5.1. seaborn

We will start our very broad discussion of data visualization with seaborn. seaborn is based on matplotlib, but is “higher-level” (i.e. easier to use) and makes what many consider nicer looking graphs (i.e. many = me).

So, why do we still look at matplotlib? I think it is good to see matplotlib, since it is the most popular way to create figures in Python. But, you should take a look at seaborn as well.

Some people, especially those coming to Python from other languages, are suggesting that you just start with seaborn instead.

seaborn comes with Anaconda and Github Codespaces, so there’s nothing to install. Just add import seaborn as sns to your set-up and you’re ready to go. In my set-up, I’ll set a theme, so that the same theme is used across all of my graphs. I’ll create my returns directly in the stocks DataFrame.

DataCamp has a seaborn tutorial as well.

There’s an example gallery as well.

You can find other examples here.

Finally, I’m going to use the df.var_name convention for pulling out variables from a DataFrame. I find it easier than df['var_name']. I’ll go back and forth in the notes, to get you use to the different styles.

# Set-up
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="white")

# Include this to have plots show up in your Jupyter notebook.
%matplotlib inline 

# Read in some eod prices
stocks = pd.read_csv('https://raw.githubusercontent.com/aaiken1/fin-data-analysis-python/main/data/tr_eikon_eod_data.csv',
                  index_col=0, parse_dates=True)  

stocks.dropna(inplace=True)  

from janitor import clean_names

stocks = clean_names(stocks)

stocks['aapl_ret'] = np.log(stocks.aapl_o / stocks.aapl_o.shift(1))  
stocks['msft_ret'] = np.log(stocks.msft_o / stocks.msft_o.shift(1))  

stocks.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2138 entries, 2010-01-04 to 2018-06-29
Data columns (total 14 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   aapl_o    2138 non-null   float64
 1   msft_o    2138 non-null   float64
 2   intc_o    2138 non-null   float64
 3   amzn_o    2138 non-null   float64
 4   gs_n      2138 non-null   float64
 5   spy       2138 non-null   float64
 6   _spx      2138 non-null   float64
 7   _vix      2138 non-null   float64
 8   eur=      2138 non-null   float64
 9   xau=      2138 non-null   float64
 10  gdx       2138 non-null   float64
 11  gld       2138 non-null   float64
 12  aapl_ret  2137 non-null   float64
 13  msft_ret  2137 non-null   float64
dtypes: float64(14)
memory usage: 250.5 KB

If you don’t have janitor installed, you’ll need to run in a code cell above your set-up code. You should only have to install a package once per code space.

pip install pyjanitor

You can read more about janitor here.

Let’s start with a line plot. We’ll plot just AAPL to start.

sns.lineplot(x=stocks.index, y=stocks.aapl_o)
plt.show();

We can also make a distribution, or histogram, as well. I’ll add what’s called the kernel density estimate (kde), which gives the distribution. We’ll do more data work like this when thinking about risk.

sns.displot(stocks.aapl_ret, kde=True, bins=50)
plt.show();

sns.jointplot(x=stocks.aapl_ret, y=stocks.msft_ret)
plt.show();

sns.jointplot(x=stocks.aapl_ret, y=stocks.msft_ret, kind='hex')
plt.show();