Bokeh – ColumnDataSource not updating whiskered-plot - python-3.x

I’m having issues with the following code (I’ve cut out large pieces but I can add them back in – these seemed like the important parts). In my main code, I set up a plot (“sectionizePlot”) which is a simple variation on another whiskered-plot
I’m looking to update them on the fly. In the same script, I’m using a heatmap (“ModifiedGenericHeatMap”) which updates fine.
Any ideas how I might update my whiskered-plot? Updating the ColumnDataSource doesn’t seem to work (which makes sense). I’m guessing that I am running into issues with adding each circle/point individually onto the plot.
One idea would be to clear the plot each time and manually add the points onto the plot, but it would need to be cleared each time, which I’m unsure of how to do.
Any help would be appreciated. I’m just a lowly Scientist trying to utilize Bokeh in Pharma research.
def ModifiedgenericHeatMap(source, maxPct):
colors = ["#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2", "#dfccce", "#ddb7b1", "#cc7878", "#933b41", "#550b1d"]
#mapper = LinearColorMapper(palette=colors, low=0, high=data['count'].max())
mapper = LinearColorMapper(palette=colors, low=0, high=maxPct)
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
globalDist = figure(title="derp",
x_range=cols, y_range=list(reversed(rows)),
x_axis_location="above", plot_width=1000, plot_height=400,
tools=TOOLS, toolbar_location='below')
globalDist.grid.grid_line_color = None
globalDist.axis.axis_line_color = None
globalDist.axis.major_tick_line_color = None
globalDist.axis.major_label_text_font_size = "5pt"
globalDist.axis.major_label_standoff = 0
globalDist.xaxis.major_label_orientation = pi / 3
globalDist.rect(x="cols", y="rows", width=1, height=1,
source=source,
fill_color={'field': 'count', 'transform': mapper},
line_color=None)
color_bar = ColorBar(color_mapper=mapper, major_label_text_font_size="5pt",
ticker=BasicTicker(desired_num_ticks=len(colors)),
# fix this via using a formatter with accounts for
formatter=PrintfTickFormatter(format="%d%%"),
label_standoff=6, border_line_color=None, location=(0, 0))
text_props = {"source": source, "text_align": "left", "text_baseline": "middle"}
x = dodge("cols", -0.4, range=globalDist.x_range)
r = globalDist.text(x=x, y=dodge("rows", 0.3, range=globalDist.y_range), text="count", **text_props)
r.glyph.text_font_size = "8pt"
globalDist.add_layout(color_bar, 'right')
globalDist.select_one(HoverTool).tooltips = [
('Well:', '#rows #cols'),
('Count:', '#count'),
]
return globalDist
def sectionizePlot(source, source_error, type, base):
print("sectionize plot created with typ: " + type)
colors = []
for x in range(0, len(base)):
colors.append(getRandomColor())
title = type + "-wise Intensity Distribution"
p = figure(plot_width=600, plot_height=300, title=title)
p.add_layout(
Whisker(source=source_error, base="base", upper="upper", lower="lower"))
for i, sec in enumerate(source.data['base']):
p.circle(x=source_error.data["base"][i], y=sec, color=colors[i])
p.xaxis.axis_label = type
p.yaxis.axis_label = "Intensity"
if (type.split()[-1] == "Row"):
print("hit a row")
conv = dict(enumerate(list("nABCDEFGHIJKLMNOP")))
conv.pop(0)
p.xaxis.major_label_overrides = conv
p.xaxis.ticker = SingleIntervalTicker(interval=1)
return p
famData = dict()
e1FractSource = ColumnDataSource(dict(count=[], cols=[], rows=[], index=[]))
e1Fract = ModifiedgenericHeatMap(e1FractSource, 100)
rowSectTotSource = ColumnDataSource(data=dict(base=[]))
rowSectTotSource_error = ColumnDataSource(data=dict(base=[], lower=[], upper=[]))
rowSectPlot_tot = sectionizePlot(rowSectTotSource,rowSectTotSource_error, "eSum Row", rowBase)
def update(selected=None):
global famData
famData = getFAMData(file_source_dt1, True)
global e1Stack
e1Fract = (famData['e1Sub'] / famData['eSum']) * 100
e1Stack = e1Fract.stack(dropna=False).reset_index()
e1Stack.columns = ["rows", "cols", "count"]
e1Stack['count'] = e1Stack['count'].apply(lambda x: round(x, 1))
e1FractSource.data = dict(cols=e1Stack["cols"], count=(e1Stack["count"]),
rows=e1Stack["rows"], index=e1Stack.index.values, codon=wells, )
rowData, colData = sectionize(famData['eSum'], rows, cols)
rowData_lower, rowData_upper = getLowerUpper(rowData)
rowBase = list(range(1, 17))
rowSectTotSource_error.data = dict(base=rowBase, lower=rowData_lower, upper=rowData_upper, )
rowSectTotSource.data = dict(base=rowData)
rowSectPlot_tot.title.text = "plot changed in update"
layout = column(e1FractSource, rowSectPlot_tot)
update()
curdoc().add_root(layout)
curdoc().title = "Specs"
print("ok")

Related

how to grey out other lines using Mouse hover?

I'm trying to grey out other lines when i hover over one of them so far i have this:
from sklearn import datasets
data_wine = datasets.load_wine (as_frame = True).frame
new_data = data_wine.drop (['proline', 'magnesium'], axis = 1)
new_data = new_data.reset_index().melt(id_vars = ['index', 'target'])
hover = alt.selection(
type="single", on="mouseover", fields=["variable"], nearest=True
)
lineplot = alt.Chart(new_data).mark_line().encode(
alt.X("variable:N"),
alt.Y("value:Q"),
alt.Color ('target:N'),
alt.Detail ('index:N'),
).properties(width = 1000)
# nearest point
point = lineplot.mark_circle().encode(
opacity=alt.value(0)
).add_selection(hover)
# highlight
singleline = lineplot.mark_line().encode(
size=alt.condition(~hover, alt.value(0.5), alt.value(3))
)
point+singleline
it looks like this and while hovering the mouse the size changes and i couldn't replace size with Color:
how can i achieve this?
new_data = data_wine.drop (['proline', 'magnesium'], axis = 1)
new_data = new_data.reset_index().melt(id_vars = ['index', 'target'])
highlight = alt.selection(type='single', on='mouseover', fields=['target'], nearest=False, bind='legend')
selection = alt.selection_multi(fields=['target'], bind='legend', on='mouseover')
lineplot=alt.Chart(new_data).mark_line().encode(
alt.X("variable:N"),
alt.Y("value:Q"),
alt.Color ('target:N'),
alt.Detail ('index:N'),
).properties(width = 1000)
# nearest point
point = lineplot.mark_circle().encode(
opacity=alt.value(0)
).add_selection(highlight)
#highlight
singleline = lineplot.mark_line().encode(
opacity=alt.condition(selection, alt.value(0.7), alt.value(0.03))
#size=alt.condition(~highlight, alt.value(1), alt.value(3))
).add_selection(selection)
point + singleline

Problem with adding smiles on photos with convolutional autoencoder

I have a dataset with images and another dataset as it's description:
There are a lot of pictures: people with and without sunglasses, smiles and other attributes. What I want to do is be able to add smiles to photos where people are not smiling.
I've started like this:
smile_ids = attrs['Smiling'].sort_values(ascending=False).iloc[100:125].index.values
smile_data = data[smile_ids]
no_smile_ids = attrs['Smiling'].sort_values(ascending=True).head(5).index.values
no_smile_data = data[no_smile_ids]
eyeglasses_ids = attrs['Eyeglasses'].sort_values(ascending=False).head(25).index.values
eyeglasses_data = data[eyeglasses_ids]
sunglasses_ids = attrs['Sunglasses'].sort_values(ascending=False).head(5).index.values
sunglasses_data = data[sunglasses_ids]
When I print them their are fine:
plot_gallery(smile_data, IMAGE_H, IMAGE_W, n_row=5, n_col=5, with_title=True, titles=smile_ids)
Plot gallery looks like this:
def plot_gallery(images, h, w, n_row=3, n_col=6, with_title=False, titles=[]):
plt.figure(figsize=(1.5 * n_col, 1.7 * n_row))
plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)
for i in range(n_row * n_col):
plt.subplot(n_row, n_col, i + 1)
try:
plt.imshow(images[i].reshape((h, w, 3)), cmap=plt.cm.gray, vmin=-1, vmax=1, interpolation='nearest')
if with_title:
plt.title(titles[i])
plt.xticks(())
plt.yticks(())
except:
pass
Then I do:
def to_latent(pic):
with torch.no_grad():
inputs = torch.FloatTensor(pic.reshape(-1, 45*45*3))
inputs = inputs.to('cpu')
autoencoder.eval()
output = autoencoder.encode(inputs)
return output
def from_latent(vec):
with torch.no_grad():
inputs = vec.to('cpu')
autoencoder.eval()
output = autoencoder.decode(inputs)
return output
After that:
smile_latent = to_latent(smile_data).mean(axis=0)
no_smile_latent = to_latent(no_smile_data).mean(axis=0)
sunglasses_latent = to_latent(sunglasses_data).mean(axis=0)
smile_vec = smile_latent-no_smile_latent
sunglasses_vec = sunglasses_latent - smile_latent
And finally:
def add_smile(ids):
for id in ids:
pic = data[id:id+1]
latent_vec = to_latent(pic)
latent_vec[0] += smile_vec
pic_output = from_latent(latent_vec)
pic_output = pic_output.view(-1,45,45,3).cpu()
plot_gallery([pic,pic_output], IMAGE_H, IMAGE_W, n_row=1, n_col=2)
def add_sunglasses(ids):
for id in ids:
pic = data[id:id+1]
latent_vec = to_latent(pic)
latent_vec[0] += sunglasses_vec
pic_output = from_latent(latent_vec)
pic_output = pic_output.view(-1,45,45,3).cpu()
plot_gallery([pic,pic_output], IMAGE_H, IMAGE_W, n_row=1, n_col=2)
But when I execute this line I don't get any faces:
add_smile(no_smile_ids)
The output:
Could someone please explain where is my mistake or why it can happen? Thanks for any help.
Added: checking the shape of pic_output:
Wild guess, but it seems you are broadcasting your images instead of permuting the axes. The former will have the undesired effect of mixing information across the batches/channels.
pic_output = pic_output.view(-1, 45, 45, 3).cpu()
should be replaced with
pic_output = pic_output.permute(0, 2, 3, 1).cpu()
Assuming tensor pic_output is already shaped like (-1, 3, 45, 45).

How to update the Figure attributes (x_range) after the Figure is already created using bokeh 1.4, programatticall?

I am trying to create a Bar Chart here, I already have figure object, but want to assign x_range later instead of inside figure object.
I have tried various techniques shown at the end, I am using Bokeh 1.4
def generate_bar_chart(
self, lst_category,lst_frequency, lst_colors, str_xlabel, str_ylabel, str_plot_title,
plot_height=700, plot_width=800, width_bar=0.6):
try:
source = ColumnDataSource(
dict(
x=lst_category,
values=lst_frequency,
color=lst_colors[:len(lst_category)]
)
)
hover_tool = HoverTool(
tooltips=[("Prod Order", "#x"), ("Frequency", "#values")]
)
p = figure(
x_range=lst_category, # I want to set it programatically afterwards, not here
title=str_plot_title,
plot_height=plot_height,
plot_width=plot_width,
background_fill_color="#f9f9f9",
)
p.add_tools(hover_tool)
p.vbar(
x="x",
top="values",
width=width_bar,
source=source,
line_color="#020B13",
)
# p.xgrid.grid_line_color = None
p.y_range.start = 0
p.x_range.range_padding = 0.2
p.xaxis.axis_label = str_xlabel
p.yaxis.axis_label = str_ylabel
p.xaxis.major_label_orientation = 1.1
p.outline_line_color = "#020B13"
p.xaxis.major_label_text_font_size = "11pt"
p.yaxis.major_label_text_font_size = "11pt"
p.yaxis.axis_label_text_font_size = "11pt"
p.xaxis.axis_label_text_font_size = "11pt"
p.yaxis.axis_label_text_font_style = "bold"
p.xaxis.axis_label_text_font_style = "bold"
p.title.text_font_size = "20pt"
show(p)
return True
except Exception as e:
if hasattr(e, "message"):
print(e.message)
else:
print(e)
# I want to do like this
p.x_range = lst_category
# Tried with:
## Not working
p.x_range.factors = lst_category
#
p.x_range=FactorRange(factors=lst_category)
Without a complete minimal reproducer, the best that can be offered is a demonstration of it working on an unrelated, but complete, toy example, which will hopefully be useful as a reference:
from bokeh.io import show
from bokeh.models import ColumnDataSource
from bokeh.models import FactorRange
from bokeh.plotting import figure
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
counts = [5, 3, 4, 2, 4, 6]
source = ColumnDataSource(data=dict(fruits=fruits, counts=counts))
p = figure(plot_height=350, toolbar_location=None, x_range=FactorRange())
p.vbar(x='fruits', top='counts', width=0.9, source=source)
p.xgrid.grid_line_color = None
p.x_range.factors = fruits
p.y_range.start = 0
p.y_range.end = 9
show(p)

How to fix 'Dropdown Menu Read' Error in Plotly Dash

I have tried to re-create the following example Towards Data Science Example shown on the web
I have written the following code which I modified to this:
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import pandas as pd
import plotly.graph_objs as go
# Step 1. Launch the application
app = dash.Dash()
# Step 2. Import the dataset
filepath = 'https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv'
st = pd.read_csv(filepath)
# range slider options
st['Date'] = pd.to_datetime(st.Date)
dates = ['2015-02-17', '2015-05-17', '2015-08-17', '2015-11-17',
'2016-02-17', '2016-05-17', '2016-08-17', '2016-11-17', '2017-02-17']
features = st.columns[1:-1]
opts = [{'label' : i, 'value' : i} for i in features]
# Step 3. Create a plotly figure
trace_1 = go.Scatter(x = st.Date, y = st['AAPL.High'],
name = 'AAPL HIGH',
line = dict(width = 2,
color = 'rgb(229, 151, 50)'))
layout = go.Layout(title = 'Time Series Plot',
hovermode = 'closest')
fig = go.Figure(data = [trace_1], layout = layout)
# Step 4. Create a Dash layout
app.layout = html.Div([
# a header and a paragraph
html.Div([
html.H1("This is my first dashboard"),
html.P("Dash is so interesting!!")
],
style = {'padding' : '50px' ,
'backgroundColor' : '#3aaab2'}),
# adding a plot
dcc.Graph(id = 'plot', figure = fig),
# dropdown
html.P([
html.Label("Choose a feature"),
dcc.Dropdown(
id='opt',
options=opts,
value=features[0],
multi=True
),
# range slider
html.P([
html.Label("Time Period"),
dcc.RangeSlider(id = 'slider',
marks = {i : dates[i] for i in range(0, 9)},
min = 0,
max = 8,
value = [1, 7])
], style = {'width' : '80%',
'fontSize' : '20px',
'padding-left' : '100px',
'display': 'inline-block'})
])
])
# Step 5. Add callback functions
#app.callback(Output('plot', 'figure'),
[Input('opt', 'value'),
Input('slider', 'value')])
def update_figure(input1, input2):
# filtering the data
st2 = st[(st.Date > dates[input2[0]]) & (st.Date < dates[input2[1]])]
# updating the plot
trace_1 = go.Scatter(x = st2.Date, y = st2['AAPL.High'],
name = 'AAPL HIGH',
line = dict(width = 2,
color = 'rgb(229, 151, 50)'))
trace_2 = go.Scatter(x = st2.Date, y = st2[input1],
name = str(input1),
line = dict(width = 2,
color = 'rgb(106, 181, 135)'))
fig = go.Figure(data = [trace_1, trace_2], layout = layout)
return fig
# Step 6. Add the server clause
if __name__ == '__main__':
app.run_server(debug = True)
When I change the feature input, it does not update the plot correctly and does not show the selected features in the plot.
Either there is something wrong with the callback function or the initialization of the graph with the second trace. But I cant figure out where the issue is.
As you are only providing two scatter traces within your callback. From both, one is static for 'AAPL.High'. So you need to limit the dropdown values to Multi=False.
Valid plots are only generated for choosing options like 'AAPL.LOW' and others like dic won't display a second trace. The callback wouldn't terminate if you would keepmulti=True the callback would stil work, if always only one option is selected. The moment you select two or more options the script will fail as it would try to find faulty data for the data return block here:
trace_2 = go.Scatter(x = st2.Date, y = st2[**MULTIINPUT**],
name = str(input1),
line = dict(width = 2,
color = 'rgb(106, 181, 135)'))
Only one column id is allowed to be passed at MULTIINPUT. If you want to introduce more traces please use a for loop.
Change the code to the following:
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import pandas as pd
import plotly.graph_objs as go
# Step 1. Launch the application
app = dash.Dash()
# Step 2. Import the dataset
filepath = 'https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv'
st = pd.read_csv(filepath)
# range slider options
st['Date'] = pd.to_datetime(st.Date)
dates = ['2015-02-17', '2015-05-17', '2015-08-17', '2015-11-17',
'2016-02-17', '2016-05-17', '2016-08-17', '2016-11-17', '2017-02-17']
features = st.columns
opts = [{'label' : i, 'value' : i} for i in features]
# Step 3. Create a plotly figure
trace_1 = go.Scatter(x = st.Date, y = st['AAPL.High'],
name = 'AAPL HIGH',
line = dict(width = 2,
color = 'rgb(229, 151, 50)'))
layout = go.Layout(title = 'Time Series Plot',
hovermode = 'closest')
fig = go.Figure(data = [trace_1], layout = layout)
# Step 4. Create a Dash layout
app.layout = html.Div([
# a header and a paragraph
html.Div([
html.H1("This is a Test Dashboard"),
html.P("Dash is great!!")
],
style = {'padding' : '50px' ,
'backgroundColor' : '#3aaab2'}),
# adding a plot
dcc.Graph(id = 'plot', figure = fig),
# dropdown
html.P([
html.Label("Choose a feature"),
dcc.Dropdown(
id='opt',
options=opts,
value=features[0],
multi=False
),
# range slider
html.P([
html.Label("Time Period"),
dcc.RangeSlider(id = 'slider',
marks = {i : dates[i] for i in range(0, 9)},
min = 0,
max = 8,
value = [1, 7])
], style = {'width' : '80%',
'fontSize' : '20px',
'padding-left' : '100px',
'display': 'inline-block'})
])
])
# Step 5. Add callback functions
#app.callback(Output('plot', 'figure'),
[Input('opt', 'value'),
Input('slider', 'value')])
def update_figure(input1, input2):
# filtering the data
st2 = st#[(st.Date > dates[input2[0]]) & (st.Date < dates[input2[1]])]
# updating the plot
trace_1 = go.Scatter(x = st2.Date, y = st2['AAPL.High'],
name = 'AAPL HIGH',
line = dict(width = 2,
color = 'rgb(229, 151, 50)'))
trace_2 = go.Scatter(x = st2.Date, y = st2[input1],
name = str(input1),
line = dict(width = 2,
color = 'rgb(106, 181, 135)'))
fig = go.Figure(data = [trace_1, trace_2], layout = layout)
return fig
# Step 6. Add the server clause
if __name__ == '__main__':
app.run_server(debug = True)
I hope this cleared things up and solved your issues. :)

How can I use the plotly dropdown menu feature to update the z value in my choropleth map?

I just want to create a menu on the plot where I'm able to change the z-value in data only. I tried looking at other examples on here: https://plot.ly/python/dropdowns/#restyle-dropdown but it was hard since the examples were not exactly similar to my plot.
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
data = [go.Choropleth(
locations = df['CODE'],
z = df['GDP (BILLIONS)'],
text = df['COUNTRY'],
colorscale = [
[0, "rgb(5, 10, 172)"],
[0.35, "rgb(40, 60, 190)"],
[0.5, "rgb(70, 100, 245)"],
[0.6, "rgb(90, 120, 245)"],
[0.7, "rgb(106, 137, 247)"],
[1, "rgb(220, 220, 220)"]
],
autocolorscale = False,
reversescale = True,
marker = go.choropleth.Marker(
line = go.choropleth.marker.Line(
color = 'rgb(180,180,180)',
width = 0.5
)),
colorbar = go.choropleth.ColorBar(
tickprefix = '$',
title = 'GDP<br>Billions US$'),
)]
layout = go.Layout(
title = go.layout.Title(
text = '2014 Global GDP'
),
geo = go.layout.Geo(
showframe = False,
showcoastlines = False,
projection = go.layout.geo.Projection(
type = 'equirectangular'
)
),
annotations = [go.layout.Annotation(
x = 0.55,
y = 0.1,
xref = 'paper',
yref = 'paper',
text = 'Source: <a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">\
CIA World Factbook</a>',
showarrow = False
)]
)
fig = go.Figure(data = data, layout = layout)
py.iplot(fig, filename = 'd3-world-map')
It's been a while since this was asked, but I figured it was still worth answering. I can't speak to how this might have changed since it was asked in 2019, but this works today.
First, I'll provide the code I used to create the new z values and the dropdown menu, then I'll provide all of the code I used to create these graphs in one chunk (easier to cut and paste...and all that).
This is the data I used for the alternate data in the z field.
import plotly.graph_objects as go
import pandas as pd
import random
z2 = df['GDP (BILLIONS)'] * .667 + 12
random.seed(21)
random.shuffle(z2)
df['z2'] = z2 # example as another column in df
print(df.head()) # validate as expected
z3 = df['GDP (BILLIONS)'] * .2 + 1000
random.seed(231)
random.shuffle(z3) # example as a series outside of df
z4 = df['GDP (BILLIONS)']**(1/3) * df['GDP (BILLIONS)']**(1/2)
random.seed(23)
random.shuffle(z4)
z4 = z4.tolist() # example as a basic Python list
To add buttons to change z, you'll add updatemenus to your layout. Each dict() is a separate dropdown option. At a minimum, each button requires a method, a label, and args. These represent what is changing (method for data, layout, or both), what it's called in the dropdown (label), and the new information (the new z in this example).
args for changes to data (where the method is either restyle or update) can also include the trace the change applies to. So if you had a bar chart and a line graph together, you may have a button that only changes the bar graph.
Using the same structure you have:
updatemenus = [go.layout.Updatemenu(
x = 1, xanchor = 'right', y = 1.15, type = "dropdown",
pad = {'t': 5, 'r': 20, 'b': 5, 'l': 30}, # around all buttons (not indiv buttons)
buttons = list([
dict(
args = [{'z': [df['GDP (BILLIONS)']]}], # original data; nest data in []
label = 'Return to the Original z',
method = 'restyle' # restyle is for trace updates
),
dict(
args = [{'z': [df['z2']]}], # nest data in []
label = 'A different z',
method = 'restyle'
),
dict(
args = [{'z': [z3]}], # nest data in []
label = 'How about this z?',
method = 'restyle'
),
dict(
args = [{'z': [z4]}], # nest data in []
label = 'Last option for z',
method = 'restyle'
)])
)]
All code used to create this graph in one chunk (includes code shown above).
import plotly.graph_objs as go
import pandas as pd
import ssl
import random
# to collect data without an error
ssl._create_default_https_context = ssl._create_unverified_context
# data used in plot
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
# z values used in buttons
z2 = df['GDP (BILLIONS)'] * .667 + 12
random.seed(21)
random.shuffle(z2)
df['z2'] = z2 # example as another column in the data frame
print(df.head()) # validate as expected
z3 = df['GDP (BILLIONS)'] * .2 + 1000
random.seed(231)
random.shuffle(z3) # example as a series outside of the data frame
z4 = df['GDP (BILLIONS)']**(1/3) * df['GDP (BILLIONS)']**(1/2)
random.seed(23)
random.shuffle(z4)
z4 = z4.tolist() # example as a basic Python list
data = [go.Choropleth(
locations = df['CODE'], z = df['GDP (BILLIONS)'], text = df['COUNTRY'],
colorscale = [
[0, "rgb(5, 10, 172)"],
[0.35, "rgb(40, 60, 190)"],
[0.5, "rgb(70, 100, 245)"],
[0.6, "rgb(90, 120, 245)"],
[0.7, "rgb(106, 137, 247)"],
[1, "rgb(220, 220, 220)"]],
reversescale = True,
marker = go.choropleth.Marker(
line = go.choropleth.marker.Line(
color = 'rgb(180,180,180)', width = 0.5)),
colorbar = go.choropleth.ColorBar(
tickprefix = '$',
title = 'GDP<br>Billions US$',
len = .6) # I added this for aesthetics
)]
layout = go.Layout(
title = go.layout.Title(text = '2014 Global GDP'),
geo = go.layout.Geo(
showframe = False, showcoastlines = False,
projection = go.layout.geo.Projection(
type = 'equirectangular')
),
annotations = [go.layout.Annotation(
x = 0.55, y = 0.1, xref = 'paper', yref = 'paper',
text = 'Source: <a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">\
CIA World Factbook</a>',
showarrow = False
)],
updatemenus = [go.layout.Updatemenu(
x = 1, xanchor = 'right', y = 1.15, type = "dropdown",
pad = {'t': 5, 'r': 20, 'b': 5, 'l': 30},
buttons = list([
dict(
args = [{'z': [df['GDP (BILLIONS)']]}], # original data; nest data in []
label = 'Return to the Original z',
method = 'restyle' # restyle is for trace updates only
),
dict(
args = [{'z': [df['z2']]}], # nest data in []
label = 'A different z',
method = 'restyle'
),
dict(
args = [{'z': [z3]}], # nest data in []
label = 'How about this z?',
method = 'restyle'
),
dict(
args = [{'z': [z4]}], # nest data in []
label = 'Last option for z',
method = 'restyle'
)])
)]
)
fig = go.Figure(data = data, layout = layout)
fig.show()

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