Choropleth Map is a type of thematic map in which a set of pre-defined areas is colored or patterned in proportion to a statistical variable that represents an aggregate summary of a geographic characteristic within each area. Choropleth maps provide an easy way to visualize how a variable varies across a geographic area or show the level of variability within a region.
Sliders can be used in plotly to change the data displayed or the style of a plot. It is a handy tool that can be used to display changes in a choropleth map.
Installation
Let’s start by installing plotly:
pip install plotly
Now, let’s Import the necessary libraries.
Python3
import pandas as pd import plotly import numpy as np |
Loading Dataset
Loading sample dataset. We’ll use this dataset to plot a choropleth map.
Python3
df = pd.read_csv('https://raw.githubusercontent\ .com/plotly/datasets/master/2011_us_ag_exports.csv') data = [dict(type='choropleth', locations = df['code'].astype(str), z=df['total exports'].astype(float), locationmode='USA-states')] |
Creating Slider
Now, let’s build our slider:
Python3
steps = [] for i in range(len(data)): step = dict(method='restyle', args=['visible', [False] * len(data)], label='Year {}'.format(i + 1980)) step['args'][1][i] = True steps.append(step) sliders = [dict(active=0, pad={"t": 1}, steps=steps)] |
Final Code with Output
Finally, let’s put all the code together and plot the choropleth map with a slider.
Python3
import pandas as pd import plotly import numpy as np plotly.offline.init_notebook_mode() # Reading sample data using pandas DataFrame df = pd.read_csv('https://raw.githubusercontent.\ com/plotly/datasets/master/2011_us_ag_exports.csv') data = [dict(type='choropleth', locations = df['code'].astype(str), z=df['total exports'].astype(float), locationmode='USA-states')] # let's create some more additional, data for i in range(5): data.append(data[0].copy()) data[-1]['z'] = data[0]['z'] * np.random.rand(*data[0]['z'].shape) # let's now create slider for map steps = [] for i in range(len(data)): step = dict(method='restyle', args=['visible', [False] * len(data)], label='Year {}'.format(i + 1980)) step['args'][1][i] = True steps.append(step) slider = [dict(active=0, pad={"t": 1}, steps=steps)] layout = dict(geo=dict(scope='usa', projection={'type': 'albers usa'}), sliders=slider) fig = dict(data=data, layout=layout) plotly.offline.iplot(fig) |
Output:
