使用Pandas計算新列

Question

基於這個問題，我想知道如何使用def（）來計算帶有Pandas的新列並使用多個參數（字符串和整數）？

具體例子：

df_joined["IVbest"] = IV(df_joined["Saison"], df_joined["Wald_Typ"], df_joined["NS_Cap"])

“Saison”，“Wald_Typ”是字符串“NS_Cap”是整數

現在我想通過這個定義運行所有這些值並再次返回一個x值：

def IV(saison, wald, ns):
    if saison == "Sommer":
        if wald == "Laubwald":
            x = ns * 0.1
        elif wald == "Nadelwald":
            x = ns * 0.2
        elif wald == "Mischwald":
            x = ns * 0.3
    elif saison == "Winter":
        if wald == "Laubwald":
            x = ns * 0.01
        elif wald == "Nadelwald":
            x = ns * 0.02
        elif wald == "Mischwald":
            x = ns * 0.03
    return x

我怎樣才能做到最好？

我嘗過類似的東西

df_joined["IVbest"] = IV(df_joined["Saison", "Wald_Typ", "NS_Cap"])

要么

df_joined["IVbest"] = df_joined["Saison", "Wald_Typ", "NS_Cap"].apply(IV)

但沒有任何作用:(

Answer 1

我認為在這種情況下最好使用6個掩碼並使用它們來執行這些行的計算：

sommer_laub = (df_joined['Saison'] == 'Sommer') & (df_joined['Wald_Typ'] == 'Laubwald')
sommer_nadel = (df_joined['Saison'] == 'Sommer') & (df_joined['Wald_Typ'] == 'Nadelwald')
sommer_misch = (df_joined['Saison'] == 'Sommer') & (df_joined['Wald_Typ'] == 'Mischwald')
winter_laub = (df_joined['Saison'] == 'Winter') & (df_joined['Wald_Typ'] == 'Laubwald')
winter_nadel = (df_joined['Saison'] == 'Winter') & (df_joined['Wald_Typ'] == 'Nadelwald')
winter_misch = (df_joined['Saison'] == 'Winter') & (df_joined['Wald_Typ'] == 'Mischwald')
df.loc[sommer_laub, 'IVbest'] = df.loc[sommer_laub,'NS_Cap'] * 0.1
df.loc[sommer_nadel, 'IVbest'] = df.loc[sommer_nadel,'NS_Cap'] * 0.2
df.loc[sommer_misch, 'IVbest'] = df.loc[sommer_misch,'NS_Cap'] * 0.3
df.loc[winter_laub, 'IVbest'] = df.loc[winter_laub,'NS_Cap'] * 0.01
df.loc[winter_nadel, 'IVbest'] = df.loc[winter_nadel,'NS_Cap'] * 0.02
df.loc[winter_misch, 'IVbest'] = df.loc[winter_misch,'NS_Cap'] * 0.03