Basic Array Creation
A NumPy array is an efficient, multidimensional container for storing and manipulating large datasets of homogeneous (same) data types. Although they are similar to Python lists, they are more memory-efficient and allow for high-performance mathematical and numerical operations.
Now, it’s time to create your first NumPy arrays. The most straightforward way to do this is by using the array()
function, passing either a list
or a tuple
as its argument.
Note
You should create NumPy arrays only from lists in all the tasks throughout our course.
Let’s take a look at an example:
As you can see, these two NumPy arrays are equal.
Specifying Data Type
The data type of the array elements is defined implicitly; however, you can specify it explicitly with the dtype
parameter:
The first integer array uses the default int64
data type, which is an 8-byte integer. The second array uses int8
, a 1-byte integer.
Other common NumPy data types include numpy.float16
, numpy.float32
, and numpy.float64
, which store 2-byte, 4-byte, and 8-byte floating point numbers, respectively.
Using int8
for small integers is more memory-efficient. However, be careful when choosing the data type (dtype
) to avoid overflow if the numbers are too large for the specified type.
Here is a list of possible data types in NumPy:
dtype | range | significant digits |
---|---|---|
np.int8 | -128...127 | |
np.int16 | -32768...32768 | |
np.int32 | -2.1 * 109...2.1 * 109 | |
np.int64 | -9.2 * 1018...9.2 * 1018 | |
np.float16 | ±(6.0 * 10-8...65504) | 3 |
np.float32 | ±(1.4 * 10-45...3.4 * 1038) | 6 |
np.float64 | ±(4.9 * 10-324...1.8 * 10308) | 15 |
Tarea
Create a 1D array named float_array
:
- Use the correct function to create a NumPy 1D array;
- Create an array with two elements (use a
list
as the first argument):1.32
,4.6
in this order; - Set the data type of its elements to
np.float16
via specifying the second argument.
¿Todo estuvo claro?
Contenido del Curso
Ultimate NumPy
3. Commonly used NumPy Functions
Ultimate NumPy
Basic Array Creation
A NumPy array is an efficient, multidimensional container for storing and manipulating large datasets of homogeneous (same) data types. Although they are similar to Python lists, they are more memory-efficient and allow for high-performance mathematical and numerical operations.
Now, it’s time to create your first NumPy arrays. The most straightforward way to do this is by using the array()
function, passing either a list
or a tuple
as its argument.
Note
You should create NumPy arrays only from lists in all the tasks throughout our course.
Let’s take a look at an example:
As you can see, these two NumPy arrays are equal.
Specifying Data Type
The data type of the array elements is defined implicitly; however, you can specify it explicitly with the dtype
parameter:
The first integer array uses the default int64
data type, which is an 8-byte integer. The second array uses int8
, a 1-byte integer.
Other common NumPy data types include numpy.float16
, numpy.float32
, and numpy.float64
, which store 2-byte, 4-byte, and 8-byte floating point numbers, respectively.
Using int8
for small integers is more memory-efficient. However, be careful when choosing the data type (dtype
) to avoid overflow if the numbers are too large for the specified type.
Here is a list of possible data types in NumPy:
dtype | range | significant digits |
---|---|---|
np.int8 | -128...127 | |
np.int16 | -32768...32768 | |
np.int32 | -2.1 * 109...2.1 * 109 | |
np.int64 | -9.2 * 1018...9.2 * 1018 | |
np.float16 | ±(6.0 * 10-8...65504) | 3 |
np.float32 | ±(1.4 * 10-45...3.4 * 1038) | 6 |
np.float64 | ±(4.9 * 10-324...1.8 * 10308) | 15 |
Tarea
Create a 1D array named float_array
:
- Use the correct function to create a NumPy 1D array;
- Create an array with two elements (use a
list
as the first argument):1.32
,4.6
in this order; - Set the data type of its elements to
np.float16
via specifying the second argument.
¿Todo estuvo claro?