如何遍历熊猫数据框中的每个列和每个单元格

Question

I have a dataframe ( training_df ) with 4 columns each containing about 150 rows. I also have function as follows:

def normalise(theMin, theMax, theVal):
    if(theMin == theVal):
        return 0
    else if(theMax == theVal):
        return 1
    return (theVal - theMin) / (theMax - theMin)

Now, what I want to do is to iterate through all four columns of my dataframe in turn and iterate through all the rows in each column and for each value in the rows (there will of course be only one cell in each row) I want to replace them with whatever value is returned from the normalise function. So I tried something like this by looking at the already asked questions in this forum:

for column in training_df:
    theMin = training_df[column].min()
    theMax = training_df[column].max()    
    for i in training_df[[column]].iterrows():
        training_df[[column[i]]] = normalise(theMin, theMax, i)

But I get a TypeError: string indices must be integers I am quite new to Python and pandas and to data mining, so if somebody could clarify this a bit would really appreciate it. Thanks in advance.

Answer 1

我将要做的 ..

df.apply(lambda x : (x-x.min())/(x.max()-x.min()))