statistics

问题 Following is my code: sklearn_tfidf = TfidfVectorizer(ngram_range= (3,3),stop_words=stopwordslist, norm='l2',min_df=0, use_idf=True, smooth_idf=False, sublinear_tf=True) sklearn_representation = sklearn_tfidf.fit_transform(documents) It generates tri gram by removing all the stopwords. What I want it to allow those TRIGRAM what have stopword in their middle ( not in start and end) Is there processor needs to be written for this. Need suggestions. 回答1: Yes, you need to supply your own analyzer

问题 Is there a module for Python to open IBM SPSS (i.e. .sav) files? It would be great if there's something up-to-date which doesn't require any additional dll files/libraries. 回答1: I have released a python package "pyreadstat" that reads SPSS (sav, zsav and por), Stata and SAS files. It is a wrapper around the C library ReadStat so it is very fast. Readstat is the library used in the back of the R library Haven, which is widely used and very robust. The package is autocontained. It does not

问题 I have numpy, statsmodel, pandas, and scipy(I think) How do I calculate the z score of a p-value and vice versa? For example if I have a p value of 0.95 I should get 1.96 in return. I saw some functions in scipy but they only run a z-test on a array. 回答1: >>> import scipy.stats as st >>> st.norm.ppf(.95) 1.6448536269514722 >>> st.norm.cdf(1.64) 0.94949741652589625 As other users noted, Python calculates left/lower-tail probabilities by default. If you want to determine the density points

问题 As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. Closed 7 years ago . I've always thought that Python's advantages are code readibility and development speed, but time and memory usage were not as good as

问题 I am in charge of developing a website which should be able to show statistics from both Apple's app store and Google Play Store to clients, so they can easily see what's going on. I have figured out some ways to get App Store's data, but the Google Play Developers statistics seem way harder to get. I've heard of scraping, but this wouldn't be a great solution, as it would probably get broken whenever the developers console gets a major update. I'm looking for something which would work like

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed last year . I'm looking for a good open source library for scala for math and statistics. Hopefully something like Apache Math or Colt, but implemented in Scala. Can anyone point me in the right direction? 回答1: Yes, there are some: Scalalab The ScalaLab project aims to provide an efficient scientific programming environment

问题 I have fitted a model where: Y ~ A + A^2 + B + mixed.effect(C) Y is continuous A is continuous B actually refers to a DAY and currently looks like this: Levels: 1 < 2 < 3 < 4 < 5 < 6 < 7 < 8 < 9 < 11 < 12 I can easily change the data type, but I'm not sure whether it is more appropriate to treat B as numeric, a factor, or as an ordered factor. AND when treated as numeric or ordered factor, I'm not quite sure how to interpret the output. When treated as an ordered factor, summary(my.model)

问题 I'm having trouble making a histogram in R. The problem is that I tell it to make 5 bins but it makes 4 and I tell to make 5 and it makes 8 of them. data <- c(5.28, 14.64, 37.25, 78.9, 44.92, 8.96, 19.22, 34.81, 33.89, 24.28, 6.5, 4.32, 2.77, 17.6, 33.26, 52.78, 5.98, 22.48, 20.11, 65.74, 35.73, 56.95, 30.61, 29.82); hist(data, nclass = 5,freq=FALSE,col="orange",main="Histogram",xlab="x",ylab="f(x)",yaxs="i",xaxs="i") Any ideas on how to fix it? 回答1: Use the breaks argument: hist(data, breaks

问题 I have data in a mysql table in long / tall format (described below) and want to convert it to wide format. Can I do this using just sql? Easiest to explain with an example. Suppose you have information on (country, key, value) for M countries, N keys (e.g. keys can be income, political leader, area, continent, etc.) Long format has 3 columns: country, key, value - M*N rows. e.g. 'USA', 'President', 'Obama' ... 'USA', 'Currency', 'Dollar' Wide format has N=16 columns: county, key1, ..., keyN

问题 I have a dataframe containing a single column of IDs and all other columns are numerical values for which I want to compute z-scores. Here's a subsection of it: ID Age BMI Risk Factor PT 6 48 19.3 4 PT 8 43 20.9 NaN PT 2 39 18.1 3 PT 9 41 19.5 NaN Some of my columns contain NaN values which I do not want to include into the z-score calculations so I intend to use a solution offered to this question: how to zscore normalize pandas column with nans? df['zscore'] = (df.a - df.a.mean())/df.a.std