怎么用最短時(shí)間高效而踏實(shí)的學(xué)習(xí)python所踊？
【IPython notebook教程】《Efficient Data Analysis with the IPython Notebook》 GitHub:O網(wǎng)頁鏈接
【開源:(Python)NLP快速流程(原型)庫broca】"a Python library for rapidly experimenting with new natural language processing (NLP) approaches"O網(wǎng)頁鏈接GitHub:O網(wǎng)頁鏈接
【高效的Python數(shù)據(jù)分析框架Ibis】O網(wǎng)頁鏈接GitHub:O網(wǎng)頁鏈接通過IPN了解Ibis:O網(wǎng)頁鏈接Slide:《Ibis: Scaling the Python Data Experience》O網(wǎng)頁鏈接云:O網(wǎng)頁鏈接
【基于Pandas/Matplotlib的高收入數(shù)據(jù)分析】《Exploring the Top Incomes Database with Pandas and Matplotlib》by Ramiro GómezO網(wǎng)頁鏈接
【幻燈:(PyData 2015)機(jī)器學(xué)習(xí)系統(tǒng)觀】《PyData 2015 Keynote: "A Systems View of Machine Learning"》by Joshua BloomO網(wǎng)頁鏈接云:O網(wǎng)頁鏈接
【Kaggle代碼(Python):分類問題重要變量的篩選和可視化】《Visualizing important variables》by saihttam in Caterpillar Tube PricingO網(wǎng)頁鏈接
【面向數(shù)據(jù)科學(xué)的NumPy/SciPy/Pandas使用速查表】《NumPy/SciPy/Pandas Cheat Sheet》O網(wǎng)頁鏈接云:O網(wǎng)頁鏈接
【Python/Pandas/Bokeh數(shù)據(jù)分析/可視化實(shí)例】《Data Analysis with Python, Pandas, and Bokeh》by Chris MetcalfO網(wǎng)頁鏈接GitHub:O網(wǎng)頁鏈接
【八個工具看Python數(shù)據(jù)生態(tài)圈的最新趨勢】《Eight Tools That Show What’s on the Horizon for the Python Data Ecosystem》by Bo Moore Including:SFrame&SGraph/Bokeh/Dask/Ibis/Splash/Petuum/Flink/PyxleyO網(wǎng)頁鏈接
