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spotify_app.py
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from h2o_wave import Q, main, app, ui, site, data
import pandas as pd
import os
import numpy as np
from datetime import datetime
@app('/spotify')
async def serve(q: Q):
q.page['Stream_History'] = ui.form_card(box='1 1 3 6', items=[
ui.text_xl('Upload your Spotify Stream History',tooltip='It should be in Json format'),
ui.file_upload(name='datasets', label='Upload', multiple=True),
])
q.client.data_path = './data'
if not os.path.exists(q.client.data_path):
os.mkdir(q.client.data_path)
if q.args.datasets:
await handle_uploaded_data(q)
await q.page.save()
async def handle_uploaded_data(q: Q):
data_path = q.client.data_path
# Download new dataset to data directory
q.client.working_file_path = await q.site.download(url=q.args.datasets[0], path=data_path)
df = pd.read_json(q.client.working_file_path)
df["endTime"] = pd.to_datetime(df["endTime"]) + pd.Timedelta(hours=3)
df["Day"] = pd.to_datetime(df["endTime"], format='%Y-%m-%d %H:%M').dt.day_name()
df["Hour"] = pd.to_datetime(df["endTime"], format='%Y-%m-%d %H:%M').dt.hour
df['Month'] = pd.to_datetime(df["endTime"], format='%Y-%m-%d %H:%M').dt.strftime('%Y-%m')
df['DayOrNot'] = np.where(pd.to_datetime(df["endTime"], format='%Y-%m-%d %H:%M').dt.dayofweek < 5,0,1 )
df["Minutes"] = df["msPlayed"] / 60000
df["Minutes"] = df["Minutes"].round(decimals=1)
df["PrevEndTime"] = df["endTime"].shift(1)
df.loc[0, 'Session_Id'] = 1
for i in range(1, len(df)):
if df.loc[i-1, 'endTime'] + pd.Timedelta(minutes=30) < df.loc[i, 'endTime'] :
df.loc[i, "Session_Id"] = df.loc[i-1, 'Session_Id'] + 1
else:
df.loc[i, 'Session_Id'] = df.loc[i-1, 'Session_Id']
df["endTime"] = df["endTime"].astype(str)
df.drop(["msPlayed"],axis=1, inplace=True)
#make a bar plot for hour analysis
df_hour = df.groupby("Hour").agg({"Minutes":"sum"}).reset_index()
df_hour["Minutes"] = df_hour["Minutes"].round(decimals=1)
hours = pd.DataFrame(list(range(0,24)))
hours.columns = ["Hour"]
hour_df = hours.merge(df_hour[["Hour","Minutes"]], how='left', on = 'Hour')
hour_df["Minutes"] = hour_df["Minutes"].fillna(0)
q.page.add('bar_plot', ui.plot_card(
box='7 1 3 3',
title='Total Minutes Played by Hour',
data=data(fields=df_hour.columns.tolist(),rows=df_hour.values.tolist(),pack=True),
plot=ui.plot([
ui.mark(type='interval',
x='=Hour', y='=Minutes',y_min=0,color='blue',x_max=23)
])
))
#make a bar plot for Day analysis
df_Day = df.groupby("Day").agg({"Minutes":"sum"}).reset_index()
df_Day["Minutes"] = df_Day["Minutes"].astype(int)
df_Day['Day'] = pd.Categorical(
df_Day['Day'], categories=["Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday"], ordered=True
)
df_Day = df_Day.sort_values('Day',ascending=True)
q.page.add('bar_plot2', ui.plot_card(
box='4 4 3 3',
title='Total Minutes Played by Day',
data=data(fields=df_Day.columns.tolist(),rows=df_Day.values.tolist(),pack=True),
plot=ui.plot([
ui.mark(type='interval',
x='=Day', y='=Minutes',y_min=0,color='blue')
])
))
#make a bar plot for month analysis
df_month = df.groupby("Month").agg({"Minutes":"sum"}).reset_index()
df_month["Minutes"] = df_month["Minutes"].astype(int)
q.page.add('bar_plot3', ui.plot_card(
box='7 4 3 3',
title='Total Minutes Played by Month',
data=data(fields=df_month.columns.tolist(),rows=df_month.values.tolist(),pack=True),
plot=ui.plot([
ui.mark(type='interval',
x='=Month', y='=Minutes',y_min=0,color='blue')
])
))
#make a bar plot for most listened artists for each month
df_monthly_artists = df.groupby(["Month","artistName"]).agg({"Minutes":"sum"}).reset_index().sort_values(["Month","artistName"],ascending=False)
df_monthly_artists["Rank"] = df_monthly_artists.groupby("Month").rank(ascending=False, method='first')["Minutes"]
df_monthly_artists = df_monthly_artists.loc[df_monthly_artists.Rank==1].sort_values(["Month"],ascending=True)
df_monthly_artists["Minutes"] = df_monthly_artists["Minutes"].astype(int)
q.page.add('bar_plot4', ui.plot_card(
box='4 1 3 3',
title='Most Played Artist for Each Month with Total Minutes Listened',
data=data(fields=df_monthly_artists.columns.tolist(),rows=df_monthly_artists.values.tolist(),pack=True),
plot=ui.plot([
ui.mark(type='interval',
x='=Month', y='=Minutes',y_min=0,color='blue',label='=artistName',label_position='top')
])
))
#make a line plot for most listened 5 artists with monthly trend
df_top = df.groupby("artistName").agg({"Minutes":"sum"}).reset_index()
df_top["Minutes"] = df_top["Minutes"].astype(int)
df_top10 = df_top.sort_values("Minutes", ascending=False).head(5)
df_artist1 = df.groupby(["Month","artistName"]).agg({"Minutes":"sum"}).reset_index()
df_artist2 = df_artist1[df_artist1.artistName.isin(df_top10.artistName)]
df_artist2["key"] = df_artist2["Month"] + df_artist2["artistName"]
months = pd.DataFrame(df_artist2["Month"].unique())
artists = pd.DataFrame(df_artist2["artistName"].unique() )
months['key'] = 0
artists['key'] = 0
months_and_artists = months.merge(artists, how='outer', on = 'key')
months_and_artists.drop('key',1, inplace=True)
months_and_artists.columns = ["Month","artistName"]
months_and_artists["key"] = months_and_artists["Month"] + months_and_artists["artistName"]
months_and_artists = pd.merge(months_and_artists, df_artist2[["key","Minutes"]], how='left', on ='key')
months_and_artists.drop('key',1, inplace=True)
months_and_artists["Minutes"] = months_and_artists["Minutes"].fillna(0)
q.page.add('line_plot5', ui.plot_card(
box='8 7 5 4',
title="Most Played Top 5 Artists and Their Monthly Listened Durations",
data=data(fields=months_and_artists.columns.tolist(),rows=months_and_artists.values.tolist(),pack=True),
plot=ui.plot([
ui.mark(type='area',
x='=Month', y='=Minutes',y_min=0,color='=artistName')
])
))
#make a info card about session count and time duration
session_count = str(df.groupby("Session_Id").agg({"Minutes":"sum"}).count()[0])
first_date = str(datetime.strptime(str(df.endTime.min()), '%Y-%m-%d %H:%M:%S').strftime('%d-%b-%Y').upper())
avg_session_duration = str(round(df.groupby("Session_Id").agg({"Minutes":"sum"})["Minutes"].mean()))
q.page.add('info_card', ui.form_card(
box='1 7 3 2',
items=[
ui.text('Your session count is '+ session_count + ' since the date ' + first_date + '.', size='l', width='400px'),
ui.separator('--------------------------------------------------------'),
ui.text('Your average session length is ' + avg_session_duration + ' minutes.', size='l', width='400px')
]
))
#make a info card about longest session
longest_session_time = int(df.groupby("Session_Id").agg({"Minutes":"sum"}).sort_values(by=["Minutes"], ascending=False).head(1)['Minutes'].values[0])
session_id = df.groupby("Session_Id").agg({"Minutes":"sum"}).sort_values(by=["Minutes"], ascending=False).head(1).reset_index()["Session_Id"]
artist_list = df[df.Session_Id.isin(session_id)].artistName.unique()
listToStr = ', '.join([str(elem) for elem in artist_list])
q.page.add('info_card2', ui.form_card(
box='1 9 3 2',
items=[
ui.text("Your longest session has a total duration of " + str(longest_session_time) + ' minutes.', size='l', width='200px'),
ui.text('In this session, you listened: ' + listToStr , size='l', width='200px')
]
))
#make a top listened artists table
df_top_artists = df.groupby("artistName").agg({"Minutes":"sum"}).reset_index()
df_top_artists["Minutes"] = df_top_artists["Minutes"].astype(int)
df_top_artists = df_top_artists.sort_values("Minutes", ascending=False).head(10)
n_rows = df_top_artists.shape[0]
q.page.add('top_artists', ui.form_card(box='10 1 3 6',
items=[ ui.table(
name='Top Artists',
columns=[ui.table_column(name=str(x), label=str(x), sortable=True) for x in df_top_artists.columns.values],
rows=[ui.table_row(name=str(i), cells=[str(df_top_artists[col].values[i]) for col in df_top_artists.columns.values])
for i in range(n_rows)]
)]
))
#make a top listened songs table
df_top_songs = df.groupby(["trackName","artistName"]).agg({"Minutes":"count"}).reset_index()
df_top_songs = df_top_songs.rename(columns= {'trackName':'trackName', 'artistName':'artistName', 'Minutes':'Count'})
df_top_songs = df_top_songs.sort_values("Count", ascending=False).head(5)
n_rows = df_top_songs.shape[0]
q.page.add('top_songs', ui.form_card(box='4 7 4 4',
items=[ ui.table(
name='Top Listened Songs',
columns=[ui.table_column(name=str(x), label=str(x), sortable=True) for x in df_top_songs.columns.values],
rows=[ui.table_row(name=str(i), cells=[str(df_top_songs[col].values[i]) for col in df_top_songs.columns.values])
for i in range(n_rows)]
)]
))
await q.page.save()