A comprehensive Python library for downloading Midcontinent Independent System Operator (MISO) public reports into pandas dataframes.
Project description
MISOReports
A comprehensive Python library for downloading Midcontinent Independent System Operator (MISO) public reports into pandas dataframes.
As of 2024-12-22, MISOReports supports reports from MISORTWDDataBroker, MISORTWDBIReporter, and MISO Market Reports, totalling to well over 120 different reports.
With MISOReports, you can skip all of the intermediate URL generation/parsing/typing steps and get any supported report's data as a dataframe with just a few lines of code. You can also choose to retrieve the raw data directly and use that instead.
For documentation and information on currently supported reports, please check out DOCUMENTATION.md.
Features
MISOReports supports these features and more:
- Downloading reports by datetime for reports that offer a datetime option
- Downloading live reports for reports without a date option
- Downloading raw report content in any of their supported formats (csv, xml, json, xls, xlsx, etc.)
- Generating target URLs for the report of your choice
Examples
Example 1:
Download a single-table report with datetime option from MISO Market Reports.
Input:
import datetime
from MISOReports.MISOReports import MISOReports
# Downloads the data offered at
# https://docs.misoenergy.org/marketreports/20241030_da_expost_ramp_mcp.xlsx.
df = MISOReports.get_df(
report_name="da_expost_ramp_mcp",
ddatetime=datetime.datetime(year=2024, month=10, day=30),
)
print(df)
Output:
Hour Ending Reserve Zone 1 - DA MCP Ramp Up Ex-Post 1 Hour ... Reserve Zone 8 - DA MCP Ramp Up Ex-Post 1 Hour Reserve Zone 8 - DA MCP Ramp Down Ex-Post 1 Hour
0 1 0.00 ... 0.00 0.0
1 2 0.00 ... 0.00 0.0
2 3 0.00 ... 0.00 0.0
3 4 0.00 ... 0.00 0.0
4 5 0.00 ... 0.00 0.0
5 6 0.17 ... 0.17 0.0
6 7 1.48 ... 1.48 0.0
7 8 0.00 ... 0.00 0.0
8 9 0.00 ... 0.00 0.0
9 10 0.00 ... 0.00 0.0
10 11 0.00 ... 0.00 0.0
11 12 1.08 ... 1.08 0.0
12 13 1.81 ... 1.81 0.0
13 14 2.56 ... 2.56 0.0
14 15 3.13 ... 3.13 0.0
15 16 5.00 ... 5.00 0.0
16 17 5.00 ... 5.00 0.0
17 18 12.85 ... 12.85 0.0
18 19 5.17 ... 5.17 0.0
19 20 0.00 ... 0.00 0.0
20 21 0.00 ... 0.00 0.0
21 22 0.00 ... 0.00 0.0
22 23 0.00 ... 0.00 0.0
23 24 0.00 ... 0.00 0.0
[24 rows x 17 columns]
Example 2:
Download a multi-table report from MISORTWDDataBroker.
Input:
from MISOReports.MISOReports import MISOReports
# Downloads the data offered at
# https://api.misoenergy.org/MISORTWDDataBroker/DataBrokerServices.asmx?messageType=gettotalload&returnType=csv.
df = MISOReports.get_df(
report_name="totalload",
)
# For multi-table reports, use a for-loop
# to iterate across the tables.
for i, table_name in enumerate(df["table_names"]):
print(table_name)
print(df["dataframes"].iloc[i].head(3))
print()
print()
Output:
ClearedMW
Load_Hour Load_Value
0 1 65871.0
1 2 65521.0
2 3 64474.0
MediumTermLoadForecast
Hour_End Load_Forecast
0 1 68614.0
1 2 66566.0
2 3 66620.0
FiveMinTotalLoad
Load_Time Load_Value
0 1900-01-01 00:00:00 68899.0
1 1900-01-01 00:05:00 68690.0
2 1900-01-01 00:10:00 68327.0
Example 3:
Download a multi-table report from MISORTWDDataBroker.
Input:
from MISOReports.MISOReports import MISOReports
# Downloads the data offered at
# https://api.misoenergy.org/MISORTWDDataBroker/DataBrokerServices.asmx?messageType=getlmpconsolidatedtable&returnType=csv.
df = MISOReports.get_df(
report_name="lmpconsolidatedtable",
)
# For multi-table reports, use a for-loop
# to iterate across the tables.
for i, table_name in enumerate(df["table_names"]):
print(table_name)
print(df["dataframes"].iloc[i].head(3))
print()
print()
Output:
Metadata
Type Timing
0 FiveMinLMP 1900-01-01 16:45:00
1 HourlyIntegratedLmp 1900-01-01 16:00:00
2 DayAheadExAnteLmp 1900-01-01 17:00:00
Data
Name LMP - FiveMinLMP MLC - FiveMinLMP MCC - FiveMinLMP REGMCP - FiveMinLMP ... MLC - DayAheadExAnteLmp MCC - DayAheadExAnteLmp LMP - DayAheadExPostLmp MLC - DayAheadExPostLmp MCC - DayAheadExPostLmp
1 EES.PERVL2_CT 17.49 -1.69 -12.64 15.0 ... -1.15 -6.1 21.0 -1.15 -6.1
2 EES.RICE1 17.91 -1.25 -12.66 15.0 ... -0.06 -6.21 21.98 -0.06 -6.21
3 EES.RVRBEND1 18.42 -0.98 -12.42 15.0 ... -0.38 -5.83 22.04 -0.38 -5.83
[3 rows x 20 columns]
Example 4:
Download a single-table report along with its text content from MISO Market Reports.
Input:
from MISOReports.MISOReports import MISOReports
# Downloads the data offered at
# https://api.misoenergy.org/MISORTWDDataBroker/DataBrokerServices.asmx?messageType=getNAI&returnType=csv.
data = MISOReports.get_data(
report_name="NAI",
file_extension="csv",
)
print("Text Content:")
print(data.response.text)
print()
print("Dataframe:")
print(data.df)
Output:
Text Content:
RefId,22-Dec-2024 - Interval 16:40 EST
Name,Value
MISO,2212.89
Dataframe:
Name Value
0 MISO 2212.89
Example 5:
Download a single-table report with datetime option from MISO Market Reports.
Input:
import datetime
from MISOReports.MISOReports import MISOReports
# Downloads the data offered at
# https://docs.misoenergy.org/marketreports/MISOdaily3042024.xml.
# Note: the above link's 304 represents
# the number of days past the start of the year,
# 2024, which aligns with the ddatetime given below.
data = MISOReports.get_data(
report_name="MISOdaily",
ddatetime=datetime.datetime(year=2024, month=10, day=30),
)
print(data.df)
Output:
PostedValue Hour Data_Code Data_Date Data_Type UTCOffset PostingType
0 64975 1 2024-10-30 DF 5 Daily
1 63868 2 2024-10-30 DF 5 Daily
2 62750 3 2024-10-30 DF 5 Daily
3 62581 4 2024-10-30 DF 5 Daily
4 63869 5 2024-10-30 DF 5 Daily
.. ... ... ... ... ... ... ...
619 1935 20 TVA 2024-10-28 FLOW 5 Daily
620 2304 21 TVA 2024-10-28 FLOW 5 Daily
621 2379 22 TVA 2024-10-28 FLOW 5 Daily
622 2343 23 TVA 2024-10-28 FLOW 5 Daily
623 2364 24 TVA 2024-10-28 FLOW 5 Daily
[624 rows x 7 columns]
Contributing
Please take a look at our CONTRIBUTING.md for details on how to contribute.
License
This project is licensed under the MIT License - see the LICENSE file for details.
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