Create dynamic and interactive graphs using plotly & cufflinks

  As we know visuals are best of data analysis for humans. So if we want to do data analysis in machine learning using python, then there are lots of libraries(plot using pandas, matplotlib, folium, seaborn) are available to create graphs, but if we just looking for graphs then we can draw the graphs but these graphs are very static.

  That means we can't create dynamic graphs using these libraries. If requirement is to create dynamic graphs then we have to move on another libraries like plotly & cufflinks.

import plotly.graph_objs as go

import plotly.offline as pyo

datax = np.random.randint(1,101,100)

datay = np.random.randint(1,101,100)

datax,datay

(array([66, 80, 27,  7, 76, 82, 75, 97, 80, 10,  2, 53, 97, 84, 29,  3, 99,
        10, 59, 75, 21, 27, 93, 64, 96, 63, 78, 89, 16, 80, 35, 39, 32, 33,
        46, 26, 59, 60, 70,  9, 72, 51, 23, 32, 49, 92, 74, 49,  7, 89, 90,
        48, 75, 94, 82, 24,  7, 81, 11, 12, 86, 47, 47, 70, 76,  9, 92,  8,
         7, 66, 44, 17, 75, 48, 77, 47, 35, 17, 41, 51,  7, 22,  5, 34, 51,
        32, 88,  6,  3, 75, 19, 25, 96, 70, 54, 44,  2,  3, 81, 28]),
 array([ 27,  57,  54,  88,  52,  72,  47,  21,  68, 100,  51,  54,  42,
         27,  52,  54,  48,  66,   3,  88,  68,  59,   7,  76,  29,  18,
          2,  14,   6,  96,  51,  56,  43,  65,  80,  96,  81,   9,  38,
         11,  58,  88,   5,  87,  67,  80,  78,  69,  84,  48,  44,  23,
         38,  36,  42, 100,  13,  96, 100,   4,  32,  69,  99,  87,  88,
         98,  50,  18,  88,  89,  86,  71,   2,   1,  75,  46,  84,  94,
         92,  87,  75,  47,  28,  92,  53,  62,  85,  38,  29,  54,  99,
         79,  91,  93,  55,  40,   5,  75,  39,  15]))

graphs_data = [go.Scatter(x=datax, y=datay, mode='markers')]

graphs_data

Scatter({
    'x': array([66, 80, 27,  7, 76, 82, 75, 97, 80, 10,  2, 53, 97, 84, 29,  3, 99, 10,
                59, 75, 21, 27, 93, 64, 96, 63, 78, 89, 16, 80, 35, 39, 32, 33, 46, 26,
                59, 60, 70,  9, 72, 51, 23, 32, 49, 92, 74, 49,  7, 89, 90, 48, 75, 94,
                82, 24,  7, 81, 11, 12, 86, 47, 47, 70, 76,  9, 92,  8,  7, 66, 44, 17,
                75, 48, 77, 47, 35, 17, 41, 51,  7, 22,  5, 34, 51, 32, 88,  6,  3, 75,
                19, 25, 96, 70, 54, 44,  2,  3, 81, 28]),
    'y': array([ 27,  57,  54,  88,  52,  72,  47,  21,  68, 100,  51,  54,  42,  27,
                 52,  54,  48,  66,   3,  88,  68,  59,   7,  76,  29,  18,   2,  14,
                  6,  96,  51,  56,  43,  65,  80,  96,  81,   9,  38,  11,  58,  88,
                  5,  87,  67,  80,  78,  69,  84,  48,  44,  23,  38,  36,  42, 100,
                 13,  96, 100,   4,  32,  69,  99,  87,  88,  98,  50,  18,  88,  89,
                 86,  71,   2,   1,  75,  46,  84,  94,  92,  87,  75,  47,  28,  92,
                 53,  62,  85,  38,  29,  54,  99,  79,  91,  93,  55,  40,   5,  75,
                 39,  15]) 

})

fig = go.Figure(data=graphs_data)

fig

pyo.plot(fig)

'temp-plot.html'

That's how we can create you create dynamic and interactive graphs, these are nice and work fine but only one drawback here is that we have to write lots of code. So to simplify this we have another library for same purpose is cufflinks. Cufflinks integrate all the plotly function to pandas library.

import cufflinks as cf

cf.go_offline()

df.iplot(x='id', y='salary', kind='scatter', mode='markers')

Also you can create 3D graph using cufflinks.

x1 = [1,2,3,4,5]

y1 = [10,20,30,40,50]

z1 = [5,4,6,7,1]

dataset = pd.DataFrame({'id':x1,'name':y1,'remarks':z1})

type(dataset)

pandas.core.frame.DataFrame

dataset.iplot(kind='surface')