def get_plot(df):
#Make plot and customize
p = Scatter(df, x='sepal_length', y='sepal_width', xlabel='Sepal Length [cm]', ylabel='Sepal Width [cm]', title='Sepal width vs. length')
p.xaxis.axis_label_text_font_size = "14pt"
p.xaxis.major_label_text_font_size = '10pt'
p.yaxis.axis_label_text_font_size = "14pt"
p.yaxis.major_label_text_font_size = '10pt'
p.title.text_font_size = '16pt'
p.add_tools(HoverTool()) #Need to configure tooltips
#Return the plot
return(p)
def main():
"""
This function will
-load data from a csv
-impute missing data with the column's mean
-perform kmneans clustering
-produce an html scatter plot
"""
#load data from a CSV to a dataframe
with open(settings["crime_data"]) as in_data:
crime_data = pd.DataFrame.from_csv(in_data, sep=',')
crime_data=crime_data.fillna(value=-999)
#load all numeric data into an array. The offense column from the crime data
#is excluded
as_array = np.asfarray(crime_data[["X","Y"]])
#number of groups
n_clusters=40
#Correct missing data
imputer = Imputer(missing_values=-999, strategy="mean")
patched = imputer.fit_transform(as_array)
#cluster data
cluster = KMeans(n_clusters=n_clusters)
cluster.fit(patched)
#assigned grouped labels to the crime data
labels = cluster.labels_
crime_data["labels"]=labels
pdict = create_ordered_dict(crime_data, "labels")
crime_data.to_csv(r'C:\users\andrew_woizesko\desktop\knn.csv')
np.savetxt(r'C:\users\andrew_woizesko\desktop\centers.csv', cluster.cluster_centers_)
#location of output graph
file_name = os.path.join("..", 'tests', "kmeans_clusters_{0}.html".format(time_stamp()))
output_file(file_name)
#create out graph
TOOLS="pan,wheel_zoom,box_zoom,reset"
scatter = Scatter(pdict.values(), title="Crime Clusters", filename=file_name, tools=TOOLS)
scatter.show()
def create_chart(df, attr1, attr2):
values = {'PTS': 15, 'TRB': 10, 'AST': 7, 'BLK': 1, 'STL': 1}
full_names = {
'PTS': 'Points',
'TRB': 'Rebounds',
'AST': 'Assists',
'BLK': 'Blocks',
'STL': 'Steals'
}
query_string1 = attr1 + " > " + str(values[attr1])
query_string2 = attr2 + " > " + str(values[attr2])
df = df.query(query_string1)
df = df.query(query_string2)
label1 = full_names[attr1] + ' PER 36'
label2 = full_names[attr2] + ' PER 36'
tooltips = [('Player', '@Player'), (attr1, '@' + attr1),
(attr2, '@' + attr2), ('Team', '@Tm')]
p = Scatter(df,
x=attr1,
y=attr2,
xlabel=label1,
ylabel=label2,
tooltips=tooltips)
output_file('stats.html')
show(p)
def scatter(df,x_axis,y_axis,title):
plot = Scatter(df, x=x_axis, y=y_axis, color=x_axis,
title=title,
legend='top_right',
xlabel=x_axis,
ylabel=y_axis)
return plot
def Test():
with sqlite3.connect('DublinMarathons.db') as conn:
cursor = conn.cursor()
cursor.execute("""
Select Splits.Split, Splits.Split_Start_Time, Splits.Split_Finish_Time, Splits.Split_Duration, RaceDetails.Year, Runner.Name
from ((Splits
INNER JOIN Runner ON Splits.RunnerID=Runner.RunnerID)
INNER JOIN RaceDetails ON Splits.RaceID=RaceDetails.RaceID)
where Runner.Name="Barry,Smyth";
""")
data = []
for row in cursor.fetchall():
data.append(row)
df = pd.DataFrame(data, columns=['Split', 'Split_Start_Time', 'Split_Finish_Time', 'Split_Duration', 'Year', 'Name'])
df.Split_Duration = df.Split_Duration.map(Convert)
TOOLS = "hover"
output_file('Scatter.html')
source=ColumnDataSource(data=df)
hover = HoverTool(tooltips=[
("Name", "$Name"),
("Split", "$Split"),
("StartTime", "$Split_Start_Time"),
("FinishTime", "$Split_Finish_Time")
])
p = Scatter(df, x='Split_Duration', y='Year', tools=TOOLS)
show(p)
def abc_detail(abc_id):
history = app.config["HISTORY"]
history.id = abc_id
abc = ABCInfo(history.get_abc())
model_probabilities = history.get_model_probabilities()
model_ids = model_probabilities.columns
model_probabilities.columns = list(
map(lambda x: "{}".format(x), model_probabilities.columns))
model_probabilities = model_probabilities.reset_index()
if len(model_probabilities) > 0:
populations = history.get_all_populations()
populations = populations[populations.t >= 0]
particles = (
history.get_nr_particles_per_population().reset_index().rename(
columns={
"index": "t",
"t": "particles"
}).query("t >= 0"))
melted = pd.melt(model_probabilities,
id_vars="t",
var_name="m",
value_name="p")
prob_plot = Bar(melted, label="t", stack="m", values="p")
prob_plot.ylabel = "p"
plot = Tabs(tabs=[
Panel(child=prob_plot, title="Probability"),
Panel(child=Scatter(x="t", y="nr_samples", data=populations),
title="Samples"),
Panel(child=Scatter(x="t", y="particles", data=particles),
title="Particles"),
Panel(child=Scatter(x="t", y="epsilon", data=populations),
title="Epsilon")
])
plot = PlotScriptDiv(*components(plot))
return render_template("abc_detail.html",
abc_id=abc_id,
plot=plot,
BOKEH=BOKEH,
model_ids=model_ids,
abc=abc)
return render_template("abc_detail.html",
abc_id=abc_id,
plot=PlotScriptDiv("", "Exception: No data found."),
BOKEH=BOKEH,
abc=abc)
def createBokehChart(self):
data = self.getWorkingPandasDataFrame()
return Scatter(data,
x=self.getKeyFields()[0],
y=self.getValueFields()[0],
xlabel=self.getKeyFields()[0],
ylabel=self.getValueFields()[0],
legend=self.showLegend(),
color=self.options.get("color"))
def create_plot(brand='AMC'):
data_df = df_auto[df_auto.brand == brand]
p = Scatter(data_df,
x='trunk',
y='length',
title="Length vs Trunk",
legend="top_left",
xlabel="Trunk",
ylabel="Length")
show(p)
def plot():
ticker = request.form['name_ticker']
apicall = 'https://www.quandl.com/api/v3/datasets/WIKI/' + ticker + '/data.csv?column_index=4&start_date=2012-11-01&end_date=2013-11-30'
apikey = '&api_key=yRdMoLRR-tk-oNmDdQpd'
strcall = apicall + apikey
response = requests.get(strcall)
df = pd.read_csv(io.BytesIO(response.content), delimiter=',', sep="\n")
#prices = (df.columns, df.shape)
p = Scatter(df,
x='sepal_length',
y='sepal_width',
title='Sepal width vs. length')
p.title.text_font_size = '16pt'
p.add_tools(HoverTool()) #Need to configure tooltips for a good HoverTool
script, div = components(p)
return render_template('home.html', script=script, div=div)
def scatter(self, dataframe, x=None, y=None, width=None, height=None, color=None, title=None,
xaxis_label=None, yaxis_label=None, label=None):
color = self.__default_options__.get('color', None) if color is None else color
width = self.__default_options__.get('width', None) if width is None else width
width, height = self._width_height(width, height)
scatter = Scatter(dataframe, x=x, y=y, width=width, height=height, color=color, title=title,
tools=TOOLS + ',hover' if label else '')
if label:
hover = scatter.select_one(dict(type=HoverTool))
hover.tooltips = [("Id", "@%s" % label)]
renderer = scatter.select_one(dict(type=GlyphRenderer))
renderer.data_source.data[label] = dataframe[label].tolist()
if xaxis_label:
scatter._xaxis.axis_label = xaxis_label
if yaxis_label:
scatter._yaxis.axis_label = yaxis_label
return scatter
def scatter(self,
dataframe,
x=None,
y=None,
width=None,
height=None,
color=None,
title=None,
xaxis_label=None,
yaxis_label=None,
label=None):
color = self.__default_options__.get('color',
None) if color is None else color
width = self.__default_options__.get('width',
None) if width is None else width
width, height = self._width_height(width, height)
scatter = Scatter(dataframe,
x=x,
y=y,
width=width,
height=height,
color=color,
title=title,
tools=TOOLS + ',hover' if label else '')
if label:
hover = scatter.select_one(dict(type=HoverTool))
hover.tooltips = [("Id", "@%s" % label)]
renderer = scatter.select_one(dict(type=GlyphRenderer))
renderer.data_source.data[label] = dataframe[label].tolist()
if xaxis_label:
scatter._xaxis.axis_label = xaxis_label
if yaxis_label:
scatter._yaxis.axis_label = yaxis_label
return scatter
def bokeh_high_level_scatter(ag2):
pal = [
'#7fc97f', '#beaed4', '#fdc086', '#ffff99', '#386cb0', '#f0027f',
'#bf5b17'
]
tooltips = [("Cat", "@Cat"), ("Dog", "@Dog"), ("Livestock", "@Livestock"),
("ZipCode", "@ZipCode")]
s = Scatter(ag2,
x='Cat',
y='Dog',
color=color('Livestock', palette=pal),
tooltips=tooltips)
output_file('bokeh_high_scatter.html')
show(s)
def eitc_plot(eitc):
'''This plots earned eitc as a function of earned income'''
p = Scatter(eitc,
x='earned_income',
y='eitc',
color='blue',
title="EITC for Earned Income",
legend='top_right',
xlabel="earned_income",
ylabel="eitc")
output_file("eitc.html")
show(p)
def scatter_plot(X, Y, xlabel='', ylabel='', title='', height=3):
dict = {'X': X, 'Y': Y}
df = pd.DataFrame(dict)
plot = Scatter(df,
'X',
'Y',
title=title,
plot_height=int(height * ht),
sizing_mode='scale_width')
plot.xaxis.axis_label = xlabel
plot.yaxis.axis_label = ylabel
return plot
def main():
# check recalculation request
if 'recalculate' in request.args:
if request.args.get('recalculate') == 'True':
betalyzer.recalculate()
# build sector betas bar chart
sector_betas = betalyzer.df_tickers.groupby('sector')['beta'].mean()
bk_sector_betas = Bar(sector_betas,
plot_width=550,
plot_height=400,
legend=None)
bk_sector_betas_script, bk_sector_betas_div = components(bk_sector_betas)
# build market cap betas bar chart
mktcap_betas = betalyzer.df_tickers.groupby(
'market_cap_decile')['beta'].mean()
bk_mc_betas = Bar(mktcap_betas,
plot_width=550,
plot_height=400,
legend=None)
bk_mc_betas_script, bk_mc_betas_div = components(bk_mc_betas)
# build market cap scatter plot
scatter = Scatter(betalyzer.df_tickers,
x='market_cap_log',
y='beta',
plot_width=550,
plot_height=400)
scatter_script, scatter_div = components(scatter)
# build line plot for top three stocks
top_tickers = betalyzer.df_tickers['ticker'].head(3)
bk_history = Line(betalyzer.df_betas[top_tickers],
plot_width=550,
plot_height=400)
bk_history_script, bk_history_div = components(bk_history)
return render_template(
'main.html',
dt_tickers=betalyzer.df_tickers.to_dict(orient='records'),
bk_sector_betas_script=bk_sector_betas_script,
bk_sector_betas_div=bk_sector_betas_div,
bk_mc_betas_script=bk_mc_betas_script,
bk_mc_betas_div=bk_mc_betas_div,
scatter_script=scatter_script,
scatter_div=scatter_div,
bk_history_script=bk_history_script,
bk_history_div=bk_history_div)
def scatter():
s = Scatter(
flowers,
title="Fisher's Iris data set", tools='tap,box_select,save',
x=blend('petal_length', name='Length'),
y=blend('petal_width', name='Width'),
color='species', palette=Spectral4, legend=True,
)
# Lets move the legend off-canvas!
legend = s.legend[0]
legend.border_line_color = None
legend.orientation = 'horizontal'
legend.location = (0, 0)
s.above.append(legend)
return s
def kmeanspp(X, k, num_iter=100):
centroids = init_centroids(X, k)
for _ in range(num_iter):
D = assign_to_clusters(X, centroids)
centroids = recalc_centroids(X, D)
df = pd.DataFrame(X)
D = [d[1] for d in D]
df = pd.concat([df, pd.Series(D)], axis=1)
df.columns = ['x', 'y', 'cluster']
p = Scatter(df, x='x', y='y', color='cluster')
show(p)
def plot_2d_scatter(df, vectorizer, text='Body', to_plot='Label'):
X = vectorizer.transform(df[text])
cos_dist = 1 - cosine_similarity(X.todense())
mds = MDS(n_components=2, dissimilarity="precomputed", random_state=0)
pos = mds.fit_transform(cos_dist) # shape (n_components, n_samples)
distance_df = pd.DataFrame(pos, columns=['x', 'y'])
distance_df[to_plot] = df[to_plot]
p = Scatter(distance_df,
x='x',
y='y',
title="MDS: White House Posts",
color=to_plot,
legend="top_right")
show(p)
def generate_scatter(table_data, form_data):
"""Generate scatter plot."""
if form_data['marker'] == '':
form_data['marker'] = None
if form_data['color'] == '':
form_data['color'] = None
plot = Scatter(table_data,
x=form_data['x'],
y=form_data['y'],
title=form_data['x'] + ' vs ' + form_data['y'],
color=form_data['color'],
marker=form_data['marker'],
tools='pan,wheel_zoom,box_zoom,reset,resize,hover,save')
plot.title.text_font_style = "bold"
output_file("output.html")
save(plot)
return build_html()
def plt_scatter1(data, param_dict, mode='object', output_path=None):
#to_date(data,'%Y-%m-01')
tooltips = create_tooltips(param_dict)
title = "{} by {} (dot colors show {})".format(param_dict['y'].upper(),
param_dict['x'].upper(),
param_dict['color'].upper())
try:
color = param_dict['color']
except KeyError:
color = 'red'
# p = figure(tooltips=tooltips)
# p.scatter(data=data, x=param_dict['x'], y=param_dict['y'], color=color, title=title,
# xlabel=param_dict['x'], ylabel=param_dict['y'],
# plot_width=1000, plot_height=600, tooltips = tooltips,
# legend_sort_field = 'color',
# legend_sort_direction = 'ascending')
p = Scatter(data=data,
x=param_dict['x'],
y=param_dict['y'],
color=color,
title=title,
xlabel=param_dict['x'],
ylabel=param_dict['y'],
plot_width=1000,
plot_height=600,
tooltips=tooltips,
legend_sort_field='color',
legend_sort_direction='ascending')
p.legend.background_fill_alpha = 0.8
try:
p.radius = param_dict['size']
except KeyError:
try:
p.marker = param_dict['color']
except KeyError:
pass
if type(data[0][param_dict['x']]) == str:
x_rng = sort_axis(data, param_dict['x'], False, True)
p.x_range = x_rng
if type(data[0][param_dict['y']]) == str:
y_rng = sort_axis(data, param_dict['y'], False, True)
p.y_range = y_rng
return do_output(p, mode, output_path)
def ticker(ticker):
# build line
line = Line(betalyzer.df_betas[ticker], plot_width=1000, plot_height=400)
bokeh_script, bokeh_div = components(line)
# build scatter
scatter = Scatter(betalyzer.df_changes.head(betalyzer.window),
x=betalyzer.market,
y=ticker,
plot_width=550,
plot_height=400)
scatter_script, scatter_div = components(scatter)
# build histogram
df_hist = betalyzer.df_changes[[ticker,
'SPY']].head(500).unstack().reset_index()
df_hist.rename(columns={'level_0': 'ticker', 0: 'change'}, inplace=True)
hist = Histogram(df_hist,
values='change',
color='ticker',
bins=20,
legend='top_right',
plot_width=550,
plot_height=400)
hist_script, hist_div = components(hist)
return render_template(
'ticker.html',
ticker=ticker,
bokeh_script=bokeh_script,
bokeh_div=bokeh_div,
scatter_script=scatter_script,
scatter_div=scatter_div,
hist_script=hist_script,
hist_div=hist_div,
window=betalyzer.window,
dt_ticker=betalyzer.df_tickers.loc[ticker].to_dict())
output_file("lines.html", title="line.py example")
vline = Line(xyvalues, title="Lines VLine", ylabel='measures', width=500, height=300,
tools=TOOLS)
hline = Line(xyvalues, title="Lines HLine", ylabel='measures', width=500, height=300,
tools=TOOLS)
int_vline = Line(xyvalues, title="Lines VLine Interp", ylabel='measures', width=500, height=300,
tools=TOOLS)
int_hline = Line(xyvalues, title="Lines HLine Interp", ylabel='measures', width=500, height=300,
tools=TOOLS)
svalues = {}
# svalues['Business'] = [(i, v) for i, v in zip(index, xyvalues['Business'])]
for k in xyvalues.columns:
svalues[k] = [(i, v) for i, v in zip(index, xyvalues[k])]
# # import pdb; pdb.set_trace()
scatter_point = Scatter(svalues, title="Scatter mouse", ylabel='measures', width=500, height=300,
legend=True,
tools=TOOLS)
scatter = Scatter(svalues, title="Scatter V Line", ylabel='measures', width=500, height=300,
legend=True,
tools=TOOLS)
int_point_line = Line(xyvalues, title="Lines Mouse Interp.", ylabel='measures', width=500, height=300,
tools=TOOLS)
point_line = Line(xyvalues, title="Lines Mouse", ylabel='measures', width=500, height=300,
tools=TOOLS)
hhover = hline.select(dict(type=HoverTool))
hhover.mode = 'hline'
hhover.line_policy = 'next'
from bokeh.charts import Scatter, output_file, show
from bokeh.sampledata.autompg import autompg as df
p = Scatter(df,
x='mpg',
y='hp',
title="HP vs MPG",
xlabel="Miles Per Gallon",
ylabel="Horsepower")
output_file("scatter.html")
show(p)
import seaborn as sns
# In[2]:
# 导入数据
exercise = sns.load_dataset('exercise')
output_notebook()
#output_file('test.html')
# * bokeh.charts
# In[3]:
# 散点图
p = Scatter(data=exercise, x='id', y='pulse', title='exercise dataset')
show(p)
# In[4]:
# 柱状图
p = Bar(data=exercise,
values='pulse',
label='diet',
stack='kind',
title='exercise dataset')
show(p)
# In[5]:
# 盒子图
s2.triangle(x, y1, size=10, color="firebrick", alpha=0.5)
# NEW: create a new plot and share only one range
s3 = figure(width=250, height=250, x_range=s1.x_range, title=None)
s3.square(x, y2, size=10, color="olive", alpha=0.5)
# NEW: put the subplots in a gridplot
p = gridplot([[s1, s2, s3]], toolbar_location=None)
# show the results
show(p)
### Example from the high-level charts tutorial
from bokeh.charts import Scatter, output_file, show
from bokeh.sampledata.autompg import autompg as df
print(df)
p = Scatter(df,
x='mpg',
y='weight',
title="HP vs weight",
color="navy",
xlabel="Miles Per Gallon",
ylabel="weight")
output_file("scatter.html")
show(p)
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()
""" This example uses the Iris data to demonstrate the specification of
combined variables using chart operations.
This specific instance uses a blend, which stacks columns, and renames
the combined column. This can be used where the column itself is a type
of categorical variable. Here, length and width are derived from the
petal and sepal measurements.
"""
from bokeh.charts import Scatter, output_file, show
from bokeh.charts.operations import blend
from bokeh.sampledata.iris import flowers as data
scatter = Scatter(
data,
x=blend('petal_length', 'sepal_length', name='length'),
y=blend('petal_width', 'sepal_width', name='width'),
color='species',
title=
'x=petal_length+sepal_length, y=petal_width+sepal_width, color=species',
legend='top_right')
output_file("iris_blend.html", title="iris_blend.py example")
show(scatter)
value_name='Count', var_name='Degree')
vline = Line(data, y='Count', color='Degree', title="Lines VLine", ylabel='measures',
tools=TOOLS)
hline = Line(data, y='Count', color='Degree', title="Lines HLine",
ylabel='measures', tools=TOOLS)
int_vline = Line(data, y='Count', color='Degree', title="Lines VLine Interp",
ylabel='measures', tools=TOOLS)
int_hline = Line(data, y='Count', color='Degree', title="Lines HLine Interp",
ylabel='measures', tools=TOOLS)
scatter_point = Scatter(data, x='Year', y='Count', color='Degree',
title="Scatter mouse", ylabel='measures', legend=True,
tools=TOOLS)
scatter = Scatter(data, x='Year', y='Count', color='Degree',
title="Scatter V Line", ylabel='measures', legend=True, tools=TOOLS)
int_point_line = Line(data, x='Year', y='Count', color='Degree',
title="Lines Mouse Interp.", ylabel='measures', tools=TOOLS)
point_line = Line(data, x='Year', y='Count', color='Degree',
title="Lines Mouse", ylabel='measures', tools=TOOLS)
hhover = hline.select(HoverTool)
hhover.mode = 'hline'
hhover.line_policy = 'next'
from bokeh.charts import Scatter, output_file, show
from bokeh.sampledata.iris import flowers as data
scatter = Scatter(data,
x='petal_length',
y='petal_width',
color='species',
marker='species',
title='Iris Dataset Color and Marker by Species',
legend=True)
output_file("iris_simple.html", title="iris_simple.py example")
show(scatter)
scatter4 = Scatter(
df, x='mpg', y='hp', color='cyl', marker='origin', title="x='mpg', y='hp', color='cyl', marker='origin'",
xlabel="Miles Per Gallon", ylabel="Horsepower", legend='top_right')
# Example with nested json/dict like data, which has been pre-aggregated and pivoted
df2 = df_from_json(data)
df2 = df2.sort('total', ascending=False)
df2 = df2.head(10)
df2 = pd.melt(df2, id_vars=['abbr', 'name'])
scatter5 = Scatter(
df2, x='value', y='name', color='variable', title="x='value', y='name', color='variable'",
xlabel="Medals", ylabel="Top 10 Countries", legend='bottom_right')
scatter6 = Scatter(flowers, x=blend('petal_length', 'sepal_length', name='length'),
y=blend('petal_width', 'sepal_width', name='width'), color='species',
title='x=petal_length+sepal_length, y=petal_width+sepal_width, color=species',
legend='top_right')
scatter6.title_text_font_size = '10pt'
output_file("scatter_multi.html", title="scatter_multi.py example")
show(vplot(
hplot(scatter0, scatter1),
hplot(scatter2, scatter3),
hplot(scatter4, scatter5),
hplot(scatter6)
))
def scatter_groups(xyvalues, fname, title, xlabel, ylabel):
TOOLS="resize,crosshair,pan,wheel_zoom,box_zoom,reset,previewsave"
scatter = Scatter(xyvalues, filename=fname, title=title,
legend ="top_left", tools=TOOLS,
xlabel=xlabel, ylabel=ylabel)
return scatter
import pandas as pd
from bokeh.charts import Scatter, output_file, show, vplot, hplot, defaults
from bokeh.charts.operations import blend
from bokeh.charts.utils import df_from_json
from bokeh.sampledata.autompg import autompg as df
from bokeh.sampledata.iris import flowers
from bokeh.sampledata.olympics2014 import data
defaults.plot_width = 450
defaults.plot_height = 400
scatter0 = Scatter(df, x='mpg', title="x='mpg'", xlabel="Miles Per Gallon")
scatter1 = Scatter(df,
x='mpg',
y='hp',
title="x='mpg', y='hp'",
xlabel="Miles Per Gallon",
ylabel="Horsepower",
legend='top_right')
scatter2 = Scatter(df,
x='mpg',
y='hp',
color='cyl',
title="x='mpg', y='hp', color='cyl'",
xlabel="Miles Per Gallon",
ylabel="Horsepower",
legend='top_right')
from bokeh.charts import Scatter, output_file, show
from bokeh.sampledata.autompg import autompg as df
p = Scatter(df, x='displ', y='hp', marker='square',
title="HP vs DISPL", legend="top_left",
xlabel="Displacement", ylabel="Horsepower")
output_file("scatter.html")
show(p)
from bokeh.charts import Scatter, output_file, show
from bokeh.sampledata.autompg import autompg as df
p = Scatter(df,
x='mpg',
y='hp',
title="HP vs MPG",
xlabel="Miles Per Gallon",
ylabel="Horsepower")
p.logo = None # remove Bokeh's logo
output_file("scatter.html")
show(p)
"""Uses bokeh for plotting"""
from bokeh.charts import Scatter, output_file, show
import pandas
DF = pandas.DataFrame(columns=['X', 'Y'])
DF['X'] = [1, 2, 3, 4, 5]
DF['Y'] = [5, 6, 4, 5, 3]
# pylint: disable=line-too-long
SC = Scatter(DF,
x='X',
y='Y',
title='Temperature Observations',
xlabel='Day of observations',
ylabel='Temperature')
output_file('../output/Scatter_charts.html')
show(SC)
color='Degree',
title="Lines VLine Interp",
ylabel='measures',
tools=TOOLS)
int_hline = Line(data,
y='Count',
color='Degree',
title="Lines HLine Interp",
ylabel='measures',
tools=TOOLS)
scatter_point = Scatter(data,
x='Year',
y='Count',
color='Degree',
title="Scatter mouse",
ylabel='measures',
legend=True,
tools=TOOLS)
scatter = Scatter(data,
x='Year',
y='Count',
color='Degree',
title="Scatter V Line",
ylabel='measures',
legend=True,
tools=TOOLS)
int_point_line = Line(data,
x='Year',