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Intermediate Python
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Run the hidden code cell below to import the data used in this course.

### Take Notes

Add notes about the concepts you've learned and code cells with code you want to keep.

`.mfe-app-workspace-11z5vno{font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;font-size:13px;line-height:20px;}`# Add your code snippets here``

### Explore Datasets

Use the DataFrames imported in the first cell to explore the data and practice your skills!

• Create a loop that iterates through the `brics` DataFrame and prints "The population of {country} is {population} million!".
• Create a histogram of the life expectancies for countries in Africa in the `gapminder` DataFrame. Make sure your plot has a title, axis labels, and has an appropriate number of bins.
• Simulate 10 rolls of two six-sided dice. If the two dice add up to 7 or 11, print "A win!". If the two dice add up to 2, 3, or 12, print "A loss!". If the two dice add up to any other number, print "Roll again!".
Unknown integration
DataFrameavailable as
events_per_city
variable
``````SELECT v.venuecity, COUNT(e.eventid) AS event_count
FROM event e
JOIN venue v ON e.venueid = v.venueid
GROUP BY v.venuecity
ORDER BY event_count DESC
LIMIT 20;``````
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
``````import plotly.express as px

events_per_city = pd.DataFrame({
'venuecity': ['New York City', 'Los Angeles', 'Las Vegas', 'Chicago', 'San Francisco', 'Washington', 'Houston', 'Detroit', 'Boston', 'Baltimore', 'Denver', 'Seattle', 'Minneapolis', 'St. Louis', 'Toronto'],
'event_count': [2647, 312, 300, 209, 194, 164, 155, 152, 144, 142, 136, 136, 131, 127, 120]
})

fig = px.bar(events_per_city, x='venuecity', y='event_count', title='Top 20 Cities by Number of Events', labels={'venuecity': 'City', 'event_count': 'Number of Events'})
fig.show()``````