How to read a numerical data or file in Python with numpy?

Last Updated : 12 Aug, 2021

Prerequisites: Numpy

NumPy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays. This article depicts how numeric data can be read from a file using Numpy.

Numerical data can be present in different formats of file :

The data can be saved in a txt file where each line has a new data point.
The data can be stored in a CSV(comma separated values) file.
The data can be also stored in TSV(tab separated values) file.

There are multiple ways of storing data in files and the above ones are some of the most used formats for storing numerical data. To achieve our required functionality numpy's loadtxt() function will be used.

Syntax: numpy.loadtxt(fname, dtype=’float’, comments=’#’, delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)

Parameters:
fname : File, filename, or generator to read. If the filename extension is .gz or .bz2, the file is first decompressed. Note that generators should return byte strings for Python 3k.
dtype : Data-type of the resulting array; default: float. If this is a structured data-type, the resulting array will be 1-dimensional, and each row will be interpreted as an element of the array.
delimiter : The string used to separate values. By default, this is any whitespace.
converters : A dictionary mapping column number to a function that will convert that column to a float. E.g., if column 0 is a date string: converters = {0: datestr2num}. Default: None.
skiprows : Skip the first skiprows lines; default: 0.

Returns: ndarray

Approach

Import module
Load file
Read numeric data
Print data retrieved.

Given below are some implementation for various file formats:

Link to download data files used :

Link1 : gfg_example1.txt
Link2 : gfg_example2.csv
Link3 : gfg_example3.tsv
Link4 : gfg_example4.csv

Example 1: Reading numerical data from text file

Python3

# Importing libraries that will be used
import numpy as np

# Setting name of the file that the data is to be extracted from in python
filename = 'gfg_example1.txt'

# Loading file data into numpy array and storing it in variable called data_collected
data_collected = np.loadtxt(filename)

# Printing data stored
print(data_collected)


# Type of data
print(
    f'Stored in : {type(data_collected)} and data type is : {data_collected.dtype}')

Output :

Example 2: Reading numerical data from CSV file.

Python3

# Importing libraries that will be used
import numpy as np

# Setting name of the file that the data is to be extracted from in python
# This is a comma separated values file
filename = 'gfg_example2.csv'

# Loading file data into numpy array and storing it in variable.
# We use a delimiter that basically tells the code that at every ',' we encounter,
# we need to treat it as a new data point.
# The data type of the variables is set to be int using dtype parameter.
data_collected = np.loadtxt(filename, delimiter=',', dtype=int)

# Printing data stored
print(data_collected)


# Type of data
print(
    f'Stored in : {type(data_collected)} and data type is : {data_collected.dtype}')

Output :

Example 3: Reading from tsv file

Python3

# Importing libraries that will be used
import numpy as np

# Setting name of the file that the data is to be extracted from in python
# This
filename = 'gfg_example3.tsv'

# Loading file data into numpy array and storing it in variable called data_collected
# We use a delimiter that basically tells the code that at every ',' we encounter,
# we need to treat it as a new data point.
data_collected = np.loadtxt(filename, delimiter='\t')

# Printing data stored
print(data_collected)


# Type of data
print(
    f'Stored in : {type(data_collected)} and data type is : {data_collected.dtype}')

Output :

Example 4: Select only particular rows and skip some rows

Python3

# Importing libraries that will be used
import numpy as np

# Setting name of the file that the data is to be extracted from in python
filename = 'gfg_example4.csv'

# Loading file data into numpy array and storing it in variable called data_collected
data_collected = np.loadtxt(
    filename, skiprows=1, usecols=[0, 1], delimiter=',')

# Printing data stored
print(data_collected)


# Type of data
print(
    f'Stored in : {type(data_collected)} and data type is : {data_collected.dtype}')