You’ll be able to usefulness please see forms to get the index of the max price in a NumPy array:
Form 1: Get Index of Max Price in One-Dimensional Array
x.argmax()
Form 2: Get Index of Max Price in Every Row of Multi-Dimensional Array
x.argmax(axis=1)
Form 3: Get Index of Max Price in Every Column of Multi-Dimensional Array
x.argmax(axis=0)
Refer to examples display learn how to usefulness each and every mode in apply.
Instance 1: Get Index of Max Price in One-Dimensional Array
Refer to code presentations learn how to get the index of the max price in a one-dimensional NumPy array:
import numpy as np
#manufacture NumPy array of values
x = np.array([2, 7, 9, 4, 4, 6, 3])
#to find index that incorporates max price
x.argmax()
2
The argmax() serve as returns a worth of 2.
This tells us that the price in index place 2 of the array incorporates the utmost price.
If we take a look at the unedited array, we will see that the price in index place 2 is 9, which is certainly the utmost price within the array.
Instance 2: Get Index of Max Price in Every Row of Multi-Dimensional Array
Refer to code presentations learn how to get the index of the max price in each and every row of a multi-dimensional NumPy array:
import numpy as np
#manufacture multi-dimentsional NumPy array
x = np.array([[4, 2, 1, 5], [7, 9, 2, 0]])
#view NumPy array
print(x)
[[4 2 1 5]
[7 9 2 0]]
#to find index that incorporates max price in each and every row
x.argmax(axis=1)
array([3, 1], dtype=int32)
From the effects we will see:
- The max price within the first row is situated in index place 3.
- The max price in the second one row is situated in index place 1.
Instance 3: Get Index of Max Price in Every Column of Multi-Dimensional Array
Refer to code presentations learn how to get the index of the max price in each and every column of a multi-dimensional NumPy array:
import numpy as np
#manufacture multi-dimentsional NumPy array
x = np.array([[4, 2, 1, 5], [7, 9, 2, 0]])
#view NumPy array
print(x)
[[4 2 1 5]
[7 9 2 0]]
#to find index that incorporates max price in each and every column
x.argmax(axis=0)
array([1, 1, 1, 0], dtype=int32)
From the effects we will see:
- The max price within the first column is situated in index place 1.
- The max price in the second one column is situated in index place 1.
- The max price within the 3rd column is situated in index place 1.
- The max price within the fourth column is situated in index place 0.
Matching: A Easy Rationalization of NumPy Axes
Backup Assets
Refer to tutorials provide an explanation for learn how to carry out alternative familiar operations in Python:
Learn how to Fill NumPy Array with Values
Learn how to Substitute Parts in NumPy Array
Learn how to Get Particular Row from NumPy Array
