data-science

问题 I am trying to drop NA values from a pandas dataframe. I have used dropna() (which should drop all NA rows from the dataframe). Yet, it does not work. Here is the code: import pandas as pd import numpy as np prison_data = pd.read_csv('https://andrewshinsuke.me/docs/compas-scores-two-years.csv') That's how you get the data frame. As the following shows, the default read_csv method does indeed convert the NA data points to np.nan . np.isnan(prison_data.head()['out_custody'][4]) Out[2]: True

问题 I am receiving the error: ValueError: Wrong number of items passed 3, placement implies 1 , and I am struggling to figure out where, and how I may begin addressing the problem. I don't really understand the meaning of the error; which is making it difficult for me to troubleshoot. I have also included the block of code that is triggering the error in my Jupyter Notebook. The data is tough to attach; so I am not looking for anyone to try and re-create this error for me. I am just looking for

问题 Before scikit-learn 0.20 we could use result.grid_scores_[result.best_index_] to get the standard deviation. (It returned for exemple: mean: 0.76172, std: 0.05225, params: {'n_neighbors': 21} ) What's the best way in scikit-learn 0.20 to get the standard deviation of the best score ? 回答1: In newer versions, the grid_scores_ is renamed as cv_results_ . Following the documentation, you need this: best_index_ : int The index (of the cv_results_ arrays) which corresponds to the best > candidate

问题 I am working with two csv files and imported as dataframe, df1 and df2 df1 has 50000 rows and df2 has 150000 rows. I want to compare (iterate through each row) the 'time' of df2 with df1, find the difference in time and return the values of all column corresponding to similar row, save it in df3 ( time synchronization ) For example, 35427949712 (of 'time' in df1) is nearest or equal to 35427949712 (of 'time' in df2), So I would like to return the contents to df1 ('velocity_x' and 'yaw') and

问题 As I was reading about tf–idf on Wiki, I was confused by what it means by the word "document". Does it mean paragraph? "The inverse document frequency is a measure of how much information the word provides, that is, whether the term is common or rare across all documents. It is the logarithmically scaled inverse fraction of the documents that contain the word, obtained by dividing the total number of documents by the number of documents containing the term, and then taking the logarithm of

问题 I have a .csv file that contains my data. I would like to do Logistic Regression , Naive Bayes and Decision Trees . I already know how to implement these. However, my teacher wants me to split the data in my .csv file into 80% and let my algorithms predict the other 20% . I would like to know how to actually split the data in that way. diabetes_df = pd.read_csv("diabetes.csv") diabetes_df.head() with open("diabetes.csv", "rb") as f: data = f.read().split() train_data = data[:80] test_data =

问题 After reading Unbalanced factor of KMeans, I am trying to understand how this works. I mean, from my examples, I can see that the less the value of the factor, the better the quality of KMeans clustering, i.e. the more balanced are its clusters. But what is the naked mathematical interpretation of this factor? Is this a known quantity or something? Here are my examples: C1 = 10 C2 = 100 pdd = [(C1,10), (C2, 100)] n = 2 <-- #clusters total = 110 <-- #points uf = 10 * 10 + 100 * 100 uf = 100100

问题 I'm converting pdf to text convertion using PyPDF2 and during this code some words are mixing, the code is shown below :- filename = 'CS1.pdf' pdfFileObj = open(filename,'rb') pdfReader = PyPDF2.PdfFileReader(pdfFileObj) num_pages = pdfReader.numPages count = 0 text = "" while count < num_pages: pageObj = pdfReader.getPage(count) count +=1 print(pageObj) text += pageObj.extractText() if text != "": text = text else: text = textract.process('/home/ayush/Ayush/1june/pdf_to_text/CS1.pdf', method

问题 I want to rewrite this code whithout for loops. for x in range(round(len(data)/128)): for i in range(128): data.iloc[x*128 , i*24:(i+1)*24] = data.iloc[(x*128)+i , 0:24].values This code moves data of 128 rows to one row(move index 1:127 -> index 0) and (index 128:255 -> index 128) and ...)). How can I rewrite it optimally? 来源： https://stackoverflow.com/questions/58141412/how-can-i-rewrite-this-code-without-for-loop

问题 I am using a module called pyhaystack to retrieve data (rest API) from a building automation system based on 'tags.' Python will return a dictionary of the data. Im trying to use pandas with an If Else statement further below that I am having trouble with. The pyhaystack is working just fine to get the data... This connects me to the automation system: (works just fine) from pyhaystack.client.niagara import NiagaraHaystackSession import pandas as pd session = NiagaraHaystackSession(uri='http: