默认会在当前目录生成 render.html 文件# 也可以传入路径参数，如 bar.render("mycharts.html")bar.re...
In [1]: import numba In [2]: def double_every_value_nonumba(x): return x * 2 In [3]: @numba.vectorize def double_every_value_withnumba(x): return x * 2 # 不带numba的自定义函数: 797 us In [4]: %timeit df["col1_doubled"] = df["a"].apply(double_every_value_nonumba) ...
# create and run a Notebook on Kaggle kaggle kernels push -k [KERNEL] -p /path/to/kernel # download code files and metadata associated with a Notebook kaggle kernels pull [KERNEL] -p /path/to/download -m 1. 2. 3. 4. 5. 6. Creating and Running a New Notebook Create a local f...
from skimage.morphology import remove_small_objectsim = rgb2gray(imread('../images/circles.jpg'))im[im > 0.5] = 1 # create binary image by thresholding with fixed threshold0.5im[im <= 0.5] = 0im = im.astype(np.bool)pylab.figure(figsize=(20,20))pylab.subplot(2,2,1), plot_image(i...
The kagglehub library provides a simple way to interact with Kaggle resources such as datasets, models, notebook outputs in Python.This library also integrates natively with the Kaggle notebook environment. This means the behavior differs when you download a Kaggle resource with kagglehub in the ...
总之,AI预测世界杯其实是对历史数据的归纳总结,而且完全依赖数据的喂养,能给出相对概率。 这和人的直觉一样,你觉得巴西会夺冠,肯定有一些过往的事实验证了你的直觉,不然就是瞎猜了。 具体代码见: https://www.kaggle.com/code/sslp23/predicting-fifa-2022-world-cup-with-ml#The-Project...
This project, developed with VS Code, Jupyter Notebook and Google Colab, uses Python (Flask, Pytorch, face_recognition, and more) and Postman (for API Testing) to develop two implementations of recognizing human faces, particularly those present in the L
cookiecutter-data-science- Project template for data science projects. nteract- Open Jupyter Notebooks with doubleclick. papermill- Parameterize and execute Jupyter notebooks,tutorial. nbdime- Diff two notebook files, Alternative GitHub App:ReviewNB. ...
Kaggle金牌得主的机器学习教程www.heywhale.com/mw/project/5f69d02471c700003078961b?from=zhihumlkj 目录 项目背景与分析 数据读入与检查 数据预处理 数据校正 缺失值填充 数据创建 数据转换 数据清洗 数据划分 探索性分析 建模分析 模型评估与优化
21. Getting started with Django | Django djangoproject.com/start 官方的Django框架介绍将帮助您进行设置,以便您可以使用Python进行Web开发。 22. LEARNING PATH: Django: Modern Web Development with Django oreilly.com/learning-pa 来自O'Reilly的这个资源有助于为Python学习Django和Web开发技能提供更多策划。