Welcome to RapidanAPI! This Python Package helps Rapidan's clients process API-accessible data and forecasts as conveniently as possible.
RapidanAPI is currently in Version 1.1.3, and we’re actively expanding the number and extensiveness of datasets available through RapidanAPI. If you have any questions about RapidanAPI or need help with its implementation, don't hesitate to reach out to us at info@rapidanenergy.com.
The easiest way to install this package is to use the package installer for Python, which enables a "pip" command. The tutorial for using the package installer can be found here: https://packaging.python.org/en/latest/tutorials/installing-packages/
The RapidanAPI package can be installed with the command "pip install RapidanAPI" either on your own computer or in a virtual environment. Installation should only take a few seconds!
Once this package is installed, you can access most datasets with a few lines of code and an API key – but endpoints like the Global Oil Balance have some extra parameters. Here is a list of endpoints and their parameters:
{% seo %} {% include head-custom.html %} {% highlight html %} Endpoint : Parameters
global_oil_balance : api_key, balance_date, columns, frequency refined_products_outlook : api_key barrels_at_risk : api_key china_risk_tracker : api_key energy_calendar : api_key us_gas_balance : api_key eu_gas_balance : api_key {% endhighlight %}
The "api_key" parameter contains your api key, which is used to identify you as a legitimate user and should be kept secret. The default usage plan allows each key to make up to 30000 requests per month. You can reach out to Rapidan at any time to inquire about API key access. Endpoints which only require the API key parameter will always pull the most current & complete version of the dataset. Here's how you can pull energy calendar data using only the “api_key” parameter:
{% seo %} {% include head-custom.html %} {% highlight python %} from RapidanAPI import energy_calendar
api_key = "YOUR_API_KEY"
df = energy_calendar(api_key)
print(df)
df.to_csv("Energy_Calendar.csv") {% endhighlight %}
This outputs a table that can be saved as a .csv, .xlsx, or other file:
{% seo %} {% include head-custom.html %} {% highlight html %} Stars Date/Event Description 0 ** Month of July Trinidad and Tobago’s Atlantic LNG (ALNG) Trai... 1 ** Month of July Russia’s Sakhalin II LNG undergoes scheduled m... 2 ** Month of July Final FERC Commissioner likely to be sworn in.... 3 * July 9 US House Financial Services Cmte hearing: Annu... 4 *** July 10 OPEC releases its July Monthly Oil Market Rep.... {% endhighlight %}
For datasets like the Global Oil Balance, parameters such as “balance_date”, “columns”, and "frequency" are included to give you extra control of the data, and allow you to retrieve historical oil balances. In the future, we plan on adding these parameters to other endpoints – such as the refined products outlook and gas balance endpoints.
The “balance_date” parameter is in YYMM format, and includes our historical oil balances from 2401 (January 2024) to present. For example, setting the date parameter as 2401 will pull data from January 2024, and setting it as 2407 will pull data from July 2024. If you want to pull the most recently updated dataset, just set this parameter as balance_date="Current" or balance_date=None.
The “columns” parameter lets you pull specific columns of data. If you want to pull the entire dataset, just set this parameter as columns="All" or columns=None. But if you want to pull specific columns, just enter one or more of the column codes listed at the bottom of this page as comma separated values. For example, columns=“OECD_CONS, OECD_SUPP” will pull OECD consumption and OECD supply.
The “frequency” parameter lets you pull either monthly or quarterly frequency data. Please note that all pre-2410 balances contain ONLY quarterly frequency data. To pull monthly/quarterly frequency data, just set this parameter as frequency="Monthly" or frequency="Quarterly". If this parameter is set as None or anything else, both monthly and quartertly data will be pulled.
Here’s an example of how you can pull all columns of our current global oil balance at a monthly frequency:
{% seo %} {% include head-custom.html %} {% highlight python %} from RapidanAPI import global_oil_balance
api_key = "YOUR_API_KEY" balance_id = "Current" columns = "All" frequency = "Monthly"
df = global_oil_balance(api_key, balance_id, columns, frequency)
print(df)
df.to_csv("Global_Oil_Balance.csv") {% endhighlight %}
Which outputs a dataframe & .csv that looks like this:
{% seo %} {% include head-custom.html %} {% highlight html %} Month (Updated 2410) OECD Consumption (mb/d) ... 0 Jan-23 44.1 ... 1 Feb-23 46.2 ... 2 Mar-23 46.1 ... 3 Apr-23 44.7 ... 4 May-23 45.9 ... 5 Jun-23 46.6 ... 6 Jul-23 45.9 ... 7 Aug-23 46.5 ... 8 Sep-23 46.1 ... 9 Oct-23 46.3 ... 10 Nov-23 46.6 ... 11 Dec-23 46.2 ... ... ... ... ... {% endhighlight %}
For your convenience, this section contains a few chunks of example code.
Here's a script that pulls and prints every dataframe available through RapidanAPI:
{% seo %} {% include head-custom.html %} {% highlight python %} from RapidanAPI import global_oil_balance, china_risk_tracker, refined_products_outlook, barrels_at_risk, eu_gas_balance, us_gas_balance, energy_calendar import pandas as pd
api_key = "YOUR_API_KEY"
global_oil_balance_df = global_oil_balance(api_key, balance_date="Current", columns="All", frequency="Monthly") refined_products_df = refined_products_outlook(api_key) barrels_at_risk_df = barrels_at_risk(api_key) china_risk_tracker_df = china_risk_tracker(api_key) eu_gas_balance_df = eu_gas_balance(api_key) us_gas_balance_df = us_gas_balance(api_key) energy_calendar_df = energy_calendar(api_key)
print("Global Oil Balance DataFrame:\n", global_oil_balance_df.head(), "\n") print("Refined Products Outlook DataFrame:\n", refined_products_df.head(), "\n") print("Barrels at Risk DataFrame:\n", barrels_at_risk_df.head(), "\n") print("China Risk Tracker DataFrame:\n", china_risk_tracker_df.head(), "\n") print("EU Gas Balance DataFrame:\n", eu_gas_balance_df.head(), "\n") print("US Gas Balance DataFrame:\n", us_gas_balance_df.head(), "\n") print("Energy Calendar DataFrame:\n", energy_calendar_df.head(), "\n") {% endhighlight %}
The following script uses the "openpyxl" package (which can be installed with the command "pip install openpyxl") to write all of the endpoints to an excel file called "Rapidan_Data.xlsx."
{% seo %} {% include head-custom.html %} {% highlight python %} from RapidanAPI import (global_oil_balance, china_risk_tracker, refined_products_outlook, barrels_at_risk, eu_gas_balance, us_gas_balance, energy_calendar) import pandas as pd
api_key = "YOUR_API_KEY"
with pd.ExcelWriter("Rapidan_Data.xlsx", engine="openpyxl") as writer: global_oil_balance(api_key, balance_date="Current", columns="All", frequency="Monthly").to_excel(writer, sheet_name="Global_Oil_Balance", index=False) refined_products_outlook(api_key).to_excel(writer, sheet_name="Refined_Products_Outlook", index=False) barrels_at_risk(api_key).to_excel(writer, sheet_name="Barrels_at_Risk", index=False) china_risk_tracker(api_key).to_excel(writer, sheet_name="China_Risk_Tracker", index=False) eu_gas_balance(api_key).to_excel(writer, sheet_name="EU_Gas_Balance", index=False) us_gas_balance(api_key).to_excel(writer, sheet_name="US_Gas_Balance", index=False) energy_calendar(api_key).to_excel(writer, sheet_name="Energy_Calendar", index=False) {% endhighlight %}
Another option is to make charts in Python, right when you pull the data. Here's an example of a script that uses the "matplotlib" and "seaborn" packages, to create some good-looking charts based on the EU Gas Balance endpoint:
{% seo %} {% include head-custom.html %} {% highlight python %} from RapidanAPI import eu_gas_balance import pandas as pd import matplotlib.pyplot as plt import seaborn as sns
api_key = "YOUR_API_KEY"
df = eu_gas_balance(api_key) date_column = df.columns[0] df[date_column] = pd.to_datetime(df[date_column], format="%B-%y", errors="coerce") df = df.dropna(subset=[date_column])
df = df[[date_column, "Total Production", "Total LNG Imports", "Total Pipeline Imports"]]
sns.set(style="whitegrid") plt.figure(figsize=(14, 8)) colors = ["#0b3d91", "#1f77b4", "#4a90e2"] labels = ["Total Production", "Total LNG Imports", "Total Pipeline Imports"] linestyles = ["-", "--", ":"]
for i, col in enumerate(labels): plt.plot(df[date_column], df[col], linestyle=linestyles[i], color=colors[i], label=col)
plt.title("Rapidan's EU Gas Balance", fontsize=20, weight="bold") plt.xlabel("Date", fontsize=14, weight="bold") plt.ylabel("Volume (bcm)", fontsize=14, weight="bold") plt.legend(title="Legend", title_fontsize="13", fontsize="12") plt.grid(True, which="both", linestyle="--", linewidth=0.5) plt.xticks(rotation=45, fontsize=12) plt.yticks(fontsize=12) plt.tight_layout()
plt.savefig("EU_Gas_Balance_Chart.png", dpi=300) plt.show() {% endhighlight %}
This outputs the following chart as a .png file:
Here is the list of unique IDs which correspond to different columns of data in our global oil balance. Please note that over time, some columns may be added or removed:
RapidanAPI is in its early stages of development, but we plan on regularly expanding the number and quality of datasets that are available. If you have any questions about RapidanAPI or need help with its implementation, don't hesitate to reach out.