如何迭代 DataFrame 列并更新列值

Question

I'm trying to iterate though the abc column and update its' value, but I keep getting errors. Any help would be appreciated.

somedata.csv:
                    ABC
0               0.00094
1               0.00094
2               0.00094
3               0.00094
...
4999            0.00094


data = pd.read_csv("somedata.csv")
abc = pd.DataFrame(data, columns = ['ABC'])

for column in abc:
    value = (abc - min(abc)) / (max(abc) - min(abc))
    abc = value

Error: numpy.core._exceptions.UFuncTypeError: ufunc 'subtract' did not contain a loop with signature matching types (dtype(' dtype('

line 72, in
value = (abc - min(abc)) / (max(abc) - min(abc))

Answer 1

So there's a few things wrong. On "abc" instantiation, finish the ")". The line value = (abc - min(abc)) / (max(abc) - min(abc)) is very confusing. abc you have as a dataframe column, so I assume you would like to update every row value of this column titled 'ABC'.

To get value = (ThisRowValue - min(abc)) / (max(abc) - min(abc)), utilize Panda's max and min

I would write this

#Separate out the column (assuming multiple columns)
abc = df['ABC']
abc = list(map(float, abc)) #Edited to turn it into float values

list2 = [] # Empty list for storage
for this_row_value in abc: #Iterate for each value in column 'ABC'
    value = (this_row_value - abc.min()) / (abc.max() - abc.min()) #Utilize Panda's Min/Max
    list2.append(value)

#Set updated values to the column values. You can also use Pandas Update.
df['ABC'] = list2

Where df is just the dataframe object.