Taking two samples from the data but with different observations Question: My data is made of about 9000 observations and 20 features (Edit – Pandas dataframe). I’ve taken a sample of 200 observations like this and conducted some analysis on it: sample_data = data.sample(n = 200) Now I want to randomely take a sample of …
Extract all possible combinations of unique elements in dict of lists Question: I have this input: d = {‘a’: [‘A’, ‘B’, ‘C’], ‘b’: [‘A’, ‘B’, ‘C’], ‘c’: [‘D’, ‘E’], ‘d’: [‘E’, ‘F’, ‘G’]} How can I extract all the possible unique samplings per list? One of the possible output is for example: d = {‘a’: …
How can I partitioning data set (csv file) with systematic sampling method?(python) Question: Here are the requirements: Partitioning data set into train data set and test data set. Systematic sampling should be used when partitioning data. The train data set should be about 80% of all data points and the test data set should be …
stratified sampling with priors in python Question: Context The common scenario of applying stratified sampling is about choosing a random sample that roughly maintains the distribution of the selected variable(s) so that it is representative. Goal: The goal is to create a function to perfrom stratified sampling but with some provided proportions of the considered …
What is the best practice to reducefilter stream data into normal data with the most used characteristics using PySpark? Question: I am working on streaming web-server records using PySpark in real-time, and I want to reducefilter the data of a certain period (Let’s say 1 week, which is 10M records) into 1M records to reach …
Sampling two normal distribution "in the same way" Question: Suppose I want to sample a normal distribution. This is straightforward through rng = numpy.random.default_rng() and then rng.normal(mean, std, size). This is also easy if I want to change the standard deviation, like rng.normal(mean, std*2, size). However, executing the two commands give "different" result. To my …
For a binary classification, how can I sample data by column so that 2/3 of the data contains zeros and 1/3 contains ones? Question: I have a large dataset containing four columns and the third column contains the binary label (a value either 0 or 1). This dataset is imbalanced – it contains much more …
SMOTE – could not convert string to float Question: I think I’m missing something in the code below. from sklearn.model_selection import train_test_split from imblearn.over_sampling import SMOTE # Split into training and test sets # Testing Count Vectorizer X = df[[‘Spam’]] y = df[‘Value’] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=40) X_resample, y_resampled = …
How to generate a random 4 digit number not starting with 0 and having unique digits? Question: This works almost fine but the number starts with 0 sometimes: import random numbers = random.sample(range(10), 4) print(”.join(map(str, numbers))) I’ve found a lot of examples but none of them guarantee that the sequence won’t start with 0. Asked …
What does replacement mean in numpy.random.choice? Question: Here explains the function numpy.random.choice. However, I am confused about the third parameter replace. What is it? And in which case will it be useful? Thanks! Asked By: wking || Source Answers: It controls whether the sample is returned to the sample pool. If you want only unique …
