How To Read Data From A File And Save It As An Array

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

Prerequisites: Numpy

NumPy is a full general-purpose assortment-processing package. Information technology provides a high-operation 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 betoken.
The information tin can be stored in a CSV(comma separated values) file.
The data tin can be likewise stored in TSV(tab separated values) file.

There are multiple ways of storing information in files and the in a higher place ones are some of the most used formats for storing numerical information. To attain our required functionality numpy's loadtxt() part volition be used.

Syntax: numpy.loadtxt(fname, dtype='bladder', 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 assortment; default: bladder. If this is a structured data-blazon, 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 catechumen that column to a float. E.yard., if column 0 is a appointment string: converters = {0: datestr2num}. Default: None.
skiprows : Skip the first skiprows lines; default: 0.

Returns: ndarray

Approach

Import module
Load file
Read numeric information
Print information retrieved.

Given beneath 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 information from text file

Python3

                import                numpy equally np              
                filename                                =                'gfg_example1.txt'              
                data_collected                                =                np.loadtxt(filename)              
                print                (data_collected)              
                impress                (              
                                f                'Stored in : {type(data_collected)} and data blazon is : {data_collected.dtype}'                )              

Output :

Output of Case 1

Instance 2: Reading numerical data from CSV file.

Python3

                import                numpy every bit np              
                filename                                =                'gfg_example2.csv'              
                data_collected                                =                np.loadtxt(filename, delimiter                =                ','                , dtype                =                int                )              
                impress                (data_collected)              
                impress                (              
                                f                'Stored in : {type(data_collected)} and data blazon is : {data_collected.dtype}'                )              

Output :

Output of Example 2

Instance 3: Reading from tsv file

Python3

                import                numpy as np              
                filename                                =                'gfg_example3.tsv'              
                data_collected                                =                np.loadtxt(filename, delimiter                =                '\t'                )              
                print                (data_collected)              
                print                (              
                                f                'Stored in : {type(data_collected)} and data type is : {data_collected.dtype}'                )              

Output :

Output of Example three

Example 4: Select only particular rows and skip some rows

Python3

                import                numpy as np              
                filename                                =                'gfg_example4.csv'              
                data_collected                                =                np.loadtxt(              
                                filename, skiprows                =                1                , usecols                =                [                0                ,                                1                ], delimiter                =                ','                )              
                print                (data_collected)              
                print                (              
                                f                'Stored in : {type(data_collected)} and data type is : {data_collected.dtype}'                )              