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

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

Dictionaries can contain key:value pairs where the values are again dictionaries.

As an example, have a look at the script where another version of europe - the dictionary you've been working with all along - is coded. The keys are still the country names, but the values are dictionaries that contain more information than just the capital.

It's perfectly possible to chain square brackets to select elements. To fetch the population for Spain from europe, for example, you need:

Empire State building prediction

``````# numpy and matplotlib imported, seed set

# Simulate random walk 500 times
all_walks = []
for i in range(500) :
random_walk = [0]
for x in range(100) :
step = random_walk[-1]
dice = np.random.randint(1,7)
if dice <= 2:
step = max(0, step - 1)
elif dice <= 5:
step = step + 1
else:
step = step + np.random.randint(1,7)
if np.random.rand() <= 0.001 :
step = 0
random_walk.append(step)
all_walks.append(random_walk)

# Create and plot np_aw_t
np_aw_t = np.transpose(np.array(all_walks))

# Select last row from np_aw_t: ends
ends = np_aw_t[-1,:]

# Plot histogram of ends, display plot
plt.hist(ends)
plt.show()
# numpy and matplotlib imported, seed set

# Simulate random walk 500 times
all_walks = []
for i in range(500) :
random_walk = [0]
for x in range(100) :
step = random_walk[-1]
dice = np.random.randint(1,7)
if dice <= 2:
step = max(0, step - 1)
elif dice <= 5:
step = step + 1
else:
step = step + np.random.randint(1,7)
if np.random.rand() <= 0.001 :
step = 0
random_walk.append(step)
all_walks.append(random_walk)

# Create and plot np_aw_t
np_aw_t = np.transpose(np.array(all_walks))

# Select last row from np_aw_t: ends
ends = np_aw_t[-1,:]

# Plot histogram of ends, display plot
plt.hist(ends)
plt.show() ``````
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### Take Notes

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

``# Add your code snippets here``
• 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.