pyarrow

问题 I'm trying to use pyarrow and i keep getting the following error. ImportError: Can not find the shared library: libhdfs3.so so i read some stackoverflow and it says that i need to set enviorment variable for ARROW_LIBHDFS_DIR. The path to libhdfs.so is /usr/local/hadoop/native/ it tried to set it in bashrc but it didn't work the conda installation doesn't seem to work i.e. conda install libhdfs3 pip install libhdfs3 conda install -c clinicalgraphics libgcrypt11 conda install libprotobuf=2.5

问题 The data I have is a kind of streaming data. And I want to store them into a single Parquet file. But Pyarrow will overwrite the Parquet file everytime. So How should I do? I try not to close the writer but it seems impossible since If I didn't close it, then I could not read this file. Here is the package: import pyarrow.parquet as pp import pyarrow as pa for name in ['LEE','LSY','asd','wer']: writer=pq.ParquetWriter('d:/test.parquet', table.schema) arrays=[pa.array([name]),pa.array([2])]

问题 The data I have is a kind of streaming data. And I want to store them into a single Parquet file. But Pyarrow will overwrite the Parquet file everytime. So How should I do? I try not to close the writer but it seems impossible since If I didn't close it, then I could not read this file. Here is the package: import pyarrow.parquet as pp import pyarrow as pa for name in ['LEE','LSY','asd','wer']: writer=pq.ParquetWriter('d:/test.parquet', table.schema) arrays=[pa.array([name]),pa.array([2])]

问题 I have tried installing it in the terminal and in juypter lab and it says that it has been successfully installed but when I run df = query_job.to_dataframe() I keep getting the error " ValueError: The pyarrow library is not installed, please install pyarrow to use the to_arrow() function.". I have no idea how to fix this. Any advice? I am trying to access data from google data studio ultimately with the code, from google.cloud import bigquery import pandas import numpy import pyarrow

问题 I have a C++ library which is built against the Apache Arrow C++ libraries, with a binding to python using Pybind. I'd like to be able to write a function in C++ to take a table constructed with PyArrow, like: void test(arrow::Table test); Passing in a PyArrow table like: tab = pa.Table.from_pandas(df) mybinding.test(tab) If I do a naive function as above, I get: TypeError: arrow_test(): incompatible function arguments. The following argument types are supported: 1. (arg0: arrow::Table) ->

问题 I am currently developing my first whole system using PySpark and I am running into some strange, memory-related issues. In one of the stages, I would like to resemble a Split-Apply-Combine strategy in order to modify a DataFrame. That is, I would like to apply a function to each of the groups defined by a given column and finally combine them all. Problem is, the function I want to apply is a prediction method for a fitted model that "speaks" the Pandas idiom, i.e., it is vectorized and

问题 I am currently developing my first whole system using PySpark and I am running into some strange, memory-related issues. In one of the stages, I would like to resemble a Split-Apply-Combine strategy in order to modify a DataFrame. That is, I would like to apply a function to each of the groups defined by a given column and finally combine them all. Problem is, the function I want to apply is a prediction method for a fitted model that "speaks" the Pandas idiom, i.e., it is vectorized and

问题 I have process A and process B. Process A opens a file, calls mmap and write to it, process B do the same but reads the same mapped region when process A has finished writing. Using mmap, process B is suppossed to read the file from memory instead of disk assuming process A has not called munmap. If I would like to deploy process A and process B to diferent containers in the same pod in Kubernetes, is memory mapped IO supposed to work the same way as the initial example? Should container B

问题 Trying to use pyarrow to access hdfs file and not able to get it working, below is the code, thank you very much in advance. [rxie@cedgedev03 code]$ python Python 2.7.12 |Anaconda 4.2.0 (64-bit)| (default, Jul 2 2016, 17:42:40) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2 Type "help", "copyright", "credits" or "license" for more information. Anaconda is brought to you by Continuum Analytics. Please check out: http://continuum.io/thanks and https://anaconda.org import pyarrow import os os

问题 Is there a special pyarrow data type I should use for columns which have lists of dictionaries when I save to a parquet file? If I save lists or lists of dictionaries as a string, I normally have to .apply(eval) the field if I read it into memory again in order for pandas to recognize the data as a list (so I can normalize it with pd.json_normalize ) column_a: [ {"id": "something", "value": "else"}, {"id": "something2", "value": "else2"}, ] column_b: ["test", "test2", "test3"] Just wondering