Load array from file python

Create an array from a .txt file in Python (Copy words from .txt file and put them as array elements)

Well, in this tutorial, you will learn how to copy data from a .txt file and put them as array elements in Python.

Python has become one of the most popular languages today for managing data and its analysis. However, the data that you need usually comes in different file formats. Such data may need to be imported into your program for different purposes.

We will be using Python’s NumPy library which provides ndarray options and various methods to efficiently operate on them.
The NumPy library provides two common methods for importing text files into NumPy arrays.
They are:

Once you import the necessary packages and set the working directory for your program, you can use any of the above two methods depending on your need.

Let us now understand both of them in detail.

numpy.loadtxt() in Python

It loads data from a text file into an array object.

numpy.loadtxt(fname,dtype) , where fname is the name of the text file and dtype allows you to specify the data type.

Let us now look into an example:

import numpy as np datafromfile=np.loadtxt("example.txt",dtype="str") print(datafromfile)

['apple' 'mango' 'grapes' 'orange' 'banana' 'kiwi']

Important Note:

Each row of the text file must contain the same number of values. Otherwise, it throws an error as shown;

import numpy as np datafromfile=np.loadtxt("example.txt",dtype="str") print(datafromfile)

ValueError: Wrong number of columns at line 2

Detailed Syntax:

numpy.loadtxt(fname,dtype, comments=’#’, delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0, encoding=’bytes’, max_rows=None, *, like=None)

Except for the filename(fname), all other parameters are optional and you can use them based on your specific needs.

numpy.genfromtxt() in Python

It is the same as loadtxt() except for the fact that it handles missing values too.

numpy.genfromtxt(fname, dtype=float, comments=’#’, delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None,excludelist=None, deletechars=” !#$%&'()*+, -./:;?@[\\]^<|>~”, replace_space=’_’, autostrip=False, case_sensitive=True, defaultfmt=’f%i’, unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None, encoding=’bytes’, *, like=None)

Even here, all parameters except the filename(fname) are optional only.
Let us look into an example for the same below;

import numpy as np datafromfile=np.genfromtxt("example.txt",dtype="str",delimiter=",") print(datafromfile)

[[‘apple’ ‘mango’] [‘grapes’ ‘orange’] [‘banana’ ‘kiwi’]]

You must know that both the above methods are very similar and perform the same functions.
However, using genfromtxt() gives you options for dealing with missing values, CSV’s etc.

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numpy.fromfile#

Construct an array from data in a text or binary file.

A highly efficient way of reading binary data with a known data-type, as well as parsing simply formatted text files. Data written using the tofile method can be read using this function.

Parameters : file file or str or Path

Open file object or filename.

Changed in version 1.17.0: pathlib.Path objects are now accepted.

Data type of the returned array. For binary files, it is used to determine the size and byte-order of the items in the file. Most builtin numeric types are supported and extension types may be supported.

New in version 1.18.0: Complex dtypes.

Number of items to read. -1 means all items (i.e., the complete file).

Separator between items if file is a text file. Empty (“”) separator means the file should be treated as binary. Spaces (” “) in the separator match zero or more whitespace characters. A separator consisting only of spaces must match at least one whitespace.

offset int

The offset (in bytes) from the file’s current position. Defaults to 0. Only permitted for binary files.

Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

More flexible way of loading data from a text file.

Do not rely on the combination of tofile and fromfile for data storage, as the binary files generated are not platform independent. In particular, no byte-order or data-type information is saved. Data can be stored in the platform independent .npy format using save and load instead.

>>> dt = np.dtype([('time', [('min', np.int64), ('sec', np.int64)]), . ('temp', float)]) >>> x = np.zeros((1,), dtype=dt) >>> x['time']['min'] = 10; x['temp'] = 98.25 >>> x array([((10, 0), 98.25)], dtype=[('time', [('min', ' 

Save the raw data to disk:

>>> import tempfile >>> fname = tempfile.mkstemp()[1] >>> x.tofile(fname) 

Read the raw data from disk:

>>> np.fromfile(fname, dtype=dt) array([((10, 0), 98.25)], dtype=[('time', [('min', ' 

The recommended way to store and load data:

>>> np.save(fname, x) >>> np.load(fname + '.npy') array([((10, 0), 98.25)], dtype=[('time', [('min', ' 

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Save Array to File & Read Array from File – NumPy

You can save numpy array to a file using numpy.save() and then later, load into an array using numpy.load().

Following is a quick code snippet where we use firstly use save() function to write array to file. Secondly, we use load() function to load the file to a numpy array.

# save array to file numpy.save(file, array) # load file to array array = numpy.load(file)

Examples

1. Save numpy array to file

In the following example: we will initialize an array; create and open a file in write binary mode; and then write the array to the file using numpy.save() method.

Python Program

import numpy as np #initialize an array arr = np.array([[[11, 11, 9, 9], [11, 0, 2, 0] ], [[10, 14, 9, 14], [0, 1, 11, 11]]]) # open a binary file in write mode file = open("arr", "wb") # save array to the file np.save(file, arr) # close the file file.close

Once you are done saving the array, do not forget to close the file.

You should observe that a new file named arr is created in your current working directory. Extension to the file is not required. But you may use an extension of your choice.

2. Load the saved numpy array from file

In this example, we will load the array from file. We will use the above example to save an array and continue that to read the array from file.

Python Program

import numpy as np #initialize an array arr = np.array([[[11, 11, 9, 9], [11, 0, 2, 0] ], [[10, 14, 9, 14], [0, 1, 11, 11]]]) # open a binary file in write mode file = open("arr", "wb") # save array to the file np.save(file, arr) # close the file file.close # open the file in read binary mode file = open("arr", "rb") #read the file to numpy array arr1 = np.load(file) #close the file print(arr1)

[[[11 11 9 9] [11 0 2 0]] [[10 14 9 14] [ 0 1 11 11]]]

We have successfully read the numpy array from file and load an object with this array.

Summary

In this Numpy Tutorial, we learned how to save a numpy array to a file, and load the numpy array from file to an object in the program.

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What is array.fromfile() in Python?

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Overview

Python is a high-level programming language that provides functionalities for several operations. The array module comes with various methods to manipulate array data structures.

One of these methods is the array.fromfile() method, which is used to append items for a file object to an array.

Syntax

Arguments

• f : This is the file object from which items are to be appended to the array.
• n : This is the number of items from f that need to be appended to the array. If f has less than n items, EOFError is raised.

Both of the parameters listed above are necessary for the method to work.

Return value

This function does not return any values.

Code example

#import modules
import os
from array import array
 
#open file object for writing
f = open('my_file.txt','wb')
 
#write array of integers to file object
array("i", [1, 2, 3, 4, 5, 6, 7, 8, 9]).tofile(f)
 
#close file
f.close()
 
#open file for reading
f = open('my_file.txt','rb')
 
#initialize array with integer type
array_one = array("i")
 
#initialize array with integer type
array_two = array("i")
 
#read 3 items from file
array_one.fromfile(f,3)
print(array_one)
 
#read 6 items from file
array_two.fromfile(f,6)
print(array_two)
 
#close file
f.close()

Code explanation

• Lines 2–3: We import the os and array module.
• Line 6: We open a new file for writing. We mention wb , which means that we are writing in binary mode.
• Line 9: We append integers to the file object f from the array, using array.tofile(f) .
• Line 12: We close the file object f .
• Line 15: We open the file object f for reading.
• Lines 18–21: We initialize the arrays with the integer type.
• Lines 24–25: We append 3 items from the file object f to array_one , using array.tromfile() . We print the array after appending the items to it.
• Line 28: We append 6 items from the file object f to array_two , using array.tromfile() . We print the array after appending the items to it.
• Line 32: We close the file object f .

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