Summary statistics by category with seaborn

Importing and Managing Financial Data in Python

Stefan Jansen

Instructor

Categorical plots with seaborn

Specialized ways to plot combinations of categorical and numerical variables
Visualize estimates of summary statistics per category
Understand how categories impact numerical variables
Compare using key metrics of distributional characteristics
Example: Mean Market Cap per Sector or IPO Year with indication of dispersion

The basics: countplot

sns.countplot(x='Sector', data=nasdaq)
plt.xticks(rotation=45)

countplot, sorted

sector_size = nasdaq.groupby('Sector').size()
order = sector_size.sort_values(ascending=False)
order.head()

Sector
Health Care          645
Finance              627
Technology           433
...

order = order.index.tolist()

['Health Care', 'Finance', ..., 'Energy', 'Transportation']

countplot, sorted

sns.countplot(x='Sector', data=nasdaq, order=order)
plt.xticks(rotation=45)
plt.title('# Observations per Sector’)

countplot, multiple categories

recent_ipos = nasdaq[nasdaq['IPO Year'] > 2014]
recent_ipos['IPO Year'] = recent_ipos['IPO Year'].astype(int)
sns.countplot(x='Sector', hue='IPO Year', data=recent_ipos)

Compare stats with PointPlot

nasdaq['IPO'] = nasdaq['IPO Year'].apply(lambda x: 'After 2000' if x > 2000 else 'Before 2000')
sns.pointplot(x='Sector', y='market_cap_m', hue='IPO', data=nasdaq)
plt.xticks(rotation=45); plt.title('Mean Market Cap')

Let's practice!

Importing and Managing Financial Data in Python