数据处理是一个当下非常热门的研究方向，通过对于大型实际场景中的数据进行建模，可以用于预测下一阶段可能出现的情况。比如我们有过去的2002年-2018年的黄金价格的数据：
该数据来源于Gitee上的一个开源项目。其中包含有：时间、开盘价、收盘价、最高价、最低价、交易数以及成交额这么几个参数。假如我们使用一个机器学习的模型去分析这个数据，也许我们可以预测在这个数据中并不存在的金价数据。如果预测的契合度较好，那么对于一些人的投资策略来说有重大意义。但是这种实际场景下的数据，往往数据量是非常大的。虽然这里我们使用到的数据只有300多KB，但是我们更多的时候不得不考虑10个GB甚至是1个TB以上的数据的处理。如果处理都无法处理，那我们如何对这些数据进行建模呢？
首先我们看一个最简单的情况，我们先不考虑性能的问题，那么我们可以使用xlrd这个工具来在python中打开和加载一个Excel表格：
# table.py
def read_table_by_xlrd():
import xlrd
workbook = xlrd.open_workbook(r'data.xls')
sheet_name = workbook.sheet_names()
print ('All sheets in the file data.xls are: {}'.format(sheet_name))
sheet = workbook.sheet_by_index(0)
print ('The cell value of row index 0 and col index 1 is: {}'.format(sheet.cell_value(0, 1)))
print ('The elements of row index 0 are: {}'.format(sheet.row_values(0)))
print ('The length of col index 1 are: {}'.format(len(sheet.col_values(1))))
if __name__ == '__main__':
read_table_by_xlrd()
上述代码的输出如下：
[dechin@dechin-manjaro gold]$ python3 table.py All sheets in the file data.xls are: ['Sheet1', 'Sheet2', 'Sheet3'] The cell value of row index 0 and col index 1 is: 开 The elements of row index 0 are: ['时间', '开', '高', '低', '收', '量', '额'] The length of col index 1 are: 3923
我们这里成功的将一个xls格式的表格加载到了python的内存中，我们可以对这些数据进行分析。如果需要对这些数据修改，可以使用openpyxl这个仓库，但是这里我们不做过多的赘述。
在python中还有另外一个非常常用且非常强大的库可以用来处理表格数据，那就是pandas，这里我们利用ipython这个工具简单展示一下使用pandas处理表格数据的方法：
[dechin@dechin-manjaro gold]$ ipython
Python 3.8.5 (default, Sep 4 2020, 07:30:14)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.19.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import pandas as pd
In [2]: !ls -l
总用量 368
-rw-r--r-- 1 dechin dechin 372736 3月 27 21:31 data.xls
-rw-r--r-- 1 dechin dechin 563 3月 27 21:42 table.py
In [3]: data = pd.read_excel('data.xls', 'Sheet1') # 读取excel格式的文件
In [4]: data.to_csv('data.csv', encoding='utf-8') # 转成csv格式的文件
In [7]: !ls -l
总用量 588
-rw-r--r-- 1 dechin dechin 221872 3月 27 21:52 data.csv
-rw-r--r-- 1 dechin dechin 372736 3月 27 21:31 data.xls
-rw-r--r-- 1 dechin dechin 563 3月 27 21:42 table.py
In [8]: !head -n 10 data.csv # 读取csv文件的头10行
,时间,开,高,低,收,量,额
0,2002-10-30,83.98,92.38,82.0,83.52,352,29373370
1,2002-10-31,83.9,83.92,83.9,83.91,66,5537480
2,2002-11-01,84.5,84.65,84.0,84.51,77,6502510
3,2002-11-04,84.9,85.06,84.9,84.99,95,8076330
4,2002-11-05,85.1,85.2,85.1,85.13,61,5193650
5,2002-11-06,84.9,84.9,84.9,84.9,1,84900
6,2002-11-07,85.0,85.15,85.0,85.14,26,2212310
7,2002-11-08,85.25,85.28,85.1,85.16,35,2981780
8,2002-11-11,85.18,85.19,85.18,85.19,65,5537050
在ipython中我们不仅可以执行python指令，还可以在前面加一个!就能够执行一些系统命令，非常的方便。csv格式的文件，其实就是用逗号跟换行符来替代常用的\t字符串进行数据的分隔。
但是，不论是使用xlrd还是pandas，我们都会面临一个同样的问题：需要把所有的数据加载到内存中进行处理。我们一般的个人电脑只有8GB-16GB的内存，就算是比较大的64GB的内存，我们也只能够在内存中对64GB以下内存大小的文件进行处理，这对于大数据场景来说远远不够。所以，下一章节中介绍的vaex就是一个很好的解决方案。另外，关于Linux下查看本地内存以及使用情况的方法如下：
[dechin@dechin-manjaro gold]$ vmstat procs -----------memory---------- ---swap-- -----io---- -system-- ------cpu----- r b 交换 空闲 缓冲 缓存 si so bi bo in cs us sy id wa st 0 0 0 35812168 328340 2904872 0 0 20 27 362 365 8 4 88 0 0 [dechin@dechin-manjaro gold]$ vmstat 2 3 procs -----------memory---------- ---swap-- -----io---- -system-- ------cpu----- r b 交换 空闲 缓冲 缓存 si so bi bo in cs us sy id wa st 1 0 0 35810916 328356 2905844 0 0 20 27 362 365 8 4 88 0 0 0 0 0 35811916 328364 2904952 0 0 0 6 613 688 1 1 99 0 0 0 0 0 35812168 328364 2904856 0 0 0 0 672 642 0 1 99 0 0
我们可以看到空闲内存大约有36GB的内存，这里我们本机一共有40GB的内存，算是比较大的了。
vaex提供了一种内存映射的数据处理方案，我们不需要将整个的数据文件加载到内存中进行处理，我们可以直接对硬盘存储进行操作。换句话说，我们所能够处理的文件大小不再受到内存大小的限制，只要在磁盘存储空间允许的范围内，我们都可以对这么大小的文件进行处理。
一般现在个人PC的磁盘最小也有128GB，远远大于内存可以承受的范围。当然，由于分区的不同，不一定能够保障所有的内存资源都能够被使用到，这里附上查看当前目录分区的可用磁盘空间大小查询的方法：
[dechin@dechin-manjaro gold]$ df -hl . 文件系统 容量 已用 可用 已用% 挂载点 /dev/nvme0n1p9 144G 57G 80G 42% /
这里可以看到我们还有80GB的可用磁盘空间，也就是说，如果我们在当前目录放一个80GB大小的表格文件，那么用pandas和xlrd都是没办法处理的，因为这已经远远超出了内存可支持的空间。但是用vaex，我们依然可以对这个文件进行处理。
在vaex的官方文档链接中也介绍有vaex的原理和优势：
与大多数的python第三方包类似的，我们可以使用pip来进行下载和管理。当然由于下载的文件会比较多，中间的过程也会较为缓慢，我们只需安静等待即可：
[dechin@dechin-manjaro gold]$ python3 -m pip install vaex
Collecting vaex
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Building wheels for collected packages: frozendict, aplus
Building wheel for frozendict (setup.py) ... done
Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=1ae5d8fe0d670f73bf3ee88453978246919197a616f0e08e601c84cc244cb238
Stored in directory: /home/dechin/.cache/pip/wheels/9b/9b/56/5713233cf7226423ab6c58c08081551a301b5863e343ba053c
Building wheel for aplus (setup.py) ... done
Created wheel for aplus: filename=aplus-0.11.0-py3-none-any.whl size=4412 sha256=9762d51c5ece813b0c5a27ff6ebc1a86e709d55edb7003dcc11272c954dd39c7
Stored in directory: /home/dechin/.cache/pip/wheels/de/93/23/3db69e1003030a764c9827dc02137119ec5e6e439afd64eebb
Successfully built frozendict aplus
Installing collected packages: pyarrow, tabulate, frozendict, aplus, python-utils, progressbar2, vaex-core, vaex-ml, vaex-viz, vaex-astro, vaex-hdf5, cachetools, vaex-server, xarray, jupyterlab-widgets, ipywidgets, ipympl, branca, shapely, traittypes, ipyleaflet, ipyvue, ipyvuetify, ipywebrtc, ipydatawidgets, pythreejs, ipyvolume, bqplot, vaex-jupyter, vaex
Attempting uninstall: ipywidgets
Found existing installation: ipywidgets 7.5.1
Uninstalling ipywidgets-7.5.1:
Successfully uninstalled ipywidgets-7.5.1
Successfully installed aplus-0.11.0 bqplot-0.12.23 branca-0.4.2 cachetools-4.2.1 frozendict-1.2 ipydatawidgets-4.2.0 ipyleaflet-0.13.6 ipympl-0.7.0 ipyvolume-0.5.2 ipyvue-1.5.0 ipyvuetify-1.6.2 ipywebrtc-0.5.0 ipywidgets-7.6.3 jupyterlab-widgets-1.0.0 progressbar2-3.53.1 pyarrow-3.0.0 python-utils-2.5.6 pythreejs-2.3.0 shapely-1.7.1 tabulate-0.8.9 traittypes-0.2.1 vaex-4.1.0 vaex-astro-0.8.0 vaex-core-4.1.0 vaex-hdf5-0.7.0 vaex-jupyter-0.6.0 vaex-ml-0.11.1 vaex-server-0.4.0 vaex-viz-0.5.0 xarray-0.17.0
在出现Successfully installed的字样之后，就代表我们已经安装成功，可以开始使用了。
由于使用其他的工具我们也可以正常的打开和读取表格文件，为了体现出使用vaex的优势，这里我们直接用ipython来对比一下两者的打开时间：
[dechin@dechin-manjaro gold]$ ipython
Python 3.8.5 (default, Sep 4 2020, 07:30:14)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.19.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import vaex
In [2]: import xlrd
In [3]: %timeit xlrd.open_workbook(r'data.xls')
46.4 ms ± 76.2 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [4]: %timeit vaex.open('data.csv')
4.95 ms ± 48.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [7]: %timeit vaex.open('data.hdf5')
1.34 ms ± 1.84 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
我们从结果中发现，打开同样的一份文件，使用xlrd需要将近50ms的时间，而vaex最低只需要1ms的时间，如此巨大的性能优势使得我们不得不对vaex给予更多的关注。关于跟其他库的对比，在这个链接中已经有人做过了，即使是对比pandas，vaex在读取速度上也有1000多倍的加速，而计算速度的加速效果在数倍，总体来说表现非常的优秀。
在上一章节的测试中，我们用到了1个没有提到过的文件：data.hdf5，这个文件其实是从data.csv转换而来的。这一章节我们主要就介绍如何将数据格式进行转换，以适配vaex可以打开和识别的格式。第一个方案是使用pandas将csv格式的文件直接转换为hdf5格式，操作类似于在python对表格数据处理的章节中将xls格式的文件转换成csv格式：
[dechin@dechin-manjaro gold]$ ipython
Python 3.8.5 (default, Sep 4 2020, 07:30:14)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.19.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import pandas as pd
In [4]: data = pd.read_csv('data.csv')
In [10]: data.to_hdf('data.hdf5','data',mode='w',format='table')
In [11]: !ls -l
总用量 932
-rw-r--r-- 1 dechin dechin 221872 3月 27 21:52 data.csv
-rw-r--r-- 1 dechin dechin 348524 3月 27 22:17 data.hdf5
-rw-r--r-- 1 dechin dechin 372736 3月 27 21:31 data.xls
-rw-r--r-- 1 dechin dechin 563 3月 27 21:42 table.py
操作完成之后在当前目录下生成了一个hdf5文件。但是这种操作方式有个弊端，就是生成的hdf5文件跟vaex不是直接适配的关系，如果直接用df = vaex.open('data.hdf5')的方法进行读取的话，输出内容如下所示：
In [3]: df Out[3]: # table 0 '(0, [83.98, 92.38, 82. , 83.52], [ 0, ... 1 '(1, [83.9 , 83.92, 83.9 , 83.91], [ 1, ... 2 '(2, [84.5 , 84.65, 84. , 84.51], [ 2, ... 3 '(3, [84.9 , 85.06, 84.9 , 84.99], [ 3, ... 4 '(4, [85.1 , 85.2 , 85.1 , 85.13], [ 4, ... ... ... 3,917 '(3917, [274.65, 275.35, 274.6 , 274.61], [ ... 3,918 '(3918, [274.4, 275.2, 274.1, 275. ], [ 391... 3,919 '(3919, [275. , 275.01, 274. , 274.19], [ ... 3,920 '(3920, [275.2, 275.2, 272.6, 272.9], [ 392... 3,921 '(3921, [272.96, 273.73, 272.5 , 272.93], [ ...
在这个数据中，丢失了最关键的索引信息，虽然数据都被正确的保留了下来，但是在读取上有非常大的不便。因此我们更加推荐第二种数据转换的方法，直接用vaex进行数据格式的转换：
