<jupyter_start><jupyter_text>Table of contents1. [About Open Power System Data](opsd)2. [About Jupyter Notebooks and GitHub](jupyter)3. [About this datapackage](datapackage)4. [Data sources](sources)5. [Naming conventions](naming)6. [License](license)       Time series: Processing Notebook              Main Notebook        Processing Notebook            This Notebook is part of the heating profiles data package contributed to the Open Power System Data platform.  1. About Open Power System Data This notebook is a contribution to the project [Open Power System Data](http://open-power-system-data.org). Open Power System Data develops a platform for free and open data for electricity system modeling. We collect, check, process, document, and provide data that are publicly available but currently inconvenient to use. More info on Open Power System Data:- [Information on the project on our website](http://open-power-system-data.org)- [Data and metadata on our data platform](http://data.open-power-system-data.org)- [Data processing scripts on our GitHub page](https://github.com/Open-Power-System-Data)  2. About Jupyter Notebooks and GitHub This file is a [Jupyter Notebook](http://jupyter.org/). A Jupyter Notebook is a file that combines executable programming code with visualizations and comments in markdown format, allowing for an intuitive documentation of the code. We use Jupyter Notebooks for combined coding and documentation. We use Python 3 as programming language. All Notebooks are stored on [GitHub](https://github.com/), a platform for software development, and are publicly available. More information on our IT-concept can be found [here](http://open-power-system-data.org/it). See also our [step-by-step manual](http://open-power-system-data.org/step-by-step) how to use the dataplatform.  3. About this datapackage This dataset comprises national time series for representing building heat pumps in power system models. The heat demand of buildings and the coefficient of performance (COP) of heat pumps is calculated for several European countries and years in an hourly resolution.      Heat demand time series for space and water heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. The profiles are year-wise scaled to 1 TWh each. For the years 2008 to 2013, the data is additionally scaled with annual statistics on the final energy consumption for heating.    COP time series for different heat sources – air, ground, and groundwater – and different heat sinks – floor heating, radiators, and water heating – are calculated based on COP and heating curves using reanalysis temperature data, spatially aggregated with respect to the heat demand, and corrected based on field measurements.    All data processing as well as the download of relevant input data is conducted in python and pandas and has been documented in the processing notebooks linked below. Please also consider and cite our Data Descriptor.  4. Data sources A complete list of data sources is provided on the [datapackage information website](http://data.open-power-system-data.org/when2heat/). They are also contained in the JSON file that contains all metadata.  5. Naming conventions<jupyter_code>import pandas as pd; pd.read_csv('input/notation.csv', index_col=list(range(4)))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.06244503078276165, 'fraction_numerical': 0.0067428906479038405, 'mean_word_length': 4.724832214765101, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '260909', 'license': 'MIT', 'n_tokens_mistral': 835, 'n_tokens_neox': 784, 'n_words': 442, 'path': 'main.ipynb', 'repo_name': 'FLomb/when2heat', 'size': '3411'}