i am currently working with plotly i have a function called plotChart that takes a dataframe as input and plots a candlestick chart. I am trying to figure out a way to pass a list of dataframes to the function plotChart and use a plotly dropdown menu to show the options on the input list by the stock name. The drop down menu will have the list of dataframe and when an option is clicked on it will update the figure in plotly is there away to do this. below is the code i have to plot a single dataframe
def make_multi_plot(df):
fig = make_subplots(rows=2, cols=2,
shared_xaxes=True,
vertical_spacing=0.03,
subplot_titles=('OHLC', 'Volume Profile'),
row_width=[0.2, 0.7])
for s in df.name.unique():
trace1 = go.Candlestick(
x=df.loc[df.name.isin([s])].time,
open=df.loc[df.name.isin([s])].open,
high=df.loc[df.name.isin([s])].high,
low=df.loc[df.name.isin([s])].low,
close=df.loc[df.name.isin([s])].close,
name = s)
fig.append_trace(trace1,1,1)
fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].BbandsMid, mode='lines',name='MidBollinger'),1,1)
fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].BbandsUpp, mode='lines',name='UpperBollinger'),1,1)
fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].BbandsLow, mode='lines',name='LowerBollinger'),1,1)
fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].vwap, mode='lines',name='VWAP'),1,1)
fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].STDEV_1, mode='lines',name='UPPERVWAP'),1,1)
fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].STDEV_N1, mode='lines',name='LOWERVWAP'),1,1)
fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].KcMid, mode='lines',name='KcMid'),1,1)
fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].KcUpper, mode='lines',name='KcUpper'),1,1)
fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].KcLow, mode='lines',name='KcLow'),1,1)
trace2 = go.Bar(
x=df.loc[df.name.isin([s])].time,
y=df.loc[df.name.isin([s])].volume,
name = s)
fig.append_trace(trace2,2,1)
# fig.update_layout(title_text=s)
graph_cnt=len(fig.data)
tr = 11
symbol_cnt =len(df.name.unique())
for g in range(tr, graph_cnt):
fig.update_traces(visible=False, selector=g)
#print(g)
def create_layout_button(k, symbol):
start, end = tr*k, tr*k+2
visibility = [False]*tr*symbol_cnt
visibility[start:end] = [True,True,True,True,True,True,True,True,True,True,True]
return dict(label = symbol,
method = 'restyle',
args = [{'visible': visibility[:-1],
'title': symbol,
'showlegend': False}])
fig.update(layout_xaxis_rangeslider_visible=False)
fig.update_layout(
updatemenus=[go.layout.Updatemenu(
active = 0,
buttons = [create_layout_button(k, s) for k, s in enumerate(df.name.unique())]
)
])
fig.show()
i am trying to add annotations to the figure it will be different for each chart below is how i had it setup for the single chart df['superTrend'] is a Boolean column
for i in range(df.first_valid_index()+1,len(df.index)):
prev = i - 1
if df['superTrend'][i] != df['superTrend'][prev] and not np.isnan(df['superTrend'][i]) :
#print(i,df['inUptrend'][i])
fig.add_annotation(x=df['time'][i], y=df['open'][i],
text= 'Buy' if df['superTrend'][i] else 'Sell',
showarrow=True,
arrowhead=6,
font=dict(
#family="Courier New, monospace",
size=20,
#color="#ffffff"
),)
I adapted an example from the plotly community to your example and created the code. The point of creation is to create the data for each subplot and then switch between them by means of buttons. The sample data is created using representative companies of US stocks. one issue is that the title is set but not displayed. We are currently investigating this issue.
import yfinance as yf
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
symbols = ['AAPL','GOOG','TSLA']
stocks = pd.DataFrame()
for s in symbols:
data = yf.download(s, start="2021-01-01", end="2021-12-31")
data['mean'] = data['Close'].rolling(20).mean()
data['std'] = data['Close'].rolling(20).std()
data['upperBand'] = data['mean'] + (data['std'] * 2)
data.reset_index(inplace=True)
data['symbol'] = s
stocks = stocks.append(data, ignore_index=True)
def make_multi_plot(df):
fig = make_subplots(rows=2, cols=1,
shared_xaxes=True,
vertical_spacing=0.03,
subplot_titles=('OHLC', 'Volume Profile'),
row_width=[0.2, 0.7])
for s in df.symbol.unique():
trace1 = go.Candlestick(
x=df.loc[df.symbol.isin([s])].Date,
open=df.loc[df.symbol.isin([s])].Open,
high=df.loc[df.symbol.isin([s])].High,
low=df.loc[df.symbol.isin([s])].Low,
close=df.loc[df.symbol.isin([s])].Close,
name=s)
fig.append_trace(trace1,1,1)
trace2 = go.Scatter(
x=df.loc[df.symbol.isin([s])].Date,
y=df.loc[df.symbol.isin([s])].upperBand,
name=s)
fig.append_trace(trace2,1,1)
trace3 = go.Bar(
x=df.loc[df.symbol.isin([s])].Date,
y=df.loc[df.symbol.isin([s])].Volume,
name=s)
fig.append_trace(trace3,2,1)
# fig.update_layout(title_text=s)
# Calculate the total number of graphs
graph_cnt=len(fig.data)
# Number of Symbols
symbol_cnt =len(df.symbol.unique())
# Number of graphs per symbol
tr = 3
# Hide setting for initial display
for g in range(tr, graph_cnt):
fig.update_traces(visible=False, selector=g)
def create_layout_button(k, symbol):
start, end = tr*k, tr*k+2
visibility = [False]*tr*symbol_cnt
# Number of graphs per symbol, so if you add a graph, add True.
visibility[start:end] = [True,True,True]
return dict(label = symbol,
method = 'restyle',
args = [{'visible': visibility[:-1],
'title': symbol,
'showlegend': True}])
fig.update(layout_xaxis_rangeslider_visible=False)
fig.update_layout(
updatemenus=[go.layout.Updatemenu(
active = 0,
buttons = [create_layout_button(k, s) for k, s in enumerate(df.symbol.unique())]
)
])
fig.show()
return fig.layout
make_multi_plot(stocks)
Related
Plotly: How to display and filter a dataframe with multiple dropdowns?
dataset = https://community.tableau.com/s/question/0D54T00000CWeX8SAL/sample-superstore-sales-excelxls
Hey, I am new to plotly as well, Following up on this question. My case is similar but with extra conditions. I need to plot the total sales for every quarter in every region filtered by "year" and "categories". I was able to reproduce the line chart in the article. But failed to reproduce a bar chart. There are 4 quarters in a year and 4 regions. Thus, at least 16 bars must be present at all times.
This is what I am trying to build:
enter image description here
import plotly.graph_objs as go
import pandas as pd
import numpy as np
file = pd.read_excel(r"Sample - Superstore.xlsx")
sales = file[['Sales','Region', 'Order Date','Category', 'State']]
sales["Quarters"] = sales['Order Date'].apply(lambda x: x.quarter)
sales["Years"] = sales['Order Date'].apply(lambda x: x.year)
df = sales.groupby(['Years','Quarters', 'Region', 'Category'], as_index = False).sum()
df_input = df.copy()
years = df['Years'].unique().tolist()
categories = df['Category'].unique().tolist()
regions = df['Region'].unique().tolist()
quarters = df['Quarters'].unique().tolist()
dfs = {}
for year in years:
dfs[year]=pd.pivot_table(df[df['Years']==year],
values='Sales',
index=['Quarters','Region'],
columns=['Category'],
aggfunc=np.sum)
# find row and column unions
common_cols = []
common_rows = []
for df in dfs.keys():
common_cols = sorted(list(set().union(common_cols,list(dfs[df]))))
common_rows = sorted(list(set().union(common_rows,list(dfs[df].index))))
df_common = pd.DataFrame(np.nan, index=common_rows, columns=common_cols)
# reshape each dfs[df] into common dimensions
dfc={}
for df_item in dfs:
#print(dfs[unshaped])
df1 = dfs[df_item].copy()
s=df_common.combine_first(df1)
df_reshaped = df1.reindex_like(s)
dfc[df_item]=df_reshaped
# plotly start
fig = go.Figure()
# for year in all_years:
# df2 = group_sales.loc[group_sales["Years"] == year]
# all_quarters =list(sorted(set(df2["Quarters"].astype(str))))
# all_regions =list(sorted(set(df2["Region"].astype(str))))
# fig.add_trace(go.Bar(x= all_quarters, y=df2.loc[df2["Region"] == all_regions[0]]["Sales"], name=all_regions[0],marker_color='blue', visible=(year== default_year)))
# fig.add_trace(go.Bar(x= all_quarters, y= df2.loc[df2["Region"] == all_regions[1]]["Sales"], name=all_regions[1], marker_color='lightblue', visible=( year == default_year)))
# fig.add_trace(go.Bar(x= all_quarters, y= df2.loc[df2["Region"] == all_regions[2]]["Sales"],name= all_regions[2],marker_color='grey', visible=(year== default_year)))
# fig.add_trace(go.Bar(x= all_quarters, y= df2.loc[df2["Region"] == all_regions[3]]["Sales"],name=all_regions[3], marker_color='red', visible=(year== default_year)))
# year_plot_names.extend([year]*4)
print(common_cols)
for col in common_cols:
# fig.add_trace(go.Bar(x= all_quarters, y=df2.loc[df2["Region"] == all_regions[0]]["Sales"], name=all_regions[0],marker_color='blue', visible=(year== default_year)))
fig.add_trace(go.Bar(x= quarters, name= regions[0],marker_color='blue', visible= True))
fig.add_trace(go.Bar(x= quarters, name= regions[1],marker_color='lightblue', visible= True))
fig.add_trace(go.Bar(x= quarters, name= regions[2],marker_color='grey', visible= True))
fig.add_trace(go.Bar(x= quarters, name= regions[3],marker_color='red', visible= True))
# fig.add_trace(go.Bar(x= regions,marker_color='blue', visible= True))
# fig.add_trace(go.Scatter(x=regions,
# visible=True,
# marker=dict(size=12, line=dict(width=2)),
# marker_symbol = 'diamond',name=col
# )
# )
fig.show()
# menu setup
updatemenu= []
# buttons for menu 1, names
buttons=[]
# create traces for each color:
# build argVals for buttons and create buttons
for df in dfc.keys():
argList = []
for col in dfc[df]:
temp = []
j = 0
# for i in range(0,4):
# temp2 = []
# for i in range(0,4):
# temp2.append(dfc[df][col].values[j])
# j+=1
# temp.append(temp2)
# argList.append(temp)
print(dfc[df][col])
argList.append(dfc[df][col].values)
argVals = [ {'y':argList}]
buttons.append(dict(method='update',
label=df,
visible=True,
args=argVals))
print(buttons)
# buttons for menu 2, colors
b2_labels = common_cols
# matrix to feed all visible arguments for all traces
# so that they can be shown or hidden by choice
b2_show = [list(b) for b in [e==1 for e in np.eye(len(b2_labels))]]
buttons2=[]
buttons2.append({'method': 'update',
'label': 'All',
'args': [{'visible': [True]*len(common_cols)}]})
# create buttons to show or hide
for i in range(0, len(b2_labels)):
buttons2.append(dict(method='update',
label=b2_labels[i],
args=[{'visible':b2_show[i]}]
)
)
# add option for button two to hide all
buttons2.append(dict(method='update',
label='None',
args=[{'visible':[False]*len(common_cols)}]
)
)
# some adjustments to the updatemenus
updatemenu=[]
your_menu=dict()
updatemenu.append(your_menu)
your_menu2=dict()
updatemenu.append(your_menu2)
updatemenu[1]
updatemenu[0]['buttons']=buttons
updatemenu[0]['direction']='down'
updatemenu[0]['showactive']=True
updatemenu[1]['buttons']=buttons2
updatemenu[1]['y']=0.6
fig.update_layout(showlegend=False, updatemenus=updatemenu)
fig.update_layout(yaxis=dict(range=[0,df_input['Sales'].max()+0.4]))
# title
fig.update_layout(
title=dict(
text= "<i>Filtering with multiple dropdown buttons</i>",
font={'size':18},
y=0.9,
x=0.5,
xanchor= 'center',
yanchor= 'top'))
# button annotations
fig.update_layout(
annotations=[
dict(text="<i>Year</i>", x=-0.4, xref="paper", y=1.1, yref="paper",
align="left", showarrow=False, font = dict(size=16, color = 'steelblue')),
dict(text="<i>Category</i>", x=-0.4, xref="paper", y=0.7, yref="paper",
align="left", showarrow=False, font = dict(size=16, color = 'steelblue')
)
])
fig.show()
I tried to do this with plotly buttons however getting two buttons to work together with a statically defined menu is challenging
it becomes very simple if dash is used with plotly as now there is callback capability that makes requirement simple.
import pandas as pd
import itertools
import plotly.graph_objects as go
from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
# Load Data
dfraw = pd.read_excel("Sample - Superstore.xls")
df = (dfraw.loc[:,['Sales','Region', 'Order Date','Category', 'State']]
.assign(Quarters=dfraw["Order Date"].dt.quarter,
Years=dfraw["Order Date"].dt.year)
.groupby(['Years','Quarters', 'Region', 'Category'], as_index=False).sum()
)
# add in totals with category "All"
df = (pd.concat([df, df.groupby(["Years","Quarters","Region"], as_index=False).agg({"Sales":"sum"}).assign(Category="All")])
.set_index(['Years', 'Region', 'Category','Quarters'])
.unstack("Region")
.droplevel(0, axis=1)
)
# colors...
config = {'Central':"blue", 'East':"lightblue", 'South':"grey", 'West':"red"}
fig = go.Figure()
# create a trace for every year, category & region
for y,cat in itertools.product(df.index.get_level_values("Years").unique(),df.index.get_level_values("Category").unique()):
dff = df.loc[(y,cat)]
for region in dff.columns:
fig.add_trace(go.Bar(x=dff.index, y=dff[region], name=region, meta=f"{y}{cat}", marker_color=config[region],
visible=(y==2014 and cat=="All"),
text=dff[region].apply(lambda v: f"{v/10**3:.0f}k"),textposition="outside")
)
# Build App
app = JupyterDash(__name__)
app.layout = html.Div([
dcc.Graph(id='graph'),
html.Label(["Year",dcc.Dropdown(id='year-dropdown', clearable=False,
value='2014', options=[{'label': year, 'value': year}
for year in df.index.get_level_values("Years").unique()])
]),
html.Label([
"Category",dcc.Dropdown(id='cat-dropdown', clearable=False, value='All',
options=[{'label': year, 'value': year}
for year in df.index.get_level_values("Category").unique()])
]),
])
# Define callback to update graph
#app.callback(
Output('graph', 'figure'),
[Input("year-dropdown", "value"),Input("cat-dropdown", "value")]
)
def update_figure(year, cat):
return fig.update_traces(visible=False).update_traces(visible=True, selector={"meta":f"{year}{cat}"}).update_layout(title=f"{year} {cat}")
# Run app and display result inline in the notebook
app.run_server(mode='inline')
I'm trying to plot the intensity of CO2 emissions per country using plotly ,
Most of the countries don't even exceed 10k, but the color range is from 0 to 35k , how do I change that?
Here's my code:
def enable_plotly_in_cell():
import IPython
from plotly.offline import init_notebook_mode
display(IPython.core.display.HTML('''<script src="/static/components/requirejs/require.js"></script>'''))
init_notebook_mode(connected=False)
data = dict(type = 'choropleth',
locations = fmmcgrf['Entity'],
locationmode = 'country names',
z = fmmcgrf['CO2'],
text = fmmcgrf['Entity'],
colorbar = {'title':'CO2'})
layout = dict(title = 'Co2',
geo = dict(showframe = False,
projection = {'type': 'winkel tripel'}))
map = go.Figure(data = [data], layout=layout)
enable_plotly_in_cell()
map.show()
I customized the color bar with the example from the official reference.
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
fig = go.Figure(data=go.Choropleth(
locations = df['CODE'],
z = df['GDP (BILLIONS)'],
text = df['COUNTRY'],
colorscale = 'Blues',
autocolorscale=False,
reversescale=True,
marker_line_color='darkgray',
marker_line_width=0.5,
colorbar_tickprefix = '$',
colorbar_tickvals=[0,10000,15000],# update
colorbar_title = 'GDP<br>Billions US$',
))
fig.update_layout(
title_text='2014 Global GDP',
geo=dict(
showframe=False,
showcoastlines=False,
projection_type='equirectangular'
),
)
fig.show()
Trying to make a choropleth map in plotly using some data I have in a csv file. Have created the following map:
my choromap
This isn't a correct display of the data however. Here is an excerpt of my csv file:
China,2447
...
Trinidad And Tobago,2
Turkey,26
Ukraine,8
United Arab Emirates,97
United States of America,2008
Based on this I'd expected China to appear in a similar colour to that which the US has loaded in, however it looks the same as countries with values of less than 200. Does anyone know what the reason for this is?
Here's my full code for reference:
import pandas as pd
import plotly as py
df = pd.read_csv('app_country_data_minus_uk.csv')
data = [dict(type='choropleth',
locations = df['Country'],
locationmode = 'country names',
z = df['Applications'],
text = df['Country'],
colorbar = {'title':'Apps per country'},
colorscale = 'Jet',
reversescale = False
)]
layout = dict(title='Application Jan-June 2018',
geo = dict(showframe=False,projection={'type':'mercator'}))
choromap = dict(data = data,layout = layout)
red = py.offline.plot(choromap,filename='world.html')
per your comment I would make sure that china is indeed 2447 and not something like 244. I would also follow the plotly documentation although you example code works.
import plotly.plotly as py
import pandas as pd
df = pd.read_csv('app_country_data_minus_uk.csv')
data = [ dict(
type = 'choropleth',
locations = df['Country'],
locationmode = 'country names',
z = df['Applications'],
colorscale = 'Jet',
reversescale = False,
marker = dict(
line = dict (
color = 'rgb(180,180,180)',
width = 0.5
) ),
colorbar = dict(
autotick = False,
tickprefix = '',
title = 'Apps per country'),
) ]
layout = dict(
title = 'app_country_data_minus_uk',
geo = dict(
showframe = True,
showcoastlines = True,
projection = dict(
type = 'Mercator'
)
)
)
fig = dict( data=data, layout=layout )
py.iplot( fig, validate=False, filename='d3-world-map' )
or if you want to plot it offline:
import plotly.plotly as py
import pandas as pd
import plotly
df = pd.read_csv('app_country_data_minus_uk.csv')
data = [ dict(
type = 'choropleth',
locations = df['Country'],
locationmode = 'country names',
z = df['Applications'],
colorscale = 'Jet',
reversescale = False,
marker = dict(
line = dict (
color = 'rgb(180,180,180)',
width = 0.5
) ),
colorbar = dict(
title = 'Apps per country'),
) ]
layout = dict(
title = 'app_country_data_minus_uk',
geo = dict(
showframe = True,
showcoastlines = True,
projection = dict(
type = 'Mercator'
)
)
)
fig = dict( data=data, layout=layout )
plotly.offline.plot(fig,filename='world.html')
If you use iplot you will be able to edit the chart and see the data in plotly to make sure your data looks correct
I have been trying to create a Geoscatter Plot with Plotly where the marker size should indicate the number of customers (row items) in one city (zip_city). I based my code on two templates from the Plotly documentation: United States Bubble Map and the aggregation part Mapping with Aggregates.
I managed to put together a code that does what I want, except for one drawback: when I hover over a bubble, I would like to see the name of the city plus number of customers (the result from the aggregation), so something like Aguadilla: 2. Can you help me on how to do this?
Here is my code (as a beginner with plotly, I am also open to code improvements):
import plotly.offline as pyo
import pandas as pd
df = pd.DataFrame.from_dict({'Customer': [111, 222, 555, 666],
'zip_city': ['Aguadilla', 'Aguadilla', 'Arecibo', 'Wrangell'],
'zip_latitude':[18.498987, 18.498987, 18.449732,56.409507],
'zip_longitude':[-67.13699,-67.13699,-66.69879,-132.33822]})
data = [dict(
type = 'scattergeo',
locationmode = 'USA-states',
lon = df['zip_longitude'],
lat = df['zip_latitude'],
text = df['Customer'],
marker = dict(
size = df['Customer'],
line = dict(width=0.5, color='rgb(40,40,40)'),
sizemode = 'area'
),
transforms = [dict(
type = 'aggregate',
groups = df['zip_city'],
aggregations = [dict(target = df['Customer'], func = 'count', enabled = True)]
)]
)]
layout = dict(title = 'Customers per US City')
fig = dict( data=data, layout=layout )
pyo.plot( fig, validate=False)
Update:
Can I access the result of the transforms argument directly in the data argument to show the number of customers per city?
You can create a list, that will contains what you want and then set text=list in data. Also do not forget specify hoverinfo='text'.
I am updated your code, so try this:
import pandas as pd
import plotly.offline as pyo
df = pd.DataFrame.from_dict({'Customer': [111, 222, 555, 666],
'zip_city': ['Aguadilla', 'Aguadilla', 'Arecibo', 'Wrangell'],
'zip_latitude':[18.498987, 18.498987, 18.449732,56.409507],
'zip_longitude':[-67.13699,-67.13699,-66.69879,-132.33822]})
customer = df['Customer'].tolist()
zipcity = df['zip_city'].tolist()
list = []
for i in range(len(customer)):
k = str(zipcity[i]) + ':' + str(customer[i])
list.append(k)
data = [dict(
type = 'scattergeo',
locationmode = 'USA-states',
lon = df['zip_longitude'],
lat = df['zip_latitude'],
text = list,
hoverinfo = 'text',
marker = dict(
size = df['Customer'],
line = dict(width=0.5, color='rgb(40,40,40)'),
sizemode = 'area'
),
transforms = [dict(
type = 'aggregate',
groups = df['zip_city'],
aggregations = [dict(target = df['Customer'], func = 'count', enabled = True)]
)]
)]
layout = dict(title = 'Customers per US City')
fig = dict(data=data, layout=layout)
pyo.plot(fig, validate=False)
Hi I'm fairly new to Python, Plotly and Jupyter Notebook. I would like to use a slider to select the number of days as the range in a query to which a graph is created from. My only issue is that I want the graph to automatically update on interaction with the slider, without having to re-run the query and graph creation. My code is below:
slider = widgets.IntSlider()
display(slider)
sliderVal = slider.value
df = pd.read_sql(f"""
SELECT CASE WHEN SiteID LIKE 3 THEN 'BLAH'
WHEN SiteID LIKE 4 THEN 'BLAHBLAH'
END AS Website,
COUNT(1) AS Count
FROM viewName
WHERE (TimeStamp > DATEADD(DAY, -{sliderVal}, GETDATE()))
GROUP BY SiteId
ORDER BY Count DESC
""", conn)
data = [go.Bar(x=df.Website, y=df.Count)]
layout = go.Layout(
xaxis=dict(
title='Website'),
yaxis=dict(
title='Exception count'),
title=f'Number of exceptions per user in the last {sliderVal} days')
chart = go.Figure(data=data, layout=layout, )
py.iplot(chart, filename='WebExceptions')
Thanks in advance!
If you do not want to rerun the query, then your data frame df must contain the results for all the values that you want the intslider widget to take, the function linked to the widget will then simply filter the data and redraw the graph with the new filtered data.
Here's an example with some dummy data:
import ipywidgets as widgets
import plotly.offline as py
import plotly.graph_objs as go
import pandas as pd
py.init_notebook_mode(connected = True)
# Dummy data, to be replaced with your query result for the range of sliderVal
df = pd.DataFrame({'Days': [1] * 3 + [2] * 4 + [3] * 5,
'Website': [1,2,3, 4,5,6,7, 8,9,10,11,12],
'Count': [10,5,30, 15,20,25,12, 18,17,30,23,27]})
def update_plot(sliderVal):
filtered_df = df.query('Days== ' + str(sliderVal))
data = [go.Bar(x = filtered_df.Website,
y = filtered_df.Count)]
layout = go.Layout(
xaxis = dict(title = 'Website'),
yaxis = dict(title = 'Exception count'),
title = f'Number of exceptions per user in the last {sliderVal} days')
chart = go.Figure(data = data, layout = layout, )
py.iplot(chart, filename = 'WebExceptions')
# links an IntSlider taking values between 1 and 3 to the update_plot function
widgets.interact(update_plot, sliderVal = (1, 3))
and here is the result with sliderVal = 2: