Plotting Data Lab

Learning Objectives

Understand the components of a point in a graph, an $x$ value, and a $y$ value
Understand how to plot a point on a graph, from a point's $x$ and $y$ value
Get a sense of how to use a graphing library, like Plotly, to answer questions about our data

Working again with our travel data

Let's again get our travel data from our excel spreadsheet.

import pandas
file_name = './cities.xlsx'
travel_df = pandas.read_excel(file_name)
cities = travel_df.to_dict('records')

cities

[{'Area': 59, 'City': 'Solta', 'Country': 'Croatia', 'Population': 1700},
 {'Area': 68, 'City': 'Greenville', 'Country': 'USA', 'Population': 84554},
 {'Area': 4758,
  'City': 'Buenos Aires',
  'Country': 'Argentina',
  'Population': 13591863},
 {'Area': 3750,
  'City': 'Los Cabos',
  'Country': 'Mexico',
  'Population': 287651},
 {'Area': 33,
  'City': 'Walla Walla Valley',
  'Country': 'USA',
  'Population': 32237},
 {'Area': 200, 'City': 'Marakesh', 'Country': 'Morocco', 'Population': 928850},
 {'Area': 491,
  'City': 'Albuquerque',
  'Country': 'New Mexico',
  'Population': 559277},
 {'Area': 8300,
  'City': 'Archipelago Sea',
  'Country': 'Finland',
  'Population': 60000},
 {'Area': 672,
  'City': 'Iguazu Falls',
  'Country': 'Argentina',
  'Population': 0},
 {'Area': 27, 'City': 'Salina Island', 'Country': 'Italy', 'Population': 4000},
 {'Area': 2731571, 'City': 'Toronto', 'Country': 'Canada', 'Population': 630},
 {'Area': 3194,
  'City': 'Pyeongchang',
  'Country': 'South Korea',
  'Population': 2581000}]

Visualizing our data with graphs

As we can see, in our list of cities, each city has a population number. Our first task will be to display the populations of our first three cities in a bar chart.

First we load the plotly library into our notebook, and we initialize this offline mode.

import plotly

plotly.offline.init_notebook_mode(connected=True)
# use offline mode to avoid initial registration

Now the next step is to build a trace. As we know our trace is a dictionary with a key of x and a key of y. We have set up a trace to look like the following: trace_first_three = {'x': x_values, 'y': y_values}.

First define x_values so that it is a list of the first three cities. Use what we learned about accessing information from lists and dictionaries to assign x_values equal to the first three countries.

x_values = []

Now use list and dictionary accessors to set y_values equal to the first three populations.

y_values = []

x_values = [cities[0]['City'], cities[1]['City'], cities[2]['City']]
y_values = [cities[0]['Population'], cities[1]['Population'], cities[2]['Population']]

Now let's plot our data.

trace_first_three_pops = {'x': x_values, 'y': y_values}


plotly.offline.iplot([trace_first_three_pops])

Note that by default, plotly sets the type of trace as a line trace. Let's make our trace a bar trace by setting the key of 'type' equal to 'bar'. We can continue to use the lists of x_values and y_values that we defined about in our new trace. Also, we can have the label match the names of the cities, by setting the key of text equal to a list of the names of the cities. Assign a list of our first three cities to the key of text.

bar_trace_first_three_pops = {'type': 'scatter'}

bar_trace_first_three_pops['type'] # 'bar'

'scatter'

plotly.offline.iplot([bar_trace_first_three_pops])

Ok, now let's plot two different traces side by side. Create another trace called bar_trace_first_three_areas that has is like our bar_trace_first_three_pops except the values are a list of areas. We will plot this side by side our bar_trace_first_three_pops in the plot below.

trace_first_three_areas = {'type': 'scatter', 'x': [], 'y': [], 'text': []}

plotly.offline.iplot([trace_first_three_pops, trace_first_three_areas])

Summary

In this section, we saw how we use data visualisations to better understand the data. We do the following. Import plotly:

import plotly

plotly.offline.init_notebook_mode(connected=True)

Then we define a trace, which is a Python dictionary.

trace = {'x': [], 'y': [], 'text': [], 'type': 'bar'}

Finally, we display our trace with a call to the following method:

plotly.offline.iplot([trace])

Easy peasy, quick and easy!

JeffKatzy/plotting-data-lab-data-science-pilot

Plotting Data Lab

Learning Objectives

Working again with our travel data

Visualizing our data with graphs

Summary