46 lines
1.1 KiB
Python
46 lines
1.1 KiB
Python
import pandas as pd
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# jahr-monat-tag
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# monat/tag/jahr (us-schreibweise)
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beverages_by_date = pd.read_csv("../data/beverages_by_date.csv",
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index_col=0)
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# zum datum konvertiert
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beverages_by_date.index = pd.to_datetime(
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beverages_by_date.index,
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format="%Y-%m-%d" # normalerweise nicht
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)
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print(beverages_by_date)
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print(beverages_by_date.index.dtype)
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print()
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sampler = beverages_by_date.resample("2W")
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for el in sampler:
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print(el)
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print(sampler)
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print(beverages_by_date.loc["2024-02-8":"2024-02-14"])
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by_weekly = beverages_by_date.resample("2W").agg({
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'coffee': ["sum", "mean", "std", "count"]
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})
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print(by_weekly)
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# bfill und ffill
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# interploate = linear
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#
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daily = beverages_by_date.resample("8h").bfill()
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print(daily.loc["2024-02-8":"2024-02-14"])
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# übung mit zeiten
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solar_df = pd.read_csv("../data/Balkonkraftwerk.csv", index_col=0)
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solar_df.index = pd.to_datetime(solar_df.index)
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print(solar_df)
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print(solar_df.columns)
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# 1) Wie sieht es im durchschnitt jeden Tag aus (D)
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# 2) An welchen Tagen war die effizientz > 35%
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# 3) Stündliche Werte interpolieren (h) (1h), (3h)
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# - Komisch
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