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Intermediate Python

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.

This is a matplotlib data visualization exercise.

Add your notes here

# Scatter plot

# Additional Customizations
plt.scatter(x = gdp_cap, y = life_exp, s = np.array(pop) * 2, c = col, alpha = 0.8)

# 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'])

# Additional customizations
plt.text(1550, 71, 'India')
plt.text(5700, 80, 'China')

# Add grid() call
plt.grid(True)

# Show the plot
plt.show()
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# 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()
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# Add your code snippets here
# Basic scatter plot, log scale
plt.scatter(gdp_cap, life_exp)
plt.xscale('log') 

# Strings
xlab = 'GDP per Capita [in USD]'
ylab = 'Life Expectancy [in years]'
title = 'World Development in 2007'

# Add axis labels
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')

# Add title
plt.title('World Development in 2007')

# After customizing, display the plot
plt.show()
1 hidden cell

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!".

Definition of countries and capital

countries = ['spain', 'france', 'germany', 'norway'] capitals = ['madrid', 'paris', 'berlin', 'oslo']

Get index of 'germany': ind_ger

ind_ger = countries.index('germany')

Use ind_ger to print out capital of Germany

print(capitals[ind_ger])

Access dictionary If the keys of a dictionary are chosen wisely, accessing the values in a dictionary is easy and intuitive. For example, to get the capital for France from europe you can use:

europe['france'] Here, 'france' is the key and 'paris' the value is returned.

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'])

Dictionary Manipulation (1) If you know how to access a dictionary, you can also assign a new value to it. To add a new key-value pair to europe you can use something like this:

europe['iceland'] = 'reykjavik'

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('poland' in europe)

Dictionariception

Remember lists? They could contain anything, even other lists. Well, for dictionaries the same holds. 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:

europe['spain']['population']

Dictionary to DataFrame (1) Pandas is an open source library, providing high-performance, easy-to-use data structures and data analysis tools for Python. Sounds promising!

The DataFrame is one of Pandas' most important data structures. It's basically a way to store tabular data where you can label the rows and the columns. One way to build a DataFrame is from a dictionary.

In the exercises that follow you will be working with vehicle data from different countries. Each observation corresponds to a country and the columns give information about the number of vehicles per capita, whether people drive left or right, and so on.

Three lists are defined in the script:

names, containing the country names for which data is available. dr, a list with booleans that tells whether people drive left or right in the corresponding country. cpc, the number of motor vehicles per 1000 people in the corresponding country. Each dictionary key is a column label and each value is a list which contains the column elements.

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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': ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt'], 
'drives_right': [True, False, False, False, True, True, True], 'cars_per_cap': [809, 731, 588, 18, 200, 70, 45]}

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

# Print cars
print(cars)
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