{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Best Business Strategies for Chinook\n", "\n", "## Introduction\n", "In this project, we will answer to some business questions about Chinook, a fictional digital music store. The database we will be using contains information about:\n", "* Invoice for each customer;\n", "* Employee assigned to each customer;\n", "* Customer;\n", "* Playlists;\n", "* Tracks;\n", "* Albums;\n", "* Music genres; \n", "* Media type of a track.\n", "\n", "Using this data we will answer the following questions:\n", "* Which is the best genre to add to the store in the USA?\n", "* Why some employees perform better?\n", "* Summary statistics about customers and purchases for each country.\n", "* Do customers tend to buy whole albums or individual tracks?\n", "* Do the protected media types influence on sales?\n", "* Which artist is used in the most playlists?\n", "* How many tracks have been purchased vs not purchased?\n", "* Is the range of tracks in the store reflective of their sales popularity?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nametype
0albumtable
1artisttable
2customertable
3employeetable
4genretable
5invoicetable
6invoice_linetable
7media_typetable
8playlisttable
9playlist_tracktable
10tracktable
11invoice_trackview
12customers_per_countryview
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" ], "text/plain": [ " name type\n", "0 album table\n", "1 artist table\n", "2 customer table\n", "3 employee table\n", "4 genre table\n", "5 invoice table\n", "6 invoice_line table\n", "7 media_type table\n", "8 playlist table\n", "9 playlist_track table\n", "10 track table\n", "11 invoice_track view\n", "12 customers_per_country view" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import libraries\n", "import sqlite3\n", "import pandas as pd\n", "import numpy as np\n", "import plotly.express as px\n", "from plotly.subplots import make_subplots\n", "import plotly.graph_objects as go\n", "\n", "db = \"chinook.db\"\n", "\n", "# Function to run queries\n", "def run_query(q):\n", " with sqlite3.connect(db) as conn:\n", " return pd.read_sql(q, conn)\n", "\n", "# Function to show all tables of database\n", "def show_tables():\n", " q = '''\n", " SELECT \n", " name,\n", " type\n", " FROM sqlite_master\n", " WHERE type IN (\"table\", \"view\");\n", " '''\n", " return run_query(q)\n", "\n", "show_tables()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned in the introduction we have various information about customers, employees, invoices, tracks and albums. Now that we are familiar with the database let's start answering the questions.\n", "\n", "## What's the Best Genre to be Added in the USA?\n", "\n", "The Chinook has signed a contract with a new label and would like to add three new artists out of four to the store on the USA market. We've been asked to analyze the existing data and decide which are the best genres to be added: Hip-Hop, Punk, Pop or Blues." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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genre_idnumber_of_tracksgenre_namegenre_percentage
01561Rock53.38
14130Alternative & Punk12.37
23124Metal11.80
31453R&B/Soul5.04
4636Blues3.43
52335Alternative3.33
6922Pop2.09
7722Latin2.09
81720Hip Hop/Rap1.90
9214Jazz1.33
101213Easy Listening1.24
1186Reggae0.57
12155Electronica/Dance0.48
13244Classical0.38
14133Heavy Metal0.29
15102Soundtrack0.19
16191TV Shows0.10
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" ], "text/plain": [ " genre_id number_of_tracks genre_name genre_percentage\n", "0 1 561 Rock 53.38\n", "1 4 130 Alternative & Punk 12.37\n", "2 3 124 Metal 11.80\n", "3 14 53 R&B/Soul 5.04\n", "4 6 36 Blues 3.43\n", "5 23 35 Alternative 3.33\n", "6 9 22 Pop 2.09\n", "7 7 22 Latin 2.09\n", "8 17 20 Hip Hop/Rap 1.90\n", "9 2 14 Jazz 1.33\n", "10 12 13 Easy Listening 1.24\n", "11 8 6 Reggae 0.57\n", "12 15 5 Electronica/Dance 0.48\n", "13 24 4 Classical 0.38\n", "14 13 3 Heavy Metal 0.29\n", "15 10 2 Soundtrack 0.19\n", "16 19 1 TV Shows 0.10" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tracks_per_genre_usa_q = '''\n", "WITH customer_usa_tracks AS\n", " (\n", " SELECT\n", " g.genre_id,\n", " COUNT(il.invoice_line_id) number_of_tracks,\n", " g.name genre_name\n", " FROM invoice i\n", " INNER JOIN invoice_line il ON i.invoice_id = il.invoice_id\n", " INNER JOIN track t ON t.track_id = il.track_id\n", " INNER JOIN genre g ON g.genre_id = t.genre_id\n", " WHERE billing_country = \"USA\"\n", " GROUP BY g.genre_id\n", " ORDER BY number_of_tracks DESC\n", " )\n", " \n", "SELECT\n", " *,\n", " ROUND((CAST(number_of_tracks AS FLOAT) / (SELECT SUM(number_of_tracks) FROM customer_usa_tracks) * 100), 2) genre_percentage\n", "FROM customer_usa_tracks;\n", "'''\n", "\n", "genre_sales_usa = run_query(tracks_per_genre_usa_q)\n", "genre_sales_usa" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": [ "lightgray", "lightgray", "lightgray", "lightgray", "lightgray", "lightgray", "lightgray", "lightgray", "lightgray", "green", "lightgray", "lightgray", "green", "lightgray", "lightgray", "green", "lightgray" ] }, "orientation": "h", "type": "bar", "x": [ 1, 2, 3, 4, 5, 6, 13, 14, 20, 22, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Sort number of tracks in ascending order\n", "genre_sales_usa = genre_sales_usa.sort_values(by=\"number_of_tracks\")\n", "\n", "# Set bar colors\n", "colors = [\"lightgray\"] * 17\n", "colors[15] = \"green\"\n", "colors[12] = \"green\"\n", "colors[9] = \"green\"\n", " \n", "# Horizontal plot\n", "fig1 = go.Figure(\n", " data=go.Bar(\n", " x=genre_sales_usa[\"number_of_tracks\"],\n", " y=genre_sales_usa[\"genre_name\"],\n", " orientation=\"h\",\n", " marker_color=colors\n", " )\n", ")\n", "\n", "# Set axes labels and plot dimensions\n", "fig1.update_layout(\n", " yaxis=dict(\n", " title=\"Genre Name\",\n", " ),\n", " xaxis=dict(\n", " title=\"# of Tracks Sold\",\n", " ),\n", " height=600,\n", " width=1000\n", ")\n", "\n", "# Set title position and aesthetic\n", "fig1.update_layout(\n", " title={\n", " \"text\": \"Number of Tracks Sold in USA per Genre\",\n", " \"x\": 0.03,\n", " \"y\": 0.9,\n", " \"font\": dict(\n", " size=20\n", " )\n", " }\n", ")\n", "\n", "\n", "# Show plot\n", "fig1.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We would recommend to Chinook to concentrate on: \n", "1. Punk;\n", "2. Blues;\n", "3. Pop.\n", "\n", "It is also clear that the Rock genre dominates over the others possessing more than 50% of the USA market.\n", "\n", "Therefore it is recommended to concentrate on these three genres but also find new labels that would provide us with some rock songs to add.\n", "\n", "## Employees' Performance\n", "\n", "Each customer of Chinook is assigned to a sales support agent. Some of the employees are performing better than the others so we've been asked to perform an analysis and figure out why is there this difference. We will be using the total dollar amount as the main criteria of employees' performance. We will also be using the whole invoice table, not only the USA one." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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employee_idfirst_namelast_namehire_datetitletotal
03JanePeacock2017-04-01 00:00:00Sales Support Agent1731.51
14MargaretPark2017-05-03 00:00:00Sales Support Agent1584.00
25SteveJohnson2017-10-17 00:00:00Sales Support Agent1393.92
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" ], "text/plain": [ " employee_id first_name last_name hire_date title \\\n", "0 3 Jane Peacock 2017-04-01 00:00:00 Sales Support Agent \n", "1 4 Margaret Park 2017-05-03 00:00:00 Sales Support Agent \n", "2 5 Steve Johnson 2017-10-17 00:00:00 Sales Support Agent \n", "\n", " total \n", "0 1731.51 \n", "1 1584.00 \n", "2 1393.92 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "employee_perf_q = '''\n", " SELECT \n", " e.employee_id, \n", " e.first_name,\n", " e.last_name,\n", " e.hire_date,\n", " e.title,\n", " ROUND(SUM(i.total), 2) total\n", " FROM employee e\n", " INNER JOIN customer c ON e.employee_id = c.support_rep_id \n", " LEFT JOIN invoice i ON i.customer_id = c.customer_id\n", " GROUP BY employee_id;\n", "'''\n", "\n", "run_query(employee_perf_q)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's clear that longer an employee worked at the store more money they have earned during the period. Additionally, they gained more experience so probably they work better over time.\n", "\n", "We can also calculate and average purchase value for each employee to see if the work experience influences on the ability to perform better trade operations." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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14MargaretParkSales Support Agent2017-05-03 00:00:001947-09-19 00:00:007.40
25SteveJohnsonSales Support Agent2017-10-17 00:00:001965-03-03 00:00:007.41
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" ], "text/plain": [ " employee_id first_name last_name title hire_date \\\n", "0 3 Jane Peacock Sales Support Agent 2017-04-01 00:00:00 \n", "1 4 Margaret Park Sales Support Agent 2017-05-03 00:00:00 \n", "2 5 Steve Johnson Sales Support Agent 2017-10-17 00:00:00 \n", "\n", " birthdate avg_purchase \n", "0 1973-08-29 00:00:00 8.17 \n", "1 1947-09-19 00:00:00 7.40 \n", "2 1965-03-03 00:00:00 7.41 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "avg_purch_empl_q = '''\n", " SELECT \n", " e.employee_id, \n", " e.first_name,\n", " e.last_name,\n", " e.title,\n", " e.hire_date,\n", " e.birthdate,\n", " ROUND(AVG(i.total),2 ) avg_purchase\n", " FROM employee e\n", " INNER JOIN customer c ON e.employee_id = c.support_rep_id \n", " LEFT JOIN invoice i ON i.customer_id = c.customer_id\n", " GROUP BY employee_id;\n", "'''\n", "\n", "run_query(avg_purch_empl_q)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see from the table that the average purchase value is not critically influenced by the work experience. Margaret Steve has more than five months more experience than Steve Johnson but the latter performs even slightly better. However, Jane Peacock has just one month more of work experience and she's performing better than Margaret Park. Another possible reason why Jane performs better is her younger age that might help her have a better relationship and communication with the customers. She's also probably more active in using digital stores herself (also Chinook) so she understands better her clients.\n", "\n", "Still the available data does not give us a clear idea of what are the factors that influence the performance. It is recommended to schedule some performance reviews with each employee to understand the situation better.\n", "\n", "## Customers' Sales Data\n", "\n", "We've been also asked to collect some information about customers for each country. In particular, we need to calculate:\n", "\n", "* Total number of customers.\n", "* Total value of sales.\n", "* Average value of sales per customer.\n", "* Average order value.\n", "\n", "Some countries have just one customer so we will need to merge them in the \"Other\" group. We will also be using the countries from the `customer` table.\n", "\n", "First, we will calculate the total number of customers per country." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countrynum_customerstotal_salesavg_sales_per_customeravg_order_value
0USA131040.4980.047.94
1Canada8535.5966.957.05
2Brazil5427.6885.547.01
3France5389.0777.817.78
4Germany4334.6283.668.16
5Czech Republic2273.24136.629.11
6United Kingdom3245.5281.848.77
7Portugal2185.1392.576.38
8India2183.1591.588.72
9Other151094.9473.007.45
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" ], "text/plain": [ " country num_customers total_sales avg_sales_per_customer \\\n", "0 USA 13 1040.49 80.04 \n", "1 Canada 8 535.59 66.95 \n", "2 Brazil 5 427.68 85.54 \n", "3 France 5 389.07 77.81 \n", "4 Germany 4 334.62 83.66 \n", "5 Czech Republic 2 273.24 136.62 \n", "6 United Kingdom 3 245.52 81.84 \n", "7 Portugal 2 185.13 92.57 \n", "8 India 2 183.15 91.58 \n", "9 Other 15 1094.94 73.00 \n", "\n", " avg_order_value \n", "0 7.94 \n", "1 7.05 \n", "2 7.01 \n", "3 7.78 \n", "4 8.16 \n", "5 9.11 \n", "6 8.77 \n", "7 6.38 \n", "8 8.72 \n", "9 7.45 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sales_by_country_q = '''\n", "/* Select all customers, orders and sales for each country,\n", "if the country has only one customer return \"Other\" to a new column,\n", "and the country name otherwise */\n", "\n", "WITH statistics_per_country AS \n", " (\n", " SELECT\n", " CASE\n", " WHEN COUNT(DISTINCT(c.customer_id)) = 1 THEN \"Other\"\n", " ELSE c.country\n", " END AS country,\n", " COUNT(DISTINCT(c.customer_id)) num_customers,\n", " COUNT(DISTINCT(i.invoice_id)) num_orders,\n", " SUM(i.total) total_sales\n", " FROM customer c\n", " INNER JOIN invoice i ON i.customer_id = c.customer_id\n", " GROUP BY c.country\n", " ),\n", "\n", "/* Sum customers, order and sales for each country,\n", "in this way all the Other countries will make a single group */\n", "\n", " sum_per_country AS\n", " (\n", " SELECT country,\n", " SUM(num_customers) num_customers,\n", " SUM(num_orders) num_orders,\n", " SUM(total_sales) total_sales\n", " FROM statistics_per_country \n", " GROUP BY country\n", " )\n", " \n", "SELECT \n", " country,\n", " num_customers,\n", " ROUND(total_sales, 2) total_sales,\n", " ROUND(CAST(total_sales AS FLOAT) / num_customers, 2) avg_sales_per_customer,\n", " ROUND(CAST(total_sales AS FLOAT) / num_orders, 2) avg_order_value\n", " FROM\n", " ( \n", " SELECT \n", " spc.*,\n", " CASE\n", " WHEN country = \"Other\" THEN 1\n", " ELSE 0\n", " END AS sort\n", " FROM sum_per_country spc\n", " )\n", "ORDER BY sort, total_sales DESC\n", "'''\n", "\n", "sales_by_country = run_query(sales_by_country_q)\n", "sales_by_country" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": [ "#1b90f0", "#a6d1f5 ", "#a6d1f5 ", "#a6d1f5 ", "#a6d1f5 ", "#a6d1f5 ", "#a6d1f5 ", "#a6d1f5 ", "#a6d1f5 ", "#a6d1f5 " ] }, "type": "bar", "x": [ "USA", "Canada", "Brazil", "France", "Germany", "Czech Republic", "United Kingdom", "Portugal", "India", "Other" ], "xaxis": "x", "y": [ 1040.49, 535.59, 427.68, 389.07, 334.62, 273.24, 245.52, 185.13, 183.15, 1094.94 ], "yaxis": "y" }, { "marker": { "color": [ " #f2e698", " #f2e698", " #f2e698", " #f2e698", " #f2e698", "#ebcc05", " #f2e698", " #f2e698", "#ebcc05", " #f2e698" ] }, "type": "bar", "x": [ "USA", "Canada", "Brazil", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Initialize 3 subplots in a row\n", "fig2 = make_subplots(\n", " rows=1, cols=3,\n", " subplot_titles=[\n", " \"Total Sales\", \n", " \"Average Sale per Customer\",\n", " \"Average Order Value\"\n", " ]\n", ")\n", "\n", "# Colors for the first plot\n", "colors1 = [\"#a6d1f5 \"] * 10\n", "colors1[0] = \"#1b90f0\"\n", "\n", "# Colors for the second plot\n", "colors2 = [\" #f2e698\"] * 10\n", "colors2[5] = \"#ebcc05\"\n", "colors2[8] = \"#ebcc05\"\n", "\n", "# First plot\n", "fig2.add_trace(\n", " go.Bar(\n", " x=sales_by_country[\"country\"],\n", " y=sales_by_country[\"total_sales\"],\n", " marker_color=colors1\n", " ),\n", " row=1,\n", " col=1\n", ")\n", "\n", "# Second plot\n", "fig2.add_trace(\n", " go.Bar(\n", " x=sales_by_country[\"country\"],\n", " y=sales_by_country[\"avg_sales_per_customer\"],\n", " marker_color=colors2\n", " ),\n", " row=1,\n", " col=2,\n", ")\n", "\n", "# Third plot\n", "fig2.add_trace(\n", " go.Bar(\n", " x=sales_by_country[\"country\"],\n", " y=sales_by_country[\"avg_order_value\"],\n", " ),\n", " row=1,\n", " col=3\n", ")\n", "\n", "# Remove legend\n", "fig2.update_layout(showlegend=False)\n", "\n", "# Axes labels\n", "fig2.update_yaxes(title_text=\"Dollars (USD)\", tickfont=dict(size=15), row=1, col=1)\n", "fig2.update_xaxes(title_text=\"Country\", row=1, col=2)\n", "for col in range(0, 3):\n", " fig2.update_xaxes(row=1, col=col, tickfont=dict(size=13))\n", "\n", "# Show plot\n", "fig2.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here what we observe:\n", "\n", "* The maximum number of total sales is in the USA that does not surprise since it has the maximum number of customers.\n", "* The average sales per customer in the Czech Republic is significantly higher than in other countries.\n", "* Portugal and India follow the Czech Republic so they may be the next countries to invest to acquire new customers.\n", "* Aside from the USA and Canada all other countries have a small number of customers so the above analysis about the Czech Republic, Portugal and India may be biased by small sample size.\n", "\n", "The recommended country to **invest is the Czech Republic** since it has a low number of customers but a great customer's potential standing out with more than 130 euros spent on average by each customer. If the management considers some additional investments, India is good countries to invest in next since it has a high average sale per customer and a high average order value. Finally, we should not forget about the **USA since it's the biggest market** so far, and we have to support the customer's interest in our store.\n", "\n", "## Albums or Individual Tracks\n", "\n", "In Chinook, it is possible to buy individual tracks and whole albums. Customers cannot buy a whole album and then add individual tracks to the same purchase. Management is currently considering changing its purchasing strategy to save money. The strategy they are considering is to purchase only the most popular tracks from each album from record companies, instead of purchasing every track from an album. We've been asked to calculate the percentage of purchases of individual track vs whole albums to figure out the preferences of customers.\n", "\n", "For the analysis we have to consider the following situations:\n", "* Some albums have only one or two tracks in them so even if a customer buys one track it's considered as a whole album.\n", "* Some customers choose manually all tracks from an album and then add some individual track to the same purchase.\n", "\n", "We can ignore the first case since our goal is maximizing profit. We also now from the previous analyses that the second case does not occur often. We will categorize each invoice as either an album purchase or not and calculate:\n", "* Number of invoices.\n", "* Percentage of the invoice for each case." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " album_purchase number_of_invoices percent (%)\n", "0 no 500 81.43\n", "1 yes 114 18.57" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "individual_vs_albums_q = '''\n", "WITH invoice_first_track AS\n", " (\n", " SELECT \n", " invoice_id,\n", " MIN(track_id) first_track_id\n", " FROM invoice_line\n", " GROUP BY 1\n", " )\n", "\n", "SELECT album_purchase,\n", " COUNT(invoice_id) number_of_invoices,\n", " ROUND((CAST(COUNT(invoice_id) AS FLOAT) / (\n", " SELECT COUNT(*) \n", " FROM invoice\n", " )) * 100, 2) \"percent (%)\" \n", "FROM (\n", " SELECT \n", " ift.*,\n", " CASE \n", " WHEN\n", " (\n", " SELECT t.track_id\n", " FROM track t\n", " WHERE t.album_id = (\n", " SELECT t2.album_id\n", " FROM track t2\n", " WHERE t2.track_id = ift.first_track_id\n", " )\n", " \n", " EXCEPT\n", " \n", " SELECT il2.track_id\n", " FROM invoice_line il2\n", " WHERE il2.invoice_id = ift.invoice_id \n", " ) IS NULL\n", " AND \n", " (\n", " SELECT il2.track_id\n", " FROM invoice_line il2\n", " WHERE il2.invoice_id = ift.invoice_id\n", " \n", " EXCEPT\n", " \n", " SELECT t.track_id \n", " FROM track t \n", " WHERE t.album_id = (\n", " SELECT t2.album_id\n", " FROM track t2\n", " WHERE t2.track_id = ift.first_track_id\n", " ) \n", " ) IS NULL\n", " THEN \"yes\"\n", " ELSE \"no\"\n", " END AS \"album_purchase\"\n", " FROM invoice_first_track ift \n", " )\n", " \n", "GROUP by album_purchase;\n", "'''\n", "\n", "tracks_vs_albums = run_query(individual_vs_albums_q)\n", "tracks_vs_albums" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A substantial part of customers (18%) does buy whole albums, therefore, it is not recommended to invest too much in purchasing only popular tracks for each album still it is recommended to investigate the possibility to buy some individual tracks for the albums that are usually not bought fully.\n", "\n", "## Protected vs. Non-protected Media Types: Impact on Sales\n", "\n", "There are two main media types: non-protected and protected. The latter was created to help publishers and content creators to protect their works from being copied (usually illegally). To this objection, there were created some tools like [DRM](https://en.wikipedia.org/wiki/Digital_rights_management). However, [it was argued](www.cbc.ca/news/technology/story/2009/08/06/tech-digital-locks-drm-tpm-rights-management-protection-measures-copyright-copy-protection.html) that this protection creates more problems than benefits like restricting users from lending the work, creating backups and even blocking them from using the content if the service is discontinued. Some of these drawbacks may have a strong impact on sales: people would prefer not to risk their money buying a protected song.\n", "\n", "Therefore, we will investigate if the non-protected media has any benefit to the protected media. We will count the number of tracks sold per each media type to figure out customers' preferences." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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protectedtracks_soldpercentage
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" ], "text/plain": [ " protected tracks_sold percentage\n", "0 Not Protected 4315 90.7\n", "1 Protected 442 9.3" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "protected_vs_non_protected_q = '''\n", " WITH protected_type AS \n", " (\n", " SELECT \n", " *,\n", " CASE \n", " WHEN name LIKE \"Protected%\" THEN \"Protected\" \n", " ELSE \"Not Protected\"\n", " END AS \"protected\"\n", " FROM media_type mt\n", " ),\n", " \n", " il_total_quantity AS \n", " (\n", " SELECT SUM(quantity) total_quantity\n", " FROM invoice_line\n", " )\n", " \n", " SELECT protected,\n", " SUM(il.quantity) tracks_sold,\n", " ROUND(CAST(SUM(il.quantity) AS FLOAT) / (SELECT total_quantity FROM il_total_quantity), 3) * 100 percentage\n", " FROM protected_type pt\n", " INNER JOIN track t ON t.media_type_id = pt.media_type_id\n", " INNER JOIN invoice_line il ON il.track_id = t.track_id\n", " GROUP BY protected\n", "'''\n", "protected_vs_non_protected = run_query(protected_vs_non_protected_q)\n", "protected_vs_non_protected" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "protected=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot: tracks sold by genre\n", "fig3 = px.bar(protected_vs_non_protected, x=\"protected\", y=\"percentage\", text=\"percentage\")\n", "\n", "# Data labels\n", "fig3.update_traces(texttemplate=\"%{text}\", textposition=\"outside\")\n", "\n", "# Title\n", "fig3.update_layout(\n", " title={\n", " \"text\":\"Protected vs not Protected Media Types Sales\",\n", " \"x\": 0.01,\n", " \"font\": dict(\n", " size=20\n", " )\n", " }\n", ")\n", "\n", "# Axes' labels and range\n", "fig3.update_xaxes(title=\"Protected or not Protected\")\n", "fig3.update_yaxes(title=\"# Sold Tracks\", range=[0, 100])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More than 90% of all sold tracks are of not protected media types, therefore it is recommended to **prefer non-protected audio files over the protected ones**.\n", "\n", "## The Most Popular Artists in Playlists\n", "\n", "The management has decided to buy more tracks by the most popular artists, it is also useful to prioritize the artists which tracks should be bought first. We've been asked by Chinook to find out the most popular artists in playlists created by users. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameno_playlists
0Eugene Ormandy68
1English Concert & Trevor Pinnock55
2Academy of St. Martin in the Fields & Sir Nevi...55
3The King's Singers53
4Berliner Philharmoniker & Herbert Von Karajan53
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" ], "text/plain": [ " name no_playlists\n", "0 Eugene Ormandy 68\n", "1 English Concert & Trevor Pinnock 55\n", "2 Academy of St. Martin in the Fields & Sir Nevi... 55\n", "3 The King's Singers 53\n", "4 Berliner Philharmoniker & Herbert Von Karajan 53" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "popular_artists_q = '''\n", " SELECT \n", " ar.name,\n", " SUM(DISTINCT(pt.playlist_id)) no_playlists\n", " FROM artist ar\n", " INNER JOIN album al ON ar.artist_id = al.artist_id\n", " INNER JOIN track t ON t.album_id = al.album_id\n", " INNER JOIN playlist_track pt ON pt.track_id = t.track_id\n", " GROUP BY 1\n", " ORDER BY 2 DESC\n", " LIMIT 5;\n", "'''\n", "\n", "popular_artists = run_query(popular_artists_q)\n", "popular_artists" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The artists that are used in most playlists are:\n", "* Eugene Ormandy.\n", "* English Concert & Trevor Pinnock.\n", "* Academy of St. Martin in the Fields.\n", "* The King's Singers.\n", "* Berliner Philharmoniker & Herbert Von Karajan.\n", "\n", "Most of them are known for their contribution in orchestras. Considering the fact that most of the tracks sold in the USA (the biggest market) are rock/punk/metal compositions **it is not recommended to prioritize the orchestras'**.\n", "\n", "## Tracks: Purchased vs. Not Purchased\n", "\n", "It is very important to understand if people tend to buy the whole range of track available in the store. We've been asked to figure out the percentage of the tracks sold out of the total available tracks." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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purchased_tracksnot_purchased_trackspercentage_purchasedtotal_tracks
01806169751.63503
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" ], "text/plain": [ " purchased_tracks not_purchased_tracks percentage_purchased total_tracks\n", "0 1806 1697 51.6 3503" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "purchased_vs_not_purchased_tracks_q = '''\n", " WITH purchased_tracks AS \n", " (\n", " SELECT il.track_id\n", " FROM invoice_line il\n", " GROUP BY il.track_id\n", " )\n", " \n", " SELECT \n", " COUNT(*) purchased_tracks,\n", " (SELECT COUNT(*) FROM track) - COUNT(*) not_purchased_tracks,\n", " ROUND(CAST(COUNT(*) AS FLOAT) / (SELECT COUNT(*) FROM track), 3) * 100 percentage_purchased,\n", " (SELECT COUNT(*) FROM track) total_tracks\n", " FROM purchased_tracks\n", " '''\n", "\n", "purchased_vs_not_purchased_tracks = run_query(purchased_vs_not_purchased_tracks_q)\n", "purchased_vs_not_purchased_tracks" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Stacked bar chart: purchased vs. not purchased\n", "fig4 = go.Figure(data=[\n", " go.Bar(name='Purchased', y=[1806], width=1, text=\"purchased_tracks\"),\n", " go.Bar(name='Not purchased', y=[1697], width=1, text=\"not_purchased_tracks\")\n", "])\n", "\n", "fig4.update_layout(\n", " height=600,\n", " width=500,\n", " barmode='stack'\n", ")\n", "\n", "# Title\n", "fig4.update_layout(\n", " title={\n", " \"text\": \"Purchased vs. Not Purchased Tracks\",\n", " \"x\": 0.08,\n", " \"y\": 0.92,\n", " \"font\": dict(\n", " size=20\n", " )\n", " }\n", ")\n", "\n", "# y axis label and remove x tick labels\n", "fig4.update_yaxes(title=\"# of sold tracks\", range=[0, 3503])\n", "fig4.update_xaxes(showticklabels=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Almost half of the available tracks in Chinook is not purchased**, therefore it is recommended to reconsider the sales policy. We will investigate this issue further and find out which genres have from this problem.\n", "\n", "We will further investigate the sales popularity in the next section.\n", "\n", "## Range of Tracks and Sales Popularity\n", "\n", "The management of Chinook wants to reduce losses from buying the tracks that are not very popular among customers. We will group tracks by genre and count:\n", "\n", "* Number of available tracks.\n", "* Number of sold tracks.\n", "* Percentage of sold tracks." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameno_tracksunique_tracks_soldpercentage_sold
0Drama6411.6
1TV Shows9322.2
2Soundtrack43511.6
3Latin57911920.6
4Classical741621.6
5Heavy Metal28725.0
6Reggae582237.9
7Jazz1306146.9
8Pop482552.1
9Alternative & Punk33217653.0
10Hip Hop/Rap352160.0
11Metal37423863.6
12Blues815669.1
13Rock129791570.5
14Alternative403485.0
15R&B/Soul615590.2
16Electronica/Dance302996.7
17Easy Listening2424100.0
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" ], "text/plain": [ " name no_tracks unique_tracks_sold percentage_sold\n", "0 Drama 64 1 1.6\n", "1 TV Shows 93 2 2.2\n", "2 Soundtrack 43 5 11.6\n", "3 Latin 579 119 20.6\n", "4 Classical 74 16 21.6\n", "5 Heavy Metal 28 7 25.0\n", "6 Reggae 58 22 37.9\n", "7 Jazz 130 61 46.9\n", "8 Pop 48 25 52.1\n", "9 Alternative & Punk 332 176 53.0\n", "10 Hip Hop/Rap 35 21 60.0\n", "11 Metal 374 238 63.6\n", "12 Blues 81 56 69.1\n", "13 Rock 1297 915 70.5\n", "14 Alternative 40 34 85.0\n", "15 R&B/Soul 61 55 90.2\n", "16 Electronica/Dance 30 29 96.7\n", "17 Easy Listening 24 24 100.0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sales_popularity_q = '''\n", " WITH available_tracks AS \n", " (\n", " SELECT\n", " g.genre_id,\n", " g.name,\n", " COUNT(*) no_tracks\n", " FROM track t\n", " INNER JOIN genre g ON g.genre_id = t.genre_id \n", " GROUP BY 1\n", " ),\n", "\n", " purchased_tracks AS \n", " (\n", " SELECT\n", " g.genre_id,\n", " g.name genre_name,\n", " COUNT(DISTINCT(il.track_id)) unique_tracks_sold \n", " FROM invoice_line il\n", " INNER JOIN track t ON t.track_id = il.track_id\n", " INNER JOIN genre g ON g.genre_id = t.genre_id\n", " GROUP BY g.name\n", " )\n", "\n", " SELECT\n", " at.name,\n", " at.no_tracks,\n", " pt.unique_tracks_sold,\n", " ROUND(CAST(pt.unique_tracks_sold AS FLOAT) / at.no_tracks, 3) * 100 percentage_sold\n", " FROM available_tracks at \n", " INNER JOIN purchased_tracks pt ON pt.genre_id = at.genre_id\n", " ORDER BY percentage_sold;\n", "'''\n", "\n", "sales_popularity = run_query(sales_popularity_q)\n", "sales_popularity.sort_values(by=\"percentage_sold\")" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "name=%{x}
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sionCtP0GAAAAAABgtqQsionCtP0GAAAAAABgtqQsionCtP0GAAAAAABgtqQsionCtP0GAAAAAABgtqQsionCtP0GAAAAAABgtqQsionCtP0GAAAAAACYRy9tv5KOn1ytva2zspGWljtpabmTOisbY99eev9SUhbFRGHafnMDAAAAAMyTi5d3uiVBXTGxtr7VUx50VjbS2vrWyLeP+3jTIGVRTBSm7Tc5AAAAAMA8GnTExPGTq+ni5Z3uxxcv7/Tcb9jt4z7eNEhZFBOFafvNDQAAAAAwj+qKiWvXd9PSciddu75b+7lht/c/x7j3nxQpi2KiMG2/uQEAAAAA5pFiQgZFMVGYtt/cAAAAAADzSDEhg6KYKEzbb24AAAAAgHnkGhMyKIqJwrT95gYAAAAAmEeDiom19a3UWdnoftxZ2Uhr61sj395Z2ei5fdj9p0HKopgoTNtvbgAAAACAeXLx8k5aWu70OLu53XOfzspG97ZqqTDK7f3FxCiPN2lSFsVEYdp+kwMAAAAAs3P85OrQHeDVHfIvbb9Se598HYQ6bX+NDCdlUUwUpu03AAAAAAAwG8dPrvb85f/xk6s9pwzKZcOgMmKYs5vbUz8FEZMhZTnyxcSJU2vpwqUrtbdduHSlp4ncPP/y2Pdp+w0AAAAAAExfPj1R0+fW1rcOnLJoHEvLnXTt+m7rXyvDSVmOZDFx6869njKhrpjIhcOtO/d6/k/1vqPcp+03AAAAAAAwfXXFRD5CIpcJS8udnlM9jVM0OFricJGyHMlioppBxcTp1TMHjn7YPP9yOr16Zqz7tP0GAAAAAABmo/8iztViIv/74uWd7u1nN7dHvmaEoyUOFynLwhYTdZ/PR0iMc5+23wAAAAAAwGwMumB19bb+cqG/rKiztr7laIlDRsqimKjk6s6NnlM3jXKf/UcfAdCSD/c+SvuPfw0AAAvnV3uP0/6jX7Ow2t8e44n/6w+/n/6PM9/ufry03EmvXXuz5z51n6v665vvpqXlTvrrm++2/vUceG27j9LP/vrxQvrbXz5unI2URTFRSaSYePf+HgCteZjeu78HAAAL5933H6Z377O42t4W4937e+n//fO/SkvLnbTzs1vdz/3W7/5B+uz/vNr9+Pe2/m3Px3/nH26kv/MPN3oe57d+9w/Sb/3uH7T+9Rzw/sP08qW99PQzjxbOP/0Xj9LP/ma/cT5SFsVEJU7lBAAAAAAM8tL2KwdO4dRvbX2re/vxk6s9t3VWNlJnZePA483ltSXe+1X6N3+yl/6XLz9aOL/9tUfp+l8/bJyPlGVhi4nTq2fScy+e6/lc3cWvh92n9R8QAAAAAACTpphonI+UZWGLiXzkQz4l06079w7cd5T7tP4DAgAAAABg0hQTjfORshzJYiIXCP36C4rN8y/33L55/uUDjzXsPq3/gAAAAAAAmDTFRON8pCxHspiYZVr/AQEAAAAAMGmKicb5SFkUE4Vp/QcEAAAAAMCkKSYa5yNlUUwUpvUfEAAAAAAAk6aYaJyPlEUxUZjWf0AAAAAAAAPdfO/DtHvv4UK69V7zzvVGionG+UhZFBOFafsHKwAAAAAw2OtvPUwv/J976Znf3V84f3rlYdp9Lzg7xUTjfKQsionCtP2DFQAAAAAY7KfXH6ann9lvfWd3G/7Dq4oJxcR8RjFRmLZ/sAIAAADAuI6fXE1Ly520tNxJnZWNA7d3VjYab69aW9/q3neU+8+aYiI4O8VE43ykLIqJwrT9gxUAAAAAxnH85Go6u7nd8/Ha+lb347X1rZ5yobOy0XN73eM1PX7bFBPB2SkmGucjZVFMFKbtH6wAAAAAMKqLl3fS0nKn8XPHT66mi5d3em7vLx+anN3cnqujJhQTwdkpJhrnI2VRTBSm7R+sAAAAADCqumLi2vXdtLTcSdeu7/b8u+72UZ6js7LhiIk5oZhQTMxrFBOFafsHKwAAAACMY2m501McTLKYOLu5faD4aJtiIjg7xUTjfKQsionCtP2DFQAAAADGkYuGftXbIsXES9uvjHVkxawoJoKzU0w0zkfKopgoTNs/WAEAAACgxNnN7Z6LW0euMTGPR0pkiong7BQTjfORsigmCtP2D1YAAAAAiMrXnKge5bC2vtVz8erOykZPcdFZ2Thw+zxd7LqfYiI4O8VE43ykLIqJwrT9gxUAAAAAxpFPuVQ9hVO/zspG9/b+0qFaRAw6LdTScqfnqIs2KSaCs1NMNM5HyqKYKEzbP1gBAAAAgMEUE8HZKSYa5yNlUUwUpu0frAAAAADAYIqJ4OwUE43zkbIoJgrT9g9WAAAAAGAwxURwdoqJxvlIWRQThWn7BysAAAAAMJhiIjg7xUTjfKQsionCtP2DFQAAAAAYTDERnJ1ionE+UhbFRGHa/sEKAAAAsMiOn1xNS8udruptZze3e26ru0+/zspG936dlY3Wvz7KKSaCs1NMNM5HyqKYKEzbP1gBAAAAFlVnZSOtrW91P15b3+opE85ubo9VLvT///7Hnwe37i2mkpkpJoKzU0w0zkfKopgoTNu/jAAAAAAW1fGTq+ml7Ve6H7+0/Uo6fnK1+/G4xcTxk6vp4uWd7scXL+/0PF7b/p8/2U//5MXFs3l+L/3NnfjcFBPB2SkmGucjZVFMFKbtX0gAAAAAiyqfquns5nbavfvkCIf87+rtWVPJcO36blpa7qRr13cbP9em//uP2t9h24bfObOvmAhSTMQoJqYfxURh2v6FBAAAALCocnFQvc5E0/07KxsDj6BQTMwvxUScYiJGMTH9KCYK0/YvJAAAAIBFtbTc6Tn1Uj5CYtD9L17eGXi7YmJ+KSbiFBMxionpRzFRmLZ/IQEAAAAsokiR0FRM7N6d/2tMKCZiFBPB2SkmGucjZVFMFKbtX0gAAAAAi2ppuZPW1re6H5/d3O4pEvpLheMnV3vu339qp7X1rZ6POysbPfdvm2IiRjERnJ1ionE+UhbFRGHa/oUEAAAAsMiaLm7dWdnoub2/ZKi75kT1/wy6HkVbFBMxiong7BQTjfORsigmCtP2LyQAAAAAFoNiIkYxEZydYqJxPlIWxURh2v6FBAAAAMBiUEzEKCaCs1NMNM5HyqKYKEzbv5AAAAAAWAyKiRjFRHB2ionG+UhZFBOFafsXEgAAAACLQTERo5gIzk4x0TgfKYtiojBt/0ICAAAAYDEoJmIUE8HZKSYa5yNlUUwUpu1fSAAAAAAsBsVEjGIiODvFRON8pCyKicK0/QsJAAAAgMWgmIhRTARnp5honI+URTFRmLZ/IQEAAAAcJn/1X/fS5b94mF79yeJ540bzjs5hFBMxiong7BQTjfORsigmCtP2L3MAAACAw+S1Nz5M/9va4u0k/rtfeZRe/QvFRIRiIk4xEaOYmH4UE4Vp+5c5AAAAwGGimIjPTjERo5gIzk4x0TgfKYtiojBt/zIHAAAAOEwUE/HZKSZiFBPB2SkmGucjZVFMFKbtX+YAAAAAh4liIj47xUSMYiI4O8VE43ykLIqJwrT9yxwAAADgMFFMxGenmIhRTARnp5honI+URTFRmLZ/mQMAAAAcJoqJ+OwUEzGKieDsFBON85GyKCYK0/YvcwAAAIDDRDERn51iIkYxEZydYqJxPlIWxURh2v5lDgAAAHCYKCbis1NMxCgmgrNTTDTOR8qy0MXEcy+eS0vLna7Tq2cO3OfCpSs999k8/3LP7W3/MgcAAAA4TBQT8dkpJmIUE8HZKSYa5yNlWdhi4rkXz6UTp9Z6Pnfi1FpPOZFLiVt37qWUUrp1515aWu6kC5eudO/T9i9zAAAAgMNEMRGfnWIiRjERnJ1ionE+UpaFLSZOnFo7cPRDLiJyTq+eOXCfzfMv95QXbf8yBwAAADhMFBPx2SkmYhQTwdkpJhrnI2VZ2GIin8YpHw2R0pMi4rkXz3U/7j86IqWD5UXbv8wBAAAADhPFRHx2iokYxURwdoqJxvlIWRa2mEjpydEP1etHVEuJlOqLias7N3oKjdu/eAhAa/bm4DUAAEAbDu+68I/feLiwxcTlv4x/3/77vYfp2wtcTNx8N77MvX59b2GLiUuv7qX/fi84u3sfpu0FLiZuvNP8fpWyLGwxsXn+5Z5rTJxePXPg4tajFBMf/fp/ANCSvUe/bv01AABAGw7zuvDr1/cXtph47fX9ou/5d/64/a+jDb9zZj/98sOPwrO78fPHC1tM/Omf76fHH8Xm9ujxr9P3Ly7m3H77a4/SrXeblzkpy0IWE/ki1ld3bvR8vv9i107lBAAAADBZTuUUn51TOcU4lVNwdk7l1DgfKYtiopL+oyH6rzmRkotfAwAAAJRQTMRnp5iIUUwEZ6eYaJyPlGUhi4mUnpQO1VM5pZTSiVNrPaVD/xEUudCoHkXR9i9zAAAAgMNEMRGfnWIiRjERnJ1ionE+UpaFLSZS+vi6EoMufp3SwQtkV69BkZJiAgAAAGAcion47BQTMYqJ4OwUE43zkbIsdDExibT9yxwAAADgMFFMxGenmIhRTARnp5honI+URTFRmLZ/mQMAAAAcJoqJ+OwUEzGKieDsFBON85GyTKSYeO7Fcz3Xazhxaq176qN8fYajmrZ/mQMAAAAcJoqJ+OwUEzGKieDsFBON85GyTKSYOHFqLV3duZFSenLB6FxSXLh0pedi0kcxbf8yBwAAADhMFBPx2SkmYhQTwdkpJhrnI2WZSDGxtNzpFhPPvXiuexHpqzs30tJyZxJPMbdp+5c5AAAAwGGimIjPTjERo5gIzk4x0TgfKctEionTq2fShUtXUkpPSor87+rRE0c1bf8yBwAAADhMFBPx2SkmYhQTwdkpJhrnI2WZSDFx68697jUlqqduWlrudI+eOKpp+5c5AAAAcHhdu77b3afSr3q/zspG9/OdlY3Gxzy7uT308dqkmIjPTjERo5gIzk4x0TgfKctEiolFTtu/zAEAAICj5ezmdlpb3+p+vLa+1VNGdFY2em6v+//Dyos2KSbis1NMxCgmgrNTTDTOR8oykWJi8/zLoduOQtr+ZQ4AAAAcLUvLnXTt+m734+MnV9PFyzvdjy9e3knHT64O/P+KifmkmIhTTMQpJmIUE9PPxC5+na8rUc1zL55z8WsAAACAEfUfLZFP9VQtKuo+1/8Y1VM4NZUYbVBMxGenmIhRTARnp5honI+UZSLFxNWdG2lpuZOu7tzofi6XErfu3JvEU8xt2v5lDgAAAPOkukP8pe1Xem47fnJ15OskZC9tvzJ3O9anPb9hJcSwYqJfZ2Vjro6gUEzEZ6eYiFFMBGenmGicj5RlYteYqJYTp1fPLEQpkZJiAgAAAHbvfryzvL+MyI6fXE1nN7d7Pm66TsLFyztz+xf/07K2vnVgJpMoJvIs2/76MsVEfHaKiRjFRHB2ionG+UhZJnrx6wuXrnRXGhYlbf8yBwAAgHmwtr7VUzxU1e0YH3Vn+aIcMdFUNox7jYlR5t8mxUR8doqJGMVEcHaKicb5SFkmWkyk9KScOHFqbdIPO7dp+5c5AAAAzIN8ZEP1VE55J3vdjvFR/+p/UYqJuqMlqrdVT8XUWdnouW//qZr65zXs6JRZU0zEZ6eYiFFMBGenmGicj5QlXExUVzSGOcpp+5c5AAAAtC2XDNW/6s8XYM4fLy13eo6oUEz0fo3DZtFZ2Rh4fY7+YqJ636XlzlyVErt3FRMls1NMxCgmgrNTTDTOR8oy8SMmFi1t/zIHAACAtg0qGaplRb5Pv2GPvQjFxKJRTMRnp5iIUUwEZ6eYaJyPlGUixcRzL56rPTJiabmTnnvx3CSeYm7T9i9zAAAAmAf9R0wM+lx2dnN7pL/kV0wcPYqJ+OwUEzGKieDsFBON85GyTKSYOHFqLV24dOXA5xfhehNt/zIHAACAebC2vtVTIJzd3B5YKORrTlSPsOg/HVGmmDh6FBPx2SkmYhQTwdkpJhrnI2WZSDGxtNxJV3duHPj81Z0brjEBAHusmQ8AACAASURBVAAAC2Jtfat7iqb+MiFfR2HQKZz6i4lcXlRVr1HB4aWYiM9OMRGjmAjOTjHROB8piyMmCtP2L3MAAACAw0QxEZ+dYiJGMRGcnWKicT5SlokUExcuXUlLy51068697udu3bmXlpY7tYXFUUrbv8wBAAAADhPFRHx2iokYxURwdoqJxvlIWSZSTKT08WmbqupO73TU0vYvcwAAAGD2br73YXr9rYfpp2/uLZw33tormp1iIj47xUSMYiI4O8VE43ykLBMrJhY1ba8IAQAAAO34l+f30289v3j+5Xf3082CuSkm4rNTTMQoJoKzU0w0zkfKopgoTNsrQQAAAEA7/vk3F3NH5+99SzERoZiIU0zEKSZiFBPTz0SKiXw9iUGOctpeCQIAAADaoZiIUUzEZ6eYiFFMBGenmGicj5RlIsXEiVNrafP8y+nqzo104tRa9/OnV8+4+DUAAACHUvUP7l7afqX7+bOb2z23dVY2ih6vbW/99cP0xlt7C+m//LeyncSKiRjFRHx2iokYxURwdoqJxvlIWSZSTOQLXd+6c6+nmOgvKo5i2l6BBAAAYLKuXd9tLA/6i4jOykZaW98KP17bfnTtYXr6mUfpH/7jxfKPnn+UfvKGYiJCMRGjmIhTTMQpJmIUE9PPRIuJ/O+cqzs3nMoJAACAQ2VtfSud3dwe+f5nN7cbj5oY9/Fm7fJfPkx/9yvt7wSatb+/9ii99rpiIkIxEaOYiFNMxCkmYhQT089EionTq2fS5vmXD/x78/zLjpgAAADgUFla7qTjJ1d7Tr107fruwPsfP7naeMTEuI83a4qJ+OwUEzGKifjsFBMxiong7BQTjfORskykmOhPdWXr1p1703iKuUnbK5AAAABMTj7t0sXLO93P5WtK9N83lw1NR0uM83htUUzEZ6eYiFFMxGenmIhRTARnp5honI+UZSrFxCKl7RVIAAAAJicXCf1HNPSXC1Vr61sDy4nI482aYiI+O8VEjGIiPjvFRIxiIjg7xUTjfKQsE7/GxKKl7RVIAAAAJquuNGgqEi5e3mk8AmLcx5s1xUR8doqJGMVEfHaKiRjFRHB2ionG+UhZFBOFaXsFEgAAgMlaW99Kx0+udj8+u7nd83H137t3P0ydlY2eIyb6Px72eG1TTMRnp5iIUUzEZ6eYiFFMBGenmGicj5RlYhe/vnDpyiQe6tCl7RVIAAAAJm9tfat77cS6IqJ6bcX+0zj1FxPDHq9tion47BQTMYqJ+OwUEzGKieDsFBON85GyTKSYuHXnXjpxam0SD3Xo0vYKJAAAAJRQTMRnp5iIUUzEZ6eYiFFMBGenmGicj5RlYqdyanKU0/YKJAAAQJPqttlL26/03Fb9y/9BF28uuT+Hg2IiPjvFRIxiIj47xUSMYiI4O8VE43ykLBMpJhY5ba9AAgAA1Ll2fbe2jMjW1rcOXBdhbX1r4OONe38OD8VEfHaKiRjFRHx2iokYxURwdoqJxvlIWaZ68esLl64c+VM8tb0CCQAAUGdtfSud3dweePvxk6vp4uWd7scXL+80Xvtg3PtzeCgm4rNTTMQoJuKzU0zEKCaCs1NMNM5HyjLVYuLqzg2ncgIAAGhBvshy9VRO167vpt27Hx9NkT8e9Lmm25ruz+GimIjPTjERo5iIz04xEaOYCM5OMdE4HynLVIuJzfMvO2ICAABgxnJpUD3C4ezmdlpa7vTcrphg965iomR2iokYxUR8doqJGMVEcHaKicb5SFnCxUQ+GmKYusLiKKXtFUgAAIB+g0qDXFYc2WLi3oIqnJtiIj47xUSMYiI+O8VEjGIiODvFRON8pCxTPWJiEdL6ijcAAECN/iMm+j931K4x8Td3PkybL+2nr/3zRwvn3/zJfroZ3el0VzFRstwpJmIUE/HZKSZiFBPB2SkmGucjZZlIMbHIaXvlGwAAoM7a+lZPcXB2c7vn47X1rdRZ2eh+3FnZSGvrWz0fV28fdv+2vXP7w/TVf7aYO52+/Uf7afe9X4Vnp5iIL3eKiRjFRHx2iokYxURwdoqJxvlIWRQThWl75RsAAGCQtfWt7ml2645u6KxsdG+vlg75trrPDbp/2xQTiolxKSbiFBMxiok4xUScYiJGMTH9KCYK0/bKNwAAAIoJxcT4FBNxiokYxUScYiJOMRGjmJh+FBOFaXvlGwAAAMWEYmJ8iok4xUSMYiJOMRGnmIhRTEw/C19MbJ5/uXsoct1FvC9cutJz++b5l3tub3vlGwAAAMWEYmJ8iok4xUSMYiJOMRGnmIhRTEw/C11MnF49k5aWOwNvz6XErTv3Ukop3bpzLy0td9KFS1e692l75RsAAADFhGJifIqJOMVEjGIiTjERp5iIUUxMPwtbTOTSoSmnV88cOEJi8/zL6fTqme7Hba98AwAAoJhQTIxPMRGnmIhRTMQpJuIUEzGKieknXExUT280zDzm9OqZ9NyL5xrv0390REoHC422V74BAABQTCgmxqeYiFNMxCgm4hQTcYqJGMXE9DORIyZOr545sAO/6fPzkKXlTjpxaq2xRKkrJq7u3Og5vdPf/urxE//tTkrHjgEAcAjt/e6Zj9froGX3HzxO7z94tJBK5vbu+48Wtpj4w3+9n+7/cj88uys/3V/YYuLqX8Xn9v4vn+ygb/vraMPvbe6nX/wy/p69+p/2FraY+PNr8WXu/i8fpXP/uv2vow2/c2Y/vXc/vsz9p5/tL2wx8cM/20sfPIjN7YMHj9K//XeLObff/tqj9PPd5mVOynJsEg9Sd9HolJ4cXXDi1NoknmLiqbuQ9XMvnuspJ0YqJj589MTN261vUAMAELP3wj/7eL0OWvTBrx6lV3+8l9Z/79HC+f3N/fSf/+teeHbvvr+/2MXEg/jsrvx0b7GLieDc3n+wv9jFxIP47Ba7mIi/V+8/2F/wYiK+zP2n/7LgxcSvguslD/YXu5i41bzMSVmOTeJBBhUTeSf+PKaudMgXt85fyzincrr1s5spHTuW/sf/9ButH8IMAMBoHvyvfz+lY8fS/edfaP21QHbx1Yetb4y34Tef2U/X3oyf4sSpnJzKaVxO5RTnVE4xTuUU51ROcU7lFONUTtPPRIqJQadsurpzY26PmKi7sHV/MVF3HYpBF79WTAAAHD6KCeaRYiI2N8WEYmJciok4xUSMYiJOMRGnmIhRTEw/Eykm8pER/UdN1J0uaV6Sj3yovubTq2d6Sod8n3zaplxcVEuYvCAqJgAADh/FBPNIMRGbm2JCMTEuxUScYiJGMRGnmIhTTMQoJqafiRQTKX28075qXi98nZMLlaxaSuRsnn+55z79RUteEBUTAACHj2LicDi7uX1gW2NpuTPw9s7KxtDH7KxsjHX/WVJMxOammFBMjEsxEaeYiFFMxCkm4hQTMYqJ6WdixcSiJi+IigkAgMNHMXE4nN3cbiwP+m/rrGyktfWtgfdfW9/q+T/D7j9rionY3BQTiolxKSbiFBMxiok4xUScYiJGMTH9KCYKkxdExQQAwOEzy2Ji2F/9r61vjf1X/7t3P0wvbb+Sjp9cbX2W057dOEc1DLv/8ZOr6eLlne7HFy/vzNUMFROxuSkmFBPjUkzEKSZiFBNxiok4xUSMYmL6CRcTdRtVgxzl5AVRMQEAcPjMupgYtrO8/+Ozm9sD73/x8k53fXuedqpPa3bV7YthX+/xk6sDj4C4dn03LS130rXru42fa5NiIjY3xYRiYlyKiTjFRIxiIk4xEaeYiFFMTD+OmChMXhAVEwAAh888FRPR+y/CERP9OisbtbM5fnJ16NEmion5pZiIU0zEKCbiFBMxiok4xUScYiJGMTH9KCYKkxdExQQAwOHT5qmchpUJnZWNxiMmskUsJvLRIoNu77+GRJViYn4pJuIUEzGKiTjFRIxiIk4xEaeYiFFMTD8TKyau7tw4cAqnqzs3JvXwc5u8IComAAAOnzYvfj3or/53735cYozyOIqJ8W93jYn5pJiIU0zEKCbiFBMxiok4xUScYiJGMTH9TKSYuHDpSlpa7qRbd+51P3frzr20tNxJFy5dmcRTzG3ygqiYAAA4fNosJgbtPH9p+5Wx/np/EYqJuutvVK8h0X97f+nT/3H/ERWdlY2B16Rog2IiNjfFhGJiXIqJOMVEjGIiTjERp5iIUUxMPxMpJk6cWqstIC5cupJOnFqbxFPMbfKCqJiA9u195rPpwRefav11HAZmBfDEvBUT4xwpkS1CMdFZ2eg5Mru/ROi/vf9IlLqjU6r/Z5xrf8xk2VBMhCgmFBPjUkzEKSZiFBNxiok4xUSMYmL6mUgxMei0Tfn0Tkc5eUFUTMB4HnzxqZSOHav13g9+mH7xrXMpHTuWfvGtcyM/pp3tozMrgCdmWUwM+6v/plM7Nd2+CMXEolFMxOammFBMjEsxEaeYiFFMxCkm4hQTMYqJ6ccRE4XJC6JiAsbz4ItPpcef+OREH3OUne0PvvhU2vvMZ1v/+tummAB4YpbFRNNf/eeLL9fJ10LoLybyERdVo1wsm/mnmIjNTTGhmBiXYiJOMRGjmIhTTMQpJmIUE9OPa0wUJi+IigkYj2KiXYoJgCfaPJUTDKKYiM1NMaGYGJdiIk4xEaOYiFNMxCkmYhQT089EiomUPj5tU1Xd6Z2OWvKCqJiA8QwrJt77wQ+7p3XKn/vg2ed7Tvn0+BOf7CkZ8s726mmiqs/R///TsWNDS4phz1l9rXXPOcrrqt6n+ny3X78x0uPv3v1w7K9rlNdUN69qmZFvz68zu/36jZSOHUsfPPv8yDMCaItignmkmIjNTTGhmBiXYiJOMRGjmIhTTMQpJmIUE9PPxIqJRU1eEBUTMJ5h15joLybqdoLvfeazB4qJ/utSPP7EJ3t2po9zxMQoz5mvhVH9f3uf+WzPjvdRXle+T3Vn/qiPX/f/hu34H+U1ffDs8z2PW1c4PP7EJw889wfPPt/z/KN8DQBtUUwwjxQTsbkpJhQT41JMxCkmYhQTcYqJOMVEjGJi+lFMFCYviIoJGM+4R0zUXQi7v2SoOz1R/33GKSZGec66++Qd+LnQGOV1DTq10rDHz/8e5yLho76mUf5fLm+q9+kvK0aZEUBbFBPMI8VEbG6KCcXEuBQTcYqJGMVEnGIiTjERo5iYfhQThckLomICxjNOMVF3Wqf8GNMqJkZ5zrxzvenIj1FfV919xnn86udHmf+oxcSw0zn1FyP56IhcOIz6NQC0RTExPW/vPkw/uvYwvfoXi+cv/qpsp5NiIjY3xYRiYlyKiTjFRIxiIk4xEaeYiFFMTD8TKSaee/FcOnFqrfvxiVNr3etMVC+IfRSTF0TFBIznKBUTw3aulxYTo+687y8RSl/T4098svYUTE3/r//2cb8GgFlTTEzP9Z9/mP73r+2nv/uVRwvn3/zgYbpZsJNYMRGbm2JCMTEuxUScYiJGMRGnmIhTTMQoJqafiRQTJ06tdS90feHSlW5JceHSlXR69cwknmJukxdExQSMZ5xiYtDpiqZZTIz6nHXXd+hXeiqnYY8/6msf5zUNKhTq/l/1e1X3fyJfA3B4vLT9Slpa7qSLl3cO3Jb/UGVpuZNe2n6l8XGOn1zt3rezsjGz1z9yMXFvMd26F5/t9Z9/mH77nyzmDgDFRIxiIk4xEaOYiFNMxCgm4hQTcYqJGMXE9DORYmJpudMtJp578Vx67sVzKaWUru7cSEvLnUk8xdwmL4iKCRjPuNeY6L9Ycr593GKi/1RDTUZ5znyUQn8JsPeZz07kGhPDHv+9H/zwQNEyytc4ymvqLxTy49a9znx0Rd33dJQZAYfTS9uvdAuFajFx7fruSGVEdvzkajq7ud3z8dr61ky+hmHFxM33fpX+5D8+TF/7548Wzje+/Shdfzs+W8WEYmJciok4xUSMYiJOMRGjmIhTTMQpJmIUE9PPRIqJ06tn0oVLV1JKT0qK/O/q0RNHNXlBVEzAeB588anG6w7U/fV93vmdy4HIERP5ftXHaXqdw55z9+7HhUXVuK9rUDExyuNXX2M2bIf/KK+p/3nz7XWvM5cWg47SGPY1AIdPLiV27354oJhYW9/qKRqaXLy8k5aWO0M/Ny1Dj5iwMRaerWJCMTEuxUScYiJGMRGnmIhRTMQpJuIUEzGKielnIsXErTv3uofeV0/dtLTc6R49cVSTF0TFBMxe0878o/ScAPOmWkrs3j1YTCwtd3pOzbS03EnXru/WPlZdCZGPuBj0fyZJMVG2MdZEMaGYGJdiIk4xEaOYiFNMxCgm4hQTcYqJGMXE9DORYmKRkxdExQRM1y++da7ntEKDrmdw2J8TYN71lxK7d3uLiVwqVIuKs5vbjUdALC13eo6wUEzMB8VEnGIiRjERp5iIUUzEKSZiFBNxiok4xUSMYmL6UUwUJi+IigmYrnyqoP5TPh215wSYd2vrWz1HQvRf4HpQqTDoAtm7dz8uIvrN4utRTJRtjDVRTCgmxqWYiFNMxCgm4hQTMYqJOMVEnGIiRjEx/SgmCpMXRMUEALCo6k7l1F9CNBUT/c5ubs/Nxa9tjCkmIhQTMYqJOMVEjGIiTjERo5iIU0zEKSZiFBPTj2KiMHlBVEwAAIuq7uLX1dM9nd3c7vm4s7KROisbtY+Vrzkxi9M47d5VTJRujDVRTCgmxqWYiFNMxCgm4hQTMYqJOMVEnGIiRjEx/SgmCpMXRMUEALCo6o6GqJ7yqf+aFP3FxEvbr8z8FE6ZYqJsY6yJYkIxMS7FRJxiIkYxEaeYiFFMxCkm4hQTMYqJ6SdcTCwtd9LVnRsH/r1oyQuiYgIA4PBRTJRtjDVRTCgmxqWYiFNMxCgm4hQTMYqJOMVEnGIiRjEx/SgmCpMXRMUEAMDho5go2xhrophQTIxLMRGnmIhRTMQpJmIUE3GKiTjFRIxiYvoJFxMnTq2lC5eupJQUE7t3FRPMt/d+8MOUjh1L7/3gh62/ljp7n/lsevDFp1p/HUfFgy8+ldKxYykdOzZXc33wxafS3mc+2/rrqDPKe2SeXz8Qp5go2xhrophQTIxLMRGnmIhRTMQpJmIUE3GKiTjFRIxiYvoJFxO37tzrORdwk6OcvCAqJphniolmt1+/0d2R369uR3T/fW6/fqPxPh88+3zt7b/41rmJfy2/+Na5lI4da/17Wmeed+wrJmBxKSbKNsaaKCYUE+NSTMQpJmIUE3GKiRjFRJxiIk4xEaOYmH4mcvFrR0woJphv81JMDNqxOy/FxChFQX/R8MGzzx8oJ6pfZ37s6uw/ePb5qe3gfvDFp0aaZRs72ed5x75iAhaXYqJsY6yJYkIxMS7FRJxiIkYxEaeYiFFMxCkm4hQTMYqJ6WcixcQiJy+IignmmWKi2ajFxAfPPp8ef+KTBz7/+BOf7Ckr6j6uPvagoywmYdRZKiZ6KSZgcSkmyjbGmigmFBPjUkzEKSZiFBNxiokYxUScYiJOMRGjmJh+FBOFyQuiYoJ5VrfTNX8uq9vhPuy0RqOc9ijLRxbU3T/vTK9eG6H/9dT9//4d8KM8Tp1Ri4m9z3y29mvs32HdVEx88Ozz4RKm7pRT/UdqjHKaqVG+F9X73H79xkjzr1uuqstd3Y79/JqrjzXOcjXq/xk2u7r3SNOc4DB6652H6UdXH6bLf7l4fnr94cCdJ4qJso2xJooJxcS4FBNxiokYxUScYiJGMRGnmIhTTMQoJqafiRUTV3duHLi2xCKc3ikviIoJ5ln/Tte66xDsfeazPTvx666NMM7tdZqOmOgvBh5/4pM9O6s/ePb5nufLO5qrnxvlceoMusZE/1/PD3qsB198qudrbzqVU/Roifw9rH5t+ftY/VzpERN5hv3f21Hmn19PfwGWX0//c+ZSojqPyHI17P+MMrv+98iwU3TBYWRjrH4uiomyjbEmignFxLgUE3GKiRjFRJxiIkYxEaeYiFNMxCgmpp+JFBMXLl1JS8uddOvOve7n8sWxL1y6MomnmNvkBVExwTzr3+lad3RA3tF8+/UbQ48gGOeaDFXjnMpplJ3A/f8v+jh18o7pUUqOfN/q5+oufl29/kP/X+MPez2DCof+UmQSxcSoR3T037f/SJGm58ylROlyNcr/GWV2o7xHFBMcdjbG6ueimCjbGGuimFBMjEsxEaeYiFFMxCkmYhQTcYqJOMVEjGJi+plIMXHi1FptAXHh0pV04tTaJJ5ibpMXRMUE86y603XQ0QH9Rwnkv5wftON82O11SouJYacTmmQxkR+v/xRNo5QDdeqKn/zX+KO8xkE73/uPfplmMdE0/7qLfA96zrzsDJr5uMvVsP8zyuyq75FB15tQTHDY2Rirn4tiomxjrIliQjExLsVEnGIiRjERp5iIUUzEKSbiFBMxionpZyLFxKDTNuXTOx3l5AVRMcE8qysmRr0Q9rC/7B/nL/9LionHn/hk7emnpllM1B2NMMo1JgY9Vn5tv/jWuZ7H7f+4TtvFxLD5j1pMpGPHuo/VdHTFuEeUNP0fxQQ8YWOsfi6KibKNsSaKCcXEuBQTcYqJGMVEnGIiRjERp5iIU0zEKCamH0dMFCYviIoJ5lndaWqadgrXmcTpnaLFxKCd3tMuJuquc1FXIAy7jkX/ERKRYmLUozWmUUyMOv9hy1X1OXMpMGw5nMTpnUaZXV1551ROHDU2xurnopgo2xhrophQTIxLMRGnmIhRTMQpJmIUE3GKiTjFRIxiYvpxjYnC5AVRMcE8G3Rh3/4dr3uf+Wy6/fqN9N4PfnhgB2zekTzK7YNex6D7jFIoDLrQ8iSKiQdffKp2J3Tda+1/HXUXSa57/OrripzKadIXvx7nezHq/PMsRr34dX85EVmuRvk/kYtf918MPt+umOAwszFWPxfFRNnGWBPFhGJiXIqJOMVEjGIiTjERo5iIU0zEKSZiFBPTz0SKiZQ+Pm1TVd3pnY5a8oKomGCe1Z2aJn+uqu7USVXVncPDbh+kej2A/HyjFAr9rzffPoliIu+k7jfo/uN83fl1938+cqqiuuuDjFoslHwvRp3/oFnm5a7ue5Hvn0uAyHI1yv8ZNru690j1cfPXqpjgMLMxVj8XxUTZxlgTxYRiYlyKiTjFRIxiIk4xEaOYiFNMxCkmYhQT08/EiolFTV4QFRMAwDyzMVY/F8VE2cZYE8WEYmJciok4xUSMYiJOMRGjmIhTTMQpJmIUE9OPYqIweUFUTAAA88zGWP1cFBNlG2NNFBOKiXEpJuIUEzGKiTjFRIxiIk4xEaeYiFFMTD+KicLkBVExAQCHy9nN7Z5TUHZWNrq3Xbu+e+AUlVnTYx4/uVr7ePPAxlj9XBQTZRtjTRQTiolxKSbiFBMxiok4xUSMYiJOMRGnmIhRTEw/ionC5AVRMQEAh0t/cdBZ2Uhr61sD7392c7vx9uMnV9PZze2ej5vuP2s2xurnopgo2xhrophQTIxLMRGnmIhRTMQpJmIUE3GKiTjFRIxiYvpRTBQmL4iKCQA43M5ubjce5bC03EnXru/W3nbx8s6BoynqPtcmG2P1c1FMlG2MNVFMKCbGpZiIU0zEKCbiFBMxiok4xUScYiJGMTH9KCYKkxdExQQAHG5NRzgMO1qiroTIp4MaVGbMmo2x+rkoJso2xpooJhQT41JMxCkmYhQTcYqJGMVEnGIiTjERo5iYfhQThckLomICAA6nfF2I6NES1ftUT+WkmJgfionpbYw1UUwoJsalmIhTTMQoJuIUEzGKiTjFRJxiIkYxMf1MtJi4defegQtE3rpzb5JPMbWcOLWWTpxaO/D5C5eu9Hw9m+df7rk9L4iKCQA43NbWt2rLibX1rZGuFTHogtltf12ZjbH6uSgmyjbGmigmFBPjUkzEKSZiFBNxiokYxUScYiJOMRGjmJh+JlpMnDi1lq7u3Oh+fHXnRu3O/nnLiVNraWm5c+C15lIilyu5eLlw6Ur3PnlBVEwAwOE26dMxDTv906zZGKufi2KibGOsiWJCMTEuxUScYiJGMRGnmIhRTMQpJuIUEzGKielnYsVE3mnfn7rPzVNOr55Jz714Lm2ef/lAMXF69cyBIyQ2z7+cTq+e6X6cF0TFBAAcLsdPrvZ83FnZOHDERNPREnX3z3LJMS+ncdq9a2NMMTGdjbEmignFxLgUE3GKiRjFRJxiIkYxEaeYiFNMxCgmpp+iYqJ/R/5hO2LiuRfPdUuGumKi/+iIlD4+iiInL4iKCQA4XDorGz2nXOovGV7afqWxXOgvJvL95+0UTpmNsfq5KCbKNsaaKCYUE+NSTMQpJmIUE3GKiRjFRJxiIk4xEaOYmH6KionN8y+npeVOeu7Fcyml+mtMzGv6i4hRi4mrOzd6Tu/06PGvn7jzXkrHjqX0G7/x8ecAmKoP9z9q/TXAYfHW248WdmPsT3+0n/Yf1c/l11/+SkrHjqWPzpytvX1v/6P0/YuLObff/tqjdPPO4/Ayd/P2o4UtJr7/7/fTw+DvqP3Hv07/8UeLObfffGY/vfX2o/Ayd/+Xjxe2mPjOH++nD/fi79cfv76/sMXE6/85vsx9uPc4/f63FnOZ+/3N/fSrgmXup2/uL2wx8drr+wXL3EfpD/+4/a+jDb9zZj998CC+zFkXjs3NunDzMidlmcipnE6vnqndiT+vyYVK/+cixcS79/fSu/f30ns/v9U9YiJ/DgBgXrz+1t7CboxduryX7rxfP5cP/94/SOnYsfTL9Y362b3/MG3/u8X9K7Gf/c1+eJn72d/sLWwxsf3vHqY77z8Mze3O/b30Hy4v5jL3m8/sp9ffiv+c231vb2GLiXN/tB9e5t69v5f+7OrewhYTP3kjvszdef9h+r0FPmLidsEy95OdhwtbTPzZX5Ysc3vp2wt8xMTuXevCEU3rwkNZF26cj5Rlohe/nvejJHJyMTFILiOcygkAOCocvl4/F6dyat4YcyqnGKdyinEqpzincopxEtskTwAAIABJREFUKqc4p3KKcSqnOKdyinMqpxincpp+JlpMpPTxjvvqBaIPQwZd/Dqfpqp6Pxe/BgAOGxtj9XNRTJRtjDVRTCgmxqWYiFNMxCgm4hQTMYqJOMVEnGIiRjEx/RQVE3XXlMinOHruxXNpabmTNs+/PJEXOu3UFRO5ZMlfU/56q0dR5AVRMQEAzDMbY/VzUUyUbYw1UUwoJsalmIhTTMQoJuIUEzGKiTjFRJxiIkYxMf0UFRMnTq2lqzs3uh9f3blxYOd+/8fzmrpiIn++Wrz0Fy15QVRMAADzzMZY/VwUE2UbY00UE4qJcSkm4hQTMYqJOMVEjGIiTjERp5iIUUxMP0XFRN31JKpHGCxC8oKomACA6bt598P04zcepld/snh+dLVsdjbG6ueimCjbGGuimFBMjEsxEaeYiFFMxCkmYhQTcYqJOMVEjGJi+pn6ERNHPXlBVEwAwGx8+18/Sk+tLJ7nX7QxNo2NMcVE2cZYE8WEYmJciok4xUSMYiJOMRGjmIhTTMQpJmIUE9NP8cWvB11jYlGSF0TFBADMho2xGBtj9XNRTAymmIhTTMQoJuIUEzGKiTjFRIxiIs66cJxiIkYxMf0UFxOLnrwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAYDZsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAaMva+lZaWu50dVY2em4/u7ndc3vW9JidlY2Bj9c2G2MxNsbq56KYGEwxEaeYiFFMxCkmYhQTcYqJGMVEnHXhOMVEjGJi+lFMFCYviIoJANpy/OTqgY/Pbm53Pz67uT1WubC2vtVz/87KRlpb32r968xsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAmBf9RcS4xcTxk6vp4uWd7scXL+8cKD/aZGMsxsZY/VwUE4MpJuIUEzGKiTjFRIxiIk4xEaOYiLMuHKeYiFFMTD+KicLkBVExAcC86KxsHDhionoKp6aS4dr13bS03EnXru82fq5NNsZibIzVz0UxMZhiIk4xEaOYiFNMxCgm4hQTMYqJOOvCcYqJGMXE9KOYKExeEBUTAMyDXEI03aezsjHwCArFxPyyMRanmIhRTMQpJmIUE3GKiRjFRJxiIkYxEWddOE4xEaOYmH4UE4XJC6JiAoC2vbT9ykgFwsXLOwPLC8XE/LIxFqeYiFFMxCkmYhQTcYqJGMVEnGIiRjERZ104TjERo5iYfhQThckLomICgDaNcqRE1lRM7N51jYl5ZWMsTjERo5iIU0zEKCbiFBMxiok4xUSMYiLOunCcYiJGMTH9KCYKkxdExQQAbWk6NdPu3Q8PlArHT66mtfWtgf9/bX2r5+POykbP/dtmYyzGxlj9XBQTgykm4hQTMYqJOMVEjGIiTjERo5iIsy4cp5iIUUxMP4qJwuQFUTEBQBvyaZbq5KMeOisbPZ/vLxnqio3q/2kqPdpgYyzGxlj9XBQTgykm4hQTMYqJOMVEjGIiTjERo5iIsy4cp5iIUUxMP4qJwuQFUTEBALNhYyzGxlj9XBQTgykm4hQTMYqJOMVEjGIiTjERo5iIsy4cp5iIUUxMP4qJwuQFUTEBUGZtfavnr/oH/ZX+S9uvjHW9g+pjvrT9SutfJ+VsjMXYGKufi2JiMMVEnGIiRjERp5iIUUzEKSZiFBNx1oXjFBMxionpRzFRmLwgKiYAytRdB+Hs5nb343zB5qXlzkjFRD7FkTLi6LExFmNjrH4uionBFBNxiokYxUScYiJGMRGnmIhRTMRZF45TTMQoJqYfxURh8oKomACYrLOb27VHTYx6xMTa+lZPscHRYWMsxsZY/VwUE4MpJuIUEzGKiTjFRIxiIk4xEaOYiLMuHKeYiFFMTD+KicLkBVExAezeHe10RJGLCi/i6Yg6Kxu1xcKoxUQ+sqI6u2vXd1v/uihnYyzGxlj9XBQTgykm4hQTMYqJOMVEjGIiTjERo5iIsy4cp5iIUUxMP4qJwuQFUTEB7N4dfjqitfWtnjKis7KR1ta3Bj7eop6O6Ozmdlpa7tTeNkoxked28fLOSI/J4WJjLMbGWP1cFBODKSbiFBMxiok4xUSMYiJOMRGjmIizLhynmIhRTEw/ionC5AVRMQHU6T8d0fGTqz07yy9e3mncyb6IpyN6afuVxqMbxikm+h+jv6xo3b0FVTg3G2MxNsbq56KYGEwxEaeYiFFMxCkmYhQTcYqJGMVEnHXhOMVEjGJi+lFMFCYviIoJoE71dER1O8sH7UDPFu10RKMc1TDOqZz6S4h5Kib+89sP06s/eZhe+fHi2fmZjbEIG2NxiokYxUScYiJGMRGnmIhRTMQpJmIUE3HWheMUEzGKielHMVGYvCAqJoB+/TvZxy0mFu10RJ2VjZGuuTGomOj//2vrWz33O7u5PVKhMSs2xuKzszEWY2Osfi6KicEUE3GKiRjFRJxiIkYxEaeYiLEuHGddOE4xEaOYmH4UE4XJC6JiAqiqOx1RtJiY+9MRTUD+Wuvkr/Xi5Z0Dt1VPc1VXbFQvRj5PpcTuXRtjJbOzMRZjY6x+LoqJwRQTcYqJGMVEnGIiRjERp5iIsS4cZ104TjERo5iYfhQThckLomICyJqOahj3GhPzfjoi4myMxWdnYyzGxlj9XBQTgykm4hQTMYqJOMVEjGIiTjERY104zrpwnGIiRjEx/SgmCpMXRMUEsHt3+OmI1ta3em7vrGyktfWtgf9/3k9HRJyNsfjsbIzF2Birn4tiYjDFRJxiIkYxEaeYiFFMxCkmYqwLx1kXjlNMxCgmph/FRGHygqiYAEY5HdHu3SflQ/58f4lx2E5HRJyNsfjsbIzF2Birn4tiYjDFRJxiIkYxEaeYiFFMxCkmYqwLx1kXjlNMxCgmph/FRGHygqiYAGAcNsbis7MxFmNjrH4uionBFBNxiokYxUScYiJGMRGnmIixLhxnXThOMRGjmJh+FBOFyQuiYgKAcdgYi8/OxliMjbH6uSgmBlNMxCkmYhQTcYqJGMVEnGIixrpwnHXhOMVEjGJi+lFMFCYviIoJAMZhYyw+OxtjMTbG6ueimBhMMRGnmIhRTMQpJmIUE3GKiRjrwnHWheMUEzGKielnYYuJC5euHDgH/IVLV4beb/P8yz235wVRMQEsrrIV68Pq5ntlX7eNsfjsbIzF2Birn4tiYjDFRJxiIkYxEaeYiFFMxCkmYqwLx1kXjlNMxCgmpp+FLSaee/Fcurpzo/vx1Z0bB8qJXErcunMvpZTSrTv3DtwnL4iKCWBR/cG/2kv/+J/uL5w/+Fc2xiJsjMXZGItTTMQoJuIUEzGKiTjFRIxiIk4xEWNdOM66cJxiIkYxMf0sbDFRl9OrZ9JzL57r+bj/CInN8y+n06tnuh/nBVExASwqG2MxNsbis7MxFmNjrH4uionBFBNxiokYxUScYiJGMRFnXTjGunCcdeE4xUSMYmL6UUxUcuLUWk8xUXd6p3wURU5eEBUTcLhdf+fD9MZbDxfSf7lpYyzCxliMjbE4G2NxiokYxUScYiJGMRGnmIhRTMRZF46xLhxnXThOMRGjmJh+FBP/f/pP25RSfTGRT/mU73fn/b105/299O7bt7rFRP4ccHj86Kd76Sv/6FH68oJZ++qj9Bc78bnd/sXD9HsLvDH233/xMDy7H+88XNiNsT/7y5Jlbi99e4E3xnbfi8/u9bf2FnZj7NKrT5adurn86u/9g5SOHUu//NpG/ex+8TBtL/DG2I2/2Q8vcz97Z29hi4ntP3mYbgd/R9x+fy/9h8uLucz95jP76fXr8Z9zN9/dW9hi4twf7YeXuTvv76U/u7q3sMXET96wLhxhXTjGunCcdeG4pnXhoawLN85HyqKYSB+XDf2nbRqlmHj80a+fePe9lI4dS+k3fuPjzwGHxo9f31/YjbHX33wUntvDvcfp97+1mCt3v7+5nz7cexye3U/f3F/YjbHXXt+PL3P7H6Xv/HH7X0cbfufMfvrbX8WXuRs/f7SwG2N/+uf76dHj+rn8+itfSenYsfTrM2drb99/9FH6/sXFnNtvf+1R2r0TX+Z2bz9a2GLi+/9+P+09+ig0t0cf/Tr9xz9fzLn95jP76a2fx9dLPnjweGGLiZf+eD893I+/X60LWxcel3XhGOvCcdaF45rWhYexLty8zElZFr6YGFRKpORUTrBIHL4en53D12Mcvh6fncPXYxy+Xj8Xp3IazKmc4pzKKcapnOKcyinGunCcdeEY68Jx1oXjnMopxqmcpp+FLiZyydBfPuT0Xww7JRe/hqPKxlh8djbGYmyMxWdnYyzGxlj9XBQTgykm4hQTMYqJOMVEjHXhOOvCMdaF46wLxykmYhQT08/CFhPPvXjuwDUl+tN/3Ylbd+4dKDLygqiYgMPNxlh8djbGYmyMxWdnYyzGxlj9XBQTgykm4hQTMYqJOMVEjHXhOOvCMdaF46wLxykmYhQT08/CFhMnTq2lpeVOrWpZsXn+5Z7b+k/5lBdExQQcbjbG4rOzMRZjYyw+OxtjMTbG6ueimBhMMRGnmIhRTMQpJmKsC8dZF46xLhxnXThOMRGjmJh+FraYmFTygqiYgMPNxlh8djbGYmyMxWdnYyzGxlj9XBQTgykm4hQTMYqJOMVEjHXhOOvCMdaF46wLxykmYhQT049iojB5QVRMwOFmYyw+OxtjMTbG4rOzMRZjY6x+LoqJwRQTcYqJGMVEnGIixrpwnHXhGOvCcdaF4xQTMYqJ6UcxUZi8ICom4HCzMRafnY2xGBtj8dnZGIuxMVY/F8XEYIqJOMVEjGIiTjERY104zrpwjHXhOOvCcYqJGMXE9KOYKExeEBUTHFUvbb+Sjp9cDd/er3rNlpe2X2n968tsjMVnZ2MsxsZYfHY2xmJsjNXPRTExmGIiTjERo5iIU0zEWBeOsy4cY104zrpwnGIiRjEx/SgmCpMXRMUER83FyzvdAqGueBh2e79r13fnroyosjEWn52NsRgbY/HZ2RiLsTFWPxfFxGCKiTjFRIxiIk4xEWNdOM66cIx14TjrwnGKiRjFxPSjmChMXhAVExxVkzpiYm19K53d3G796xnExlh8djbGYmyMxWdnYyzGxlj9XBQTgykm4hQTMYqJOMVEjHXhOOvCMdaF46wLxykmYhQT049iojB5QVRMcFRNqpjIR1ZUT+V07fpu619fZmMsPjsbYzE2xuKzszEWY2Osfi6KicEUE3GKiRjFRJxiIsa6cJx14RjrwnHWheMUEzGKielHMVGYvCAqJjiqJlFM5NM4Xby80/3c2c3ttLTcaf3ry2yMxWdnYyzGxlh8djbGYmyM1c9FMTGYYiJOMRGjmIhTTMRYF46zLhxjXTjOunCcYiJGMTH9KCYKkxdExQRH1SSLif4jJPrLijbZGIvPzsZYjI2x+OxsjMXYGKufi2JiMMVEnGIiRjERp5iIsS4cZ104xrpwnHXhOMVEjGJi+lFMFCYviIoJjqpJnsqpv4RQTLTPxlicjbEYG2NxNsbiFBMxiok4xUSMYiJOMRFjXTjOunCMdeE468JxiokYxcT0o5goTF4QFRMcVdFiorOykTorG92P19a3eu53dnN7pEJjVmyMxWdnYyzGxlh8djbGYmyM1c9FMTGYYiJOMRGjmIhTTMRYF46zLhxjXTjOunCcYiJGMTH9KCYKkxdExQRHzcXLOz0Xql5a7qSzm9sj395fTOzefVJO5PvOUymxe9fGWMnsbIzF2BiLz87GWIyNsfq5KCYGU0zEKSZiFBNxiokY68Jx1oVjrAvHWReOU0zEKCamH8VEYfKCqJiAw83GWHx2NsZibIzFZ2djLMbGWP1cFBODKSbiFBMxiok4xUSMdeE468Ix1oXjrAvHKSZiFBPTj2KiMHlBVEzA4WZjLD47G2MxNsbis7MxFmNjrH4uionBFBNxiokYxUScYiLGunCcdeEY68Jx1oXjFBMxionpRzFRmLwgKibgcLMxFp+djbEYG2Px2dkYi7ExVj8XxcRgiok4xUSMYiJOMRFjXTjOunCMdeE468JxiokYxcT0o5goTF4QFRNwuNkYi8/OxliMjbH47GyMxdgYq5+LYmIwxUScYiJGMRGnmIixLhxnXTjGunCcdeE4xUSMYmL6UUwUJi+Iigk43GyMxWdnYyzGxlh8djbGYmyM1c9FMTGYYiJOMRGjmIhTTMRYF46zLhxjXTjOunCcYiJGMTH9KCYKkxdExQQcbjbG4rOzMRZjYyw+u/+vvftpsWu7EgPuz6MvoVEG9Q0CNdBMY2egmqQnBR2TOHI5ge5ApzBNwavMOo8GCTpGEHC7g6CxOkRtcBSHYIhFRFuzxG752T4ZFLtq3333OWefdev+3b8frMHTq6cnHc69e6299h/FWCwUY/XnojExHhoT8dCYiIXGRDw0JmIhF46HXDgWcuF4yIXjoTERC42J7dOY2FB6ETUmxCHE//7Vr4f3H/5x+G//vb94//PNkjvFWPzZKcZioRiLPzvFWCwUY/XnojExHhoT8dCYiIXGRDw0JmIhF46HXDgWcuF4yIXjoTERC42J7dOY2FB6ETUmxCHEL/7Pr4d//e++DP/sj37bXfyH/6gYi4RiLB6KsVgoxuKhGIuHxkQsNCbioTERC42JeGhMxEIuHA+5cCzkwvGQC8dDYyIWGhPbpzGxofQiakyIQwjFmGJsaSjG4qEYi4ViLB6KsXhoTMRCYyIeGhOx0JiIh1w4FnLheMiFYyEXjodcOB4aE7HQmNg+jYkNpRdRY0IcQijGFGNLQzEWD8VYLBRj8VCMxUNjIhYaE/HQmIiFxkQ85MKxkAvHQy4cC7lwPOTC8dCYiIXGxPZpTGwovYgaE+IQQjGmGFsairF4KMZioRiLh2IsHhoTsdCYiIfGRCw0JuIhF46FXDgecuFYyIXjIReOh8ZELDQmtk9jYkPpRdSYEIcQijHF2NJQjMVDMRYLxVg8FGPx0JiIhcZEPDQmYqExEQ+5cCzkwvGQC8dCLhwPuXA8NCZioTGxfRoTG0ovosaEOIRQjCnGloZiLB6KsVgoxuKhGIuHxkQsNCbioTERC42JeMiFYyEXjodcOBZy4XjIheOhMRELjYnt05jYUHoRNSbEIYRiTDG2NBRj8VCMxUIxFg/FWDw0JmKhMREPjYlYaEzEQy4cC7lwPOTCsZALx0MuHA+NiVhoTGyfxsSG0ouoMSEOIRRjirGloRiLh2IsFoqxeCjG4qExEQuNiXhoTMRCYyIecuFYyIXjIReOhVw4HnLheGhMxEJjYvs0JjaUXkSNCXEIoRhTjC0NxVg8FGOxUIzFQzEWD42JWGhMxENjIhYaE/GQC8dCLhwPuXAs5MLxkAvHQ2MiFhoT26cxsaH0ImpMiEMIxZhibGkoxuKhGIuFYiweirF4aEzEQmMiHhoTsdCYiIdcOBZy4XjIhWMhF46HXDgeGhOx0JjYPo2JDaUXUWPiOOLiOz8Ynjx9dh/Pvv3dR/35fYdiTDG2NBRj8VCMxUIxFg/FWDw0JmKhMREPjYlYaEzEQy4cC7lwPOTCsZALx0MuHA+NiVhoTGyfxsSG0ouoMXEc8U/+6Yu1f/7e9deP9vP7DsWYYmxpKMbioRiLhWIsHoqxeGhMxEJjIh4aE7HQmIiHXDgWcuF4yIVjIReOh1w4HhoTsdCY2D6NiQ2lF3FfjYmvvv7R2uT5VOSr/7/6+kc7/bMeYnzv+utFuyCW/vyuQzGmGFsairF4KMZioRiLh2IsHhoTsdCYiIfGRCw0JuIhF46FXDgecuFYyIXjIReOh8ZELDQmtk9jYkPpRdx1Y+KHP35/32BoaUz83c9+qRlRiWff/u6iHRBLf37XoRhTjC0NxVg8FGOxUIzFQzEWD42JWGhMxENjIhYaE/GQC8dCLhwPuXAs5MLxkAvHQ2MiFhoT26cxsaH0Ih76jomL7/zgoCfU9xHfu/56ePL02dZ+fh+hGFOMLQ3FWDwUY7FQjMVDMRYPjYlYaEzEQ2MiFhoT8ZALx0IuHA+5cCzkwvGQC8dDYyIWGhPbpzGxofQiHnpjIu2syI9y+ruf/XKnf9ZDiq++/tGiZ7D05/cVijHF2NJQjMVDMRYLxVg8FGPx0JiIhcZEPDQmYqExEQ+5cCzkwvGQC8dCLhwPuXA8NCZioTGxfRoTG0ov4iE3JtIxTj/88fv7XzuG1f/bilPcKZFCMaYYWxqKsXgoxmKhGIuHYiweGhOx0JiIh8ZELDQm4iEXjoVcOB5y4VjIheMhF46HxkQsNCa2T2NiQ+lFPIbGRLnav2xW9BDPvv3dycury38/9/OHFooxxdjSUIzFQzEWC8VYPBRj8dCYiIXGRDw0JmKhMREPuXAs5MLxkAvHQi4cD7lwPDQmYqExsX0aExtKL+IhNyZ++at6E6K3xkRq0NQiPYe8EdHy84cWijHF2NJQjMVDMRYLxVg8FGPx0JiIhcZEPDQmYqExEQ+5cCzkwvGQC8dCLhwPuXA8NCZioTGxfRoTG0ov4qE1JsqV/hff+cHKz33v+uumhoY4rlCMKcaWhmIsHoqxWCjG4qEYi4fGRCw0JuKhMRELjYl4yIVjIReOh1w4FnLheMiF46ExEQuNie3TmNhQehF33Zj44Y/fr63i/9711/f/vnYE0cV3fnD/s5oSpxmKMcXY0lCMxUMxFgvFWDwUY/HQmIiFxkQ8NCZioTERD7lwLOTC8ZALx0IuHA+5cDw0JmKhMbF9GhMbSi/ivnZMCJGHYkwxtjQUY/FQjMVCMRYPxVg8NCZioTERD42JWGhMxEMuHAu5cDzkwrGQC8dDLhwPjYlYaExsn8bEhtKLqDEhDiEUY4qxpaEYi4diLBaKsXgoxuKhMRELjYl4aEzEQmMiHnLhWMiF4yEXjoVcOB5y4XhoTMRCY2L7NCY2lF5EjQlxCKEYU4wtDcVYPBRjsVCMxUMxFg+NiVhoTMRDYyIWGhPxkAvHQi4cD7lwLOTC8ZALx0NjIhYaE9unMbGh9CJqTIhDCMWYYmxpKMbioRiLhWIsHoqxeGhMxEJjIh4aE7HQmIiHXDgWcuF4yIVjIReOh1w4HhoTsdCY2D6NiQ2lF1FjQjxa/INiLBKKsVgoxuKhGIuFYiweirF4aEzEQmMiHhoTsdCYiIdcOBZy4XjIhWMhF46HXDgeGhOx0JjYPo2JDaUXUWPiceM/v/3N8M//xZfu4l/+238c/uvPFGORUIzFQjEWD8VYLBRj8VCMxUNjIhYaE/HQmIiFxkQ85MKxkAvHQy4cC7lwPOTC8dCYiIXGxPZpTMx4/ebt8OTps/u4vn218u/Ti1hrTPzPj78Z/uYnvxl+9Lf9xd/+/WYDrWJMMbY0FGOxUIzFQzEWC8VYPBRj8dCYiIXGRDw0JmIhF46HXDgWcuF4yIVjIReOh1w4HhoTsdCY2D6NiQmpKfHx0+dhGIbh46fPw5Onz4bXb97e/0x6EWuNiZ/9r18Pf/SdL8Pzb/+2u/j6rxRjkVCMxUMxFgvFWDwUY7FQjMVDMRYPjYlYaEzEQ2MiFnLheMiFYyEXjodcOBZy4XjIheOhMRELjYnt05iY8PzFy7UdEte3r4bnL17e/3N6EccaE4oxxdiSUIzFQzEWC8VYPBRjsVCMxUMxFg+NiVhoTMRDLhwLuXA85MKxkAvHQy4cC7lwPOTC8dCYiIXGxPZpTEwod0cMw8MuiiS9iBoTq6EYi4ViLB6KsVgoxuKhGIuFYiweirF4aEzEQmMiHnLhWMiF4yEXjoVcOB5y4VjIheMhF46HxkQsNCa2T2NiQq0x8e79h5Xjne59/jwM3/qWEEIIIYSyq/7IAAAfBUlEQVQ4xri6quaDv//9H4Y3f/1l+OOr/uJP//zL8OlXvw/n0p/+4XfDn/75/v8e+4g3f/1l+N3v/xB6bn8YhuFv3u3/77CP+Dd/9mX4+S++Cb9z//fXvx/+/Vf7/3vsI/7yP30Zvvld/PP67qe/Hf74+/v/e+w6vvsnX4affoi/c7/95vfDV3+x/7/HPuKrv/gyfPkm/s79/f/4ZvhXf7L/v8fO4/tfhp/89Lfh5/bN7/4w/OVfHcDfYw9xfftl+H+/ib9zP//FN8P3/2z/f499xH/5yZfhD7G0RC68QS7MvG/t+w9wyBY1JgAAAAAAgFkaExNajnICAAAAAADaaUxMeP7i5XB5dbPya+Xl1wAAAAAAQDuNiQlpd0Q6tunjp8/VXRQAAAAAAEAbjYkZ17evhidPn93H9e2rff+RAAAAAADgaGlMQEW65BwAAAAAgMelMdGBdASVuzGWSbtlPDd2JX1Wy7ttYJu8dzHG1s2cnV94dgt559g140Ocz2uM5xbn2bFr3rk4zy7GcztNGhMduLy6uf/wOo5qOc9tufz4M8+uXfqsEpO/c+4Caue9izG2bs6zW6Z853zPLZcKWu9cG+NDXHp2Z+cX+/6jHBVja5xntzljxDLeuTjPLsZzO00aEycuHUmUilcdxuWev3g5nJ1f3E94vnv/Yd9/pINWK2IVZvPKzyrt8ruAPn76fP8s0z8zznsXMza2WlW8jEnPdmPvnM9uu9dv3t7nwHKSecaHuPTsrm9fedcWULfGyUs2Z4xYxjsXVxtfzTXN89xOl8bEiTs7v1gbWBWz7VKCkp5VXmhQ9/zFy2oBUXsXeeD5xEx9JlPDQnNi3NR7Z9J4XO25pXfR+9bGpOcytXfOpGe7NB68e/9huLy6MdHZYMn44Htv1dn5xXB5dTO8e/+h+q4ZW+um6laTTtPkJZsxRiznnYsbG1/dczrNcztdGhMnLE2q1xI5xWybqTOwJSx1eWKX+/jpsxUUI6Y+q0ybO6f++YuXK00L59o/SO9dralj0njc2HNLv64Ya2NFYruxMeLy6sYzbJAm0dNnsxwXEuPDg6Xjg1XtD/Kx4PWbtyvPJd/haRJl1dzYWn7/+bw+kJdsxhixnHcubmp8zXnfVrU+N46TxsQJmyoS0gebcWMT7MnZ+YUvxhEpwTOp2UZBH5MmR6YaOnlibAfFqnTEWm0LrEnjceVze/3m7f2Yqvnappxo0gibVjsO0ZEJbWrfZbV3zfiwasn44NmtyidOrm9f3X9G00rPcjc2d2rv3NhRTt65VfKSOGNEjHcubmp8Tbxv61qeG8dLY+JEzX2Z2TExb2pgta14XnoHNW+mtSQeJu3q0rNrYRJvVfnepWIivWe+3+rK55aasL7rliknmtIFdoqNdemdK5+RFdfz8u+0JI0F+ZhrfFhlfIgr85J8h0R6D62CXTf3zuV8XlfJS+KMETFL37kyf+m5np36rku8b+tanhvHTWPiBKUvs6lkxJfdtLmz1Z2fuOr5i5fV92lu10nvWj6rw2DSbsySBqv7Eh5MvXfpOZk0WVd7bte3rzyrGde3r1aeWXlkQu3nfVbv1N65NB7kv1Y2K3ofI9JzOzu/uM/X0jtX2zFcjg89LwZYOj4YWx/U7vArj1uTF6+beudqn0Pv3AN5SYwxIm7pO1cu5uz5qKe58TU9Q99xq1qf2zD0/dk8dhoTJygVrWNf+OWkgNVPq1ouuJ6aEJ17/qdo7JmNXbRuEuVO5F2pTdr1Ogi3HOWU/1xvz2dMeu9qyhUp5We152dYe26v37wdHQs8uwfls5gaX8vPda/jwzBMf1aT8iLAnov+5PmLl6NHv5QT6+X4kI716HVX8ZLxoSVf7kntvStZGLZuKhcuFz7J51Ztmpf0Or4aI+KWvnPDMFTHjR7fvZa633fcuiXzJRZzHi+NiRM1tr0pfVjLC+tMnjxoSTbK7mz+3/Y6IZAG0vzZ1VbGmkRZlf7+rQlumdBNJcg9PNOWVSX585HwjStXpPiszkvvU8mzqyufVa1hUX6mU95iMm/d2FGA6Rx71pUTnfln9eOnz963EbUVi2PfcyYC6srvNvXXg1ouXMvXvHPz5CWbMUYsN/bO5btR8uOeet7RM1f3l59T48S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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot\n", "fig5 = px.bar(sales_popularity, x=\"name\", y=\"percentage_sold\", text=\"percentage_sold\", width=1000, height=500)\n", "\n", "# Data labels\n", "fig5.update_traces(texttemplate=\"%{text:.1f}\", textposition=\"outside\")\n", "\n", "# Title\n", "fig5.update_layout(\n", " title={\n", " \"text\": \"Percentage of Sold Tracks per Genre\",\n", " \"x\": 0.03,\n", " \"y\": 0.97,\n", " \"font\": dict(\n", " size=20\n", " )\n", " }\n", ")\n", "\n", "# Text in the box\n", "fig5.update_layout(\n", " annotations=[\n", " dict(\n", " x=3,\n", " y=50,\n", " text=\"Eight genres have
less than 50% of tracks sold\",\n", " showarrow=False,\n", " font=dict(\n", " size=15, color=\"red\"\n", " )\n", " )\n", " ]\n", ")\n", "\n", "# Emphasize genres with less than 50% of tracks sold\n", "fig5.add_shape(\n", " # unfilled Rectangle\n", " type=\"rect\",\n", " x0=-0.45,\n", " y0=60,\n", " x1=7.5,\n", " y1=0,\n", " line=dict(\n", " color=\"Red\",\n", " ),\n", " )\n", "\n", "# x and y labels\n", "fig5.update_xaxes(title=\"Genre name\", tickfont=dict(size=14))\n", "fig5.update_yaxes(title=\"% of sold tracks\", tickfont=dict(size=14), range=[0,115])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can observe eight genres - **Drama, TV Shows, Soundtrack, Latin, Classical, Heavy Metal, Reggae, and Jazz** - have less than 50% of tracks sold out of all available tracks for these genres. Chinook should address this problem by excluding these tracks from its library to reduce losses.\n", "\n", "## Is the Range of Tracks in the Store Reflective of Their Sales Popularity?\n", "\n", "Lastly, we will draw a scatter plot to understand if the range of tracks reflects their sales popularity. To avoid the range of points to be too wide we will initially exclude the genres that have more than 200 available tracks (Rock, Latin, Metal and Alternative & Punk). And then we will draw a separate scatter plot for the latter genres." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameno_tracksunique_tracks_soldpercentage_sold
13Rock129791570.5
3Latin57911920.6
11Metal37423863.6
9Alternative & Punk33217653.0
7Jazz1306146.9
1TV Shows9322.2
12Blues815669.1
4Classical741621.6
0Drama6411.6
15R&B/Soul615590.2
6Reggae582237.9
8Pop482552.1
2Soundtrack43511.6
14Alternative403485.0
10Hip Hop/Rap352160.0
16Electronica/Dance302996.7
5Heavy Metal28725.0
17Easy Listening2424100.0
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Filter genres with less than 200 available tracks\n", "less_than_200 = sales_popularity[\"no_tracks\"] < 200\n", "\n", "# Initialize 2 subplots in a row\n", "fig6 = make_subplots(\n", " rows=1, cols=2,\n", " subplot_titles=[\n", " \"Sold Tracks vs. Available Tracks
For genres with less than 200 items available\", \n", " \"Sold Tracks vs. Available Tracks
For genres with more than 200 items available\"\n", " ]\n", ")\n", "\n", "# First plot\n", "fig6.add_trace(\n", " go.Scatter(\n", " x=sales_popularity[less_than_200][\"no_tracks\"],\n", " y=sales_popularity[less_than_200][\"percentage_sold\"],\n", " mode=\"markers\",\n", " text=sales_popularity[less_than_200][\"name\"]\n", " ),\n", " row=1,\n", " col=1\n", ")\n", "\n", "# Second plot\n", "fig6.add_trace(\n", " go.Scatter(\n", " x=sales_popularity[~less_than_200][\"no_tracks\"],\n", " y=sales_popularity[~less_than_200][\"percentage_sold\"],\n", " mode=\"markers\",\n", " text=sales_popularity[~less_than_200][\"name\"]\n", " ),\n", " row=1,\n", " col=2\n", ")\n", "\n", "# Remove legend\n", "fig6.update_layout(showlegend=False)\n", "\n", "# Axes labels\n", "fig6.update_yaxes(title_text=\"Percentage of Tracks Sold\", tickfont=dict(size=15), row=1, col=1)\n", "fig6.update_yaxes(title_text=\"Percentage of Tracks Sold\", tickfont=dict(size=15), row=1, col=2, range=[0, 100])\n", "\n", "for i in range(1, 3):\n", " fig6.update_xaxes(title_text=\"Available Tracks\", row=1, col=i)\n", "\n", "# Show plot\n", "fig6.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is no connection between the range of available tracks and sales popularity.\n", "\n", "## Conclusions \n", "\n", "In this project, we analyzed a database of the fictional digital music store Chinook and provided the best business strategies to follow in order to reduce losses and increase profits. In particular, Chinook should:\n", "\n", "* Should buy tracks by the following artists: Red Tone (Red Tone), Meteor and the Girls (Pop), Slim Jim Bites (Blues).\n", "* Should concentrate on the Rock genre since it possess more than 50% of the USA market\n", "* Expand in the Czech Republic which has the best potential customer sales.\n", "* Do not forget about the USA market since it's the biggest and the most important one.\n", "* Prefer buying not-protected media type to protected.\n", "* Reconsider its purchasing strategy, since almost half of the available tracks in Chinook is not purchased, especially in the genres of Drama, TV Shows, Soundtrack, Latin, Classical, Heavy Metal, Reggae, and Jazz that have less than 50% of the available tracks sold." ] } ], "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": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 }