Add values from ferien-api and holidays library to pandas dataframe by date-CodePudding

I want to insert vacation and holiday dates to my pandas dataframe but can't figure out how... Something doesn't work out with the dates of my dataframe and the dates from the ferien-api and the holidays library. Here is my code:

import pandas as pd
from geopy.geocoders import Nominatim

def add_externals():
    geolocator = Nominatim(user_agent="plantgrid", timeout=3)
    location = geolocator.geocode("Schulweg 23, 26203 Wardenburg")
    federal_state = "NI"
    df = pd.DataFrame(pd.read_csv("Schachtschneider_further.csv", header=0))
    print("----- Adding external data -----")
    df["Auf. Datum"] = pd.to_datetime(df["Auf. Datum"])
    df.index = df["Auf. Datum"].dt.date
    df = WeatherIngest.add_daily_weather_data(df=df, location=location)
    df = CalendarIngest.add_public_holidays(df=df, federal_state=federal_state)
    df = CalendarIngest.add_school_holidays(df=df, federal_state=federal_state)
    df.to_csv("Schachtschneider_externals.csv")

The add_public_holidays function:

import pandas as pd
import holidays

def add_public_holidays(df: pd.DataFrame, federal_state: str):
    federal_state_holidays = holidays.CountryHoliday(country='Germany', prov=federal_state, years=[2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])
    if 'public_holiday' not in df.columns:
        df['public_holiday'] = 'no'
    for date in df.index:
        if date in federal_state_holidays:
            df.loc[date, 'public_holiday'] = federal_state_holidays[date]
    
    return df

And the add_school_holidays:

import pandas as pd
import ferien

def add_school_holidays(df: pd.DataFrame, federal_state: str): 
    for year in range(2001, 2021):
        federal_state_school_holidays = ferien.state_vacations(state_code=federal_state, year=year)
        if 'school_holiday' not in df.columns:
            df['school_holiday'] = 'no'
        for date in df.index:
            print(date)
            for vac in federal_state_school_holidays:
                if vac.start.date() <= date <= vac.end.date():
                    df.at[date, 'school_holiday'] = vac.name
                
    return df

The add_daily_weather_data function with the calc_daily_mean_weather_values function:

import pandas as pd
from datetime import datetime
from geopy.location import Location
from wetterdienst.provider.dwd.observation import DwdObservationRequest, DwdObservationPeriod, DwdObservationResolution, DwdObservationParameter, DwdObservationDataset
from dwdweather import DwdWeather

def calc_daily_mean_weather_values(location: Location) -> pd.Series:
    request = DwdObservationRequest(parameter = DwdObservationDataset.CLIMATE_SUMMARY,
                                         resolution=DwdObservationResolution.DAILY, 
                                         period=DwdObservationPeriod.HISTORICAL, start_date=datetime(2001, 1, 11), 
                                         end_date=datetime(2020, 10, 28), tidy=True, humanize=True, 
                                         si_units=True).filter_by_station_id(station_id=[963])
    df = request.values.all().df
    
    return df


def add_daily_weather_data(df: pd.DataFrame, location: Location):
    daily_mean_weather_values = calc_daily_mean_weather_values(location=location)
    
    temperature = daily_mean_weather_values.loc[daily_mean_weather_values['parameter'] == 'temperature_air_200']
    temperature_values = temperature['value'].to_list()
    temperature_values = [x - 273.15 for x in temperature_values]
    df['mean_temp'] = temperature_values
    
    humidity = daily_mean_weather_values.loc[daily_mean_weather_values['parameter'] == 'humidity']
    humidity_values = humidity['value'].to_list()
    df['mean_humid'] = humidity_values
        
    precipitation_height = daily_mean_weather_values.loc[daily_mean_weather_values['parameter'] == 'precipitation_height']
    precipitation_height_values = precipitation_height['value'].to_list()
    df['mean_prec_height_mm'] = precipitation_height_values
    df['total_prec_height_mm'] = [x * 24 for x in precipitation_height_values]
    
    sunshine_duration = daily_mean_weather_values.loc[daily_mean_weather_values['parameter'] == 'sunshine_duration']
    sunshine_duration_values = sunshine_duration['value'].to_list()
    df['mean_sun_dur_min'] = sunshine_duration_values
    df['total_sun_dur_h'] = [x * 24 / 60 for x in sunshine_duration_values] 
    
    return df

I'm trying around for really long now but do not come to a conclusion. Maybe someone already had a similar project and knows how to help me.

The problem is that the dates from the pandas dataframe and the dates from the ferien-api or holidays library, respectively, aren't from the same datatype and don't recognize each other, that's why I receive a data frame with two dates/ two index ('Auf. Datum'):

Auf. Datum,Auf. 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ranunculoides,Ballota,Baptisia,Barbarea,Basilikum,Begonia,Belamcanda chinensis,Berberis,Bergenia,Bergprimel,Berkleya purpurea,Beta,Bigelowia nutallii,Birne,Birnenquitte h,Bistorta,Blechnum,Bletilla,Bluetenschleier,Bluetenwoge,Blumenzwiebel,Blutwurz 'Plenum',Boehmeria sieboldiana,Boltonia,Borago,Bouteloua,Brassica,Briza,Brombeere,Brunnera,Bryonia,Buddleja,Buglossoides,Buphthalmum,Butomus,Buxus,Calamagrostis,Calamintha,Calceolaria,Calendula,Calla,Callitriche,Caltha,Camassia,Campanula,Canna,Capsicum frutescens,Cardamine,Cardiocrinum,Carduncellus,Carex,Carlina,Carpinus,Carum,Caryopteris,Catananche,Centaurea,Centaurium,Centranthus,Cephalaria,Cerastium,Ceratophyllum,Ceratostigma,Ceterach officinarum,Chaenarrhinum,Chamaemelum,Chamomilla recutita,Chasmanthium,Cheilanthes lanosa,Cheiranthus cheiri,Chelidonium,Chelone,Chenopodium,Chiastophyllum,Chionodoxa,Chrypogon gryllos,Chrysanthemum,Cichorium,Cimicifuga,Cirsium,Cistus,Claytonia sibirica,Clematis,Cochlearia 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africanus,Eriophorum,Eriophyllum,Erodium,Eryngium,Erysimum,Erythronium,Eucalyptus,Eucomis,Euonymus,Eupatorium,Euphorbia,Exotischer Bluetensaum,Fallopia,Farn,Festuca,Filipendula,Foeniculum,Fontinalis,Forsythia,Fragaria,Francoa sonchifolia varsonchifoliahne Bil,Freiland-Orchidee 'Formosana',Fritillaria,Fruehlingsstauden,Fuchsia,Funkie,Gaillardia,Galanthus nivalis ssp nivaliswiebel,Galega,Galium,Galtonia,Gartenaurikel rot,Gaura,Genista sagittalis,Gentiana,Geranium,Geum,Gillenia,Gladiolus,Glechoma,Globularia,Glyceria,Glycyrrhiza,Goniolimon,Graeser in Sorten,Gratiola officinalis,Gunnera,Gymnocarpium dryopteris,Gynostemma,Gypsophila,Hakonechloa,Halimiocistus,Hamamelis,Haplopappus lyalii,Hauswurz,Havelschwan 'Yellow Satellit',Hedera,Heimischer Bluetensaum,Helenium,Helianthemum,Helianthus,Helichrysum,Helictotrichon,Heliopsis,Helleborus,Helonias bullata,Hemerocallis,Hepatica,Heracium aurantiacum,Herbstanemonen 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Enzian,Stephanandra,Stevia,Stipa,Stokesia,Stratiotes,Stratoides,Strobelianthes,Strobilanthes atropurpureus,Succisa pratensis,Suesskirsche,Suessklee,Symphyandra pendula,Symphytum,Syringa,Tagetes,Tanacetum,Taraxacum pseudoroseum,Taxus,Telekia,Tellima,Telypteris palustris,Teppichsedum,Tetrapanapapyrifera 'Rex',Teucrium,Thalictrum,Thelypteris palustris,Thermopsis,Thymian,Thymus,Tiarella,Topinambour,Townsendia rothrockii,Trachystemon,Tradescantia,Traenendes Herz,Trapa,Tray,Trays,Tricyrtis,Trifolium,Trillium,Trollius,Tropaeolum,Tulipa,Tussilago farfara,Typha,Uncinia,Unicinia 'Rubra',Utricularia,Uvularia,Vaccinium myrtillus,Valeriana,Vancouveria hexandra,Veratrum,Verbascum,Verbena,Vernonia,Veronica,Viburnum,Vinca,Viola,Wahlenbergia albomarginata,Waldsteinia,Wasserhahnenfuss,Weigela,Weinrebe C /,Wisteria sinensis  /,Woodsia,Wulfenia carinthiaca,Yucca,Zantedeschia,Zigadenus elegans,Zitronemelisse gelb/bunt,Zitronenverbene,Zizania,mean_temp,mean_humid,mean_prec_height_mm,total_prec_height_mm,mean_sun_dur_min,total_sun_dur_h,public_holiday,school_holiday
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CodePudding user response：

I solved my problem by changing every date object to datetime.date.

def add_externals():
geolocator = Nominatim(user_agent='plantgrid', timeout=3)
location = geolocator.geocode('Schulweg 23, 26203 Wardenburg')
federal_state = 'NI'
df = pd.DataFrame(pd.read_csv('Schachtschneider_further.csv', header=0)) # , index_col=0
print("----- Adding external data -----")
df['Auf. Datum'] = pd.to_datetime(df['Auf. Datum'])
df['Auf. Datum'] = df['Auf. Datum'].dt.date
df.index = df['Auf. Datum']
df = WeatherIngest.add_daily_weather_data(df=df, location=location)
df = CalendarIngest.add_public_holidays(df=df, federal_state=federal_state)
df = CalendarIngest.add_school_holidays(df=df, federal_state=federal_state)

def add_public_holidays(df: pd.DataFrame, federal_state: str):
    federal_state_holidays = holidays.CountryHoliday(country='Germany', prov=federal_state, years=[2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])
    if 'public_holiday' not in df.columns:
        df['public_holiday'] = 'no'
    for date in df.index:
        if date in federal_state_holidays:
            df.at[date, 'public_holiday'] = federal_state_holidays[date]
    
    return df


def add_school_holidays(df: pd.DataFrame, federal_state: str): # , date: datetime.date
    for year in range(2001, 2021):
        federal_state_school_holidays = ferien.state_vacations(state_code=federal_state, year=year)
        if 'school_holiday' not in df.columns:
            df['school_holiday'] = 'no'
        index = df['Auf. Datum']
        for date in index:
            for vac in federal_state_school_holidays:
                if vac.start.date() <= date <= vac.end.date():
                    df.at[date, 'school_holiday'] = vac.name
                
    return df