Last active
February 8, 2020 23:48
-
-
Save benkamphaus/04ed8cba003bbc2b2edf707f558a3472 to your computer and use it in GitHub Desktop.
Analysis of my streaming revenue
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Earnings Report Exploration\n", | |
| "\n", | |
| "Looking at breakdown of streaming pays from major services. This is pretty basic, but maybe fun to look at?\n", | |
| "\n", | |
| "Note: package management in data science land is sheer insanity and Docker (or various isolated/containerized cloud packaging) the only reasonable solution; thus project structure matches my typical docker compose setup." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "%cd /project/" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "from matplotlib import pyplot as plt" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Reading and poking at the data we see some various quality issues, missing error ranges at reporting boundary, etc." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Start of reporting period</th>\n", | |
| " <th>End of reporting period</th>\n", | |
| " <th>Store</th>\n", | |
| " <th>Store service</th>\n", | |
| " <th>Country of sale or stream</th>\n", | |
| " <th>Album</th>\n", | |
| " <th>UPC</th>\n", | |
| " <th>Track</th>\n", | |
| " <th>ISRC</th>\n", | |
| " <th>Quantity of sales or streams</th>\n", | |
| " <th>Gross earnings (USD)</th>\n", | |
| " <th>Net earnings (USD)</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>2018-04-01</td>\n", | |
| " <td>2018-05-05</td>\n", | |
| " <td>ITunes</td>\n", | |
| " <td>iTunes Store</td>\n", | |
| " <td>US</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>Walking the Manifold</td>\n", | |
| " <td>CAGOO1804133</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1.400000</td>\n", | |
| " <td>1.400000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2018-04-01</td>\n", | |
| " <td>2018-05-05</td>\n", | |
| " <td>ITunes</td>\n", | |
| " <td>iTunes Store</td>\n", | |
| " <td>US</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>Merlin Sickness</td>\n", | |
| " <td>CAGOO1804127</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1.400000</td>\n", | |
| " <td>1.400000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2018-04-01</td>\n", | |
| " <td>2018-05-05</td>\n", | |
| " <td>ITunes</td>\n", | |
| " <td>iTunes Store</td>\n", | |
| " <td>US</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>To Andromeda (Silver Wings)</td>\n", | |
| " <td>CAGOO1804130</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1.400000</td>\n", | |
| " <td>1.400000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>2018-04-01</td>\n", | |
| " <td>2018-05-05</td>\n", | |
| " <td>ITunes</td>\n", | |
| " <td>iTunes Store</td>\n", | |
| " <td>US</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>Departure</td>\n", | |
| " <td>CAGOO1804134</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1.400000</td>\n", | |
| " <td>1.400000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>2018-04-01</td>\n", | |
| " <td>2018-05-05</td>\n", | |
| " <td>ITunes</td>\n", | |
| " <td>iTunes Store</td>\n", | |
| " <td>US</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>Raid on the Caleuche</td>\n", | |
| " <td>CAGOO1804129</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1.400000</td>\n", | |
| " <td>1.400000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>2018-04-01</td>\n", | |
| " <td>2018-05-05</td>\n", | |
| " <td>ITunes</td>\n", | |
| " <td>iTunes Store</td>\n", | |
| " <td>US</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>The Physical Option</td>\n", | |
| " <td>CAGOO1804128</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1.400000</td>\n", | |
| " <td>1.400000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>2018-04-01</td>\n", | |
| " <td>2018-05-05</td>\n", | |
| " <td>ITunes</td>\n", | |
| " <td>iTunes Store</td>\n", | |
| " <td>US</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>Orbital Resonance</td>\n", | |
| " <td>CAGOO1804131</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1.400000</td>\n", | |
| " <td>1.400000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>2018-04-01</td>\n", | |
| " <td>2018-05-05</td>\n", | |
| " <td>ITunes</td>\n", | |
| " <td>iTunes Store</td>\n", | |
| " <td>US</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>Falling Toward Resurgam (Bonus Track)</td>\n", | |
| " <td>CAGOO1804132</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1.400000</td>\n", | |
| " <td>1.400000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>2018-03-30</td>\n", | |
| " <td>2018-05-03</td>\n", | |
| " <td>ITunes</td>\n", | |
| " <td>Apple Music</td>\n", | |
| " <td>AU</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>Walking the Manifold</td>\n", | |
| " <td>CAGOO1804133</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.006435</td>\n", | |
| " <td>0.006435</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>2018-03-30</td>\n", | |
| " <td>2018-05-03</td>\n", | |
| " <td>ITunes</td>\n", | |
| " <td>Apple Music</td>\n", | |
| " <td>AU</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>Merlin Sickness</td>\n", | |
| " <td>CAGOO1804127</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.006435</td>\n", | |
| " <td>0.006435</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Start of reporting period End of reporting period Store Store service \\\n", | |
| "0 2018-04-01 2018-05-05 ITunes iTunes Store \n", | |
| "1 2018-04-01 2018-05-05 ITunes iTunes Store \n", | |
| "2 2018-04-01 2018-05-05 ITunes iTunes Store \n", | |
| "3 2018-04-01 2018-05-05 ITunes iTunes Store \n", | |
| "4 2018-04-01 2018-05-05 ITunes iTunes Store \n", | |
| "5 2018-04-01 2018-05-05 ITunes iTunes Store \n", | |
| "6 2018-04-01 2018-05-05 ITunes iTunes Store \n", | |
| "7 2018-04-01 2018-05-05 ITunes iTunes Store \n", | |
| "8 2018-03-30 2018-05-03 ITunes Apple Music \n", | |
| "9 2018-03-30 2018-05-03 ITunes Apple Music \n", | |
| "\n", | |
| " Country of sale or stream Album UPC Track \\\n", | |
| "0 US NaN NaN Walking the Manifold \n", | |
| "1 US NaN NaN Merlin Sickness \n", | |
| "2 US NaN NaN To Andromeda (Silver Wings) \n", | |
| "3 US NaN NaN Departure \n", | |
| "4 US NaN NaN Raid on the Caleuche \n", | |
| "5 US NaN NaN The Physical Option \n", | |
| "6 US NaN NaN Orbital Resonance \n", | |
| "7 US NaN NaN Falling Toward Resurgam (Bonus Track) \n", | |
| "8 AU NaN NaN Walking the Manifold \n", | |
| "9 AU NaN NaN Merlin Sickness \n", | |
| "\n", | |
| " ISRC Quantity of sales or streams Gross earnings (USD) \\\n", | |
| "0 CAGOO1804133 2 1.400000 \n", | |
| "1 CAGOO1804127 2 1.400000 \n", | |
| "2 CAGOO1804130 2 1.400000 \n", | |
| "3 CAGOO1804134 2 1.400000 \n", | |
| "4 CAGOO1804129 2 1.400000 \n", | |
| "5 CAGOO1804128 2 1.400000 \n", | |
| "6 CAGOO1804131 2 1.400000 \n", | |
| "7 CAGOO1804132 2 1.400000 \n", | |
| "8 CAGOO1804133 1 0.006435 \n", | |
| "9 CAGOO1804127 1 0.006435 \n", | |
| "\n", | |
| " Net earnings (USD) \n", | |
| "0 1.400000 \n", | |
| "1 1.400000 \n", | |
| "2 1.400000 \n", | |
| "3 1.400000 \n", | |
| "4 1.400000 \n", | |
| "5 1.400000 \n", | |
| "6 1.400000 \n", | |
| "7 1.400000 \n", | |
| "8 0.006435 \n", | |
| "9 0.006435 " | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df = pd.read_csv(\"data/earnings-report.csv\")\n", | |
| "df.head(10)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Mostly pandas figured out types Ok, but datetime encoding usually worth it." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Start of reporting period object\n", | |
| "End of reporting period object\n", | |
| "Store object\n", | |
| "Store service object\n", | |
| "Country of sale or stream object\n", | |
| "Album object\n", | |
| "UPC float64\n", | |
| "Track object\n", | |
| "ISRC object\n", | |
| "Quantity of sales or streams int64\n", | |
| "Gross earnings (USD) float64\n", | |
| "Net earnings (USD) float64\n", | |
| "dtype: object" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.dtypes" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Start of reporting period</th>\n", | |
| " <th>End of reporting period</th>\n", | |
| " <th>Store</th>\n", | |
| " <th>Store service</th>\n", | |
| " <th>Country of sale or stream</th>\n", | |
| " <th>Album</th>\n", | |
| " <th>UPC</th>\n", | |
| " <th>Track</th>\n", | |
| " <th>ISRC</th>\n", | |
| " <th>Quantity of sales or streams</th>\n", | |
| " <th>Gross earnings (USD)</th>\n", | |
| " <th>Net earnings (USD)</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>2018-04-01</td>\n", | |
| " <td>2018-05-05</td>\n", | |
| " <td>ITunes</td>\n", | |
| " <td>iTunes Store</td>\n", | |
| " <td>US</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>Walking the Manifold</td>\n", | |
| " <td>CAGOO1804133</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1.4</td>\n", | |
| " <td>1.4</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2018-04-01</td>\n", | |
| " <td>2018-05-05</td>\n", | |
| " <td>ITunes</td>\n", | |
| " <td>iTunes Store</td>\n", | |
| " <td>US</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>Merlin Sickness</td>\n", | |
| " <td>CAGOO1804127</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1.4</td>\n", | |
| " <td>1.4</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2018-04-01</td>\n", | |
| " <td>2018-05-05</td>\n", | |
| " <td>ITunes</td>\n", | |
| " <td>iTunes Store</td>\n", | |
| " <td>US</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>To Andromeda (Silver Wings)</td>\n", | |
| " <td>CAGOO1804130</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1.4</td>\n", | |
| " <td>1.4</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Start of reporting period End of reporting period Store Store service \\\n", | |
| "0 2018-04-01 2018-05-05 ITunes iTunes Store \n", | |
| "1 2018-04-01 2018-05-05 ITunes iTunes Store \n", | |
| "2 2018-04-01 2018-05-05 ITunes iTunes Store \n", | |
| "\n", | |
| " Country of sale or stream Album UPC Track \\\n", | |
| "0 US NaN NaN Walking the Manifold \n", | |
| "1 US NaN NaN Merlin Sickness \n", | |
| "2 US NaN NaN To Andromeda (Silver Wings) \n", | |
| "\n", | |
| " ISRC Quantity of sales or streams Gross earnings (USD) \\\n", | |
| "0 CAGOO1804133 2 1.4 \n", | |
| "1 CAGOO1804127 2 1.4 \n", | |
| "2 CAGOO1804130 2 1.4 \n", | |
| "\n", | |
| " Net earnings (USD) \n", | |
| "0 1.4 \n", | |
| "1 1.4 \n", | |
| "2 1.4 " | |
| ] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "date_fields = ['Start of reporting period', 'End of reporting period']\n", | |
| "for date_field in date_fields:\n", | |
| " df[date_field] = pd.to_datetime(df[date_field]) \n", | |
| "df.head(3)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Just verifying the degree of variation in reporting range. Monthly-ish." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(Timedelta('-30 days +21:42:24.698963'), Timedelta('3 days 13:46:15.560952'))" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df['Date range'] = df['Start of reporting period'] - df['End of reporting period']\n", | |
| "df['Date range'].mean(), df['Date range'].std()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Store/Service Breakdown\n", | |
| "\n", | |
| "Just want to figure out the relationship between store and service. Reasonably straightforward. I.e., ITunes is store, Apple Music is the\n", | |
| "streaming service. Some are both, etc. Both filtering out noise of\n", | |
| "one-off listens on services without much representation." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "ITunes 8945\n", | |
| "Spotify 7756\n", | |
| "YouTube 411\n", | |
| "GooglePlay 349\n", | |
| "VKontakte 207\n", | |
| "Deezer 124\n", | |
| "BOOM App UMA 30\n", | |
| "Name: Store, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "store_counts = df['Store'].value_counts()\n", | |
| "store_counts[store_counts > 10]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Apple Music 8670\n", | |
| "Spotify 7756\n", | |
| "Google Play Music Streaming 341\n", | |
| "Content ID 264\n", | |
| "iTunes Match 234\n", | |
| "VKontakte 207\n", | |
| "Streaming 176\n", | |
| "Deezer 124\n", | |
| "iTunes Store 41\n", | |
| "BOOM App UMA 30\n", | |
| "Name: Store service, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "services = df['Store service'].value_counts()\n", | |
| "services[services > 10]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## The Big Two\n", | |
| "\n", | |
| "Vast majority of my plays/streaming income are via Spotify and Apple Music, so deeper dive into those follows." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "apple_music = df[(df['Store'] == 'ITunes') & \\\n", | |
| " (df['Store service'] == 'Apple Music')]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>UPC</th>\n", | |
| " <th>Quantity of sales or streams</th>\n", | |
| " <th>Gross earnings (USD)</th>\n", | |
| " <th>Net earnings (USD)</th>\n", | |
| " <th>Date range</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>0.0</td>\n", | |
| " <td>8670.000000</td>\n", | |
| " <td>8670.000000</td>\n", | |
| " <td>8670.000000</td>\n", | |
| " <td>8670</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>4.520992</td>\n", | |
| " <td>0.029707</td>\n", | |
| " <td>0.029707</td>\n", | |
| " <td>-30 days +17:09:45.467128</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>9.690443</td>\n", | |
| " <td>0.068153</td>\n", | |
| " <td>0.068153</td>\n", | |
| " <td>3 days 06:46:47.279287</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>-34 days +00:00:00</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.005012</td>\n", | |
| " <td>0.005012</td>\n", | |
| " <td>-34 days +00:00:00</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>0.010099</td>\n", | |
| " <td>0.010099</td>\n", | |
| " <td>-27 days +00:00:00</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>0.025090</td>\n", | |
| " <td>0.025090</td>\n", | |
| " <td>-27 days +00:00:00</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>299.000000</td>\n", | |
| " <td>2.201731</td>\n", | |
| " <td>2.201731</td>\n", | |
| " <td>-27 days +00:00:00</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " UPC Quantity of sales or streams Gross earnings (USD) \\\n", | |
| "count 0.0 8670.000000 8670.000000 \n", | |
| "mean NaN 4.520992 0.029707 \n", | |
| "std NaN 9.690443 0.068153 \n", | |
| "min NaN 1.000000 0.000000 \n", | |
| "25% NaN 1.000000 0.005012 \n", | |
| "50% NaN 2.000000 0.010099 \n", | |
| "75% NaN 4.000000 0.025090 \n", | |
| "max NaN 299.000000 2.201731 \n", | |
| "\n", | |
| " Net earnings (USD) Date range \n", | |
| "count 8670.000000 8670 \n", | |
| "mean 0.029707 -30 days +17:09:45.467128 \n", | |
| "std 0.068153 3 days 06:46:47.279287 \n", | |
| "min 0.000000 -34 days +00:00:00 \n", | |
| "25% 0.005012 -34 days +00:00:00 \n", | |
| "50% 0.010099 -27 days +00:00:00 \n", | |
| "75% 0.025090 -27 days +00:00:00 \n", | |
| "max 2.201731 -27 days +00:00:00 " | |
| ] | |
| }, | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "apple_music.describe()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Just a sanity check to verify that w/digital only streams/sales, this difference doesn't matter." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0" | |
| ] | |
| }, | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "len(apple_music[apple_music['Gross earnings (USD)'] != \\\n", | |
| " apple_music['Net earnings (USD)']])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Simple Average Stream Rate\n", | |
| "\n", | |
| "Just a quick check reveals average pay per stream (truncated) to be:\n", | |
| " \n", | |
| "- Apple: `0.0065`\n", | |
| "- Spotify: `0.0038`" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "39197" | |
| ] | |
| }, | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "streams = apple_music['Quantity of sales or streams'].sum()\n", | |
| "streams" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "257.56376144663363" | |
| ] | |
| }, | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "payout = apple_music['Gross earnings (USD)'].sum()\n", | |
| "payout" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0.00657100700172548" | |
| ] | |
| }, | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "payout / streams" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Start of reporting period</th>\n", | |
| " <th>End of reporting period</th>\n", | |
| " <th>Store</th>\n", | |
| " <th>Store service</th>\n", | |
| " <th>Country of sale or stream</th>\n", | |
| " <th>Album</th>\n", | |
| " <th>UPC</th>\n", | |
| " <th>Track</th>\n", | |
| " <th>ISRC</th>\n", | |
| " <th>Quantity of sales or streams</th>\n", | |
| " <th>Gross earnings (USD)</th>\n", | |
| " <th>Net earnings (USD)</th>\n", | |
| " <th>Date range</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>118</th>\n", | |
| " <td>2018-05-01</td>\n", | |
| " <td>2018-05-31</td>\n", | |
| " <td>Spotify</td>\n", | |
| " <td>Spotify</td>\n", | |
| " <td>BR</td>\n", | |
| " <td>PatternShift EP</td>\n", | |
| " <td>8.007395e+11</td>\n", | |
| " <td>Merlin Sickness</td>\n", | |
| " <td>CAGOO1804127</td>\n", | |
| " <td>2</td>\n", | |
| " <td>0.003650</td>\n", | |
| " <td>0.003650</td>\n", | |
| " <td>-30 days</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>119</th>\n", | |
| " <td>2018-05-01</td>\n", | |
| " <td>2018-05-31</td>\n", | |
| " <td>Spotify</td>\n", | |
| " <td>Spotify</td>\n", | |
| " <td>US</td>\n", | |
| " <td>PatternShift EP</td>\n", | |
| " <td>8.007395e+11</td>\n", | |
| " <td>Merlin Sickness</td>\n", | |
| " <td>CAGOO1804127</td>\n", | |
| " <td>4</td>\n", | |
| " <td>0.008624</td>\n", | |
| " <td>0.008624</td>\n", | |
| " <td>-30 days</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>120</th>\n", | |
| " <td>2018-05-01</td>\n", | |
| " <td>2018-05-31</td>\n", | |
| " <td>Spotify</td>\n", | |
| " <td>Spotify</td>\n", | |
| " <td>US</td>\n", | |
| " <td>PatternShift EP</td>\n", | |
| " <td>8.007395e+11</td>\n", | |
| " <td>Merlin Sickness</td>\n", | |
| " <td>CAGOO1804127</td>\n", | |
| " <td>28</td>\n", | |
| " <td>0.086129</td>\n", | |
| " <td>0.086129</td>\n", | |
| " <td>-30 days</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>121</th>\n", | |
| " <td>2018-05-01</td>\n", | |
| " <td>2018-05-31</td>\n", | |
| " <td>Spotify</td>\n", | |
| " <td>Spotify</td>\n", | |
| " <td>DE</td>\n", | |
| " <td>PatternShift EP</td>\n", | |
| " <td>8.007395e+11</td>\n", | |
| " <td>Merlin Sickness</td>\n", | |
| " <td>CAGOO1804127</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.002963</td>\n", | |
| " <td>0.002963</td>\n", | |
| " <td>-30 days</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>122</th>\n", | |
| " <td>2018-05-01</td>\n", | |
| " <td>2018-05-31</td>\n", | |
| " <td>Spotify</td>\n", | |
| " <td>Spotify</td>\n", | |
| " <td>GB</td>\n", | |
| " <td>PatternShift EP</td>\n", | |
| " <td>8.007395e+11</td>\n", | |
| " <td>Merlin Sickness</td>\n", | |
| " <td>CAGOO1804127</td>\n", | |
| " <td>9</td>\n", | |
| " <td>0.068183</td>\n", | |
| " <td>0.068183</td>\n", | |
| " <td>-30 days</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Start of reporting period End of reporting period Store Store service \\\n", | |
| "118 2018-05-01 2018-05-31 Spotify Spotify \n", | |
| "119 2018-05-01 2018-05-31 Spotify Spotify \n", | |
| "120 2018-05-01 2018-05-31 Spotify Spotify \n", | |
| "121 2018-05-01 2018-05-31 Spotify Spotify \n", | |
| "122 2018-05-01 2018-05-31 Spotify Spotify \n", | |
| "\n", | |
| " Country of sale or stream Album UPC Track \\\n", | |
| "118 BR PatternShift EP 8.007395e+11 Merlin Sickness \n", | |
| "119 US PatternShift EP 8.007395e+11 Merlin Sickness \n", | |
| "120 US PatternShift EP 8.007395e+11 Merlin Sickness \n", | |
| "121 DE PatternShift EP 8.007395e+11 Merlin Sickness \n", | |
| "122 GB PatternShift EP 8.007395e+11 Merlin Sickness \n", | |
| "\n", | |
| " ISRC Quantity of sales or streams Gross earnings (USD) \\\n", | |
| "118 CAGOO1804127 2 0.003650 \n", | |
| "119 CAGOO1804127 4 0.008624 \n", | |
| "120 CAGOO1804127 28 0.086129 \n", | |
| "121 CAGOO1804127 1 0.002963 \n", | |
| "122 CAGOO1804127 9 0.068183 \n", | |
| "\n", | |
| " Net earnings (USD) Date range \n", | |
| "118 0.003650 -30 days \n", | |
| "119 0.008624 -30 days \n", | |
| "120 0.086129 -30 days \n", | |
| "121 0.002963 -30 days \n", | |
| "122 0.068183 -30 days " | |
| ] | |
| }, | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "spotify = df[(df['Store'] == 'Spotify') & \\\n", | |
| " (df['Store service'] == 'Spotify')]\n", | |
| "spotify.head(5)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "57590" | |
| ] | |
| }, | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "sp_streams = spotify['Quantity of sales or streams'].sum()\n", | |
| "sp_streams" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "220.11416147851116" | |
| ] | |
| }, | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "sp_payout = spotify['Gross earnings (USD)'].sum()\n", | |
| "sp_payout" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0.0038220899718442642" | |
| ] | |
| }, | |
| "execution_count": 18, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "sp_payout / sp_streams" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Apple also tends to pay me slightly more on time. Both only caught up\n", | |
| "to last year ranges." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(Timestamp('2019-11-30 00:00:00'), Timestamp('2019-12-26 00:00:00'))" | |
| ] | |
| }, | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "(spotify['End of reporting period'].max(),\n", | |
| " apple_music['End of reporting period'].max())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "_Note_ re: Fiddly Bits in `pandas`\n", | |
| "\n", | |
| "This copy is because I'm going to assign new columns, and because\n", | |
| "pandas is structured to avoid copy and provide views into other\n", | |
| "data frames, it would otherwise complain a bunch at me." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "spotify = spotify.copy()\n", | |
| "apple_music = apple_music.copy()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Per stream histogram\n", | |
| "\n", | |
| "What's behind an average pay per stream? Well, there's a complicated breakdown by country, free or paid user on Spotify, etc. I'm not going to go into that entire mess,\n", | |
| "but will at least provide a histogram of steam counts per payout bin\n", | |
| "for both services." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "spotify['Per stream (USD)'] = spotify['Gross earnings (USD)'] / \\\n", | |
| " spotify['Quantity of sales or streams']" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "spotify['Per stream (USD)'].hist(bins=100)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "apple_music['Per stream (USD)'] = apple_music['Gross earnings (USD)'] / \\\n", | |
| " apple_music['Quantity of sales or streams']" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "apple_music['Per stream (USD)'].hist(bins=100)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Yeah, the range breaks default spread by naive binning. Looks like we have to something smart in setting axis range." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(0.0034317150283437775, 0.0020821268824345127)" | |
| ] | |
| }, | |
| "execution_count": 25, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "spotify['Per stream (USD)'].mean(), spotify['Per stream (USD)'].std()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(0.006918049667291591, 0.0056609539997116605)" | |
| ] | |
| }, | |
| "execution_count": 26, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "apple_music['Per stream (USD)'].mean(), apple_music['Per stream (USD)'].std()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Apple music has the larger spread, so we use its range bounds to estimate a good extent for the plot." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0.02390091166642657" | |
| ] | |
| }, | |
| "execution_count": 27, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "0.006918049667291591 + 3*0.0056609539997116605" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 28, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<Figure size 1332x756 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "apple_music['Per stream (USD)'].hist(bins=100, range=(0.0, 0.025), alpha=0.6)\n", | |
| "spotify['Per stream (USD)'].hist(bins=100, range=(0.0, 0.025),alpha=0.6)\n", | |
| "fig = plt.gcf()\n", | |
| "fig.set_size_inches(18.5, 10.5)\n", | |
| "plt.ylabel('Stream count')\n", | |
| "plt.xlabel('Payment per stream (USD)')\n", | |
| "plt.title('Spotify (Orange) vs. Apple Music (Blue) Payment Per Stream')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Wrap-up\n", | |
| "\n", | |
| "Yeah, Apple Music pays more. Spotify has (at least recently) been pretty good to me re: discovery (getting into Discover Weekly plays, etc. is a better deal for an artist looking to find listeners than about any other streaming feature out there). What do I make of all this?\n", | |
| "\n", | |
| "# 🤷♂️\n", | |
| "\n", | |
| "If this was fun to read, you can try to go out and [contribute some of your own stats](https://open.spotify.com/artist/6MNFOvQOGj0BYmHJa6tVIV?si=LFaJVGhqRO2hNBAL3ujj5A)! 😉" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.7.4" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 4 | |
| } | |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment