第numpyarray找出符合条件的数并赋值的示例代码目录1.直接利用条件索引2.利用numpy.where3.直接逻辑运算 在python中利用numpy array进行数据处理,经常需要找出符合某些要求的数据位置,有时候还需要对这些位
y = np.array([1,5,6,8,1,7,3,6,9])# Where y is greater than 5, returns index positionnp.where(y>5)array([2, 3, 5, 7, 8], dtype=int64),)# First will replace the values that match the condition,# second will replace the values t...
numpy.where(condition to check, x, y) Return x or y as elements based on condition check. condition array_like, bool x,y x is returned if condition is True, y otherwise Examples : Updating data We will create an array by using arange(). We will multiply each element by 3 if they...
import numpy as np # Create three 1D NumPy arrays array_1 = np.array([1, 2, 3, 4, 5]) array_2 = np.array([10, 20, 30, 40, 50]) condition_array = np.array([True, False, True, False, True]) # Use np.where ufunc to create a new array based on the condition result...
cond = np.mod(array, 2)==1 cond array([False, True, False, True, False, False, False, True, False, True, False, True])# Use extract to get the values np.extract(cond, array) array([ 1, 19, 11, 13, 3])# Apply condition on extract directly ...
np.mod(array,2)==1condarray([False,True,False,True,False,False,False,True,False,True,False,True])# Use extract to get the valuesnp.extract(cond,array)array([1,19,11,13,3])# Apply condition on extract directlynp.extract(((array<3) | (array>15)),array)array([0,1,19,16,18,2]...
Array converted to a float type: [ 1. 2. 3. 4.] Click me to see the sample solution8. 2D Array (Border 1, Inside 0)Write a NumPy program to create a 2D array with 1 on the border and 0 inside. Expected Output:Original array: [[ 1. 1. 1. 1. 1.] ... [ 1. 1....
The strides of the array tell us that you have to skip 8 bytes (one value) to move to the next column, but 32 bytes (4 values) to get to the same position in the next row. As such, the strides for the array will be (32,8). Note that if you set the data type to int32,...
Boolean indexing allows us to filter elements from an array based on a specific condition. In NumPy, boolean indexing allows us to filter elements from an array based on a specific condition. We use boolean masks to specify the condition. Before we learn
This could be a single value, in which case, that value will be the output wheneverconditionisTrue. But this can also be an array or array-like object, such as a list. If it’s an array-like object, the output of np.where will be the item in theoutput-if-truearray that correspond...