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Intermediate Python

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

import matplotlib.pyplot as plt
year = [1950, 1970, 1990, 2010]
pop = [2.519, 3.692, 5.263, 6.972]
plt.plot(year, pop)
plt.show()
plt.scatter(year, pop)
plt.show()
 
# Print the last item from year and pop
print(year[-1])
print(pop[-1])


# Import matplotlib.pyplot as plt
import matplotlib.pyplot as plt

# Make a line plot: year on the x-axis, pop on the y-axis
plt.plot(year, pop)

# Display the plot with plt.show()
plt.show()
# Change the line plot below to a scatter plot
plt.scatter(gdp_cap, life_exp)
# Put the x-axis on a logarithmic scale
plt.xscale('log')
# Show plot
plt.show()

HISTOGRAM

# Create histogram of life_exp data
plt.hist(life_exp)

# Display histogram
plt.show()
# Build histogram with 5 bins
plt.hist(life_exp, 5)

# Show and clean up plot
plt.show()
plt.clf()

# Build histogram with 20 bins
plt.hist(life_exp, 20)


# Show and clean up again
plt.show()
plt.clf()
# Import the course packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Import the two datasets
gapminder = pd.read_csv("datasets/gapminder.csv")
brics = pd.read_csv("datasets/brics.csv")

bins es la cantidad de columnas

# Build histogram with 5 bins
plt.hist(life_exp, 5)
# Show and clean up plot
plt.show()
plt.clf()
# Build histogram with 20 bins
plt.hist(life_exp, 20)
# Show and clean up again
plt.show()
plt.clf()
# 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(xlab)
plt.ylabel(ylab)

# Add title

plt.title(title)
# After customizing, display the plot
plt.show()

# Definition of tick_val and tick_lab
tick_val = [1000, 10000, 100000]
tick_lab = ['1k', '10k', '100k']

# Adapt the ticks on the x-axis
plt.xticks(tick_val, tick_lab)

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

# Specify c and alpha inside plt.scatter()
#C ES COLOR

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'])


# Show the plot
plt.show()

Diccionary

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!".
#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]
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