04_visualization.py

"""
CLASS:  Visualization
"""

# imports
import pandas as pd
import matplotlib.pyplot as plt

# import the data available at https://raw.githubusercontent.com/justmarkham/DAT5/master/data/drinks.csv
drinks = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT5/master/data/drinks.csv')

'''
Visualization
'''

# bar plot of number of countries in each continent
drinks.continent.value_counts().plot(kind='bar', title='Countries per Continent')
plt.xlabel('Continent')
plt.ylabel('Count')
plt.show()                                  # show plot window (if it doesn't automatically appear)
plt.savefig('countries_per_continent.png')  # save plot to file

# bar plot of average number of beer servings (per adult per year) by continent
drinks.groupby('continent').beer_servings.mean().plot(kind='bar', title='Average Number of Beer Servings By Continent')
plt.ylabel('Average Number of Beer Servings Per Year')
plt.show()

# histogram of beer servings (shows the distribution of a numeric column)
drinks.beer_servings.hist(bins=20)
plt.title("Distribution of Beer Servings")
plt.xlabel('Beer Servings')
plt.ylabel('Frequency')
plt.show()

# density plot of beer servings (smooth version of a histogram)
drinks.beer_servings.plot(kind='density', xlim=(0,500))
plt.title("Distribution of Beer Servings")
plt.xlabel('Beer Servings')
plt.show()

# grouped histogram of beer servings (shows the distribution for each group)
drinks.beer_servings.hist(by=drinks.continent)
plt.show()

drinks.beer_servings.hist(by=drinks.continent, sharex=True)
plt.show()

drinks.beer_servings.hist(by=drinks.continent, sharex=True, sharey=True)
plt.show()

drinks.beer_servings.hist(by=drinks.continent, sharey=True, layout=(2, 3))   # change layout (new in pandas 0.15.0)
plt.show()

# boxplot of beer servings by continent (shows five-number summary and outliers)
drinks.boxplot(column='beer_servings', by='continent')
plt.show()

# scatterplot of beer servings versus wine servings
drinks.plot(kind='scatter', x='beer_servings', y='wine_servings', alpha=0.3)
plt.show()

# same scatterplot, except point color varies by 'spirit_servings'
# note: must use 'c=drinks.spirit_servings' prior to pandas 0.15.0
drinks.plot(kind='scatter', x='beer_servings', y='wine_servings', c='spirit_servings', colormap='Blues')
plt.show()

# same scatterplot, except all European countries are colored red
colors = np.where(drinks.continent=='EU', 'r', 'b')
drinks.plot(x='beer_servings', y='wine_servings', kind='scatter', c=colors)
plt.show()

# Scatter matrix
pd.scatter_matrix(drinks)
plt.show()


##########################################
############    Exercise 1    ############
##########################################

# 1. Generate a plot showing the average number of total litres of pure alcohol
# by continent.
drinks.groupby('continent').total_litres_of_pure_alcohol.mean().plot(kind='bar')
plt.show()

# 2. Illustrate the relationship between spirit servings and total litres of 
# pure alcohol.  What kind of relationship is there?
drinks.plot(kind='scatter', x='spirit_servings', y='total_litres_of_pure_alcohol', alpha=0.4)
plt.show()

# 3. Generate one plot that shows the distribution of spirit servings for each 
# continent.
drinks.spirit_servings.hist(by=drinks.continent, sharex=True, sharey=True)
plt.show()


##########################################
#############    Homework    #############
##########################################
'''
Use the automotive mpg data (https://raw.githubusercontent.com/justmarkham/DAT5/master/data/auto_mpg.txt) 
to complete the following parts.  Please turn in your code for each part.  
Before each code chunk, give a brief description (one line) of what the code is
doing (e.g. "Loads the data" or "Creates scatter plot of mpg and weight").  If 
the code output produces a plot or answers a question, give a brief
interpretation of the output (e.g. "This plot shows X,Y,Z" or "The mean for 
group A is higher than the mean for group B which means X,Y,Z").
'''

'''
Part 1
Produce a plot that compares the mean mpg for the different numbers of cylinders.
'''

'''
Part 2
Use a scatter matrix to explore relationships between different numeric variables.
'''

'''
Part 3
Use a plot to answer the following questions:
-Do heavier or lighter cars get better mpg?
-How are horsepower and displacement related?
-What does the distribution of acceleration look like?
-How is mpg spread for cars with different numbers of cylinders?
-Do cars made before or after 1975 get better average mpg? (Hint: You need to 
create a new column that encodes whether a year is before or after 1975.)
'''