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 …
Spliting a list into n uneven buckets with all combinations Question: I have a list like: lst = [1,2,3,4,5,6,7,8,9,10] and I want to get the combination of all splits for a given n bucket without changing the order of the list. Output exp for n=3: [ [1],[2],[3,4,5,6,7,8,9,10], [1],[2,3],[4,5,6,7,8,9,10], [1],[2,3,4],[5,6,7,8,9,10], . . . [1,2,3,4,5,6,7,8],[9],[10], ] Python …
Losing index information when using dask.dataframe.to_parquet() with partitioning Question: When I was using dask=1.2.2 with pyarrow 0.11.1 I did not observe this behavior. After updating (dask=2.10.1 and pyarrow=0.15.1), I cannot save the index when I use to_parquet method with given partition_on and write_index arguments. Here I have created a minimal example which shows the issue: …
Pandas: Sampling a DataFrame Question: I’m trying to read a fairly large CSV file with Pandas and split it up into two random chunks, one of which being 10% of the data and the other being 90%. Here’s my current attempt: rows = data.index row_count = len(rows) random.shuffle(list(rows)) data.reindex(rows) training_data = data[row_count // 10:] testing_data …
