Data Science, Tutorials

Visualizing Historical Yield Curves with Plotly and Python

Posted on: 30 June 2022
Updated on: 24 March 2023
Written by: Pierian Training

In this blog post we’ll explore visualizing the Yield Curve through a cool 3D Surface Plot, since we’ll be exploring 3 dimensions of data: the rate yields, the publication date, and the rate periods.

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Grabbing Yield Curve for 2022¶

We can use the pandas library to directly read the HTML table from the treasury website:

In [13]:

import pandas as pd

In [14]:

year = 2022
url = f"https://home.treasury.gov/resource-center/data-chart-center/interest-rates/TextView?type=daily_treasury_yield_curve&field_tdr_date_value={year}"
table_list = pd.read_html(url)

In [15]:

df = table_list[0]

In [16]:

df.head()

Out[16]:

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	Date	20 YR	30 YR	Extrapolation Factor	8 WEEKS BANK DISCOUNT	COUPON EQUIVALENT	52 WEEKS BANK DISCOUNT	COUPON EQUIVALENT.1	1 Mo	2 Mo	3 Mo	6 Mo	1 Yr	2 Yr	3 Yr	5 Yr	7 Yr	10 Yr	20 Yr	30 Yr
0	01/03/2022	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.05	0.06	0.08	0.22	0.40	0.78	1.04	1.37	1.55	1.63	2.05	2.01
1	01/04/2022	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.06	0.05	0.08	0.22	0.38	0.77	1.02	1.37	1.57	1.66	2.10	2.07
2	01/05/2022	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.05	0.06	0.09	0.22	0.41	0.83	1.10	1.43	1.62	1.71	2.12	2.09
3	01/06/2022	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.04	0.05	0.10	0.23	0.45	0.88	1.15	1.47	1.66	1.73	2.12	2.09
4	01/07/2022	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.05	0.05	0.10	0.24	0.43	0.87	1.17	1.50	1.69	1.76	2.15	2.11

Cleaning the Data¶

We can see that there is a bit of unwanted data and columns in the dataframe above. Let’s clean it up and create a function to clean up future dataframes from the website:

In [17]:

df.columns

Out[17]:

Index(['Date', '20 YR', '30 YR', 'Extrapolation Factor',
       '8 WEEKS BANK DISCOUNT', 'COUPON EQUIVALENT', '52 WEEKS BANK DISCOUNT',
       'COUPON EQUIVALENT.1', '1 Mo', '2 Mo', '3 Mo', '6 Mo', '1 Yr', '2 Yr',
       '3 Yr', '5 Yr', '7 Yr', '10 Yr', '20 Yr', '30 Yr'],
      dtype='object')

In [18]:

df['Date'] = pd.to_datetime(df['Date'])
df = df.drop(['20 YR', '30 YR', 'Extrapolation Factor',
       '8 WEEKS BANK DISCOUNT', 'COUPON EQUIVALENT', '52 WEEKS BANK DISCOUNT',
       'COUPON EQUIVALENT.1'],axis=1).set_index('Date')

In [19]:

df

Out[19]:

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	1 Mo	2 Mo	3 Mo	6 Mo	1 Yr	2 Yr	3 Yr	5 Yr	7 Yr	10 Yr	20 Yr	30 Yr
Date
2022-01-03	0.05	0.06	0.08	0.22	0.40	0.78	1.04	1.37	1.55	1.63	2.05	2.01
2022-01-04	0.06	0.05	0.08	0.22	0.38	0.77	1.02	1.37	1.57	1.66	2.10	2.07
2022-01-05	0.05	0.06	0.09	0.22	0.41	0.83	1.10	1.43	1.62	1.71	2.12	2.09
2022-01-06	0.04	0.05	0.10	0.23	0.45	0.88	1.15	1.47	1.66	1.73	2.12	2.09
2022-01-07	0.05	0.05	0.10	0.24	0.43	0.87	1.17	1.50	1.69	1.76	2.15	2.11
…	…	…	…	…	…	…	…	…	…	…	…	…
2022-06-23	1.12	1.54	1.65	2.44	2.78	3.01	3.12	3.14	3.16	3.09	3.45	3.21
2022-06-24	1.19	1.60	1.73	2.51	2.83	3.04	3.13	3.18	3.19	3.13	3.51	3.26
2022-06-27	1.16	1.57	1.79	2.56	2.89	3.08	3.21	3.24	3.27	3.20	3.56	3.31
2022-06-28	1.12	1.59	1.79	2.55	2.88	3.10	3.21	3.25	3.27	3.20	3.55	3.30
2022-06-29	1.12	1.53	1.78	2.55	2.88	3.06	3.13	3.15	3.17	3.10	3.46	3.22

123 rows × 12 columns

Function for Cleaning¶

Now that we have the cleaning steps we can easily create a function for this as well as resetting the dataframe index to be the Date column:

In [20]:

def clean_df(df):
    df['Date'] = pd.to_datetime(df['Date'])
    df = df.set_index('Date')
    df = df.drop(['20 YR', '30 YR', 'Extrapolation Factor',
       '8 WEEKS BANK DISCOUNT', 'COUPON EQUIVALENT', '52 WEEKS BANK DISCOUNT',
       'COUPON EQUIVALENT.1','2 Mo'],axis=1)
    return df

In [11]:

df = clean_df(df)

In [7]:

df

Out[7]:

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	1 Mo	3 Mo	6 Mo	1 Yr	2 Yr	3 Yr	5 Yr	7 Yr	10 Yr	20 Yr	30 Yr
Date
2022-01-03	0.05	0.08	0.22	0.40	0.78	1.04	1.37	1.55	1.63	2.05	2.01
2022-01-04	0.06	0.08	0.22	0.38	0.77	1.02	1.37	1.57	1.66	2.10	2.07
2022-01-05	0.05	0.09	0.22	0.41	0.83	1.10	1.43	1.62	1.71	2.12	2.09
2022-01-06	0.04	0.10	0.23	0.45	0.88	1.15	1.47	1.66	1.73	2.12	2.09
2022-01-07	0.05	0.10	0.24	0.43	0.87	1.17	1.50	1.69	1.76	2.15	2.11
…	…	…	…	…	…	…	…	…	…	…	…
2022-06-23	1.12	1.65	2.44	2.78	3.01	3.12	3.14	3.16	3.09	3.45	3.21
2022-06-24	1.19	1.73	2.51	2.83	3.04	3.13	3.18	3.19	3.13	3.51	3.26
2022-06-27	1.16	1.79	2.56	2.89	3.08	3.21	3.24	3.27	3.20	3.56	3.31
2022-06-28	1.12	1.79	2.55	2.88	3.10	3.21	3.25	3.27	3.20	3.55	3.30
2022-06-29	1.12	1.78	2.55	2.88	3.06	3.13	3.15	3.17	3.10	3.46	3.22

123 rows × 11 columns

Visualizing a Heat Map of Data¶

For a single year, we can visualize this in the form of a heatmap, which is essentially a flattened surface plot, where the Z value in this case is the actual interest yield on the treasury bill, denoted by the color:

In [21]:

import seaborn as sns

In [22]:

sns.heatmap(df)

Out[22]:

<AxesSubplot:ylabel='Date'>

Grabbing Data for Multiple Years¶

Our heatmap from above looks good. Let’s loop through a few years to grab and clean dataframes from the treasury website to get a lot of data to concatenate together:

In [24]:

for year in range(2010,2021):
    url = f"https://home.treasury.gov/resource-center/data-chart-center/interest-rates/TextView?type=daily_treasury_yield_curve&field_tdr_date_value={year}"
    table_list = pd.read_html(url)
    df = pd.concat([df,clean_df(table_list[0])]) 

In [25]:

df

Out[25]:

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	1 Mo	2 Mo	3 Mo	6 Mo	1 Yr	2 Yr	3 Yr	5 Yr	7 Yr	10 Yr	20 Yr	30 Yr
Date
2022-01-03	0.05	0.06	0.08	0.22	0.40	0.78	1.04	1.37	1.55	1.63	2.05	2.01
2022-01-04	0.06	0.05	0.08	0.22	0.38	0.77	1.02	1.37	1.57	1.66	2.10	2.07
2022-01-05	0.05	0.06	0.09	0.22	0.41	0.83	1.10	1.43	1.62	1.71	2.12	2.09
2022-01-06	0.04	0.05	0.10	0.23	0.45	0.88	1.15	1.47	1.66	1.73	2.12	2.09
2022-01-07	0.05	0.05	0.10	0.24	0.43	0.87	1.17	1.50	1.69	1.76	2.15	2.11
…	…	…	…	…	…	…	…	…	…	…	…	…
2020-12-24	0.09	NaN	0.09	0.09	0.10	0.13	0.17	0.37	0.66	0.94	1.46	1.66
2020-12-28	0.09	NaN	0.11	0.11	0.11	0.13	0.17	0.38	0.65	0.94	1.46	1.67
2020-12-29	0.08	NaN	0.10	0.12	0.11	0.12	0.17	0.37	0.66	0.94	1.47	1.67
2020-12-30	0.06	NaN	0.08	0.09	0.12	0.12	0.17	0.37	0.66	0.93	1.46	1.66
2020-12-31	0.08	NaN	0.09	0.09	0.10	0.13	0.17	0.36	0.65	0.93	1.45	1.65

2876 rows × 12 columns

In [26]:

df = df.sort_index()

In [27]:

df

Out[27]:

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	1 Mo	2 Mo	3 Mo	6 Mo	1 Yr	2 Yr	3 Yr	5 Yr	7 Yr	10 Yr	20 Yr	30 Yr
Date
2010-01-04	0.05	NaN	0.08	0.18	0.45	1.09	1.66	2.65	3.36	3.85	4.60	4.65
2010-01-05	0.03	NaN	0.07	0.17	0.41	1.01	1.57	2.56	3.28	3.77	4.54	4.59
2010-01-06	0.03	NaN	0.06	0.15	0.40	1.01	1.60	2.60	3.33	3.85	4.63	4.70
2010-01-07	0.02	NaN	0.05	0.16	0.40	1.03	1.62	2.62	3.33	3.85	4.62	4.69
2010-01-08	0.02	NaN	0.05	0.15	0.37	0.96	1.56	2.57	3.31	3.83	4.61	4.70
…	…	…	…	…	…	…	…	…	…	…	…	…
2022-06-23	1.12	1.54	1.65	2.44	2.78	3.01	3.12	3.14	3.16	3.09	3.45	3.21
2022-06-24	1.19	1.60	1.73	2.51	2.83	3.04	3.13	3.18	3.19	3.13	3.51	3.26
2022-06-27	1.16	1.57	1.79	2.56	2.89	3.08	3.21	3.24	3.27	3.20	3.56	3.31
2022-06-28	1.12	1.59	1.79	2.55	2.88	3.10	3.21	3.25	3.27	3.20	3.55	3.30
2022-06-29	1.12	1.53	1.78	2.55	2.88	3.06	3.13	3.15	3.17	3.10	3.46	3.22

2876 rows × 12 columns

Looks like we still have the 2 month, which was not available in earlier years, let’s remove it:

In [28]:

df = df.drop('2 Mo',axis=1)

In [29]:

df.head()

Out[29]:

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	1 Mo	3 Mo	6 Mo	1 Yr	2 Yr	3 Yr	5 Yr	7 Yr	10 Yr	20 Yr	30 Yr
Date
2010-01-04	0.05	0.08	0.18	0.45	1.09	1.66	2.65	3.36	3.85	4.60	4.65
2010-01-05	0.03	0.07	0.17	0.41	1.01	1.57	2.56	3.28	3.77	4.54	4.59
2010-01-06	0.03	0.06	0.15	0.40	1.01	1.60	2.60	3.33	3.85	4.63	4.70
2010-01-07	0.02	0.05	0.16	0.40	1.03	1.62	2.62	3.33	3.85	4.62	4.69
2010-01-08	0.02	0.05	0.15	0.37	0.96	1.56	2.57	3.31	3.83	4.61	4.70

Creating a 3D Surface Plot¶

Now we can use Plotly to easily create a 3D surface plot to visualize the timeline of the curves and how they have changed over time. Pay close attention to the short term yields dropping to zero at certain times!

In [31]:

import plotly.graph_objects as go
import pandas as pd
import numpy as np

In [ ]:

x = df.columns
y = df.index
z = df.to_numpy()

fig = go.Figure(data=[go.Surface(z=z, x=x, y=y)])
fig.update_layout(title='Yield Curves',
                  scene = {"aspectratio": {"x": 1, "y": 1, "z": 0.4}})
fig.show()

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Visualizing Historical Yield Curves with Plotly and Python

Your FREE Guide to Become a Data Scientist

Grabbing Yield Curve for 2022¶

Cleaning the Data¶

Function for Cleaning¶

Visualizing a Heat Map of Data¶

Grabbing Data for Multiple Years¶

Creating a 3D Surface Plot¶

Pierian Training

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