Python NumPy genfromtxt() – Complete Tutorial

In this Python NumPy tutorial, we will learn how to use the numpy genfromtxt() function in NumPy array Python. With the Python NumPy genfromtxt function, we will cover these topics.

• Python numpy genfromtxt CSV
• Python numpy genfromtxt converters
• Python numpy genfromtxt columns
• Python numpy loadtxt
• Python numpy loadtxt string
• Python numpy loadtxt skip first row
• Python numpy loadtxt example
• Python numpy loadtxt dtype
• Python numpy loadtxt skip column
• Python numpy loadtxt usecol
• Python numpy loadtxt dtype integer
• Python numpy loadtxt skip last rows
• Python numpy loadtxt skip header
• Python numpy loadtxt could not convert string to float

Python numpy genfromtxt

• In this section, we will discuss how to load the data from a text file by using Python numpy.genfromtxt() function.
• In Python, this function is used to generate an array from a text file with missing values and different data types like float, string object, etc.
• This method is available in the NumPy package module and it is used to read the file that contains datatype into an array format.

Syntax:

Let’s have a look at the Syntax and understand the working of the Python numpy genfromtxt() function

numpy.genfromtxt
                (
                 fname,
                 dtype=<class 'float'>,
                 comments='#',
                 delimiter=None,
                 skip_header=0,
                 skip_footer=0,
                 converters=None,
                 missing_values=None,
                 filling_values=None,
                 usecols=None,
                 names=None,
                 excludelist=None,
                 replace_space='_',
                 autostrip=False,
                 case_sensitive=True,
                 unpack=None,
                 loose=True,
                 invalid_raise=True,
                 max_rows=None,
                 encoding='bytes',
                 like=None,
                )

• It consists of a few parameters
  • fname: This parameter indicates the filename where the filename extension will be gz or bz2 and the file is passing through the genfromtxt() function.
  • dtype: It is used for the data type of the array and if we mention the dtype as ‘None’, then it will automatically produce data type depending on the values of that column.
  • comments: This is an optional parameter and indicates the start of a comment in an array.
  • delimiter: By default, it takes none value and if we want to split the values then we can easily use this parameter.
  • skip_header: It is an optional parameter and it represents that we have to skip the number of lines from the starting of the file.
  • skip_footer: This parameter indicates that we have to skip the number of lines from the ending of the file.
  • converters: It is used to convert the data of a column to a value and also provide default values for missing values.
  • missing_values: It is also an optional parameter and set of strings corresponding to missing data.
  • filling_values: This parameter is used as default when the input data are missing.
  • usecols: By default, it takes none value, and if the value is 0 and it is used for reading the column values.

Example:

Let’s take an example and check how to load the data from a text file by using the Python numpy.genfromtxt() function

Source Code:

from io import StringIO
import numpy as np
 
new_string = StringIO("15.9,20,Japan")
new_result = np.genfromtxt(new_string, dtype= 'S5', encoding = None, delimiter=",")
print("File data:",new_result)

In the above code, we imported the StringIO and numpy library and then create a string by using the stringIO() method and which is used to Unicode of string in data. After that, we have declared the variable ‘new_result’ and assigned a numpy.genfromtext() function and within this function we have passed the string, datatype, and delimiter as an argument.

Here is the implementation of the following given code

Also, check: Python NumPy Normalize + Examples

Python numpy genfromtxt CSV

• In this Program, we will discuss how to read a CSV file by using the Python numpy.genfromtxt() function.
• To do this task first we will create a CSV file in which we are going to insert integer numbers. Now we want to load those numbers in our Output data. In this Program first, we imported the numpy library for numpy.genfromtxt() function.
• Within this function, we are going to assign the CSV file as an argument. Once you will print ‘new_data’ then the output will display the data array.

Source Code:

import numpy as np
 
new_data = np.genfromtxt('final.csv', delimiter = ',',dtype='int64')
print(new_data)

Here is the Screenshot of the following given code

Also, read: Python NumPy read CSV

Python numpy genfromtxt converters

• In this section, we will discuss how the sequence of strings can be converted in Python numpy genfromtxt() function.
• To perform this particular task we are going to use the converter concept and which is used to convert the data of a column to a value.
• By default its Syntax is convertors ={ 3: lambda s: float(s or 0)). In this example, we have to convert the input string into array values. In such cases, we should declare the conversion method along with the strip argument.

Syntax:

Here is the Syntax of Python numpy.genfromtxt() function

numpy.genfromtxt
                (
                 fname,
                 dtype=<class 'float'>,
                 comments='#',
                 delimiter=None,
                 skip_header=0,
                 skip_footer=0,
                 converters=None,
                 missing_values=None,
                 filling_values=None,
                 usecols=None,
                 names=None,
                 excludelist=None,
                 replace_space='_',
                 autostrip=False,
                 case_sensitive=True,
                 unpack=None,
                 loose=True,
                 invalid_raise=True,
                 max_rows=None,
                 encoding='bytes',
                 like=None,
                )

Source Code:

import numpy as np
from io import StringIO

new_info = "15,5.6,0, 18, 23.4, 9"

new_conv = lambda y: int(y.strip() or 0)
new_result=np.genfromtxt(StringIO(new_info), delimiter=",")
print("Conversion orf string to array:",new_result)

Here is the implementation of the following given code

Read: Python NumPy Savetxt + Examples

Python numpy genfromtxt columns

• Here we can see how to see how to set the columns in Python numpy.genfromtext() function.
• To do this task we are going to use the usecols() argument and this parameter takes a Sequence of integer values to the indices of the column. In this example, the first column has an index of 0 that indicates we want to display only the first column elements.
• Inside this parameter, we have used the negative integer as a regular index that represents we want to display only the last column elements.

Example:

import numpy as np
from io import StringIO

new_data = u"29,5,18, 56, 98, 23"
result=np.genfromtxt(StringIO(new_data), usecols=(0, -1))
print(result)

Here is the Output of the following given code

Read: Python NumPy argsort

Python numpy loadtxt

• In this section, we will learn how to load a text file in Python numpy array by using the numpy.loadtxt() function.
• In Python the numpy.loadtxt() function is used to load data from a text file and this method is available in the NumPy package module.
• In this method each row in the text file have must be the same number of values.

Syntax:

Let’s have a look at the Syntax and understand the working of Python numpy.loadtxt() function

numpy.loadtxt
             (
              fname,
              dtype=<class 'float'>,
              comments='#',
              delimiter=None,
              converters=None,
              skiprows=0,
              usecols=None,
              unpack=False,
              ndim=0,
              encoding='bytes',
              max_rows=None,
              like=None
             )

Example:

Let’s take an example and check how to load a text file in Python numpy array

Source Code:

import numpy as np
 
new_result=np.loadtxt('test9.txt',dtype='float')
print(new_result)

In the following code, we have a load the text file in the NumPy array by using the data type(dtype) parameter. In this example, we have mentioned the float datatype in the parameter. Once you will print ‘new_resuilt’ then the output displays the floating values in an array.

You can refer to the below Screenshot

Read: Python NumPy Delete – Complete Tutorial

Python numpy loadtxt string

• In this Program, we will discuss how to load a text file to a numpy array by using the string parameter.
• In this example, we have to use the dtype parameter to the NumPy.loadtxt() function and this parameter defines the resulting array data type that is a string.
• To perform this task first we will import the numpy library and then declare a variable ‘new-output’. Now by using the numpy.loadtxt() function we will pass text file and dtype=’str’ as an argument.

Source Code:

import numpy as np
 
new_output=np.loadtxt('test9.txt',dtype='str')
print(new_output)

Here is the implementation of the following given code

As you can see in the Screenshot the output displays the string values in an array.

Read: Python NumPy Minimum tutorial

Python numpy loadtxt skip first row

• In this section, we will discuss how to use the skip rows parameter in Python numpy.loadtxt() function.
• In this function, the skiprows parameter value is 0 and it is used to skip the first rows.
• In this example, we have used the numpy.loadtxt() function and within this function, we have applied the CSV file and skiprows as an argument.
• Once you will print ‘new_data’ then the output will display the array but the first element has been removed from the input array.

Example:

import numpy as np
 
new_data = np.loadtxt('final.csv',delimiter=',',skiprows=1,dtype='int64')
print("Skipped rows from array:",new_data)

Here is the execution of the following given code

Read: Python NumPy Data types

Python numpy loadtxt example

• In this section, we will discuss how to use the numpy.loadtxt() function in Python.
• In this example, we are going to apply specific parameters in Python numpy.loadtxt() function. To do this task first we will import the numpy and then create a string by using the StringIO function.
• Now create a variable ‘new_result’ and assign the numpy.loadtxt() function. Inside the function we have used string,delimiter,usecols,dtype and unpack=True as an argument.

Example:

Let’s take an example and check how to use these arguments in NumPy.loadtxt() function

Source Code:

from io import StringIO   
import numpy as np
  
new_string = StringIO("16, 8, 97, \n14, 17")
new_result =np.loadtxt(new_string, delimiter =', ', usecols =(0, 1),unpack = True,dtype='int64')
print(new_result)

Here is the output of the following given code

Read: Python Numpy unique

Python numpy loadtxt dtype

• Here we can see how to use the datatype parameter in Python numpy.loadtxt() function.
• This parameter defines the data type for the numpy array and by default, it takes float datatype. As per the condition if we are taking float values in a string then by default the result will show in decimal values.
• Each row is interpreted as a value of the NumPy array and a number of fields must match the number of columns in the dtype parameter.

Syntax:

Here is the Syntax of Python numpy.loadtxt() function

numpy.loadtxt
             (
              fname,
              dtype=<class 'float'>,
              comments='#',
              delimiter=None,
              converters=None,
              skiprows=0,
              usecols=None,
              unpack=False,
              ndim=0,
              encoding='bytes',
              max_rows=None,
              like=None
             )

Example:

Let’s take an example and check how to use the type parameter in Python numpy.loadtxt() function

Source Code:

from io import StringIO   
import numpy as np

new_str = StringIO("F 32 86 Oliva\nM 45 32 John")
result=np.loadtxt(new_str, dtype={'names': ('stu_gender', 'student_age', 'student_id','student_name'),
                     'formats': ('S1', 'i4', 'f4','S5')})
print(result)

Here is the Output of the following given code

Read: Python NumPy Count – Useful Guide

Python numpy loadtxt skip column

• In this section, we will discuss how to load the data from a text file without the first column in Python.
• Here we can use the concept of Python numpy.loadtxt() function and within this function, we have used the usecols parameter for skipping the first column elements.
• This parameter defines the columns to be read by default it takes none value for all columns read and if you want to extract specific columns then we have to mention the usecols parameter in the function.

import numpy as np
 
new_values = np.loadtxt('test9.txt', usecols=(1, 2), unpack=True)
print("Skipped columns from array:",new_values)

In the above code, we imported the numpy library and then use the numpy.loadtxt() function in which we have assigned the text file and usecols() as an argument. Once you will print ‘new_values’ then the result displays the array in which the first column has been removed.

Here is the Screenshot of the following given code

Read: Python NumPy Replace

Python numpy loadtxt usecol

• In this Program, we will discuss how to apply the usecol parameter in NumPy.loadtxt() function.
• We have already cover this parameter on Python numpy loadtxt skip column topic.

Python numpy loadtxt dtype integer

• Here we can use the type parameter and assign integer datatype in Python numpy.loadtxt() function.
• This parameter defines the resulting array datatype by default it is set as float but in this example, we will set dtype=’integer’.
• In this Program, we have to load the text file by using the dtype parameter in numpy.loadtxt() function.

Example:

Let’s take an example and check how to use the type parameter in Numpy.loadtxt() function

Source Code:

import numpy as np
 
result=np.loadtxt('test9.txt',dtype='int')
print(result)

You can refer to the below Screenshot

Read: Python NumPy Matrix Multiplication

Python numpy loadtxt skip last rows

• In this section, we will discuss how to skip the last row in Python numpy.loadtxt() function.
• Here we will exclude the last line of the text file. To do this task we are going to use the numpy.loadtxt() function and within this function we have to open the text file in reading mode by using the open() function.
• We have to use the slicing method [:-1] for skipping the last row in a given array. Once you will print ‘new_data’ then the output displays the updated array.

Example:

import numpy as np
 
new_data = np.loadtxt(open('test9.txt','rt').readlines()[:-1],dtype='int64')
print("Skipped last row from array:",new_data)

Input array

Here is the execution of the following given code

As you can see in the Screenshot the last row has been removed from the array.

Read: Python NumPy diff with examples

Python numpy loadtxt skip header

• In this section, we will discuss how to skip the header section in an array by using Python numpy.loadtxt() function.
• By using the numpy.loadtxt() function we can easily solve this problem. In this example we have to consider the first line of the text file will be the header and after that, we are going to use the skiprows argument and assign the value 1. Once you will print ‘ski_head’ then the output will display the updated array and the first line of the array has been removed.

Syntax:

Here is the Syntax of Python numpy.loadtxt() function

numpy.loadtxt
             (
              fname,
              dtype=<class 'float'>,
              comments='#',
              delimiter=None,
              converters=None,
              skiprows=0,
              usecols=None,
              unpack=False,
              ndim=0,
              encoding='bytes',
              max_rows=None,
              like=None
             )

Example:

import numpy as np
 
ski_head = np.loadtxt('test9.txt',dtype='int64',skiprows=1)
print("Skipped header from array:",ski_head)

You can refer to the below Screenshot

Read: Python NumPy Indexing

Python numpy loadtxt could not convert string to float

• In this section, we will discuss how to solve the value error “could not convert string to float” by using Python numpy.loadtxt() function.
• To do this task we are going to import the numpy library and then use the numpy.loadtxt() function and within this function we are going to apply the delimiter parameter and it is used to separate the values.
• In this example, we have just created a text file and inserted the values. Now we have to load the data from a text file.

Source Code:

import numpy as np
 
new_values = np.loadtxt('test9.txt',delimiter=';')
print(new_values)

Here is the implementation of the following given code

As you can see in the Screenshot the error occurs could not convert string to float: ‘Array values’

Solution: Python numpy loadtxt could not convert string to float

Here is the Solution of could not convert string to float

In this example, we have just updated the text file with a CSV file

Source Code:

import numpy as np
 
new_result =np.loadtxt('final.csv', delimiter=';')
print(new_result)

Here is the Screenshot of the following given code

As you can see in the Screenshot the output displays the array values.

You may also like to read the following tutorials on Python NumPy.

• Python NumPy append + 9 Examples
• Python NumPy Matrix + Examples
• Python NumPy Sum + Examples
• Python NumPy Median + Examples
• Python NumPy Arange + Examples
• Python Copy NumPy Array

So, in this tutorial, we have learned how to use the numpy genfromtext() function in NumPy array Python. Moreover, with the Python NumPy genfromtext function, we have covered these examples.

• Python numpy genfromtxt CSV
• Python numpy genfromtxt converters
• Python numpy genfromtxt columns
• Python numpy loadtxt
• Python numpy loadtxt string
• Python numpy loadtxt skip first row
• Python numpy loadtxt example
• Python numpy loadtxt dtype
• Python numpy loadtxt skip column
• Python numpy loadtxt usecol
• Python numpy loadtxt dtype integer
• Python numpy loadtxt skip last rows
• Python numpy loadtxt skip header
• Python numpy loadtxt could not convert string to float