- 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
Let's again get our travel data from our excel spreadsheet. If we do not already have pandas
and xldr
for retrieving data from excel, we should install it now.
!pip install pandas
!pip install xlrd
import pandas
file_name = './cities.xlsx'
travel_df = pandas.read_excel(file_name)
cities = travel_df.to_dict('records')
Press shift + enter to run the code above.
cities
[{'Area': 4758,
'City': 'Buenos Aires',
'Country': 'Argentina',
'Population': 2891000},
{'Area': 2731571,
'City': 'Toronto',
'Country': 'Canada',
'Population': 2800000},
{'Area': 3194,
'City': 'Pyeongchang',
'Country': 'South Korea',
'Population': 2581000},
{'Area': 200, 'City': 'Marakesh', 'Country': 'Morocco', 'Population': 928850},
{'Area': 491,
'City': 'Albuquerque',
'Country': 'New Mexico',
'Population': 559277},
{'Area': 3750,
'City': 'Los Cabos',
'Country': 'Mexico',
'Population': 287651},
{'Area': 68, 'City': 'Greenville', 'Country': 'USA', 'Population': 84554},
{'Area': 8300,
'City': 'Archipelago Sea',
'Country': 'Finland',
'Population': 60000},
{'Area': 33,
'City': 'Walla Walla Valley',
'Country': 'USA',
'Population': 32237},
{'Area': 27, 'City': 'Salina Island', 'Country': 'Italy', 'Population': 4000},
{'Area': 59, 'City': 'Solta', 'Country': 'Croatia', 'Population': 1700},
{'Area': 672,
'City': 'Iguazu Falls',
'Country': 'Argentina',
'Population': 0}]
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 names 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 cities.
x_values = [cities[0]['City'], cities[1]['City'], cities[2]['City']]
Now use list and dictionary accessors to set y_values
equal to the first three populations.
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 our variable text_values
equal to a list of names of the first three cities. Then we pass the information as the appropriate dictionary value in our trace.
text_values = []
bar_trace_first_three_pops = {'type': 'scatter', 'text': text_values}
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. First, create another trace called bar_trace_first_three_areas
that is like our bar_trace_first_three_pops
except the values are a list of areas. We will display this side by side along our bar_trace_first_three_pops
in the plot below.
bar_trace_first_three_areas = {'type': 'scatter', 'x': [], 'y': [], 'text': []}
bar_trace_first_three_pops = {'type': 'scatter', 'x': [], 'y': [], 'text': []}
plotly.offline.iplot([bar_trace_first_three_pops, bar_trace_first_three_areas])
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!