Visualize the distribution of your data

Importing and Managing Financial Data in Python

Stefan Jansen

Instructor

Always look at your data!

Identical metrics can represent very different data

Introducing seaborn plots

Many attractive and insightful statistical plots
Based on matplotlib
Swiss Army knife: seaborn.distplot()
- Histogram
- Kernel Density Estimation (KDE)
- Rugplot

10 year treasury: trend and distribution

ty10 = web.DataReader('DGS10', 'fred', date(1962, 1, 1))
ty10.info()

DatetimeIndex: 15754 entries, 1962-01-02 to 2022-05-20
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype  
 --  ------  --------------  -----  
 0   DGS10   15083 non-null  float64

ty10.describe()

              DGS10
mean       6.291073
std        2.851161
min        1.370000
25%        4.190000
50%        6.040000
...

10 year treasury: time series trend

ty10.dropna(inplace=True) # Avoid creation of copy

ty10.plot(title='10-year Treasury'); plt.tight_layout()

10 year treasury: historical distribution

import seaborn as sns
sns.distplot(ty10)

10 year treasury: trend and distribution

ax = sns.distplot(ty10)
ax.axvline(ty10['DGS10'].median(), color='black', ls='--')

Let's practice!

Importing and Managing Financial Data in Python