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app.py
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app.py
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###----------------------LIBRARIES----------------------------###
from ast import In
from msilib.schema import Component
from tarfile import USTAR_FORMAT
import pandas as pd
import numpy as np
import datetime as dt
import plotly.express as px # (version 4.7.0 or higher)
import plotly.graph_objects as go
import dash_bootstrap_components as dbc # pip install dash_bootstrap_components
from dash import Dash, dcc, html, Input, Output, State # pip install dash (version 2.0.0 or higher)
###-----------------LOADING & PREPARING DATA----------------------------###
#Setting a DataFrame
df = pd.read_csv('data.csv')
df.drop_duplicates(keep = 'first', inplace = True)
df.usage_date = df.usage_date.apply(lambda x: dt.datetime.strptime(x.split()[0], '%Y-%m-%d'))
df.user_created_date = df.user_created_date.apply(lambda x: dt.datetime.strptime(x.split()[0], '%Y-%m-%d')if x is not np.nan else x)
df = df.set_index('usage_date',drop = True)[['cust_id', 'source_duration', 'transcoded_duration', 'video_duration',"user_created_date"]].sort_index(ascending = True)
df['new_customer'] = df.user_created_date.apply(lambda x: True if x > df.index[-1] - dt.timedelta(days = 90)
else False)
###-------------------------------------- CONSTANTS -------------------------------------------------------###
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = Dash(external_stylesheets=external_stylesheets)
#app = Dash(__name__)
# Standard Colors
color_font = 'rgb(243,247,246)'
color_background = 'rgb(31,38,48)'
color_container = 'rgb(37,46,63)'
clear_color_chart = 'rgb(38,254,184)'
dark_color_chart = 'rgb(41,152,119)'
# Chart Tamplate
layout = go.Layout(
autosize=True,
margin=dict(l=30, r=30, b=20, t=40),
hovermode="closest",
grid = None,
plot_bgcolor=color_background,
paper_bgcolor=color_container,
legend=dict(font=dict(size=10), orientation="h"),
title="Title Name",
font = dict(
size = 12,
color = color_font))
#Filter paramters
#Date Granularity - Dropdown List
date_granularity = {'Day': 'D', 'Month': 'M', 'Year': 'Y'}
#end and start date
start_date = dt.date(2022,1,1)
end_date = dt.date(2022,10,21)
# Top N - Dropdown List
top = [5,10,15,20]
###--------------------------------- DASH COMPONENTS - LAYOUT----------------------------------------###
app.layout = html.Div([
dcc.Store(id="aggregate_data", data = [], storage_type = 'memory'),
# empty Div to trigger javascript file for graph resizing
html.Div(id="output-clientside"),
#Header
html.Div(
[
html.Div(
[
html.Img(
src=app.get_asset_url("logo.svg"),
id="plotly-image",
style={
"height": "80px",
"width": "auto",
"margin-bottom": "25px",
},
)
],
className="one-third column",
),
#Title
html.Div(
[
html.Div(
[
html.H3(
"Livepeer Customer Usage",
style={"margin-bottom": "0px",
"margin-right": "300px",
"align-items":"flex-start",
"color": "rgb(38,254,184)"},
),
]
)
],
className="one-half column",
id="title")
],style = {'margin-top': '0%'}),
#Filters division
html.Div(
[
html.Div([
#Date Picker
html.P('Date Range', className = 'control_label'),
dcc.DatePickerRange(
id='my-date-picker-range',
min_date_allowed= start_date,
max_date_allowed = end_date,
start_date = start_date,
end_date= end_date,
display_format = "MM/DD/YYYY",
start_date_placeholder_text = "MM/DD/YYYY",
style = {'width':'50%'},
className = 'dcc_control'),
#Date Granunality
html.P("Date Granularity", className = 'control_label'),
dcc.Dropdown(
id = 'granunality-drop',
options= [{"label": i, "value": i} for i in ['Day','Month','Year']],
value = 'Day',
clearable = False,
style = {'width':'60%'},
className = 'dcc_control'),
#Ranking
html.P("Ranking Top Users", className = 'control_label'),
dcc.Dropdown(
options = [{"label": i, "value": i} for i in top],
id = 'rank-drop',
clearable = False,
value = 5,
style = {'width':'30%'},
className = 'dcc_control')],
#Customize
#html.P("Customers", className = 'control_label'),
#dcc.Dropdown(
# id="customers-drop",
# options= [{"label": i, "value": i} for i in list(set(df.cust_id.to_list())) +['All']],
# value = 'All',
#clearable = False,
#multi= False,
#style = {'width':'50%'},
#className="dcc_control",
#)
#],
className="pretty_container twelve columns",
id="cross-filter-options"
)
] ),
#Big Numbers Section
html.Div([
html.Div([
html.Div
([],
id="transcode",
className="mini_container",
),
html.Div(
[],
id="source",
className="mini_container",
),
html.Div(
[],
id="video",
className="mini_container",
)
],
style = {'display': 'flex', 'width': '100%'},
id="info-container"
),
], style = {'display':'inline-block'}),
#Tab Section
html.Div([
html.Div([
dcc.Tabs([
dcc.Tab(label = 'Transcoded Duration', value = 'transcoded_duration'),
dcc.Tab(label = 'Source Duration', value = 'source_duration'),
dcc.Tab(label = 'Video Duration', value = 'video_duration')
],
id ='tabs',
value = 'transcoded_duration',
style = {'width': 'auto', 'margin': '12px'},
className = "pretty_container twelve columns"),
html.Div([
],
id = 'tabs-content-graphs')
])
])
])
####-------------------------------- CONNECTING PLOTLY GRAPHS WITH DASH COMPONENTS--------------####
# Date granunality, Date range, clients
@app.callback(
Output(component_id = "aggregate_data", component_property = 'data'),
[Input(component_id = 'my-date-picker-range', component_property = 'start_date'),
Input(component_id = 'my-date-picker-range', component_property = 'end_date'),
Input(component_id = 'granunality-drop', component_property = 'value')]
)
def data_filtered(start_date,end_date,time):
df_filter = df.loc[start_date:end_date].to_period(date_granularity[time]).reset_index()
df_filter = df_filter.reset_index(drop = True)
df_filter.usage_date = df_filter.usage_date.apply(lambda x: x.to_timestamp())
data_dict = df_filter.to_dict(orient = 'record')
return data_dict
#Big Numbers
@app.callback(
[Output('transcode', 'children'),
Output('source', 'children'),
Output('video', 'children')],
[Input("aggregate_data",'data')],prevent_initial_call=True)
def display_numbers(json_dict):
#Converting Json dict to dataframe
df = pd.DataFrame(json_dict)
df = df.set_index('usage_date')
# Metrics Accumulated
df_total = df.sum(axis = 0, numeric_only = True)
def format_num(text,numb):
return html.Div([
html.P(text, className = "text-big-numbers"),
html.Br(),
html.P(numb, className = "big-numbers"),
])
def human_format(num):
num = float('{:.3g}'.format(num))
magnitude = 0
while abs(num) >= 1000:
magnitude += 1
num /= 1000.0
return '{}{}'.format('{:f}'.format(num).rstrip('0').rstrip('.'), ['', 'K', 'M', 'B', 'T'][magnitude])
transcode = format_num("Transcoded Duration",human_format(df_total['transcoded_duration']))
source = format_num("Source Duration",human_format(df_total['source_duration']))
video = format_num("Video Duration",human_format(df_total['video_duration']))
return transcode, source, video
#Tabs, Top customers
@app.callback(
Output(component_id = 'tabs-content-graphs', component_property = 'children'),
[Input(component_id = 'tabs', component_property = 'value')],
[Input(component_id = 'rank-drop', component_property = 'value')],
[Input(component_id = "aggregate_data", component_property = 'data')],prevent_initial_call=True)
def render_content(tab,N,json_dict):
#Converting Json dict to dataframe
df = pd.DataFrame(json_dict)
df = df.set_index('usage_date')
#Defining Graph Objects
# Metrics Over Time
df_overtime = df.groupby(by = 'usage_date').agg({'source_duration': np.sum,
'transcoded_duration': np.sum,
'video_duration': np.sum})
fig_overtime = go.Figure(data = [go.Bar(x = df_overtime.index, y = df_overtime[tab])], layout = layout)
fig_overtime.update_traces(marker_color = clear_color_chart)
fig_overtime.update_layout(title_text='{} - Over Time '.format(" ".join(tab.split('_')).capitalize()))
# Metrics Total Running
df_cumsum = df.groupby(by = 'usage_date').agg({'source_duration': np.sum,
'transcoded_duration': np.sum,
'video_duration': np.sum}).cumsum(axis =0)
fig_total_running= go.Figure(layout = layout)
fig_total_running.add_trace(go.Scatter(x =df_cumsum.index,y = df_cumsum[tab], fill='tozeroy'))
fig_total_running.update_traces(marker_color= clear_color_chart, marker_line_color= dark_color_chart,
marker_line_width=1.5, opacity=0.6)
fig_total_running.update_layout(title_text='{} - Total Running '.format(" ".join(tab.split('_')).capitalize()))
# Metrics Variation Overtime
df_pct_change = df.groupby(by = 'usage_date').agg({'source_duration': np.sum,
'transcoded_duration': np.sum,
'video_duration': np.sum}).pct_change().drop(df.index[0],axis = 0).replace(np.inf,1)
fig_pct_change = go.Figure(layout= layout)
fig_pct_change.add_trace(go.Scatter(x = df_pct_change.index, y = df_pct_change[tab], mode = 'lines', name = 'lines'))
fig_pct_change.update_traces(marker_color = clear_color_chart)
fig_pct_change.update_layout(title_text='{} - Percent Variation Over Time'.format(" ".join(tab.split('_')).capitalize()))
# Histogram
fig_hist = go.Figure(data = [go.Histogram(x = df[tab], nbinsx = 200)], layout = layout)
fig_hist.update_traces(marker_color = dark_color_chart)
fig_hist.update_layout(title_text='{} - Histogram of Customer Usage'.format(" ".join(tab.split('_')).capitalize()))
#Ranking Chart
df_rank = df.groupby(by = 'cust_id').agg({'source_duration': np.sum,
'transcoded_duration': np.sum,
'video_duration': np.sum})[[tab]].sort_values(by =tab, ascending = False)
df_top = df_rank.copy()
df_top['ranking'] = range(1,len(df_top.index) +1)
df_top = df_top.iloc[:N,:].reset_index().assign(rank_id = lambda x: x['ranking'].apply(lambda x: str(x) + ' - ') + x['cust_id'])
colors = [dark_color_chart] * N
colors[0] = 'crimson'
fig_rank = go.Figure(data = [go.Bar(x = df_top['rank_id'], y = df_top[tab], marker_color = colors)], layout = layout)
fig_rank.update_layout(title_text='Top {} Customers by {}'.format(N," ".join(tab.split('_')).capitalize()))
#Ranking charts for future potencials
#Dataframe for new clients - Clients who created their account within the last 3 months
df_potencial = df.copy()
df_new = df_potencial[df_potencial.new_customer == True]
df_rank_new = df_new.groupby(by = 'cust_id').agg({'source_duration': np.sum,
'transcoded_duration': np.sum,
'video_duration': np.sum})[[tab]].sort_values(by =tab, ascending = False)
df_rank_new ['ranking'] = range(1,len(df_rank_new.index) +1)
df_rank_new = df_rank_new .iloc[:N,:].reset_index().assign(rank_id = lambda x: x['ranking'].apply(lambda x: str(x) + ' - ') + x['cust_id'])
colors = [dark_color_chart] * N
colors[0] = 'crimson'
fig_rank_new = go.Figure(data = [go.Bar(x = df_rank_new['rank_id'], y = df_rank_new[tab], marker_color = colors)], layout = layout)
fig_rank_new.update_layout(title_text='Top {} New Customers by {} (New customers in the past 3 months)'.format(N," ".join(tab.split('_')).capitalize()), title_font_size = 14)
#Return Graphs
return html.Div([
html.Div([
html.Div(
[dcc.Graph(figure = fig_overtime,id="overtime-graph",
style = {'width':'auto',
'height': '100%',
'margin': '10px'})],
),
html.Div(
[dcc.Graph(figure = fig_rank, id="total-running-grapth",style ={'width':'auto',
'height': '100%',
'margin': '10px'})],
),
html.Div(
[dcc.Graph(figure = fig_hist,id="total-running-grapth",style ={'width':'auto',
'height': '100%',
'margin': '10px'})]
),
], className = 'six columns'),
html.Div([
html.Div(
[dcc.Graph(figure = fig_total_running, id="overtime-graph",style = {'width':'auto',
'height': '100%',
'margin': '10px'})],
),
html.Div(
[dcc.Graph( figure = fig_rank_new, id="total-running-grapth",style ={'width':'auto',
'height': '100%',
'margin': '10px'})],
),
html.Div(
[dcc.Graph(figure = fig_pct_change, id="total-running-grapth",style ={'width':'auto',
'height': '100%',
'margin': '10px'})]
)
], className = 'six columns')
], className="pretty_container twelve columns")
if __name__ == '__main__':
app.run_server(debug=True)