Data Visualization Using Python and Plotly

Data Visualization

• Data Visualization allows us to quickly interpret the data and adjust different variables to see their effect
• Technology is increasingly making it easier for us to do so.

Why to Visualize Data?

• To observe the patterns
• Identify the extreme values that could be anomalies
• Easy Interpretation

Popular plotting libraries in Python

Python offers multiple graphing libraries that offers diverse features.

Here we are going to talk about only one library which is plotly.

Plotly

Plotly provides online graphing, analytics, and statistics tools for individuals and collaboration, as well as scientific graphing libraries for Python, R, MATLAB, Perl, Julia, Arduino, and REST. It is used to create interactive plots.

The dataset we are going to use for this topic is a cars dataset which you can download from Kaggle.

The subtopics we are going to cover here are as follows:

• Bar charts
• Scatter plots
• Histograms
• Box and whiskers plot
• Line plots
• Pie charts
• Donut charts
• Heat Map
• Contour plots
• Quiver plots

So, Let’s get started

1) Bar Plot

What is a bar plot?

A bar plot is a plot that presents categorical data with rectangular bars with lengths proportional to the counts that they represent

When to use bar plot?

To represent the frequency distribution of categorical variables.

A bar diagram makes it easy to compare sets of data between different groups

Let’s see an example of how to plot a bar diagram

First import the necessary libraries

import pandas as pd
import numpy as np
import plotly.offline as pyo
import plotly. graph_objs as go

Then the read the csv file into Jupyter Notebook. We are going to plot the bar chart between the price column and num_of_cylinder column.

Syntax

fig = go. Figure ([go. Bar (x = data.num_of_cylinders, y = data. price)])
fig. show ()

Output:

 When you take the cursor at any point on the graph you will able to see the values at that point.

2) Scatter plot

What is a scatter plot?

A scatter plot is a set of points that represents the values obtained for two different variables plotted on a horizontal and vertical axes

When to use scatter plots?

Scatter plots are used to convey the relationship between two numerical variables

Scatter plots are sometimes called correlation plots because they show how two variables are correlated

Let’s see an example

First import the necessary libraries

import pandas as pd
import numpy as np
import plotly.offline as pyo
import plotly. graph_objs as go

Then read the csv file in Jupyter Notebook. We are going to plot the

Scatter plot  between city_mpg and highway_mpg column.

Syntax

fig = go.Figure([go.Scatter(x = data.city_mpg , y = data.highway_mpg, mode = 'markers', marker = dict(size = 10 , color = 'red' , symbol = 'square'))])
fig.show()

On X axis we have city_mpg variable and on Y axis we have highway_mpg variable. Mode we have provide markers so that the graph we see is not a continuous line you can also modify the markers inside the dict () method. You can access some of its properties like size, color, symbol etc.

Output:

To make a line graph you just have to change the mode parameter from markers to lines.

3) Histogram

What is a histogram?

It is a graphical representation of data using bars of different heights

Histogram groups numbers into ranges and the height of each bar depicts the frequency of each range or bin

When to use histograms?

To represent the frequency distribution of numerical variables.

Let’s see an example

First import the necessary libraries

import pandas as pd
import numpy as np
import plotly.offline as pyo
import plotly. graph_objs as go

Then read the csv file in Jupyter Notebook. We are going to plot the

Histogram between city_mpg and highway_mpg column.

Syntax

trace0 = go.Histogram(x = cars_data.city_mpg , name = 'City_mpg' , opacity = 0.5)
trace1 = go.Histogram(x = cars_data.highway_mpg , name = 'Highway_mpg', opacity = 0.5)
data = [trace0,trace1]
layout = go.Layout(title = 'city vs highway mpg')
fig = go.Figure(data = data , layout = layout)
pyo.plot( fig )

Output:

4) Box and Whiskers plot

A box and whisker plot is a diagram that shows the statistical distribution of a set of data. This makes it easy to see how data is distributed along a number line.

Let’s see an example

First import the necessary libraries

import pandas as pd
import numpy as np
import plotly. offline as pyo
import plotly. graph_objs as go

Syntax

trace2 = go. Box (y = cars_data. price, name = 'Price')
trace3 = go. Box (y = cars data. city_mpg, name = 'City_mpg')
data = [trace2, trace3]
layout = go. Layout (title = 'Box and Whisker plot')
fig = go. Figure (data = data, layout = layout)
pyo. plot(fig)

Output:

5) Pie chart

Pie charts are generally used to show percentage or proportional data and usually the percentage represented by each category is provided next to the corresponding slice of pie. Pie charts are good for displaying data for around 6 categories or fewer.

Let’s see an example

First import the necessary libraries

import pandas as pd
import numpy as np
import plotly. offline as pyo
import plotly. graph_objs as go

Syntax

labels=['price','city_mpg','highway_mpg','horsepower']
values = [1200,345,666,1000]
fig = go.Figure(data=[go.Pie(labels = labels, values=values)])
fig.show()

Output:

6) Donut Chart

Pie chart and Donut chart both are same. To create a Donut chart just add one more parameter that is hole.

Let’s see an example

Syntax

labels=['price','city_mpg','highway_mpg','horsepower']
values = [1200,345,666,1000]
fig = go.Figure(data=[go.Pie(labels = labels, values=values, hole = .3)])
fig.show() 

 Output:

7) Heat map

Heat Maps are graphical representations of data that utilize color-coded systems. The primary purpose of Heat Maps is to better visualize the volume of locations within a dataset and assist in directing viewers towards areas on data visualizations that matter most.

Let’s see an example

Syntax

fig = go. Figure (data=go. Heatmap (
                   z= [[10, 2, 30, 5, 1], [20, 10, 6, 80, 30], [35, 60, 1, -10, 45]],
                   x= ['Jan', 'Feb', 'March', 'April', 'May'],
                   y= ['Summer', 'Winter', 'Monsoon']))
fig. show () 

Output:

8) Contour plot

A contour plot is a graphical technique for representing a 3-dimensional surface by plotting constant z slices, called contours, on a 2-dimensional format. That is, given a value for z, lines are drawn for connecting the (x, y) coordinates where that z value occurs.

Let’s see an example

Syntax

fig = go. Figure (data =
     go.Contour (z= [[10, 10.625, 12.5, 15.65, 30],
          [5.625, 6.25, 8.125, 10.25, 15.625],
          [2.5, 3.125, 5., 18.125, 1.5],
          [3.625, 1.25, 3.125, 0.25, 10.625],
          [1, 2.625, 2.545, 7.625, 10]],
     colorscale='Electric',
))
fig. show ()

Output:

9) Quiver plot

A quiver plot displays velocity vectors as arrows with components (u, v) at the points (x, y). quiver (x, y, u, v) plots vectors as arrows at the coordinates specified in each corresponding pair of elements in x and y.

Syntax

import plotly. figure_factory as ff
x = [12,23,45,62.66]
y = [91.3,45,66,9]
u = [12,3,45,6.678]
v = [12,33,44,54]
fig = ff. create_quiver (x, y, u, v)
fig. show ()

Output:

In this way we have seen how to create interactive graphs using plotly.