# Example 1: Creation of 1D array arr1=np.array([10,20,30]) print("My 1D array:\n",arr1) # Example 2: Create a 2D numpy array arr2 = np.array([[10,20,30],[40,50,60]]) print("My 2D numpy array:\n", arr2) # Exampl
importnumpyasnp# Create a range objectmy_range=range(1,10)# Convert range object to arrayarr_from_range=np.array(my_range)print("Array from range object:",arr_from_range) After executing the above code, we get the following output − ...
#coding=gbk from cgi import test import torch import numpy as np import torch.nn as nn import torch.optim as optim import torch.utils.data as Data import torch.nn.functional as F from data_process import get_data dtype = torch.FloatTensor device = torch.device("cuda" if torch.cuda.is_a...
Example 2: Create NumPy Matrix Filled with NaNs # Import numpyimportnumpyasnp# Create matrix filled with NaNsarr=np.full((3,3),np.NaN)# Display the matrixprint("The matrix is:\n",arr,"\n") Output
The np.zeros_like() function creates an array of zeros with the same shape and type as a given array. I find this incredibly useful when I need to create a result array that matches an input array. # Create a sample array original = np.array([[1, 2, 3], [4, 5, 6]]) ...
The array returned by np.arange() uses a half-open interval, which excludes the endpoint of the range. This behavior is similar to range() but different from np.linspace(). These differences can be a bit confusing initially, but you’ll get used to them as you start using these ...
{cudf.__version__}")forxin[np_array,cp_array,pd_series]:print("\n"+"="*80+"\n")assertx.dtype==dtypetry:cudf.Series(x)exceptExceptionasexc:print(f"Failed to create cudf.Series from{type(x)}!")traceback.print_exception(exc)else:print(f"Succeeded in creating cudf.Series from{type...
@@ -1,13 +0,0 @@ -// Next.js API route support: https://nextjs.org/docs/api-routes/introduction -import type { NextApiRequest, NextApiResponse } from 'next' - -type Data = { - name: string -} - -export default function handler( - req: NextApiRequest, - res: NextApi...
o = np.linspace(0, 4, 9)#return 9 evenly spaced values from 0 to 4o Output: array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. ]) resizechanges the shape and size of arrayin-place. o.resize(3, 3) o Output:
Use np.where ufunc: Use the np.where "ufunc" to create a new array result_array based on the condition applied to condition_array. If the condition is True, the corresponding element from ‘array_1’ is chosen; otherwise, the element from ‘array_2’ is chosen. ...