如何预处理和加载“大数据"tsv 文件转换为 python 数据框? [英] How to preprocess and load a "big data" tsv file into a python dataframe?
问题描述
我目前正在尝试将以下大型制表符分隔文件导入 Python 中类似数据框的结构中——当然，我使用的是 pandas 数据框，尽管我对其他选项持开放态度.
I am currently trying to import the following large tab-delimited file into a dataframe-like structure within Python---naturally I am using pandas dataframe, though I am open to other options.
这个文件有几 GB 大小，不是标准的 tsv 文件——它坏了，即行有不同的列数.一行可能有 25 列，另一行可能有 21 列.
This file is several GB in size, and is not a standard tsv file---it is broken, i.e. the rows have a different number of columns. One row may have 25 columns, another has 21.
以下是数据示例:
Col_01: 14 .... Col_20: 25 Col_21: 23432 Col_22: 639142
Col_01: 8 .... Col_20: 25 Col_22: 25134 Col_23: 243344
Col_01: 17 .... Col_21: 75 Col_23: 79876 Col_25: 634534 Col_22: 5 Col_24: 73453
Col_01: 19 .... Col_20: 25 Col_21: 32425 Col_23: 989423
Col_01: 12 .... Col_20: 25 Col_21: 23424 Col_22: 342421 Col_23: 7 Col_24: 13424 Col_25: 67
Col_01: 3 .... Col_20: 95 Col_21: 32121 Col_25: 111231
如您所见，其中一些列的顺序不正确...
As you can see, some of these columns are not in the correct order...
现在，我认为将此文件导入数据帧的正确方法是预处理数据，以便您可以输出具有 NaN 值的数据帧，例如
Now, I think the correct way to import this file into a dataframe is to preprocess the data such that you can output a dataframe with NaN values, e.g.
Col_01 .... Col_20 Col_21 Col22 Col23 Col24 Col25
8 .... 25 NaN 25134 243344 NaN NaN
17 .... NaN 75 2 79876 73453 634534
19 .... 25 32425 NaN 989423 NaN NaN
12 .... 25 23424 342421 7 13424 67
3 .... 95 32121 NaN NaN NaN 111231
更复杂的是，这是一个非常大的文件，有几 GB 大小.
To make this even more complicated, this is a very large file, several GB in size.
通常，我尝试分块处理数据，例如
Normally, I try to process the data in chunks, e.g.
import pandas as pd
for chunk in pd.read_table(FILE_PATH, header=None, sep=' ', chunksize=10**6):
# place chunks into a dataframe or HDF
但是，我认为没有办法先分块预处理"数据，然后再用分块将数据读入pandas.read_table().你会怎么做?有哪些预处理工具可用——也许是 sed?awk?
However, I see no way to "preprocess" the data first in chunks, and then use chunks to read the data into pandas.read_table(). How would you do this? What sort of preprocessing tools are available---perhaps sed? awk?
这是一个具有挑战性的问题，因为数据的大小和加载到数据帧之前必须完成的格式设置.任何帮助表示赞赏.
This is a challenging problem, due to the size of the data and the formatting that must be done before loading into a dataframe. Any help appreciated.
推荐答案
$ cat > pandas.awk
BEGIN {
PROCINFO["sorted_in"]="@ind_str_asc" # traversal order for for(i in a)
}
NR==1 { # the header cols is in the beginning of data file
# FORGET THIS: header cols from another file replace NR==1 with NR==FNR and see * below
split($0,a," ") # mkheader a[1]=first_col ...
for(i in a) { # replace with a[first_col]="" ...
a[a[i]]
printf "%6s%s", a[i], OFS # output the header
delete a[i] # remove a[1], a[2], ...
}
# next # FORGET THIS * next here if cols from another file UNTESTED
}
{
gsub(/: /,"=") # replace key-value separator ": " with "="
split($0,b,FS) # split record from ","
for(i in b) {
split(b[i],c,"=") # split key=value to c[1]=key, c[2]=value
b[c[1]]=c[2] # b[key]=value
}
for(i in a) # go thru headers in a[] and printf from b[]
printf "%6s%s", (i in b?b[i]:"NaN"), OFS; print ""
}
数据样本(pandas.txt):
Col_01 Col_20 Col_21 Col_22 Col_23 Col_25
Col_01: 14 Col_20: 25 Col_21: 23432 Col_22: 639142
Col_01: 8 Col_20: 25 Col_22: 25134 Col_23: 243344
Col_01: 17 Col_21: 75 Col_23: 79876 Col_25: 634534 Col_22: 5 Col_24: 73453
Col_01: 19 Col_20: 25 Col_21: 32425 Col_23: 989423
Col_01: 12 Col_20: 25 Col_21: 23424 Col_22: 342421 Col_23: 7 Col_24: 13424 Col_25: 67
Col_01: 3 Col_20: 95 Col_21: 32121 Col_25: 111231
$ awk -f pandas.awk -pandas.txt
Col_01 Col_20 Col_21 Col_22 Col_23 Col_25
14 25 23432 639142 NaN NaN
8 25 NaN 25134 243344 NaN
17 NaN 75 5 79876 634534
19 25 32425 NaN 989423 NaN
12 25 23424 342421 7 67
3 95 32121 NaN NaN 111231
所有需要的列都应该在数据文件头中.处理时收集头信息可能不是什么大工作，只需将数据保存在数组中并在最后打印，也许在版本 3 中.
All needed cols should be in the data file header. It's probably not a big job to collect the headers while processing, just keep the data in arrays and print in the end, maybe in version 3.
如果您从与数据文件 (pandas.txt) 不同的文件 (cols.txt) 读取标头，请执行脚本 (pandas.txt).awk):
If you read the headers from a different file (cols.txt) than the data file (pandas.txt), execute the script (pandas.awk):
$ awk -F pandas.awk cols.txt pandas.txt
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