问题描述
我正在使用当前使用大型（> 5GB）.csv文件运行的系统。为了提高性能，我正在测试（A）从磁盘创建数据帧的不同方法（熊猫VS ），以及（B）将结果存储到磁盘的其他方法（.csv VS 文件）。
I am working with a system that currently operates with large (>5GB) .csv files. To increase performance, I am testing (A) different methods to create dataframes from disk (pandas VS dask) as well as (B) different ways to store results to disk (.csv VS hdf5 files).
为了测试性能，我做了以下操作：
In order to benchmark performance, I did the following:
def dask_read_from_hdf():
results_dd_hdf = dd.read_hdf('store.h5', key='period1', columns = ['Security'])
analyzed_stocks_dd_hdf = results_dd_hdf.Security.unique()
hdf.close()
def pandas_read_from_hdf():
results_pd_hdf = pd.read_hdf('store.h5', key='period1', columns = ['Security'])
analyzed_stocks_pd_hdf = results_pd_hdf.Security.unique()
hdf.close()
def dask_read_from_csv():
results_dd_csv = dd.read_csv(results_path, sep = ",", usecols = [0], header = 1, names = ["Security"])
analyzed_stocks_dd_csv = results_dd_csv.Security.unique()
def pandas_read_from_csv():
results_pd_csv = pd.read_csv(results_path, sep = ",", usecols = [0], header = 1, names = ["Security"])
analyzed_stocks_pd_csv = results_pd_csv.Security.unique()
print "dask hdf performance"
%timeit dask_read_from_hdf()
gc.collect()
print""
print "pandas hdf performance"
%timeit pandas_read_from_hdf()
gc.collect()
print""
print "dask csv performance"
%timeit dask_read_from_csv()
gc.collect()
print""
print "pandas csv performance"
%timeit pandas_read_from_csv()
gc.collect()
我的发现是：
dask hdf performance
10 loops, best of 3: 133 ms per loop
pandas hdf performance
1 loop, best of 3: 1.42 s per loop
dask csv performance
1 loop, best of 3: 7.88 ms per loop
pandas csv performance
1 loop, best of 3: 827 ms per loop
当可以比.csv更快地访问hdf5存储，并且dask比pandas更快地创建数据帧时，为什么hdf5的dask速度比csv的dask慢？我在做错什么吗？
When hdf5 storage can be accessed faster than .csv, and when dask creates dataframes faster than pandas, why is dask from hdf5 slower than dask from csv? Am I doing something wrong?
何时从HDF5存储对象创建dask数据帧对性能有意义？
推荐答案
HDF5在处理数字数据时效率最高，我猜您正在读取单个字符串列，这是它的弱点。
HDF5 is most efficient when working with numerical data, I'm guessing you are reading a single string column, which is its weakpoint.
通过使用 Categorical 来存储字符串，并假设基数相对较低（高数字），可以大大提高HDF5字符串数据的性能。重复值）
Performance of string data with HDF5 can be dramatically improved by using a Categorical to store your strings, assuming relatively low cardinality (high number of repeated values)
这是从前不久开始的，但是这里的一篇不错的博客文章正是经过了这些考虑。
It's from a little while back, but a good blog post here going through exactly these considerations.http://matthewrocklin.com/blog/work/2015/03/16/Fast-Serialization
您可能还会看到使用镶木地板-它类似于HDF5，因为它是二进制格式，但是
You may also look at using parquet - it is similar to HDF5 in that it is a binary format, but is column oriented, so a single column selection like this will likely be faster.
最近（2016年至2017年），为实现对镶木地板的快速本机读取器进行了大量工作， > pandas，下一个主要发行版本的熊猫（ 0.21 ）将具有 to_parquet 和 pd内置的.read_parquet 函数。
Recently (2016-2017) there has been significant work to implement a fast native reader of parquet->pandas, and the next major release of pandas (0.21) will have to_parquet and pd.read_parquet functions built in.
这篇关于从CSV导入时，与HDF5相比，为什么 pandas 和dask性能更好？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！