# we fill a df with the data of interest and create a groupby pandas object
df = flowers[["petal_length", "petal_width", "species"]]
xyvalues = g = df.groupby("species")
# here we only drop that groupby object into a dict ..
pdict = OrderedDict()
for i in g.groups.keys():
labels = g.get_group(i).columns
xname = labels[0]
yname = labels[1]
x = getattr(g.get_group(i), xname)
y = getattr(g.get_group(i), yname)
pdict[i] = zip(x, y)
# any of the following commented are valid Scatter inputs
#xyvalues = pdict
#xyvalues = pd.DataFrame(xyvalues)
#xyvalues = xyvalues.values()
#xyvalues = np.array(xyvalues.values())
TOOLS = "resize,crosshair,pan,wheel_zoom,box_zoom,reset,previewsave"
scatter = Scatter(xyvalues,
filename="iris_scatter.html",
tools=TOOLS,
ylabel='petal_width',
facet=False)
scatter.title("iris dataset").legend("top_left")
scatter.width(600).height(400).show()
# Testing random data
df = pandas.DataFrame(columns=['X', 'Y'])
df['X'] = [1, 2, 3, 4, 5]
df['Y'] = [6, 7, 8, 9, 10]
p = Scatter(df, x='X', y='Y', title="Temperature Observation",
xlabel='Days of observations', ylabel='Temperature')
output_file('Scatter_charts.html') # Generate an HTML file
# show(p) # Show
# ----- (2) Test using bokeh.plotting (recommended for more customization) -----
# p = figure(plot_width=500, plot_height=400, title='Earthquake')
p = figure(plot_width=500, plot_height=400)
# help(p)
p.title = 'Earthquake'
p.title_text_color = 'Orange'
p.title_text_font = 'Times'
p.title_text_font_style = 'italic'
p.yaxis.minor_tick_line_color = None
p.xaxis.axis_label = 'Times'
p.yaxis.axis_label = 'Value'
# quad may also be used
p.circle([4, 8, 12], [7, 14, 21], size=17, color='orange', alpha=0.5)
# p.triangle(df['X'], df['Y'], size=10, color='red', alpha=0.5)
p.triangle(df['X'], df['Y'], size=[5, 10, 15, 20, 25], color='red', alpha=0.5)
output_file('Scatter_plotting.html')
# show(p)
# ----- (3) Exercise: Reading from excel -----
from bokeh.charts import Scatter
# we fill a df with the data of interest and create a groupby pandas object
df = flowers[["petal_length", "petal_width", "species"]]
xyvalues = g = df.groupby("species")
# here we only drop that groupby object into a dict ..
pdict = OrderedDict()
for i in g.groups.keys():
labels = g.get_group(i).columns
xname = labels[0]
yname = labels[1]
x = getattr(g.get_group(i), xname)
y = getattr(g.get_group(i), yname)
pdict[i] = zip(x, y)
# any of the following commented are valid Scatter inputs
#xyvalues = pdict
#xyvalues = pd.DataFrame(xyvalues)
#xyvalues = xyvalues.values()
#xyvalues = np.array(xyvalues.values())
TOOLS="resize,crosshair,pan,wheel_zoom,box_zoom,reset,previewsave"
scatter = Scatter(
xyvalues, filename="iris_scatter.html", tools=TOOLS, ylabel='petal_width',
facet=False
)
scatter.title("iris dataset").legend("top_left")
scatter.width(600).height(400).show()
from bokeh.sampledata.iris import flowers
# we fill a df with the data of interest and create a groupby pandas object
df = flowers[["petal_length", "petal_width", "species"]]
g = df.groupby("species")
# here we only drop that groupby object into our Scatter chart
from bokeh.charts import Scatter
scatter = Scatter(g, filename="iris_scatter.html")
scatter.title("iris dataset, gp_by_input").legend("top_left").width(600).height(400).show()
