Numpy Concepts: Difference between revisions
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One of NumPy's primary uses is as a container for data to be passed between algorithms and libraries. NumPy is one of the most important foundational packages for numerical computing in Python and a dependency for [[Pandas_Concepts#Overview|Pandas]]. | One of NumPy's primary uses is as a container for data to be passed between algorithms and libraries. NumPy is one of the most important foundational packages for numerical computing in Python and a dependency for [[Pandas_Concepts#Overview|Pandas]]. | ||
NumPy does not provide time series manipulation, which is present in [[Pandas_Concepts#Overview|Pandas]]. | |||
=<tt>ndarray</tt>= | =<tt>ndarray</tt>= | ||
Revision as of 19:21, 17 May 2024
External
Internal
Overview
NumPy is the short for Numerical Python. It provides data structures, algorithms and library glue needed for most scientific applications involving numerical data in Python. NumPy contains:
- A fast and efficient multidimensional array object
ndarray. For numerical data, NumPy arrays are more efficient for storing and manipulating data than other built-in Python data structures. - Functions for performing element-wise computations with arrays, and mathematical operations between arrays.
- Tools for reading and writing array-based datasets to disk.
- Linear algebra operations, Fourier transform, random number generation.
- A mature C API to enable Python extensions and native C or C++ code to access NumPy's data structures.
One of NumPy's primary uses is as a container for data to be passed between algorithms and libraries. NumPy is one of the most important foundational packages for numerical computing in Python and a dependency for Pandas.
NumPy does not provide time series manipulation, which is present in Pandas.
ndarray
Used to implement a Pandas Series.
Import Convention
import numpy as np