python 'numpy.ndarray' object has no attribute 'apply'
时间: 2024-01-30 15:03:40 浏览: 51
'numpy.ndarray'对象没有属性'apply'是因为NumPy的ndarray对象没有内置的'apply'方法。'apply'方法是Pandas中的一个函数,用于对数据进行逐行或逐列的操作。如果你想在NumPy的ndarray对象上使用'apply'方法,你需要先将其转换为Pandas的DataFrame对象。
以下是一个示例代码,演示如何将NumPy的ndarray对象转换为Pandas的DataFrame对象,并使用'apply'方法对数据进行操作:
```python
import numpy as np
import pandas as pd
# 创建一个NumPy的ndarray对象
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
# 将ndarray对象转换为DataFrame对象
df = pd.DataFrame(arr)
# 使用'apply'方法对数据进行操作
result = df.apply(lambda x: x.sum(), axis=0) # 对每一列求和
print(result)
```
输出结果为每一列的求和值:
```
0 12
1 15
2 18
dtype: int64
```
请注意,这里使用的是Pandas的'apply'方法,而不是NumPy的'apply'方法。因此,如果你想在NumPy的ndarray对象上使用'apply'方法,你需要先将其转换为Pandas的DataFrame对象。
相关问题
'numpy.ndarray' object has no attribute 'apply'
'numpy.ndarray' object确实没有apply方法,因为apply是pandas中的方法。如果想对numpy数组中的每个元素应用一个函数,可以使用numpy的vectorize方法。例如:
```python
import numpy as np
arr = np.array([1, 2, 3, 4])
def square(x):
return x**2
vfunc = np.vectorize(square)
result = vfunc(arr)
print(result) # 输出 [ 1 4 9 16]
```
'numpy.ndarray' object has no attribute 'applymap'
The 'numpy.ndarray' object does not have an 'applymap' attribute because the 'applymap' function is specific to pandas DataFrames and Series objects, not numpy arrays.
If you want to apply a function element-wise to a numpy array, you can use the 'numpy.vectorize' function or a loop to achieve this. Here's an example using 'numpy.vectorize':
```python
import numpy as np
# Define a sample function
def square(x):
return x**2
# Create a numpy array
arr = np.array([1, 2, 3, 4, 5])
# Create a vectorized version of the function
vec_square = np.vectorize(square)
# Apply the function element-wise to the array
result = vec_square(arr)
print(result)
```
Output:
```
[ 1 4 9 16 25]
```
In this example, the 'square' function is applied element-wise to the 'arr' numpy array using 'np.vectorize'. The resulting array 'result' contains the squared values of each element in 'arr'.