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Use Python for data analysis: This book is a detailed version of Python's pandas package. Learn this to master some basic pandas commands. However, this is not the point, because pandas is too slow to produce a large amount of data and may collapse (I don't know if there is any improvement now-!) The point is, through studying this book, I feel a little about the data operation and am familiar with the basic data operation process. All the operations in this book can be replaced by native Python, and Pandas package is not needed. Finding feelings is very important.　　该剧是讲述罗伊帝这名医生除去监-狱原医疗科长，自己成为新的医疗科长后，孤注一掷成长为掌控名牌大学医院理事会的医生的长成剧。《不完美的她》以独立女性为视角，讲述了林绪之凭借心中的爱与光亮，在黑暗中寻找光明，守护希望，温暖治愈、感动人心的女性“自我救赎”的故事。More articles are published in the public number "Muke Da".那和尚也无意再顾俞大猷，只如鬼影一般在战团中游荡，专找软柿子捏，袭人背后。For codes of the same length, theoretically, the further the coding distance between any two categories, the stronger the error correction capability. Therefore, when the code length is small, the theoretical optimal code can be calculated according to this principle. However, it is difficult to effectively determine the optimal code when the code length is slightly larger. In fact, this is an NP-hard problem. However, we usually do not need to obtain theoretical optimal codes, because non-optimal codes can often produce good enough classifiers in practice. On the other hand, it is not that the better the theoretical properties of coding, the better the classification performance, because the machine learning problem involves many factors, such as dismantling multiple classes into two "class subsets", and the difficulty of distinguishing the two class subsets formed by different dismantling methods is often different, that is, the difficulty of the two classification problems caused by them is different. Therefore, one theory has a good quality of error correction, but it leads to a difficult coding for the two-classification problem, which is worse than the other theory, but it leads to a simpler coding for the two-classification problem, and it is hard to say which is better or weaker in the final performance of the model.