如何在熊猫中将价值分配给多价值

Question

I want to assign All SkinThickness zero value with the mean of each patient lies in certain range of Age .

So I grouped data-frame by Age to get the mean of SkinThickness for each age range.

In order to assign every Zero value in SkinThickness Column to the corresponding mean value computed from the age grouping.

ageSkinMean = df_clean.groupby("Age_Class")["SkinThickness"].mean()
>>> ageSkinMean

Age_Class
21-22 years     82.163399
23-25 years    103.171429
26-30 years     91.170254
31-38 years     80.133028
39-47 years     73.685851
48-58 years     89.130233
60+ years       40.899160
Name: Insulin, dtype: float64

Currently I'm running such insufficient code ... which takes too long time for using iterrows()

start = time.time()
for i, val in df_clean[df_clean.SkinThickness == 0].iterrows():
    if val[7] < 22:
        df_clean.loc[i, "SkinThickness"] = ageSkinMean[0]
    elif val[7] < 25:
        df_clean.loc[i, "SkinThickness"] = ageSkinMean[1]
    elif val[7] < 30:
        df_clean.loc[i, "SkinThickness"] = ageSkinMean[2]
    elif val[7] < 38:
        df_clean.loc[i, "SkinThickness"] = ageSkinMean[3]
    elif val[7] < 47:
        df_clean.loc[i, "SkinThickness"] = ageSkinMean[4]
    elif val[7] < 58:
        df_clean.loc[i, "SkinThickness"] = ageSkinMean[5]
    else:
        df_clean.loc[i, "SkinThickness"] = ageSkinMean[6]
print(time.time() - start)

I wonder if there exist any pandas optimization to such block of code to run faster

Answer 1

You can use pandas transform function to replace SkinThickness 0 value with mean values

    age_skin_thickness_mean = df_clean.groupby('Age_Class')['SkinThickness'].mean()

    def replace_with_mean_thickness(row):
       row['SkinThickness'] = age_skin_thickness_mean[row['Age_Class']]
       return row

    df_clean.loc[df_clean['SkinThickness'] == 0] = df_clean.loc[df_clean['SkinThickness'] == 0].transform(replace_with_mean_thickness, axis=1)

All rows having SkinThickness == 0 in df_clean will now have SkinThickness equal to their age group mean value.