API tutorial 4 - Process query result data and save to file¶

Repository: https://github.com/openego/oedialect

Please report bugs and improvements here: https://github.com/OpenEnergyPlatform/examples/issues
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In [1]:

__copyright__ = "Zentrum für nachhaltige Energiesysteme Flensburg"
__license__   = "GNU Affero General Public License Version 3 (AGPL-3.0)"
__url__       = "https://github.com/openego/data_processing/blob/master/LICENSE"
__author__    = "wolfbunke"

__copyright__ = "Zentrum für nachhaltige Energiesysteme Flensburg" __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" __url__ = "https://github.com/openego/data_processing/blob/master/LICENSE" __author__ = "wolfbunke"

Introduction¶

This is an important information!

This is an information!

This is your task!

This tutorial gives you an overview of the OpenEnergy Platform and how you can work with the REST-full-HTTP API in Python.
The full API documentaion can be found on ReadtheDocs.io.

Part IV - How to work with the OpenEnergy Platform (OEP)¶

0 Setup token
1 Select data
2 Make a pandas dataframe
3 Make calculations
4 Save results as csv and excel files

Part IV¶

0. Setup token¶

Do not push your token to GitHub!

In [ ]:

import requests
import pandas as pd
from IPython.core.display import HTML

from token_config import oep_url, get_oep_token

# token
your_token = get_oep_token()

import requests import pandas as pd from IPython.core.display import HTML from token_config import oep_url, get_oep_token # token your_token = get_oep_token()

1. Select data¶

In [ ]:

# select powerplant data
schema = 'supply'
table = 'ego_dp_conv_powerplant'
where = 'version=v0.2.10'

conv_powerplants = requests.get(oep_url+'/api/v0/schema/'+schema+'/tables/'+table+'/rows/?where='+where, )
conv_powerplants.status_code

# select powerplant data schema = 'supply' table = 'ego_dp_conv_powerplant' where = 'version=v0.2.10' conv_powerplants = requests.get(oep_url+'/api/v0/schema/'+schema+'/tables/'+table+'/rows/?where='+where, ) conv_powerplants.status_code

Response [200] succesfully selected data!
Response [404] table doesn't exist!

2. Make a pandas dataframe¶

In [ ]:

df_pp = pd.DataFrame(conv_powerplants.json())

df_pp = pd.DataFrame(conv_powerplants.json())

3. Make calculations¶

Get an overview of your DataFrame:

In [ ]:

df_pp.info()

df_pp.info()

Sum the installed Capacity by fuels and add the unit MW in a new column.

In [ ]:

results = df_pp[['capacity','fuel']].groupby('fuel').sum()
results['units'] = 'MW'
results

results = df_pp[['capacity','fuel']].groupby('fuel').sum() results['units'] = 'MW' results

Create a csv file from the data frame with a suitable name and configure the representation of the different data types.

In [ ]:

# Write DataFrame as csv with desired column seperator and representation of the datatypes
results.to_csv(
    'Conventional_powerplants_germany.csv',
    sep=',',
    float_format='%.3f',
    decimal='.',
    date_format='%Y-%m-%d',
    encoding='utf-8'
)

# Write DataFrame as csv with desired column seperator and representation of the datatypes results.to_csv( 'Conventional_powerplants_germany.csv', sep=',', float_format='%.3f', decimal='.', date_format='%Y-%m-%d', encoding='utf-8' )

The file can be found in the /api folder.

Create a .xlsx file with a suitable name and two sheets. The sheets must contain the data from 1. result and 2. df_pp.

In [ ]:

# Write the results as xlsx file
writer = pd.ExcelWriter('Conventional_powerplants_germany.xlsx', engine='xlsxwriter')

# write results of installed Capacity by fuels
results.to_excel(writer, index=False, sheet_name='Installed Capacities by fuel')

# write orgininal data in second sheet
df_pp.to_excel(writer, index=False, sheet_name='Conventional Powerplants')

# Close the Pandas Excel writer and output the Excel file.
writer.save()

# Write the results as xlsx file writer = pd.ExcelWriter('Conventional_powerplants_germany.xlsx', engine='xlsxwriter') # write results of installed Capacity by fuels results.to_excel(writer, index=False, sheet_name='Installed Capacities by fuel') # write orgininal data in second sheet df_pp.to_excel(writer, index=False, sheet_name='Conventional Powerplants') # Close the Pandas Excel writer and output the Excel file. writer.save()

The file can be found in the /api folder.