The Python programming language has become more and more  popular in handling data analysis and processing because of its certain  unique advantages. It’s easy to read and maintain. pandas, with a rich  library of functions and methods packaged in it, is a fast, flexible and  easy to use data analysis and manipulation tool built on top of Python.  It is one of the big boosters to make Python an efficient and powerful  data analysis environment.  

  pandas is memory-based. It does a great job when the  to-be-manipulated data can fit into the memory. It is inconvenient, even  unable, to deal with big data, which can’t be wholly loaded into the  memory. Large files, however, like those containing data imported from  the database or downloaded from the web, are common in real-world  businesses. We need to have ways to manage them. How? That’s what I’d  like to say something about.

  By “big data” here, I am not talking about the TB or PB level  data that requires distributed processing. I mean the GB level file  data that can’t fit into the normal PC memory but can be held on disk.  This is the more common type of big file processing scenario.

  Since a big file can’t be loaded into the memory at once, we  often need to retrieve it line by line or chunk by chunk for further  processing. Both Python and pandas support this way of retrieval, but  they don’t have cursors. Because of the absence of a cursor mechanism,  we need to write code to implement the chunk-by-chunk retrieval in order  to use it in functions and methods; sometimes we even have to write  code to implement functions and methods. Here I list the typical  scenarios of big file processing and their code examples to make you  better understand Python’s way of dealing with them.

I. Aggregation

  A simple aggregation is to traverse values in the target  column and to perform calculation according to the specified aggregate  operation, such as the sum operation that adds up traversed values; the  count operation that records the number of traversed values; and the  mean operation that adds up and counts the traversed values and then  divides the sum by the number. Here let’s look at how Python does a sum.

  Below is a part of a file:

  

  To calculate the total sales amount, that is, doing sum over the amount column:

  1. Retrieve file line by line

total=0

with open("orders.txt",'r') as f:

      line=f.readline()

    while   True:

          line = f.readline()

        if   not line:

              break

          total += float(line.split("\t")[4])

print(total)

 

Open the   file

Read the header   row

 

Read detail   data line by line

Reading   finishes when all lines are traversed

 

Get   cumulated value

  2. Retrieve file chunk by chunk in pandas

  pandas supports data retrieval chunk by chunk. Below is the workflow diagram:

  

import pandas as pd

chunk_data =   pd.read_csv("orders.txt",sep="\t",chunksize=100000)

total=0

for chunk in chunk_data:

      total+=chunk['amount'].sum()

print(total)

 

Retrieve   the file chunk by chunk; each contains 100,000 lines

 

 

Add up   amounts of all chunks

 

  Pandas is good at retrieval and processing in large chunks.  In theory, the bigger the chunk size, the faster the processing. Note  that the chunk size should be able to fit into the available memory. If  the chunksize is set as 1, it is a line-by-line retrieval, which is  extremely slow. So I do not recommend a line-by-line retrieval when  handling large files in pandas.

II. Filtering

  The workflow diagram for filtering in pandas:

  

  Similar to the aggregation, pandas will divide a big file into multiple chunks (n), filter each data chunk and concatenate the filtering results.

  To get the sales records in New York state according to the above file:

  1. With small data sets

import pandas as pd

chunk_data =   pd.read_csv("orders.txt",sep="\t",chunksize=100000)

chunk_list = []

 

for chunk in chunk_data:

      chunk_list.append(chunk[chunk.state=="New York"])

res = pd.concat(chunk_list)

print(res)

 

 

 

Define an   empty list for storing the result set

 

Filter   chunk by chunk

 

 

Concatenate   filtering results

  2. With big data sets

import pandas as pd

chunk_data =   pd.read_csv("orders.txt",sep="\t",chunksize=100000)

n=0

for chunk in chunk_data:

      need_data = chunk[chunk.state=='New York']

    if n ==   0:

          need_data.to_csv("orders_filter.txt",index=None)

          n+=1

    else:

          need_data.to_csv("orders_filter.txt",index=None,mode='a',header=None)

 

 

 

 

 

For the   result set of processing the first chunk, write it to the target file with   headers retained and index removed

 

For the   result sets of processing other chunks, append them to the target file with   both headers and index removed

  The logic of doing aggregates and filters is simple. But as  Python doesn’t provide the cursor data type, we need to write a lot of  code to get them done.

III. Sorting

  The workflow diagram for sorting in pandas:

  

  Sorting is complicated because you need to:

  1.   Retrieve one chunk each time;

  2.   Sort this chunk;

  3.   Write the sorting result of each chunk to a temporary file;

  4.   Maintain a list of k elements (k is the number of chunks) into which a row of data in each temporary file is put;

  5.   Sort records in the list by the sorting field (same as the sort direction in step 2);

  6.   Write the record with smallest (in ascending order) or largest (in descending order) value to the result file;

  7.   Put another row from each temporary file to the list;

  8.   Repeat step 6, 7 until all records are written to the result file.

  To sort the above file by amount in ascending order, I write a  complete Python program of implementing the external sorting algorithm:

import pandas as pd

import os

import time

import shutil

import uuid

import traceback

 

def parse_type(s):

    if   s.isdigit():

          return int(s)

    try:

        res   = float(s)

          return res

    except:

          return s

   

def pos_by(by,head,sep):

    by_num   = 0

    for col   in head.split(sep):

        if   col.strip()==by:

              break

          else:

              by_num+=1

    return   by_num

 

def   merge_sort(directory,ofile,by,ascending=True,sep=","):

   

with open(ofile,'w') as outfile:

       

          file_list = os.listdir(directory)

       

          file_chunk = [open(directory+"/"+file,'r') for file in file_list]

          k_row = [file_chunk[i].readline()for i in range(len(file_chunk))]

        by   = pos_by(by,k_row[0],sep)

       

          outfile.write(k_row[0])

    k_row =   [file_chunk[i].readline()for i in range(len(file_chunk))]

k_by = [parse_type(k_row[i].split(sep)[by].strip())  for i in range(len(file_chunk))]

 

with open(ofile,'a') as outfile:

       

          while True:

              for i in range(len(k_by)):

                  if i >= len(k_by):

                    break

                 

                  sorted_k_by = sorted(k_by) if ascending else sorted(k_by,reverse=True)

                  if k_by[i] == sorted_k_by[0]:

                    outfile.write(k_row[i])

                    k_row[i] =   file_chunk[i].readline()

                    if not k_row[i]:

                        file_chunk[i].close()

                        del(file_chunk[i])

                        del(k_row[i])

                        del(k_by[i])

                    else:

                        k_by[i] =   parse_type(k_row[i].split(sep)[by].strip())

              if len(k_by)==0:

                  break

 

   

def   external_sort(file_path,by,ofile,tmp_dir,ascending=True,chunksize=50000,sep=',',
usecols=None,index_col=None):

os.makedirs(tmp_dir,exist_ok=True)

 

    try:

          data_chunk = pd.read_csv(file_path,sep=sep,usecols=usecols,index_col=index_col,chunksize=chunksize)

        for   chunk in data_chunk:

              chunk = chunk.sort_values(by,ascending=ascending)

              chunk.to_csv(tmp_dir+"/"+"chunk"+str(int(time.time()*10**7))+str(uuid.uuid4())+".csv",index=None,sep=sep)

          merge_sort(tmp_dir,ofile=ofile,by=by,ascending=ascending,sep=sep)

    except   Exception:

          print(traceback.format_exc())

      finally:

          shutil.rmtree(tmp_dir, ignore_errors=True)

 

 

if __name__ == "__main__":

    infile   = "D:/python_question_data/orders.txt"

    ofile =   "D:/python_question_data/extra_sort_res_py.txt"

    tmp =   "D:/python_question_data/tmp"

      external_sort(infile,'amount',ofile,tmp,ascending=True,chunksize=1000000,sep='\t')

 

 

 

 

 

 

 

Function

Parse data   type for the string

 

 

 

 

 

 

Function

Find the   position of the column name by which records are ordered in the headers

 

 

 

 

 

 

Function

External   merge sort

 

 

 

 

List   temporary files

 

Open a   temporary file

 

Read the   headers

 

Get the   position of column name by which records are ordered among the headers

Export the   headers

Read the   first line of detail data

 

Maintain a   list of k elements to store k sorting column values

 

 

 

Perform   sort in the order of the list

Export the   row with the smallest value

Read and   process temporary files one by one

 

 

 

 

If the file   traversal isn’t finished, continue reading and update the list

Finish   reading the file

 

 

 

Function

External   sort

 

 

Create a   directory to store the temporary files

 

Retrieve   the file chunk by chunk

 

 

Sort the   chunks one by one

 

 

Write the   sorted file

 

External   merge sort

 

 

Delete the temporary   directory

 

Main   program

 

Call the   external sort function

  Python handles the external sort using line-by-line merge  & write. I didn’t use pandas because it is incredibly slow when  doing the line-wise retrieval. Yet it is fast to do the chunk-wise merge  in pandas. You can compare their speeds if you want to.

  The code is too complicated compared with that for  aggregation and filtering. It’s beyond a non-professional programmer’s  ability. The second problem is that it is slow to execute.

  The third problem is that it is only for standard structured  files and single column sorting. If the file doesn’t have a header row,  or if there are variable number of separators in rows, or if the sorting  column contains values of nonstandard date format, or if there are  multiple sorting columns, the code will be more complicated.

IV. Grouping

  It’s not easy to group and summarize  a big file in Python, too. A convenient way out is to sort the file by  the grouping column and then to traverse the ordered file during which  neighboring records are put to same group if they have same grouping  column values and a record is put to a new group if its grouping column  value is different from the previous one. If a result set is too large,  we need to write grouping result before the memory lose its hold.

  It’s convenient yet slow because a full-text sorting is  needed. Generally databases use the hash grouping to increase speed.  It’s effective but much more complicated. It’s almost impossible for  non-professionals to do that.

So, it’s inconvenient and difficult to handle big files with  Python because of the absence of cursor data type and relevant  functions. We have to write all the code ourselves and the code is  inefficient.

  If only there was a language that a non-professional programmer can handle to process large files. Luckily, we have esProc SPL.

  It’s convenient and easy to use. Because SPL is designed to  process structured data and equipped with a richer library of functions  than pandas and the built-in cursor data type. It handles large files  concisely, effortlessly and efficiently.

  1. Aggregation

A

1

=file(file_path).cursor@tc()

2

=A1.total(sum(col))

  2. Filtering

A

B

1

=file(file_path).cursor@tc()

2

=A1.select(key==condition)

3

=A2.fetch()

/ Fetch data from a small result set

4

=file(out_file).export@tc(A2)

/ Write a large result set to a target   file

  3. Sorting

A

1

=file(file_path).cursor@tc()

2

=A1.sortx(key)

3

=file(out_file).export@tc(A2)

  4. Grouping

A

B

1

=file(file_path).cursor@tc()

2

=A1.groups(key;sum(coli):total)

/ Return a small result set directly

3

=A1.groupx(key;sum(coli):total)

4

=file(out_file).export@tc(A3)

/ Write a large result set to a target   file

  SPL also employs the above-mentioned HASH algorithm to effectively increase performance.

  SPL has the embedded parallel processing ability to be able  to make the most use of the multi-core CPU to boost performance. A @m  option only enables a function to perform parallel computing.

A

1

=file(file_path).cursor@mtc()

2

=A1.groups(key;sum(coli):total)

  There are a lot of Python-version parallel programs, but none is simple enough.