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Formatting hover text when plotting with hvplot

I am trying to use hvplot.line to plot 2 y variables in a line chart. My goal is to format the hover text to some format I want (say 1 decimal). I used the standard method to format them in bokeh's hovertool and try to pass it with ".opts(tools=)".
But the formatting does not reflect in the plot. I specified the format should '0.0',but the hover text still shows 3 decimal. What did I do wrong?
My code looks like something below:
import pandas as pd
import numpy as np
import hvplot.pandas
hvplot.extension('bokeh')
from numpy import random
from bokeh.models import HoverTool
df=pd.DataFrame({'length':np.linspace(0,4000,6),
'slope':np.linspace(1.7,2.4,6),
'Direction':np.linspace(1.2,-0.5,6),
'clearance':random.rand(6),
'weight':random.rand(6)},)
hover=HoverTool(tooltips=[('clearance','#clearance{0.0}'),('weight','#weight{0.0}')])
df.hvplot.line(x='length',y=['slope','Direction'],invert=True,hover_cols=['clearance','weight']).opts(tools=[hover])
But if I reduce the number of y variable to just 1. It works fine.
Replace the last line of code to be:
df.hvplot.line(x='length',y=['Direction'],invert=True,hover_cols=['clearance','weight']).opts(tools=[hover])

You can pass the tools to the plot call as a keyword argument.
Change your code
# df.hvplot.line(x='length',y=['slope','Direction'], hover_cols=['clearance','weight'], invert=True).opts(tools=[hover])
df.hvplot.line(x='length',y=['slope','Direction'], hover_cols=['clearance','weight'], tools=[hover], invert=True)
and your hovertool with your formatter is applied.
Minimal Example
import hvplot.pandas
import numpy as np
import pandas as pd
from bokeh.models import HoverTool
hvplot.extension('bokeh')
df=pd.DataFrame({
'length':np.linspace(0,4000,6),
'slope':np.linspace(1.7,2.4,6),
'Direction':np.linspace(1.2,-0.5,6),
'clearance':np.random.rand(6),
'weight':np.random.rand(6)}
)
hover=HoverTool(tooltips=[('clearance','#clearance{0.0}'),('weight','#weight{0.0}')])
df.hvplot.line(
x='length',
y=['slope','Direction'],
hover_cols=['clearance','weight'],
tools=[hover],
invert=True
)
Output

Plotly: How to add vertical lines at specified points?

I have a data frame plot of a time series along with a list of numeric values at which I'd like to draw vertical lines. The plot is an interactive one created using the cufflinks package. Here is an example of three time series in 1000 time values, I'd like to draw vertical lines at 500 and 800. My attempt using "axvlinee" is based upon suggestions I've seen for similar posts:
import numpy as np
import pandas as pd
import cufflinks
np.random.seed(123)
X = np.random.randn(1000,3)
df=pd.DataFrame(X, columns=['a','b','c'])
fig=df.iplot(asFigure=True,xTitle='time',yTitle='values',title='Time Series Plot')
fig.axvline([500,800], linewidth=5,color="black", linestyle="--")
fig.show()
The error message states 'Figure' object has no attribute 'axvline'.
I'm not sure whether this message is due to my lack of understanding about basic plots or stems from a limitation of using igraph.

The answer:
To add a line to an existing plotly figure, just use:
fig.add_shape(type='line',...)
The details:
I gather this is the post you've seen since you're mixing in matplotlib. And as it has been stated in the comments, axvline has got nothing to do with plotly. That was only used as an example for how you could have done it using matplotlib. Using plotly, I'd either go for fig.add_shape(go.layout.Shape(type="line"). But before you try it out for yourself, please b aware that cufflinks has been deprecated. I really liked cufflinks, but now there are better options for building both quick and detailed graphs. If you'd like to stick to one-liners similat to iplot, I'd suggest using plotly.express. The only hurdle in your case is changing your dataset from a wide to a long format that is preferred by plotly.express. The snippet below does just that to produce the following plot:
Code:
import numpy as np
import pandas as pd
import plotly.express as px
from plotly.offline import iplot
#
np.random.seed(123)
X = np.random.randn(1000,3)
df=pd.DataFrame(X, columns=['a','b','c'])
df['id'] = df.index
df = pd.melt(df, id_vars='id', value_vars=df.columns[:-1])
# plotly line figure
fig = px.line(df, x='id', y='value', color='variable')
# lines to add, specified by x-position
lines = {'a':500,'c':700,'a':900,'b':950}
# add lines using absolute references
for k in lines.keys():
#print(k)
fig.add_shape(type='line',
yref="y",
xref="x",
x0=lines[k],
y0=df['value'].min()*1.2,
x1=lines[k],
y1=df['value'].max()*1.2,
line=dict(color='black', width=3))
fig.add_annotation(
x=lines[k],
y=1.06,
yref='paper',
showarrow=False,
text=k)
fig.show()

Not sure if this is what you want, adding two scatter seems to work:
np.random.seed(123)
X = np.random.randn(1000,3)
df=pd.DataFrame(X, columns=['a','b','c'])
fig = df.iplot(asFigure=True,xTitle='time',yTitle='values',title='Time Series Plot')
fig.add_scatter(x=[500]*100, y=np.linspace(-4,4,100), name='lower')
fig.add_scatter(x=[800]*100, y=np.linspace(-4,4,100), name='upper')
fig.show()
Output:

How to create a multi-line plot title in bokeh?

How do you create a multiline plot title in bokeh?... same question as https://github.com/bokeh/bokeh/issues/994
Is this resolved yet?
import bokeh.plotting as plt
plt.output_file("test.html")
plt.text(x=[1,2,3], y = [0,0,0], text=['hello\nworld!', 'hello\nworld!', 'hello\nworld!'], angle = 0)
plt.show()
Additionally, can the title text string accept rich text?

In recent versions of Bokeh, labels and text glyphs can accept newlines in the text, and these will be rendered as expected. For multi-line titles, you will have to add explicit Title annotations for each line you want. Here is a complete example:
from bokeh.io import output_file, show
from bokeh.models import Title
from bokeh.plotting import figure
output_file("test.html")
p = figure(x_range=(0, 5))
p.text(x=[1,2,3], y = [0,0,0], text=['hello\nworld!', 'hello\nworld!', 'hello\nworld!'], angle = 0)
p.add_layout(Title(text="Sub-Title", text_font_style="italic"), 'above')
p.add_layout(Title(text="Title", text_font_size="16pt"), 'above')
show(p)
Which produces:
Note that you are limited to the standard "text properties" that Bokeh exposes, since the underlying HTML Canvas does not accept rich text. If you need something like that it might be possible with a custom extension

You can add a simple title to your plot with this:
from bokeh.plotting import figure, show, output_file
output_file("test.html")
p = figure(title="Your title")
p.text(x=[1,2,3], y = [0,0,0], text=['hello\nworld!', 'hello\nworld!', 'hello\nworld!'], angle = 0)
show(p)
Addendum
Here is a working example for plotting a pandas dataframe for you to copy/paste into a jupyter notebook. It's neither elegant nor pythonic. I got it a long time ago from various SO posts. Sorry, that I don't remember which ones anymore, so I can't cite them.
Code
# coding: utf-8
from bokeh.plotting import figure, show
from bokeh.io import output_notebook
import pandas as pd
import numpy as np
# Create some data
np_arr = np.array([[1,1,1], [2,2,2], [3,3,3], [4,4,4]])
pd_df = pd.DataFrame(data=np_arr)
pd_df
# Convert for multi-line plotting
data = [row[1].as_matrix() for row in pd_df.iterrows()]
num_lines = len(pd_df)
cols = [pd_df.columns.values] * num_lines
data
# Init bokeh output for jupyter notebook - Adjust this to your needs
output_notebook()
# Plot
p = figure(plot_width=600, plot_height=300)
p.multi_line(xs=cols, ys=data)
show(p)
Plot

python bokeh plot how to format axis display

the y axis ticks seem to be formatting numbers like 500000000 to 5.000e+8. Is there a way to control the display so that it displays as 500000000?
using python 2.7, bokeh 0.5.2
i m trying out the timeseries example at bokeh tutorials page
The tutorial plots 'Adj Close' against 'Date' but i'm plotting with 'Volume' against 'Date'

You can also use NumeralTickFormatter as used in the toy plot below. The other possible values in place of '00' are listed here.
import pandas as pd
import numpy as np
from bokeh.plotting import figure, output_file, show
from bokeh.models import NumeralTickFormatter
df = pd.DataFrame(np.random.randint(0, 90000000000, (10,1)), columns=['my_int'])
p = figure(plot_width=700, plot_height=280, y_range=[0,100000000000])
output_file("toy_plot_with_commas")
for index, record in df.iterrows():
p.rect([index], [record['my_int']/2], 0.8, [record['my_int']], fill_color="red", line_color="black")
p.yaxis.formatter=NumeralTickFormatter(format="00")
show(p)

You have to add the option p.left[0].formatter.use_scientific = False to your code. In the timeseries tutorial, it'd be:
p1 = figure(title="Stocks")
p1.line(
AAPL['Date'],
AAPL['Adj Close'],
color='#A6CEE3',
legend='AAPL',
)
p1.left[0].formatter.use_scientific = False # <---- This forces showing 500000000 instead of 5.000e+8 as you want
show(VBox(p1, p2))

Six subplots with the same number of xticklabels in matplotlib

I am really struggling with matplotlib, escpecially with the axis settings. My goal is to set up 6 subplots in one figure, which all display different datasets but have the same amount of ticklabels.
The relevant part of my sourcecode looks like:
graph4.py:
# Import Matolotlib Modules #
import matplotlib as mpl
from matplotlib.figure import Figure
from matplotlib.backends.backend_gtkagg import FigureCanvasGTKAgg as FigureCanvas
from matplotlib import ticker
import matplotlib.pyplot as plt
mpl.rcParams['font.sans-serif']='Arial' #set font to arial
# Import GTK Modules #
import gtk
#Import System Modules #
import sys
# Import Numpy Modules #
from numpy import genfromtxt
import numpy
# Import Own Modules #
import mysubplot as mysp
class graph4():
weekdays = ['Montag', 'Dienstag', 'Mittwoch', 'Donnerstag', 'Freitag', 'Samstag']
def __init__(self, graphview):
#create new Figure
self.figure = Figure(figsize=(100,100), dpi=75)
#create six subplots within self.figure
self.subplot = []
for j in range(6):
self.subplot.append(self.figure.add_subplot(321 + j))
self.__conf_subplots__() #configure title, xlabel, ylabel and grid of all subplots
#to make it look better
self.figure.subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.96, wspace=0.2, hspace=0.6)
#Matplotlib <-> GTK
self.canvas = FigureCanvas(self.figure) # a gtk.DrawingArea
self.canvas.set_flags(gtk.HAS_FOCUS|gtk.CAN_FOCUS)
self.canvas.grab_focus()
self.canvas.show()
graphview.pack_start(self.canvas, True, True)
#add labels and grid to all subplots
def __conf_subplots__(self):
index = 0
for i in self.subplot:
mysp.conf_subplot(i, 'Zeit', 'Menge', graph4.weekdays[index], True)
i.plot([], [], 'bo') #empty plot
index +=1
def plot(self, filename_list):
index = 0
for filename in filename_list:
data = genfromtxt(filename, delimiter=',') #load data from filename
if data.size != 0: #only if file isn't empty
if index <= len(self.subplot): #plot every file on a different subplot
mysp.plot(self.subplot[index],data[0:, 1], data[0:, 0])
index +=1
self.canvas.draw()
def clear_plot(self):
#clear axis of all subplots
for i in self.subplot:
i.cla()
self.__conf_subplots__()
mysubplot.py: (helper module)
# Import Matplotlib Modules
from matplotlib.axes import Subplot
import matplotlib.dates as md
import matplotlib.pyplot as plt
# Import Own Modules #
import mytime as myt
# Import Numpy Modules #
import numpy as np
def conf_subplot(subplot, xlabel, ylabel, title, grid):
if(xlabel != None):
subplot.set_xlabel(xlabel)
if(ylabel != None):
subplot.set_ylabel(ylabel)
if(title != None):
subplot.set_title(title)
subplot.grid(grid)
#rotate xaxis labels
plt.setp(subplot.get_xticklabels(), rotation=30, fontsize=12)
#display date on xaxis
subplot.xaxis.set_major_formatter(md.DateFormatter('%H:%M:%S'))
subplot.xaxis_date()
def plot(subplot, x, y):
subplot.plot(x, y, 'bo')
I think the best way to explain what goes wrong is with the use of screenshots. After I start my application, everything looks good:
If I double click a 'Week'-entry on the left, the method clear_plot() in graph4.py is called to reset all subplots. Then a list of filenames is passed to the method plot() in graph4.py. The method plot() opens each file and plots each dataset on a different subplot. So after I double click a entry, it looks like:
As you can see, each subplot has a different number of xtick labels, which looks pretty ugly to me. Therefore, I am looking for a solution to improve this. My first approach was to set the ticklabels manually with xaxis.set_ticklabels(), so that each subplot has the same number of ticklabels. However, as strange as it sounds, this only works on some datasets and I really don't know why. On some datasets, everything works fine and on other datasets, matplotlib is basically doing what it wants and displays xaxis labels that I didn't specify. I also tried FixedLocator(), but I got the same result. On some datasets it is working and on others, matplotlib is using a different number of xtick labels.
What am I doing wrong?
Edit:
As #sgpc suggested, I tried to use pyplot. My sourcecode now looks like this:
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.backends.backend_gtkagg import FigureCanvasGTKAgg as FigureCanvas
import matplotlib.dates as md
mpl.rcParams['font.sans-serif']='Arial' #set font to arial
import gtk
import sys
# Import Numpy Modules #
from numpy import genfromtxt
import numpy
# Import Own Modules #
import mysubplot as mysp
class graph2():
weekdays = ['Montag', 'Dienstag', 'Mittwoch', 'Donnerstag', 'Freitag', 'Samstag']
def __init__(self, graphview):
self.figure, temp = plt.subplots(ncols=2, nrows=3, sharex = True)
#2d array -> list
self.axes = [ y for x in temp for y in x]
#axis: date
for i in self.axes:
i.xaxis.set_major_formatter(md.DateFormatter('%H:%M:%S'))
i.xaxis_date()
#make space and rotate xtick labels
self.figure.autofmt_xdate()
#Matplotlib <-> GTK
self.canvas = FigureCanvas(self.figure) # a gtk.DrawingArea
self.canvas.set_flags(gtk.HAS_FOCUS|gtk.CAN_FOCUS)
self.canvas.grab_focus()
self.canvas.show()
graphview.pack_start(self.canvas, True, True)
def plot(self, filename_list):
index = 0
for filename in filename_list:
data = genfromtxt(filename, delimiter=',') #get dataset
if data.size != 0: #only if file isn't empty
if index < len(self.axes): #print each dataset on a different subplot
self.axes[index].plot(data[0:, 1], data[0:, 0], 'bo')
index +=1
self.canvas.draw()
#not yet implemented
def clear_plot(self):
pass
If I plot some datasets, I get the following output:
http://i.imgur.com/3ngYTNr.png (sorry, I still don't have enough reputation to embedd pictures)
Moreover, I am not really sure if sharing the x-axis is a really good idea, because it is possible that the x-values differ in every subplot (for example: in the first subplot, the x-values ranges from 8:00am - 11:00am and in the second subplot the x-values ranges from 7:00pm - 9:00pm).
If I get rid of sharex = True, I get the following output:
http://i.imgur.com/rxHeSyJ.png (sorry, I still don't have enough reputation to embedd pictures)
As you can see, the output now looks better. However now, the labels on the x-axes are not updated. I assume that is because the last suplots are empty.
My next attempt was to use an axis for each subplot. Therefore, I made this changes:
for i in self.axes:
plt.setp(i.get_xticklabels(), visible=True, rotation = 30) #<-- I added this line...
i.xaxis.set_major_formatter(md.DateFormatter('%H:%M:%S'))
i.xaxis_date()
#self.figure.autofmt_xdate() #<--changed this line
self.figure.subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.96, wspace=0.2, hspace=0.6) #<-- and added this line
Now I get the following output:
i.imgur.com/TmA1goE.png (sorry, I still don't have enough reputation to embedd pictures)
So with this attempt, I am basically struggling with the same problem as with Figure() and add_subplot().
I really don't know, what else I could try to make it work...

I would strongly recommend you to use pyplot.subplots() with sharex=True:
fig, axes = subplots(ncols=2, nrows=3, sharex= True)
Then you access each axes using:
ax = axes[i,j]
And you can plot doing:
ax.plot(...)
To control the number of ticks for each AxesSubplot you can use:
ax.locator_params(axis='x', nbins=6)
OBS: axis can be 'x', 'y' or 'both'