Rakesh Kumar

  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, ...