如何将整数列转换为[0,1]的值范围？[副本]

Question

我有一个仅包含int值的数据集，我想将这些int值转换为[0,1]的范围。我这里有两种方法已经奏效了。不过，我想试试带有pandas的。现在我的问题是，如何将每列转换为[0,1]的值范围，而不违反值，即更改值。例如，因为3代表一个类。

# My first try without pandas:


# Load a CSV file
def load_csv(filename):
    dataset = list()
    with open(filename, 'r') as file:
         csv_reader = reader(file)
         for row in csv_reader:
             if not row:
                 continue
             dataset.append(row)
    return dataset

# Find the min and max values for each column
def dataset_minmax(dataset):
    minmax = list()
    stats = [[min(column), max(column)] for column in zip(*dataset)]
    return stats
 
# Rescale dataset columns to the range 0-1
def normalize_dataset(dataset, minmax):
    for row in dataset:
        for i in range(len(row)-1):
            row[i] = (row[i] - minmax[i][0]) / (minmax[i][1] - minmax[i][0])
minmax = dataset_minmax(dataset)
normalize_dataset(dataset, minmax)

Dataframe:
import pandas as pd
d = {'int1': [1, 2, 1], 'int2': [3, 4, 5]}
# df = pd.read_csv('test.csv')
df = pd.DataFrame(data=d)

print(df['int1'].min()) # What I found, it give me the min and max back
print(df['int1'].max())

Answer 1

这将根据每个系列中的最大值将每个系列转换为[0，1]：

import pandas as pd
d = {'int1': [1, 2, 1], 'int2': [3, 4, 5]}
df = pd.DataFrame(data=d)
df = df.apply(lambda col: col.div(col.max()))
#    int1  int2
# 0   0.5   0.6
# 1   1.0   0.8
# 2   0.5   1.0