Conteúdo do Curso
Ultimate NumPy
Ultimate NumPy
Copying Arrays
Often, you need to create a copy of an array to make changes without affecting the original array.
Simple Assignment
First, we'll discuss why we can't simply create another variable using array_2 = array_1
, where array_1
is our original array. Let's look at an example:
import numpy as np array_1 = np.array([1, 2, 3]) array_2 = array_1 # Setting the first element of array_2 to 10 array_2[0] = 10 print(array_1)
We changed the value of the first element of array_2
to 10
, but this assignment also changed the value of the first element of array_1
to 10
.
Note
With
array_2 = array_1
, you are not creating a new array; instead, you are creating a reference to the same array in memory. Therefore, any changes made toarray_2
will also affectarray_1
.
To solve this problem, we could write array_2 = np.array([1, 2, 3])
, but that would mean writing the same code twice. Remember the key principle in coding: Don't repeat yourself.
ndarray.copy() Method
Luckily, NumPy has an ndarray.copy()
method as a solution to this problem. Let's see it in action:
import numpy as np array_1 = np.array([1, 2, 3]) # Copying the contents of array_1 array_2 = array_1.copy() # Setting the first element of array_2 to 10 array_2[0] = 10 print(f'Initial array: {array_1}') print(f'Modified copy: {array_2}')
Now, we have created a new array for array_2
with the same elements as array_1
.
For 2D arrays, the copying procedure is exactly the same.
numpy.copy() Function
Instead of the .copy()
method, we can also use the copy()
function, which takes the array as its parameter: array_2 = np.copy(array_1)
.
Both the function and the method work the same; however, there is one nuance. They both have the order
parameter, which specifies the memory layout of the array, but their default values are different. If you want to read more about it, here is the documentation: ndarray.copy, numpy.copy.
Tarefa
- Create a copy of
sales_data_2021
using the correct method of a NumPy array and store it insales_data_2022
. - Assign the NumPy array with elements 390 and 370 to the last two elements of the first row (1D array) of
sales_data_2022
. Use a positive index and a slice with only negativestart
specified for indexingsales_data_2022
.
Obrigado pelo seu feedback!
Copying Arrays
Often, you need to create a copy of an array to make changes without affecting the original array.
Simple Assignment
First, we'll discuss why we can't simply create another variable using array_2 = array_1
, where array_1
is our original array. Let's look at an example:
import numpy as np array_1 = np.array([1, 2, 3]) array_2 = array_1 # Setting the first element of array_2 to 10 array_2[0] = 10 print(array_1)
We changed the value of the first element of array_2
to 10
, but this assignment also changed the value of the first element of array_1
to 10
.
Note
With
array_2 = array_1
, you are not creating a new array; instead, you are creating a reference to the same array in memory. Therefore, any changes made toarray_2
will also affectarray_1
.
To solve this problem, we could write array_2 = np.array([1, 2, 3])
, but that would mean writing the same code twice. Remember the key principle in coding: Don't repeat yourself.
ndarray.copy() Method
Luckily, NumPy has an ndarray.copy()
method as a solution to this problem. Let's see it in action:
import numpy as np array_1 = np.array([1, 2, 3]) # Copying the contents of array_1 array_2 = array_1.copy() # Setting the first element of array_2 to 10 array_2[0] = 10 print(f'Initial array: {array_1}') print(f'Modified copy: {array_2}')
Now, we have created a new array for array_2
with the same elements as array_1
.
For 2D arrays, the copying procedure is exactly the same.
numpy.copy() Function
Instead of the .copy()
method, we can also use the copy()
function, which takes the array as its parameter: array_2 = np.copy(array_1)
.
Both the function and the method work the same; however, there is one nuance. They both have the order
parameter, which specifies the memory layout of the array, but their default values are different. If you want to read more about it, here is the documentation: ndarray.copy, numpy.copy.
Tarefa
- Create a copy of
sales_data_2021
using the correct method of a NumPy array and store it insales_data_2022
. - Assign the NumPy array with elements 390 and 370 to the last two elements of the first row (1D array) of
sales_data_2022
. Use a positive index and a slice with only negativestart
specified for indexingsales_data_2022
.
Obrigado pelo seu feedback!
Copying Arrays
Often, you need to create a copy of an array to make changes without affecting the original array.
Simple Assignment
First, we'll discuss why we can't simply create another variable using array_2 = array_1
, where array_1
is our original array. Let's look at an example:
import numpy as np array_1 = np.array([1, 2, 3]) array_2 = array_1 # Setting the first element of array_2 to 10 array_2[0] = 10 print(array_1)
We changed the value of the first element of array_2
to 10
, but this assignment also changed the value of the first element of array_1
to 10
.
Note
With
array_2 = array_1
, you are not creating a new array; instead, you are creating a reference to the same array in memory. Therefore, any changes made toarray_2
will also affectarray_1
.
To solve this problem, we could write array_2 = np.array([1, 2, 3])
, but that would mean writing the same code twice. Remember the key principle in coding: Don't repeat yourself.
ndarray.copy() Method
Luckily, NumPy has an ndarray.copy()
method as a solution to this problem. Let's see it in action:
import numpy as np array_1 = np.array([1, 2, 3]) # Copying the contents of array_1 array_2 = array_1.copy() # Setting the first element of array_2 to 10 array_2[0] = 10 print(f'Initial array: {array_1}') print(f'Modified copy: {array_2}')
Now, we have created a new array for array_2
with the same elements as array_1
.
For 2D arrays, the copying procedure is exactly the same.
numpy.copy() Function
Instead of the .copy()
method, we can also use the copy()
function, which takes the array as its parameter: array_2 = np.copy(array_1)
.
Both the function and the method work the same; however, there is one nuance. They both have the order
parameter, which specifies the memory layout of the array, but their default values are different. If you want to read more about it, here is the documentation: ndarray.copy, numpy.copy.
Tarefa
- Create a copy of
sales_data_2021
using the correct method of a NumPy array and store it insales_data_2022
. - Assign the NumPy array with elements 390 and 370 to the last two elements of the first row (1D array) of
sales_data_2022
. Use a positive index and a slice with only negativestart
specified for indexingsales_data_2022
.
Obrigado pelo seu feedback!
Often, you need to create a copy of an array to make changes without affecting the original array.
Simple Assignment
First, we'll discuss why we can't simply create another variable using array_2 = array_1
, where array_1
is our original array. Let's look at an example:
import numpy as np array_1 = np.array([1, 2, 3]) array_2 = array_1 # Setting the first element of array_2 to 10 array_2[0] = 10 print(array_1)
We changed the value of the first element of array_2
to 10
, but this assignment also changed the value of the first element of array_1
to 10
.
Note
With
array_2 = array_1
, you are not creating a new array; instead, you are creating a reference to the same array in memory. Therefore, any changes made toarray_2
will also affectarray_1
.
To solve this problem, we could write array_2 = np.array([1, 2, 3])
, but that would mean writing the same code twice. Remember the key principle in coding: Don't repeat yourself.
ndarray.copy() Method
Luckily, NumPy has an ndarray.copy()
method as a solution to this problem. Let's see it in action:
import numpy as np array_1 = np.array([1, 2, 3]) # Copying the contents of array_1 array_2 = array_1.copy() # Setting the first element of array_2 to 10 array_2[0] = 10 print(f'Initial array: {array_1}') print(f'Modified copy: {array_2}')
Now, we have created a new array for array_2
with the same elements as array_1
.
For 2D arrays, the copying procedure is exactly the same.
numpy.copy() Function
Instead of the .copy()
method, we can also use the copy()
function, which takes the array as its parameter: array_2 = np.copy(array_1)
.
Both the function and the method work the same; however, there is one nuance. They both have the order
parameter, which specifies the memory layout of the array, but their default values are different. If you want to read more about it, here is the documentation: ndarray.copy, numpy.copy.
Tarefa
- Create a copy of
sales_data_2021
using the correct method of a NumPy array and store it insales_data_2022
. - Assign the NumPy array with elements 390 and 370 to the last two elements of the first row (1D array) of
sales_data_2022
. Use a positive index and a slice with only negativestart
specified for indexingsales_data_2022
.