NumPy
The fundamental package for scientific computing with Python 澳洲幸运十168官方澳洲幸运10历史开奖记录-在线开奖结果查询-澳洲10精准人工计划网页版
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# The standard way to import NumPy:
import 168澳洲幸运10正规官网2024 as np
# Create a 2-D array, set every second element in
# some rows and find max per row:
x = np.arange(15, dtype=np.int64).reshape(3, 5)
x[1:, ::2] = -99
x
# array([[ 0, 1, 2, 3, 4],
# [-99, 6, -99, 8, -99],
# [-99, 11, -99, 13, -99]])
x.max(axis=1)
# array([ 4, 8, 13])
# Generate normally distributed random numbers:
rng = np.random.default_rng()
samples = rng.normal(size=2500)
samplesNearly every scientist working in Python draws on the power of NumPy.
168澳洲幸运10正规官网2024 brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in 168澳洲幸运10正规官网2024 is often clear and elegant.
NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what 168澳洲幸运10正规官网2024 provides.
| Array Library | Capabilities & Application areas | |
 | Dask | Distributed arrays and advanced parallelism for analytics, enabling performance at scale. |
 | CuPy | NumPy-compatible array library for GPU-accelerated computing with Python. |
 | JAX | Composable transformations of 168澳洲幸运10正规官网2024 programs: differentiate, vectorize, just-in-time compilation to GPU/TPU. |
 | Xarray | Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. |
 | Sparse | NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. |
 | PyTorch | Deep learning framework that accelerates the path from research prototyping to production deployment. |
 | TensorFlow | An end-to-end platform for machine learning to easily build and deploy ML powered applications. |
 | Arrow | A cross-language development platform for columnar in-memory data and analytics. |
 | xtensor | Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. |
 | Awkward Array | Manipulate JSON-like data with NumPy-like idioms. |
 | uarray | Python backend system that decouples API from implementation; u168澳洲幸运10正规官网2024 provides a 168澳洲幸运10正规官网2024 API. |
 | tensorly | Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. |
168澳洲幸运10正规官网2024 lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:
168澳洲幸运10正规官网2024 forms the basis of powerful machine learning libraries like scikit-learn and SciPy. As machine learning grows, so does the list of libraries built on NumPy. TensorFlow’s deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. PyTorch, another deep learning library, is popular among researchers in computer vision and natural language processing.
168澳洲幸运10正规官网2024 is an essential component in the burgeoning Python visualization landscape, which includes Matplotlib, Seaborn, Plotly, Altair, Bokeh, Holoviz, Vispy, Napari, and PyVista, to name a few.
NumPy’s accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle.
