Here are a few question-based article titles based on your content: * **Windows Scipy Installation Error: Why Can\'t I Find Lapack/Blas Resources?** * **Scipy Installation on Windows 7: How to Overc-Python Tutorial-php.cn

Here are a few question-based article titles based on your content: * **Windows Scipy Installation Error: Why Can\'t I Find Lapack/Blas Resources?** * **Scipy Installation on Windows 7: How to Overc

DDD

Oct 25, 2024 am 10:40 AM

Here are a few question-based article titles based on your content:

* **Windows Scipy Installation Error: Why Can't I Find Lapack/Blas Resources?**
* **Scipy Installation on Windows 7: How to Overcome the Lapack/Blas Resource Issue?**
* **Missing Lapac

Windows Scipy Install: No Lapack/Blas Resources Found

Unable to install Scipy due to missing Lapack/Blas resources? Don't despair! Here's the ultimate solution for solving this error on Windows 7.

Troubleshooting the Error

The error "numpy.distutils.system_info.NotFoundError: no lapack/blas resources found" indicates that the necessary Lapack/Blas libraries are not available during Scipy installation. Despite having Microsoft Visual Studio C installed and successfully installing other packages, this issue can persist.

Avoid Binary Installations

Using the Windows binary installation method is not necessary. Here's a step-by-step guide to resolve the issue:

Download Prebuilt Binaries: Visit the link provided in the solution: http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy.
Select the Appropriate File: Choose the distribution file that matches your Python version and system architecture (e.g., scipy-0.16.0-cp27-none-win_amd64.whl).

Install Using Pip: Navigate to the download location and run the following command:

pip install [Local File Location]\[Your specific file such as scipy-0.16.0-cp27-none-win_amd64.whl]

Additional Requirements

Ensure that the following prerequisites are met before proceeding:

Install Visual Studio 2015/2013 with Python Tools
Install Visual Studio C Compiler for Python (File Name: VCForPython27.msi)
Install your desired version of Python (File Name: python-2.7.10.amd64.msi)

By following these steps, you can successfully install Scipy without encountering the Lapack/Blas error on Windows 7.

The above is the detailed content of Here are a few question-based article titles based on your content: * **Windows Scipy Installation Error: Why Can\'t I Find Lapack/Blas Resources?** * **Scipy Installation on Windows 7: How to Overc. For more information, please follow other related articles on the PHP Chinese website!

Statement

The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

How to Use Python to Find the Zipf Distribution of a Text FileMar 05, 2025 am 09:58 AM

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

How Do I Use Beautiful Soup to Parse HTML?Mar 10, 2025 pm 06:54 PM

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

Mathematical Modules in Python: StatisticsMar 09, 2025 am 11:40 AM

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

How to Perform Deep Learning with TensorFlow or PyTorch?Mar 10, 2025 pm 06:52 PM

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

Serialization and Deserialization of Python Objects: Part 1Mar 08, 2025 am 09:39 AM

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

What are some popular Python libraries and their uses?Mar 21, 2025 pm 06:46 PM

The article discusses popular Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Django, Flask, and Requests, detailing their uses in scientific computing, data analysis, visualization, machine learning, web development, and H

How to Create Command-Line Interfaces (CLIs) with Python?Mar 10, 2025 pm 06:48 PM

This article guides Python developers on building command-line interfaces (CLIs). It details using libraries like typer, click, and argparse, emphasizing input/output handling, and promoting user-friendly design patterns for improved CLI usability.

Scraping Webpages in Python With Beautiful Soup: Search and DOM ModificationMar 08, 2025 am 10:36 AM

This tutorial builds upon the previous introduction to Beautiful Soup, focusing on DOM manipulation beyond simple tree navigation. We'll explore efficient search methods and techniques for modifying HTML structure. One common DOM search method is ex

See all articles