Conteúdo do Curso
Getting into NumPy Basics
Creating NumPy Arrays
In this section, we'll begin with the fundamentals by incorporating the NumPy library into our work and proceeding to construct our initial NumPy
arrays. Renowned for their role in facilitating robust numerical computing in Python, NumPy
arrays are multi-dimensional arrays that excel in efficiency. While they bear resemblance to the native list structure in Python, NumPy
arrays introduce certain limitations that differentiate them from lists:
NumPy
arrays are characterized by a fixed size, distinguishing them from lists which can be dynamically resized;- They also enforce a uniform data type across all elements, meaning a
NumPy
array must contain elements of the same type (e.g., exclusively integers or floating-point numbers).
These constraints are what make NumPy
arrays particularly adept for specific operations, especially for element-wise operations like addition and multiplication.
Due to their ability to handle vast datasets with greater efficiency than traditional lists or data structures, NumPy
arrays have become a staple in the realm of scientific computing and data analysis, enabling users to execute comprehensive operations on large data sets with ease.
Tarefa
- Generate a one-dimensional array containing numbers from
1
to5
. - Form a two-dimensional array with dimensions 2x3, placing the numbers
1
,2
,3
in the first row and4
,5
,6
in the second row. - Construct a
NumPy
array of size 3x3 and populate it entirely with zeros.
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In this section, we'll begin with the fundamentals by incorporating the NumPy library into our work and proceeding to construct our initial NumPy
arrays. Renowned for their role in facilitating robust numerical computing in Python, NumPy
arrays are multi-dimensional arrays that excel in efficiency. While they bear resemblance to the native list structure in Python, NumPy
arrays introduce certain limitations that differentiate them from lists:
NumPy
arrays are characterized by a fixed size, distinguishing them from lists which can be dynamically resized;- They also enforce a uniform data type across all elements, meaning a
NumPy
array must contain elements of the same type (e.g., exclusively integers or floating-point numbers).
These constraints are what make NumPy
arrays particularly adept for specific operations, especially for element-wise operations like addition and multiplication.
Due to their ability to handle vast datasets with greater efficiency than traditional lists or data structures, NumPy
arrays have become a staple in the realm of scientific computing and data analysis, enabling users to execute comprehensive operations on large data sets with ease.
Tarefa
- Generate a one-dimensional array containing numbers from
1
to5
. - Form a two-dimensional array with dimensions 2x3, placing the numbers
1
,2
,3
in the first row and4
,5
,6
in the second row. - Construct a
NumPy
array of size 3x3 and populate it entirely with zeros.