{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing the number of opportunites based on artist advance" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from plotnine import *\n", "from plotnine import options\n", "options.figure_size = (12, 8)\n", "from dfply import *\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "streams = pd.read_feather( '../../data/pop_5_release.feather')\n", "\n", "def streams_based_on(price, pps = 5e-3, pop_5_slippage = 0.15, artist_adv = 0.8):\n", " return price/(pps*(1-pop_5_slippage)*artist_adv)\n", "\n", "two_limit = streams_based_on(2e3)\n", "min_limit = streams_based_on(5e3)\n", "tenk_lim = streams_based_on(10e3)\n", "twenty_lim = streams_based_on(20e3)\n", "fifty_lim = streams_based_on(50e3)\n", "\n", "streams['Artist_Advance'] = pd.cut(streams['streams'], \n", " bins = [0,two_limit, min_limit, tenk_lim, twenty_lim, fifty_lim, 100e20], \n", " labels = ['<2k','5k', '10k', '20k', '30k', '>50k'])\n", "\n", "actual_stream_counts = streams.groupby('Artist_Advance')['streams'].agg([len, min, np.mean, np.median,max])\n", "\n", "actual_stream_counts.columns = ['Number of tracks', 'Min streams', 'Mean streams', 'Median streams', 'Max Streams']" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Number of tracksMin streamsMean streamsMedian streamsMax Streams
Artist_Advance
<2k1248101.082879e+0537760.5581970
5k1715889009.344912e+05882289.01466000
10k9314870002.117939e+062039966.02897000
20k6529490004.042791e+063997000.05767000
30k4060220008.885636e+068481000.014650000
>50k64148844007.826420e+0729884500.0740764000
\n", "
" ], "text/plain": [ " Number of tracks Min streams Mean streams Median streams \\\n", "Artist_Advance \n", "<2k 1248 10 1.082879e+05 37760.5 \n", "5k 171 588900 9.344912e+05 882289.0 \n", "10k 93 1487000 2.117939e+06 2039966.0 \n", "20k 65 2949000 4.042791e+06 3997000.0 \n", "30k 40 6022000 8.885636e+06 8481000.0 \n", ">50k 64 14884400 7.826420e+07 29884500.0 \n", "\n", " Max Streams \n", "Artist_Advance \n", "<2k 581970 \n", "5k 1466000 \n", "10k 2897000 \n", "20k 5767000 \n", "30k 14650000 \n", ">50k 740764000 " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "actual_stream_counts" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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track_spyidvaluespyidrelease_datefirst_seendays_since_releasestreamspop_5_datestreams_100_dateArtist_Advance
03QcFgVYo5UngnkoHtUKQPo423QcFgVYo5UngnkoHtUKQPo2018-02-272018-03-03 08:15:12.814499510650742018-03-042018-06-065k
16AOkhCjwfOSDj8uwsgfXxF86AOkhCjwfOSDj8uwsgfXxF2018-02-262018-03-03 03:39:07.475948583652018-03-032018-06-06<2k
20oMS8S85Ztgj1nzgHs69X5440oMS8S85Ztgj1nzgHs69X52018-02-272018-03-03 03:39:07.178301513000002018-03-042018-06-065k
37CfqRcuy4WTP7928BziSW067CfqRcuy4WTP7928BziSW02018-02-272018-03-03 03:39:07.1363625276372018-03-042018-06-06<2k
42NAihRUyr75yyMEuuc0BvP162NAihRUyr75yyMEuuc0BvP2018-02-262018-03-03 03:39:06.87920951860002018-03-032018-06-06<2k
\n", "
" ], "text/plain": [ " track_spyid value spyid release_date \\\n", "0 3QcFgVYo5UngnkoHtUKQPo 42 3QcFgVYo5UngnkoHtUKQPo 2018-02-27 \n", "1 6AOkhCjwfOSDj8uwsgfXxF 8 6AOkhCjwfOSDj8uwsgfXxF 2018-02-26 \n", "2 0oMS8S85Ztgj1nzgHs69X5 44 0oMS8S85Ztgj1nzgHs69X5 2018-02-27 \n", "3 7CfqRcuy4WTP7928BziSW0 6 7CfqRcuy4WTP7928BziSW0 2018-02-27 \n", "4 2NAihRUyr75yyMEuuc0BvP 16 2NAihRUyr75yyMEuuc0BvP 2018-02-26 \n", "\n", " first_seen days_since_release streams pop_5_date \\\n", "0 2018-03-03 08:15:12.814499 5 1065074 2018-03-04 \n", "1 2018-03-03 03:39:07.475948 5 8365 2018-03-03 \n", "2 2018-03-03 03:39:07.178301 5 1300000 2018-03-04 \n", "3 2018-03-03 03:39:07.136362 5 27637 2018-03-04 \n", "4 2018-03-03 03:39:06.879209 5 186000 2018-03-03 \n", "\n", " streams_100_date Artist_Advance \n", "0 2018-06-06 5k \n", "1 2018-06-06 <2k \n", "2 2018-06-06 5k \n", "3 2018-06-06 <2k \n", "4 2018-06-06 <2k " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "streams.head()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ggplot(streams, aes('release_date')) + geom_bar(aes(fill='Artist_Advance'))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ggplot(streams >> mask(X.release_date > '2018-02-01', X.release_date < '2018-03-01'), aes('release_date')) + geom_bar(aes(fill='Artist_Advance'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:whitelist]", "language": "python", "name": "conda-env-whitelist-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }