{
    "info": {
        "author": "Moi Je",
        "author_email": "arthur.feyt@kaizen-solutions.net",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6"
        ],
        "description": "# Forecastor for Buildings' Consumption\n\nGo check ce lien pour r\u00c3\u00a9diger le README: \n[Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)\n\n### Installation\nCommand line:\n*pip install building_energy_forecastor*\n\n### Features' list for preprocessing data from *src/building_preprocess*\n* **day_of_week(date_serie)**: Takes a pandas.Series of dates and returns a pandas.Series of corresponding week days\n(['Monday', 'Tuesday', ...]).\n* **set_time_index(df, timeindex='Timestamp')**: Set the time column as index of the dataframe df. By default the column's\nlabel is 'Timestamp'.\n* **time_to_cycle(df, timeindex='Timestamp')**: From the 3rd competitor of the [Forecast challenge](https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/)\nby Schneider Electric. Add column to a copy of df containing cosinus and sinus functions of the time of the day, the month of the year and the day of the year.\n* **add_weather(df, weather, timeindex='Timestamp', freq_temp='D')**: From the 3rd competitor of the [Forecast challenge](https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/) by Schneider Electric.\nAdds the weather data to the training dataset (*df* here) merging the two dataframes on the 'Timestamp' and rouding the time\nvalue in weather to the precised freq_temp ('D' by default).\n* **fill_temperature(df, tempindex='Temperature')**: fill the NaN values in the tempindex column by computing the mean on the\ntwo closest framing values.\n\n### Model functions from *src/building_model*\n* **building_regressor()**: Returns a linear regressor from Scikit-learn.\n* **building_train(reg, X, y)**: Trains the regressor with X the data and Y the targeted values.\n* **building_prediction(reg, X)**: Returns a pandas.DataFrame showing the prediction of the regressor *reg* given the data *X*.",
        "description_content_type": "text/markdown",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://gitlab.com/KZSLAB/building_energy_forecastor.git",
        "keywords": "",
        "license": "",
        "maintainer": "",
        "maintainer_email": "",
        "name": "building-energy-forecastor",
        "package_url": "https://pypi.org/project/building-energy-forecastor/",
        "platform": "",
        "project_url": "https://pypi.org/project/building-energy-forecastor/",
        "project_urls": {
            "Homepage": "https://gitlab.com/KZSLAB/building_energy_forecastor.git"
        },
        "release_url": "https://pypi.org/project/building-energy-forecastor/0.0.3/",
        "requires_dist": null,
        "requires_python": "",
        "summary": "Pour l'instant fait pas grand chose",
        "version": "0.0.3"
    },
    "last_serial": 5545541,
    "releases": {
        "0.0.1": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "2e54e6573ea85b936b89d9d07932ea67",
                    "sha256": "0f15223f9e91f122a994f992fd18bf971d3f8a1d3b65a66922be8c78f19dae3f"
                },
                "downloads": -1,
                "filename": "building_energy_forecastor-0.0.1-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "2e54e6573ea85b936b89d9d07932ea67",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": null,
                "size": 5498,
                "upload_time": "2019-07-11T13:15:48",
                "url": "https://files.pythonhosted.org/packages/59/65/ddd0a127993a7e9cb03b33456fd6855f94aa29e00bae0014e11e82273974/building_energy_forecastor-0.0.1-py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "0f9842beb78da3e61ffaeb4dccbf110c",
                    "sha256": "8273e7acae2a80407f7f11b213f22c237c41456e980767016e03a85320bb88ab"
                },
                "downloads": -1,
                "filename": "building_energy_forecastor-0.0.1.tar.gz",
                "has_sig": false,
                "md5_digest": "0f9842beb78da3e61ffaeb4dccbf110c",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 3818,
                "upload_time": "2019-07-11T13:15:50",
                "url": "https://files.pythonhosted.org/packages/f0/ac/3bac7b58e2cbb5cbaf8970b2e3809929107320f45a4507eab44a62307627/building_energy_forecastor-0.0.1.tar.gz"
            }
        ],
        "0.0.2": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "1a2fca4bd03c931eb7fc91d3312cb8ec",
                    "sha256": "9c46ad088e3080323c1dd33f6feec0ad75e6fc3935e4e5e7a5d3cd6c6a6fd7b9"
                },
                "downloads": -1,
                "filename": "building_energy_forecastor-0.0.2.tar.gz",
                "has_sig": false,
                "md5_digest": "1a2fca4bd03c931eb7fc91d3312cb8ec",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 6623,
                "upload_time": "2019-07-17T12:00:05",
                "url": "https://files.pythonhosted.org/packages/f8/ba/ba57f0e098469eb822e8142657d34e7354e57066fcb17e21b2fbaa426f02/building_energy_forecastor-0.0.2.tar.gz"
            }
        ],
        "0.0.3": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "d6d2d825a8f85a49f7ad7d1262dee77a",
                    "sha256": "dff50382878a3daedeef941681a1f633c122fe034a63928284c816203a8cf339"
                },
                "downloads": -1,
                "filename": "building_energy_forecastor-0.0.3.tar.gz",
                "has_sig": false,
                "md5_digest": "d6d2d825a8f85a49f7ad7d1262dee77a",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 6617,
                "upload_time": "2019-07-17T12:12:08",
                "url": "https://files.pythonhosted.org/packages/28/33/f56425a630644e6162ab0b0b6b3fea7d60208d866f071737e355451b6680/building_energy_forecastor-0.0.3.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "d6d2d825a8f85a49f7ad7d1262dee77a",
                "sha256": "dff50382878a3daedeef941681a1f633c122fe034a63928284c816203a8cf339"
            },
            "downloads": -1,
            "filename": "building_energy_forecastor-0.0.3.tar.gz",
            "has_sig": false,
            "md5_digest": "d6d2d825a8f85a49f7ad7d1262dee77a",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 6617,
            "upload_time": "2019-07-17T12:12:08",
            "url": "https://files.pythonhosted.org/packages/28/33/f56425a630644e6162ab0b0b6b3fea7d60208d866f071737e355451b6680/building_energy_forecastor-0.0.3.tar.gz"
        }
    ]
}