Werte von 0-1 zu 0-100 angepasst closes #12
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4487932f1b
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d8d2d1e757
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@ -77,7 +77,7 @@ def region_movingAverage(id: int, scrape_date_start_min: str):
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values = df.select(pl.exclude("dates"))
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values = df.select(pl.exclude("dates"))
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sum_hor = values.sum_horizontal()
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sum_hor = values.sum_horizontal()
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sum_hor = sum_hor / num_properties[arrayCunter] / 2
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sum_hor = sum_hor / num_properties[arrayCunter] / 2 * 100
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arrayCunter += 1
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arrayCunter += 1
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newDF = dates.with_columns(sum_hor=pl.Series(sum_hor))
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newDF = dates.with_columns(sum_hor=pl.Series(sum_hor))
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@ -109,6 +109,5 @@ def region_movingAverage(id: int, scrape_date_start_min: str):
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# Add moving_averages to df
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# Add moving_averages to df
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outDF = outDF.with_columns(moving_averages=pl.Series(moving_averages))
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outDF = outDF.with_columns(moving_averages=pl.Series(moving_averages))
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result = {'dates':outDF.get_column('dates').to_list(), 'cap_earlierTimeframe':outDF.get_column('sum_hor_predict').to_list(), 'cap_laterTimeframe':outDF.get_column('sum_hor_actual').to_list(), 'movAvg':outDF.get_column('moving_averages').to_list(),}
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result = {'dates':outDF.get_column('dates').to_list(), 'cap_earlierTimeframe':outDF.get_column('sum_hor_predict').to_list(), 'cap_laterTimeframe':outDF.get_column('sum_hor_actual').to_list(), 'movAvg':outDF.get_column('moving_averages').to_list(),}
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return result
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return result
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