dask

问题 Can any one explain how to rectify this error Where do i get a detailed info of dask Can it replace pandas. How is it different from other dataframes, is it fast in processing Code: import dask.dataframe as dd df = dd.demo.make_timeseries('2000-01-01', '2000-12-31', freq='10s', partition_freq='1M',dtypes={'name': str, 'id': int, 'x': float, 'y': float}) print df o/p: Traceback (most recent call last): File "C:/Users/divya.nagandla/PycharmProjects/python/supressions1/dask.py", line 1, in

问题 Can any one explain how to rectify this error Where do i get a detailed info of dask Can it replace pandas. How is it different from other dataframes, is it fast in processing Code: import dask.dataframe as dd df = dd.demo.make_timeseries('2000-01-01', '2000-12-31', freq='10s', partition_freq='1M',dtypes={'name': str, 'id': int, 'x': float, 'y': float}) print df o/p: Traceback (most recent call last): File "C:/Users/divya.nagandla/PycharmProjects/python/supressions1/dask.py", line 1, in

问题 I'm using dask-distributed with a local setup (LocalCluster with 5 workers) on a dask.delayed workload. Most of the work is done by the vtk Python bindings. Since vtk is C++ based I think that means the workers don't release the GIL when in a long-running statement. When I run the workload, my terminal prints out a bunch of errors like this: Traceback (most recent call last): File "C:\Users\patri\AppData\Local\Continuum\anaconda3\lib\site-packages\distributed\comm\core.py", line 221, in

问题 I have a Dask Dataframe for which I would like to compute skewness for a list of columns and if this skewness exceeds a certain threshold, I correct it using log transformation. I am wondering whether there is a more efficient way of making correct_skewness() function work on multiple columns in parallel by removing the for loop in the correct_skewness() function below: import dask import dask.array as da from scipy import stats # Create a dataframe df = dask.datasets.timeseries() df.head()

问题 I am trying to perform the below operation using python: for n in range (1,100) : routedat = xr.open_dataset(route_files[n]) lsmdat = xr.open_dataset(lsm_files[n]) routedat = reformat_LIS_output(routedat) lsmdat = reformat_LIS_output(lsmdat) for i in range(1,len(stations)): start_date = stations[i]['Streamflow (cumecs)'].first_valid_index() lis_date = routedat['time'][0].values gauge_id = valid_stations[i] gauge_lat = meta_file.loc[gauge_id,'Latitude'] gauge_lon = meta_file.loc[gauge_id,

问题 Here is the summary of what I'm doing: At first, I do this by normal multiprocessing and pandas package: Step 1. Get the list of files name which I'm gonna to read import os files = os.listdir(DATA_PATH + product) Step 2. loop over the list from multiprocessing import Pool import pandas as pd def readAndWriteCsvFiles(file): ### Step 2.1 read csv file into dataframe data = pd.read_csv(DATA_PATH + product + "/" + file, parse_dates=True, infer_datetime_format=False) ### Step 2.2 do some

问题 Here is the summary of what I'm doing: At first, I do this by normal multiprocessing and pandas package: Step 1. Get the list of files name which I'm gonna to read import os files = os.listdir(DATA_PATH + product) Step 2. loop over the list from multiprocessing import Pool import pandas as pd def readAndWriteCsvFiles(file): ### Step 2.1 read csv file into dataframe data = pd.read_csv(DATA_PATH + product + "/" + file, parse_dates=True, infer_datetime_format=False) ### Step 2.2 do some

问题 Here is the summary of what I'm doing: At first, I do this by normal multiprocessing and pandas package: Step 1. Get the list of files name which I'm gonna to read import os files = os.listdir(DATA_PATH + product) Step 2. loop over the list from multiprocessing import Pool import pandas as pd def readAndWriteCsvFiles(file): ### Step 2.1 read csv file into dataframe data = pd.read_csv(DATA_PATH + product + "/" + file, parse_dates=True, infer_datetime_format=False) ### Step 2.2 do some

问题 Here is the summary of what I'm doing: At first, I do this by normal multiprocessing and pandas package: Step 1. Get the list of files name which I'm gonna to read import os files = os.listdir(DATA_PATH + product) Step 2. loop over the list from multiprocessing import Pool import pandas as pd def readAndWriteCsvFiles(file): ### Step 2.1 read csv file into dataframe data = pd.read_csv(DATA_PATH + product + "/" + file, parse_dates=True, infer_datetime_format=False) ### Step 2.2 do some

问题 client = Client('127.0.0.1:8786',direct_to_workers=True) future1 = client.submit(Counter, workers= 'ninja',actor=True) counter1 = future1.result() print(counter1) All is well but what if the client gets restarted? How do I get the actor back from the worker called ninja? 回答1: There is no user-facing way to do this as of 2019-03-06 I recommend raising a feature request issue 来源： https://stackoverflow.com/questions/54918699/actors-and-dask-workers