 0 1 \n",
"495399 call on me (ryan riback remix) 1 0 \n",
"495400 uptown funk 0 1 \n",
"495401 blow your mind (mwah) 1 0 \n",
"495402 stand by your man 0 1 \n",
"495403 catch & release 0 1 \n",
"495404 galway girl 0 1 \n",
"495405 in the night 0 1 \n",
"495406 skin (wilkinson remix) 0 1 \n",
"495407 now or never (nieuw op 3fm) 1 0 \n",
"495408 fly away 0 1 \n",
"495409 fly away 0 1 \n",
"495410 issues 1 0 \n",
"495411 slide 0 1 \n",
"495412 you don't know me 0 1 \n",
"495413 no way down 0 1 \n",
"\n",
" radio_popularity yt_popularity spotify_score_may_8 StationNr \n",
"0 1.0 100.0 65.0 1 \n",
"1 1.0 100.0 65.0 2 \n",
"2 1.0 100.0 65.0 2 \n",
"3 1.0 100.0 65.0 2 \n",
"4 1.0 100.0 65.0 2 \n",
"5 1.0 100.0 65.0 2 \n",
"6 1.0 100.0 65.0 2 \n",
"7 1.0 97.0 0.0 2 \n",
"8 0.0 74.0 80.0 2 \n",
"9 0.0 74.0 80.0 2 \n",
"10 1.0 63.0 42.0 1 \n",
"11 1.0 63.0 42.0 1 \n",
"12 1.0 63.0 42.0 1 \n",
"13 1.0 63.0 42.0 1 \n",
"14 1.0 63.0 42.0 1 \n",
"15 1.0 63.0 42.0 1 \n",
"16 2.0 62.0 0.0 2 \n",
"17 1.0 62.0 0.0 2 \n",
"18 11.0 62.0 0.0 3 \n",
"19 9.0 62.0 0.0 3 \n",
"20 9.0 62.0 0.0 3 \n",
"21 4.0 62.0 0.0 1 \n",
"22 4.0 62.0 0.0 1 \n",
"23 5.0 61.0 71.0 2 \n",
"24 5.0 61.0 71.0 2 \n",
"25 5.0 61.0 71.0 2 \n",
"26 5.0 61.0 71.0 2 \n",
"27 5.0 61.0 71.0 2 \n",
"28 5.0 61.0 71.0 2 \n",
"29 5.0 61.0 71.0 2 \n",
"... ... ... ... ... \n",
"495384 5.0 NaN NaN 6 \n",
"495385 7.0 NaN NaN 6 \n",
"495386 3.0 NaN NaN 6 \n",
"495387 13.0 NaN NaN 6 \n",
"495388 3.0 NaN NaN 6 \n",
"495389 0.0 NaN NaN 6 \n",
"495390 33.0 NaN NaN 6 \n",
"495391 5.0 NaN NaN 6 \n",
"495392 6.0 NaN NaN 6 \n",
"495393 2.0 NaN NaN 6 \n",
"495394 40.0 NaN NaN 6 \n",
"495395 1.0 NaN NaN 6 \n",
"495396 10.0 NaN NaN 6 \n",
"495397 1.0 NaN NaN 6 \n",
"495398 11.0 NaN NaN 6 \n",
"495399 24.0 NaN NaN 6 \n",
"495400 3.0 NaN NaN 6 \n",
"495401 9.0 NaN NaN 6 \n",
"495402 0.0 NaN NaN 6 \n",
"495403 11.0 NaN NaN 6 \n",
"495404 5.0 NaN NaN 6 \n",
"495405 17.0 NaN NaN 6 \n",
"495406 0.0 NaN NaN 6 \n",
"495407 1.0 NaN NaN 6 \n",
"495408 5.0 NaN NaN 6 \n",
"495409 5.0 NaN NaN 6 \n",
"495410 9.0 NaN NaN 6 \n",
"495411 9.0 NaN NaN 6 \n",
"495412 13.0 NaN NaN 6 \n",
"495413 0.0 NaN NaN 6 \n",
"\n",
"[495414 rows x 12 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. TL;DR Samenvatting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Het aantal nummers van vrouwelijke en mannelijke artiesten wat wordt gedraaid op de radio verschilt significant.\n",
"- Er is geen effect van tijd op de ratio vrouw/man van gedraaide nummers (tenminste, als je de data van alle stations meeneemt in de analyse).\n",
"- Vrouwelijke artiesten zijn populairder dan mannelijke artiesten (behalve als we naar de Youtube-ratings kijken). Dit is interessant: waarom worden ze dan minder gedraaid? Wellicht moeten vrouwen populairder zijn dan mannen om gedraaid te worden op de radio. Dit is een fenomeen wat je vaak ziet in vakgebieden waarin seksisme voorkomt: vrouwen moeten vaak een stuk beter zijn in hun werk om dezelfde waardering te krijgen als een man. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Gender Verhouding"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Je had zelf al gezien dat er een groot verschil is in de hoeveelheid vrouwelijke en mannelijke artiesten die worden gedraaid op de radio. Laten we om dit effect te formaliseren d.m.v. een simpele t-test eens kijken of dit effect ook door 'toeval' had kunnen ontstaan."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/paulakaanders/anaconda/lib/python2.7/site-packages/ipykernel_launcher.py:122: MatplotlibDeprecationWarning: The set_axis_bgcolor function was deprecated in version 2.0. Use set_facecolor instead.\n"
]
},
{
"data": {
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hYfT397Nx48Zxt9XT00NxcTFxcXESL2UCiAIXBGFULl68yNmzZ9myZQtFRUUk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\nHR3l0qVLWK1W1q1bR1paGhkZGSF7vsDsypKSkrCtcBFiPpI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Sgb/EfN3tA0aA55RSK3oL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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1277d0190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"GenderVerhouding(data1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In deze grafiek hierboven kun je zien hoeveel nummers werden gedraaid van vrouwelijke en mannelijke artiesten. Zoals je zelf al had gezien, is het aantal gedraaide nummers van vrouwelijke artiesten heel veel lager dan dat van mannelijke artiesten. De individuele datapunten zijn de afzonderlijke radiostations, en het is te zien dat deze niet veel van elkaar verschillen betreffende gender verhouding. De 3 asterisken bovenaan de grafiek duiden aan dat dit een significant verschil is met p-waarde < 0.001 (namelijk p=0.0000856, zie analyse hieronder). Dit betekent dat de kans dat er geen ongelijkheid is tussen mannen en vrouwen en dit effect door puur toeval is ontstaan ~0.009% is. Dat is natuurlijk een heel erg kleine kans, dus er is duidelijk een verschil."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"%R -i DFnew"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"\tWelch Two Sample t-test\n",
"\n",
"data: DFnew$female and DFnew$male\n",
"t = -6.9686, df = 8.5221, p-value = 8.562e-05\n",
"alternative hypothesis: true difference in means is not equal to 0\n",
"95 percent confidence interval:\n",
" -64440.75 -32651.25\n",
"sample estimates:\n",
"mean of x mean of y \n",
" 17011.5 65557.5 \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"t.test(DFnew$female,DFnew$male, paired=FALSE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Tijd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"De data bevat een timestamp waardoor we weten wanneer ieder nummer is gedraaid. We kunnen dus zien of deze gender verhouding verandert afhankelijk van de tijd op de dag. Ik heb voor het gemak de dag verdeelt in 8 delen van 3 uur."
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/paulakaanders/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:166: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
"/Users/paulakaanders/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:167: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
"/Users/paulakaanders/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:168: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
"/Users/paulakaanders/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:169: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
"/Users/paulakaanders/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:170: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
"/Users/paulakaanders/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:171: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
"/Users/paulakaanders/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:172: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
"/Users/paulakaanders/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:173: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n"
]
},
{
"data": {
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866oMy74u7X+/Rm0838NE/QF3IJPpeU8ex+O34NaDk7L0cfCniP9z2u2ZRva82bt+IsN9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\niIjnKKCIiIiI5/x/WA6lqHCUs5sAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f91cbd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"GenderTijd(data_tijd)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Eerder vond ik dat vrouwen iets meer gedraaid werden tussen middernacht en 3 uur 's nachts, maar kijkend naar deze grafiek lijkt erop dat dit effect werd gedreven door één of twee stations waar dit inderdaad het geval is. Als ik in de analyse controleer voor het feit dat de data gegroepeerd is in verschillende radio stations, dan verdwijnt het effect inderdaad (als je geïnteresseerd bent in deze analyses voor ieder radiostation apart, dan hoor ik het wel. Het leek mij niet per se superinteressant om te weten dat alleen 3fm meer vrouwen op een bepaald tijdstip draait, dus ik heb dat in deze samenvatting weggelaten). Gemiddeld genomen, echter, valt er niet veel interessants op te merken. De richtlijn die ik in mijn analyses over het algemeen gebruik is dat als er in de 'raw data' niets interessants te zien valt, het niet veel uithaalt om er nog een statistische test op los te laten, omdat je van tevoren toch al weet dat áls het effect al significant is, het effect zelf waarschijnlijk maar heel klein is."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Populariteit"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Voor deze analyse heb ik de data gereduceerd zodat ieder nummer er maar één keer instaat en ik kan kijken naar de populariteit van ieder nummer van iedere artiest in deze dataset. Bovendien heb ik alleen de muzieknummers in de analyse meegenomen waarvan een populariteit-ranking beschikbaar was. Ik weet dat je zelf twijfels hebt over de betrouwbaarheid van deze populariteitsdata, dus neem de volgende resultaten met zoveel korrels zout als je zelf wil."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Youtube-populariteit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Laten we eerst kijken naar de distributie van Youtube-populariteit in de volledige dataset."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x113a6bbd0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ydPjwYSUnJ2v69Olqbm6WJO3du1cul0uS5HK5tGfPHklSU1OToqOj5XA45HK59PLLLysQ\nCKijo0ONjY2aP3++0U6FA8sK79NkWGO4n5ghcwyXEzOcmBmaGPc701tvvVVZWVnKycnR1KlT9a1v\nfUsFBQVyu91av369uru7lZiYqC1btkiSVq9erdLSUnk8HkVFRQX/lCY9PV0tLS3Kzs5WIBBQUVGR\n4uLizPcMAIArxGaN9EvLMPfcuruUEX821Mu4qNoTvUouel4xMbGhXsqIbDYpPj5GHR18LDRezHBi\nMEdzzNDc0AxNcAQkAAAMEVMAAAwRUwAADBFTAAAMEVMAAAwRUwAADBFTAAAMEVMAAAwRUwAADBFT\nAAAMEVMAAAwRUwAADBFTAAAMEVMAAAwRUwAADBFTAAAMEVMAAAwRUwAADBFTAAAMEVMAAAwRUwAA\nDBFTAAAMEVMAAAwRUwAADBFTAAAMEVMAAAwRUwAADBFTAAAMEVMAAAwRUwAADBFTAAAMEVMAAAwR\nUwAADBFTAAAMEVMAAAwRUwAADBFTAAAMEVMAAAyNO6a1tbXKycmR2+3Wxo0bJUnHjx/XkiVL5Ha7\nVVhYqN7eXklST0+PVq5cqYyMDOXm5qq1tTV4P+Xl5XK73Vq0aJHq6uoMdwcAgCtvXDH985//rEce\neUQ///nPVVVVpf/5n//Rb3/7WxUXF6u4uFg1NTWaM2eOtm/fLkmqqKhQUlKSqqurtWbNGpWUlEiS\nDh06pJaWFlVXV6uyslIbN25UV1fXxO0dAABXwLhi+sorrygjI0N2u12RkZHaunWr5syZo+7ubs2d\nO1eSlJeXp4MHD0qS6uvrlZOTI0lKSUlRR0eH2traVFtbq6ysLEVERCghIUHz5s1TbW3tBO0aAABX\nRuR4bvThhx8qKipK+fn58vl8WrBggW6//XY5HI7gdRISEtTe3i5J8nq9F1xmt9vV3t4un883bLvX\n6x3vvgAAEBLjiunAwICampq0c+dORUdH60c/+pGmT58+7HoREeff+A4ODo54mWVZw7bbbLbxLCns\n2GznT+FoaF3hur7JgBlODOZojhmam4jZjSumCQkJSk1N1axZsyRJd955p1paWuT3+4PX8fv9cjqd\nkiSn0ymfzxc8P3SZw+GQz+cL3sbn8yk1NXXcOxMubFNsiouLUWxsTKiXcklxceG9vsmAGU4M5miO\nGYbWuGJ6xx13aO3aterq6lJ0dLQaGhp0xx136O2331Zzc7OSk5O1d+9euVwuSZLL5dKePXu0atUq\nNTU1KTo6Wg6HQy6XS7t27VJmZqbOnDmjxsZGFRUVTeT+hYQVsNTZ2a2+vvB8qWiznf/B6+zs1ggf\nDmAMmOHEYI7mmKG5oRmaGFdMb7rpJt13331avny5+vv7NX/+fOXm5urb3/621q9fr+7ubiUmJmrL\nli2SpNWrV6u0tFQej0dRUVEqKyuTJKWnp6ulpUXZ2dkKBAIqKipSXFyc0Q6FC8tS2D+xJ8Mawx0z\nnBjM0RwzDC2bNdIvLsPcc+vuUkb82VAv46JqT/Qqueh5xcTEhnopI7LZpPj4GHV08Ep2vJjhxGCO\n5pihuaEZmuAISAAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgipgAA\nGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgi\npgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYA\nABgipgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgyjmlZWZkefPBBSdLx48e1ZMkSud1uFRYW\nqre3V5LU09OjlStXKiMjQ7m5uWptbQ3evry8XG63W4sWLVJdXZ3pcgAAuOKMYnr06FHt378/eH7t\n2rUqLi5WTU2N5syZo+3bt0uSKioqlJSUpOrqaq1Zs0YlJSWSpEOHDqmlpUXV1dWqrKzUxo0b1dXV\nZbIkAACuuHHH9OzZs3ryySdVUFAgSWpvb1dPT4/mzp0rScrLy9PBgwclSfX19crJyZEkpaSkqKOj\nQ21tbaqtrVVWVpYiIiKUkJCgefPmqba21nSfAAC4osYd0w0bNujHP/6xrrrqKkmS1+uVw+EIXp6Q\nkKD29vYRL7Pb7Wpvb5fP5xu23ev1jndJAACEROR4brR7925dffXVSklJ0b59+yRJgUBg2PUiIs63\nenBwcMTLLMsatt1ms41nSWHHZjt/CkdD6wrX9U0GzHBiMEdzzNDcRMxuXDGtqamR3+/X4sWL9fHH\nH+vcuXOy2Wzy+/3B6/j9fjmdTkmS0+mUz+cLnh+6zOFwyOfzBW/j8/mUmppqsj9hwTbFpri4GMXG\nxoR6KZcUFxfe65sMmOHEYI7mmGFojSumv/zlL4P//V//9V96/fXX9dhjj8nj8ai5uVnJycnau3ev\nXC6XJMnlcmnPnj1atWqVmpqaFB0dLYfDIZfLpV27dikzM1NnzpxRY2OjioqKJmTHQskKWOrs7FZf\nX3i+VLTZzv/gdXZ2a4QPBzAGzHBiMEdzzNDc0AxNjCumF1NeXq7169eru7tbiYmJ2rJliyRp9erV\nKi0tlcfjUVRUlMrKyiRJ6enpamlpUXZ2tgKBgIqKihQXFzeRSwoZy1LYP7EnwxrDHTOcGMzRHDMM\nLZs10i8uw9xz6+5SRvzZUC/jompP9Cq56HnFxMSGeikjstmk+PgYdXTwSna8mOHEYI7mmKG5oRma\n4AhIAAAYIqYAABgipgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgi\npgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYA\nABgipgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgipgAAGCKmAAAYIqYAABgipgAAGCKmAAAY\nIqYAABgipgAAGCKmAAAYIqYAABgad0yfffZZeTweeTwePfjgg+rv79fx48e1ZMkSud1uFRYWqre3\nV5LU09OjlStXKiMjQ7m5uWptbQ3eT3l5udxutxYtWqS6ujrzPQIA4AobV0z/8Ic/aN++fdq9e7eq\nqqoUCAS0c+dOFRcXq7i4WDU1NZozZ462b98uSaqoqFBSUpKqq6u1Zs0alZSUSJIOHTqklpYWVVdX\nq7KyUhs3blRXV9fE7R0AAFfAuGJ61VVXacOGDZo2bZok6frrr9d7772nnp4ezZ07V5KUl5engwcP\nSpLq6+uVk5MjSUpJSVFHR4fa2tpUW1urrKwsRUREKCEhQfPmzVNtbe1E7BcAAFfMuGI6e/bsYDQ7\nOzu1c+dOXXfddbLb7cHrJCQkqL29XZLk9XrlcDiCl9ntdrW3t8vn8w3b7vV6x7UjAACESqTJjT/6\n6CMVFBRo6dKlSk5OVn19/QWXR0Scb/Xg4OCw20ZERMiyrGHbbTabyZLChs12/hSOhtYVruubDJjh\nxGCO5pihuYmY3bhj+s4776igoEAFBQVavny5Tp06Jb/fH7zc7/fL6XRKkpxOp3w+X/D80GUOh0M+\nny94G5/Pp9TU1PEuKWzYptgUFxej2NiYUC/lkuLiwnt9kwEznBjM0RwzDK1xxfT06dPKz8/Xv//7\nvystLU2SdM0112jatGlqbm5WcnKy9u7dK5fLJUlyuVzas2ePVq1apaamJkVHR8vhcMjlcmnXrl3K\nzMzUmTNn1NjYqKKiognbuVCxApY6O7vV1xeeLxVttvM/eJ2d3RrhwwGMATOcGMzRHDM0NzRDE+OK\n6fPPP69z585p27Zt2rZtm6TzwSwvL9f69evV3d2txMREbdmyRZK0evVqlZaWyuPxKCoqSmVlZZKk\n9PR0tbS0KDs7W4FAQEVFRYqLizPaoXBhWQr7J/ZkWGO4Y4YTgzmaY4ahZbNG+sVlmHtu3V3KiD8b\n6mVcVO2JXiUXPa+YmNhQL2VENpsUHx+jjg5eyY4XM5wYzNEcMzQ3NEMTHAEJAABDxBQAAEPEFAAA\nQ8QUAABDxBQAAEPEFAAAQ8QUAABDxBQAAEPEFAAAQ8QUAABDxBQAAEPEFAAAQ8QUAABDxBQAAEPE\nFAAAQ8QUAABDxBQAAEPEFAAAQ8QUAABDxBQAAEPEFAAAQ8QUAABDxBQAAEPEFAAAQ8QUAABDxBQA\nAEPEFAAAQ8QUAABDxBQAAEPEFAAAQ8QUAABDxBQAAEPEFAAAQ8QUAABDxBQAAEORoV7A/0Z9AwEd\nP/6eoqNnhHopI+rr+0wzZ0br3LkBWVaoV3Nx3/jGdZo2bVqolwEAoyKmXwJvV6+sX5fouvjoUC9l\nRE1/7NDXZ04P2/VJ0nvebp34/lpdd903Q72UEdls0ty5N4V6GQDCBDH9klwXH60bnDGhXsaITnR8\nEtbrk86vUbVPSGEa/D91fKKZM5+Tw3FtqJcCIAwQU4StcA8+AAwJi3+AVFNTo8zMTC1cuFDbtm0L\n9XIAALgsIY+p3+/X5s2bVVlZqerqah07dkwNDQ2hXhYAAGMW8pgeOXJEKSkpmjlzpiIjI5Wdna2D\nBw+GelkAAIxZyH9n6vP5ZLfbg+ftdru8Xm8IVwSM7rOBQb3//vs6c+aTsP3zos8++0yS9JWvfCXE\nK7m4yfBnWvyJFsYi5DG1RvgJstlsl7zNac3Qy2Hc2/c/7ZW94+NQL+OiPjrTG+oljCrc1/j6idPq\n/+BBfe3/TA/1Ui7qzT+flT3mK6zRwF/O9uqbSx4O2z/Rks7/mZbXG62zZ8P3hV24s9mk+Ph5RvcR\n8pg6HA41NzcHz/t8Pjmdzkve5ic/3f5lL8vIvaFewCi+F+oFjEG4rzHc1wfgygr570znz5+vo0eP\nqrOzU/39/aqqqpLL5Qr1sgAAGDObNdLnrFfYf//3f+tnP/uZ+vr6lJaWprVr14Z6SQAAjFlYxBQA\ngMks5B/zAgAw2RFTAAAMEVMAAAwRUwAADE26mHJQ/PF59tln5fF45PF49OCDD6q/v1/Hjx/XkiVL\n5Ha7VVhYqN7e8D5QQrgoKyvTgw8+KEnM8DLV1tYqJydHbrdbGzdulMQML9eBAweUmZmpzMxMlZWV\nSWKGY9HT0yOPx6NTp05JuvjMenp6tHLlSmVkZCg3N1etra1jewBrEvH5fNYdd9xhnT592urv77dW\nrFhhvfrqq6FeVth76623rMzMTKu3t9eyLMtau3at9eyzz1rZ2dlWc3OzZVmWVVFRYT3xxBOhXOak\n8Nprr1kpKSnWunXrLMuyrKysLGY4Rh9++KH1ve99z/J6vVZ/f7/1T//0T1Z9fT3Pw8tw7tw5a968\nedbp06etgYEBKy8vz3rttdd4Ho7id7/7nZWZmWklJSVZf/nLXyzLuvjP7qOPPmpt27bNsizLOnr0\nqLV06dIxPcakemfKQfHH56qrrtKGDRuCxxe9/vrr9d5776mnp0dz586VJOXl5THLUZw9e1ZPPvmk\nCgoKJEnt7e3M8DK88sorysjIkN1uV2RkpLZu3ao5c+aou7ubGY5RIBDQ4OCgent7NTAwoIGBAUVG\nRvI8HMVLL72kDRs2KCEhQZLU1tZ20ZnV19crJydHkpSSkqKOjg61t7eP+hghP5zg5eCg+OMze/Zs\nzZ49W5LU2dmpnTt36gc/+MEFH18kJCSM6Qnz12zDhg368Y9/rLa2NkmS1+uVw+EIXs4ML+3DDz9U\nVFSU8vPz5fP5tGDBAt1+++3M8DLMmDFDhYWFcrvdmjZtmr7zne9o6tSpzHAUmzZtuuD8pX52v3iZ\n3W5Xe3v7qIe5nVTvTK1xHBQf/7+PPvpId999t5YuXark5ORhl0dETKqnwxW1e/duXX311UpJSQk+\nDwOBwLDrMcOLGxgY0KuvvqrHH39cu3fv1ttvv63XX3992PWY4cW9++672rdvn+rq6vTqq6/KZrON\n+P3PzPDSBgcHh20bmtmlLruUSfXOdDwHxcd577zzjgoKClRQUKDly5fr1KlT8vv9wcv9fj+zvISa\nmhr5/X4tXrxYH3/8sc6dOyebzcYML0NCQoJSU1M1a9YsSdKdd96plpYWZngZGhoalJKSEpxhTk6O\nduzYwQzOgWSaAAAIDElEQVQvk9PpvOjMnE7nBW3x+/0XvFO9mEn18oWD4o/P6dOnlZ+fr4cffljL\nly+XJF1zzTWaNm1a8MXJ3r17meUl/PKXv1RVVZX279+vwsJCLViwQI899hgzvAx33HGHjhw5oq6u\nLgUCATU0NOjv/u7vmOFluPHGG9XQ0KBz587JsiwdPnxYycnJmj59OjO8DJf6/5/L5dKePXskSU1N\nTYqOjh5TTCfVO1O73a7i4mKtWLEieFD8tLS0UC8r7D3//PM6d+6ctm3bFvxzIpfLpfLycq1fv17d\n3d1KTEzUli1bQrzSyYcZjt1NN92k++67T8uXL1d/f7/mz5+v3Nxcffvb32aGY3TrrbcqKytLOTk5\nmjp1qr71rW+poKBAbrebGV6mi/3srl69WqWlpfJ4PIqKigr++dFoONA9AACGJtXHvAAAhCNiCgCA\nIWIKAIAhYgoAgCFiCgCAIWIKAIAhYoq/esXFxbr77rsvOFylZVm699579fDDD4/rPv/whz8Ev6bt\nUv7jP/5DDz300LgeY0hTU5OSkpK0ePHi4Mnj8ei5554zut9LWbBggX73u99d9u0WL16s06dPS5Lu\nvfde+Xy+UW9z33336d1335UkrV+/Xm+++eZlPy7wZZtUB20AvgyPPPKI/uEf/kHPPPOM8vPzJZ0/\n0MWZM2e0ffv2cd3n+++/P6aDjU/UsaWvueYa7d+/P3je6/UqIyND1113nW6//fYJeYyJ8Pk1Hjly\nZMTjbX/Rf/7nf15wm8WLF38pawNM8M4Uf/W++tWvauvWrdq+fbvee+89/fGPf9QvfvELVVRUKCoq\nSnv27FFWVpays7O1fPlyvf3225Kkffv26V/+5V+C93PgwAHddddd+vOf/6ynnnpKb731ln7yk5/o\n9ddfV3p6evB6b7zxhhYsWCDp/Dvg1tZW3X333fJ4PLr//vvV2dkp6fyXFJeWlionJ0dZWVl66KGH\n9Mknn4xpnxwOh6677jr96U9/kqRL7sM999yj/Px8eTwe/eAHP9CJEyckSevWrbvgxURpaWnwCFqf\nt3//fi1dulQ5OTlyuVzB6zQ1NSkjI0PLly9XRkaGurq6dMMNN8jr9aqoqEiSlJ+fr5MnT8rn86mw\nsDC4r48//njwiwQWLFigY8eOqaysTD6fT//2b/824gHygVAipoCkv/3bv1VhYaHWrVundevW6ZFH\nHlFiYqIaGxv1s5/9TM8884wOHDigH/3oR/rhD3+orq6ui95XYmKiVq9erZtvvlnl5eWjvvs6efKk\nysvLVVVVpa9//evauHGjpPNfG/U3f/M32rdvn15++WVFR0friSeeGNP+vPHGG3r//feVkpIy6j68\n9dZbWrdunaqqqpSenq61a9dKOv+u+fPvnEd6F93b26sXX3xRTz/9tPbt26cXXnhBTz/9tM6ePStJ\n+uCDD/TTn/5U1dXVio2NDd5uaD927Nihb3zjGyouLlZGRob27dunvXv36qOPPrrgY2qbzaaSkhLZ\n7XZt2rRJ8+bNG9McgCuFj3mB/88999yjV155Rddee62+//3vS5JeffVVLVq0KPilwrfeeqvi4+P1\n+9///pIf0V7OUToXLlyo+Ph4See/pHjoywhqa2v1+9//Xi+//LIkqb+/X3FxcSPeR1tbW/Djz/7+\nfl111VV67LHHdOONN2rz5s2X3If58+drzpw5kqSlS5fq8ccfD8ZwtP2YPn26fvGLX6iurk4ffvih\nPvjgA1mWpd7eXknnvykmMTHxkvdx7tw5NTY26uzZs8F3wp999pmmT59+6cEBYYSYAp+TmJioa6+9\nNnh+pJgMDg5qcHBQNpvtgsv7+/tHvM/Rrvf5KAcCAU2dOjX4OE888YRuuOEGSdInn3xy0ce4+uqr\nL/h95Oddah8kacqUKcOuO7Tt87ft6+sbdj/t7e1aunSp8vLydPPNNysvL0+/+c1vgrcbSxCH1lFZ\nWamYmBhJUldXF9/JiUmFZyvwBZ8PyG233aaamprgvzptaGhQe3u7brnlFs2cOVMnTpzQp59+qoGB\nAdXV1QVvN2XKFA0MDEiSZs2aJb/fr46ODlmWpUOHDl3weLW1tTp9+rQsy9ILL7wQ/AdDt912m557\n7jkNDg4qEAiopKREW7duvez9udQ+WJalxsZGnTp1SpL04osvau7cuYqJidGsWbP0zjvvSJLOnDmj\nN954Y9h9t7S0aMaMGbr//vt122236be//a0syxrxi9O/aGhGM2bM0C233KJnnnlGkvTpp58qPz9f\nL7744rDbREZGBucKhBPemQJf8Pl3iikpKbr//vuVn58vy7L01a9+VT//+c8VGxur7373u5o7d67+\n/u//Xna7XampqTp27Jgk6ZZbblFFRYXy8/O1Y8cO5eXlKS8vTwkJCbrzzjuDj2Gz2XTDDTfogQce\n0Mcff6zrr79ejzzyiCTpoYce0qZNm5SVlaVAIKCbbrpJ69atG3XNX3SpfZDO/2Ol9evXy+v1yul0\nBr+K6p//+Z/1k5/8RIsWLdLXvvY1paamDrvv733ve9q/f78WLlyo6OhozZkzR9/85jfV2tqqqKio\nYev6/Pn09HTdc8892rp1q5544gk9+uij8ng86u/v1+23365777132OOlpaWpuLhYDz/8cPAfcQHh\ngK9gA/6K7du3T1VVVXr22WdDvRRgUuNjXuCv2Bf/xS6A8eGdKQAAhnhnCgCAIWIKAIAhYgoAgCFi\nCgCAIWIKAIAhYgoAgCFiCgCAof8H9CFgfEDYyusAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113a6b690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"YoutubeDistr()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In deze grafiek kan je zien hoevaak een nummer een bepaalde populariteitsrating heeft. Je kan zien dat lage ratings heel veel voorkomen, en hogere ratings maar zelden. Dit is omdat er veel gevallen zijn waarin de youtube-rating '0' is (9113 gevallen), en ik weet niet of dit een betrouwbare rating is of dat er '0' is ingevoerd omdat we de ware rating niet weten. Er zijn namelijk nummers die meerdere keren voorkomen in de dataset die soms een Youtube-populariteit van '0' hebben en soms een waarde boven de 0. Om die reden heb ik alleen gekeken naar Youtube-ratings boven de 0. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 0-waarden geëxcludeerd"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x123252e10>"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ejIjMzL+apm00m9S4m8dIdeP9CY+iZeZbm16ezgH6fFIdeAxYzXqZuSsifgu4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\n9/n0oAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x123252b50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"YoutubeDistrExcl()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"De distributie van de Youtube-populariteit variabele is nog steeds niet normaal verdeeld, maar wel meer nu dat alleen waarden boven de 0 meegeteld worden."
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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pNZgkaZLo5CymI4CtgY8Bq1Cu4voiymXAdwX263JtkqQe6uQQ0w7AAZn55ZZ5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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x117f9a6d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"GenderYoutubeExcl()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Er is geen verschil tussen Youtube-populariteit van mannen en vrouwen (p>0.05, zie onderstaande analyse). Houd er rekening mee dat ik in deze analyse slechts 4027 nummers mee heb kunnen nemen aangezien daar een Youtube-rating van boven de 0 van beschikbaar was."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%R -i female\n",
"%R -i male"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"\tWelch Two Sample t-test\n",
"\n",
"data: female and male\n",
"t = -0.41067, df = 1007.5, p-value = 0.6814\n",
"alternative hypothesis: true difference in means is not equal to 0\n",
"95 percent confidence interval:\n",
" -1.1455483 0.7490498\n",
"sample estimates:\n",
"mean of x mean of y \n",
" 8.033473 8.231722 \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"t.test(female,male)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Radio-populariteit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ik zal nu dezelfde figuren maken en analyses uitvoeren voor radio-populariteit. (Ik zie geen reden om te geloven dat de 0-waarden niet meegenomen moeten worden, aangezien je deze variabele zelf hebt samengesteld. Ik hoor het wel als je daar anders over denkt)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Bn26ukfSUiHgesB9wajcLkyT1Vie7mOZRbut5c0ScA/wVeCHlWkmTge9GxMkD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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1184c4cd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"GenderRadio()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Als we kijken naar de radio-populariteit van vrouwelijke en mannelijke artiesten zien we wederom een significant verschil (p<0.001, zie analyse hieronder). Deze analyse is gedaan over 14,182 nummers (die waarvan een radio-populariteit waarde beschikbaar was)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%R -i female\n",
"%R -i male"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"\tWelch Two Sample t-test\n",
"\n",
"data: female and male\n",
"t = 4.2089, df = 2695, p-value = 2.65e-05\n",
"alternative hypothesis: true difference in means is not equal to 0\n",
"95 percent confidence interval:\n",
" 0.2616675 0.7181346\n",
"sample estimates:\n",
"mean of x mean of y \n",
"1.4665428 0.9766417 \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"t.test(female,male)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Spotify"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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rxRGXAZdm5hcPcZtvGrldW31L0b8Afg345WnWOJlhvvcs3KZl5uWjJt9AdXec\nqda5ftTkjwNfP9T3lTqBASt9vxcDx1PdahKAzLwrIi4ChuonFP0V1a0nXwnMBq7JzM8CRMQ64OeA\ng1Q3t38P1U3BbwGWRsRfZObr6hHoyVQPzF4KfKIeJa4Dlo+6gfsDVA9smPR+spn5nYj4ErCiXu81\nwEfq/RgGPpKZfzBZ/RHxduDnMvON9TZ+HrgsM19fv02jbl8F/Feq25guAR4E3paZ36736w+obuL/\nbqpbzN1MdTTglcCyepn/Afw6VegeQ/V4xXdn5v8dGS3Xf86jesTdgcz86GR9IHUaz8FKo2TmHuA/\nAX8aEU9GxB9GxDuAv87MgXqxfwE8kJmvAN4O3BYRvRFxCXAhcFZmnkEVIp+luk/sZVSPvRv9lKjh\nzLyQ6tmSb8/MG6hGz5fCPz+ObMkk4Try+Dwi4iSqm73fFRELgU8D6zLzx6geFv1fIuLfTFZ/E90z\nMoJ9D3BDZp4FnEI1Mn/LqOW+mJkrM/Puep3h+hDvg1SH2m8HrqEK6FX1aP4BYNOo9xnOzD+hembs\nxwxXdSMDVhojM2+kGnG9g+qQ5juBxyLi5HqRgczcXC/7t8D/Ac4G/h3wuyNBnJmbqB5rdSqjwnAK\nHwN+sX79TqqHvU/kCxHxdxHxENWI9A/q9c8EnsrM++o6+oBPUQXt8CT1T3UIeGQfLgNmR8Q1wO9Q\nBfZxo5a7f+rd5KeANwNfqc9HrwVGnnw9tq+a7Tupo3iIWBolIl4L/HhmfojqkX+fB66LiPuAnwbu\npDr8O1oD+C7VL6xjw+AYDuF7lpn3R8RgRLwZ+A+MOm87jn8+BztmH8YLpFl870Hx49V/kCpgR6/7\nwjHLjQTwfcA/UD3C6zPAD49Zb98kNY84Brhq5ClZETEbmD/mfca+r9RVHMFK368fuDYizhlpiIgT\ngV6qQ5wACyLirfW8s4BlwL3A54BLI2JePe9y4Fmqq30P0nzQfgy4Cfh8Zu6exj78DfDSiHhdXcfJ\nVGH9BaogHFv/cqoLpPqrppgTEcdSnf8cCbeR86/HA6+mOtR7B9XVy6v4XnhP5iDVY+qg6qsr62AF\n+DCwedR7NcZZR+oqBqw0SmYm1fnE6yJie0Q8QnVe9FdHDrkCQ8D5EfEV4LeBf5+ZezPz96hGuA9E\nxKPAzwJvzsxh4BGqB2F/pQ6vyfwhsIgqZCcy4aiuDuXzgRvqw8efA34lM++aoP4L6qujv0B1ePcx\nqlHqw2Pj2h2BAAAAn0lEQVTfLzOfAzYCfxURXwY+CnyR6jD4pHVRnU+9ISJ+EfhA/T4P1n21DLhk\n1DZGtvNnwDsi4lcm2a7UkXwerHQI6qtwv56ZxUZVEXEecG1mvrrAtpdRuH5JFc/BSoeu2G+lEXEX\n1X/f+dlS74HnNKXDwhGsJEkFeA5WkqQCDFhJkgowYCVJKsCAlSSpAANWkqQCDFhJkgowYCVJKuD/\nA/NrqYXeoferAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118423810>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"SpotifyDistr()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hierboven zie je de distributie van de Spotify-populariteit variabele. Dit is een vreemde distributie: er zijn veel nummers met een extreem lage rating (0) en de rest van de populariteit-scale is normaal-verdeeld. Ik vraag me daarom wederom af of de ranking 0 soms is gegeven in gevallen waar de populariteit onbekend was? Daarom doe ik deze analyse wederom met alleen die data met een Spotify-populariteit boven de 0 (ik hoefde deze keer gelukkig slechts 2407 nummers te excluderen hierdoor)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 0-waarden geëxcludeerd"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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KMGAlSSrAgJUkqQADVpKkAgxYSZIKMGAlSSrAgJUkqQADVpKkAvbpdgGS1Kue3TXGpqH1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\nVpKkAv4/5i6hb9L8jD4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118423b50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"SpotifyDistrExcl()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dit is nu de distributie van Spotify-populariteit."
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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4cC5EZr5lLAuVJI2vbgLi9cBdlIlyL+Hhs6rnAS9ofh7gn2dcS5IeYbqZKDe7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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a8f4750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"GenderSpotifyExcl()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vrouwelijke artiesten zijn wederom significant populairder dan mannelijke artiesten (p<0.001, zie analyse hieronder). Voor deze analyse heb ik 10,575 nummers meegenomen."
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%R -i female\n",
"%R -i male"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"\tWelch Two Sample t-test\n",
"\n",
"data: female and male\n",
"t = 5.4664, df = 2522.2, p-value = 5.043e-08\n",
"alternative hypothesis: true difference in means is not equal to 0\n",
"95 percent confidence interval:\n",
" 1.626060 3.445231\n",
"sample estimates:\n",
"mean of x mean of y \n",
" 47.84954 45.31389 \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"t.test(female,male)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Suggestie"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wat deze dataset niet bevat, maar wat mij wel heel interessant lijkt om te analyseren is de genderverhouding per muziekgenre. Ik weet niet of dat data is die je nog ergens hebt liggen, maar ik kan me zo voorstellen dat de man-vrouw verhouding veel varieert tussen genres. Ik kan namelijk zelf op heel veel namen van vrouwelijke pop-artiesten komen, maar niet zoveel vrouwelijke (of mixed-gender) rockbands. Als we dit formeel kunnen analyseren, kunnen we kijken of sommige subgebieden binnen de muziekwereld wellicht seksistischer zijn dan anderen, en zo ja, waarom dan?"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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