def encode_color(self):
AltairChart.encode_color(self)
self.add_text()
# Setting tooltip as non-null
self.chart = self.chart.configure_mark(
tooltip=alt.TooltipContent("encoding"))
self.code += f"""chart = chart.configure_mark(tooltip=alt.TooltipContent('encoding'))"""
def initialize_chart(self):
x_attr = self.view.get_attr_by_channel("x")[0]
y_attr = self.view.get_attr_by_channel("y")[0]
x_min = self.view.x_min_max[x_attr.attribute][0]
x_max = self.view.x_min_max[x_attr.attribute][1]
y_min = self.view.y_min_max[y_attr.attribute][0]
y_max = self.view.y_min_max[y_attr.attribute][1]
chart = alt.Chart(self.data).mark_circle().encode(
x=alt.X(x_attr.attribute,
scale=alt.Scale(domain=(x_min, x_max)),
type=x_attr.data_type),
y=alt.Y(y_attr.attribute,
scale=alt.Scale(domain=(y_min, y_max)),
type=y_attr.data_type))
chart = chart.configure_mark(tooltip=alt.TooltipContent(
'encoding')) # Setting tooltip as non-null
chart = chart.interactive() # Enable Zooming and Panning
#####################################
## Constructing Altair Code String ##
#####################################
self.code += "import altair as alt\n"
dfname = "df" # TODO: Placeholder (need to read dynamically via locals())
self.code += f'''
chart = alt.Chart({dfname}).mark_circle().encode(
x=alt.X('{x_attr.attribute}',scale=alt.Scale(domain=({x_min}, {x_max})),type='{x_attr.data_type}'),
y=alt.Y('{y_attr.attribute}',scale=alt.Scale(domain=({y_min}, {y_max})),type='{y_attr.data_type}')
)
chart = chart.configure_mark(tooltip=alt.TooltipContent('encoding')) # Setting tooltip as non-null
chart = chart.interactive() # Enable Zooming and Panning
'''
return chart
def initialize_chart(self):
# return NotImplemented
x_attr = self.vis.get_attr_by_channel("x")[0]
y_attr = self.vis.get_attr_by_channel("y")[0]
chart = alt.Chart(self.data).mark_rect().encode(
x=alt.X('xBinStart', type='quantitative', axis=alt.Axis(title=x_attr.attribute), bin = alt.BinParams(binned=True)),
x2=alt.X2('xBinEnd'),
y=alt.Y('yBinStart', type='quantitative', axis=alt.Axis(title=y_attr.attribute), bin = alt.BinParams(binned=True)),
y2=alt.Y2('yBinEnd'),
opacity = alt.Opacity('count',type='quantitative',scale=alt.Scale(type="log"),legend=None)
)
chart = chart.configure_scale(minOpacity=0.1,maxOpacity=1)
chart = chart.configure_mark(tooltip=alt.TooltipContent('encoding')) # Setting tooltip as non-null
chart = chart.interactive() # Enable Zooming and Panning
####################################
# Constructing Altair Code String ##
####################################
self.code += "import altair as alt\n"
# self.code += f"visData = pd.DataFrame({str(self.data.to_dict(orient='records'))})\n"
self.code += f"visData = pd.DataFrame({str(self.data.to_dict())})\n"
self.code += f'''
chart = alt.Chart(visData).mark_rect().encode(
x=alt.X('xBinStart', type='quantitative', axis=alt.Axis(title='{x_attr.attribute}'), bin = alt.BinParams(binned=True)),
x2=alt.X2('xBinEnd'),
y=alt.Y('yBinStart', type='quantitative', axis=alt.Axis(title='{y_attr.attribute}'), bin = alt.BinParams(binned=True)),
y2=alt.Y2('yBinEnd'),
opacity = alt.Opacity('count',type='quantitative',scale=alt.Scale(type="log"),legend=None)
)
chart = chart.configure_mark(tooltip=alt.TooltipContent('encoding')) # Setting tooltip as non-null
'''
return chart
def encode_color(
self
): # override encode_color in AltairChart to enforce add_text occurs afterwards
AltairChart.encode_color(self)
self.add_text()
self.chart = self.chart.configure_mark(tooltip=alt.TooltipContent(
'encoding')) # Setting tooltip as non-null
self.code += f'''chart = chart.configure_mark(tooltip=alt.TooltipContent('encoding'))'''
def initialize_chart(self):
x_attr = self.vis.get_attr_by_channel("x")[0]
y_attr = self.vis.get_attr_by_channel("y")[0]
x_attr_abv = x_attr.attribute
y_attr_abv = y_attr.attribute
if len(x_attr.attribute) > 25:
x_attr_abv = x_attr.attribute[:15] + "..." + x_attr.attribute[-10:]
if len(y_attr.attribute) > 25:
y_attr_abv = y_attr.attribute[:15] + "..." + y_attr.attribute[-10:]
x_min = self.vis.min_max[x_attr.attribute][0]
x_max = self.vis.min_max[x_attr.attribute][1]
y_min = self.vis.min_max[y_attr.attribute][0]
y_max = self.vis.min_max[y_attr.attribute][1]
x_attr.attribute = x_attr.attribute.replace(".", "")
y_attr.attribute = y_attr.attribute.replace(".", "")
chart = (
alt.Chart(self.data)
.mark_circle()
.encode(
x=alt.X(
x_attr.attribute,
scale=alt.Scale(domain=(x_min, x_max)),
type=x_attr.data_type,
axis=alt.Axis(title=x_attr_abv),
),
y=alt.Y(
y_attr.attribute,
scale=alt.Scale(domain=(y_min, y_max)),
type=y_attr.data_type,
axis=alt.Axis(title=y_attr_abv),
),
)
)
# Setting tooltip as non-null
chart = chart.configure_mark(tooltip=alt.TooltipContent("encoding"))
chart = chart.interactive() # Enable Zooming and Panning
#####################################
## Constructing Altair Code String ##
#####################################
self.code += "import altair as alt\n"
dfname = "placeholder_variable"
self.code += f"""
chart = alt.Chart({dfname}).mark_circle().encode(
x=alt.X('{x_attr.attribute}',scale=alt.Scale(domain=({x_min}, {x_max})),type='{x_attr.data_type}', axis=alt.Axis(title='{x_attr_abv}')),
y=alt.Y('{y_attr.attribute}',scale=alt.Scale(domain=({y_min}, {y_max})),type='{y_attr.data_type}', axis=alt.Axis(title='{y_attr_abv}'))
)
chart = chart.configure_mark(tooltip=alt.TooltipContent('encoding')) # Setting tooltip as non-null
chart = chart.interactive() # Enable Zooming and Panning
"""
return chart
def initialize_chart(self):
self.tooltip = False
x_attr = self.view.get_attr_by_channel("x")[0]
y_attr = self.view.get_attr_by_channel("y")[0]
self.code += "import altair as alt\n"
# self.code += f"visData = pd.DataFrame({str(self.data.to_dict(orient='records'))})\n"
self.code += f"visData = pd.DataFrame({str(self.data.to_dict())})\n"
if (x_attr.data_model == "measure"):
agg_title = get_agg_title(x_attr)
y_attr_field = alt.Y(y_attr.attribute,
type=y_attr.data_type,
axis=alt.Axis(labelOverlap=True))
x_attr_field = alt.X(x_attr.attribute,
type=x_attr.data_type,
title=agg_title)
y_attr_field_code = f"alt.Y('{y_attr.attribute}', type= '{y_attr.data_type}', axis=alt.Axis(labelOverlap=True))"
x_attr_field_code = f"alt.X('{x_attr.attribute}', type= '{x_attr.data_type}', title='{agg_title}')"
if (y_attr.sort == "ascending"):
y_attr_field.sort = "-x"
y_attr_field_code = f"alt.Y('{y_attr.attribute}', type= '{y_attr.data_type}', axis=alt.Axis(labelOverlap=True), sort ='-x')"
else:
agg_title = get_agg_title(y_attr)
x_attr_field = alt.X(x_attr.attribute,
type=x_attr.data_type,
axis=alt.Axis(labelOverlap=True))
y_attr_field = alt.Y(y_attr.attribute,
type=y_attr.data_type,
title=agg_title)
x_attr_field_code = f"alt.X('{x_attr.attribute}', type= '{x_attr.data_type}', axis=alt.Axis(labelOverlap=True))"
y_attr_field_code = f"alt.Y('{y_attr.attribute}', type= '{y_attr.data_type}', title='{agg_title}')"
if (x_attr.sort == "ascending"):
x_attr_field.sort = "-y"
x_attr_field_code = f"alt.X('{x_attr.attribute}', type= '{x_attr.data_type}', axis=alt.Axis(labelOverlap=True),sort='-y')"
chart = alt.Chart(self.data).mark_bar().encode(y=y_attr_field,
x=x_attr_field)
# TODO: tooltip messes up the count() bar charts
# Can not do interactive whenever you have default count measure otherwise output strange error (Javascript Error: Cannot read property 'length' of undefined)
#chart = chart.interactive() # If you want to enable Zooming and Panning
chart = chart.configure_mark(tooltip=alt.TooltipContent(
'encoding')) # Setting tooltip as non-null
self.code += f'''
chart = alt.Chart(visData).mark_bar().encode(
y = {y_attr_field_code},
x = {x_attr_field_code},
)
chart = chart.configure_mark(tooltip=alt.TooltipContent('encoding')) # Setting tooltip as non-null
'''
return chart
def plotAltairLineChart(self):
""" Function to output Altair line chart
Returns
-------
Altair line chart
"""
# Date that will be used to indicate "Data as of user chosen date"
iso_date = self.covid19_date.strftime('%Y-%m-%d')
# Altair charting API only charts dataframe columns, not index, so need to apply reset_index()
alt_chart = alt.Chart(self.dfByState()[:iso_date]
.query("State_Province in(@self.state_province)")
.reset_index()
.rename(columns={'index': 'Date'})
).mark_line().encode(
x=alt.X(title='Date', field='Date', type='temporal'),
y=alt.Y(title='# of ' + self.confirmed_deaths, field='Qty_Confirmed',
type='quantitative', scale=alt.Scale(type=self.ylog)
),
color=alt.Color(field='State_Province', type='nominal',
legend=alt.Legend(title="State/Province")
),
tooltip=[alt.Tooltip(field='State_Province', type= 'nominal'),
alt.Tooltip(field='Qty_Confirmed', type= 'quantitative'),
alt.Tooltip(field='Date', type= 'temporal')
]
)
# To create filled circles in the legend per
# https://github.com/altair-viz/altair/issues/1206
points = alt.Chart(self.dfByState()[:iso_date]
.query("State_Province in(@self.state_province)")
.reset_index()
.rename(columns={'index': 'Date'})
).mark_circle(size=0).encode(
color='State_Province'
)
# To add hover tips, but make it less sensitive per
# https://github.com/altair-viz/altair/issues/1812
tooltips = alt_chart.mark_point(size=100, opacity=0,
tooltip=alt.TooltipContent("data")
)
alt_chart = alt_chart + points + tooltips
return alt_chart.properties(
title='COVID-19 ' + self.confirmed_deaths,
width='container',
height=400
)
def initializeChart(self):
# UP TO HERE: Broken because self.expandUnderspecified() in dataObj does not run when there are multiple object, we should not rely on spec
# measures = list(filter(lambda x: x.dataModel=="measure" if hasattr(x,"dataModel") else False,self.dobj.spec))
xAttr = self.dobj.getObjFromChannel("x")[0].columnName
yAttr = self.dobj.getObjFromChannel("y")[0].columnName
chart = alt.Chart(self.dataURL).mark_circle().encode(
x=alt.X(xAttr, scale=alt.Scale(zero=False)),
y=alt.Y(yAttr, scale=alt.Scale(zero=False)))
chart = chart.configure_mark(tooltip=alt.TooltipContent(
'encoding')) # Setting tooltip as non-null
chart = chart.interactive(
) # If you want to enable Zooming and Panning
return chart
def covid19TimeSeriesByCountry(covid19_date: Date,
confirmed_deaths: str,
country: List[str] = ['US'],
ylog: bool = False) -> PanelRow:
"""Function that returns a Panel dashboard displaying confirmed COVID-19 cases
It is using Panel's "Reactive functions" API: https://panel.holoviz.org/user_guide/APIs.html
Parameters
----------
covid19_date : Date
End date of data you wish to obtain up to
country : List[str]
One or more countries for which you would like to obtain data for (default='US')
confirmed_deaths : str
Option to choose # of confirmed cases or # of deaths from Covid-19
ylog: bool
Whether or not to apply log scaling to y-axis. Default is False
Returns
-------
Panel object
"""
iso_date: str = covid19_date.strftime('%Y-%m-%d')
# Source of COVID-19 data
if confirmed_deaths == 'Confirmed Cases':
url: str = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
else:
url: str = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'
df: DataFrame = pd.read_csv(url)
df_countries: DataFrame = (df.drop(
columns=['Province/State', 'Lat', 'Long']).groupby(
'Country/Region').agg('sum').sort_values(
by=df.columns[-1],
ascending=False).transpose().reset_index().melt(
id_vars='index', value_name='Qty').rename(
columns={
'index': 'Date',
'Country/Region': 'Country_Region'
}).set_index('Date'))
df_countries.index: DateTimeIndex = [
datetime.strptime(date, '%m/%d/%y') for date in df_countries.index
]
# Convert data to "wide format" since hvplot does not work with "long format"
df_countries_wide = df_countries.pivot(columns='Country_Region',
values='Qty')
alt_chart: Altair = alt.Chart(df_countries[:iso_date].query(
"Country_Region in(@country)").reset_index().rename(
columns={'index': 'Date'})).mark_line().encode(
x=alt.X(title='Date', field='Date', type='temporal'),
y=alt.Y(title='# of ' + confirmed_deaths,
field='Qty',
type='quantitative',
scale=alt.Scale(type=ylog)),
color=alt.Color(field='Country_Region',
type='nominal',
legend=alt.Legend(title="Country/Region")),
tooltip=[
alt.Tooltip(field='Country_Region', type='nominal'),
alt.Tooltip(field='Qty', type='quantitative'),
alt.Tooltip(field='Date', type='temporal')
])
# https://github.com/altair-viz/altair/issues/1812
alt_chart: Altair = alt_chart + alt_chart.mark_point(
size=100, opacity=0, tooltip=alt.TooltipContent("data"))
# If only one country is selected, then also provide a data table containing counts by date
if len(country) == 1:
panel_app: PanelRow = pn.Row(
# Add Altair chart
alt_chart.properties(
title='COVID-19 ' + confirmed_deaths,
width=
'container', # Have to use 'container' option, otherwise, layout is
height=400),
# Add hvplot table using data in wide format
df_countries_wide[:iso_date].loc[:, country].sort_values(
by=df_countries_wide[:iso_date].loc[:, country].columns[0],
ascending=False
).merge(df_countries_wide[:iso_date].loc[:, country].sort_values(
by=df_countries_wide[:iso_date].loc[:, country].columns[0],
ascending=False).diff(),
how='inner',
left_index=True,
right_index=True).reset_index().rename(
columns={
'index': 'Date',
f'{country[0]}_x': 'Cum. Qty',
f'{country[0]}_y': 'Difference'
}).hvplot.table(sortable=True,
selectable=True,
width=300,
height=500))
# Else provide just the Altair line chart
else:
panel_app: PanelRow = pn.Row(
alt_chart.properties(title='COVID-19 ' + confirmed_deaths,
width=700,
height=400))
return panel_app
def initialize_chart(self):
self.tooltip = False
x_attr = self.vis.get_attr_by_channel("x")[0]
y_attr = self.vis.get_attr_by_channel("y")[0]
if (x_attr.data_model == "measure"):
agg_title = get_agg_title(x_attr)
measure_attr = x_attr.attribute
y_attr_field = alt.Y(y_attr.attribute, type= y_attr.data_type, axis=alt.Axis(labelOverlap=True))
x_attr_field = alt.X(x_attr.attribute, type= x_attr.data_type, title=agg_title)
y_attr_field_code = f"alt.Y('{y_attr.attribute}', type= '{y_attr.data_type}', axis=alt.Axis(labelOverlap=True))"
x_attr_field_code = f"alt.X('{x_attr.attribute}', type= '{x_attr.data_type}', title='{agg_title}')"
if (y_attr.sort=="ascending"):
y_attr_field.sort="-x"
y_attr_field_code = f"alt.Y('{y_attr.attribute}', type= '{y_attr.data_type}', axis=alt.Axis(labelOverlap=True), sort ='-x')"
else:
agg_title = get_agg_title(y_attr)
measure_attr = y_attr.attribute
x_attr_field = alt.X(x_attr.attribute, type = x_attr.data_type,axis=alt.Axis(labelOverlap=True))
y_attr_field = alt.Y(y_attr.attribute,type=y_attr.data_type,title=agg_title)
x_attr_field_code = f"alt.X('{x_attr.attribute}', type= '{x_attr.data_type}', axis=alt.Axis(labelOverlap=True))"
y_attr_field_code = f"alt.Y('{y_attr.attribute}', type= '{y_attr.data_type}', title='{agg_title}')"
if (x_attr.sort=="ascending"):
x_attr_field.sort="-y"
x_attr_field_code = f"alt.X('{x_attr.attribute}', type= '{x_attr.data_type}', axis=alt.Axis(labelOverlap=True),sort='-y')"
k=10
topK_code = ""
if len(self.data)>k: # Truncating to only top k
remaining_bars = len(self.data)-k
self.data = self.data.nlargest(k,measure_attr)
text = alt.Chart(self.data).mark_text(
x=155,
y=142,
align="right",
color = "#ff8e04",
fontSize = 11,
text=f"+ {remaining_bars} more ..."
)
topK_code = f'''text = alt.Chart(visData).mark_text(
x=155,
y=142,
align="right",
color = "#ff8e04",
fontSize = 11,
text=f"+ {remaining_bars} more ..."
)
chart = chart + text
'''
chart = alt.Chart(self.data).mark_bar().encode(
y = y_attr_field,
x = x_attr_field
)
if (topK_code!=""):
chart = chart + text
# TODO: tooltip messes up the count() bar charts
# Can not do interactive whenever you have default count measure otherwise output strange error (Javascript Error: Cannot read property 'length' of undefined)
#chart = chart.interactive() # If you want to enable Zooming and Panning
chart = chart.configure_mark(tooltip=alt.TooltipContent('encoding')) # Setting tooltip as non-null
self.code += "import altair as alt\n"
# self.code += f"visData = pd.DataFrame({str(self.data.to_dict(orient='records'))})\n"
self.code += f"visData = pd.DataFrame({str(self.data.to_dict())})\n"
self.code += f'''
chart = alt.Chart(visData).mark_bar().encode(
y = {y_attr_field_code},
x = {x_attr_field_code},
)
{topK_code}
chart = chart.configure_mark(tooltip=alt.TooltipContent('encoding')) # Setting tooltip as non-null
'''
return chart
def initialize_chart(self):
# return NotImplemented
x_attr = self.vis.get_attr_by_channel("x")[0]
y_attr = self.vis.get_attr_by_channel("y")[0]
x_attr_abv = str(x_attr.attribute)
y_attr_abv = str(y_attr.attribute)
if len(x_attr_abv) > 25:
x_attr_abv = x_attr.attribute[:15] + "..." + x_attr.attribute[-10:]
if len(y_attr_abv) > 25:
y_attr_abv = y_attr.attribute[:15] + "..." + y_attr.attribute[-10:]
if isinstance(x_attr.attribute, str):
x_attr.attribute = x_attr.attribute.replace(".", "")
if isinstance(y_attr.attribute, str):
y_attr.attribute = y_attr.attribute.replace(".", "")
chart = (alt.Chart(self.data).mark_rect().encode(
x=alt.X(
"xBinStart",
type="quantitative",
axis=alt.Axis(title=x_attr_abv),
bin=alt.BinParams(binned=True),
),
x2=alt.X2("xBinEnd"),
y=alt.Y(
"yBinStart",
type="quantitative",
axis=alt.Axis(title=y_attr_abv),
bin=alt.BinParams(binned=True),
),
y2=alt.Y2("yBinEnd"),
opacity=alt.Opacity(
"count",
type="quantitative",
scale=alt.Scale(type="log"),
legend=None,
),
))
chart = chart.configure_scale(minOpacity=0.1, maxOpacity=1)
# Setting tooltip as non-null
chart = chart.configure_mark(tooltip=alt.TooltipContent("encoding"))
chart = chart.interactive() # Enable Zooming and Panning
####################################
# Constructing Altair Code String ##
####################################
self.code += "import altair as alt\n"
# self.code += f"visData = pd.DataFrame({str(self.data.to_dict(orient='records'))})\n"
self.code += f"visData = pd.DataFrame({str(self.data.to_dict())})\n"
self.code += f"""
chart = alt.Chart(visData).mark_rect().encode(
x=alt.X('xBinStart', type='quantitative', axis=alt.Axis(title='{x_attr_abv}'), bin = alt.BinParams(binned=True)),
x2=alt.X2('xBinEnd'),
y=alt.Y('yBinStart', type='quantitative', axis=alt.Axis(title='{y_attr_abv}'), bin = alt.BinParams(binned=True)),
y2=alt.Y2('yBinEnd'),
opacity = alt.Opacity('count',type='quantitative',scale=alt.Scale(type="log"),legend=None)
)
chart = chart.configure_mark(tooltip=alt.TooltipContent('encoding')) # Setting tooltip as non-null
"""
return chart
def covid19TimeSeriesByState(covid19_date: Date,
state_province: List[str],
confirmed_deaths: List[str],
ylog: bool = False) -> PanelColumn:
"""Function that returns a Panel dashboard displaying confirmed COVID-19 cases
It is using Panel's "Reactive functions" API: https://panel.holoviz.org/user_guide/APIs.html
Parameters
----------
covid19_date : Date
End date of data you wish to obtain up to
state_province : str
State for which you would like to obtain data for (default='Ohio')
confirmed_deaths : str
Option to choose # of confirmed cases or deaths due to COVID-19
ylog : bool
To enable log scaling or not on y-axis. Log scale can be useful to easily discern growth rate.
Returns
-------
Panel object
"""
# Panel's date widget returns date in ISO-8601 format
iso_date: str = covid19_date.strftime('%Y-%m-%d')
# Get geo json data at state county level based on FIPS
with urlopen(
'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
) as response:
geo_data: dict = json.load(response)
# Source of COVID-19 data
# To leverage Plotly's choropleth_mapbox function, need to have FIPS as fixed length(n=5) values consisting of leading zeros
if confirmed_deaths == 'Confirmed Cases':
url: str = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv'
else:
url: str = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv'
df: DataFrame = pd.read_csv(url,
converters={
'FIPS':
lambda x: int(float(x)) if x != '' else x
}).query("FIPS != ''")
df['FIPS']: Series = df['FIPS'].astype('str').str.zfill(5)
if 'Population' in df.columns:
df.drop(columns='Population', inplace=True)
df_by_state: DataFrame = (df.drop(columns=[
'UID', 'iso2', 'iso3', 'code3', 'FIPS', 'Admin2', 'Country_Region',
'Lat', 'Long_', 'Combined_Key'
]).groupby('Province_State').agg('sum').transpose().reset_index().melt(
id_vars='index', var_name='State_Province',
value_name='Qty_Confirmed').rename(columns={
'index': 'Date'
}).set_index('Date'))
df_by_state.index: DateTimeIndex = [
datetime.strptime(date, '%m/%d/%y') for date in df_by_state.index
]
# Prepare data for hvplot tables
# https://stackoverflow.com/questions/53052914/selecting-non-adjacent-columns-by-column-number-pandas
df_by_state_ts: DataFrame = df.iloc[:, np.r_[6, 11:df.shape[1]]].groupby(
'Province_State').agg('sum').transpose()
df_by_counties: DataFrame = df.query("not Lat==0").iloc[:, [6, 10, -1]]
# Prepare data for Plotly choropleth map
df_choropleth: DataFrame = df.iloc[:, np.r_[4, 5, 6, df.shape[1] - 1]]
df_choropleth: DataFrame = df_choropleth.rename(columns={
df_choropleth.columns[3]: 'Confirmed_Cases',
'Admin2': 'County'
})
# Initialize plotly choropleth map
plotly_chart: Plotly = px.choropleth_mapbox(
df_choropleth.query("Province_State in@state_province"),
geojson=geo_data,
locations='FIPS',
color='Confirmed_Cases',
color_discrete_map="Viridis",
mapbox_style="carto-positron",
zoom=3.5,
center={
"lat": 37.0902,
"lon": -95.7129
},
opacity=0.5,
hover_name='County',
width=1400,
height=700)
plotly_chart.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
# Initialize altair chart
alt_chart: Altair = alt.Chart(df_by_state[:iso_date].query(
"State_Province in(@state_province)").reset_index().rename(
columns={'index': 'Date'})).mark_line().encode(
x=alt.X(title='Date', field='Date', type='temporal'),
y=alt.Y(title='# of ' + confirmed_deaths,
field='Qty_Confirmed',
type='quantitative',
scale=alt.Scale(type=ylog)),
color=alt.Color(field='State_Province',
type='nominal',
legend=alt.Legend(title="State/Province")),
tooltip=[
alt.Tooltip(field='State_Province', type='nominal'),
alt.Tooltip(field='Qty_Confirmed', type='quantitative'),
alt.Tooltip(field='Date', type='temporal')
])
# https://github.com/altair-viz/altair/issues/1812
alt_chart: Altair = alt_chart + alt_chart.mark_point(
size=100, opacity=0, tooltip=alt.TooltipContent("data"))
# If only one state is selected, then also provide data tables containing counts by counties, by date, and counties choropleth map
if len(state_province) == 1:
# Layout the panel app such that the column will consist of a row of line chart, data table by counties, and data table by state
# then a plotly choropleth below them
panel_app: PanelColumn = pn.Column(
pn.Row(
# Add Altair line chart of cum. covid19 cases or deaths
alt_chart.properties(
title='COVID-19 ' + confirmed_deaths,
width=
'container', # Have to use 'container' option, otherwise, layout is
height=400),
# A data table of counts by counties
df_by_counties.query(
"Province_State == @state_province").sort_values(
by=df_by_counties.columns[2], ascending=False).drop(
columns='Province_State', axis='columns').rename(
columns={
'Combined_Key':
'County',
df_by_counties.columns[2]:
'Qty as of ' + df_by_counties.columns[2]
}).hvplot.table(sortable=True,
selectable=True,
width=250,
height=500),
# A data table of counts by state with difference between rows
(df_by_state_ts.loc[:,
state_province].sort_values(
by=state_province,
ascending=False).rename(
columns={
'index': 'Date',
state_province[0]: 'Cum. Qty'
})
).merge(
(df_by_state_ts.loc[:, state_province].sort_values(
by=state_province, ascending=False).rename(
columns={
'index': 'Date',
state_province[0]: 'Cum. Qty'
})).diff(),
how='inner',
left_index=True,
right_index=True).rename(columns={
'Cum. Qty_x': 'Cum. Qty',
'Cum. Qty_y': 'Difference'
}).reset_index().rename(columns={
'index': 'Date'
}).hvplot.table(sortable=True,
selectable=True,
width=300,
height=500),
width=1200,
sizing_mode='stretch_width'),
# A plotly choropleth Figure
plotly_chart)
# if more than one state is chosen, just provide Altair line chart, as it would not be feasible
# to try to provide data tables and choropleth maps for several states
else:
panel_app: PanelColumn = pn.Column(
pn.Row(
# Add Altair line chart of cum. covid19 cases
alt_chart.properties(title='COVID-19 ' + confirmed_deaths,
width=700,
height=400)))
return panel_app
def plotAltairLineChart(self):
""" Returns an Altair line chart.
Returns
-------
An Altair line chart.
"""
df = self.getData(self.confirmed_deaths)
iso_date: str = self.covid19_date.strftime('%Y-%m-%d')
# Altair requires dataframe to be in "long format"
df_countries = (df.drop(columns=['Province/State', 'Lat', 'Long'])
.groupby('Country/Region').agg('sum')
.sort_values(by=df.columns[-1], ascending=False)
.transpose()
.reset_index()
.melt(id_vars='index', value_name='Qty')
.rename(columns={'index': 'Date',
'Country/Region': 'Country_Region'
}
)
.set_index('Date')
)
# Make index values actual datetime objects so that we can
# leverage Panda's date filtering API
df_countries.index = [datetime.strptime(day, '%m/%d/%y')
for day in df_countries.index
]
alt_chart = alt.Chart(df_countries[: iso_date]
.query("Country_Region in(@self.country)")
.reset_index()
.rename(columns={'index': 'Date'})
).mark_line().encode(
x=alt.X(title='Date', field='Date', type='temporal'),
y=alt.Y(title='# of ' + self.confirmed_deaths, field='Qty',
type='quantitative', scale=alt.Scale(type=self.ylog)
),
color=alt.Color(field='Country_Region', type='nominal',
legend=alt.Legend(title="Country/Region")
),
tooltip=[alt.Tooltip(field='Country_Region', type= 'nominal'),
alt.Tooltip(field='Qty', type= 'quantitative'),
alt.Tooltip(field='Date', type= 'temporal')
]
)
# To create filled circles in the legend per
# https://github.com/altair-viz/altair/issues/1206
points = alt.Chart(df_countries[: iso_date]
.query("Country_Region in(@self.country)")
.reset_index()
.rename(columns={'index': 'Date'})
).mark_circle(size=0).encode(
color='Country_Region'
)
# To add hover tips, but make it less sensitive per
# https://github.com/altair-viz/altair/issues/1812
tooltips = alt_chart.mark_point(size=100, opacity=0,
tooltip=alt.TooltipContent("data")
)
alt_chart = alt_chart + points + tooltips
return alt_chart.properties(
title='COVID-19 ' + self.confirmed_deaths,
width='container',
height=400
)
columns={
0: "correlation",
"level_0": "first",
"level_1": "second"
})
correlation_data["correlation_label"] = correlation_data["correlation"].map(
"{:.2f}".format)
base_correlation_plot = altair.Chart(correlation_data).encode(x="second:O",
y="first:O")
# Text layer with correlation labels
# Colors are for easier readability
correlation_plot_text = base_correlation_plot.mark_text(
tooltip=altair.TooltipContent("encoding")).encode(
text="correlation_label",
color=altair.condition(altair.datum.correlation > 0.5,
altair.value("white"), altair.value("black")))
correlation_plot = base_correlation_plot.mark_rect(
tooltip=altair.TooltipContent("encoding")).encode(color=altair.Color(
"correlation:Q",
scale=altair.Scale(domain=[-1, 0, 1],
range=["DarkBlue", "White", "DarkRed"],
type="linear")))
CORRELATION_MATRIX = correlation_plot + correlation_plot_text
(CORRELATION_MATRIX_COLUMN,
CORRELATION_OBSERVATION_COLUMN) = streamlit.columns([2, 1])
