def fig_c002(regions=["Lombardia", "Veneto"], norm="fraction"):
value_name = "Valore % sul Tot." if norm == "fraction" else "Valore"
yformat = ".1%" if norm == "fraction" else ".0"
data = get_pop_covid_regions()
covid_lomb_ven = data[data.region.isin(regions)]
covid_lomb_ven = covid_lomb_ven.melt(
id_vars=["time", "region"],
value_vars=["n_home_quarantine", "n_hospitalized", "n_intensive_care"],
var_name="Variabile",
value_name=value_name,
)
covid_lomb_ven["Variabile"] = covid_lomb_ven["Variabile"].replace(labels)
fig = px.area(
covid_lomb_ven.sort_values(by=["time", "Variabile"], ascending=False),
x="time",
y=value_name,
color="Variabile",
color_discrete_sequence=["#EF553B", "#FF7F0E", "#54A24B"],
facet_col="region",
labels=labels,
groupnorm=norm,
template="plotly_white",
)
fig.update_xaxes(title=None)
fig.add_annotation(
font=dict(size=9, color="grey"),
showarrow=False,
yref="paper",
xref="paper",
text="Fonte: ISTAT, Dipartimento di Protezione Civile Italiana",
y=-0.15,
yanchor="top",
)
fig.update_layout(
title=dict(
text=f"<br><b>Composizione dei Casi COVID-19 Attivi:"
" Lombardia e Veneto</b></br>"
f'<span style="font-size: 12px">{value_name} - Clicca sulle Voci '
"nella Legenda per Selezionare o Deselezionare</span>",
x=0.5,
y=0.98,
),
legend=dict(orientation="h", y=-0.2, yanchor="top", title=None),
yaxis=dict(tickformat=yformat),
margin={
"l": 60,
"r": 30,
"t": 130,
"b": 100,
"autoexpand": False
},
dragmode=False,
width=650,
height=450,
)
watermark(fig)
return fig
def fig_a006():
df = pd.read_csv(
"https://raw.githubusercontent.com/buildnn/covid-19-ita/master/"
"data/migrazione_sanitaria_gimbe_2019-06.csv"
)
df["Saldo"] = ((df["Crediti"] - df["Debiti"]) / 1000000).round(1)
df = df.sort_values("Saldo").dropna(subset=["Saldo"])
fig_a006 = px.bar(
df,
y="Regione",
x="Saldo",
orientation="h",
color="Saldo",
color_continuous_midpoint=0,
color_continuous_scale="Temps_r",
width=500,
height=700,
range_x=(-450, 810),
template="plotly_white",
labels={"Saldo": "Saldo (mln€)"},
title="<br><b>Saldo per Prestazioni verso Non Residenti</b></br>"
'<span style="font-size: 12px;">Anno 2017 - Milioni di Euro</span>',
)
fig_a006.layout.coloraxis.showscale = False
fig_a006.update_layout(
margin={"l": 120, "r": 30, "t": 80, "b": 100, "autoexpand": False},
legend=dict(orientation="h", y=-0.78, title=None),
yaxis=dict(title=None),
xaxis=dict(tickformat="+0", tick0=-400, dtick=200),
title=dict(x=0.5, y=0.98),
)
fig_a006.add_annotation(
text="<br>Fonte: Osservatorio GIMBE, ACISMOM e OPBG indicano "
"rispettivamente Ospedale Pediatrico Bambino Gesù <br>e Associazione "
"dei Cavalieri Italiani del Sovrano Militare Ordine di Malta, "
"contabilizzati separatamente",
align="center",
font=dict(size=9, color="grey"),
showarrow=False,
xref="paper",
yref="paper",
x=-0.28,
y=-0.15,
)
watermark(fig_a006)
return fig_a006
def plot_veneto_lombardy(df, y, title):
x = "time"
fig = px.line(
df.round(2),
x=x,
y=y,
color="region",
width=600,
height=400,
template="plotly_white",
labels={
"tot_n_hospitalized_anchored": "N. Ospedalizzati,"
" 25 Feb = 1",
"n_intensive_care_anchored": "N. Ricoveri in Terapia Intensiva,"
" 25 Feb = 1",
"time": "Data",
"region": "Regione",
},
range_x=(
pd.to_datetime("2020-02-25"),
df.time.max() + pd.to_timedelta(10, "D"),
),
line_shape="spline",
)
fig.update_traces(line=dict(width=1.0))
for trace in fig.data:
name = trace["name"]
color = trace["line"]["color"]
lom = df.loc[(df.time == df.time.max()) & (df.region == name)]
fig.add_trace(
go.Scatter(
x=lom[x],
y=lom[y],
text=lom["region"],
textposition="middle right",
mode="markers+text",
hoverinfo="skip",
marker=dict(color=color),
))
fig.update_layout(
margin={
"l": 40,
"r": 20,
"b": 40,
"t": 80,
"autoexpand": False
},
title=dict(
text=title,
x=0.5,
y=0.96,
),
xaxis=dict(title=None),
yaxis_tickformat="20",
# legend=dict(orientation="h", yanchor="top", y=-.15, title=None),
showlegend=False,
)
fig.add_annotation(
text="Fonte: Dipartimento di Protezione Civile",
font=dict(size=9, color="grey"),
showarrow=False,
xref="paper",
yref="paper",
y=-0.14,
)
watermark(fig)
return fig
def fig_b004():
df = pd.read_csv("https://docs.google.com/spreadsheets/d/"
"1VwmTC47fQdnuVFizvI7eTs3xPPFtJwU4/export?format=csv&"
"id=1VwmTC47fQdnuVFizvI7eTs3xPPFtJwU4&gid=831947660")
fig = go.Figure()
fig.add_traces([
go.Scatter(
x=df["Anno"],
y=df["Fondo Sanitario Nazionale (2008 =100)"].round(1),
name="Fondo Sanitario Nazionale (2008=100)",
mode="lines+markers",
line=dict(width=1.0),
# marker=dict(symbol=134)
),
go.Scatter(
x=df["Anno"],
y=df["Indice prezzi sanità 2008=100"].round(1),
name="Indice Prezzi Sanità (2008=100)",
mode="lines+markers",
line=dict(width=1.0),
# marker=dict(symbol=134)
),
go.Scatter(
x=df["Anno"],
y=df["migrazione sanitaria 2008=100"].round(1),
name="Migrazione Sanitaria (2008=100)",
mode="lines+markers",
line=dict(width=1.0),
# marker=dict(symbol=134)
),
])
w = 500
h = 400
lm = 40
rm = 40
bm = 120
tm = 80
fig.update_layout(
title=dict(
text="<b>Fondi per la Sanità, Prezzi e Migrazione Sanitaria</b>"
'<br><span style="font-size: 12;">Andamento negli anni '
"(standardizzato, 2008=1)</span>",
x=0.5,
),
template="plotly_white",
legend=dict(
orientation="h",
x=0.5,
y=-70 / (h + bm + tm),
yanchor="top",
xanchor="center",
),
width=w,
height=h,
margin={
"l": lm,
"r": rm,
"b": bm,
"t": tm,
"autoexpand": False,
},
)
fig.add_annotation(
text="Fonti: Eurostat, Ministero della Sanità, "
"Elaborazioni su dati Ilsole24Ore/Conferenza Stato-Regioni",
xref="paper",
yref="paper",
yanchor="bottom",
font=dict(size=9, color="grey"),
y=-340 / (h + bm + tm),
showarrow=False,
)
watermark(fig, annot_y=-270 / (h + bm + tm), logo_y=-235 / (h + bm + tm))
return fig
def fig_c001(y="test_pthab", regions=["Lombardia", "Veneto"]):
data = get_pop_covid_regions()
covid_lomb_ven = data[data.region.isin(regions)]
ita_mean = data.groupby("time", as_index=False).mean()
fig = px.line(
map_names(covid_lomb_ven),
x=map_names("time"),
y=map_names(y),
color=map_names("region"),
template="plotly_white",
labels=labels,
width=600,
height=450,
)
fig.update_traces(
mode="lines",
line=dict(width=1.0),
marker=dict(symbol=134),
)
fig.add_trace(
go.Scatter(
x=ita_mean["time"],
y=ita_mean[y],
line=dict(dash="dot", color="firebrick"),
mode="lines",
name="Media ITA",
), )
for trace in fig.data:
fig.add_trace(
go.Scatter(
x=trace["x"][[-1]],
y=trace["y"][[-1]],
text=trace["name"],
textposition="middle right",
marker=dict(color=trace["line"]["color"]),
mode="markers+text",
name=trace["name"],
showlegend=False,
))
fig.add_annotation(
font=dict(size=9, color="grey"),
showarrow=False,
yref="paper",
xref="paper",
text="Fonte: ISTAT, Dipartimento di Protezione Civile Italiana",
y=-0.1,
yanchor="top",
)
fig.update_layout(
title=dict(
text=f"<b>{labels[y]}: Lombardia e Veneto</b>",
x=0.5,
),
xaxis=dict(
range=[
data["time"].min(),
data["time"].max() + pd.to_timedelta(10, "d"),
],
title=None,
),
margin={
"l": 60,
"r": 30,
"t": 100,
"b": 100,
"autoexpand": False
},
legend=dict(
orientation="h",
title=None,
xanchor="center",
x=0.5,
y=-0.15,
yanchor="top",
),
dragmode=False,
)
watermark(fig)
return fig
def plot(df,
x,
y,
pl_kwargs,
labels,
title,
update_layout_kwargs,
line_shape=None):
fig = px.line(df,
x=x,
y=y,
color="geo",
template="plotly_white",
title=title,
labels=labels,
line_shape=line_shape,
**pl_kwargs)
for n, trace in enumerate(fig.select_traces()):
trace["line"]["width"] = 0.75
trace["mode"] = "lines+markers"
trace["marker"] = {"symbol": 133, "opacity": 0.5, "size": 3}
trace = go.Scatter(
x=trace["x"][-1:],
y=trace["y"][-1:],
name=trace["name"],
text=[trace["name"]],
showlegend=False,
mode="markers",
textposition="middle right",
marker=dict(color=trace["line"]["color"]),
)
if trace["name"] in [
"Italy",
"South Korea",
"China",
"Canada",
"Australia",
"United States",
"Germany",
"United Kingdom",
"Spain",
]:
trace["mode"] = "markers+text"
fig.add_trace(trace)
update_kwa = dict(
width=400,
margin={
"r": 20,
"b": 180,
"t": 80,
"l": 70,
"autoexpand": False
},
height=750,
title={"x": 0.5},
showlegend=False,
legend=dict(orientation="v", x=0.0, y=1.0, title=None),
xaxis=dict(
rangeslider=dict(visible=True),
type="date" if x == "time" else "linear",
),
)
update_kwa.update(update_layout_kwargs)
fig.update_layout(**update_kwa)
return watermark(fig)
def fig_a005():
medici_df = pd.read_csv(
"https://docs.google.com/spreadsheets/d/e/"
"2PACX-1vRNSiOhlGO4r2Dh2cSr2zJbQ-iUfqqbQojs"
"aTe4z1omzhuIjI_fzJCoD3EFNzgCeXMeX-3cVsZK1hZS/pub?output=csv"
)
medici_df = medici_df.query("Anno == 2017")
medici_df = medici_df.dropna(subset=["Regione"])
medici_df = medici_df.replace("Emilia_Romagna", "Emilia-Romagna")
medici_df = medici_df.replace("Friuli_Ven_Giu", "Friuli-Venezia Giulia")
medici_df = medici_df.replace("PA_bolzano", "P.A. Bolzano")
medici_df = medici_df.replace("PA_trento", "P.A. Trento")
medici_df = medici_df[medici_df.Regione != ""]
medici_df.loc[
medici_df.Adempiente.isna() | (medici_df.Adempiente == ""),
"Adempiente",
] = "Non Sottoposta a Verifica"
fig_a005 = px.bar(
medici_df,
x="Regione",
y="LEA",
# hover_data=["Adempiente"],
color="Adempiente", # subset.time.astype(str),
color_discrete_map={
"Non Sottoposta a Verifica": "#BAB0AC",
"Non Adempiente": "#E45756",
"Adempiente": "#54A24B",
},
# range_y=(0, 1060),
width=650,
height=400,
# barmode="group",
template="plotly_white",
labels={"LEA": "Punteggio LEA"},
color_discrete_sequence=px.colors.qualitative.Dark24,
title="<br><b>Punteggi LEA (Livelli Essenziali di Assistenza)</b></br>"
'<span style="font-size: 12px;">Anno 2017</span>',
)
fig_a005.add_shape(
# Line Vertical
dict(
type="line",
x0=-1.0,
y0=160,
x1=21.0,
y1=160,
line=dict(
color="#D45113",
width=1,
# dash="dot",
),
opacity=1.0,
)
)
fig_a005.update_layout(
margin={"l": 50, "r": 20, "t": 80, "b": 170, "autoexpand": False},
legend=dict(orientation="h", y=-0.78, title=None),
xaxis=dict(title=None),
title=dict(x=0.5, y=0.96),
)
fig_a005.add_annotation(
text="Fonte: Rielaborazione BuildNN & Tortuga da Ministero della "
"Salute",
font=dict(size=9, color="grey"),
showarrow=False,
xref="paper",
yref="paper",
y=-1.04,
)
fig_a005.add_annotation(
x=-0.5,
y=160,
xref="x",
yref="y",
xanchor="left",
yanchor="middle",
text="<b>Livello Minimo</b>",
showarrow=False,
font=dict(family="Courier New, monospace", size=10, color="#ffffff"),
align="left",
bordercolor="#c7c7c7",
borderwidth=0,
borderpad=0,
bgcolor="#D45113",
# opacity=0.75
)
watermark(fig_a005)
return fig_a005
def fig_a004():
es_unit_value_map, es_geo_value_map = get_es_maps()
es_isco08_value_map = dict(prep_eurostat.var["eurostat"]["isco08"])
hlth_rs_prsrg = prep_eurostat.parse_eurostat_dataset("hlth_rs_prsrg")
hlth_rs_prsrg = hlth_rs_prsrg.query("unit == 'P_HTHAB'").drop(
columns=["unit"]
)
# curative_beds = hlth_rs_prsrg.query("facility == 'HBEDT_CUR'").drop(
# columns=["facility"]
# )
hsp_pers = hlth_rs_prsrg.loc[
hlth_rs_prsrg["geo"].str.startswith("IT")
| hlth_rs_prsrg["geo"].isin(["DE", "FR"])
]
with warnings.catch_warnings(record=True):
hsp_pers["time"] = hsp_pers["time"].dt.year
hsp_pers["isco08"] = hsp_pers["isco08"].replace(es_isco08_value_map)
hsp_pers["Regione"] = hsp_pers["geo"].replace(es_geo_value_map)
# --- CHARTS ---
field = "isco08"
cls = "Nurses and midwives"
subset = hsp_pers.query("time == 2008 | time == 2016")
subset = (
subset[subset[field] == cls]
.rename(columns={"value": cls})
.drop(columns=[field])
)
subset["time"] = subset["time"].astype(str)
subset["level"] = subset["geo"].apply(len)
subset = subset.sort_values(by=["time", "level"])
fig_a004 = px.bar(
subset,
x="Regione",
y=cls,
hover_data=["geo"],
color="time",
category_orders={0: "2008", 1: "2016"},
range_y=(0, 1060),
width=500,
height=400,
barmode="group",
template="plotly_white",
labels={cls: "Personale per 100.000 ab."},
color_discrete_sequence=px.colors.qualitative.Dark24,
title="<br><b>Personale Infermieristico e Ostetrico</b></br>"
'<span style="font-size: 12px;">Unità di Personale '
"per 100.000 abitanti: Confronto 2008 vs. 2016</span>",
)
fig_a004.add_shape(
# Line Vertical
dict(
type="line",
x0=2.5,
y0=-10,
x1=2.5,
y1=900,
line=dict(
color="grey",
width=1,
# dash="dot",
),
opacity=0.5,
)
)
fig_a004.update_layout(
margin={"l": 50, "r": 20, "t": 80, "b": 150, "autoexpand": False},
legend=dict(orientation="h", y=-0.58, title=None),
xaxis=dict(title=None),
title=dict(x=0.5, y=0.96),
annotations=[
dict(
text="Fonte: Rielaborazione BuildNN & Tortuga da Eurostat",
font=dict(size=9, color="grey"),
showarrow=False,
xref="paper",
yref="paper",
y=-0.84,
)
],
)
watermark(fig_a004, logo_y=-.75, annot_y=-.82)
return fig_a004
def fig_a001():
es_unit_value_map, es_geo_value_map = get_es_maps()
es_facility_value_map = dict(prep_eurostat.var["eurostat"]["facility"])
hlth_rs_bdsrg = prep_eurostat.parse_eurostat_dataset("hlth_rs_bdsrg")
hlth_rs_bdsrg = hlth_rs_bdsrg.query("unit == 'P_HTHAB'").drop(
columns=["unit"]
)
# curative_beds = hlth_rs_bdsrg.query("facility == 'HBEDT_CUR'").drop(
# columns=["facility"]
# )
hsp_beds = hlth_rs_bdsrg.loc[
hlth_rs_bdsrg["geo"].str.startswith("IT")
| hlth_rs_bdsrg["geo"].isin(["DE", "FR"])
]
with warnings.catch_warnings(record=True):
hsp_beds["time"] = hsp_beds["time"].dt.year
hsp_beds["facility"] = hsp_beds["facility"].replace(
es_facility_value_map
)
hsp_beds["Regione"] = hsp_beds["geo"].replace(es_geo_value_map)
# --- CHARTS ---
subset = hsp_beds.query("time == 2001 | time == 2017")
subset = subset[subset.facility.str.startswith("Avail")]
subset["time"] = subset["time"].astype(str)
subset["level"] = subset["geo"].apply(len)
subset = subset.sort_values(by=["time", "level"])
fig_a001 = px.bar(
subset,
x="Regione",
y="value",
hover_data=["geo"],
color="time",
category_orders={0: "2017", 1: "2001"},
width=500,
height=400,
barmode="group",
template="plotly_white",
labels={"value": "Totale Posti Letto per 100.000 ab."},
color_discrete_sequence=px.colors.qualitative.Dark24,
title="<br><b>Totale Posti Letto nelle Strutture Ospedaliere</b></br>"
'<span style="font-size: 12px;">Posti Letto '
"per 100.000 abitanti: Confronto 2001 vs. 2017</span>",
)
fig_a001.add_shape(
# Line Vertical
dict(
type="line",
x0=2.5,
y0=-10,
x1=2.5,
y1=900,
line=dict(
color="grey",
width=1,
# dash="dot",
),
opacity=0.5,
)
)
fig_a001.update_layout(
margin={"l": 50, "r": 20, "t": 80, "b": 150, "autoexpand": False},
legend=dict(orientation="h", y=-0.58, title=None),
xaxis=dict(title=None),
title=dict(x=0.5, y=0.96),
annotations=[
dict(
text="Fonte: Rielaborazione BuildNN & Tortuga da Eurostat",
font=dict(size=9, color="grey"),
showarrow=False,
xref="paper",
yref="paper",
y=-0.84,
)
],
)
watermark(fig_a001, logo_y=-.75, annot_y=-.82)
return fig_a001
def fig_a002():
# -- PHARMA
pharma = (
prep_salutegov.parse_dataset("pharmacies")
.groupby("region_code")[["pharmacy_code"]]
.nunique()
.reset_index()
.rename(columns={"pharmacy_code": "n_pharmacies"})
)
asl = (
prep_salutegov.parse_dataset("asl_comuni_pop")
.replace("", np.nan)
.dropna(subset=["region_code"])
)
asl["TOTALE"] = (
asl["TOTALE"]
.str.replace(".", "")
.str.replace(",", ".")
.replace("", np.nan)
.astype(float)
)
pop = asl.groupby(["region_code"]).agg(
population=pd.NamedAgg("TOTALE", "sum"),
region=pd.NamedAgg("region", "first"),
)
pharma["population"] = pop.reindex(pharma["region_code"].values)[
"population"
].values
pharma["region"] = (
pop.reindex(pharma["region_code"].values)["region"].str.title().values
)
pharma.loc[
pharma.region_code == "04", "region"
] = "Trentino-Alto Adige/Südtirol"
pharma["hab_per_pharma"] = (
(pharma["population"] / pharma["n_pharmacies"]).round(0).astype(int)
)
fig_a002 = px.choropleth(
pharma,
geojson=GDF_REG,
color="hab_per_pharma",
hover_data=["region", "n_pharmacies", "population"],
locations="region_code",
# range_color=(0, 3.5),
center={"lat": 42.65, "lon": 12.4},
featureidkey="properties.reg_istat_code",
projection="mercator",
template="plotly_white",
color_continuous_scale=px.colors.diverging.Temps,
labels={
"region_code": "Cod. Regione",
"region": "Regione",
"hab_per_pharma": "Ab. per Farmacia",
"n_pharmacies": "Numero Farmacie",
"population": "Numero Abitanti",
},
# line_shape="spline",
)
fig_a002.update_geos(fitbounds="locations", visible=False).update_layout(
width=600, height=600
)
fig_a002.update_layout(
margin={"l": 80, "r": 140, "b": 80, "t": 100, "autoexpand": False},
title=dict(
text="<br><b>Abitanti per Farmacia</b></br>"
'<span style="font-size: 11px;">Anno 2018 - Meno è Meglio</span>',
x=0.5,
y=0.96,
),
xaxis=dict(title=None),
yaxis_tickformat="20",
legend=dict(orientation="h", yanchor="top", y=-0.15, title=None),
# showlegend=False,
annotations=[
dict(
text="Fonte: Rielaborazione BuildNN & Tortuga da Ministero "
"della Salute",
font=dict(size=9, color="grey"),
showarrow=False,
xref="paper",
yref="paper",
y=-0.0,
)
],
)
watermark(fig_a002)
return fig_a002
def fig_e001():
regioni = prep_dpc_regions()
id_vars = [
"time",
"country",
"region_code",
"region",
"latitude",
"longitude",
"epidemic_start",
"epidemic_age",
]
value_vars = [
"n_hospitalized",
"n_intensive_care",
# 'n_home_quarantine',
# 'being_tested',
"new_deceased",
"new_discharged",
]
hosp_melted = regioni.melt(id_vars=id_vars, value_vars=value_vars)
hosp_melted
f1 = (
px.area(
hosp_melted[
hosp_melted.region.isin(["Lombardia", "Veneto"])
].replace(
{
"n_hospitalized": "Ospedalizzati (no T.I.)",
"n_intensive_care": "Terapia Intensiva",
# 'n_home_quarantine',
# 'being_tested',
"new_deceased": "Decessi",
"new_discharged": "Dimessi/Guariti",
}
),
labels={"value": "Valore", "time": "Dara", "region": "Regione"},
x="time",
y="value",
color="variable",
color_discrete_sequence=px.colors.qualitative.T10,
facet_col="region",
template="plotly_white",
title="<b>Tamponi vs Impatto COVID-19 sugli Ospedali</b><br>"
'<span style="font-size: 12px;">Confronto tra Veneto e Lombardia'
"</span>",
)
.update_layout(
margin={"t": 100, "autoexpand": False},
title=dict(y=0.92, x=0.5),
width=820,
height=450,
yaxis=dict(title=None, tickformat="d"),
legend=dict(orientation="h", title=None, y=-0.13),
dragmode=False,
)
.update_xaxes(title=None)
)
f2 = px.bar(
regioni[regioni.region.isin(["Lombardia", "Veneto"])],
x="time",
y="being_tested",
color_discrete_sequence=["steelblue"],
facet_col="region",
opacity=0.5,
).update_traces(hoverinfo="skip").update_layout(dragmode=False)
for n, trace in enumerate(f2.select_traces()):
trace["name"] = "Tamponi Effettuati"
if n == 0:
trace["showlegend"] = True
f1.add_trace(trace, 1, n + 1)
f1.add_annotation(
text="Fonte: Dipartimento di Protezione Civile",
font=dict(size=9, color="grey"),
showarrow=False,
xref="paper",
yref="paper",
y=-0.28,
)
watermark(f1)
return f1
def bar_line_plot(
df,
x="date_report",
bary=[ # col_name, label, color, trace_kwargs
("new_positives", "Nuovi Casi", "#FF8A5B", {}),
("new_deaths", "Decessi", "#EA526F", {}),
("new_discharged", "Dimessi/Guariti", "#25CED1", {}),
],
barmode="stack",
liney=(
"being_tested",
"Test Effettuati",
"firebrick",
{"mode": "lines+markers"},
),
yscale=100,
secondaryy=False,
y1_title="<b>N. Casi -- N. Test Effettuati/100</b>",
y2_title="<b>N. Test Effettuati</b>",
title="<br><b>Andamento del Contagio in Korea del Sud</b></br>"
'<span style="font-size: 13px;">'
" Casi Attivi, Dimessi/Guariti, Decessi e Test Effettuati</span>",
fonte="Fonte: Korea Centers for Desease Control and Prevention",
):
fig = make_subplots(specs=[[{"secondary_y": secondaryy}]])
for col_name, label, color, trace_kwargs in bary:
trace = (
go.Bar(
x=df[x],
y=df[col_name],
name=label,
marker=dict(color=color),
opacity=0.75,
**trace_kwargs
),
)
fig.add_trace(trace[0], secondary_y=False)
fig.update_layout(barmode=barmode)
# -- TESTS
col_name, label, color, trace_kwargs = liney
line_trace = go.Scatter(
x=df[x],
y=df[col_name] / yscale,
name=label,
line=dict(color=color, width=0.75,),
marker=dict(symbol=134),
**trace_kwargs
)
fig.add_trace(line_trace, secondary_y=secondaryy)
if secondaryy:
fig.update_yaxes(
title=dict(text=y2_title, font=dict(size=11)),
range=(0, (df[liney[0]] / yscale).max() * 1.1),
secondary_y=True,
showgrid=False,
)
fig.update_yaxes(
title=dict(text=y1_title, font=dict(size=11)), secondary_y=False
)
fig.update_layout(
margin={"t": 80, "l": 40, "r": 20, "autoexpand": False},
title=dict(text=title, x=0.5,),
template="plotly_white",
width=750,
height=500,
legend=dict(orientation="h"),
annotations=[
dict(
text=fonte,
font=dict(size=9, color="grey"),
showarrow=False,
xref="paper",
yref="paper",
y=-0.24,
)
],
)
watermark(fig)
return fig
def line_double_trace(
elements,
secondary_elements=None,
main_line_mode="lines+markers+text",
secondary_line_dash="solid",
main_line_width=1.75,
secondary_line_width=0.75,
label_format="<b>{:.0f}</b>",
label_each=3,
primary_y_title="<b>Numero Tamponi Eseguiti</b>",
secondary_y_title="<b>Nuovi Casi Positivi</b>",
title="<br><b>Numero di Tamponi Eseguiti vs Nuovi Casi COVID-19</b></br>"
'<span style="font-size: 12;">Differenza tra italia e Korea del '
"Sud</span>",
xaxis_title="N. Giorni Trascorsi dal 100° Caso Ufficiale",
source="Fonte: Dipartimento di Protezione Civile Italiana, "
"Korea Centers for Desease Control and Prevention</p>",
source_y=-0.49,
primary_y_kwargs={},
secondary_y_kwargs={},
layout_kwargs={},
):
"""
elements = [(x: array, y: array, name: str, color: str, textcolor: str)]
secondary_elements = [(x: array, y: array, name: str, color: str)]
"""
fig = make_subplots(
specs=[[{"secondary_y": True if secondary_elements else False}]]
)
for x, y, name, color, textcolor in elements:
fig.add_trace(
go.Scatter(
x=x,
y=y,
text=[
label_format.format(x) if n % label_each == 0 else ""
for n, x in enumerate(y)
],
name=name,
# --- LINE
textposition="top center",
textfont=dict(color=textcolor),
line=dict(color=color, width=main_line_width,),
mode=main_line_mode,
marker=dict(color=color),
line_shape="spline",
# ---
opacity=0.75,
showlegend=True,
)
)
if secondary_elements:
for x, y, name, color in secondary_elements:
fig.add_trace(
go.Scatter(
x=x,
y=y,
name=name,
# --- LINE
line=dict(
color=color,
width=secondary_line_width,
dash=secondary_line_dash,
),
mode="lines",
line_shape="spline",
# ---
opacity=0.75,
showlegend=True,
),
secondary_y=True,
)
# Primary
fig.update_yaxes(
title=dict(text=primary_y_title, font=dict(size=11)),
secondary_y=False,
**primary_y_kwargs
)
# Secondary
fig.update_yaxes(
title=dict(text=secondary_y_title, font=dict(size=11)),
secondary_y=True,
showgrid=False,
**secondary_y_kwargs
)
_layout_kwargs_std = dict(
title=dict(text=title, x=0.5, y=0.98),
margin={"l": 40, "r": 10, "t": 80, "b": 140, "autoexpand": False},
template="plotly_white",
width=700,
height=450,
showlegend=True,
legend=dict(orientation="h", y=-0.22, x=0.5, xanchor="center"),
# yaxis=dict(range=(0, 400)),
xaxis=dict(title=xaxis_title),
annotations=[
dict(
font=dict(size=9, color="grey"),
showarrow=False,
yref="paper",
xref="paper",
text=source,
y=source_y,
)
],
)
_layout_kwargs_std.update(layout_kwargs)
fig.update_layout(**_layout_kwargs_std)
watermark(fig)
return fig
yaxis={"title": None},
annotations=[
dict(
text=
"Fonte: Fonte: Open Data Ministero dell'Economia e Finanze.",
showarrow=False,
yref="paper",
y=mb * 0.9,
font={
"color": "grey",
"size": 9
},
)
],
)
watermark(f).show()
bilancio_data = bilancio.loc[bilancio["Descrizione Programma"].isin(
programmi + p2)]
fig = make_subplots(
rows=2,
cols=1,
# shared_xaxes=True,
row_heights=[800, 1250],
column_widths=[450],
vertical_spacing=0.03,
specs=[[{
"type": "table"
}], [{
"type": "sunburst"
}]],
)
