Python

Pandas Read_csv Multiprocessing

If you’ve used pandas before, you’re probably aware of the fantastic capabilities and tools for data processing. We used pandas to read data files and transform them into a variety of interesting summaries. The typical process pipeline would begin with a CSV-formatted text file containing data.

We’d read data into a pandas DataFrame and experiment with various transformations. Keep on understanding to know more about the concept of Pandas read csv multiprocessing. Apart from loading the CSV file, you’ll learn about the numerous characteristics of the pandas read csv function, as well as the options that may be changed to improve the read csv function’s output.

Syntax of Pandas.read_csv

Below you can find the syntax of pandas.read_csv for your better understanding.

This method returns a two-dimensional data structure with labelled axes from a CSV file.

How to Read a CSV file?

The pandas read_csv() function is widely used to read a CSV file into a Python pandas DataFrame. Additionally, it also supports reading any delimited file.

CSV files are basically plain text files used to hold 2-dimensional data in a human-readable format. They are commonly used in the industry to communicate large batch files across organizations. In some rare cases, these files can also be used to store metadata.

We’ll read the data from the csv file created on our machine. The sample data file that we have built specifically to run the commands is shown below. Although this file contains a little amount of data, the commands can be used on larger files to improve data processing.

The pandas read csv function may read a csv file in various ways, depending on the requirements. For example, you can use custom separators, read only specific columns/rows, etc. All of the cases are covered one by one below.

Call the pandas function read csv() with the file location as input to read a CSV file.

The picture below illustrates how to read data from a specific csv file. The pandas module is first imported, and then the file location for the read csv function is specified.

import pandas
d_frame = pandas.read_csv("C:\\Users\\\\Desktop\\demo.csv")
d_frame.head()

The fetched results are shown below.

How can I make a column header out of any row?

This section will guide you to set any row as a column header with the help of simple steps.

import pandas
d_frame = pandas.read_csv("C:\\Users\\\\Desktop\\demo.csv")
print(d_frame.head())

This is the outcome. As you can see, row 0 was found to be a promising fit for the header. It can provide a clear-cut explanation of the figures presented in the table. While you read the CSV, use the header option to make this 0 row a header.

The following code demonstrates that the row numbering, including column headers, begins at 0. You can see that the value of header is set to ‘1’ in the second line of code.

import pandas
d_frame = pandas.read_csv("C:\\Users\\\\Desktop\\demo.csv", header=1)
print(d_frame.head())

The updated header is shown in the following result once the code has been executed.

How to Load CSV Without Column Headers?

There’s a chance the CSV file you’re loading lacks a column header. By default, the first row is measured as a column header.

You can define the header as None to prevent any row from being interpreted as a column header. Pandas will be required to begin constructing numbered columns at 0.

import pandas
d_frame = pandas.read_csv("C:\\Users\\\\Desktop\\demo.csv", header=None)
print(d_frame.head())

The attached image shows no headers, as you can see.

Pandas Read_csv Multiprocessing Examples

The above section of this article helped you to get familiar with the basics of Pandas read_csv. Now let’s cover some major pandas read_csv multiprocessing examples to understand better.

Example 1:

While reading a file, Pandas’ read table method can take chunksize as an argument and return an iterator. This means that you can process chunksize rows in individual DataFrames at a time. The separate results can then be combined.

The code snippet below demonstrates how to read files in smaller parts and handle each individually. Let’s take a look at the reference code below.

The pandas module is loaded first, and the file path is specified. We created a function (called d_frame) to process the data dataframe. The main function, in which the read_table function is utilized, is then written. After that, each data frame is processed, and the result is shown.

import pandas
path = "C:\\Users\\\\Desktop\\demo.csv"
size = 10
def d_frame(dframe):
        return len(dframe)
if __name__ == '__main__':
        reader = pandas.read_table(path, chunksize=size)
        res = 0
        for dframe in reader:
                res += d_frame(dframe)
        print (res)

The number of rows in the file is displayed on the screen below.

Example 2:

You may also enhance performance by adding a multiprocessing twist to it. Here’s a multiprocessing version of the previous sample. The following code sample should be self-explanatory. The goal is to process a block of data asynchronously by placing it into a multiprocessing pool queue. Each pool process will complete the task and provide the result.

Please remember that the Pool must be created within the __main__ block. This is the case because just one primary process should establish the pool and distribute it asynchronously among the several processes.

import pandas
import multiprocessing as mp
path = "C:\\Users\\\\Desktop\\demo.csv"
size = 10
def d_frame(dframe):
        return len(dframe)
if __name__ == '__main__':
        reader = pandas.read_table(path, chunksize=size)
        pool = mp.Pool(4)
       
        funclist = []
        for df in reader:
                # process each data frame
                f = pool.apply_async(d_frame,[df])
                funclist.append(f)
        res = 0
        for f in funclist:
                res += f.get(timeout=10)
        print(res)

The following is the resultant screen of the code above.

Conclusion:

Python’s huge ecosystem of data-centric Python packages makes it a good language for data analysis. Pandas is one of these packages, and it makes importing and analyzing data a breeze. Using an iterator, Pandas allows you to read big csv files in segments. It is no longer necessary to load the complete csv file into memory before starting to process it. We’ve gone through this concept in-depth, with examples in this post.

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Kalsoom Bibi

Hello, I am a freelance writer and usually write for Linux and other technology related content