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Intermediate Python

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# Pre-defined lists
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr =  [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]

# Import pandas as pd
import pandas as pd

# Create dictionary my_dict with three key:value pairs: my_dict
my_dict = {"country":names, "drives_right":dr, "cars_per_cap":cpc}

# Build a DataFrame cars from my_dict: cars
cars = pd.DataFrame (my_dict)

# Print cars
print (cars)

Intermediate Python

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

# Dictionary of dictionaries
europe = { 'spain': { 'capital':'madrid', 'population':46.77 },
           'france': { 'capital':'paris', 'population':66.03 },
           'germany': { 'capital':'berlin', 'population':80.62 },
           'norway': { 'capital':'oslo', 'population':5.084 } }


# Print out the capital of France
print (europe ["france"] ["capital"])

# Create sub-dictionary data
data = {"capital":"rome", "population":59.83}

# Add data to europe under key 'italy'
europe ["italy"] = data

# Print europe
print (europe)
# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin', 'norway':'oslo' }

# Print out the keys in europe
print (europe.keys())

# Print out value that belongs to key 'norway'
print (europe["norway"])

# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin', 'norway':'oslo' }

# Add italy to europe
europe ["italy"] = "rome"

# Print out italy in europe
print ("italy" in europe)

# Add poland to europe
europe ["poland"] = "warsaw"

# Print europe
print (europe)

# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'bonn',
          'norway':'oslo', 'italy':'rome', 'poland':'warsaw',
          'australia':'vienna' }

# Update capital of germany
europe ["germany"] = "berlin"

# Remove australia
del (europe ["australia"])

# Print europe
print (europe)

Take Notes

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

# Import numpy as np
import numpy as np

# Store pop as a numpy array: np_pop
np_pop = np.array (pop)

# Double np_pop
np_pop = np_pop *2 

# Update: set s argument to np_pop
plt.scatter(gdp_cap, life_exp, s = np_pop)

# Previous customizations
plt.xscale('log') 
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')
plt.xticks([1000, 10000, 100000],['1k', '10k', '100k'])

# Display the plot
plt.show()

Add your notes here

# 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!".
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