[英]Find maximum value in array for a given temperature range
Study Gas Surfactant Surfactant Concentration Additive Additive
Concentration LiquidPhase Quality Pressure (Psi) Temperature (C) Shear Rate (/Sec) Halflife (Min) Viscosity Color
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 50 0 0 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 50 0 51 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 50 0 61 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 50 0 75 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 50 0 105 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 50 0 0 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 50 0 12 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 50 0 25 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 50 0 34 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 50 0 48 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 100 0 0 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 100 0 0.1 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 100 0 0.5 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 100 0 0.79 0 yellow
Thakore 2020 N2 AOS 1 w% None None DI Water 0.95 10 100 0 0.9 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 50 0 0 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 50 0 26 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 50 0 72 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 50 0 84 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 50 0 120 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 0 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 0.33 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 1 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 1.9 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 2.4 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 0 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 0.2 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 1 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 1.3 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 1.9 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 0 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 0.1 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 0.2 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 0.26 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 100 0 0.3 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 150 0 0 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 150 0 0.05 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 150 0 0.08 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 150 0 0.13 0 yellow
Thakore 2020 N2 AOS 1 w% Guar 0.36 w% DI Water 0.95 10 150 0 0.2 0 yellow
上面顯示的是我的數組示例。 我如何找到給定溫度下“半衰期”的最大值? 假設我想從溫度為 25 度的每個元素中找到“半衰期”的最大值。 有沒有一種優雅的方法來做到這一點?
我嘗試在for
循環中循環,將溫度拆分為單獨的列表,然后使用大量if
語句,找到每個列表的最大值並將其編譯回主列表。 這非常丑陋且耗時,我想知道是否有更好的方法來做到這一點。 請告訴我!
基本上我在這里將 excel 文件加載到 Pandas 數據框中:
dv = pd.read_excel('data.xlsx')
然后我清理它並將其重命名為“已清理”,這並不重要,只是提一下。
ahmed17 = cleaned[cleaned.Study == "Ahmed 2017"]
ahmed18 = cleaned[cleaned.Study == "Ahmed 2018"]
alzo = cleaned[cleaned.Study == "Alzobaidi 2017"]
reid = cleaned[cleaned.Study == "Reidenbach 1986"]
har = cleaned[cleaned.Study == "Harris 1987"]
chen = cleaned[cleaned.Study == "Chen, Y. 2016b"]
yan = cleaned[cleaned.Study == "Yanqing Wang 2017"]
hut = cleaned[cleaned.Study == "Hutchins 2005"]
tha = cleaned[cleaned.Study == "Thakore 2020"]
ha = cleaned[cleaned.Study == "Harris 1995"]
從那里,我將清理過的數據框分成單獨的研究,這個項目是一個文獻組合。
trace1 = go.Scatter(y=ahmed17[selected_y], x=ahmed17[selected_x])
最后,我將每個單獨的研究加載到跟蹤中,並將它們顯示在圖表中。 Selected y 和 selected x 是字符串,例如“Temperature (C)”和“Halflife (Min)”。
我需要做的是,在將數組拆分為單獨的研究之前,找到相對於每個溫度(0,50,100,150,200,250,300)的最大“半衰期”並將它們編譯成單獨的列表,然后將它們編譯成同一個列表。 從那里我可以將列表分成單獨的研究,我很高興。 我曾嘗試使用以下內容來做到這一點:
tha25 = [x for x in tha[selected_x] if x == 25]
要將 thakore 研究分成 25 度列表,然后從那里找到最大值。 但是我為我編譯的列表得到了一堆 Nan 值,我不確定我是否正確地拆分了列表。
如果您原始的、清理過的數據框是 df ,那么試試這個:
(df[df['Temperature']==25])['Halflife'].max()
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