Embarking on a data exploration voyage led me to the captivating realm of geo-visualization. Armed with the Pandas and Plotly Express libraries, I started to plot 10 distinct coordinates on the canvas of the United States. A carefully crafted DataFrame, harboring the latitude and longitude treasures, paved the way for a seamless visualization.
In this hands-on endeavor, the px.scatter_geo function from Plotly Express emerged as the navigational compass. With an elegant command, it breathed life into a geographical scatter plot, effortlessly placing each coordinate on the map. The canvas, representing the vast expanse of the USA, became a tapestry of visual insights.
A mere collection of latitude and longitude points metamorphosed into a visual symphony, painting a vivid picture of geographic distribution. The result, a map adorned with 10 distinctive location points, offers a glance into the spatial narrative concealed within the data.
import pandas as pd
import plotly.express as px
# Define the coordinates
data = {‘Latitude’: [37.7749, 34.0522, 41.8781, 40.7128, 36.7783, 32.7767, 39.9526, 33.7490, 35.2271, 42.3601],
‘Longitude’: [-122.4194, -118.2437, -87.6298, -74.0060, -119.4179, -96.7970, -75.1652, -84.3880, -80.8431, -71.0589]}
# Create a DataFrame
df = pd.DataFrame(data)
# Charting the course on the USA map
fig = px.scatter_geo(df, lat=’Latitude’, lon=’Longitude’, scope=’usa’, color_discrete_map={‘Longitude’: ‘red’},
title=’USA Map with 10 Location Points’)
# Navigational customizations
fig.update_geos(bgcolor=’yellow’) # Set the background color to yellow
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This expedition into geo-visualization serves as more than a technical exercise; it’s a window into the vast possibilities that unfold when data meets creativity. As I eagerly anticipate incorporating these newfound skills into future analyses, the map becomes not just a visual output but a milestone in a continuous journey of learning and discovery. The data-driven adventure continues, promising more maps and stories yet to be unveiled. Link to the colab is attached below.
https://colab.research.google.com/drive/1eCYBis6ltDbwZaZH8psz1P5FcMxrBNZv#scrollTo=ZST-7yzE8eF6