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plots.py
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plots.py
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import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import os
import utils
MAPBOX_TOKEN = os.environ.get('MAPBOX_TOKEN')
def map_weekdays(x):
if x == 0:
return "Monday"
elif x == 1:
return "Tuesday"
elif x == 2:
return "Wednesday"
elif x == 3:
return "Thursday"
elif x == 4:
return "Friday"
elif x == 5:
return "Saturday"
return "Sunday"
def map_months(x):
if x == 1:
return "January"
elif x == 2:
return "February"
elif x == 3:
return "March"
elif x == 4:
return "April"
elif x == 5:
return "May"
elif x == 6:
return "June"
elif x == 7:
return "July"
elif x == 8:
return "August"
elif x == 9:
return "September"
elif x == 10:
return "October"
elif x == 11:
return "November"
return "December"
def choropleth_map(df, geojson, center, metric, color_scale):
fig = px.choropleth_mapbox(df, geojson=geojson, locations='full_fips', color=metric,
color_continuous_scale=color_scale,
range_color=(0, df[metric].max()),
mapbox_style='stamen-toner',
zoom=6, center = {"lat": center[0], "lon": center[1]},
opacity=0.4,
hover_data= ['county', metric],
labels={metric: utils.get_metric_labels(metric), 'full_fips': 'FIPS'},
)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0}, height = 500, width = 800)
return fig
def bar_chart(df):
fig = px.bar(df, x='category_name', y='benefit', text= 'benefit', width = 250, height = 340)
fig.update_traces(texttemplate='%{text:.2s}', textposition='inside')
fig.update_yaxes(visible=False)
fig.update_xaxes(title='')
fig.update_layout(
margin=dict(l=1, r=1, t=30, b=0),
paper_bgcolor='rgba(0, 0, 0, 0)',
legend={'title_text':''},
plot_bgcolor='rgba(0, 0, 0, 0)',
title_text= '',
title_x = 0.5)
return fig
def cum_sales(df, county=False, custom=False):
if county == False:
width = 610
height = 340
elif custom == True:
width = 500
height = 250
else:
width = 590
height = 340
fig = go.Figure()
fig.add_trace(go.Scatter(x=df.date, y=df.cum_sum_benefit,
mode='lines+markers',
name='cum. benefits'))
fig.add_trace(go.Scatter(x=df.date, y=df.cum_sum_cost,
mode='lines+markers',
name='cum. costs'))
fig.add_trace(go.Scatter(x=df.date, y=df.cum_sum_profit,
mode='lines+markers',
name='cum. profits'))
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='LightGrey')
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='LightGrey')
fig.update_layout(
width = width,
height = height,
hovermode="x unified",
title_x=0.5,
margin=dict(l=1, r=1, t=30, b=0),
paper_bgcolor='rgba(0, 0, 0, 0)',
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1,
title_text=''
),
plot_bgcolor='rgba(0, 0, 0, 0)')
return fig
def dist_plot(df, profit):
fig = px.violin(df, x='profit', log_x=True, width=300, height=180)
fig.update_xaxes(range=[0, 5.5])
fig.add_vline(x=profit, line_width=2, line_dash="dash", line_color="#ba1e7f")
fig.update_layout(
title_x=0.5,
margin=dict(l=1, r=1, t=30, b=0),
paper_bgcolor='rgba(0, 0, 0, 0)')
return fig
def waterfall_chart(df):
fig = go.Figure(go.Waterfall(
orientation = "v",
measure = ["relative", "relative", "total"],
x = ["Revenues", "Costs", "Profits"],
textposition = "outside",
text = [utils.human_format(round(df.benefit.sum(),1)), utils.human_format(round(-1*df.cost.sum(),1)), utils.human_format(round(df.profit.sum(),1))],
y = [df.benefit.sum(), -1*df.cost.sum(), df.profit.sum()],
connector = {"line":{"color":"rgb(63, 63, 63)"}},
))
fig.update_layout(
width = 530,
height = 300,
paper_bgcolor='rgba(0, 0, 0, 0)',
plot_bgcolor='rgba(0, 0, 0, 0)',
showlegend = False,
margin=dict(l=0, r=0, t=0, b=0),
yaxis_range=[0,round(df.benefit.sum(),1)+1000000]
)
return fig
def weekday_pie_chart(df):
df['weekday'] = [x.weekday() for x in df.date]
df['weekday'] = df['weekday'].apply(map_weekdays)
df_to_pie = df.groupby('weekday', as_index=False).sum()[['weekday', 'profit']]
fig = px.pie(df_to_pie, values='profit', names='weekday', hole=0.5)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(
width = 200,
paper_bgcolor='rgba(0, 0, 0, 0)',
plot_bgcolor='rgba(0, 0, 0, 0)',
showlegend = False,
margin=dict(l=0, r=0, t=0, b=0)
)
return fig
def month_pie_chart(df):
df['month'] = df.date.dt.month
df['month'] = df['month'].apply(map_months)
df_to_pie = df.groupby('month', as_index=False).count()[['month', 'invoice_and_item_number']]
fig = px.pie(df_to_pie, values='invoice_and_item_number', names='month', hole=0.4)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(
width = 200,
paper_bgcolor='rgba(0, 0, 0, 0)',
plot_bgcolor='rgba(0, 0, 0, 0)',
showlegend = False,
margin=dict(l=0, r=0, t=0, b=0)
)
return fig
def get_mos_profitable_items(df):
most_profitable_items = df[['item_description', 'state_bottle_cost', 'state_bottle_retail']]
most_profitable_items['margin_%'] = round((most_profitable_items.state_bottle_retail-df.state_bottle_cost)/df.state_bottle_retail*100,1)
df_items = most_profitable_items.sort_values('margin_%', ascending=False).head(10).drop_duplicates().reset_index(drop=True).drop([1,2]).reset_index(drop=True)
return [{'item': df_items['item_description'][0], 'margin': df_items['margin_%'][0]},
{'item': df_items['item_description'][1], 'margin': df_items['margin_%'][1]},
{'item': df_items['item_description'][2], 'margin': df_items['margin_%'][2]}
]
def monthly_waterfall(df):
df = df.sort_values('date', ascending=True).set_index('date')
df = df.groupby(pd.Grouper(freq="M")).sum()[['benefit', 'cost', 'profit']]
measure = ['relative', 'relative', 'total']*12
x = ["Dec. 2020 (Rev.)", "Dec. 2020 (Cost)", "Dec. 2020 (Profit)", "Jan. 2021 (Rev.)","Jan. 2021 (Cost)","Jan. 2021 (Profit)", "Feb. 2021 (Rev.)","Feb. 2021 (Cost)","Feb. 2021 (Profit)",
"Mar. 2021 (Rev.)","Mar. 2021 (Cost)","Mar. 2021 (Profit)", "Apr. 2021 (Rev.)","Apr. 2021 (Cost)","Apr. 2021 (Profit)",
"May 2021 (Rev.)","May 2021 (Cost)","May 2021 (Profit)", "Jun. 2021 (Rev.)", "Jun. 2021 (Cost)", "Jun. 2021 (Profit)", "Jul. 2021 (Rev.)","Jul. 2021 (Cost)","Jul. 2021 (Profit)",
"Aug. 2021 (Rev.)","Aug. 2021 (Cost)","Aug. 2021 (Profit)", "Sep. 2021 (Rev.)","Sep. 2021 (Cost)","Sep. 2021 (Profit)",
"Oct. 2021 (Rev.)","Oct. 2021 (Cost)","Oct. 2021 (Profit)", "Nov. 2021 (Rev.)","Nov. 2021 (Cost)","Nov. 2021 (Profit)"]
y_list = []
for index, row in df.iterrows():
y_list.append(row.benefit)
y_list.append(row.cost*-1)
y_list.append(row.profit)
fig = go.Figure(go.Waterfall(
orientation = "v",
measure = measure,
x = x,
y = y_list,
connector = {"line":{"color":"rgb(63, 63, 63)"}},
))
fig.update_layout(
width = 1360,
height = 225,
paper_bgcolor='rgba(0, 0, 0, 0)',
plot_bgcolor='rgba(0, 0, 0, 0)',
showlegend = False,
margin=dict(l=0, r=0, t=0, b=0)
)
for v_line in [2.5, 5.5, 8.5, 11.5, 14.5, 17.5, 20.5, 23.5, 26.5, 29.5, 32.5]:
fig.add_vline(x=v_line, line_width=1, line_dash="dash", line_color="black")
fig.update_xaxes(tickvals=[1.25, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34],
ticktext=["Dec. 2020", "Jan. 2021", "Feb. 2021", "Mar. 2021",
"Apr. 2021", "May. 2021", "Jun. 2021", "Jul. 2021",
"Aug. 2021", "Sep. 2021", "Oct. 2021", "Nov. 2021"])
fig.update_yaxes(title_text="USD")
return fig
def line_chart_invoices(df, counties):
df = df.sort_values('date', ascending=True).set_index('date')
df_sorted = df.groupby(pd.Grouper(freq="D")).count()[['invoice_and_item_number']]
df_sorted['invoice_mean'] = df_sorted.invoice_and_item_number/df.county.nunique()
df_sorted = df_sorted.reset_index()
df_sorted['weekday'] = [x.weekday() for x in df_sorted.date]
df_to_plot = df_sorted[(df_sorted['weekday'] != 5) & (df_sorted['weekday'] != 6)]
v_lines = ["2020-12-31","2021-01-31","2021-02-28","2021-03-31","2021-04-30","2021-05-31",
"2021-06-30","2021-07-31","2021-08-31","2021-09-30","2021-10-31","2021-11-30"]
tick_vals = ["2020-12-15","2021-01-15","2021-02-15","2021-03-15","2021-04-15","2021-05-15",
"2021-06-15","2021-07-15","2021-08-15","2021-09-15","2021-10-15","2021-11-15"]
fig = go.Figure()
fig.add_trace(go.Scatter(x=df_to_plot['date'], y=df_to_plot['invoice_mean'],
mode='lines',
name='State average'))
if counties:
for county in counties:
df_county_sorted = df[df['county'] == county].groupby(pd.Grouper(freq="D")).count()[['invoice_and_item_number']]
df_county_sorted = df_county_sorted.reset_index()
df_county_sorted['weekday'] = [x.weekday() for x in df_county_sorted.date]
df_county_to_plot = df_county_sorted[(df_county_sorted['weekday'] != 5) & (df_county_sorted['weekday'] != 6)]
fig.add_trace(go.Scatter(x=df_county_to_plot["date"],
y=df_county_to_plot["invoice_and_item_number"],
name= county.capitalize(), line = dict(dash='dot'), opacity=0.85))
for v_line in v_lines:
fig.add_vline(x=v_line, line_width=1, line_dash="dash", line_color="black")
fig.update_xaxes(tickvals=tick_vals,
ticktext=["Dec. 2020", "Jan. 2021", "Feb. 2021", "Mar. 2021",
"Apr. 2021", "May. 2021", "Jun. 2021", "Jul. 2021",
"Aug. 2021", "Sep. 2021", "Oct. 2021", "Nov. 2021"])
fig.update_yaxes(title_text="Number of invoices")
fig.update_layout(
width = 1350,
height = 250,
hovermode="x unified",
hoverlabel=dict(
bgcolor="white",
font_size=14,
font_family="Arial sans serif"
),
paper_bgcolor='rgba(0, 0, 0, 0)',
plot_bgcolor='rgba(0, 0, 0, 0)',
showlegend = True,
legend=dict(
orientation="h",
yanchor="bottom",
xanchor="right",
y=1.01,
x=0.8
),
margin=dict(l=0, r=0, t=0, b=0)
)
return fig