I am not able to display graph using python datashader

I downloaded and installed datasheder using the below steps:

git clone https://github.com/bokeh/datashader.git  
cd datashader  
conda install -c bokeh --file requirements.txt  
python setup.py install

After that, I have run the code using terminal like `python data.py, but no graph is displayed; nothin is being displayed.

I am not sure if I've follwed the right steps here, can somebody help me display the graphs? Here is my code:

import pandas as pd
import numpy as np
import xarray as xr
import datashader as ds
import datashader.glyphs
import datashader.transfer_functions as tf
from collections import OrderedDict

np.random.seed(1)
num=10000

dists = {cat: pd.DataFrame(dict(x=np.random.normal(x,s,num),
                            y=np.random.normal(y,s,num),
                            val=val,cat=cat))
     for x,y,s,val,cat in 
     [(2,2,0.01,10,"d1"), (2,-2,0.1,20,"d2"), (-2,-2,0.5,30,"d3"), (-2,2,1.0,40,"d4"), (0,0,3,50,"d5")]}

df = pd.concat(dists,ignore_index=True)
df["cat"]=df["cat"].astype("category")
df.tail()

tf.shade(ds.Canvas().points(df,'x','y'))
glyph = ds.glyphs.Point('x', 'y')
canvas = ds.Canvas(plot_width=200, plot_height=200, x_range=(-8,8)y_range=(-8,8))
from datashader import reductions
reduction = reductions.count()

from datashader.core import bypixel
agg = bypixel(df, canvas, glyph, reduction)
agg
canvas.points(df, 'x', 'y', agg=reductions.count())
tf.shade(canvas.points(df,'x','y',agg=reductions.count()))
tf.shade(canvas.points(df,'x','y',agg=reductions.any()))
tf.shade(canvas.points(df,'x','y',agg=reductions.mean('y')))
tf.shade(50-canvas.points(df,'x','y',agg=reductions.mean('val')))
agg  = canvas.points(df, 'x', 'y')
tf.shade(agg.where(agg>=np.percentile(agg,99)))
tf.shade(np.sin(agg))
aggc = canvas.points(df, 'x', 'y', ds.count_cat('cat'))
aggc
tf.shade(aggc.sel(cat='d3'))
agg_d3_d5=aggc.sel(cat=['d3', 'd5']).sum(dim='cat')
tf.shade(agg_d3_d5)

I haven't tried your code, but there is nothing in there that would actually display the image. Each shade() call creates an image in memory, but then nothing is done with it here. If you were in a Jupyter notebook environment and the shade() call were the last item in the cell, it would display automatically, but the regular Python prompt doesn't have such "rich display" support. So you can either save it to an image file on disk (using e.g. utils/export_image), or you can assign the result of shade() to a variable and then pass that to a Bokeh or Matplotlib or other plot, as you prefer. But you have to do something with the image if you want to see it.