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Python Pandas DataFrame.sample() 函数从一个 DataFrame 中随机生成一行或一列的样本。样本可以包含多行或多列。

pandas.DataFrame.sample() 语法

DataFrame.sample(
    n=None, frac=None, replace=False, weights=None, random_state=None, axis=None
)

参数

返回值

它返回一个 Series 或 DataFrame。返回的 Series 或 DataFrame 是一个调用器，包含从原始 DataFrame 中随机选择的 n 个元素。

示例代码：DataFrame.sample()

默认情况下，函数返回一个包含行的样本，即 axis=1。

import pandas as pd

dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 75, 4: 95},
                    'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
                    'Obtained Marks': {0: 56, 1: 75, 2: 82, 3: 64, 4: 67}})
print(dataframe)

我们的 DataFrame 为，

   Attendance    Name  Obtained Marks
0          60  Olivia              56
1         100    John              75
2          80   Laura              82
3          75     Ben              64
4          95   Kevin              67

这个函数的所有参数都是可选的。如果我们在执行这个函数时没有传递任何参数，它将返回一个随机的行作为输出。

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: 100, 2: 80, 3: 75, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: 56, 1: 75, 2: 82, 3: 64, 4: 67},
    }
)
dataframe1 = dataframe.sample()
print(dataframe1)

输出 1：

   Attendance Name  Obtained Marks
3          75  Ben              64

输出 2：

   Attendance   Name  Obtained Marks
4          95  Kevin              67

输出 1 和输出 2 显示了同一个程序的两次执行情况。每次这个函数都会从给定的 DataFrame 中产生一个随机的行样本。

示例代码：DataFrame.sample() 提取列

要在样本中生成列，我们将简单地把我们的轴改为 1。

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: 100, 2: 80, 3: 75, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: 56, 1: 75, 2: 82, 3: 64, 4: 67},
    }
)
dataframe1 = dataframe.sample(n=1, axis=1)
print(dataframe1)

输出：

     Name
0  Olivia
1    John
2   Laura
3     Ben
4   Kevin

该函数已经生成了一个单一列的样本作为输出。列的数量由参数 n=1 设置。

示例代码：DataFrame.sample() 生成数据的一部分

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: 100, 2: 80, 3: 75, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: 56, 1: 75, 2: 82, 3: 64, 4: 67},
    }
)
dataframe1 = dataframe.sample(frac=0.5)
print(dataframe1)

输出：

   Attendance   Name  Obtained Marks
3          75    Ben              64
4          95  Kevin              67
1         100   John              75

返回的样本是原始数据的 50%。

示例代码：DataFrame.sample() 对 DataFrame 进行过采样

如果 frac>1，那么参数 replace 应该是 True，以允许同一行可以被多次取样，否则，它将引发 ValueError。

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: 100, 2: 80, 3: 75, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: 56, 1: 75, 2: 82, 3: 64, 4: 67},
    }
)
dataframe1 = dataframe.sample(frac=1.5, replace=True)
print(dataframe1)

输出：

   Attendance   Name  Obtained Marks
3          75     Ben              64
0          60  Olivia              56
1         100    John              75
2          80   Laura              82
1         100    John              75
2          80   Laura              82
0          60  Olivia              56
4          95   Kevin              67

如果 replace 被设置为 False，同时 frac 大于 1，则会产生 ValueError。

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: 100, 2: 80, 3: 75, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: 56, 1: 75, 2: 82, 3: 64, 4: 67},
    }
)
dataframe1 = dataframe.sample(frac=1.5, replace=False)
print(dataframe1)

输出：

Traceback (most recent call last):
  File "..\test.py", line 6, in <module>
    dataframe1 = dataframe.sample(frac=1.5, replace=False)
  File "..\lib\site-packages\pandas\core\generic.py", line 5044, in sample
    raise ValueError(
ValueError: Replace has to be set to `True` when upsampling the population `frac` > 1.

示例代码：DataFrame.sample() 和 weights

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: 100, 2: 80, 3: 75, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: 56, 1: 75, 2: 82, 3: 64, 4: 67},
    }
)
dataframe1 = dataframe.sample(n=2, weights="Attendance")
print(dataframe1)

输出：

   Attendance   Name  Obtained Marks
1         100   John              75
4          95  Kevin              67

这里，在返回的样本中选择 Attendance 列中数值较大的行。