def create_stats():
##############################################################
# LINE PLOT
###############################################################
line_df = get_views.get_view_by_name('ventas_diarias')
line_fig = px.line(line_df,
x = 'fecha_compra',
y = 'promedio_ventas',
hover_data=['volumen_ventas'])
line_fig.update_layout(
height=500
)
###############################################################
# BAR PLOT
###############################################################
bar_df = get_views.get_view_by_name('venta_ciudad_tienda').head(10)
bar_fig = px.bar(bar_df
, y = 'ciudad_tienda', x = 'tienda_x_ciudad'
, color = 'canal'
, hover_data=['canal']
, barmode = 'stack'
, orientation = 'h'
)
bar_fig.update_layout(
autosize=True,
#width=1000,
height=500,
legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
yaxis={'categoryorder':'total ascending'}
)
#################################################################################
# Here the layout for the plots to use.
#################################################################################
stats = [
dbc.Col(
dbc.Card(
dcc.Graph(figure=bar_fig, id="bar"),body=True, color="dark"
),
width={"size": 5, "offset": 2},
),
dbc.Col(
dbc.Card(
dcc.Graph(figure=line_fig, id="line"),body=True, color="dark"
),
width=5
)
]
return stats
def set_cities_options(chosen_state):
clean2 = get_views.get_view_by_name('canal_edad_tipo_ciudad_cluster')
dff = clean2[clean2['ciudad_tienda'] == chosen_state]
return [{
'label': c,
'value': c
} for c in sorted(dff['cluster_id'].unique())]
def update_graph(selected_ciudad, selected_cluster):
clean = get_views.get_view_by_name('canal_edad_tipo_ciudad_cluster')
df = clean[clean['ciudad_tienda'] == selected_ciudad]
df10 = df['cluster_id'].unique()
if selected_cluster is None:
df2 = get_views.get_view_by_name('canal_edad_tipo_ciudad_cluster')
dff = df2[(df2['ciudad_tienda'] == selected_ciudad)]
fig = px.sunburst(dff,
path=['canal', 'edad', 'tipo_articulo'],
values='volumen_pesos',
color='canal',
title="Total sales for the city of: {}".format(
selected_ciudad))
fig.update_traces(textinfo='label+percent entry')
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
else:
if selected_cluster in list(df10):
df2 = get_views.get_view_by_name(
'canal_edad_tipo_ciudad_cluster')
dff = df2[(df2['ciudad_tienda'] == selected_ciudad)
& df2['cluster_id'].isin([selected_cluster])]
fig = px.sunburst(
dff,
path=['canal', 'edad', 'tipo_articulo'],
values='volumen_pesos',
color='canal',
title="Total sales for the city of: {}".format(
selected_ciudad))
fig.update_traces(textinfo='label+percent entry')
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
else:
df2 = get_views.get_view_by_name(
'canal_edad_tipo_ciudad_cluster')
dff = df2[(df2['ciudad_tienda'] == selected_ciudad)]
fig = px.sunburst(
dff,
path=['canal', 'edad', 'tipo_articulo'],
values='volumen_pesos',
color='canal',
title="Total sales for the city of: {}".format(
selected_ciudad))
fig.update_traces(textinfo='label+percent entry')
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
return fig
def map_cities(ciudad):
line_df = get_views.get_view_by_name('ventas_diarias_ciudad')
line_df["ciudad_tienda"] = line_df["ciudad_tienda"].apply(
lambda x: x.capitalize())
line_df = line_df[line_df["ciudad_tienda"] == ciudad]
fig6 = px.line(line_df,
x='fecha_compra',
y='volumen_ventas',
hover_data=['promedio_ventas'])
return open('maps/' + ciudad + '_map.html', 'r').read(), fig6
def create_map():
'''
This functions creates the map off Colombia where the company has stores.
params:
none
returns:
creates an html for Colombia where each city has information about
customers purchase frecuency
'''
df_for_map = get_views.get_view_by_name('tiendas_frecuencia')
df_for_map["Radio_for_map"]=((df_for_map["valor_neto"])/df_for_map["valor_neto"].mean())*10+5
# Create the map:
m_prueba = folium.Map(location=[6.461508, -75.000000],max_zoom=18, zoom_start=6, tiles="cartodbpositron")
circle="""
<svg version='1.1' xmlns='http://www.w3.org/2000/svg'
width='25' height='25' viewBox='0 0 120 120'>
<circle cx='60' cy='60' r='50'
fill={} />
</svg>
"""
group1=folium.FeatureGroup(name=circle.format('#FF0000')+"<FONT SIZE=2>Freq. [1,1.25]</font> ")
m_prueba.add_child(group1)
group2=folium.FeatureGroup(name=circle.format('#FF7070')+"<FONT SIZE=2>Freq. [1.25,1.35]</font>", show=False)
m_prueba.add_child(group2)
group3=folium.FeatureGroup(name=circle.format('#FF6B22')+"<FONT SIZE=2>Freq. [1.35,1.5]</font>" , show=False)
m_prueba.add_child(group3)
group4=folium.FeatureGroup(name=circle.format('#0FFB0E')+"<FONT SIZE=2>Freq. [1.5,1.8]</font>", show=False)
m_prueba.add_child(group4)
group5=folium.FeatureGroup(name=circle.format('#019F00')+"<FONT SIZE=2>Freq. > 1.8</font>", show=False)
m_prueba.add_child(group5)
for i in range(df_for_map.shape[0]):
sales=round(df_for_map.loc[i,"valor_neto"]/1000000,1)
sales=f"${sales}M"
html_ = """
<h2 style="margin-bottom:-10"; align="center">{}</h2>""".format(df_for_map.loc[i,'punto_venta']) + """ <br>
<b>Total ventas (1 año):</b> {}""".format(sales) + """ <br/>
<b>Frec. de venta (1 año):</b> {}""".format(round(df_for_map.loc[i,'frequency'],4)) + """ <br/>
<b>Centro comercial:</b> {}""".format(df_for_map.loc[i,'centro_comercial']) + """ <br/>
<b>Canal:</b> {}""".format(df_for_map.loc[i,'canal']) + """ <br/>
<b>Codigo tienda:</b> {}""".format(df_for_map.loc[i,'codigo_tienda']) + """ <br/>
<b>IDGEO:</b> {}""".format(df_for_map.loc[i,'id_geo'])
iframe = branca.element.IFrame(html=html_, width=300, height=250)
popup = folium.Popup(iframe, max_width=305, parse_html=True)
if (df_for_map.loc[i,"frequency"] > 1) & (df_for_map.loc[i,"frequency"] <= 1.25):
folium.CircleMarker(radius=df_for_map.loc[i,"Radio_for_map"],
location=[df_for_map.loc[i,"latitude"],df_for_map.loc[i,"longitude"]],
popup=popup,
color="black",fill=True,fill_color="#FF0000",weight=1,
fill_opacity=0.7,
tooltip=f"<FONT SIZE=4><b>Ventas</b>:{sales}</font>").add_to(group1)
elif (df_for_map.loc[i,"frequency"] > 1.25) & (df_for_map.loc[i,"frequency"] <= 1.35):
folium.CircleMarker(radius=df_for_map.loc[i,"Radio_for_map"],
location=[df_for_map.loc[i,"latitude"],df_for_map.loc[i,"longitude"]],
popup=popup,color="black",fill=True,fill_color="#FF7070",weight=1,
fill_opacity=0.7,
tooltip=f"<FONT SIZE=4><b>Ventas</b>:{sales}</font>").add_to(group2)
elif (df_for_map.loc[i,"frequency"] > 1.35) & (df_for_map.loc[i,"frequency"] <= 1.5):
folium.CircleMarker(radius=df_for_map.loc[i,"Radio_for_map"],
location=[df_for_map.loc[i,"latitude"],df_for_map.loc[i,"longitude"]],
popup=popup,color="black",fill=True,fill_color="#FF6B22",weight=1,
fill_opacity=0.8,
tooltip=f"<FONT SIZE=4><b>Ventas</b>:{sales}</font>").add_to(group3)
elif (df_for_map.loc[i,"frequency"] > 1.5) & (df_for_map.loc[i,"frequency"] <= 1.8):
folium.CircleMarker(radius=df_for_map.loc[i,"Radio_for_map"],
location=[df_for_map.loc[i,"latitude"],df_for_map.loc[i,"longitude"]],
popup=popup,color="black",fill=True,fill_color="#8EFF94",weight=1,
fill_opacity=0.8,
tooltip=f"<FONT SIZE=4><b>Ventas</b>:{sales}</font>").add_to(group4)
else:
folium.CircleMarker(radius=df_for_map.loc[i,"Radio_for_map"],
location=[df_for_map.loc[i,"latitude"],df_for_map.loc[i,"longitude"]],
popup=popup,color="black",fill=True,fill_color="#2B9A00",weight=1,
fill_opacity=0.8,
tooltip=f"<FONT SIZE=4><b>Ventas</b>:{sales}</font>").add_to(group5)
folium.LayerControl(collapsed=False).add_to(m_prueba)
m_prueba.save('maps/Colombia_map.html')
##############################
# Map Layout
##############################
map = html.Div(
[
html.H5("Sales frequency and amount per store"),
html.P("Full Country"),
# Place the main graph component here:
html.Iframe(srcDoc = open('maps/Colombia_map.html','r').read()
, id="COL_map",width='100%',height=526)
],
#className="ds4a-body",
)
return map
def create_map_cities():
'''
This functions creates the maps off all cities where the company has stores.
params:
none
returns:
creates an html for all the cities where each map has information about
customers purchase frecuency
'''
df_for_map = get_views.get_view_by_name('tiendas_frecuencia')
df_for_map["Radio_for_map"]=((df_for_map["valor_neto"])/df_for_map["valor_neto"].mean())*10+5
# Create dictionary with cities, location and zoom:
locations_dict ={'ciudad': ['Medellín', 'Bogotá', 'Barrancabermeja', 'Cali', 'Santa Marta', 'Cartagena', 'Yopal',
'Chía', 'Armenia', 'Villavicencio', 'Ipiales', 'Pasto', 'Bucaramanga', 'Cúcuta',
'Tunja', 'Pitalito', 'Barranquilla', 'Valledupar', 'Popayán', 'Ibagué', 'Montería',
'Riohacha', 'Ocaña', 'Girardot', 'Rionegro', 'Neiva', 'San Andres', 'Apartadó',
'Yumbo', 'Manizales', 'La Ceja', 'Aguachica', 'Envigado', 'Pereira', 'Duitama',
'Sogamoso', 'Arauca', 'Sincelejo', 'Florencia', 'Cartago', 'Palmira'],
'location': [[6.2501125,-75.5803933], [4.683925, -74.087004], [7.064098, -73.856063],
[3.427341, -76.521280], [11.235145, -74.192268], [10.397664, -75.506810],
[5.333906, -72.395035], [4.877329, -74.034782], [4.540616, -75.674956],
[4.136571, -73.626914], [0.826007, -77.640272], [1.211466, -77.277567],
[7.107962, -73.113924], [7.902127, -72.506138], [5.550740, -73.349890],
[1.852723, -76.048825], [10.992978, -74.806297], [10.469399, -73.251555],
[2.457041, -76.592210], [4.434196, -75.197765], [8.749607, -75.879484],
[11.537468, -72.912597], [8.247508, -73.356539], [4.302750, -74.801321],
[6.147482, -75.375835], [2.932439, -75.282356], [12.561642, -81.717021],
[7.883654, -76.625723], [3.582594, -76.489090], [5.061344, -75.5049307],
[6.029851, -75.428421], [8.307962, -73.612180], [6.168872, -75.584505],
[4.811958, -75.709814], [5.823173, -73.031070], [5.724299, -72.924044],
[7.081708, -70.753947], [9.302024, -75.396304], [1.617380, -75.609830],
[4.746874, -75.921079], [3.531945, -76.297079]],
'zoom': [12, 11, 14, 12, 14, 12, 14, 13, 13, 14, 14, 13, 13, 12, 14, 14, 13, 13, 13, 13,
14, 13, 14, 14, 13, 13, 13, 14, 14, 14, 14, 14, 14, 13, 14, 14, 14, 14, 14, 14, 14]
}
# Create the map:
locations_df = pd.DataFrame(locations_dict)
for index, row in locations_df.iterrows():
m_prueba = folium.Map(location=row[1],max_zoom=18, zoom_start=row[2])
circle="""
<svg version='1.1' xmlns='http://www.w3.org/2000/svg'
width='25' height='25' viewBox='0 0 120 120'>
<circle cx='60' cy='60' r='50'
fill={} />
</svg>
"""
group1=folium.FeatureGroup(name=circle.format('#FF0000')+"<FONT SIZE=2>Freq. [1,1.25]</font> ")
m_prueba.add_child(group1)
group2=folium.FeatureGroup(name=circle.format('#FF7070')+"<FONT SIZE=2>Freq. [1.25,1.35]</font>")
m_prueba.add_child(group2)
group3=folium.FeatureGroup(name=circle.format('#FF6B22')+"<FONT SIZE=2>Freq. [1.35,1.5]</font>" )
m_prueba.add_child(group3)
group4=folium.FeatureGroup(name=circle.format('#0FFB0E')+"<FONT SIZE=2>Freq. [1.5,1.8]</font>")
m_prueba.add_child(group4)
group5=folium.FeatureGroup(name=circle.format('#019F00')+"<FONT SIZE=2>Freq. > 1.8</font>")
m_prueba.add_child(group5)
for i in range(df_for_map.shape[0]):
sales=round(df_for_map.loc[i,"valor_neto"]/1000000,1)
sales=f"${sales}M"
html_ = """
<h2 style="margin-bottom:-10"; align="center">{}</h2>""".format(df_for_map.loc[i,'punto_venta']) + """ <br>
<b>Total ventas (1 año):</b> {}""".format(sales) + """ <br/>
<b>Frec. de venta (1 año):</b> {}""".format(round(df_for_map.loc[i,'frequency'],4)) + """ <br/>
<b>Centro comercial:</b> {}""".format(df_for_map.loc[i,'centro_comercial']) + """ <br/>
<b>Canal:</b> {}""".format(df_for_map.loc[i,'canal']) + """ <br/>
<b>Codigo tienda:</b> {}""".format(df_for_map.loc[i,'codigo_tienda']) + """ <br/>
<b>IDGEO:</b> {}""".format(df_for_map.loc[i,'id_geo'])
iframe = branca.element.IFrame(html=html_, width=300, height=250)
popup = folium.Popup(iframe, max_width=305, parse_html=True)
if (df_for_map.loc[i,"frequency"] > 1) & (df_for_map.loc[i,"frequency"] <= 1.25):
folium.CircleMarker(radius=df_for_map.loc[i,"Radio_for_map"],
location=[df_for_map.loc[i,"latitude"],df_for_map.loc[i,"longitude"]],
popup=popup,
color="black",fill=True,fill_color="#FF0000",weight=1,
fill_opacity=0.7,
tooltip=f"<FONT SIZE=4><b>Ventas</b>:{sales}</font>").add_to(group1)
elif (df_for_map.loc[i,"frequency"] > 1.25) & (df_for_map.loc[i,"frequency"] <= 1.35):
folium.CircleMarker(radius=df_for_map.loc[i,"Radio_for_map"],
location=[df_for_map.loc[i,"latitude"],df_for_map.loc[i,"longitude"]],
popup=popup,color="black",fill=True,fill_color="#FF7070",weight=1,
fill_opacity=0.7,
tooltip=f"<FONT SIZE=4><b>Ventas</b>:{sales}</font>").add_to(group2)
elif (df_for_map.loc[i,"frequency"] > 1.35) & (df_for_map.loc[i,"frequency"] <= 1.5):
folium.CircleMarker(radius=df_for_map.loc[i,"Radio_for_map"],
location=[df_for_map.loc[i,"latitude"],df_for_map.loc[i,"longitude"]],
popup=popup,color="black",fill=True,fill_color="#FF6B22",weight=1,
fill_opacity=0.8,
tooltip=f"<FONT SIZE=4><b>Ventas</b>:{sales}</font>").add_to(group3)
elif (df_for_map.loc[i,"frequency"] > 1.5) & (df_for_map.loc[i,"frequency"] <= 1.8):
folium.CircleMarker(radius=df_for_map.loc[i,"Radio_for_map"],
location=[df_for_map.loc[i,"latitude"],df_for_map.loc[i,"longitude"]],
popup=popup,color="black",fill=True,fill_color="#8EFF94",weight=1,
fill_opacity=0.8,
tooltip=f"<FONT SIZE=4><b>Ventas</b>:{sales}</font>").add_to(group4)
else:
folium.CircleMarker(radius=df_for_map.loc[i,"Radio_for_map"],
location=[df_for_map.loc[i,"latitude"],df_for_map.loc[i,"longitude"]],
popup=popup,color="black",fill=True,fill_color="#2B9A00",weight=1,
fill_opacity=0.8,
tooltip=f"<FONT SIZE=4><b>Ventas</b>:{sales}</font>").add_to(group5)
folium.LayerControl(collapsed=True).add_to(m_prueba)
map_name = row[0]+'_map.html'
m_prueba.save('maps/'+map_name)
from data_fetch import get_views
from get_callbacks import return_callbacks
card_main = dbc.Card(
[
dbc.CardBody(
[
html.H4("Analysis of sales values by city and items offered by OFFCORSS", className="card-title"),
html.P(
"choose the city to see the main items sold in each of the stores.",
className="card-text",
),
dcc.Dropdown(id="ciudades-dpdn",placeholder='Ciudad...',
options=[{'label': i, 'value': i} for i in sorted(get_views.get_view_by_name('canal_edad_tipo_ciudad_cluster').drop(get_views.get_view_by_name('canal_edad_tipo_ciudad_cluster')[(get_views.get_view_by_name('canal_edad_tipo_ciudad_cluster')['ciudad_tienda'] == 'MEDELLIN') | (get_views.get_view_by_name('canal_edad_tipo_ciudad_cluster')['ciudad_tienda'] == 'BOGOTÁ') ].index)['ciudad_tienda'].unique())],
multi=False,
value='AGUACHICA',
clearable=False,
persistence=False,
persistence_type='memory',
style={'width': "60%",'color':'black'}),
html.P(
"select the cluster you want to view",
className="card-text",
),
dcc.Dropdown(id='cluster-dpdn',placeholder='cluster', options=[], multi=False,clearable=True,value=None,persistence=True,persistence_type='memory',style={'width': "60%",'color':'black'}),
html.P(
"Choose several categories to observe the profits produced",
className="card-text",
),
def barp(selected_cluster, selected_ciudad, select):
clean = get_views.get_view_by_name('canal_edad_tipo_ciudad_cluster')
df = clean[clean['ciudad_tienda'] == selected_ciudad]
df10 = df['cluster_id'].unique()
if (selected_cluster is None and select
== 'grupo_articulo') or (selected_cluster not in list(df10)
and select == 'grupo_articulo'):
dff = get_views.get_view_by_name(
'ciudad_tienda_grupo_articulo_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)]
df_bar = df_bar.sort_values('volumen_pesos',
ascending=False).head(10)
fig5 = px.bar(
df_bar,
y='grupo_articulo',
x="volumen_pesos",
color='grupo_articulo',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
elif (selected_cluster is None
and select == 'canal') or (selected_cluster not in list(df10)
and select == 'canal'):
dff = get_views.get_view_by_name('ciudad_tienda_canal_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)]
df_bar = df_bar.sort_values('volumen_pesos', ascending=False)
fig5 = px.bar(
df_bar,
y='canal',
x="volumen_pesos",
color='canal',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
elif (selected_cluster is None
and select == 'sublinea') or (selected_cluster not in list(df10)
and select == 'sublinea'):
dff = get_views.get_view_by_name('ciudad_tienda_sublinea_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)]
df_bar = df_bar.sort_values('volumen_pesos', ascending=False)
fig5 = px.bar(
df_bar,
y='sublinea',
x="volumen_pesos",
color='sublinea',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
elif (selected_cluster is None
and select == 'saldo') or (selected_cluster not in list(df10)
and select == 'saldo'):
dff = get_views.get_view_by_name('ciudad_tienda_saldo_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)]
df_bar = df_bar.sort_values('volumen_pesos', ascending=False)
fig5 = px.bar(
df_bar,
y='saldo',
x="volumen_pesos",
color='saldo',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
elif (selected_cluster is None and select
== 'tipo_tejido') or (selected_cluster not in list(df10)
and select == 'tipo_tejido'):
dff = get_views.get_view_by_name(
'ciudad_tienda_tipo_tejido_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)]
df_bar = df_bar.sort_values('volumen_pesos', ascending=False)
fig5 = px.bar(
df_bar,
y='tipo_tejido',
x="volumen_pesos",
color='tipo_tejido',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
elif (selected_cluster is None and select
== 'tipo_articulo') or (selected_cluster not in list(df10)
and select == 'tipo_articulo'):
dff = get_views.get_view_by_name(
'ciudad_tienda_tipo_articulo_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)]
df_bar = df_bar.sort_values('volumen_pesos',
ascending=False).head(10)
fig5 = px.bar(
df_bar,
y='tipo_articulo',
x="volumen_pesos",
color='tipo_articulo',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
elif (selected_cluster is None
and select == 'mes_venta') or (selected_cluster not in list(df10)
and select == 'mes_venta'):
dff = get_views.get_view_by_name('ciudad_tienda_mes_venta_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)]
df_bar = df_bar.sort_values('volumen_pesos', ascending=False)
fig5 = px.bar(
df_bar,
y='mes_venta',
x="volumen_pesos",
color='mes_venta',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
elif (selected_cluster in list(df10) and select == 'grupo_articulo'):
dff = get_views.get_view_by_name(
'ciudad_tienda_grupo_articulo_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)
& dff['cluster_id'].isin([selected_cluster])]
df_bar = df_bar.sort_values('volumen_pesos',
ascending=False).head(10)
fig5 = px.bar(
df_bar,
y='grupo_articulo',
x="volumen_pesos",
color='grupo_articulo',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
elif (selected_cluster in list(df10) and select == 'canal'):
dff = get_views.get_view_by_name('ciudad_tienda_canal_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)
& dff['cluster_id'].isin([selected_cluster])]
df_bar = df_bar.sort_values('volumen_pesos', ascending=False)
fig5 = px.bar(
df_bar,
y='canal',
x="volumen_pesos",
color='canal',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
elif (selected_cluster in list(df10) and select == 'sublinea'):
dff = get_views.get_view_by_name('ciudad_tienda_sublinea_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)
& dff['cluster_id'].isin([selected_cluster])]
df_bar = df_bar.sort_values('volumen_pesos', ascending=False)
fig5 = px.bar(
df_bar,
y='sublinea',
x="volumen_pesos",
color='sublinea',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
elif (selected_cluster in list(df10) and select == 'saldo'):
dff = get_views.get_view_by_name('ciudad_tienda_saldo_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)
& dff['cluster_id'].isin([selected_cluster])]
df_bar = df_bar.sort_values('volumen_pesos', ascending=False)
fig5 = px.bar(
df_bar,
y='saldo',
x="volumen_pesos",
color='saldo',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
elif (selected_cluster in list(df10) and select == 'tipo_tejido'):
dff = get_views.get_view_by_name(
'ciudad_tienda_tipo_tejido_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)
& dff['cluster_id'].isin([selected_cluster])]
df_bar = df_bar.sort_values('volumen_pesos', ascending=False)
fig5 = px.bar(
df_bar,
y='tipo_tejido',
x="volumen_pesos",
color='tipo_tejido',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
elif (selected_cluster in list(df10) and select == 'mes_venta'):
dff = get_views.get_view_by_name('ciudad_tienda_mes_venta_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)
& dff['cluster_id'].isin([selected_cluster])]
df_bar = df_bar.sort_values('volumen_pesos', ascending=False)
fig5 = px.bar(
df_bar,
y='mes_venta',
x="volumen_pesos",
color='mes_venta',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
elif (selected_cluster in list(df10) and select == 'tipo_articulo'):
dff = get_views.get_view_by_name(
'ciudad_tienda_tipo_articulo_cluster')
df_bar = dff[(dff['ciudad_tienda'] == selected_ciudad)
& dff['cluster_id'].isin([selected_cluster])]
df_bar = df_bar.sort_values('volumen_pesos',
ascending=False).head(10)
fig5 = px.bar(
df_bar,
y='tipo_articulo',
x="volumen_pesos",
color='tipo_articulo',
orientation="h",
hover_name="ciudad_tienda",
title="Sales generated according to: {}".format(select))
else:
print('no found')
return fig5
def update(selected_cluster, selected_ciudad, choose):
clean = get_views.get_view_by_name('canal_edad_tipo_ciudad_cluster')
df = clean[clean['ciudad_tienda'] == selected_ciudad]
df10 = df['cluster_id'].unique()
if selected_cluster is None and choose == 'edad':
dff = get_views.get_view_by_name('edad_count_avg_cluster')
df_line = dff[(dff['ciudad_tienda'] == selected_ciudad)]
fig3 = px.scatter(
df_line,
x="fecha_compra",
y='ventas_promedio',
color=choose,
hover_data=['cantidad_compras'],
title="Trend of sales according to the annual course of : {}".
format(choose))
elif selected_cluster is None and choose == 'canal':
dff = get_views.get_view_by_name('count_avg_cluster')
df_line = dff[(dff['ciudad_tienda'] == selected_ciudad)]
fig3 = px.scatter(
df_line,
x="fecha_compra",
y='ventas_promedio',
color=choose,
hover_data=['cantidad_compras'],
title="Trend of sales according to the annual course of : {}".
format(choose))
elif selected_cluster in list(df10) and choose == 'edad':
dff = get_views.get_view_by_name('edad_count_avg_cluster')
df_line = dff[(dff['ciudad_tienda'] == selected_ciudad)
& dff['cluster_id'].isin([selected_cluster])]
fig3 = px.scatter(
df_line,
x="fecha_compra",
y='ventas_promedio',
color=choose,
hover_data=['cantidad_compras'],
title="Trend of sales according to the annual course of : {}".
format(choose))
elif selected_cluster in list(df10) and choose == 'canal':
dff = get_views.get_view_by_name('count_avg_cluster')
df_line = dff[(dff['ciudad_tienda'] == selected_ciudad)
& dff['cluster_id'].isin([selected_cluster])]
fig3 = px.scatter(
df_line,
x="fecha_compra",
y='ventas_promedio',
color=choose,
hover_data=['cantidad_compras'],
title="Trend of sales according to the annual course of : {}".
format(choose))
elif selected_cluster not in list(df10) and choose == 'canal':
dff = get_views.get_view_by_name('count_avg_cluster')
df_line = dff[(dff['ciudad_tienda'] == selected_ciudad)]
fig3 = px.scatter(
df_line,
x="fecha_compra",
y='ventas_promedio',
color=choose,
hover_data=['cantidad_compras'],
title="Trend of sales according to the annual course of : {}".
format(choose))
else:
dff = get_views.get_view_by_name('edad_count_avg_cluster')
df_line = dff[(dff['ciudad_tienda'] == selected_ciudad)]
fig3 = px.scatter(
df_line,
x="fecha_compra",
y='ventas_promedio',
color=choose,
hover_data=['cantidad_compras'],
title="Trend of sales according to the annual course of : {}".
format(choose))
return fig3
def create_map():
#############################
# Load paths
#############################
#############################
# Load map data
#############################
df_for_map = get_views.get_view_by_name('tiendas_frecuencia')
df_for_map["Radio_for_map"] = df_for_map["valor_neto"] / 37000000
# Create the map:
m_prueba = folium.Map(location=[5.543949, -73.917579],
max_zoom=18,
zoom_start=5)
tooltip = 'Click me!'
group1 = folium.FeatureGroup(name="Freq [1,1.25]")
m_prueba.add_child(group1)
group2 = folium.FeatureGroup(name="Freq [1.25,1.35]", show=False)
m_prueba.add_child(group2)
group3 = folium.FeatureGroup(name="Freq [1.35,1.5]", show=False)
m_prueba.add_child(group3)
group4 = folium.FeatureGroup(name="Freq [1.5,1.8]", show=False)
m_prueba.add_child(group4)
group5 = folium.FeatureGroup(name="Freq > 1.8", show=False)
m_prueba.add_child(group5)
for i in range(df_for_map.shape[0]):
sales = df_for_map.loc[i, "valor_neto"]
if (df_for_map.loc[i, "frequency"] >
1) & (df_for_map.loc[i, "frequency"] <= 1.25):
folium.CircleMarker(radius=df_for_map.loc[i, "Radio_for_map"],
location=[
df_for_map.loc[i, "latitude"],
df_for_map.loc[i, "longitude"]
],
popup=df_for_map.loc[i, "centro_comercial"],
color="black",
fill=True,
fill_color="red",
weight=1,
fill_opacity=0.5,
tooltip=f"Sales:{sales}").add_to(group1)
elif (df_for_map.loc[i, "frequency"] >
1.25) & (df_for_map.loc[i, "frequency"] <= 1.35):
folium.CircleMarker(radius=df_for_map.loc[i, "Radio_for_map"],
location=[
df_for_map.loc[i, "latitude"],
df_for_map.loc[i, "longitude"]
],
popup=df_for_map.loc[i, "centro_comercial"],
color="black",
fill=True,
fill_color="blue",
weight=1,
fill_opacity=0.5,
tooltip=f"Sales:{sales}").add_to(group2)
elif (df_for_map.loc[i, "frequency"] >
1.35) & (df_for_map.loc[i, "frequency"] <= 1.5):
folium.CircleMarker(radius=df_for_map.loc[i, "Radio_for_map"],
location=[
df_for_map.loc[i, "latitude"],
df_for_map.loc[i, "longitude"]
],
popup=df_for_map.loc[i, "centro_comercial"],
color="black",
fill=True,
fill_color="coral",
weight=1,
fill_opacity=0.5,
tooltip=f"Sales:{sales}").add_to(group3)
elif (df_for_map.loc[i, "frequency"] >
1.5) & (df_for_map.loc[i, "frequency"] <= 1.8):
folium.CircleMarker(radius=df_for_map.loc[i, "Radio_for_map"],
location=[
df_for_map.loc[i, "latitude"],
df_for_map.loc[i, "longitude"]
],
popup=df_for_map.loc[i, "centro_comercial"],
color="black",
fill=True,
fill_color="green",
weight=1,
fill_opacity=0.5,
tooltip=f"Sales:{sales}").add_to(group4)
else:
folium.CircleMarker(radius=df_for_map.loc[i, "Radio_for_map"],
location=[
df_for_map.loc[i, "latitude"],
df_for_map.loc[i, "longitude"]
],
popup=df_for_map.loc[i, "centro_comercial"],
color="black",
fill=True,
fill_color="purple",
weight=1,
fill_opacity=0.5,
tooltip=f"Sales:{sales}").add_to(group5)
folium.TileLayer('cartodbpositron').add_to(m_prueba)
folium.LayerControl(collapsed=False).add_to(m_prueba)
m_prueba.save('Colombia_map.html')
##############################
# Map Layout
##############################
map = html.Div(
[
# Place the main graph component here:
html.Iframe(srcDoc=open('Colombia_map.html', 'r').read(),
id="COL_map",
width='100%',
height=600)
],
className="ds4a-body",
)
return map