dask

问题 We are trying out dask_yarn version 0.3.0 (with dask 0.18.2) because of the conflicts between the boost-cpp i'm running with pyarrow version 0.10.0 We are trying to read a csv file from hdfs - however we get an error when running dd.read_csv('hdfs:///path/to/file.csv') since it is trying to use hdfs3. ImportError: Can not find the shared library: libhdfs3.so From the documentation it seems that there is an option to use pyarrow . What is the correct syntax/configuration to do so? 回答1: Try

问题 We are trying out dask_yarn version 0.3.0 (with dask 0.18.2) because of the conflicts between the boost-cpp i'm running with pyarrow version 0.10.0 We are trying to read a csv file from hdfs - however we get an error when running dd.read_csv('hdfs:///path/to/file.csv') since it is trying to use hdfs3. ImportError: Can not find the shared library: libhdfs3.so From the documentation it seems that there is an option to use pyarrow . What is the correct syntax/configuration to do so? 回答1: Try

问题 I'm building a Django app that enables users to upload a CSV via a form using a FormField. Once the CSV is imported I use the Pandas read_csv(filename) command to read in the CSV so I can do some processing on the CSV using Pandas. I've recently started learning the really useful Dask library because the size of the uploaded files can be larger than memory. Everything works fine when using Pandas pd.read_csv(filename) but when I try and use Dask dd.read_csv(filename) I get the error "

问题 I'm building a Django app that enables users to upload a CSV via a form using a FormField. Once the CSV is imported I use the Pandas read_csv(filename) command to read in the CSV so I can do some processing on the CSV using Pandas. I've recently started learning the really useful Dask library because the size of the uploaded files can be larger than memory. Everything works fine when using Pandas pd.read_csv(filename) but when I try and use Dask dd.read_csv(filename) I get the error "

问题 What exactly I want to achieve in terms of input output is a cross - join Input Example df = pd.DataFrame(columns = ['A', 'val'], data = [['a1', 23],['a2', 29], ['a3', 39]]) print(df) A val 0 a1 23 1 a2 29 2 a3 39 Output Example: df['key'] = 1 df.merge(df, how = "outer", on ="key") A_x val_x key A_y val_y 0 a1 23 1 a1 23 1 a1 23 1 a2 29 2 a1 23 1 a3 39 3 a2 29 1 a1 23 4 a2 29 1 a2 29 5 a2 29 1 a3 39 6 a3 39 1 a1 23 7 a3 39 1 a2 29 8 a3 39 1 a3 39 How I achieve this for a large dataset with

问题 What exactly I want to achieve in terms of input output is a cross - join Input Example df = pd.DataFrame(columns = ['A', 'val'], data = [['a1', 23],['a2', 29], ['a3', 39]]) print(df) A val 0 a1 23 1 a2 29 2 a3 39 Output Example: df['key'] = 1 df.merge(df, how = "outer", on ="key") A_x val_x key A_y val_y 0 a1 23 1 a1 23 1 a1 23 1 a2 29 2 a1 23 1 a3 39 3 a2 29 1 a1 23 4 a2 29 1 a2 29 5 a2 29 1 a3 39 6 a3 39 1 a1 23 7 a3 39 1 a2 29 8 a3 39 1 a3 39 How I achieve this for a large dataset with

问题 What exactly I want to achieve in terms of input output is a cross - join Input Example df = pd.DataFrame(columns = ['A', 'val'], data = [['a1', 23],['a2', 29], ['a3', 39]]) print(df) A val 0 a1 23 1 a2 29 2 a3 39 Output Example: df['key'] = 1 df.merge(df, how = "outer", on ="key") A_x val_x key A_y val_y 0 a1 23 1 a1 23 1 a1 23 1 a2 29 2 a1 23 1 a3 39 3 a2 29 1 a1 23 4 a2 29 1 a2 29 5 a2 29 1 a3 39 6 a3 39 1 a1 23 7 a3 39 1 a2 29 8 a3 39 1 a3 39 How I achieve this for a large dataset with

问题 I have a numpy array that i want to add as a column in a existing dask dataframe. enc = LabelEncoder() nparr = enc.fit_transform(X[['url']]) I have ddf of type dask dataframe. ddf['nurl'] = nparr ??? Any elegant way to achieve above please? Python PANDAS: Converting from pandas/numpy to dask dataframe/array This does not solve my issue as i want numpy array into existing dask dataframe. 回答1: You can convert the numpy array to a dask Series object, then merge it to the dataframe. You will need

问题 I'm trying to save by Dask Dataframe to parquet on the same machine as the dask scheduler/workers are located. However, I have trouble during this. My Dask setup : My python script is executed on my local machine (laptop 16 GB RAM), but the script creates a Dask client to a Dask scheduler running on a remote machine (a server with 400 GB RAM for parallel computations). The Dask scheduler and workers are all located on the same server, thus they all share the same file system, locally

问题 I'm trying to save by Dask Dataframe to parquet on the same machine as the dask scheduler/workers are located. However, I have trouble during this. My Dask setup : My python script is executed on my local machine (laptop 16 GB RAM), but the script creates a Dask client to a Dask scheduler running on a remote machine (a server with 400 GB RAM for parallel computations). The Dask scheduler and workers are all located on the same server, thus they all share the same file system, locally