- 文章信息
- 作者: kaiwu
- 点击数:537
Introduction to Python
Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with numpy.
Introduction to SQL
Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.
Introduction to R
Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.
Intermediate Python
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Hugo Bowne-Anderson
Data Scientist at DataCamp
Data Science for Everyone
An introduction to data science with no coding involved.
Hadrien Lacroix
Curriculum Manager at DataCamp
Introduction to Data Science in Python
Dive into data science using Python and learn how to effectively analyze and visualize your data.
Hillary Green-Lerman
Lead Data Scientist, Looker
Joining Data in SQL
Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.
Chester Ismay
Data Science Evangelist at DataRobot
Data Manipulation with pandas
Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.
Richie Cotton
Curriculum Architect at DataCamp
Supervised Learning with scikit-learn
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
Hugo Bowne-Anderson
Data Scientist at DataCamp
Machine Learning for Everyone
An introduction to machine learning with no coding involved.
Hadrien Lacroix
Curriculum Manager at DataCamp
Introduction to Tableau
Get started with Tableau, a widely used business intelligence (BI) and analytics software to explore, visualize, and securely share data.
Carl Rosseel
Curriculum Manager at Datacamp
Intermediate R
Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.
Python Data Science Toolbox (Part 1)
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Hugo Bowne-Anderson
Data Scientist at DataCamp
Introduction to Data Visualization with Matplotlib
Learn how to create, customize, and share data visualizations using Matplotlib.
Ariel Rokem
Senior Data Scientist, University of Washington
Python Data Science Toolbox (Part 2)
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Hugo Bowne-Anderson
Data Scientist at DataCamp
- 文章信息
- 作者: kaiwu
- 点击数:693
中国入境旅游者中外国人的人次数:月度数据
类别:月度
区间:1994-2015
单位:万人次
备注:2016年开始都是半年的数据
中国入境旅游者中外国人的人次数:季度数据
类别:月度
区间:1994-2015
单位:万人次
备注:2016年开始都是半年的数据
中国入境旅游者中外国人的人次数:年度数据
累月:年度
区间:1994-2019
单位:万人次
季度 | 中国入境旅游者中外国人的人次数(万人次) |
1994Q1 | 103.86 |
1994Q2 | 135.26 |
1994Q3 | 138.27 |
1994Q4 | 140.82 |
1995Q1 | 114.64 |
1995Q2 | 144.3 |
1995Q3 | 164.46 |
1995Q4 | 165.27 |
1996Q1 | 139.34 |
1996Q2 | 172.57 |
1996Q3 | 178.61 |
1996Q4 | 183.92 |
1997Q1 | 159.86 |
1997Q2 | 186.55 |
1997Q3 | 199.94 |
1997Q4 | 196.45 |
1998Q1 | 156.42 |
1998Q2 | 181.55 |
1998Q3 | 182.67 |
1998Q4 | 190.13 |
1999Q1 | 171.39 |
1999Q2 | 213.41 |
1999Q3 | 225.75 |
1999Q4 | 232.68 |
2000Q1 | 208.75 |
2000Q2 | 261.56 |
2000Q3 | 275.26 |
2000Q4 | 274.12 |
2001Q1 | 233.79 |
2001Q2 | 294.55 |
2001Q3 | 301.73 |
2001Q4 | 292.57 |
2002Q1 | 274.42 |
2002Q2 | 339.95 |
2002Q3 | 392.3 |
2002Q4 | 337.28 |
2003Q1 | 320.59 |
2003Q2 | 141.54 |
2003Q3 | 307.92 |
2003Q4 | 370.24 |
2004Q1 | 329.39 |
2004Q2 | 417.67 |
2004Q3 | 457.83 |
2004Q4 | 488.36 |
2005Q1 | 435.98 |
2005Q2 | 526.1 |
2005Q3 | 532.19 |
2005Q4 | 531.24 |
2006Q1 | 463.24 |
2006Q2 | 557.65 |
2006Q3 | 590.16 |
2006Q4 | 609.98 |
2007Q1 | 545.67 |
2007Q2 | 665.86 |
2007Q3 | 690.83 |
2007Q4 | 708.61 |
2008Q1 | 625.28 |
2008Q2 | 638.77 |
2008Q3 | 571.22 |
2008Q4 | 597.26 |
2009Q1 | 482.71 |
2009Q2 | 537.91 |
2009Q3 | 578.01 |
2009Q4 | 595.12 |
2010Q1 | 575.07 |
2010Q2 | 680.02 |
2010Q3 | 690.8 |
2010Q4 | 666.8 |
2011Q1 | 585.77 |
2011Q2 | 701.47 |
2011Q3 | 715.18 |
2011Q4 | 708.58 |
2012Q1 | 629.61 |
2012Q2 | 715.99 |
2012Q3 | 692.63 |
2012Q4 | 680.93 |
2013Q1 | 604.18 |
2013Q2 | 671.73 |
2013Q3 | 660.4 |
2013Q4 | 692.72 |
2014Q1 | 584.33 |
2014Q2 | 668.81 |
2014Q3 | 668.23 |
2014Q4 | 714.71 |
2015Q1 | 561.31 |
2015Q2 | 675.05 |
2015Q3 | 663.61 |
2015Q4 | 698.57 |
- 文章信息
- 作者: kaiwu
- 点击数:807
将视频的字幕文件(.srt)转换为文本文件(祛除时间行)
给定目录下,所有字幕文件,批量转换
参考文件
https://stackoverflow.com/questions/51073045/parsing-transcript-srt-files-into-readable-text
https://pythongeeks.org/rename-files-in-python/
https://github.com/byroot/pysrt
print('covert %d srt file to txt file' % (i+1)
#covert 67 srt file to txt file
- 文章信息
- 作者: kaiwu
- 点击数:538
卓克的总结是:1在病毒浓度较高的环境里,通过普通外科口罩被吸入的病毒就足以导致感染;在病毒浓度较低的环境里,戴口罩的人就安全很多。2在防止病毒传染给其他人方面,N95口罩和外科口罩不相上下,而想防止被感染,最好还是佩戴N95口罩。3要想从科上严谨地证实一个观念的是非常困难的,既要有数学模型、实测结果,还要与现实高度吻合。
Masking out air sharing
The effectiveness of masks in preventing the transmission of severe acute respiratory syndrome coronavirus 2 has been debated since the beginning of the COVID-19 pandemic. One important question is whether masks are effective despite the forceful expulsion of respiratory matter during coughing and sneezing. Cheng et al. convincingly show that most people live in conditions in which the airborne virus load is low. The probability of infection changes nonlinearly with the amount of respiratory matter to which a person is exposed. If most people in the wider community wear even simple surgical masks, then the probability of an encounter with a virus particle is even further limited. In indoor settings, it is impossible to avoid breathing in air that someone else has exhaled, and in hospital situations where the virus concentration is the highest, even the best-performing masks used without other protective gear such as hazmat suits will not provide adequate protection.
Science, abg6296, this issue p. 1439
Abstract
Airborne transmission by droplets and aerosols is important for the spread of viruses. Face masks are a well-established preventive measure, but their effectiveness for mitigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is still under debate. We show that variations in mask efficacy can be explained by different regimes of virus abundance and are related to population-average infection probability and reproduction number. For SARS-CoV-2, the viral load of infectious individuals can vary by orders of magnitude. We find that most environments and contacts are under conditions of low virus abundance (virus-limited), where surgical masks are effective at preventing virus spread. More-advanced masks and other protective equipment are required in potentially virus-rich indoor environments, including medical centers and hospitals. Masks are particularly effective in combination with other preventive measures like ventilation and distancing.
- 文章信息
- 作者: kaiwu
- 点击数:749
在2021年7月22日,DeepMind和欧洲分子生物学实验室(European Bioinformatics Institute)联合宣布,两家合作,用自己开发的人工智能分析工具AlphaFold2预测了36.5万种蛋白质的结构,然后把这些结果做成数据库,免费给全球所有科研人员使用。
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the 3-D structure that a protein will adopt based solely on its amino acid sequence, the structure prediction component of the ‘protein folding problem’8, has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even where no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.