In this post, we are going to learn about how to create numpy array of specific types and dimensions using numpy library functions np.tile() ,np.empty(),np.zeros(),np.array() with examples by using these examples.
1. np.tile() create numpy array of specify type
The numpy. tile() create and return a new array after repeating the given element number of reps. In the below example, we have created a 1D array by using tile() in which the number is 9 and reps is 10.
We have created a three-dimensional array in which the number is 9 and reps is (3,3).
import numpy as np
print(np.tile(9, 10))
print(np.tile(9, (3,3)))
Output
[9 9 9 9 9 9 9 9 9 9]
[[9 9 9]
[9 9 9]
[9 9 9]]
Tile(): to create an array of specific type
- In this example, we have created an array of specific types using np. array()
- Passed the array created by using np.array() to tile() function along with shape(1,1)
import numpy as np
arr = np.array([[3,6,9,12],[12,15,18,21]],dtype='i')
nparr = np.tile(arr,(1,1))
print(nparr)
Output
[[ 3 6 9 12]
[12 15 18 21]]
2. np.empty() to create numpy array of specific type
In this example, we have created a numpy array of space(3,3) specify type int. The numpy. empty(shape, type) return a new array of given shape and type without initializing any value
import numpy as np
nparr = np.empty((3, 3), dtype=int)
print(nparr)
Output
[[ 0 0 0]
[ 0 0 0]
[920 0 0]]
3. np.empty() to create numpy array of specific type
In this example, we will learn how to create a numpy array of specific dimensional using np. full(shape,fill_value, type) function that returns a new array of a given shape and fills all elements with the same value that passed the fillvalue parameter
We have created an array of shapes (3,3),fillvalue=9 specify the dtype as int.
import numpy as np
nparr = np.full((3,3), 9, dtype = int)
print(nparr)
Output
[[9 9 9]
[9 9 9]
[9 9 9]]
4.np.zeros() to create numpy array of specific type
The python numpy.zeros(shape,dtype) function returns a new array of given shape and type and fills the array with zero. In this example, we have created a numpy array of shapes (3,3, type =int and filled it with values 90 instead of zeros.
import numpy as np
nparr = np.zeros((3,3), dtype=int) + 90
print(nparr)
Output
[[90 90 90]
[90 90 90]
[90 90 90]]
5. numpy.array to create array of specify type
In this python program example, we have created an array of the specific type, passing an array with the datatype. It will create an array of the specified type and dimensional. We can change the datatype of the numpy array as per the need datatype selected from datatype.
| character used | Datatype |
| i | represent an integer data type |
| b | used for boolean datatype |
| u | used for unsigned integer |
| f | float type |
| c | complex float |
| m | time delta |
| M | Datetime |
| O | object |
| S | string |
| U | Unicode string |
Python Program to Create numpy array of specific type and dimension
import numpy as np
nparr = np.array([[3,6,9,12],[12,15,18,21]],dtype='i')
print(nparr)
Output
[[ 3 6 9 12]
[12 15 18 21]]
Summary
In this post, we have learned how to Create numpy array of specific type and dimension using numpy library function np.tile() ,np.empty(),np.zeros(),np.array().