How to Efficiently Import CSV Data into NumPy Record Arrays?

Efficiently Import CSV Data into NumPy Record Arrays

In NumPy, a common task is to import data from a CSV file into a record array . A record array is a structured data type that allows for efficient access to data organized into columns. Direct Method: Using Numpy.genfromtxt() Unlike R functions like read.table() and read.delim(), which directly import CSV data into R's dataframe, NumPy does not provide this functionality directly. However, the numpy.genfromtxt() function can be used by setting the delimiter keyword to a comma to achieve a similar result:

Alternative Method: Using csv.reader() and numpy.core.records.fromrecords()

If the direct method using numpy.genfromtxt() does not suit your needs, you can Use a combination of csv.reader() and numpy.core.records.fromrecords(). This method includes the following:

import numpy as np

# Read CSV data into a record array
my_data = np.genfromtxt('my_file.csv', delimiter=',')

# Print the record array
print(my_data)

Using csv.reader() to parse the CSV and create a list of permissions.

Using numpy.core.records.fromrecords() To convert the list of permissions to an array record.

1. code:

Choosing the appropriate method depends on various factors such as CSV file size, data structure, and specific performance needs.

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