def test_barplot_stacked(df_fruits):
"Test for stacked Barplot"
# Create Scatterplot:
arguments = dict(
ylabel="Price per Unit [€]",
title="Fruit prices per Year",
stacked=True,
alpha=0.6,
show_figure=False,
)
p_bar = df_fruits.plot_bokeh(kind="bar", **arguments)
p_bar_accessor = df_fruits.plot_bokeh.bar(**arguments)
p_bar_pandas_backend = df_fruits.plot(kind="bar", **arguments)
p_bar_accessor_pandas_backend = df_fruits.plot.bar(**arguments)
# Output plot as HTML:
output = pandas_bokeh.row([p_bar, p_bar_accessor])
with open(os.path.join(DIRECTORY, "Plots", "Barplot_stacked.html"),
"w") as f:
f.write(pandas_bokeh.embedded_html(output))
assert True
def test_basic_lineplot_pyspark(spark):
"""Test for basic lineplot with Pyspark"""
# Create basic lineplot:
np.random.seed(42)
df = pd.DataFrame(
{
"Google": np.random.randn(1000) + 0.2,
"Apple": np.random.randn(1000) + 0.17
},
index=pd.date_range("1/1/2000", periods=1000),
)
df.index.name = "Date"
df = df.cumsum()
df = df + 50
df = spark.createDataFrame(df.reset_index())
p_basic_lineplot = df.plot_bokeh(kind="line", x="Date", show_figure=False)
p_basic_lineplot_accessor = df.plot_bokeh.line(x="Date", show_figure=False)
# Output plot as HTML:
output = pandas_bokeh.row([p_basic_lineplot, p_basic_lineplot_accessor])
with open(os.path.join(directory, "Plots", "Basic_lineplot_PySpark.html"),
"w") as f:
f.write(pandas_bokeh.embedded_html(output))
assert True
def test_pointplot():
"Test for pointplot"
x = np.arange(-3, 3, 0.1)
y2 = x**2
y3 = x**3
df = pd.DataFrame({"x": x, "Parabula": y2, "Cube": y3})
arguments = dict(
x="x",
xticks=range(-3, 4),
size=5,
colormap=["#009933", "#ff3399"],
title="Pointplot (Parabula vs. Cube)",
marker="x",
show_figure=False,
)
p_pointplot = df.plot_bokeh(kind="point", **arguments)
p_pointplot_accessor = df.plot_bokeh.point(**arguments)
p_pointplot_pandas_backend = df.plot(kind="point", **arguments)
p_pointplot_accessor_pandas_backend = df.plot.point(**arguments)
# Output plot as HTML:
output = pandas_bokeh.row([p_pointplot, p_pointplot_accessor])
with open(os.path.join(DIRECTORY, "Plots", "Pointplot.html"), "w") as f:
f.write(pandas_bokeh.embedded_html(output))
assert True
def test_scatterplot_2(df_iris):
"Test 2 for scatterplot"
# Change one value to clearly see the effect of the size keyword
df_iris.loc[13, "sepal length (cm)"] = 15
# Make scatterplot:
# Create Scatterplot:
arguments = dict(
x="petal length (cm)",
y="sepal width (cm)",
category="species",
title="Iris DataSet Visualization",
size="sepal length (cm)",
show_figure=False,
)
p_scatter = df_iris.plot_bokeh(kind="scatter", **arguments)
p_scatter_accessor = df_iris.plot_bokeh.scatter(**arguments)
p_scatter_pandas_backend = df_iris.plot(kind="scatter", **arguments)
p_scatter_accessor_pandas_backend = df_iris.plot.scatter(**arguments)
# Output plot as HTML:
output = pandas_bokeh.row([p_scatter, p_scatter_accessor])
with open(os.path.join(DIRECTORY, "Plots", "Scatterplot_2.html"),
"w") as f:
f.write(pandas_bokeh.embedded_html(output))
assert True
def test_lineplot_with_points(df_stock):
"Test for lineplot with data points:"
# Create complex lineplot:
arguments = dict(
figsize=(600, 450),
title="Apple vs Google",
xlabel="Date",
ylabel="Stock price [$]",
yticks=[0, 100, 200, 300, 400],
ylim=(100, 200),
xlim=("2001-01-01", "2001-02-01"),
colormap=["red", "blue"],
plot_data_points=True,
plot_data_points_size=10,
marker="asterisk",
show_figure=False,
)
p_lineplot_with_points = df_stock.plot_bokeh(kind="line", **arguments)
p_lineplot_with_points_accessor = df_stock.plot_bokeh.line(**arguments)
p_lineplot_with_points_pandas_backend = df_stock.plot(kind="line",
**arguments)
p_lineplot_with_points_accessor_pandas_backend = df_stock.plot.line(
**arguments)
# Output plot as HTML:
output = pandas_bokeh.row(
[p_lineplot_with_points, p_lineplot_with_points_accessor])
with open(os.path.join(DIRECTORY, "Plots", "Lineplot_with_points.html"),
"w") as f:
f.write(pandas_bokeh.embedded_html(output))
assert True
def test_lineplot_with_points():
"Test for lineplot with data points:"
# Create complex lineplot:
np.random.seed(42)
df = pd.DataFrame(
{"Google": np.random.randn(1000) + 0.2, "Apple": np.random.randn(1000) + 0.17},
index=pd.date_range("1/1/2000", periods=1000),
)
df.index.name = "Date"
df = df.cumsum()
df = df + 50
arguments = dict(
figsize=(600, 450),
title="Apple vs Google",
xlabel="Date",
ylabel="Stock price [$]",
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",
show_figure=False,
)
p_lineplot_with_points = df.plot_bokeh(kind="line", **arguments)
p_lineplot_with_points_accessor = df.plot_bokeh.line(**arguments)
# Output plot as HTML:
output = pandas_bokeh.row([p_lineplot_with_points, p_lineplot_with_points_accessor])
with open(os.path.join(directory, "Plots", "Lineplot_with_points.html"), "w") as f:
f.write(pandas_bokeh.embedded_html(output))
assert True
def test_basic_lineplot(df_stock):
"""Test for basic lineplot"""
# Create basic lineplot:
p_basic_lineplot = df_stock.plot_bokeh(kind="line", show_figure=False)
p_basic_lineplot_accessor = df_stock.plot_bokeh.line(show_figure=False)
p_basic_lineplot_pandas_backend = df_stock.plot(
kind="line",
fontsize_title=30,
fontsize_label=25,
fontsize_ticks=15,
fontsize_legend=40,
show_figure=False,
)
p_basic_lineplot_accessor_pandas_backend = df_stock.plot.line(
fontsize_title=30,
fontsize_label=25,
fontsize_ticks=15,
fontsize_legend=40,
show_figure=False,
)
# Output plot as HTML:
output = pandas_bokeh.row([p_basic_lineplot, p_basic_lineplot_accessor])
with open(os.path.join(DIRECTORY, "Plots", "Basic_lineplot.html"),
"w") as f:
f.write(pandas_bokeh.embedded_html(output))
assert True
def test_barplot_stacked():
"Test for stacked Barplot"
data = {
"fruits": ["Apples", "Pears", "Nectarines", "Plums", "Grapes", "Strawberries"],
"2015": [2, 1, 4, 3, 2, 4],
"2016": [5, 3, 3, 2, 4, 6],
"2017": [3, 2, 4, 4, 5, 3],
}
df = pd.DataFrame(data).set_index("fruits")
# Make scatterplot:
# Create Scatterplot:
arguments = dict(
ylabel="Price per Unit [€]",
title="Fruit prices per Year",
stacked=True,
alpha=0.6,
show_figure=False,
)
p_bar = df.plot_bokeh(kind="bar", **arguments)
p_bar_accessor = df.plot_bokeh.bar(**arguments)
# Output plot as HTML:
output = pandas_bokeh.row([p_bar, p_bar_accessor])
with open(os.path.join(directory, "Plots", "Barplot_stacked.html"), "w") as f:
f.write(pandas_bokeh.embedded_html(output))
assert True
def test_scatterplot_2():
"Test 2 for scatterplot"
# Load Iris Dataset:
df = pd.read_csv(
r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/Documentation/Testdata/iris/iris.csv"
)
#Change one value to clearly see the effect of the size keyword
df.loc[13, "sepal length (cm)"] = 15
#Make scatterplot:
# Create Scatterplot:
arguments = dict(
x="petal length (cm)",
y="sepal width (cm)",
category="species",
title="Iris DataSet Visualization",
size="sepal length (cm)",
show_figure=False,
)
p_scatter = df.plot_bokeh(kind="scatter", **arguments)
p_scatter_accessor = df.plot_bokeh.scatter(**arguments)
# Output plot as HTML:
output = pandas_bokeh.row([p_scatter, p_scatter_accessor])
with open(os.path.join(directory, "Plots", "Scatterplot_2.html"),
"w") as f:
f.write(pandas_bokeh.embedded_html(output))
assert True
def test_complex_lineplot():
"""Test for complexd lineplot"""
# Create complex lineplot:
np.random.seed(42)
df = pd.DataFrame(
{
"Google": np.random.randn(1000) + 0.2,
"Apple": np.random.randn(1000) + 0.17
},
index=pd.date_range("1/1/2000", periods=1000),
)
df.index.name = "Date"
df = df.cumsum()
df = df + 50
arguments = dict(
figsize=(600, 450),
y="Apple",
title="Apple vs Google",
xlabel="Date",
ylabel="Stock price [$]",
yticks=[0, 100, 200, 300, 400],
ylim=(0, 400),
toolbar_location=None,
colormap=["red", "blue"],
hovertool_string="""<img
src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/fa/Apple_logo_black.svg/170px-Apple_logo_black.svg.png"
height="42" alt="@imgs" width="42"
style="float: left; margin: 0px 15px 15px 0px;"
border="2"></img> Apple
<h4> Stock Price: </h4> @{Apple}""",
panning=False,
zooming=False,
show_figure=False,
)
p_complex_lineplot = df.plot_bokeh(kind="line", **arguments)
p_complex_lineplot_accessor = df.plot_bokeh.line(**arguments)
# Output plot as HTML:
output = pandas_bokeh.row(
[p_complex_lineplot, p_complex_lineplot_accessor])
with open(os.path.join(directory, "Plots", "Complex_lineplot.html"),
"w") as f:
f.write(pandas_bokeh.embedded_html(output))
assert True
def test_basic_stepplot(df_stock):
"""Test for basic stepplot"""
p_basic_stepplot = df_stock.plot_bokeh(kind="step", show_figure=False)
p_basic_stepplot_accessor = df_stock.plot_bokeh.step(show_figure=False)
p_basic_stepplot_pandas_backend = df_stock.plot(kind="step",
show_figure=False)
p_basic_stepplot_accessor_pandas_backend = df_stock.plot.step(
show_figure=False)
# Output plot as HTML:
output = pandas_bokeh.row([p_basic_stepplot, p_basic_stepplot_accessor])
with open(os.path.join(DIRECTORY, "Plots", "Basic_stepplot.html"),
"w") as f:
f.write(pandas_bokeh.embedded_html(output))
assert True
def test_complex_lineplot(df_stock):
"""Test for complexd lineplot"""
# Create complex lineplot:
arguments = dict(
figsize=(600, 450),
y="Apple",
title="Apple vs Google",
xlabel="Date",
ylabel="Stock price [$]",
yticks=[0, 100, 200, 300, 400],
ylim=(0, 400),
toolbar_location=None,
colormap=["red", "blue"],
hovertool_string="""<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
<h4> Stock Price: </h4> @{Apple}""",
panning=False,
zooming=False,
show_figure=False,
)
p_complex_lineplot = df_stock.plot_bokeh(kind="line", **arguments)
p_complex_lineplot_accessor = df_stock.plot_bokeh.line(**arguments)
p_complex_lineplot_pandas_backend = df_stock.plot(kind="line", **arguments)
p_complex_lineplot_accessor_pandas_backend = df_stock.plot.line(
**arguments)
# Output plot as HTML:
output = pandas_bokeh.row([
p_complex_lineplot,
p_complex_lineplot_accessor,
p_complex_lineplot_pandas_backend,
p_complex_lineplot_accessor_pandas_backend,
])
with open(os.path.join(DIRECTORY, "Plots", "Complex_lineplot.html"),
"w") as f:
f.write(pandas_bokeh.embedded_html(output))
assert True
def test_histogram(df_hist):
"Test for histograms"
# 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,
)
# Top-on-Top Histogram (Default):
p_tt_pandas_backend = df_hist.plot.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_pandas_backend = df_hist.plot(
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_pandas_backend = df_hist.plot.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([
pandas_bokeh.row(p_tt, p_tt_pandas_backend),
pandas_bokeh.row(p_ss, p_ss_pandas_backend),
pandas_bokeh.row(p_stack, p_stack_pandas_backend),
])
pandas_bokeh.output_file(os.path.join(DIRECTORY, "Plots",
"Histogram.html"))
pandas_bokeh.save(layout)
