python - 如何在 python pandas 中逐一阅读专栏？

Question

我已经从 URL 读取文件如下:

    url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"

    names = ['sepal length', 'sepal width', 'petal length', 'petal width',  'class']

    data = pd.read_csv(url, names=names)

    print(data.shape)

    print(data)

现在，我想阅读一列并进行一些处理(可能是最小值、最大值或标准偏差、r 分数等)，然后再次阅读另一列并进行一些处理。

有什么办法可以在 scikit learn/pandas/python 中实现吗？

Answer 1

你可以使用describe:

data.describe()

输出:

       sepal length  sepal width  petal length  petal width
count    150.000000   150.000000    150.000000   150.000000
mean       5.843333     3.054000      3.758667     1.198667
std        0.828066     0.433594      1.764420     0.763161
min        4.300000     2.000000      1.000000     0.100000
25%        5.100000     2.800000      1.600000     0.300000
50%        5.800000     3.000000      4.350000     1.300000
75%        6.400000     3.300000      5.100000     1.800000
max        7.900000     4.400000      6.900000     2.500000

或单列:

data['petal length'].describe()

输出:

count    150.000000
mean       3.758667
std        1.764420
min        1.000000
25%        1.600000
50%        4.350000
75%        5.100000
max        6.900000
Name: petal length, dtype: float64

或者您可以使用 apply 和 lambda 来按列进行一些自定义处理。

data.apply(lambda x: x.describe())

输出:

        sepal length  sepal width  petal length  petal width        class
25%         5.100000     2.800000      1.600000     0.300000          NaN
50%         5.800000     3.000000      4.350000     1.300000          NaN
75%         6.400000     3.300000      5.100000     1.800000          NaN
count     150.000000   150.000000    150.000000   150.000000          150
freq             NaN          NaN           NaN          NaN           50
max         7.900000     4.400000      6.900000     2.500000          NaN
mean        5.843333     3.054000      3.758667     1.198667          NaN
min         4.300000     2.000000      1.000000     0.100000          NaN
std         0.828066     0.433594      1.764420     0.763161          NaN
top              NaN          NaN           NaN          NaN  Iris-setosa
unique           NaN          NaN           NaN          NaN            3