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How to Read CSV Files with NumPy?

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  • Last Updated : 19 Dec, 2021

In this article, we will discuss how to read CSV files with Numpy in Python.

Dataset in use:

 

Method 1: Using loadtxt method 

To import data from a text file, we will use the NumPy loadtxt() method. To use this function we need to make sure that the count of entries in each line of the text document should be equal. In Python, numpy.load() is used to load data from a text file, with the goal of being a quick read for basic text files.

Syntax: 

numpy.loadtxt('data.csv')

Example: Loading csv from loadtxt method

Python3




import numpy as np
  
# using loadtxt()
arr = np.loadtxt("sample_data.csv",
                 delimiter=",", dtype=str)
display(arr)


Output:

Method 2: Using genfromtxt method

The genfromtxt() method is used to import the data from a text document. We can specify how to handle the missing values in our dataset in case if there are.

Syntax:

numpy.genfromtxt('data.csv')

Example: Loading CSV from genfromtxt method

Python3




import numpy as np
  
# using genfromtxt()
arr = np.genfromtxt("sample_data.csv",
                    delimiter=",", dtype=str)
display(arr)


Output:

Method 3: Using CSV module

csv.reader() reads each line of the CSV file. We read data line by line and then convert each line to a list of items.

Syntax:

csv.reader(x)

Here x denotes each line of the CSV file.

Example: Loading CSV using csv reader

Python3




import numpy as np
  
# Importing csv module
import csv
  
  
with open("sample_data.csv", 'r') as x:
    sample_data = list(csv.reader(x, delimiter=","))
  
sample_data = np.array(sample_data)
display(sample_data)


Output:


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