05_pandas_visualization.py

'''
CLASS: Visualization with Pandas (and Matplotlib)
'''

import pandas as pd
import matplotlib.pyplot as plt

# read in the drinks data
drink_cols = ['country', 'beer', 'spirit', 'wine', 'liters', 'continent']
drinks = pd.read_csv('drinks.csv', header=0, names=drink_cols, na_filter=False)

'''
Histogram: show the distribution of a numerical variable
'''

# sort the beer column and split it into 3 groups
drinks.beer.order().values

# compare with histogram
drinks.beer.plot(kind='hist', bins=3)

# try more bins
drinks.beer.plot(kind='hist', bins=20)

# add title and labels
drinks.beer.plot(kind='hist', bins=20, title='Histogram of Beer Servings')
plt.xlabel('Beer Servings')
plt.ylabel('Frequency')

# compare with density plot (smooth version of a histogram)
drinks.beer.plot(kind='density', xlim=(0, 500))

# stacked histogram with multiple variables
drinks[['beer', 'spirit', 'wine']].plot(kind='hist', stacked=True)

'''
Scatter Plot: show the relationship between two numerical variables
'''

# select the beer and wine columns and sort by beer
drinks[['beer', 'wine']].sort('beer').values

# compare with scatter plot
drinks.plot(kind='scatter', x='beer', y='wine')

# add transparency
drinks.plot(kind='scatter', x='beer', y='wine', alpha=0.3)

# vary point color by spirit servings
drinks.plot(kind='scatter', x='beer', y='wine', c='spirit', colormap='Blues')

# scatter matrix of three numerical columns
pd.scatter_matrix(drinks[['beer', 'spirit', 'wine']])

# increase figure size
pd.scatter_matrix(drinks[['beer', 'spirit', 'wine']], figsize=(10, 8))

'''
Bar Plot: show a numerical comparison across different categories
'''

# count the number of countries in each continent
drinks.continent.value_counts()

# compare with bar plot
drinks.continent.value_counts().plot(kind='bar')

# calculate the average beer/spirit/wine amounts for each continent
drinks.groupby('continent').mean().drop('liters', axis=1)

# side-by-side bar plots
drinks.groupby('continent').mean().drop('liters', axis=1).plot(kind='bar')

# stacked bar plots
drinks.groupby('continent').mean().drop('liters', axis=1).plot(kind='bar', stacked=True)

'''
Box Plot: show quartiles (and outliers) for one or more numerical variables
'''

# show "five-number summary" for beer
drinks.beer.describe()

# compare with box plot
drinks.beer.plot(kind='box')

# include multiple variables
drinks.drop('liters', axis=1).plot(kind='box')

'''
Line Plot: show the trend of a numerical variable over time
'''

# read in the ufo data
ufo = pd.read_csv('ufo.csv')
ufo['Time'] = pd.to_datetime(ufo.Time)
ufo['Year'] = ufo.Time.dt.year

# count the number of ufo reports each year (and sort by year)
ufo.Year.value_counts().sort_index()

# compare with line plot
ufo.Year.value_counts().sort_index().plot()

# don't use a line plot when there is no logical ordering
drinks.continent.value_counts().plot()

'''
Grouped Box Plots and Grouped Histograms: show one plot for each group
'''

# reminder: box plot of beer servings
drinks.beer.plot(kind='box')

# reminder: histogram of beer servings
drinks.beer.plot(kind='hist')

# box plot of beer servings grouped by continent
drinks.boxplot(column='beer', by='continent')

# histogram of beer servings grouped by continent
drinks.beer.hist(by=drinks.continent)

# share the x axes
drinks.beer.hist(by=drinks.continent, sharex=True)

# share the x and y axes
drinks.beer.hist(by=drinks.continent, sharex=True, sharey=True)

# change the layout
drinks.beer.hist(by=drinks.continent, layout=(2, 3))

# box plot of all numeric columns grouped by continent
drinks.boxplot(by='continent')

'''
Assorted Functionality
'''

# saving a plot to a file: run all four lines at once
drinks.beer.plot(kind='hist', bins=20, title='Histogram of Beer Servings')
plt.xlabel('Beer Servings')
plt.ylabel('Frequency')
plt.savefig('beer_histogram.png')

# list available plot styles
plt.style.available

# change to a different style
plt.style.use('ggplot')