def craft_get():
df_company = pd.DataFrame(get_data(query_craft),
columns=[
'company', 'state', '2008', '2009', '2010',
'2011', '2012', '2013', '2014', '2015',
'2016', '2017', '2018'
])
df_companies = df_company.replace({"state": states})
df_companies = df_companies.astype({
"2008": int,
"2009": int,
"2010": int,
"2011": int,
"2012": int,
"2013": int,
"2014": int,
"2015": int,
"2016": int,
"2017": int,
"2018": int
})
craft_data = df_companies.to_html(index=False,
classes="display compact",
table_id="example")
return render_template("craft.html", craft_data=craft_data)
def numco():
df3 = pd.DataFrame(dframe(query_num_co),
columns=[
'states', '2008', '2009', '2010', '2011', '2012',
'2013', '2014', '2015', '2016', '2017'
])
df4 = df3.replace({"states": states})
df4 = df4.astype({
"2008": int,
"2009": int,
"2010": int,
"2011": int,
"2012": int,
"2013": int,
"2014": int,
"2015": int,
"2016": int,
"2017": int
})
df_numco = pd.DataFrame(get_data(query_numco), columns=['state', '2018'])
df_numco = df_numco.astype({"2018": int})
df4 = df4.merge(df_numco, left_on='states', right_on='state')
df4 = df4.drop('state', 1)
numco_data = df4.to_html(index=False,
classes="display compact",
table_id="example")
return render_template("numco.html", numco_data=numco_data)
def states_get():
df = pd.DataFrame(dframe(query_state),
columns=['states', '2008', '2009', '2010'])
df1 = df.replace({"states": states})
df1 = df1.astype({"2008": int, "2009": int, "2010": int})
df1 = df1.drop(df1[df1['2008'] == 0].index)
print(df1)
df_brew = pd.DataFrame(get_data(query_brew),
columns=[
'state', '2011', '2012', '2013', '2014', '2015',
'2016', '2017', '2018'
])
df_brew = df_brew.astype({
"2011": int,
"2012": int,
"2013": int,
"2014": int,
"2015": int,
"2016": int,
"2017": int,
"2018": int
})
df1 = df1.merge(df_brew, left_on='states', right_on='state')
df1 = df1.drop('state', 1)
state_data = df1.to_html(index=False,
classes="display compact",
table_id="example")
return render_template("states.html", state_data=state_data)
def graph():
query_graph = "select sum(count * price) as value \
from sales, products \
where products.id=sales.id \
group by extract(month from sales.sale_date) \
order by extract(month from sales.sale_date)"
line_labels=labels
line_values=[item for item, in get_data(query_graph)]
return render_template('chart.html', title='Monthly Beer Sales', max=200, labels=line_labels, values=line_values)
def graph_sales():
query_graph = "SELECT SUM(total) AS total \
FROM sales_mined \
WHERE extract(year from date_of_order)=2017 \
GROUP BY date_trunc('month', date_of_order) \
ORDER BY date_trunc('month', date_of_order)"
line_labels=labels
line_values=[item for item, in get_data(query_graph)]
return render_template('chart.html', title='Sales 2017', max=35000, labels=line_labels, values=line_values)
def current():
df_current = pd.DataFrame(get_data(query_current),
columns=[
'state', 'produced', 'population',
'number of breweries', 'rank by breweries',
'breweries per capita', 'capita rank'
])
current_data = df_current.to_html(index=False,
classes="display compact",
table_id="example")
return render_template("current.html", current_data=current_data)
def salary():
df_salary = pd.DataFrame(get_data(query_salary),
columns=[
'size', 'principal', 'brewmaster', 'mngr',
'head', 'assistant', 'cellar', 'packmng',
'canner', 'other'
])
print(df_salary)
salary_data = df_salary.to_html(index=True,
classes="display compact",
table_id="example")
return render_template("salary.html", salary_data=salary_data)
def population():
df_pop = pd.DataFrame(
get_data(query_population),
columns=['state', 'population', 'percent(21+)', 'adults(21+)'])
df_pop = df_pop.astype({"percent(21+)": float, "adults(21+)": int})
df_pop['percent(21+)'] = df_pop['percent(21+)'] * 100
df_pop['percent(21+)'] = df_pop['percent(21+)'].round(2)
df_pop['percent(21+)'] = df_pop['percent(21+)'].astype(str) + '%'
pop_data = df_pop.to_html(index=False,
classes="display compact",
table_id="example")
return render_template("population.html", pop_data=pop_data)
def craft_get():
craft_data = "".join(craft_temp % (company, state, barrels2008, barrels2009, barrels2010, barrels2011, barrels2012, barrels2013, barrels2014, barrels2015, barrels2016, barrels2017, barrels2018)
for company, state, barrels2008, barrels2009, barrels2010, barrels2011, barrels2012, barrels2013, barrels2014, barrels2015, barrels2016, barrels2017, barrels2018 in get_data(query_craft))
def dframe(query):
state_list = get_data(query)
try:
unit_type = flask_args.get('unit_type')[0].decode("utf-8")
except:
unit_type = "Celcius"
try:
theme = flask_args.get('theme')[0].decode("utf-8")
except:
theme = "default"
gdf = gpd.GeoDataFrame.from_file(join(dirname(__file__), 'data/states', 'gz_2010_us_040_00_20m.shp'))[['NAME', 'geometry']]
yr = 2018
gdf.columns = ['state', 'geometry']
gdf = gdf.drop(gdf.index[26])
gdf = gdf.drop(gdf.index[7])
df_company = pd.DataFrame(get_data(query_craft), columns =['company','state','2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018'])
df_companies = df_company.replace({"state": states})
df_companies = df_companies.astype({"2008": int, "2009": int, "2010": int, "2011": int, "2012": int, "2013": int, "2014": int, "2015": int, "2016": int, "2017": int, "2018": int})
df = pd.DataFrame(dframe(query_state), columns =['states', '2008', '2009', '2010'])
df1 = df.replace({"states": states})
df1 = df1.astype({"2008": int, "2009": int, "2010": int})
df_brew = pd.DataFrame(get_data(query_brew), columns =['state', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018'])
df_brew = df_brew.astype({"2011": int, "2012": int, "2013": int, "2014": int, "2015": int, "2016": int, "2017": int, "2018": int})
df1 = df1.merge(df_brew, left_on = 'states', right_on = 'state')
df1 = df1.drop('state', 1)
df3 = pd.DataFrame(dframe(query_num_co), columns =['states', '2008', '2009', '2010', '2011','2012', '2013', '2014', '2015', '2016', '2017'])
df4 = df3.replace({"states": states})
df4 = df4.astype({"2008": int, "2009": int, "2010": int, "2011": int, "2012": int, "2013": int, "2014": int, "2015": int, "2016": int, "2017": int})
def sales_mined_get():
if if_exist('sales_mined')[0][0] == False:
data_xls = pd.read_excel('jkbg2017sales_mined.xlsx', sheet_name = ['Recreational', 'Medical'], index_col=None, header = 1)
df = pd.concat(data_xls[frame] for frame in data_xls.keys())
df['Date of Order'] = pd.to_datetime(df['Date of Order'])
df['Total'] = df['Total'].astype(float)
df_sales_mined = df.iloc[:,[0,3,4,6,7]]
df_sales_mined = df_sales_mined.drop_duplicates()
df_sales_mined.rename(columns = lambda x: x.strip().lower().replace(" ", "_"), inplace=True)
for index, row in df_sales_mined.iterrows():
add_sales_mined(row['buyer_name'], row['classification'], row['date_of_order'], row['product_count'], row['total'])
query_sales_mined = "select buyer_name, classification, date_of_order, product_count, total \
from sales_mined \
order by date_of_order"
sales_mined_data = "".join(sales_mined_temp % (buyer_name, classification, date_of_order, product_count, total) for buyer_name, classification, date_of_order, product_count, total in get_data(query_sales_mined))
return render_template("sales_mined.html", sales_mined_data = sales_mined_data)
def clients_get():
#data_xls = pd.read_excel('jkbg2017sales_mined.xlsx', 'Recreational', index_col=None)
#data_xls.to_csv('csvfile.csv', encoding='utf-8', index=False)
if if_exist('clientsdb')[0][0] == False:
data_xls = pd.read_excel('jkbg2017sales_mined.xlsx', sheet_name = ['Recreational', 'Medical'], index_col=None, header = 1)
df = pd.concat(data_xls[frame] for frame in data_xls.keys())
df_clients = df.iloc[:,[0,8,9,11]]
df_clients = df_clients.drop_duplicates()
df_clients.rename(columns = lambda x: x.strip().lower().replace(" ", "_"), inplace=True)
for index, row in df_clients.iterrows():
add_clients(row['buyer_name'], row['bill_street'], row['bill_city'], row['buyer_licenses'])
query_clients = "select client_id, buyer_name, bill_street, bill_city, buyer_licenses \
from clientsdb \
order by client_id"
clients_data = "".join(clients_temp % (c_id, buyer_name, bill_street, bill_city, buyer_licenses) for c_id, buyer_name, bill_street, bill_city, buyer_licenses in get_data(query_clients))
return render_template("clients.html", clients_data = clients_data)
def dframe(query):
state_list = get_data(query)
for item in state_list:
if len(item[0]) != 2:
state_list.remove(item)
return state_list
def sales_get():
query_sales = "select id, sale_date, count \
from sales \
order by id"
sales_data = "".join(sales_temp % (s_id, sale_date, count) for s_id, sale_date, count in get_data(query_sales))
return render_template("sales.html", sales_data = sales_data)
def products_get():
query_products = "select id, price, name \
from products \
order by id"
product_data = "".join(POST % (id, price, name) for id, price, name in get_data(query_products))
return render_template("products.html", product_data = product_data)
def modify_doc(doc):
def callback(event):
selected = geosource.selected.indices
yr = slider.value
data = get_json(str(yr), selected[0])
sgeosource.geojson = data[0]
text_year.text = str(yr)
text_state.text = data[1]
text_beer.text = str(f"{data[2]:,d}")
text_numco.text = str(numco(data[1], str(yr)))
text_rank.text = str(rank(data[1], str(yr)))
text_rate.text = str(rate(data[1], str(yr))) + '%'
text_adults.text = str(adults(data[1])) + '%'
text_capita.text = str(capita(data[1], str(yr)))
company_selection.labels = company(data[1])
initial_companies = [
company_selection.labels[i] for i in company_selection.active
]
src.data = make_dataset(initial_companies).data
coordinates = xysource(data[1], df1)
ycoordinate = [i / 100000 for i in coordinates[1]]
statesource.data = dict(x=coordinates[0], y=ycoordinate)
coordinatesplot = xysource(data[1], df4)
numcosource.data = dict(x=coordinatesplot[0], y=coordinatesplot[1])
splot.visible = True
tplot.visible = True
def xysource(state, df5):
df2 = df5.loc[df5['states'] == state]
df2 = df2.drop('states', 1)
list_x = [int(i) for i in list(df2)]
list_y = df2.values[0]
return [list_x, list_y]
flask_args = doc.session_context.request.arguments
try:
plot_title = flask_args.get('plot_title')[0].decode("utf-8")
except:
plot_title = "Sea Surface Temperature at 43.18, -70.43"
try:
dataset = flask_args.get('dataset')[0].decode("utf-8")
except:
dataset = "Dataset One"
try:
unit_type = flask_args.get('unit_type')[0].decode("utf-8")
except:
unit_type = "Celcius"
try:
theme = flask_args.get('theme')[0].decode("utf-8")
except:
theme = "default"
gdf = gpd.GeoDataFrame.from_file(
join(dirname(__file__), 'data/states', 'gz_2010_us_040_00_20m.shp'))[[
'NAME', 'geometry', 'GEO_ID', 'CENSUSAREA'
]]
yr = 2018
gdf.columns = ['state', 'geometry', 'geo_id', 'censusarea']
gdf = gdf.drop(gdf.index[26])
gdf = gdf.drop(gdf.index[7])
df_company = pd.DataFrame(get_data(query_craft),
columns=[
'company', 'state', '2008', '2009', '2010',
'2011', '2012', '2013', '2014', '2015',
'2016', '2017', '2018'
])
df_companies = df_company.replace({"state": states})
df_companies = df_companies.astype({
"2008": int,
"2009": int,
"2010": int,
"2011": int,
"2012": int,
"2013": int,
"2014": int,
"2015": int,
"2016": int,
"2017": int,
"2018": int
})
df = pd.DataFrame(dframe(query_state),
columns=['states', '2008', '2009', '2010'])
df1 = df.replace({"states": states})
df1 = df1.astype({"2008": int, "2009": int, "2010": int})
df_brew = pd.DataFrame(get_data(query_brew),
columns=[
'state', '2011', '2012', '2013', '2014', '2015',
'2016', '2017', '2018'
])
df_brew = df_brew.astype({
"2011": int,
"2012": int,
"2013": int,
"2014": int,
"2015": int,
"2016": int,
"2017": int,
"2018": int
})
df1 = df1.merge(df_brew, left_on='states', right_on='state')
df1 = df1.drop('state', 1)
df3 = pd.DataFrame(dframe(query_num_co),
columns=[
'states', '2008', '2009', '2010', '2011', '2012',
'2013', '2014', '2015', '2016', '2017'
])
df4 = df3.replace({"states": states})
df4 = df4.astype({
"2008": int,
"2009": int,
"2010": int,
"2011": int,
"2012": int,
"2013": int,
"2014": int,
"2015": int,
"2016": int,
"2017": int
})
df_numco = pd.DataFrame(get_data(query_numco), columns=['state', '2018'])
df_numco = df_numco.astype({"2018": int})
df4 = df4.merge(df_numco, left_on='states', right_on='state')
df4 = df4.drop('state', 1)
df_pop = pd.DataFrame(get_data(query_population),
columns=['state', 'population', 'percent', 'adults'])
df_pop = df_pop.astype({"percent": float, "adults": int})
df_pop['percent'] = df_pop['percent'] * 100
def numco(state, year):
merged = df4.loc[df4['states'] == state]
numco_text = merged.iloc[0][year]
return numco_text
def rank(state, year):
df_rank = df4[['states', year]]
df = df_rank.sort_values(year, ascending=False)
df = df.reset_index(drop=True)
idx = df.index[df['states'] == state]
idx = idx[0] + 1
return idx
def rate(state, year):
rate = (100 * df4.loc[df4['states'] == state][year].values[0]
) / df4[year].sum()
return rate.round(2)
def adults(state):
adults = df_pop.loc[df_pop['state'] == state]['percent'].values[0]
return adults.round(2)
def capita(state, year):
adults = df_pop.loc[df_pop['state'] == state]['adults'].values[0]
num = numco(state, year)
kf = 100000
capita = (num / adults) * kf
print(capita)
return capita.round(1)
def get_json(year, idx):
df_yr = df1[['states', year]]
df_yr.columns = ['states', 'year']
merged = gdf.merge(df_yr, left_on='state', right_on='states')
state_text = ''
beer_text = ''
if idx is not None:
merged = merged.iloc[[idx]]
state_text = merged.iloc[0]['state']
beer_text = merged.iloc[0]['year']
merged_json = json.loads(merged.to_json())
json_data = json.dumps(merged_json)
return [json_data, state_text, beer_text]
#Input GeoJSON source that contains features for plotting.
geosource = GeoJSONDataSource(geojson=get_json('2018', None)[0])
#Define a sequential multi-hue color palette.
palette = ['#084594', '#2171b5', '#4292c6', '#6baed6', '#9ecae1']
#palette = d3['Category20b'][4]
#Reverse color order so that dark blue is highest obesity.
palette = palette[::-1]
#Instantiate LinearColorMapper that linearly maps numbers in a range, into a sequence of colors.
color_mapper = LinearColorMapper(palette=palette,
low=0,
high=2500000,
nan_color='#d9d9d9')
#Define custom tick labels for color bar.
tick_labels = {
'0': '0%',
'5': '5%',
'10': '10%',
'15': '15%',
'20': '20%',
'25': '25%',
'30': '30%',
'35': '35%',
'40': '>40%'
}
#Add hover tool
hover = HoverTool(tooltips=[('State', '@state'),
('brew', '@year' + " bbls")],
toggleable=False)
#Create color bar.
color_bar = ColorBar(color_mapper=color_mapper,
label_standoff=10,
width=590,
height=20,
border_line_color=None,
location=(0, 0),
orientation='horizontal',
major_label_overrides=tick_labels)
#Create figure object.
plot = figure(plot_width=640,
plot_height=420,
aspect_scale=0.85,
title='State Craft Beer Sales 2018')
plot.add_tools(hover)
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = None
plot.background_fill_color = "#F5F5F5"
#Add patch renderer to figure.
plot.axis.visible = False
initial_map = Patches(xs='xs',
ys='ys',
fill_color={
'field': 'year',
'transform': color_mapper
},
line_color='white',
line_width=0.15,
fill_alpha=0.5)
selected_state = Patches(line_width=1,
fill_color={
'field': 'year',
'transform': color_mapper
},
line_color='#00022b',
fill_alpha=0.5)
nonselected_state = Patches(line_width=0.15,
fill_color={
'field': 'year',
'transform': color_mapper
},
line_color='white',
fill_alpha=0.5)
plot.add_glyph(geosource,
initial_map,
selection_glyph=selected_state,
nonselection_glyph=nonselected_state)
#Specify figure layout.
plot.add_tools(TapTool())
plot.on_event(Tap, callback)
plot.add_layout(color_bar, 'below')
splot = figure(plot_width=320,
plot_height=300,
match_aspect=True,
title=None,
toolbar_location=None)
tplot = figure(plot_width=320,
plot_height=300,
title=None,
toolbar_location=None,
tools='')
sales_plot = figure(
plot_width=320,
plot_height=300,
title="Sales 2008-2018",
x_axis_label='Year',
y_axis_label='Barrels of Craft Beer Produced (x 100 000)',
toolbar_location=None)
numco_plot = figure(plot_width=320,
plot_height=300,
title="Number of Companies 2008-2018",
x_axis_label='Year',
y_axis_label='Number of craft beer breweries',
toolbar_location=None)
splot.axis.visible = False
tplot.axis.visible = False
splot.grid.visible = False
tplot.grid.visible = False
splot.outline_line_width = 0
tplot.outline_line_width = 0
# splot.visible = False
# tplot.visible = False
sgeosource = GeoJSONDataSource(geojson=get_json('2018', 2)[0])
splot.patches('xs',
'ys',
source=sgeosource,
fill_color='#131E3A',
line_color='black',
line_width=0.25,
fill_alpha=1)
year_source = ColumnDataSource({'year': ['2018']})
text_year = Label(x=250,
y=250,
x_units='screen',
y_units='screen',
text='2018',
text_color='#131E3A',
text_font_size='16pt',
text_font_style='bold')
text_state = Label(x=0,
y=250,
x_units='screen',
y_units='screen',
text='California',
text_color='#131E3A',
text_font_size='16pt',
text_font_style='bold')
text_beer = Label(x=145,
y=220,
x_units='screen',
y_units='screen',
text='1,032,369',
text_color='#013240',
text_font_size='28pt',
text_font_style='bold',
text_font='Roboto',
text_baseline='middle',
text_align='right',
x_offset=25)
text_val1 = Label(x=175,
y=222,
x_units='screen',
y_units='screen',
text='Barrels of Craft Beer',
text_color='#013220',
text_font_size='11pt',
text_font_style='normal',
text_font='Roboto')
text_val2 = Label(x=175,
y=208,
x_units='screen',
y_units='screen',
text='Produced per Year',
text_color='#013220',
text_font_size='11pt',
text_font_style='normal',
text_font='Roboto')
text_numco = Label(x=145,
y=180,
x_units='screen',
y_units='screen',
text='750',
text_color='#013240',
text_font_size='28pt',
text_font_style='bold',
text_font='Roboto',
text_baseline='middle',
text_align='right',
x_offset=25)
text_val3 = Label(x=175,
y=182,
x_units='screen',
y_units='screen',
text='Craft Breweries',
text_color='#013220',
text_font_size='11pt',
text_font_style='normal',
text_font='Roboto')
text_val4 = Label(x=175,
y=168,
x_units='screen',
y_units='screen',
text='Operating per Year',
text_color='#013220',
text_font_size='11pt',
text_font_style='normal',
text_font='Roboto')
text_rank = Label(x=145,
y=140,
x_units='screen',
y_units='screen',
text='1',
text_color='#013240',
text_font_size='28pt',
text_font_style='bold',
text_font='Roboto',
text_baseline='middle',
text_align='right',
x_offset=25)
text_val5 = Label(x=175,
y=142,
x_units='screen',
y_units='screen',
text='Most Craft',
text_color='#013220',
text_font_size='11pt',
text_font_style='normal',
text_font='Roboto')
text_val6 = Label(x=175,
y=128,
x_units='screen',
y_units='screen',
text='Breweries in the US',
text_color='#013220',
text_font_size='11pt',
text_font_style='normal',
text_font='Roboto')
text_rate = Label(x=145,
y=100,
x_units='screen',
y_units='screen',
text='5%',
text_color='#013240',
text_font_size='28pt',
text_font_style='bold',
text_font='Roboto',
text_baseline='middle',
text_align='right',
x_offset=25)
text_val7 = Label(x=175,
y=102,
x_units='screen',
y_units='screen',
text='of US Craft Breweries',
text_color='#013220',
text_font_size='11pt',
text_font_style='normal',
text_font='Roboto')
text_val8 = Label(x=175,
y=88,
x_units='screen',
y_units='screen',
text='',
text_color='#013220',
text_font_size='11pt',
text_font_style='normal',
text_font='Roboto')
text_adults = Label(x=145,
y=60,
x_units='screen',
y_units='screen',
text='75%',
text_color='#013240',
text_font_size='28pt',
text_font_style='bold',
text_font='Roboto',
text_baseline='middle',
text_align='right',
x_offset=25)
text_val9 = Label(x=175,
y=62,
x_units='screen',
y_units='screen',
text='21+ Adults in state',
text_color='#013220',
text_font_size='11pt',
text_font_style='normal',
text_font='Roboto')
text_val10 = Label(x=175,
y=48,
x_units='screen',
y_units='screen',
text='',
text_color='#013220',
text_font_size='11pt',
text_font_style='normal',
text_font='Roboto')
text_capita = Label(x=145,
y=20,
x_units='screen',
y_units='screen',
text='2.8%',
text_color='#013240',
text_font_size='28pt',
text_font_style='bold',
text_font='Roboto',
text_baseline='middle',
text_align='right',
x_offset=25)
text_val11 = Label(x=175,
y=22,
x_units='screen',
y_units='screen',
text='Breweries per',
text_color='#013220',
text_font_size='11pt',
text_font_style='normal',
text_font='Roboto')
text_val12 = Label(x=175,
y=8,
x_units='screen',
y_units='screen',
text='100,000 21+ Adults',
text_color='#013220',
text_font_size='11pt',
text_font_style='normal',
text_font='Roboto')
coordinates = xysource('California', df1)
ycoordinate = [i / 100000 for i in coordinates[1]]
statesource = ColumnDataSource(dict(x=coordinates[0], y=ycoordinate))
coordinatesplot = xysource('California', df4)
numcosource = ColumnDataSource(
dict(x=coordinatesplot[0], y=coordinatesplot[1]))
sales_plot.line('x', 'y', source=statesource, line_width=3, line_alpha=0.6)
numco_plot.line('x', 'y', source=numcosource, line_width=3, line_alpha=0.6)
tplot.add_layout(text_year)
tplot.add_layout(text_state)
tplot.add_layout(text_beer)
tplot.add_layout(text_val1)
tplot.add_layout(text_val2)
tplot.add_layout(text_numco)
tplot.add_layout(text_val3)
tplot.add_layout(text_val4)
tplot.add_layout(text_rank)
tplot.add_layout(text_val5)
tplot.add_layout(text_val6)
tplot.add_layout(text_rate)
tplot.add_layout(text_val7)
tplot.add_layout(text_val8)
tplot.add_layout(text_adults)
tplot.add_layout(text_val9)
tplot.add_layout(text_val10)
tplot.add_layout(text_capita)
tplot.add_layout(text_val11)
tplot.add_layout(text_val12)
# glyph = Text(x="x", y="y", text="text", angle=0.3, text_color="#96deb3")
# tplot.add_glyph(source, glyph)
def update_plot(attr, old, new):
yr = slider.value
new_data = get_json(str(yr), None)[0]
geosource.geojson = new_data
plot.title.text = 'State Craft Beer Sales, %d' % yr
# Make a slider object: slider
slider = Slider(title='Year', start=2008, end=2018, step=1, value=2018)
slider.on_change('value', update_plot)
def make_dataset(company_list):
xs = []
ys = []
colors = []
labels = []
# src_circle = []
for i, company in enumerate(company_list):
df_co = df_companies.loc[df_companies['company'] == company]
df_co = df_co.drop(['company', 'state'], axis=1)
list_x = [int(i) for i in list(df_co)]
list_y = df_co.values[0]
xs.append(list_x)
ys.append(list_y)
colors.append(company_colors[i])
labels.append(company)
new_src = ColumnDataSource(data={
'x': xs,
'y': ys,
'color': colors,
'label': labels
})
# for i in range(0, len(new_src.data['x'])):
# print (i)
# src_circle.append(ColumnDataSource(data={'x': new_src.data['x'][i], 'y': new_src.data['y'][i]}))
return new_src
def make_plot(src):
p = figure(plot_width=800,
plot_height=420,
title='Craft Beer Sales by State Companies 2008-2018',
x_axis_label='Year',
y_axis_label='Barrels of Craft Beer Produced, bbls')
p.multi_line('x',
'y',
color='color',
line_alpha=0.6,
hover_line_alpha=1.0,
legend='label',
line_width=3,
name='needshover',
source=src)
# for i in src_circle:
# p.circle('x', 'y', fill_color="white", color = 'grey', size=8, source = i)
hover = HoverTool(names=['needshover'],
tooltips=[('Company', '@label'), ('Year', '$x{int}'),
('Barrels', '$data_y' + " bbls")])
p.add_tools(hover)
p.yaxis.formatter = NumeralTickFormatter(format="0.0 a")
p.legend.location = "top_left"
return p
def update(attr, old, new):
companies_to_plot = [
company_selection.labels[i] for i in company_selection.active
]
new_src = make_dataset(companies_to_plot)
src.data.update(new_src.data)
def company(state):
available_companies = df_companies.loc[df_companies['state'] ==
state][:]
sort_companies = list(set(available_companies['company'][:20]))
sort_companies.sort()
return sort_companies
company_selection = CheckboxGroup(labels=company('California'),
active=[0, 1])
company_selection.on_change('active', update)
initial_companies = [
company_selection.labels[i] for i in company_selection.active
]
company_colors = Category20_16
company_colors.sort()
src = make_dataset(initial_companies)
# src_circle = make_dataset(initial_companies)[1]
plot_state = make_plot(src)
controls = WidgetBox(company_selection, width=200)
l1 = row(controls, plot_state)
if plot_title == '1':
r = row([plot, tplot], sizing_mode='stretch_height')
doc.add_root(column(r, l1))
else:
l = layout([[plot], [splot, tplot], [numco_plot, sales_plot]])
doc.add_root(column(slider, l))
doc.theme = Theme(filename=f"theme-{theme}.yaml")
