<jupyter_start><jupyter_text>Seattle Landmarks<jupyter_code>import pandas as pd
data = pd.read_csv("Landmarks.csv", names=('City Feature', 'Common Name', 'Address', 'Website', 'Latitude', 'Longitude'))
data
website_data = data[pd.notnull(data['Website'])]<jupyter_output><empty_output><jupyter_text>What percentage of the landmarks have websites?<jupyter_code>'{:0.2f}% of the landmarks have websites.'.format((len(website_data)/len(data)) * 100)

school_data = data[data['Common Name'].str.contains("School")]<jupyter_output><empty_output><jupyter_text>How many schools are considered Landmarks?<jupyter_code>'{} of the landmarks are schools.'.format(len(school_data))<jupyter_output><empty_output><jupyter_text>How evenly dispersed are the Landmarked schools?<jupyter_code>import matplotlib.pyplot as plt
plt.scatter(school_data["Latitude"], school_data["Longitude"], c="blue", label="unbound by Charles River")
plt.xlabel("Latitude"), plt.ylabel("Longitude"), plt.title("The dispersal of landmarked schools.")
plt.show()<jupyter_output><empty_output><jupyter_text>How close are the landmarked libraries to the landmarked schools?<jupyter_code>library_data = data[data['Common Name'].str.contains("Library")]
plt.scatter(school_data["Latitude"], school_data["Longitude"], c="blue", label="Schools")
plt.scatter(library_data["Latitude"], library_data["Longitude"], c="red", label="Libraries")
plt.xlabel("Latitude"), plt.ylabel("Longitude"), plt.title("How close are the schools to the libraries")
plt.legend(loc='upper left')
plt.show()<jupyter_output><empty_output><jupyter_text>How many landmarks are around the space needle?<jupyter_code>space_needle = data[data['Common Name'].str.contains("Space Needle")]
data = data[data["Common Name"] != "Common Name"]
plt.scatter(data["Latitude"], data["Longitude"])
plt.scatter(space_needle["Latitude"], space_needle["Longitude"], c="red", label="Space Needle")
plt.xlabel("Latitude"), plt.ylabel("Longitude"), plt.title("How many other landmarks are around the space needle?")
plt.legend(loc='upper left')
plt.show()<jupyter_output><empty_output>
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