/dailyproduction#
This endpoint returns the daily gas production model for the lower 48, informed from pipeline scrapes, state data, and our short-term production forecast.
Endpoint#
POST |
https://hyperion.api.synmax.com/v3/dailyproduction |
Usage#
Python#
- daily_production(aggregate_by=[None], end_date=None, region=[None], start_date=None, sub_region=[None])#
Returns the daily gas production model for the lower 48.
Object returned is a pandas DataFrame.
⮞ Setup the client.
from synmax.hyperion import HyperionApiClient, ApiPayload
access_token = 'your access token goes here'
client = HyperionApiClient(access_token=access_token)
For a more detailed guide on setting up the Hyperion client, please see the Quickstart page.
⮞ Use parameters to narrow down your request.
payload = ApiPayload(start_date='2021-05-01', end_date='2022-06-25', sub_region=['S LA', 'West - TX'])
Tip
Narrow down your request to receive a faster response. Using no parameters will fetch the entire dataset which may take a very long time.
⮞ Call the function.
result_df = client.daily_production(payload)
⮞ Print the results or save them for later.
# Print the entire response as a string.
print(result_df.to_string())
# Create a CSV file from the output.
result_df.to_csv('output.csv')
# Print the number of items per column.
print(result_df.count())
For more information on outputting to different formats, please see the pandas DataFrame documentation.
Parameters#
Query#
Parameter |
Type |
Description |
Example |
---|---|---|---|
region |
str arr |
Limit search by a list of regions (clusters of multiple major producing basins). |
|
sub_region |
str arr |
Limit search by a list of subregions (clusters of several counties forming a major producing basin). |
|
start_date |
str [“YYYY-MM-DD”] |
Limit search to a start date. |
|
end_date |
str [“YYYY-MM-DD”] |
Limit search to an end date. |
|
aggregate_by |
str arr |
Aggregate data by a list of parameters. |
|
Aggregation#
Aggregation provides an overview of overall productions levels for a particular operator or across a given region during a given timeframe.
Data is always implicitly aggregated by date. This means all other column data are grouped by date.
Supported parameters: date
, region
Response#
Parameter |
Type |
Description |
Example |
---|---|---|---|
date |
str [“YYYY-MM-DD”] |
Forecasted month (unsynced). |
“2022-11-01” |
dry_gas |
float |
Gas volume in BCF (billions of standard cubic feet of gas). |
23.842 |
region |
str |
Aggregation of multiple major producing basins. |
“GULF” |
sub_region |
str |
Aggregation of several counties, typically representing a major producing basin. |
“S LA” |
Note on Dry Gas Production
The dry gas production information for each day is not fully available on that same day, as some pipeline nomination data is still missing. The complete daily production data will be ready by the next day. So, for example, if you want the full data for April 12, 2023, you’ll need to wait until April 13, 2023, to get it.
Responses#
Normal Output#
date |
dry_gas |
region |
sub_region |
---|---|---|---|
2020-06-08 |
0.3046666666666667 |
southeast |
AL |
2020-06-09 |
0.3046666666666667 |
southeast |
AL |
2020-06-10 |
0.3046666666666667 |
southeast |
AL |
2020-06-11 |
0.3046666666666667 |
southeast |
AL |
2020-06-12 |
0.3046666666666667 |
southeast |
AL |
2020-06-13 |
0.3046666666666667 |
southeast |
AL |
2020-06-14 |
0.3046666666666667 |
southeast |
AL |
2020-06-15 |
0.3046666666666667 |
southeast |
AL |
2020-06-16 |
0.3046666666666667 |
southeast |
AL |
2020-06-17 |
0.3046666666666667 |
southeast |
AL |
Note
Output data will return as “null” when input data is invalid or does not exist.
Aggregated Output#
Aggregated by…
payload = {
'aggregate_by': 'date'
}
date |
dry_gas |
---|---|
2022-11-01 |
97.74523344287729 |
2022-11-02 |
97.839740496358 |
2022-11-03 |
98.18611891481966 |
2022-11-04 |
98.37531152889612 |
2022-11-05 |
97.96266564040262 |
2022-11-06 |
98.49851707562956 |
2022-11-07 |
98.82688826428509 |
2022-11-08 |
98.72588165917888 |
2022-11-09 |
98.61454742178606 |
2022-11-10 |
98.79910324791804 |
payload = {
'aggregate_by': 'region'
}
date |
dry_gas |
region |
---|---|---|
2022-11-01 |
38.514448780936654 |
gulf |
2022-11-01 |
11.109955559762613 |
midwest |
2022-11-01 |
32.86035389122039 |
northeast |
2022-11-01 |
0.3401935483870968 |
southeast |
2022-11-01 |
14.920281662570506 |
west |
2022-11-02 |
38.73388477928677 |
gulf |
2022-11-02 |
11.025759682976869 |
midwest |
2022-11-02 |
32.91385410008344 |
northeast |
2022-11-02 |
0.3401935483870968 |
southeast |
2022-11-02 |
14.826048385623825 |
west |
Tip
To better explore using the aggregation functionality interactively, consider using our Excel Add-in. By familiarizing yourself with the available options, you can more efficiently query and process the data for your specific needs. To learn how to use the SynMax Excel Add-in, please refer to our Excel Add-in Documentation.