def plot_ApplevsGoogle_1():
plotname = inspect.stack()[0][3][5:]
pandas_bokeh.output_file(os.path.join(PLOT_DIR, f"{plotname}.html"))
df = df_stocks()
plot = df.plot_bokeh(kind="line")
def test_mapplot(df_mapplot):
"Mapplot test"
kwargs = dict(
x="longitude",
y="latitude",
hovertool_string="""<h2> @{name} </h2>
<h3> Population: @{pop_max} </h3>""",
tile_provider="STAMEN_TERRAIN_RETINA",
size="size",
figsize=(900, 600),
title="World cities with more than 1.000.000 inhabitants",
show_figure=False,
)
p_map = df_mapplot.plot_bokeh(kind="map", **kwargs)
p_map_accessor = df_mapplot.plot_bokeh.map(**kwargs)
p_map_pandas_backend = df_mapplot.plot(kind="map", **kwargs)
p_map_accessor_pandas_backend = df_mapplot.plot.map(**kwargs)
layout = pandas_bokeh.plot_grid([[p_map, p_map_accessor]],
plot_width=450,
plot_height=300,
show_plot=False)
pandas_bokeh.output_file(os.path.join(DIRECTORY, "Plots", "Mapplot.html"))
pandas_bokeh.save(layout)
def plot_Scatterplot():
plotname = inspect.stack()[0][3][5:]
pandas_bokeh.output_file(os.path.join(PLOT_DIR, f"{plotname}.html"))
df = df_iris()
df = df.sample(frac=1)
#Create Bokeh-Table with DataFrame:
from bokeh.models.widgets import DataTable, TableColumn
from bokeh.models import ColumnDataSource
data_table = DataTable(
columns=[TableColumn(field=Ci, title=Ci) for Ci in df.columns],
source=ColumnDataSource(df.head(10)),
)
#Create Scatterplot:
p_scatter = df.plot_bokeh.scatter(
x="petal length (cm)",
y="sepal width (cm)",
category="species",
title="Iris DataSet Visualization",
show_figure=False)
#Combine Div and Scatterplot via grid layout:
pandas_bokeh.plot_grid([[data_table, p_scatter]], plot_width=400, plot_height=350)
def test_pieplot():
"Test Pieplot"
p_pie = df_pie = pd.read_csv(
os.path.join(test_sets_directory, "Bundestagswahl", "Bundestagswahl.csv")
)
p_pie = df_pie.plot_bokeh.pie(
x="Partei",
y="2017",
colormap=["blue", "red", "yellow", "green", "purple", "orange", "grey"],
title="Results of German Bundestag Election 2017",
show_figure=False,
)
p_pie_multiple = df_pie.plot_bokeh(
kind="pie",
x="Partei",
colormap=["blue", "red", "yellow", "green", "purple", "orange", "grey"],
title="Results of German Bundestag Elections [2002-2017]",
line_color="grey",
show_figure=False,
)
layout = pandas_bokeh.plot_grid(
[[p_pie, p_pie_multiple]], plot_width=450, plot_height=300, show_plot=False
)
pandas_bokeh.output_file(os.path.join(directory, "Plots", "Pieplot.html"))
pandas_bokeh.save(layout)
def test_pieplot(df_election):
"Test Pieplot"
p_pie = df_election.plot_bokeh.pie(
x="Partei",
y="2017",
colormap=[
"blue", "red", "yellow", "green", "purple", "orange", "grey"
],
title="Results of German Bundestag Election 2017",
show_figure=False,
)
p_pie_multiple = df_election.plot_bokeh(
kind="pie",
x="Partei",
colormap=[
"blue", "red", "yellow", "green", "purple", "orange", "grey"
],
title="Results of German Bundestag Elections [2002-2017]",
line_color="grey",
show_figure=False,
)
p_pie_pandas_backend = df_election.plot.pie(
x="Partei",
y="2017",
colormap=[
"blue", "red", "yellow", "green", "purple", "orange", "grey"
],
title="Results of German Bundestag Election 2017",
show_figure=False,
)
p_pie_multiple_pandas_backend = df_election.plot(
kind="pie",
x="Partei",
colormap=[
"blue", "red", "yellow", "green", "purple", "orange", "grey"
],
title="Results of German Bundestag Elections [2002-2017]",
line_color="grey",
show_figure=False,
)
layout = pandas_bokeh.plot_grid(
[
[p_pie, p_pie_multiple],
[p_pie_pandas_backend, p_pie_multiple_pandas_backend],
],
plot_width=450,
plot_height=300,
show_plot=False,
)
pandas_bokeh.output_file(os.path.join(DIRECTORY, "Plots", "Pieplot.html"))
pandas_bokeh.save(layout)
def test_area_plots(df_energy):
"Test area plots"
p_area = df_energy.plot_bokeh.area(
x="Year",
stacked=True,
legend="top_left",
colormap=["brown", "orange", "black", "grey", "blue", "green"],
title="Worldwide energy consumption split by energy source",
ylabel="Million tonnes oil equivalent",
ylim=(0, 16000),
show_figure=False,
)
p_area_normed = df_energy.plot_bokeh(
kind="area",
x="Year",
stacked=True,
normed=100,
legend="bottom_left",
colormap=["brown", "orange", "black", "grey", "blue", "green"],
title="Worldwide energy consumption split by energy source",
ylabel="Million tonnes oil equivalent",
show_figure=False,
)
p_area_pandas_backend = df_energy.plot.area(
x="Year",
stacked=True,
legend="top_left",
colormap=["brown", "orange", "black", "grey", "blue", "green"],
title="Worldwide energy consumption split by energy source",
ylabel="Million tonnes oil equivalent",
ylim=(0, 16000),
show_figure=False,
)
p_area_normed_pandas_backend = df_energy.plot(
kind="area",
x="Year",
stacked=True,
normed=100,
legend="bottom_left",
colormap=["brown", "orange", "black", "grey", "blue", "green"],
title="Worldwide energy consumption split by energy source",
ylabel="Million tonnes oil equivalent",
show_figure=False,
)
layout = pandas_bokeh.plot_grid([[p_area, p_area_normed]],
plot_width=450,
plot_height=300,
show_plot=False)
pandas_bokeh.output_file(os.path.join(DIRECTORY, "Plots", "Areaplot.html"))
pandas_bokeh.save(layout)
def plot_Barplot():
plotname = inspect.stack()[0][3][5:]
pandas_bokeh.output_file(os.path.join(PLOT_DIR, f"{plotname}.html"))
df = df_fruits()
df.plot_bokeh.bar(ylabel="Price per Unit [€]",
title="Fruit prices per Year",
alpha=0.6)
def test_basic_lineplot_rangetool(df_stock):
"""Test for basic lineplot above with a rangetool extension"""
p_basic_lineplot_accessor_pandas_backend = df_stock.plot.line(
show_figure=False, rangetool=True)
# Output plot as HTML:
pandas_bokeh.output_file(
os.path.join(DIRECTORY, "Plots", "Basic_lineplot_rangetool.html"))
pandas_bokeh.save(p_basic_lineplot_accessor_pandas_backend)
assert True
def plot_df_lines_bokeh(df,
x_col,
y_cols,
figures_path,
filename,
save_flag=False):
columns_names = list(df.columns)
if save_flag:
outfp = os.path.join(figures_path, filename + ".html")
output_file(outfp)
pandas_bokeh.output_file(outfp)
warnings.filterwarnings("ignore")
df = df.set_index(x_col, drop=True)[y_cols]
df["t"] = df.index.values
if x_col == "data" or x_col == "datetime":
df.t = df.t.apply(lambda d: str(d.date()))
p = df.plot.line(marker="o",
plot_data_points=True,
panning=True,
figsize=(900, 500),
legend="top_left",
show_figure=False,
rangetool=False,
hovertool_string="""
t: @t <br/>
""")
hovers = p.select(dict(type=HoverTool))
flag_first = True
for hover in hovers:
hover.tooltips = []
if flag_first:
for y_col in ["t"] + y_cols:
hover.tooltips += [
(y_col, '@{' + y_col + '}'),
]
flag_first = False
if save_flag:
item_text = json.dumps(json_item(p, filename))
output_path = os.path.join(figures_path, filename + ".json")
with open(output_path, "w+") as out_f:
out_f.write(item_text)
save(p)
p.background_fill_alpha = 0
p.border_fill_alpha = 0
return p
def plot_rangetool():
plotname = inspect.stack()[0][3][5:]
pandas_bokeh.output_file(os.path.join(PLOT_DIR, f"{plotname}.html"))
ts = pd.Series(np.random.randn(1000),
index=pd.date_range("1/1/2000", periods=1000))
df = pd.DataFrame(np.random.randn(1000, 4),
index=ts.index,
columns=list("ABCD"))
df = df.cumsum()
df.plot_bokeh(rangetool=True)
def test_histogram():
"Test for histograms"
import numpy as np
df_hist = pd.DataFrame(
{
"a": np.random.randn(1000) + 1,
"b": np.random.randn(1000),
"c": np.random.randn(1000) - 1,
},
columns=["a", "b", "c"],
)
# Top-on-Top Histogram (Default):
p_tt = df_hist.plot_bokeh.hist(
bins=np.linspace(-5, 5, 41),
vertical_xlabel=True,
hovertool=False,
title="Normal distributions (Top-on-Top)",
line_color="black",
show_figure=False,
)
# Side-by-Side Histogram (multiple bars share bin side-by-side) also accessible via
# kind="hist":
p_ss = df_hist.plot_bokeh(
kind="hist",
bins=np.linspace(-5, 5, 41),
histogram_type="sidebyside",
vertical_xlabel=True,
hovertool=False,
title="Normal distributions (Side-by-Side)",
line_color="black",
show_figure=False,
)
# Stacked histogram:
p_stack = df_hist.plot_bokeh.hist(
bins=np.linspace(-5, 5, 41),
histogram_type="stacked",
vertical_xlabel=True,
hovertool=False,
title="Normal distributions (Stacked)",
line_color="black",
show_figure=False,
)
layout = pandas_bokeh.column([p_tt, p_ss, p_stack])
pandas_bokeh.output_file(os.path.join(directory, "Plots", "Histogram.html"))
pandas_bokeh.save(layout)
def plot_Stepplot():
plotname = inspect.stack()[0][3][5:]
pandas_bokeh.output_file(os.path.join(PLOT_DIR, f"{plotname}.html"))
df = df_parabula_cube()
plot = df.plot_bokeh.step(
x="x",
xticks=range(-1, 1),
colormap=["#009933", "#ff3399"],
title="Pointplot (Parabula vs. Cube)",
mode="after",
figsize=(800, 300),
)
def plot_Pointplot():
plotname = inspect.stack()[0][3][5:]
pandas_bokeh.output_file(os.path.join(PLOT_DIR, f"{plotname}.html"))
df = df_parabula_cube()
plot = df.plot_bokeh.point(
x="x",
xticks=range(-3, 4),
size=5,
colormap=["#009933", "#ff3399"],
title="Pointplot (Parabula vs. Cube)",
marker="x",
)
def plot_lines_dashboard_ita(cases_df, figures_path, geo_name,
plot_dashboard_flag):
for save_flag in [False]:
p1 = plot_df_lines_bokeh(cases_df, "data", [
"totale_tamponi",
"totale_casi",
], figures_path, "tamponi", save_flag)
p2 = plot_df_lines_bokeh(cases_df, "data", [
"totale_attualmente_positivi", "totale_deceduti",
"totale_dimessi_guariti"
], figures_path, "totali_principali", save_flag)
p3 = plot_df_lines_bokeh(cases_df, "data", [
"nuovi_positivi", "nuovi_attualmente_positivi", "nuovi_deceduti",
"nuovi_dimessi_guariti"
], figures_path, "nuovi_principali", save_flag)
p4 = plot_df_lines_bokeh(cases_df, "data", [
"tasso_positivi_tamponi", "tasso_nuovi_positivi",
"tasso_mortalita", "tasso_guarigione"
], figures_path, "tassi_principali", save_flag)
p5 = plot_df_lines_bokeh(cases_df, "data", [
"attualmente_isolamento_domiciliare",
"attualmente_ricoverati",
"attualmente_terapia_intensiva",
], figures_path, "dettaglio_pazienti_attuali", save_flag)
p6 = plot_df_lines_bokeh(cases_df, "data", [
"tasso_ricoverati_con_sintomi",
"tasso_terapia_intensiva",
"tasso_terapia_intensiva_ricoverati",
], figures_path, "tassi_condizioni_cliniche", save_flag)
if plot_dashboard_flag:
outfp = os.path.join(figures_path, geo_name + ".html")
output_file(outfp)
pandas_bokeh.output_file(outfp)
plot_grid = pandas_bokeh.plot_grid([
[p1, p2],
[p3, p4],
[p5, p6],
],
toolbar_location="left")
# pandas_bokeh.save(plot_grid)
save(plot_grid)
def plot_Barplot3():
plotname = inspect.stack()[0][3][5:]
pandas_bokeh.output_file(os.path.join(PLOT_DIR, f"{plotname}.html"))
df = df_fruits()
p_bar = df.plot_bokeh.bar(
ylabel="Price per Unit [€]",
title="Fruit prices per Year",
alpha=0.6,
show_figure=False,
)
p_stacked_bar = df.plot_bokeh.bar(
ylabel="Price per Unit [€]",
title="Fruit prices per Year",
stacked=True,
alpha=0.6,
show_figure=False,
)
# Reset index, such that "fruits" is now a column of the DataFrame:
df.reset_index(inplace=True)
# Create horizontal bar (via kind keyword):
p_hbar = df.plot_bokeh(
kind="barh",
x="fruits",
xlabel="Price per Unit [€]",
title="Fruit prices per Year",
alpha=0.6,
legend="bottom_right",
show_figure=False,
)
# Create stacked horizontal bar (via barh accessor):
p_stacked_hbar = df.plot_bokeh.barh(
x="fruits",
stacked=True,
xlabel="Price per Unit [€]",
title="Fruit prices per Year",
alpha=0.6,
legend="bottom_right",
show_figure=False,
)
pandas_bokeh.plot_grid([[p_bar, p_stacked_bar], [p_hbar, p_stacked_hbar]],
plot_width=450)
def test_histogram_average_diplay(df_hist):
"Test average & cumulative function of histogram"
p_hist = df_hist.plot_bokeh.hist(
y=["a", "b"],
bins=np.arange(-4, 6.5, 0.5),
normed=100,
vertical_xlabel=True,
ylabel="Share[%]",
title="Normal distributions (normed)",
show_average=True,
xlim=(-4, 6),
ylim=(0, 30),
show_figure=False,
)
p_hist_cum = df_hist.plot_bokeh.hist(
y=["a", "b"],
bins=np.arange(-4, 6.5, 0.5),
normed=100,
cumulative=True,
vertical_xlabel=True,
ylabel="Share[%]",
title="Normal distributions (normed & cumulative)",
show_figure=False,
)
p_hist_cum_pandas_backend = df_hist.plot.hist(
by=["a", "b"],
bins=np.arange(-4, 6.5, 0.5),
normed=100,
cumulative=True,
vertical_xlabel=True,
ylabel="Share[%]",
title="Normal distributions (normed & cumulative)",
show_figure=False,
)
p_average = pandas_bokeh.plot_grid(
[[p_hist, p_hist_cum, p_hist_cum_pandas_backend]],
plot_width=450,
plot_height=300,
show_plot=False,
)
pandas_bokeh.output_file(
os.path.join(DIRECTORY, "Plots",
"Histogram_average_and_cumulative.html"))
pandas_bokeh.save(p_average)
def test_autosizing(df_fruits):
"""
Autoscaling test
"""
kwargs = dict(figsize=(500, 200),
sizing_mode="scale_width",
show_figure=False)
p_autoscale = df_fruits.plot_bokeh(kind="bar", **kwargs)
pandas_bokeh.output_file(os.path.join(DIRECTORY, "Plots",
"AutoScale.html"))
pandas_bokeh.save(p_autoscale)
assert True
def totals_plot(self, countries=None):
total_df = self.calc_totals(countries)
total_df['currently_sick'] = (total_df.confirmed - total_df.deaths -
total_df.recovered)
total_df = total_df.drop('confirmed', axis=1)
c_string = self.countries_to_string(countries)
pandas_bokeh.output_file(os.path.join('figures', 'totals.html'))
curdoc().theme = Theme(json=jt)
total_df.plot_bokeh.area(
figsize=(1500, 750),
title=f'Total COVID-19 numbers, {c_string}' + self.data_disclaimer,
ylabel='Number of individuals affected (stacked)',
stacked=True,
sizing_mode='scale_both')
def plot_comparison_df_bokeh(df,
y_cols,
figures_path,
filename,
save_flag=False):
columns_names = list(df.columns)
if save_flag:
outfp = os.path.join(figures_path, filename + ".html")
output_file(outfp)
pandas_bokeh.output_file(outfp)
warnings.filterwarnings("ignore")
df.index.name = "t"
p = df.plot.line(marker="o",
plot_data_points=True,
panning=True,
figsize=(1200, 600),
legend="top_left",
show_figure=False,
rangetool=False,
hovertool_string="""
t: @t <br/>
""")
hovers = p.select(dict(type=HoverTool))
flag_first = True
for hover in hovers:
hover.tooltips = []
if flag_first:
for y_col in df.columns:
hover.tooltips += [
(y_col, '@{' + y_col + '}'),
]
flag_first = False
if save_flag:
item_text = json.dumps(json_item(p, filename))
output_path = os.path.join(figures_path, filename + ".json")
with open(output_path, "w+") as out_f:
out_f.write(item_text)
save(p)
return p
def test_histogram_average_diplay():
"Test average & cumulative function of histogram"
import numpy as np
df_hist = pd.DataFrame(
{
"a": np.random.randn(1000) + 1,
"b": np.random.randn(1000),
"c": np.random.randn(1000) - 1,
},
columns=["a", "b", "c"],
)
p_hist = df_hist.plot_bokeh.hist(
y=["a", "b"],
bins=np.arange(-4, 6.5, 0.5),
normed=100,
vertical_xlabel=True,
ylabel="Share[%]",
title="Normal distributions (normed)",
show_average=True,
xlim=(-4, 6),
ylim=(0, 30),
show_figure=False,
)
p_hist_cum = df_hist.plot_bokeh.hist(
y=["a", "b"],
bins=np.arange(-4, 6.5, 0.5),
normed=100,
cumulative=True,
vertical_xlabel=True,
ylabel="Share[%]",
title="Normal distributions (normed & cumulative)",
show_figure=False,
)
p_average = pandas_bokeh.plot_grid(
[[p_hist, p_hist_cum]], plot_width=450, plot_height=300, show_plot=False
)
pandas_bokeh.output_file(
os.path.join(directory, "Plots", "Histogram_average_and_cumulative.html")
)
pandas_bokeh.save(p_average)
def plot_Scatterplot2():
plotname = inspect.stack()[0][3][5:]
pandas_bokeh.output_file(os.path.join(PLOT_DIR, f"{plotname}.html"))
df = df_iris()
df = df.sample(frac=1)
#Change one value to clearly see the effect of the size keyword
df.loc[13, "sepal length (cm)"] = 15
#Make scatterplot:
p_scatter = df.plot_bokeh.scatter(
x="petal length (cm)",
y="sepal width (cm)",
category="species",
title="Iris DataSet Visualization with Size Keyword",
size="sepal length (cm)")
def draw_graph(df, key_selected):
plots = []
result_type_name = key_selected.replace("_", " ").capitalize()
for alg in set(sorted(df[k0_algorithm].values)):
alg_name = alg
alg_type = "scheduling"
if "heuristic" in alg:
alg_name = "OGSA"
elif "optimal" in alg:
alg_name = "Optimisation model"
if "fw" in alg:
alg_name += " with FW"
alg_type = "pricing"
df_selected = df.loc[lambda df2: df[k0_algorithm] == alg,
lambda df2: [k0_households_no, key_selected]]
df_selected = df_selected.set_index(k0_households_no)
y_label = ""
if "time" in key_selected:
y_label = "Run time (seconds)"
elif "iteration" in key_selected:
y_label = "Iteration"
elif "reduction" in key_selected:
y_label = key_selected.replace("_", " ").capitalize()
p_line = df_selected.plot_bokeh(
kind="line",
title="{} of {}, {}".format(result_type_name, alg_type,
alg_name),
xlabel="Number of Households",
ylabel=y_label,
show_figure=False,
sizing_mode="scale_width",
)
if "reduction" in key_selected:
p_line.yaxis.formatter = NumeralTickFormatter(format='0 %')
p_line.legend.location = "top_left"
p_line.y_range.start = 0
plots.append(p_line)
pandas_bokeh.output_file("{}{}.html".format(dt_folder,
result_type_name))
grid = pandas_bokeh.plot_grid(plots, ncols=2, show_plot=False)
pandas_bokeh.save(grid)
def plot_ApplevsGoogle_3():
plotname = inspect.stack()[0][3][5:]
pandas_bokeh.output_file(os.path.join(PLOT_DIR, f"{plotname}.html"))
df = df_stocks()
plot = df.plot_bokeh(
figsize=(800, 450),
title="Apple vs Google",
xlabel="Date",
ylabel="Stock price [$]",
yticks=[0, 100, 200, 300, 400],
ylim=(100, 200),
xlim=("2001-01-01", "2001-02-01"),
colormap=["red", "blue"],
plot_data_points=True,
plot_data_points_size=10,
marker="asterisk",
)
def simple_plot(self, countries=['Germany', 'Austria', 'Italy']):
if type(countries) != list:
raise TypeError('countries argument accepts type list, '
f'got {type(countries)} instead')
confirmed = self.confirmed_df.loc[:, (countries, slice(None),
slice(None), slice(None))]
confirmed.columns = (
confirmed.columns.droplevel(3).droplevel(2).droplevel(1))
pandas_bokeh.output_file(os.path.join('figures', 'simple_plot.html'))
curdoc().theme = Theme(json=jt)
confirmed.plot_bokeh.line(figsize=(1500, 750),
title="simple plot",
plot_data_points=True,
plot_data_points_size=5,
marker="circle",
sizing_mode='scale_both')
def lineplot(x: str,
y: list,
data,
title="Line Plot",
output_file="",
**lineplot_kwargs):
"""
Plots a line plot.
Parameters
----------
x : str
X axis column
y : list
Y axis column
data : Dataframe
Dataframe
title : str, optional
Title of the plot, by default 'Line Plot'
output_file : str, optional
If a name is provided save the plot to an html file, by default ''
"""
y.append(x)
data_copy = data[y].copy()
data_copy = data_copy.set_index(x)
xlabel = lineplot_kwargs.pop("xlabel", x)
p_line = data_copy.plot_bokeh.line(title=title,
xlabel=xlabel,
**lineplot_kwargs)
if output_file: # pragma: no cover
if Path(output_file).suffix == ".html":
pandas_bokeh.output_file(os.path.join(IMAGE_DIR, output_file))
else:
export_png(p_line, os.path.join(IMAGE_DIR, output_file))
def plot_Histogram():
plotname = inspect.stack()[0][3][5:]
pandas_bokeh.output_file(os.path.join(PLOT_DIR, f"{plotname}.html"))
df = df_hist()
# Top-on-Top Histogram (Default):
p1 = df.plot_bokeh.hist(
bins=np.linspace(-5, 5, 41),
vertical_xlabel=True,
hovertool=False,
title="Normal distributions (Top-on-Top)",
line_color="black",
show_figure=False,
)
# Side-by-Side Histogram (multiple bars share bin side-by-side) also accessible via
# kind="hist":
p2 = df.plot_bokeh(
kind="hist",
bins=np.linspace(-5, 5, 41),
histogram_type="sidebyside",
vertical_xlabel=True,
hovertool=False,
title="Normal distributions (Side-by-Side)",
line_color="black",
show_figure=False,
)
# Stacked histogram:
p3 = df.plot_bokeh.hist(
bins=np.linspace(-5, 5, 41),
histogram_type="stacked",
vertical_xlabel=True,
hovertool=False,
title="Normal distributions (Stacked)",
line_color="black",
show_figure=False,
)
pandas_bokeh.plot_grid([[p1], [p2], [p3]])
def plot_ApplevsGoogle_2():
plotname = inspect.stack()[0][3][5:]
pandas_bokeh.output_file(os.path.join(PLOT_DIR, f"{plotname}.html"))
df = df_stocks()
plot = df.plot_bokeh(
figsize=(800, 450),
y="Apple",
title="Apple vs Google",
xlabel="Date",
ylabel="Stock price [$]",
yticks=[0, 100, 200, 300, 400],
ylim=(0, 400),
toolbar_location=None,
colormap=["red", "blue"],
hovertool_string=r"<img src='https://upload.wikimedia.org/wikipedia/commons/thumb/f/fa/Apple_logo_black.svg/170px-Apple_logo_black.svg.png' height='42' alt='@imgs' width='42' style='float: left; margin: 0px 15px 15px 0px;' border='2' ></img> Apple \n\n<h4> Stock Price: </h4> @{Apple}",
panning=False,
zooming=False,
)
def test_mapplot():
"Mapplot test"
df_mapplot = pd.read_csv(os.path.join(test_sets_directory, "populated places","populated_places.csv"))
df_mapplot["size"] = df_mapplot["pop_max"] / 1000000
p_map = df_mapplot.plot_bokeh.map(
x="longitude",
y="latitude",
hovertool_string="""<h2> @{name} </h2>
<h3> Population: @{pop_max} </h3>""",
tile_provider="STAMEN_TERRAIN_RETINA",
size="size",
figsize=(900, 600),
title="World cities with more than 1.000.000 inhabitants",
show_figure=False)
pandas_bokeh.output_file(os.path.join(directory, "Plots", "Mapplot.html"))
pandas_bokeh.save(p_map)
def lineplot(x: str,
y: list,
data,
title="Line Plot",
output_file="",
**lineplot_kwargs):
"""
Plots a line plot.
Parameters
----------
x : str
X axis column
y : list
Y axis column
data : Dataframe
Dataframe
title : str, optional
Title of the plot, by default 'Line Plot'
output_file : str, optional
If a name is provided save the plot to an html file, by default ''
"""
data_copy = data[[x] + y].copy()
data_copy = data_copy.set_index(x)
xlabel = lineplot_kwargs.pop("xlabel", x)
p_line = data_copy.plot_bokeh.line(title=title,
xlabel=xlabel,
**lineplot_kwargs)
if output_file:
pandas_bokeh.output_file(output_file)
pandas_bokeh.save(p_line)
def plot_lines_dashboard(cases_df, figures_path, geo_name,
plot_dashboard_flag):
if plot_dashboard_flag:
save_flag = False
outfp = os.path.join(figures_path, geo_name + ".html")
output_file(outfp)
pandas_bokeh.output_file(outfp)
else:
save_flag = True
p1 = plot_df_lines_bokeh(cases_df, "datetime", [
"total_cases",
], figures_path, "cases", save_flag)
p2 = plot_df_lines_bokeh(cases_df, "datetime",
["total_deaths", "total_recovered"], figures_path,
"main_totals", save_flag)
p3 = plot_df_lines_bokeh(cases_df, "datetime", [
"new_positives", "new_currently_positives", "new_deaths",
"new_recovered"
], figures_path, "main_new", save_flag)
p4 = plot_df_lines_bokeh(
cases_df, "datetime",
["rate_new_positives", "rate_deaths", "rate_recovered"], figures_path,
"main_rate", save_flag)
if plot_dashboard_flag:
plot_grid = pandas_bokeh.plot_grid([
[p1, p2],
[p3, p4],
],
toolbar_location="left")
# pandas_bokeh.save(plot_grid)
save(plot_grid)
