Chapter 08: Creating a Full Analysis Report

Activity 15: Generating Visualization Using Plotly

1. Import all the required libraries and packages into the Jupyter notebook. Make sure to read the data from bank.csv into the Spark DataFrame.

2. Import the libraries for Plotly, as illustrated here:

   import plotly.graph_objs as go
   from plotly.plotly import iplot
   import plotly as py

3. Now, for visualization in Plotly, we need to initiate an offline session. Use the following command (requires version >= 1.9.0):

   from plotly import __version__
   from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
   print(__version__)

4. Now Plotly is initiated offline. Use the following command to start a Plotly notebook:

   import plotly.plotly as py
   import plotly.graph_objs as go
   
   init_notebook_mode(connected=True)

   After starting the Plotly notebook, we can use Plotly to generate many types of graphs, such as a bar graph, a boxplot, or a scatter plot, and convert the entire output into a user interface or an app that is supported by Python's Flask framework.

5. Now, plot each graph using Plotly:

   Bar graph:

   df = pd.read_csv('bank.csv', sep=';')
   data = [go.Bar(x=df.y,
               y=df.balance)]
   
   py.iplot(data)

   The bar graph is as follows:

   

   Figure 8.18: Bar graph

   Scatter plot:

   py.iplot([go.Histogram2dContour(x=df.balance, y=df.age, contours=dict(coloring='heatmap')),
          go.Scatter(x=df.balance, y=df.age, mode='markers', marker=dict(color='red', size=8, opacity=0.3))], show_link=False)

   The scatter plot is as follows:

   

   Figure 8.19: Scatter plot

   Boxplot:

   plot1 = go.Box(
       y=df.age,
       name = 'age of the customers',
       marker = dict(
           color = 'rgb(12, 12, 140)',
       )
   )
   py.iplot([plot1])

   The boxplot is as follows:

   

   Figure 8.20: Boxplot