7. Using APIs for Data Imports #
This chapter starts by using NASDAQ Data Link to download some BTC price and return data. We’ll also see our first set of simulations. I’ll then show you how to use Pandas Data Reader.
This is also our first time using an API. Their API, or Application Programming Interface, let’s us talk to a remote data storage system and pull in what we need. APIs are more general, though, and are used whenever you need one application to talk to another.
We’ll use the NASDAQ Data Link. They also have Python specific instructions.
You can read about the install on their package page.
We can again use pip to install packages via the command line or in your Jupyter notebook.
pip install nasdaq-data-link
To install a package directly in your notebook (e.g. in Google Colabs), use the ! pip convention.
! pip install nasdaq-data-link
When you sign-up for NASDAQ Data Link, you’ll get an API Key. You will need to add this key to the set-up to access the NASDAQ data using Quandl.
I have saved my key locally and am bringing it in with quandl.read_key, so that it isn’t publicly available. You don’t need that bit of code.
You can also install pandas-datareader using pip.
pip install pandas-datareader
Again, add the ! pip if you’re in Google Colab.
Finally, for a large set of APIs for access data, check out Rapid API. Some are free, others you have to pay for. You’ll need to get an access API key for each one. This is a great way to get data for projects!. More on this at the end of these notes.
Let’s do our usual sort of set-up code.
# Set-up
import nasdaqdatalink # You could also do something like: import nasdaqdatalink as ndl
import pandas_datareader as pdr
import numpy as np
import pandas as pd
import datetime as dt
import matplotlib as mpl
import matplotlib.pyplot as plt
# Include this to have plots show up in your Jupyter notebook.
%matplotlib inline
# nasdaqdatalink.ApiConfig.api_key = 'YOUR_KEY_HERE'
nasdaqdatalink.read_key()
#nasdaqdatalink.read_key(filepath="/data/.corporatenasdaqdatalinkapikey")
#print(nasdaqdatalink.ApiConfig.api_key)
gdp = nasdaqdatalink.get('FRED/GDP')
gdp
| Value | |
|---|---|
| Date | |
| 1947-01-01 | 243.164 |
| 1947-04-01 | 245.968 |
| 1947-07-01 | 249.585 |
| 1947-10-01 | 259.745 |
| 1948-01-01 | 265.742 |
| ... | ... |
| 2020-10-01 | 21477.597 |
| 2021-01-01 | 22038.226 |
| 2021-04-01 | 22740.959 |
| 2021-07-01 | 23202.344 |
| 2021-10-01 | 23992.355 |
300 rows × 1 columns
btc = nasdaqdatalink.get('BCHAIN/MKPRU')
btc.tail()
| Value | |
|---|---|
| Date | |
| 2024-01-04 | 42854.95 |
| 2024-01-05 | 44190.10 |
| 2024-01-06 | 44181.10 |
| 2024-01-07 | 43975.63 |
| 2024-01-08 | 43928.07 |
btc['ret'] = btc.pct_change().dropna()
btc = btc.loc['2015-01-01':,['Value', 'ret']]
btc.plot()
<AxesSubplot:xlabel='Date'>
Well, that’s not a very good graph. The returns and price levels are in different units. Let’s use an f print to show and format the average BTC return.
print(f'Average return: {100 * btc.ret.mean():.2f}%')
Average return: 0.22%
Let’s make a cumulative return chart and daily return chart. We can then stack these on top of each other. I’ll use the .sub(1) method to subtract 1 from the cumulative product. You see this a lot in the DataCamps.
btc['ret_g'] = btc.ret.add(1) # gross return
btc['ret_c'] = btc.ret_g.cumprod().sub(1) # cummulative return
btc
| Value | ret | ret_g | ret_c | |
|---|---|---|---|---|
| Date | ||||
| 2015-01-01 | 316.15 | 0.001425 | 1.001425 | 0.001425 |
| 2015-01-02 | 314.81 | -0.004238 | 0.995762 | -0.002819 |
| 2015-01-03 | 270.93 | -0.139386 | 0.860614 | -0.141812 |
| 2015-01-04 | 276.80 | 0.021666 | 1.021666 | -0.123218 |
| 2015-01-05 | 263.17 | -0.049241 | 0.950759 | -0.166392 |
| ... | ... | ... | ... | ... |
| 2024-01-04 | 42854.95 | -0.046799 | 0.953201 | 134.745803 |
| 2024-01-05 | 44190.10 | 0.031155 | 1.031155 | 138.974976 |
| 2024-01-06 | 44181.10 | -0.000204 | 0.999796 | 138.946468 |
| 2024-01-07 | 43975.63 | -0.004651 | 0.995349 | 138.295629 |
| 2024-01-08 | 43928.07 | -0.001082 | 0.998918 | 138.144979 |
3295 rows × 4 columns
We can now make a graph using the fig, axs method. This is good review! Again, notice that semi-colon at the end. This suppresses some annoying output in the Jupyter notebook.
fig, axs = plt.subplots(2, 1, sharex=True, sharey=False, figsize=(10, 6))
axs[0].plot(btc.ret_c, 'g', label = 'BTC Cumulative Return')
axs[1].plot(btc.ret, 'b', label = 'BTC Daily Return')
axs[0].set_title('BTC Cumulative Returns')
axs[1].set_title('BTC Daily Returns')
axs[0].legend()
axs[1].legend();
I can make the same graph using the .add_subplot() syntax. The method above gives you some more flexibility, since you can give both plots the same x-axis.
fig = plt.figure(figsize=(10, 6))
ax1 = fig.add_subplot(2, 1, 1)
ax1.plot(btc.ret_c, 'g', label = 'BTC Cumulative Return')
ax2 = fig.add_subplot(2, 1, 2)
ax2.plot(btc.ret, 'b', label = 'BTC Daily Return')
ax1.set_title('BTC Cumulative Returns')
ax2.set_title('BTC Daily Returns')
ax1.legend()
ax2.legend()
plt.subplots_adjust(wspace=0.5, hspace=0.5);
Let’s put together some ideas, write a function, and run a simulation. We’ll use something called geometric brownian motion (GBM). What is GBM? It is a particular stochastic differential equation. But, what’s important for us is the idea, which is fairly simple. Here’s the formula:
(7.1)#\[\begin{align}
dS = \mu S dt + \sigma S dW_t
\end{align}\]
This says that the change in the stock price has two components - a drift, or average increase over time, and a shock that it is random at each point in time. The shock is scaled by the standard deviation of returns that you use. So, larger standard deviation, the bigger the shocks can be. This is basically the simplest way that you can model an asset price.
The shocks are what make the price wiggle around around, or else it would just go up over time, based on the drift value that we use.
And, I’ll stress - we aren’t predicting here, so to speak. We are trying to capture some basic reality about how an asset moves and then seeing what is possible in the future. We aren’t making a statement about whether we think an asset is overvalued or undervalued, will go up or down, etc.
You can solve this equation to get the value of the asset at any point in time t. You just need to know the total of all of the shocks at time t.
(7.2)#\[\begin{align}
S(t) = S(0) \exp \left(\left(\mu - \frac{1}{2}\sigma^2\right)t + \sigma W(t)\right)
\end{align}\]
T = 30 # How long is our simulation? Let's do 31 days (0 to 30 the way Python counts)
N = 30 # number of time points in the prediction time horizon, making this the same as T means that we will simulate daily returns
S_0 = btc.Value[-1] # initial BTC price
N_SIM = 100 # How many simulations to run?
mu = btc.ret.mean()
sigma = btc.ret.std()
This is the basic syntax for writing a function in Python. We saw this earlier, back when doing “Comp 101”. Remember, in Python, indentation matters!
def simulate_gbm(s_0, mu, sigma, n_sims, T, N):
dt = T/N # One day
dW = np.random.normal(scale = np.sqrt(dt),
size=(n_sims, N)) # The random part
W = np.cumsum(dW, axis=1)
time_step = np.linspace(dt, T, N)
time_steps = np.broadcast_to(time_step, (n_sims, N))
S_t = s_0 * np.exp((mu - 0.5 * sigma ** 2) * time_steps + sigma * np.sqrt(time_steps) * W)
S_t = np.insert(S_t, 0, s_0, axis=1)
return S_t
Nothing happens when we define a function. We’ve just created something called simulate_gbm that we can now use just like any other Python function.
We can look at each piece of the function code, with some numbers hard-coded, to get a sense of what’s going on. This gets tricky - keep track of the dimensions. I think that’s the hardest part. How many numbers are we creating in each array? What do they mean?
# Creates 100 rows of 30 random numbers from the standard normal distribution.
dW = np.random.normal(scale = np.sqrt(1),
size=(100, 30))
# cumulative sum along each row
W = np.cumsum(dW, axis=1)
# Array with numbers from 1 to 30
time_step = np.linspace(1, 30, 30)
# Expands that to be 100 rows of numbers from 1 to 30. This is going to be the t in the formula above. So, for the price on the 30th day, we have t=30.
time_steps = np.broadcast_to(time_step, (100, 30))
# This is the formula from above to find the value of the asset any any point in time t. np.exp is the natural number e. W is the cumulative sum of all of our random shocks.
S_t = S_0 * np.exp((mu - 0.5 * sigma ** 2) * time_steps + sigma * np.sqrt(time_steps) * W)
# This inserts the initial price at the start of each row.
S_t = np.insert(S_t, 0, S_0, axis=1)
We can look at these individually, too.
dW
array([[-5.34806895e-01, 6.26811104e-01, -2.55951226e-01, ...,
1.00737530e+00, 5.78126790e-01, 3.88150560e-01],
[ 5.99873560e-01, 6.39561247e-02, 2.14466737e+00, ...,
-5.38069263e-01, -8.58050801e-01, -5.16026856e-01],
[ 1.51657474e+00, 1.24924862e+00, -6.52424136e-01, ...,
1.86145843e-01, -7.61330775e-01, 1.72615783e+00],
...,
[-5.47916337e-01, 8.63074955e-01, 2.48968864e-02, ...,
-9.00298605e-01, -1.60692342e+00, 1.61836970e+00],
[-3.78820915e-02, 5.76213842e-02, -8.58066629e-01, ...,
-2.21380687e-01, -4.67827145e-01, 1.21468252e+00],
[ 1.07314421e+00, -2.75410825e-01, 2.37539507e-01, ...,
-5.58011413e-01, -1.38785865e+00, -1.02008786e-03]])
time_steps
array([[ 1., 2., 3., ..., 28., 29., 30.],
[ 1., 2., 3., ..., 28., 29., 30.],
[ 1., 2., 3., ..., 28., 29., 30.],
...,
[ 1., 2., 3., ..., 28., 29., 30.],
[ 1., 2., 3., ..., 28., 29., 30.],
[ 1., 2., 3., ..., 28., 29., 30.]])
len(time_steps)
100
np.shape(time_steps)
(100, 30)
I do this kind of step-by-step break down all of the time. It’s the only way I can understand what’s going on.
We can then use our function. This returns an narray.
gbm_simulations = simulate_gbm(S_0, mu, sigma, N_SIM, T, N)
And, we can plot all of the simulations. I’m going to use pandas to plot, save to ax, and the style the ax.
gbm_simulations_df = pd.DataFrame(np.transpose(gbm_simulations))
# plotting
ax = gbm_simulations_df.plot(alpha=0.2, legend=False)
ax.set_title('BTC Simulations', fontsize=16);
The y-axis has a very wide range, since some extreme values are possible, given this simulation.
7.1. Using pandas-datareader and yfinance#
The pandas data-reader API lets us access additional data sources, such as FRED.
There are also API that let you access the same data. For example, Yahoo! Finance has several, like yfinance. I think that the Yahoo! Finance access for pandas-datareader was broken in a recent update. See my comments below.
Lots of developers have written APIs to access different data sources.
Note
Different data sources might require API keys. Sometimes you have to pay. Always read the documentation.
Here’s another FRED example, but using pandas-datareader.
start = dt.datetime(2010, 1, 1)
end = dt.datetime(2013, 1, 27)
gdp = pdr.DataReader('GDP', 'fred', start, end)
gdp.head
<bound method NDFrame.head of GDP
DATE
2010-01-01 14764.610
2010-04-01 14980.193
2010-07-01 15141.607
2010-10-01 15309.474
2011-01-01 15351.448
2011-04-01 15557.539
2011-07-01 15647.680
2011-10-01 15842.259
2012-01-01 16068.805
2012-04-01 16207.115
2012-07-01 16319.541
2012-10-01 16420.419
2013-01-01 16648.189>
As mentioned, the pandas data-reader and yfinance APIs let you pull stock data from Yahoo! Finance. However, Yahoo! Finance keeps breaking what you need to scrape the data, so these packages can sometimes be unreliable. However, we can get aspects of them to work.
Here’s some basic set-up that gets yfinance working.
# The usual type of set-up.
import pandas as pd
import numpy as np
import bt as bt
import ffn as ffn
# This will get our plots to automatically show up.
%matplotlib inline
# As of Dec 2022, looks like yfinance broke the ffn/bt data import. Add this to get it to work. See https://github.com/pmorissette/ffn/issues/185
import yfinance as yf
yf.pdr_override()
I’ll do the example from the yfinance webpage. This brings in information on MSFT as a yfinance ticker object. It looks like a JSON file to me. See below for more on JSON as a storage type.
msft = yf.Ticker("MSFT")
# get all stock info
msft.info
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
Input In [21], in <cell line: 4>()
1 msft = yf.Ticker("MSFT")
3 # get all stock info
----> 4 msft.info
File /opt/anaconda3/lib/python3.9/site-packages/yfinance/ticker.py:138, in Ticker.info(self)
136 @property
137 def info(self) -> dict:
--> 138 return self.get_info()
File /opt/anaconda3/lib/python3.9/site-packages/yfinance/base.py:1020, in TickerBase.get_info(self, proxy)
1018 def get_info(self, proxy=None) -> dict:
1019 self._quote.proxy = proxy
-> 1020 data = self._quote.info
1021 return data
File /opt/anaconda3/lib/python3.9/site-packages/yfinance/scrapers/quote.py:555, in Quote.info(self)
551 @property
552 def info(self) -> dict:
553 if self._info is None:
554 # self._scrape(self.proxy) # decrypt broken
--> 555 self._fetch(self.proxy)
557 self._fetch_complementary(self.proxy)
559 return self._info
File /opt/anaconda3/lib/python3.9/site-packages/yfinance/scrapers/quote.py:706, in Quote._fetch(self, proxy)
703 self._already_fetched = True
704 modules = ['summaryProfile', 'financialData', 'quoteType',
705 'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
--> 706 result = self._data.get_raw_json(
707 _BASIC_URL_ + f"/{self._data.ticker}", params={"modules": ",".join(modules), "ssl": "true"}, proxy=proxy
708 )
709 result["quoteSummary"]["result"][0]["symbol"] = self._data.ticker
710 query1_info = next(
711 (info for info in result.get("quoteSummary", {}).get("result", []) if info["symbol"] == self._data.ticker),
712 None,
713 )
File /opt/anaconda3/lib/python3.9/site-packages/yfinance/data.py:209, in TickerData.get_raw_json(self, url, user_agent_headers, params, proxy, timeout)
207 def get_raw_json(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
208 response = self.get(url, user_agent_headers=user_agent_headers, params=params, proxy=proxy, timeout=timeout)
--> 209 response.raise_for_status()
210 return response.json()
File /opt/anaconda3/lib/python3.9/site-packages/requests/models.py:1021, in Response.raise_for_status(self)
1016 http_error_msg = (
1017 f"{self.status_code} Server Error: {reason} for url: {self.url}"
1018 )
1020 if http_error_msg:
-> 1021 raise HTTPError(http_error_msg, response=self)
HTTPError: 401 Client Error: Unauthorized for url: https://query2.finance.yahoo.com/v10/finance/quoteSummary/MSFT?modules=summaryProfile%2CfinancialData%2CquoteType%2CdefaultKeyStatistics%2CassetProfile%2CsummaryDetail&ssl=true
In VS Code, you can open the rest of that in a text editor (see the message) and look at every variable in there. You can pull specific information out this object.
# Get the sector.
msft.info['sector']
'Technology'
Here’s something a bit more complex. I’ll pull the first company officer. Note the indexing, starting at 0, the usual Python way.
msft.info['companyOfficers'][0]
{'maxAge': 1,
'name': 'Mr. Satya Nadella',
'age': 55,
'title': 'Chairman & CEO',
'yearBorn': 1967,
'fiscalYear': 2022,
'totalPay': 12676750,
'exercisedValue': 0,
'unexercisedValue': 0}
You can drill down even more. Honestly, I was guessing a bit at how to access this data. This just seemed like a “Python” or “JSON” way to do it and it worked.
msft.info['companyOfficers'][0]['totalPay']
12676750
They have recent accounting data, too.
msft.info['grossMargins']
0.68522
Here’s two years of price, volume, dividend, and split data. Remember when we looked at return calculations? You need the dividends if you’re going to accurately calculate returns. You also need the stock splits, or you’ll be comparing prices pre- and post- splits, getting funky returns!
hist = msft.history(period="2y")
hist
| Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|
| Date | |||||||
| 2021-05-03 00:00:00-04:00 | 248.910705 | 249.843887 | 246.671100 | 247.397995 | 19626600 | 0.0 | 0.0 |
| 2021-05-04 00:00:00-04:00 | 246.523756 | 246.759509 | 241.406051 | 243.400085 | 32756100 | 0.0 | 0.0 |
| 2021-05-05 00:00:00-04:00 | 244.647597 | 245.079804 | 241.465007 | 242.103485 | 21901300 | 0.0 | 0.0 |
| 2021-05-06 00:00:00-04:00 | 242.083851 | 245.433442 | 240.355036 | 245.305740 | 26491100 | 0.0 | 0.0 |
| 2021-05-07 00:00:00-04:00 | 247.682876 | 249.794795 | 246.720242 | 247.987396 | 27032900 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 2023-04-26 00:00:00-04:00 | 296.700012 | 299.570007 | 292.730011 | 295.369995 | 64599200 | 0.0 | 0.0 |
| 2023-04-27 00:00:00-04:00 | 295.970001 | 305.200012 | 295.250000 | 304.829987 | 46462600 | 0.0 | 0.0 |
| 2023-04-28 00:00:00-04:00 | 304.010010 | 308.929993 | 303.309998 | 307.260010 | 36446700 | 0.0 | 0.0 |
| 2023-05-01 00:00:00-04:00 | 306.970001 | 308.600006 | 305.149994 | 305.559998 | 21294100 | 0.0 | 0.0 |
| 2023-05-02 00:00:00-04:00 | 307.760010 | 309.165009 | 303.910004 | 305.410004 | 26404431 | 0.0 | 0.0 |
504 rows × 7 columns
As noted on their webpage, you can pull multiple stocks, by ticker, at once.
tickers = yf.Tickers('msft aapl goog')
tickers.tickers['AAPL'].history(period="1mo")
| Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|
| Date | |||||||
| 2023-04-03 00:00:00-04:00 | 164.270004 | 166.289993 | 164.220001 | 166.169998 | 56976200 | 0.0 | 0.0 |
| 2023-04-04 00:00:00-04:00 | 166.600006 | 166.839996 | 165.110001 | 165.630005 | 46278300 | 0.0 | 0.0 |
| 2023-04-05 00:00:00-04:00 | 164.740005 | 165.050003 | 161.800003 | 163.759995 | 51511700 | 0.0 | 0.0 |
| 2023-04-06 00:00:00-04:00 | 162.429993 | 164.960007 | 162.000000 | 164.660004 | 45390100 | 0.0 | 0.0 |
| 2023-04-10 00:00:00-04:00 | 161.419998 | 162.029999 | 160.080002 | 162.029999 | 47716900 | 0.0 | 0.0 |
| 2023-04-11 00:00:00-04:00 | 162.350006 | 162.360001 | 160.509995 | 160.800003 | 47644200 | 0.0 | 0.0 |
| 2023-04-12 00:00:00-04:00 | 161.220001 | 162.059998 | 159.779999 | 160.100006 | 50133100 | 0.0 | 0.0 |
| 2023-04-13 00:00:00-04:00 | 161.630005 | 165.800003 | 161.419998 | 165.559998 | 68445600 | 0.0 | 0.0 |
| 2023-04-14 00:00:00-04:00 | 164.589996 | 166.320007 | 163.820007 | 165.210007 | 49337200 | 0.0 | 0.0 |
| 2023-04-17 00:00:00-04:00 | 165.089996 | 165.389999 | 164.029999 | 165.229996 | 41516200 | 0.0 | 0.0 |
| 2023-04-18 00:00:00-04:00 | 166.100006 | 167.410004 | 165.649994 | 166.470001 | 49923000 | 0.0 | 0.0 |
| 2023-04-19 00:00:00-04:00 | 165.800003 | 168.160004 | 165.539993 | 167.630005 | 47720200 | 0.0 | 0.0 |
| 2023-04-20 00:00:00-04:00 | 166.089996 | 167.869995 | 165.559998 | 166.649994 | 52456400 | 0.0 | 0.0 |
| 2023-04-21 00:00:00-04:00 | 165.050003 | 166.449997 | 164.490005 | 165.020004 | 58311900 | 0.0 | 0.0 |
| 2023-04-24 00:00:00-04:00 | 165.000000 | 165.600006 | 163.889999 | 165.330002 | 41949600 | 0.0 | 0.0 |
| 2023-04-25 00:00:00-04:00 | 165.190002 | 166.309998 | 163.729996 | 163.770004 | 48714100 | 0.0 | 0.0 |
| 2023-04-26 00:00:00-04:00 | 163.059998 | 165.279999 | 162.800003 | 163.759995 | 45498800 | 0.0 | 0.0 |
| 2023-04-27 00:00:00-04:00 | 165.190002 | 168.559998 | 165.190002 | 168.410004 | 64902300 | 0.0 | 0.0 |
| 2023-04-28 00:00:00-04:00 | 168.490005 | 169.850006 | 167.880005 | 169.679993 | 55209200 | 0.0 | 0.0 |
| 2023-05-01 00:00:00-04:00 | 169.279999 | 170.449997 | 168.639999 | 169.589996 | 52472900 | 0.0 | 0.0 |
| 2023-05-02 00:00:00-04:00 | 170.089996 | 170.350006 | 167.539993 | 168.539993 | 48425696 | 0.0 | 0.0 |
I wasn’t able to figure out how to get historical financial statement data out of yfinance. I would suggest using our Bloomberg terminals or Factset to do that. Easy enough to just pull historical data, by ticker, into Excel or a CSV file from those data sources. You can then import that into Python to use as part of a trading signal or just to do some basic comparisons and graphs.
7.2. Data Details - Using APIs#
This notes above use the NASDAQ Datalink API to pull some BTC data. Now, I’ll discuss using this API more generally, as well as using Rapid API, another website with a variety of data options. I’ll also show you an API from Github.
As mentioned above, APIs are ways for one program or piece of software to talk to another. In our case, we’re using them to get data. That data might come in as a pandas DataFrame, ready to use. Other times, it might come in as something called a JSON file. We’ll have to do a bit more work with this common data structure.
7.2.1. NASDAQ API - Another Example#
Let’s look at the NASDAQ API one more time. Once you log in, you’ll see the home page below. Note the strip across the upper-left, that has API, Python, Excel, etc. You can use the NASDAQ API in a variety of settings. There’s a SEARCH FOR DATA box at the top.
Fig. 7.1 NASDAQ API homepage.#
If you click EXPLORE next to the search box, you’re taken to a list of all of their data. Much of it is premium - you have to pay. However, you can filter for free data. There’s free data for house prices, gold and silver markets, IMF macro data, the Fed, etc. Much of this free data comes from Quandl, which was purchased by Nasdaq recently.
Quandl has been completely integrated by NASDAQ now, though you will see legacy instructions on the website that refer to its older API commands.
Fig. 7.2 Exploring NASDAQ data options.#
Let’s look at the Zillow data, the first option presented when I look for free data. I’ve used them in labs and exams.
Fig. 7.3 NASDAQ has an API for Zillow housing data.#
Each the data APIs shows you samples of what you can access. So, we see an example table with data for a particular indicator and region. We also see a table that has a list of all of the indicators and what they measure. Finally, we see a table with all of the regions and what they represent.
This data structure makes it clear that we can download value data and then merge in ID and region descriptions if needed. But, how do we do that? See the tab in the upper-left, with DATA highlighted? You can click on DOCUMENTATION and USAGE to learn more. We’ll look at a quick example here.
Click USAGE and then the Python icon. You’ll seen an example that lets you filter by a single indicator_id and region. It has your API key and the .get_table method.
However, note the quandl stuff. They haven’t transitioned this code yet. You’ll need to do a pip install for quandl.
Also, we didn’t use .get_table above for BTC. The Zillow data is stored differently.
Make sure that you include your API key. You can input it directly, using the code that they provide. I’m using a different way to do the key that doesn’t require me to type my API key into this publicly available code.
#! pip install quandl
# Bring in quandl for downloading data
import quandl
# quandl.ApiConfig.api_key = 'YOUR_KEY_HERE'
quandl.read_key()
You need that paginate=True in there in order to download all of the available data. Without it, it will only pull the first 10,000 rows. Using paginate extends the limit to 1,000,000 rows, or observations. Now, note that this could be a lot of data! You might need to download the data in chunks to get what you want.
Let’s try pulling in the indicator_id ZATT for all regions.
# zillow = quandl.get_table('ZILLOW/DATA', indicator_id = 'ZATT', paginate=True)
I’ve commented out the code above, because I know it will exceed the download limit! So, we need to be more selective.
If you look on the NASDAQ Zillow documentation page, you’ll see the three tables that you can download, the variables inside of each, and what you’re allowed to filter on. You unfortunately can’t filter on date in the ZILLOW/DATA table. Other data sets, like FRED, do let you specify start and end dates. Every API is different.
You can find examples of how to filter and sub-select your data on the NASDAQ website: https://docs.data.nasdaq.com/docs/python-tables
However, you can filter on region_id. Let’s pull the ZILLOW/REGIONS table to see what we can use.
regions = quandl.get_table('ZILLOW/REGIONS', paginate=True)
regions
| region_id | region_type | region | |
|---|---|---|---|
| None | |||
| 0 | 99999 | zip | 98847; WA; Wenatchee, WA; Chelan County; Pesha... |
| 1 | 99998 | zip | 98846; WA; Okanogan County; Pateros |
| 2 | 99997 | zip | 98845; WA; Wenatchee; Douglas County; Palisades |
| 3 | 99996 | zip | 98844; WA; Okanogan County; Oroville |
| 4 | 99995 | zip | 98843; WA; Wenatchee, WA; Douglas County; Orondo |
| ... | ... | ... | ... |
| 89300 | 100000 | zip | 98848; WA; Moses Lake, WA; Grant County; Quincy |
| 89301 | 10000 | city | Bloomington; MD; Garrett County |
| 89302 | 1000 | county | Echols County; GA; Valdosta, GA |
| 89303 | 100 | county | Bibb County; AL; Birmingham-Hoover, AL |
| 89304 | 10 | state | Colorado |
89305 rows × 3 columns
What if we just want cities?
cities = regions[regions.region_type == 'city']
cities
| region_id | region_type | region | |
|---|---|---|---|
| None | |||
| 10 | 9999 | city | Carrsville; VA; Virginia Beach-Norfolk-Newport... |
| 20 | 9998 | city | Birchleaf; VA; Dickenson County |
| 56 | 9994 | city | Wright; KS; Dodge City, KS; Ford County |
| 124 | 9987 | city | Weston; CT; Bridgeport-Stamford-Norwalk, CT; F... |
| 168 | 9980 | city | South Wilmington; IL; Chicago-Naperville-Elgin... |
| ... | ... | ... | ... |
| 89203 | 10010 | city | Atwood; KS; Rawlins County |
| 89224 | 10008 | city | Bound Brook; NJ; New York-Newark-Jersey City, ... |
| 89254 | 10005 | city | Chanute; KS; Neosho County |
| 89290 | 10001 | city | Blountsville; AL; Birmingham-Hoover, AL; Bloun... |
| 89301 | 10000 | city | Bloomington; MD; Garrett County |
28131 rows × 3 columns
cities.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 28131 entries, 10 to 89301
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 region_id 28131 non-null object
1 region_type 28131 non-null object
2 region 28131 non-null object
dtypes: object(3)
memory usage: 879.1+ KB
I like to look and see what things are stored as, too. Remember, the object type is very generic.
There are 28,131 rows of cities! How about counties?
counties = regions[regions.region_type == 'county']
counties
| region_id | region_type | region | |
|---|---|---|---|
| None | |||
| 94 | 999 | county | Durham County; NC; Durham-Chapel Hill, NC |
| 169 | 998 | county | Duplin County; NC |
| 246 | 997 | county | Dubois County; IN; Jasper, IN |
| 401 | 995 | county | Donley County; TX |
| 589 | 993 | county | Dimmit County; TX |
| ... | ... | ... | ... |
| 89069 | 1003 | county | Elmore County; AL; Montgomery, AL |
| 89120 | 1002 | county | Elbert County; GA |
| 89204 | 1001 | county | Elbert County; CO; Denver-Aurora-Lakewood, CO |
| 89302 | 1000 | county | Echols County; GA; Valdosta, GA |
| 89303 | 100 | county | Bibb County; AL; Birmingham-Hoover, AL |
3097 rows × 3 columns
Can’t find the regions you want? You could export the whole thing to a CSV file and explore it in Excel. This will show up in whatever folder you currently have as your home in VS Code.
counties.to_csv('counties.csv', index = True)
You can also open up the Variables window at the top of VS Code (or the equivalent in Google Colab) and scroll through the file, looking for the region_id values that you want.
Finally, you can search the text in a column directly. Let’s find counties in NC.
nc_counties = counties[counties['region'].str.contains("; NC")]
nc_counties
| region_id | region_type | region | |
|---|---|---|---|
| None | |||
| 94 | 999 | county | Durham County; NC; Durham-Chapel Hill, NC |
| 169 | 998 | county | Duplin County; NC |
| 2683 | 962 | county | Craven County; NC; New Bern, NC |
| 4637 | 935 | county | Chowan County; NC |
| 4972 | 93 | county | Ashe County; NC |
| ... | ... | ... | ... |
| 87475 | 1180 | county | Martin County; NC |
| 87821 | 1147 | county | Lenoir County; NC; Kinston, NC |
| 88578 | 1059 | county | Greene County; NC |
| 88670 | 1049 | county | Graham County; NC |
| 88823 | 1032 | county | Gaston County; NC; Charlotte-Concord-Gastonia,... |
100 rows × 3 columns
nc_counties.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 100 entries, 94 to 88823
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 region_id 100 non-null object
1 region_type 100 non-null object
2 region 100 non-null object
dtypes: object(3)
memory usage: 3.1+ KB
There are 100 counties in NC, so this worked. Now, we can save these regions to a list and use that to pull data.
By exploring the data like this, you can maybe find the region_id values that you want and give them as a list. I’m also going to use the qopts = option to name the columns that I want to pull. This isn’t necessary here, since I want all of the columns, but I wanted to show you that you could do this.
nc_county_list = nc_counties['region_id'].to_list()
zillow_nc = quandl.get_table('ZILLOW/DATA', indicator_id = 'ZATT', paginate = True, region_id = nc_county_list, qopts = {'columns': ['indicator_id', 'region_id', 'date', 'value']})
zillow_nc.head(25)
| indicator_id | region_id | date | value | |
|---|---|---|---|---|
| None | ||||
| 0 | ZATT | 999 | 2023-03-31 | 541442.450359 |
| 1 | ZATT | 999 | 2023-02-28 | 542934.945251 |
| 2 | ZATT | 999 | 2023-01-31 | 546725.963769 |
| 3 | ZATT | 999 | 2022-12-31 | 539928.263825 |
| 4 | ZATT | 999 | 2022-11-30 | 542585.370376 |
| 5 | ZATT | 999 | 2022-10-31 | 598375.000000 |
| 6 | ZATT | 999 | 2022-09-30 | 601490.000000 |
| 7 | ZATT | 999 | 2022-08-31 | 606467.000000 |
| 8 | ZATT | 999 | 2022-07-31 | 607902.000000 |
| 9 | ZATT | 999 | 2022-06-30 | 608465.000000 |
| 10 | ZATT | 999 | 2022-05-31 | 597607.000000 |
| 11 | ZATT | 999 | 2022-04-30 | 585235.000000 |
| 12 | ZATT | 999 | 2022-03-31 | 572754.000000 |
| 13 | ZATT | 999 | 2022-02-28 | 556641.000000 |
| 14 | ZATT | 999 | 2022-01-31 | 538430.000000 |
| 15 | ZATT | 999 | 2021-12-31 | 516010.000000 |
| 16 | ZATT | 999 | 2021-11-30 | 506505.000000 |
| 17 | ZATT | 999 | 2021-10-31 | 498382.000000 |
| 18 | ZATT | 999 | 2021-09-30 | 486909.000000 |
| 19 | ZATT | 999 | 2021-08-31 | 475100.000000 |
| 20 | ZATT | 999 | 2021-07-31 | 469689.000000 |
| 21 | ZATT | 999 | 2021-06-30 | 456018.000000 |
| 22 | ZATT | 999 | 2021-05-31 | 443594.000000 |
| 23 | ZATT | 999 | 2021-04-30 | 435319.000000 |
| 24 | ZATT | 999 | 2021-03-31 | 427213.000000 |
Hey, there’s Durham County!
zillow_nc.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 26510 entries, 0 to 26509
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 indicator_id 26510 non-null object
1 region_id 26510 non-null object
2 date 26510 non-null datetime64[ns]
3 value 26510 non-null float64
dtypes: datetime64[ns](1), float64(1), object(2)
memory usage: 828.6+ KB
Now you can filter by date if you like. And, you could pull down multiple states this way, change the variable type, etc. You could also merge in the region names using region_id as your key.
7.2.2. Using Rapid API#
Another data option is Rapid API. There’s all types of data here - markets, sports, gambling, housing, etc. People will write their own APIs, perhaps interfacing with the websites that contain the information. They can then publish their APIs on this webpage. Many have free options, some you have to pay for. There are thousands here, so you’ll have to dig around.
Fig. 7.4 Main Rapid API webpage#
One you have an account, you’ll be able to subscribe to different APIs. You probably want the data to have a free option.
The quick start guide is here.
Luckily, all of the APIs here tend to have the same structures. These are called REST APIs. This stands for “Representational State Transfer” and is just a standardized way for computers to talk to each other. They are going to use a standard data format, like JSON. More on this below.
You can read more on their API Learn page.
We’ll look at one example, Pinnacle Odds, which has some sports gambling information: https://rapidapi.com/tipsters/api/pinnacle-odds/
Once you’ve subscribed, you see the main endpoint screen.
Fig. 7.5 Pinnacle Odds endpoint page. I’ve blocked my API key with two different windows.#
At the top, you’ll see Endpoints, About, Tutorials, Discussions, and Pricing. Click around to read more about the API.
We are currently on Endpoints. Endpoints are basically like URLs. They are where different tables of data live. We are going to use this page to figure out the data that we need. And, the webpage page will also create the Python code needed to download the data!
You can start on the left of the screen. You’ll see a list of the different tables available. I’ll try List of Sports in this example. You’ll see why in a minute.
You’ll note that the middle section now changed. This is where you can filter and ask for particular types of data from that table. In this case, there are no options to change.
On the right, you’ll see Code Snippets. The default is Node.js, a type of Javascript. We don’t want that. Click the dropdown box and look for Python. They have three ways, using three different packages, to interface with the API from Python and download the data. I’ll pick Requests - it seemed to work below.
This will change the code. You’ll see the package import, your API key, the host, and the data request. You can click Copy Code.
But, before we run this on our end, let’s click Test Endpoint. That’s the blue box in the middle. Then, click Results on the left and Body. By doing this, we essentially just ran that code in the browser. We can see what data we’re going to get. This is a JSON file with 9 items. Each item has 6 keys. You can see what the keys are - they are giving us the ids for each sport. For example, “Soccer” is “id = 1”.
This is very helpful! We need to know these id values if we want to pull particular sports.
For fun, let’s pull this simple JSON file on our end. I’ve copied and pasted the code below. It didn’t like the print function, so I just dropped it. I am again loading in my API key from an separate file. You’ll use your own.
import requests
from dotenv import load_dotenv # For my .env file which contains my API keys locally
import os # For my .env file which contains my API keys locally
load_dotenv() # For my .env file which contains my API keys locally
RAPID_API_KEY = os.getenv('RAPID_API_KEY')
url = "https://pinnacle-odds.p.rapidapi.com/kit/v1/sports"
headers = {
"X-RapidAPI-Key": RAPID_API_KEY,
"X-RapidAPI-Host": "pinnacle-odds.p.rapidapi.com"
}
sports_ids = requests.request("GET", url, headers=headers)
print(sports_ids.text)
[{"id":1,"p_id":29,"name":"Soccer","last":1681571699,"special_last":1681571658,"last_call":1681571702},{"id":2,"p_id":33,"name":"Tennis","last":1681571703,"special_last":1681556172,"last_call":1681571705},{"id":3,"p_id":4,"name":"Basketball","last":1681571705,"special_last":1681571632,"last_call":1681571706},{"id":4,"p_id":19,"name":"Hockey","last":1681571474,"special_last":1681568468,"last_call":1681571707},{"id":5,"p_id":34,"name":"Volleyball","last":1681571707,"last_call":1681571709},{"id":6,"p_id":18,"name":"Handball","last":1681571709,"last_call":1681571710},{"id":7,"p_id":15,"name":"American Football","last":1681571125,"special_last":1681520390,"last_call":1681571711},{"id":8,"p_id":22,"name":"Mixed Martial Arts","last":1681571386,"special_last":1681571395,"last_call":1681571697},{"id":9,"p_id":3,"name":"Baseball","last":1681571697,"special_last":1681571666,"last_call":1681571698}]
We can turn that response file into a JSON file. This is what it wants to be!
All of the code that follows is also commented out so that it doesn’t run every time I edit this online book. The output from the code is still there, however.
sports_ids_json = sports_ids.json()
sports_ids_json
[{'id': 1,
'p_id': 29,
'name': 'Soccer',
'last': 1681571699,
'special_last': 1681571658,
'last_call': 1681571702},
{'id': 2,
'p_id': 33,
'name': 'Tennis',
'last': 1681571703,
'special_last': 1681556172,
'last_call': 1681571705},
{'id': 3,
'p_id': 4,
'name': 'Basketball',
'last': 1681571705,
'special_last': 1681571632,
'last_call': 1681571706},
{'id': 4,
'p_id': 19,
'name': 'Hockey',
'last': 1681571474,
'special_last': 1681568468,
'last_call': 1681571707},
{'id': 5,
'p_id': 34,
'name': 'Volleyball',
'last': 1681571707,
'last_call': 1681571709},
{'id': 6,
'p_id': 18,
'name': 'Handball',
'last': 1681571709,
'last_call': 1681571710},
{'id': 7,
'p_id': 15,
'name': 'American Football',
'last': 1681571125,
'special_last': 1681520390,
'last_call': 1681571711},
{'id': 8,
'p_id': 22,
'name': 'Mixed Martial Arts',
'last': 1681571386,
'special_last': 1681571395,
'last_call': 1681571697},
{'id': 9,
'p_id': 3,
'name': 'Baseball',
'last': 1681571697,
'special_last': 1681571666,
'last_call': 1681571698}]
That’s JSON. I was able to show the whole thing in the notebook.
Let’s get that into a pandas DataFrame now. To do that, we have to know a bit about how JSON files are structured. This one is easy. pd.json_normalize is a useful tool here.
sports_ids_df = pd.json_normalize(data = sports_ids_json)
sports_ids_df
| id | p_id | name | last | special_last | last_call | |
|---|---|---|---|---|---|---|
| 0 | 1 | 29 | Soccer | 1681571699 | 1.681572e+09 | 1681571702 |
| 1 | 2 | 33 | Tennis | 1681571703 | 1.681556e+09 | 1681571705 |
| 2 | 3 | 4 | Basketball | 1681571705 | 1.681572e+09 | 1681571706 |
| 3 | 4 | 19 | Hockey | 1681571474 | 1.681568e+09 | 1681571707 |
| 4 | 5 | 34 | Volleyball | 1681571707 | NaN | 1681571709 |
| 5 | 6 | 18 | Handball | 1681571709 | NaN | 1681571710 |
| 6 | 7 | 15 | American Football | 1681571125 | 1.681520e+09 | 1681571711 |
| 7 | 8 | 22 | Mixed Martial Arts | 1681571386 | 1.681571e+09 | 1681571697 |
| 8 | 9 | 3 | Baseball | 1681571697 | 1.681572e+09 | 1681571698 |
What do all of those columns mean? I don’t know! You’d want to read the documentation for your API.
Also, note how I’m changing the names of my objects as I go. I want to keep each data structure in memory - what I originally downloaded, the JSON file, the DataFrame. This way, I don’t overwrite anything and I won’t be forced to download the data all over again.
Now, let’s see if we can pull some actual data. I notice that id = 3 is Basketball. Cool. Let’s try for some NBA data. Go back to the left of the Endpoint page and click on List of archive events. The middle will change and you’ll have some required and optional inputs. I know I want sport_id to be 3. But I don’t want all basketball. Just the NBA. So, I notice the league_ids option below. But I don’t know the number of the NBA.
OK, back to the left side. See List of leagues? Click that. I put in sport_id = 3. I then click Test Endpoint. I go to Results, select Body, and then Expand All. I do a CTRL-F to look for “NBA”.
And I find a bunch of possibilities! NBA games. Summer League. D-League. Summer League! If you’re betting on NBA Summer League, please seek help. Let’s use the regular NBA. That’s league_id = 487.
Back to List of archive events. I’ll add that league ID to the bottom of the middle. I set the page_num to 1000. I then click Test Endpoint and look at what I get.
Nothing! That’s an empty looking file on the right. Maybe this API doesn’t keep archived NBA? Who knows.
Let’s try another endpoint. Click on List of markets. Let’s see what this one has. In the middle, I’ll again use the codes for basketball and the NBA. I’ll set is_have_odds to True. Let’s test the endpoint and see what we get.
Fig. 7.6 Some NBA odds for this weekend.#
We can expand the result and look at the data structure. This is a more complicated one. I see 8 items under events. These correspond to the 8 games this weekend. Then, under each event, you can keep drilling down. The level 0 is kind of like the header for that event. It has the game, the start time, the teams, etc. You’ll see 4 more keys under periods. Each of these is a different betting line, with money lines, spreads, what I think are over/under point totals, etc.
Anyway, the main thing here is that we have indeed pulled some rather complex looking data. That data is current for upcoming games, not historical. But, we can still pull this in and use it to see how to work with a more complex JSON structure.
I’ll copy and paste the code again.
import requests
url = "https://pinnacle-odds.p.rapidapi.com/kit/v1/markets"
querystring = {"sport_id":"3","league_ids":"487","is_have_odds":"true"}
headers = {
"X-RapidAPI-Key": RAPID_API_KEY,
"X-RapidAPI-Host": "pinnacle-odds.p.rapidapi.com"
}
current = requests.request("GET", url, headers=headers, params=querystring)
print(current.text)
{"sport_id":3,"sport_name":"Basketball","last":1681571705,"last_call":1681571706,"events":[{"event_id":1570671674,"sport_id":3,"league_id":487,"league_name":"NBA","starts":"2023-04-15T17:10:00","last":1681571077,"home":"Philadelphia 76ers","away":"Brooklyn 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Does that query string above make sense now?
I’ll convert that data to JSON below and peak at it.
current_json = current.json()
current_json
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'sport_name': 'Basketball',
'last': 1681571705,
'last_call': 1681571706,
'events': [{'event_id': 1570671674,
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'league_id': 487,
'league_name': 'NBA',
'starts': '2023-04-15T17:10:00',
'last': 1681571077,
'home': 'Philadelphia 76ers',
'away': 'Brooklyn Nets',
'event_type': 'prematch',
'parent_id': None,
'resulting_unit': 'Regular',
'is_have_odds': True,
'periods': {'num_0': {'line_id': 2067360473,
'number': 0,
'cutoff': '2023-04-15T17:10:00Z',
'period_status': 1,
'money_line': {'home': 1.263, 'draw': None, 'away': 4.18},
'spreads': {'-8.5': {'hdp': -8.5,
'home': 1.909,
'away': 2.0,
'max': 25000.0},
'-6.0': {'hdp': -6.0, 'home': 1.588, 'away': 2.48, 'max': 25000.0},
'-6.5': {'hdp': -6.5, 'home': 1.636, 'away': 2.37, 'max': 25000.0},
'-7.0': {'hdp': -7.0, 'home': 1.684, 'away': 2.28, 'max': 25000.0},
'-7.5': {'hdp': -7.5, 'home': 1.746, 'away': 2.19, 'max': 25000.0},
'-8.0': {'hdp': -8.0, 'home': 1.819, 'away': 2.09, 'max': 25000.0},
'-9.0': {'hdp': -9.0, 'home': 1.99, 'away': 1.9, 'max': 25000.0},
'-9.5': {'hdp': -9.5, 'home': 2.08, 'away': 1.826, 'max': 25000.0},
'-10.0': {'hdp': -10.0, 'home': 2.16, 'away': 1.757, 'max': 25000.0},
'-10.5': {'hdp': -10.5, 'home': 2.25, 'away': 1.704, 'max': 25000.0},
'-11.0': {'hdp': -11.0, 'home': 2.34, 'away': 1.653, 'max': 25000.0}},
'totals': {'213.5': {'points': 213.5,
'over': 1.862,
'under': 2.03,
'max': 5000.0},
'211.0': {'points': 211.0, 'over': 1.632, 'under': 2.35, 'max': 5000.0},
'211.5': {'points': 211.5, 'over': 1.68, 'under': 2.27, 'max': 5000.0},
'212.0': {'points': 212.0, 'over': 1.714, 'under': 2.21, 'max': 5000.0},
'212.5': {'points': 212.5, 'over': 1.763, 'under': 2.15, 'max': 5000.0},
'213.0': {'points': 213.0, 'over': 1.806, 'under': 2.09, 'max': 5000.0},
'214.0': {'points': 214.0, 'over': 1.9, 'under': 1.98, 'max': 5000.0},
'214.5': {'points': 214.5, 'over': 1.952, 'under': 1.925, 'max': 5000.0},
'215.0': {'points': 215.0, 'over': 2.01, 'under': 1.869, 'max': 5000.0},
'215.5': {'points': 215.5, 'over': 2.06, 'under': 1.826, 'max': 5000.0},
'216.0': {'points': 216.0, 'over': 2.11, 'under': 1.775, 'max': 5000.0}},
'team_total': {'home': {'points': 111.5, 'over': 1.909, 'under': 1.943},
'away': {'points': 102.5, 'over': 1.869, 'under': 1.98}},
'meta': {'number': 0,
'max_spread': 25000.0,
'max_money_line': 15000.0,
'max_total': 5000.0,
'max_team_total': 2000.0}},
'num_1': {'line_id': 2067360475,
'number': 1,
'cutoff': '2023-04-15T17:10:00Z',
'period_status': 1,
'money_line': {'home': 1.363, 'draw': None, 'away': 3.32},
'spreads': {'-5.0': {'hdp': -5.0,
'home': 1.909,
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'number': 4,
'cutoff': '2023-04-16T21:40:00Z',
'period_status': 1,
'money_line': {'home': 1.571, 'draw': None, 'away': 2.53},
'spreads': {'-2.5': {'hdp': -2.5,
'home': 1.97,
'away': 1.917,
'max': 500.0},
'-0.5': {'hdp': -0.5, 'home': 1.641, 'away': 2.34, 'max': 500.0},
'-1.0': {'hdp': -1.0, 'home': 1.699, 'away': 2.23, 'max': 500.0},
'-1.5': {'hdp': -1.5, 'home': 1.787, 'away': 2.11, 'max': 500.0},
'-2.0': {'hdp': -2.0, 'home': 1.869, 'away': 2.0, 'max': 500.0},
'-3.0': {'hdp': -3.0, 'home': 2.07, 'away': 1.813, 'max': 500.0},
'-3.5': {'hdp': -3.5, 'home': 2.17, 'away': 1.74, 'max': 500.0},
'-4.0': {'hdp': -4.0, 'home': 2.31, 'away': 1.657, 'max': 500.0},
'-4.5': {'hdp': -4.5, 'home': 2.42, 'away': 1.602, 'max': 500.0}},
'totals': {'56.0': {'points': 56.0,
'over': 1.934,
'under': 1.952,
'max': 500.0},
'54.0': {'points': 54.0, 'over': 1.636, 'under': 2.36, 'max': 500.0},
'54.5': {'points': 54.5, 'over': 1.709, 'under': 2.23, 'max': 500.0},
'55.0': {'points': 55.0, 'over': 1.769, 'under': 2.14, 'max': 500.0},
'55.5': {'points': 55.5, 'over': 1.847, 'under': 2.04, 'max': 500.0},
'56.5': {'points': 56.5, 'over': 2.01, 'under': 1.869, 'max': 500.0},
'57.0': {'points': 57.0, 'over': 2.12, 'under': 1.781, 'max': 500.0},
'57.5': {'points': 57.5, 'over': 2.22, 'under': 1.714, 'max': 500.0},
'58.0': {'points': 58.0, 'over': 2.36, 'under': 1.636, 'max': 500.0}},
'team_total': {'home': {'points': 29.0, 'over': 1.9, 'under': 1.952},
'away': {'points': 26.5, 'over': 1.833, 'under': 2.03}},
'meta': {'number': 4,
'max_spread': 500.0,
'max_money_line': 500.0,
'max_total': 500.0,
'max_team_total': 500.0}}}},
{'event_id': 1571135863,
'sport_id': 3,
'league_id': 487,
'league_name': 'NBA',
'starts': '2023-04-17T02:40:00',
'last': 1681571641,
'home': 'Denver Nuggets',
'away': 'Minnesota Timberwolves',
'event_type': 'prematch',
'parent_id': None,
'resulting_unit': 'Regular',
'is_have_odds': True,
'periods': {'num_0': {'line_id': 2067398680,
'number': 0,
'cutoff': '2023-04-17T02:40:00Z',
'period_status': 1,
'money_line': {'home': 1.353, 'draw': None, 'away': 3.45},
'spreads': {'-7.5': {'hdp': -7.5,
'home': 1.961,
'away': 1.943,
'max': 6500.0},
'-5.0': {'hdp': -5.0, 'home': 1.621, 'away': 2.4, 'max': 6500.0},
'-5.5': {'hdp': -5.5, 'home': 1.671, 'away': 2.3, 'max': 6500.0},
'-6.0': {'hdp': -6.0, 'home': 1.724, 'away': 2.21, 'max': 6500.0},
'-6.5': {'hdp': -6.5, 'home': 1.787, 'away': 2.12, 'max': 6500.0},
'-7.0': {'hdp': -7.0, 'home': 1.869, 'away': 2.04, 'max': 6500.0},
'-8.0': {'hdp': -8.0, 'home': 2.05, 'away': 1.854, 'max': 6500.0},
'-8.5': {'hdp': -8.5, 'home': 2.14, 'away': 1.781, 'max': 6500.0},
'-9.0': {'hdp': -9.0, 'home': 2.22, 'away': 1.714, 'max': 6500.0},
'-9.5': {'hdp': -9.5, 'home': 2.32, 'away': 1.666, 'max': 6500.0},
'-10.0': {'hdp': -10.0, 'home': 2.42, 'away': 1.613, 'max': 6500.0}},
'totals': {'224.5': {'points': 224.5,
'over': 1.943,
'under': 1.943,
'max': 2000.0},
'222.0': {'points': 222.0, 'over': 1.714, 'under': 2.21, 'max': 2000.0},
'222.5': {'points': 222.5, 'over': 1.757, 'under': 2.15, 'max': 2000.0},
'223.0': {'points': 223.0, 'over': 1.8, 'under': 2.09, 'max': 2000.0},
'223.5': {'points': 223.5, 'over': 1.847, 'under': 2.04, 'max': 2000.0},
'224.0': {'points': 224.0, 'over': 1.892, 'under': 1.99, 'max': 2000.0},
'225.0': {'points': 225.0, 'over': 1.99, 'under': 1.884, 'max': 2000.0},
'225.5': {'points': 225.5, 'over': 2.04, 'under': 1.84, 'max': 2000.0},
'226.0': {'points': 226.0, 'over': 2.1, 'under': 1.793, 'max': 2000.0},
'226.5': {'points': 226.5, 'over': 2.15, 'under': 1.757, 'max': 2000.0},
'227.0': {'points': 227.0, 'over': 2.21, 'under': 1.709, 'max': 2000.0}},
'team_total': {'home': {'points': 115.5, 'over': 1.877, 'under': 1.98},
'away': {'points': 108.5, 'over': 1.917, 'under': 1.934}},
'meta': {'number': 0,
'max_spread': 6500.0,
'max_money_line': 3500.0,
'max_total': 2000.0,
'max_team_total': 1000.0}},
'num_1': {'line_id': 2067398688,
'number': 1,
'cutoff': '2023-04-17T02:40:00Z',
'period_status': 1,
'money_line': {'home': 1.467, 'draw': None, 'away': 2.85},
'spreads': {'-4.0': {'hdp': -4.0,
'home': 1.917,
'away': 1.97,
'max': 3000.0},
'-2.0': {'hdp': -2.0, 'home': 1.645, 'away': 2.33, 'max': 3000.0},
'-2.5': {'hdp': -2.5, 'home': 1.694, 'away': 2.24, 'max': 3000.0},
'-3.0': {'hdp': -3.0, 'home': 1.757, 'away': 2.15, 'max': 3000.0},
'-3.5': {'hdp': -3.5, 'home': 1.833, 'away': 2.06, 'max': 3000.0},
'-4.5': {'hdp': -4.5, 'home': 2.01, 'away': 1.877, 'max': 3000.0},
'-5.0': {'hdp': -5.0, 'home': 2.09, 'away': 1.8, 'max': 3000.0},
'-5.5': {'hdp': -5.5, 'home': 2.18, 'away': 1.729, 'max': 3000.0},
'-6.0': {'hdp': -6.0, 'home': 2.26, 'away': 1.68, 'max': 3000.0}},
'totals': {'115.0': {'points': 115.0,
'over': 1.952,
'under': 1.934,
'max': 1000.0},
'113.0': {'points': 113.0, 'over': 1.724, 'under': 2.2, 'max': 1000.0},
'113.5': {'points': 113.5, 'over': 1.775, 'under': 2.12, 'max': 1000.0},
'114.0': {'points': 114.0, 'over': 1.826, 'under': 2.06, 'max': 1000.0},
'114.5': {'points': 114.5, 'over': 1.884, 'under': 2.0, 'max': 1000.0},
'115.5': {'points': 115.5, 'over': 2.01, 'under': 1.869, 'max': 1000.0},
'116.0': {'points': 116.0, 'over': 2.1, 'under': 1.8, 'max': 1000.0},
'116.5': {'points': 116.5, 'over': 2.17, 'under': 1.74, 'max': 1000.0},
'117.0': {'points': 117.0, 'over': 2.28, 'under': 1.68, 'max': 1000.0}},
'team_total': {'home': {'points': 59.5, 'over': 1.917, 'under': 1.934},
'away': {'points': 55.5, 'over': 1.943, 'under': 1.909}},
'meta': {'number': 1,
'max_spread': 3000.0,
'max_money_line': 1500.0,
'max_total': 1000.0,
'max_team_total': 500.0}},
'num_3': {'line_id': 2067398001,
'number': 3,
'cutoff': '2023-04-17T02:40:00Z',
'period_status': 1,
'money_line': {'home': 1.54, 'draw': None, 'away': 2.62},
'spreads': {'-2.5': {'hdp': -2.5,
'home': 1.934,
'away': 1.952,
'max': 1500.0},
'-0.5': {'hdp': -0.5, 'home': 1.609, 'away': 2.4, 'max': 1500.0},
'-1.0': {'hdp': -1.0, 'home': 1.666, 'away': 2.29, 'max': 1500.0},
'-1.5': {'hdp': -1.5, 'home': 1.757, 'away': 2.15, 'max': 1500.0},
'-2.0': {'hdp': -2.0, 'home': 1.833, 'away': 2.05, 'max': 1500.0},
'-3.0': {'hdp': -3.0, 'home': 2.03, 'away': 1.847, 'max': 1500.0},
'-3.5': {'hdp': -3.5, 'home': 2.13, 'away': 1.775, 'max': 1500.0},
'-4.0': {'hdp': -4.0, 'home': 2.26, 'away': 1.684, 'max': 1500.0},
'-4.5': {'hdp': -4.5, 'home': 2.37, 'away': 1.625, 'max': 1500.0}},
'totals': {'57.5': {'points': 57.5,
'over': 1.9,
'under': 1.99,
'max': 750.0},
'55.5': {'points': 55.5, 'over': 1.645, 'under': 2.34, 'max': 750.0},
'56.0': {'points': 56.0, 'over': 1.694, 'under': 2.25, 'max': 750.0},
'56.5': {'points': 56.5, 'over': 1.769, 'under': 2.14, 'max': 750.0},
'57.0': {'points': 57.0, 'over': 1.826, 'under': 2.07, 'max': 750.0},
'58.0': {'points': 58.0, 'over': 1.98, 'under': 1.9, 'max': 750.0},
'58.5': {'points': 58.5, 'over': 2.06, 'under': 1.826, 'max': 750.0},
'59.0': {'points': 59.0, 'over': 2.15, 'under': 1.757, 'max': 750.0},
'59.5': {'points': 59.5, 'over': 2.24, 'under': 1.704, 'max': 750.0}},
'team_total': {'home': {'points': 30.0, 'over': 1.884, 'under': 1.961},
'away': {'points': 27.5, 'over': 1.9, 'under': 1.952}},
'meta': {'number': 3,
'max_spread': 1500.0,
'max_money_line': 750.0,
'max_total': 750.0,
'max_team_total': 500.0}},
'num_4': {'line_id': 2067398697,
'number': 4,
'cutoff': '2023-04-17T02:40:00Z',
'period_status': 1,
'money_line': {'home': 1.68, 'draw': None, 'away': 2.3},
'spreads': {'-1.5': {'hdp': -1.5,
'home': 1.934,
'away': 1.952,
'max': 500.0},
'1.0': {'hdp': 1.0, 'home': 1.54, 'away': 2.56, 'max': 500.0},
'0.5': {'hdp': 0.5, 'home': 1.617, 'away': 2.39, 'max': 500.0},
'-0.5': {'hdp': -0.5, 'home': 1.757, 'away': 2.15, 'max': 500.0},
'-1.0': {'hdp': -1.0, 'home': 1.833, 'away': 2.05, 'max': 500.0},
'-2.0': {'hdp': -2.0, 'home': 2.03, 'away': 1.847, 'max': 500.0},
'-2.5': {'hdp': -2.5, 'home': 2.13, 'away': 1.775, 'max': 500.0},
'-3.0': {'hdp': -3.0, 'home': 2.26, 'away': 1.684, 'max': 500.0},
'-3.5': {'hdp': -3.5, 'home': 2.36, 'away': 1.625, 'max': 500.0}},
'totals': {'57.0': {'points': 57.0,
'over': 1.97,
'under': 1.917,
'max': 500.0},
'55.0': {'points': 55.0, 'over': 1.671, 'under': 2.29, 'max': 500.0},
'55.5': {'points': 55.5, 'over': 1.746, 'under': 2.17, 'max': 500.0},
'56.0': {'points': 56.0, 'over': 1.813, 'under': 2.09, 'max': 500.0},
'56.5': {'points': 56.5, 'over': 1.892, 'under': 1.99, 'max': 500.0},
'57.5': {'points': 57.5, 'over': 2.05, 'under': 1.84, 'max': 500.0},
'58.0': {'points': 58.0, 'over': 2.16, 'under': 1.757, 'max': 500.0},
'58.5': {'points': 58.5, 'over': 2.25, 'under': 1.694, 'max': 500.0},
'59.0': {'points': 59.0, 'over': 2.39, 'under': 1.621, 'max': 500.0}},
'team_total': {'home': {'points': 29.5, 'over': 2.0, 'under': 1.854},
'away': {'points': 28.0, 'over': 2.02, 'under': 1.84}},
'meta': {'number': 4,
'max_spread': 500.0,
'max_money_line': 500.0,
'max_total': 500.0,
'max_team_total': 500.0}}}}]}
Wow, that’s a lot of stuff. OK, now this is the tricky part. How do we get this thing into a pandas DataFrame? This is where we really have to think carefully. What do we actually want? Remember, a DataFrame, at its simplest, looks like a spreadsheet, with rows and columns. How could this thing possibly look like that?
current_df = pd.json_normalize(data = current_json)
current_df
| sport_id | sport_name | last | last_call | events | |
|---|---|---|---|---|---|
| 0 | 3 | Basketball | 1681571705 | 1681571706 | [{'event_id': 1570671674, 'sport_id': 3, 'leag... |
We need to flatten this file. JSON files are nested. That’s what all those brackets are doing. Let’s think a little more about that.
Fig. 7.7 JSON structure. Source: https://towardsdatascience.com/all-pandas-json-normalize-you-should-know-for-flattening-json-13eae1dfb7dd#
JSON files are like dictionaries, as you can see in the picture above. There’s a key and a value. However, they can get complicated where there’s a list of dictionaries embedded in the same data structure. You can think of navigating them like working through the branches of a tree. Which branch do you want?
To do this, we’ll use the pd.json_normalize method. We’ve just used it, but that was with a simple JSON file. It didn’t really work with the current odds data, unless we add more arguments.
You can read more here.
Everything is packed into that events column. Let’s flatten it. This will take every item in it and convert it into a new column. Keys will be combined together to create compound names that combine different levels.
current_df_events = pd.json_normalize(data = current_json, record_path=['events'])
current_df_events
| event_id | sport_id | league_id | league_name | starts | last | home | away | event_type | parent_id | ... | periods.num_0.totals.222.0.under | periods.num_0.totals.222.0.max | periods.num_0.totals.222.5.points | periods.num_0.totals.222.5.over | periods.num_0.totals.222.5.under | periods.num_0.totals.222.5.max | periods.num_0.totals.223.0.points | periods.num_0.totals.223.0.over | periods.num_0.totals.223.0.under | periods.num_0.totals.223.0.max | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1570671674 | 3 | 487 | NBA | 2023-04-15T17:10:00 | 1681571077 | Philadelphia 76ers | Brooklyn Nets | prematch | None | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 1570671683 | 3 | 487 | NBA | 2023-04-16T00:40:00 | 1681570378 | Sacramento Kings | Golden State Warriors | prematch | None | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 1570671684 | 3 | 487 | NBA | 2023-04-17T00:10:00 | 1681568411 | Phoenix Suns | Los Angeles Clippers | prematch | None | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 1570671685 | 3 | 487 | NBA | 2023-04-15T22:10:00 | 1681571621 | Cleveland Cavaliers | New York Knicks | prematch | None | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 1570794181 | 3 | 487 | NBA | 2023-04-15T19:40:00 | 1681570462 | Boston Celtics | Atlanta Hawks | prematch | None | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 5 | 1570800108 | 3 | 487 | NBA | 2023-04-16T19:10:00 | 1681567037 | Memphis Grizzlies | Los Angeles Lakers | prematch | None | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 6 | 1571130758 | 3 | 487 | NBA | 2023-04-16T21:40:00 | 1681565359 | Milwaukee Bucks | Miami Heat | prematch | None | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 7 | 1571135863 | 3 | 487 | NBA | 2023-04-17T02:40:00 | 1681571641 | Denver Nuggets | Minnesota Timberwolves | prematch | None | ... | 2.21 | 2000.0 | 222.5 | 1.757 | 2.15 | 2000.0 | 223.0 | 1.8 | 2.09 | 2000.0 |
8 rows × 856 columns
list(current_df_events)
['event_id',
'sport_id',
'league_id',
'league_name',
'starts',
'last',
'home',
'away',
'event_type',
'parent_id',
'resulting_unit',
'is_have_odds',
'periods.num_0.line_id',
'periods.num_0.number',
'periods.num_0.cutoff',
'periods.num_0.period_status',
'periods.num_0.money_line.home',
'periods.num_0.money_line.draw',
'periods.num_0.money_line.away',
'periods.num_0.spreads.-8.5.hdp',
'periods.num_0.spreads.-8.5.home',
'periods.num_0.spreads.-8.5.away',
'periods.num_0.spreads.-8.5.max',
'periods.num_0.spreads.-6.0.hdp',
'periods.num_0.spreads.-6.0.home',
'periods.num_0.spreads.-6.0.away',
'periods.num_0.spreads.-6.0.max',
'periods.num_0.spreads.-6.5.hdp',
'periods.num_0.spreads.-6.5.home',
'periods.num_0.spreads.-6.5.away',
'periods.num_0.spreads.-6.5.max',
'periods.num_0.spreads.-7.0.hdp',
'periods.num_0.spreads.-7.0.home',
'periods.num_0.spreads.-7.0.away',
'periods.num_0.spreads.-7.0.max',
'periods.num_0.spreads.-7.5.hdp',
'periods.num_0.spreads.-7.5.home',
'periods.num_0.spreads.-7.5.away',
'periods.num_0.spreads.-7.5.max',
'periods.num_0.spreads.-8.0.hdp',
'periods.num_0.spreads.-8.0.home',
'periods.num_0.spreads.-8.0.away',
'periods.num_0.spreads.-8.0.max',
'periods.num_0.spreads.-9.0.hdp',
'periods.num_0.spreads.-9.0.home',
'periods.num_0.spreads.-9.0.away',
'periods.num_0.spreads.-9.0.max',
'periods.num_0.spreads.-9.5.hdp',
'periods.num_0.spreads.-9.5.home',
'periods.num_0.spreads.-9.5.away',
'periods.num_0.spreads.-9.5.max',
'periods.num_0.spreads.-10.0.hdp',
'periods.num_0.spreads.-10.0.home',
'periods.num_0.spreads.-10.0.away',
'periods.num_0.spreads.-10.0.max',
'periods.num_0.spreads.-10.5.hdp',
'periods.num_0.spreads.-10.5.home',
'periods.num_0.spreads.-10.5.away',
'periods.num_0.spreads.-10.5.max',
'periods.num_0.spreads.-11.0.hdp',
'periods.num_0.spreads.-11.0.home',
'periods.num_0.spreads.-11.0.away',
'periods.num_0.spreads.-11.0.max',
'periods.num_0.totals.213.5.points',
'periods.num_0.totals.213.5.over',
'periods.num_0.totals.213.5.under',
'periods.num_0.totals.213.5.max',
'periods.num_0.totals.211.0.points',
'periods.num_0.totals.211.0.over',
'periods.num_0.totals.211.0.under',
'periods.num_0.totals.211.0.max',
'periods.num_0.totals.211.5.points',
'periods.num_0.totals.211.5.over',
'periods.num_0.totals.211.5.under',
'periods.num_0.totals.211.5.max',
'periods.num_0.totals.212.0.points',
'periods.num_0.totals.212.0.over',
'periods.num_0.totals.212.0.under',
'periods.num_0.totals.212.0.max',
'periods.num_0.totals.212.5.points',
'periods.num_0.totals.212.5.over',
'periods.num_0.totals.212.5.under',
'periods.num_0.totals.212.5.max',
'periods.num_0.totals.213.0.points',
'periods.num_0.totals.213.0.over',
'periods.num_0.totals.213.0.under',
'periods.num_0.totals.213.0.max',
'periods.num_0.totals.214.0.points',
'periods.num_0.totals.214.0.over',
'periods.num_0.totals.214.0.under',
'periods.num_0.totals.214.0.max',
'periods.num_0.totals.214.5.points',
'periods.num_0.totals.214.5.over',
'periods.num_0.totals.214.5.under',
'periods.num_0.totals.214.5.max',
'periods.num_0.totals.215.0.points',
'periods.num_0.totals.215.0.over',
'periods.num_0.totals.215.0.under',
'periods.num_0.totals.215.0.max',
'periods.num_0.totals.215.5.points',
'periods.num_0.totals.215.5.over',
'periods.num_0.totals.215.5.under',
'periods.num_0.totals.215.5.max',
'periods.num_0.totals.216.0.points',
'periods.num_0.totals.216.0.over',
'periods.num_0.totals.216.0.under',
'periods.num_0.totals.216.0.max',
'periods.num_0.team_total.home.points',
'periods.num_0.team_total.home.over',
'periods.num_0.team_total.home.under',
'periods.num_0.team_total.away.points',
'periods.num_0.team_total.away.over',
'periods.num_0.team_total.away.under',
'periods.num_0.meta.number',
'periods.num_0.meta.max_spread',
'periods.num_0.meta.max_money_line',
'periods.num_0.meta.max_total',
'periods.num_0.meta.max_team_total',
'periods.num_1.line_id',
'periods.num_1.number',
'periods.num_1.cutoff',
'periods.num_1.period_status',
'periods.num_1.money_line.home',
'periods.num_1.money_line.draw',
'periods.num_1.money_line.away',
'periods.num_1.spreads.-5.0.hdp',
'periods.num_1.spreads.-5.0.home',
'periods.num_1.spreads.-5.0.away',
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That’s a lot of columns! Do you see what it did? When I flattened the file, it worked its way through the dictionary and key values. So, periods to num_0 to totals to the various over/under values, etc. It then combined those permutations to create new columns, where each value is separated by a period. Then value at the end of the chain is what goes into the DataFrame.
That’s a quick introduction to Rapid API and dealing with its JSON output. Every API is different - you’ll have to play around.
7.2.3. Data on Kaggle#
Kaggle is also a great source for data. You can search their data sets here.
Searching for finance, I see one on consumer finance complaints that looks interesting. The Kaggle page describes the data, gives you a data dictionary, and some examples.
The data for Kaggle contests is usually pretty clean already. That said, you’ll usually have to do at least some work to get it ready to look at.