Python NumPy Data types

In this Python tutorial, we will learn how to use Data types in NumPy Python. Also, we will cover these topics.

  • NumPy data types string
  • NumPy data types size
  • NumPy data types float
  • NumPy data types list
  • NumPy data types datetime
  • NumPy data types change
  • Numpy data types range
  • Numpy data types check
  • NumPy data types with examples
  • Numpy array with different data types
  • NumPy array with mixed data types
  • NumPy empty data types
  • NumPy random data types
  • NumPy data types tuple
  • name two numpy data types for text
  • NumPy data types uint8
  • Numpy change data type uint8
  • NumPy data type string not understood
  • NumPy data types in Pandas
  • Data types in NumPy loadtxt
  • Numpy get datatype of element
  • NumPy change datatype of column
  • NumPy data types overflow
  • Numpy datatype must provide an itemsize
  • NumPy add data types
  • Numpy structured array data types
  • astypes data types numpy
  • Numpy same datatypes
  • NumPy data types float 32
  • Numpy genfromtxt multiple data types
  • NumPy data type hierarchy
  • NumPy complex data types
  • Numpy data type length
  • NumPy data type custom
  • Numpy data type void
  • Numpy data type max value
  • Numpy data types binary
  • Numpy data types bool
  • Numpy numeric data type
  • Numpy vectorize data type not understood
  • Numpy data types memory size

Python Numpy Data types

  • In this section we will discuss how to use Numpy data types in Python.
  • In Python, the NumPy module provides a numeric datatype object and it is used to implement the fixed size of the array. Datatypes are basically used for defining a variable with a specific type.
  • There are mainly three types of data types in Python.
    • NumPy numeric Types
    • NumPy string Types
    • NumPy scaler types
  • The main several data types supported by NumPy Python are:
    • np.bool_: This type is used to return boolean values like (True or False).
    • np.int: It is the default type of integer and C-type long.
    • intc: It is similar to C integer and it indicates 32 or 64 bit integer.
    • intp: This type indicates for indexing.
    • int8: By default its range value is -128 to 127.
    • int16: It is a 16 bit integer and by default its range value is (-32768 to 32767).
    • int32: it is for set 32-bit integer and the range value is (-2147483648 to 2147483647).
    • int64: By default its value is set at (-9223372036854775808 to 9223372036854775807).
    • uint8: It is a 8 bit integer and range value is (0 to 255).
    • uint16: It is a 16 bit unsigned integer and range value is (0 to 65535).
    • uint32: This is a unsigned integer with 32 bit and its value is (0 to 4294967295).
    • uint64: It is a 8 byte unsigned integer and range value is (0 to 18446744073709551615).

NumPy DataType

Numpy datatype is used on Python programming and datatype objects execute the fixed size of memory and the elements of the array have the same datatype.

Syntax:

Here is the Syntax of the dtype object

numpy.dtype
           (
            object,
            align,
            copy
           )

Example:

Let’s take an example and understand the data type of the array

import numpy as np

new_array = np.array([45, 62, 25, 76])
print(new_array.dtype)

In the above code, we have used the np.array() function to create an array and then take dtype as an argument in the print statement.

Here is the Screenshot of the following given code

Python Numpy Data type
Python Numpy Data type

As you can see in the Screenshot the output is int32 datatype object

Another example to check the data type object in NumPy Python

Source Code:

import numpy as np 

my_arr = np.dtype('i4')
print (my_arr)

You can refer to the below Screenshot

Python Numpy data type
Python Numpy data type

Read: Python NumPy Array

NumPy data types string

  • Here we can discuss how to use Data type string in NumPy Python.
  • In this example, we are going to create an array by using the np.array() function and then use dtype as an argument in a print statement and allow us to define the string datatype ‘u6’ that indicates the unsigned integer.

Example:

import numpy as np

new_array = np.array(['Micheal', 'John', 'George'])
print(new_array.dtype)

Here is the implementation of the following given code

NumPy data types string
NumPy data types string

Read: Check if NumPy Array is Empty in Python

NumPy data types size

  • In this Program, we will discuss how to use data type size in NumPy Python.
  • Here we can use the ndarray.size() method for getting the total number of items in an array along with the given axis.

Syntax:

Here is the Syntax of numpy.size() method

ndarray.size()

Source Code:

import numpy as np

new_val = np.array([[98,45,67],[17,28,35]])
result= new_val.size
print(result)

In the above code, we have created a numpy array by using the np.array() function and then using ndarray.size() method and it will count the number of items in the array.

You can refer to the below Screenshot

NumPy data types size
NumPy data types size

Read: Python NumPy zeros

NumPy data types float

  • In this section we will discuss how to check float datatype in NumPy Python.
  • In this example first, we are going to create an array by using the np.array function and assigning decimal numbers to it. Now use dtype as an argument in the print statement.
  • Once you will print ‘new_array’ then the output will display the data type of the given array.

Example:

import numpy as np

new_array = np.array([89.9, 17.9, 45.1])
print(new_array.dtype)

Here is the execution of the following given code

NumPy data types float
NumPy data types float

As you can see in the Screenshot the output is ‘float64’

Read: Python NumPy Sum

NumPy data types list

We have already covered this topic in Python NumPy datatype.

NumPy data types datetime

  • In this Program, we will discuss how to use datatime datatype in NumPy Python.
  • In Python, the datetime() method is used to get the date in an array in a year-month-date format.

Syntax:

Here is the Syntax of numpy.datetime() method

numpy.datetime64(date)

Note: This method always returns the date in the format of ‘yyy-mm-dd’

Source Code:

import numpy as np

new_array = np.array(np.datetime64('1998-06-14'))
print(new_array)

In the above code first, we have imported a numpy library and then create a numpy array by using the np.array() function in which we have assigned a datetime64() method. Once you will print ‘new_array’ then the output will display the same date.

Here is the output of the following given code

NumPy data types datetime
NumPy data type datetime

Read: Python NumPy arange

NumPy data types change

  • In this section, we will discuss how to change the data type in numpy Python.
  • To perform this particular task we are going to use ndarray.astype() method. For example, suppose we have an array of type float64 and now we want to convert into int32 by using the astype() method.
  • This method takes a parameter which is the target data type and this function is a typecasting.

Syntax:

Here is the Syntax of astype() method

ndarray.astype
              (
               dtype,
               order='K',
               casting='unsafe',
               subok=True,
               copy=True
              )

Example:

import numpy as np

new_arr = np.array([78.3, 17.5, 15.2, 17.1, 19.2])
new_result = new_arr.astype('i')
print(new_result)

In the above code, we have created a numpy array by using the np.array() method. Now we are going to convert float numbers to integers numbers by using the astype() function and within this method, we have pass ‘i’ as an argument.

You can refer to the below Screenshot

NumPy data types change
NumPy data types change

Another example to change the data type in the NumPy array

In this program, we are going to use the np.arange() method for creating an array, and within this method, we have to assign the datatype =’int64′. Now use the view and slicing method and get the data type in a floating number.

Source Code:

import numpy as np

new_arr = np.arange(8, dtype='int64')
result = new_arr.view('float64')
result[:] = new_arr
print(result)

Here is the output of the following given code

NumPy data types change
NumPy data types change

Read: Python NumPy append

Numpy data types range

  • Here we can see the range of datatype in NumPy Python.
  • To do this task we are going to use the np.iinfo() method. In Python, the np.iinfo() method shows machine limits for integer data types.

Syntax:

Here is the Syntax of np.iinfo() method

numpy.info(dtype)

Source Code:

import numpy as np

a=np.iinfo(np.uint64).max
print(a)

In the above code first, we have created a variable ‘a’ and then use the np.iinfo() method for getting the maximum range of the array.

Here is the Screenshot of the following given code

Numpy data types range
Numpy data types range

Read: Python NumPy matrix

Numpy data types check

  • Let us see how to check the data type in NumPy Python.
  • To perform this particular task we have created a numpy array by using the np.array() method and then use the dtype() method for checking the data type in Python.

Source Code:

import numpy as np

new_array = np.array([78, 145, 193, 223, 892, 956])
print( new_array.dtype)

Here is the Screenshot of the following given code

Numpy data types check
Numpy data types check

Read: Python sort NumPy array

NumPy data types with examples

  • Let us see how to use data types in NumPy Python and we will also cover related examples.
  • In this example, we are going to use the unsigned integer in the dtype method as an argument along with we will also use int 8 and it will be replaced with ‘i1’.

Source Code:

import numpy as np 

arr1 = np.dtype('i4')
new_arr = np.dtype(np.int64) 
new_val = np.dtype([('John',np.int8)])
print(arr1)
print(new_arr)
print(new_val)

You can refer to the below Screenshot

NumPy data types with examples
NumPy data types with examples

Read: Python NumPy concatenate

Numpy array with different data types

  • In this section, we will discuss how to apply different data types in the NumPy array by using Python.
  • Now we will create an example in which we are going to use integer datatype for numeric values. After that, we use the astype() method for changing the decimal values into integers.
  • Now we are going to apply the bool datatype it will return a boolean value that is true or false.

Example:

import numpy as np

new_arr1 = np.array(['16', '7', '19'], dtype='i')# int datatype
print(new_arr1)
new_arr2 = np.array([98.7, 45.2, 89.4])# astype data type
result = new_arr2.astype(int)
print(result)
new_val = np.array([4, 2, 5])
new_output = new_val.astype(bool) # bool datatype
print(new_output)

Here is the execution of the following given code

Numpy array with different data types
Numpy array with different data types

Read: Python NumPy linspace

NumPy array with mixed data types

  • In this section, we will discuss how to mix data types in NumPy array by using Python.
  • We have already covered this example in our previous topic(NumPy array with different types). In this example, we have created a simple numpy array and assigned integer, string values to it.
  • Now use the datatype ‘S4’, ‘i4’ and once you will print ‘res’ then the output will display the same input array.

Example:

import numpy as np

res = np.array([("john", 15, 14), ("George", 13, 21)], dtype='|S4, i4, i4')
print(res)

Here is the implementation of the following given code

NumPy array with mixed data types
NumPy array with mixed data types

Read: Python NumPy log

NumPy empty data types

  • Here we can use the empty method in the NumPy array by using Python.
  • In Python, the empty() function is used to initialize an array without giving any shape and it always returns a new shape with random values. This method is available in the Python numpy module package.

Example:

import numpy as np  

new_arr = np.empty([4, 3])  
print(new_arr)

Here is the Screenshot of the following given code

NumPy empty data types
NumPy empty data types

Read: Python NumPy where with examples

NumPy random data types

  • In this Program, we will discuss how to use data type in random method by using NumPy Python.
  • In Python, the random() method is used for generating random numbers in a given shape. This method will always return an array that contains random values.

Source Code:

import numpy as np  

new_arr = np.random.randn(4, 5).astype('f')
print(new_arr)

Here is the execution of the following given code

NumPy random data types
NumPy random data types

Read: Python NumPy read CSV

NumPy data types tuple

  • Let us see how to use data type in the tuple by using NumPy Python.
  • In this example we are going to use tuples in numpy array along with data type.

Example:

import numpy as np  

new_arr= np.array([('pos', (78, 15, 28)), ('sop', (10, 26, 87))], dtype='3a, 3i')
print(new_arr)

Here is the execution of the following given code

NumPy data types tuple
NumPy data types tuple

Read: Python NumPy to list

Name two numpy data types for text

  • In this section, we will discuss how to use two numpy data types in Python.
  • By using datatype ‘S’ and ‘i4’ we are going to create an example and display the result. ‘S’ and ‘i4’ are the numeric data types in numpy Python.

Example:

import numpy as np

new_array1 = np.array([89, 45, 21, 98], dtype='S')
new_array2 = np.array([91, 22, 87, 65], dtype='i4')
print(new_array1.dtype)
print(new_array2.dtype)

Here is the output of the following given code

Name two numpy data types for text
Name two numpy data types for text

As you can see in the Screenshot the output is ‘int32’ and ‘s2’.

Read: Python NumPy square

NumPy data types uint8

  • Here we can discuss how to use uint8 datatype in NumPy array Python.
  • In Python uint8 datatype indicates unsigned integer and it consists of 8 bits with positive range values from 0 to 255.
  • This datatype store information about the type byte order and bit-width with ‘C’unsigned character.

Source Code:

import numpy as np 

new_arr = np.dtype('uint8') 
print (new_arr)
new_val = np.array([87, 45, 12, 98], dtype='uint8')
print(new_val.dtype)

You can refer to the below Screenshot

NumPy data types uint8
NumPy data types uint8

Read: Python NumPy absolute value

NumPy data type string not understood

  • In this Program, we will discuss how to solve the numpy data type string not understand the problem.
  • To do this task first we are going to use numpy.loadtxt() function and this method is used to load data from a CSV or text file and within this function we have assign CSV file ‘final.csv’ and dtype=’string’.
  • Once you will print ‘new_val’ then the output will raise an error data type ‘string’ not understanding that indicates the data type could not understand the string type.

Example:

import numpy

new_val = numpy.loadtxt ('final.csv', dtype= 'string', delimiter=',')
print (new_val)

Here is the Screenshot of the following given code

NumPy data type string not understood
NumPy data type string not understood

Solution

In this example, we are going the change the datatype ‘string’ with ‘str’ because np.str() works only in Python3.

Source Code:

import numpy

new_val = numpy.loadtxt ('final.csv', dtype= 'str', delimiter=',')
print (new_val)

Here is the Output of the following given code

Solution NumPy data type string not understood
Solution NumPy data type string not understood

Read: Python NumPy Average

NumPy data types in Pandas

  • In this section, we will discuss how to use numpy datatypes in Pandas by using Python.
  • To perform this particular task we are going to use the pd.dataframe() method for creating a dataframe and assigning integer and string values to it. Now use dtype() attribute and it will display the numpy dtype() class.

Example:

import pandas as pd

df = pd.DataFrame({'M': [17,'N',98.4]})
c=df['M'].dtype
f=type(df['M'].dtype)
print(c)
print(f)

You can refer to the below Screenshot

NumPy data types in Pandas
NumPy data types in Pandas

Read: Python NumPy nan

Data types in NumPy loadtxt

  • Here we can see how to use the loadtxt() function in the Numpy array along with datatype by using Python.
  • In Python, the numpy package provides a function that is loadtxt() and it is used for loading data from a CSV or text file.

Syntax:

Here is the Syntax of numpy.loadtxt() method

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

new_array = numpy.loadtxt ('final.csv', dtype= 'int', delimiter=',')
print(new_array)

In the above code, we have created ‘new_array’ and assigned a CSV file ‘final.csv’ along with datatype that is ‘int’. Once you will print ‘new_array’ then the output will display only integer values.

You can refer to the below Screenshot

Data types in NumPy loadtxt
Data types in NumPy loadtxt

Read: Python NumPy empty array

Numpy get datatype of element

  • In this section, we will discuss how to get the data type of the element in Numpy Python.
  • By using dtype() method we can easily get the datatype of every element. In this example we take only integer numbers in an argument.Once you will print the variable ‘m’ ‘d’ and ‘u’ then the output will display the numpy datatype.

Source Code:

import numpy as np

c=np.array([1])
m=c.dtype
print(m)

d= type(c)
print(d)

u= type(c[0])
print(u)
z=type(c[0].item())
print(z)

Here is the Screenshot of the following given code

Numpy get datatype of element
Numpy get datatype of element

Read: Python NumPy shape

NumPy change datatype of column

  • In this section, we will discuss how to change the data type of a particular column.
  • By using the np.ones() method we can easily create an array and pass ‘f’ as an argument that represents the values will be in decimal number. Now change the specific column and iterate through the slicing [:] method.

Example:

import numpy as np
import pandas as pd

y = np.ones((2, 2), dtype='f')
y[:, 1] = y[:, 1].astype('i')
print(type(y[:, 1][0]))

y_pd = pd.DataFrame(y)
y_pd[1] = y_pd[1].astype(np.int16)
print(type(y_pd[1][0]))

You can refer to the below Screenshot

NumPy change datatype of column
NumPy change the datatype of a column

Read: Python NumPy Random

NumPy data types overflow

  • Let us see how to overflow data types in NumPy Python.
  • In Python the size of an integer is flexible and every data type can store to some extent when value exceeds their limit then it becomes overflow solution is change the data type.

Example:

import numpy as np

a=np.arange(4000000,dtype='int64').reshape(4000,1000)
print(a)

In the above code first, we have imported a numpy library and then use the np.arange() function for creating a numpy array in which we have assigned a type along with np.reshape().

Here is the output of the following given code

NumPy data types overflow
NumPy data types overflow

Read: Python NumPy Split

Numpy datatype must provide an itemsize

  • In this section, we will discuss how to solve the error ‘NumPy datatype must provide an itemsize.
  • In this example we are going to use the concept of np.matrix and np.transpose() method.
  • In Python, a matrix is like an array object and we can generate the array by using the np.array() method, and the transpose() method is used to change column items into row items.

Example:


import numpy as np

arr = np.array([['1', '2'], ['3', '4']])
new_val = np.matrix(arr)
result = new_val.transpose()
print(new_val*result)

In the above code, we have created an array and then transpose method for changing the elements. Once you will print ‘new_val*result’ then the output will display the error ‘data type must provide an itemsize’. Now we are going to solve this error.

Here is the Screenshot of the following given code

Numpy datatype must provide an itemsize
Numpy datatype must provide an itemsize

Solution:

In Python when we are using the transpose method and the matrix elements are in string form then it will raise an error so we have to convert the elements of the matrix from strings to integers.

Code Snippet:

import numpy as np

arr = np.array([[1, 2], [3, 4]])
new_val = np.matrix(arr)
result = new_val.transpose()
print(new_val*result)

Here is the output of the following given code

Solution Numpy datatype must provide an itemsize
Solution Numpy datatype must provide an itemsize

NumPy add data types

  • In this section, we will discuss how to add data types in NumPy Python.
  • To perform this particular task we are going to use ndarray.astype() method. For example, suppose we have an array of type int32 and now we want to convert into float32 by using the astype() method and within this function, we have assigned a‘f’ keyword into it.

Source Code:

import numpy as np

my_array = np.array([65, 34, 19, 81, 79])
new_output = my_array.astype('f')
print(new_output)

You can refer to the below Screenshot

NumPy add data types
NumPy add data types

Numpy structured array data types

  • Here we can see how to create a structured array along with data type in NumPy Python.
  • In Python structured array stores data of any type and size. It is basically homogenous and creates a numpy array with elements and each item in an array should be a structure.

Example:

import numpy as np

new_arr = np.array([('William', 67, 95.9), ('Micheal', 43, 11.2)],
	dtype=[('Stu_name', ('str', 15)), ('stu_id', 'i'), ('stu_marks', 'f')])

print("Structured array:",new_arr)

Here is the implementation of the following given code

Numpy structured array data types
Numpy structured array data types

astypes data types numpy

  • In this program, we will discuss how to use the astype() function in NumPy Python.
  • In Python astype() function is used for changing the datatype in the NumPy array. For e.g suppose we have nan and decimal values in a numpy array. Now we want to change the nan and decimal values with integers.
  • To do this task we are going to apply the astype() method and within this method, we have assigned an int attribute for getting the integer values.

Example:

import numpy as np

new_arr = np.array([[71.2, np.nan], [786.3, np.nan], [1875.46, np.inf]])

result = new_arr.astype(int)
print(result)

Here is the output of the following given code

astypes data types numpy
astypes data types numpy

Numpy same datatypes

  • In this section, we will discuss how to use the same data type in NumPy Python.
  • In this example, we are going to use np.int32 that represents the 32-bit integer and this datatype can be store both types of values negative as well as positives.
  • Now we will create an array and assign integers value as an argument along with datatype that is ‘i4’. Once you will print ‘arr1’ and ‘arr2.dtype’ then it will display same datatype ‘int32’.

Source Code:

import numpy as np  

arr1 = np.dtype(np.int32) 
arr2 = np.array([54,34,33],dtype='i4')  
print(arr1)  
print(arr2.dtype)

Here is the implementation of the following given code

Numpy same datatypes
Numpy same datatypes

NumPy data types float 32

  • Let us see how to use the float32 data type in NumPy Python.
  • In Python, the float32 indicates the dynamic range value by using a decimal point for e.g suppose you have an array in which you have to assign an integers value. Now float32 datatype() method will help to convert a number (integer) with a floating number.

Example:

import numpy as np 

new_val = np.dtype([('info',np.float32)]) 
new_arr = np.array([(45,),(78,),(93,)], dtype = new_val) 
print(new_arr)

print(new_arr.dtype)
NumPy data types float 32
NumPy data types float 32

As you can see in the Screenshot the output is displaying the decimal numbers

Numpy genfromtxt multiple data types

  • In this section, we will discuss how to use multiple data types in the genfromtxt() method by using NumPy Python.
  • In Python, the genfromtext() is used to load the data from CSV or text files and this function always returns elements as an array and if you set the usemask in a parameter as true then it will return a masked array.

Syntax:

Here is the Syntax of genfromtext() method

numpy.genfromtxt
                (
                 fname,
                 dtype=<class 'float'>,
                 comments='#',
                 delimiter=None,
                 skip_header=0,
                 skip_footer=0,
                 convertors=None,
                 missing_values=None,
                 filling_values=None,
                 usecols=None,
                 names=None,
                 excludelist=None,
                )

Source Code:

import numpy as np

new_val = np.genfromtxt("test9.txt", dtype=None, encoding=None)
print(new_val)

print(new_val.dtype)

In the above code, we have used the np.genfromtext() method in which we have assigned a CSV file “test9.txt” along with datatype. Once you will print ‘new_val.dtype’ then the output will display the datatype with input value

Here is the Screenshot of the following given code

Numpy genfromtxt multiple data types
Numpy genfromtxt multiple data types

NumPy data type hierarchy

  • In this Program, we will discuss how to use hierarchy datatype in NumPy Python.
  • In this example, we are going to use different floating datatypes like float16,float32,float64. In Python, these floating data types are used for changing the integer values with decimal values.

Example:

import numpy as np

d = np.float16("56.7")
c = np.float32(d)
m = np.float64(c)
print(d == c == m)
print(d, c, m)

You can refer to the below Screenshot

NumPy data type hierarchy
NumPy data type hierarchy

NumPy complex data types

  • In this Program, we will discuss the data type complex in NumPy Python.
  • By using np.clongdouble() we can perform this task. In Python, this function represented the extended precision floats that are real and imaginary part(complex).

Source Code:

import numpy as np

y = np.clongdouble(2+3j)
print(y.dtype)

In the above code, we have used the np.clongdouble() method and assigned the complex number to it. Once you will print ‘y.dtype’ then the output will display the data type of that complex number.

You can refer to the below Screenshot

NumPy complex data types
NumPy complex data types

Numpy data type length

  • In this section, we will discuss how to measure the length of data type in NumPy Python.
  • In this example, we are going to use the size() and itemsize() methods. In Python, the itemsize() is used for the size of an array element in bytes. In simple words, we can say it will return the length of each item of the numpy array in bytes.

Syntax:

Here is the Syntax of the itemsize() method

narray.itemsize()

Example:

import numpy as np

new_value = np.array([167,342,986,554],dtype='i4')

print(new_value.size)
print(new_value.itemsize)

In the above code, we have created an array by using the np.array() method in which we have assigned the datatype and integer values. Now we want to check the length of an array by using the item.size() method.

Here is the implementation of the following given code

Numpy data type length
Numpy data type length

NumPy data type custom

  • Here we can see how to custom data type in NumPy Python.
  • In Python str() method is used for custom objects for updating the output and it is a human-readable version of our custom object. While in the case of the repr() method it will always return the object representation in string format and it is used to rebuild the object again.
  • In this example, if you declare a repr() properly then the eval() method will create a new custom object.

Source Code:

class Person:
    def __init__(self, stu_name, stu_age):
        self.stu_name = stu_name
        self.stu_age = stu_age

    def __str__(self):
        return f'Person({self.stu_name})'

    def __repr__(self):
        return f"Person(name='{self.stu_name}', age={self.stu_age})"
new_val = Person('Micheal', stu_age=65)
print(repr(new_val))

Here is the implementation of the following given code

NumPy data type custom
NumPy data type custom

Numpy data type void

  • In this program, we will discuss how to use void data type in NumPy Python.
  • In Python void data type there is no operation and values in it. In this example, we have used ‘V4’ in the dtype() attribute. Once you will print ‘a’ then the output will display the ‘V4’ data type.

Example:

import numpy as np 

a = np.dtype('V4')
print (a)

Here is the output of the following given code

Numpy data type void
Numpy data type void

Numpy data type max value

  • In this section, we will discuss how to get max value by using data type in NumPy Python.
  • To do this task we are going to use the np.iinfo() method it will help the user to get the max value. In Python, this function is used for machine integer limits and by default, its maximum value is 2147483647.

Source Code:

import numpy as np 

new_array = np.array([78, 145, 193, 223, 892, 956])
print(new_array.dtype)
result = np.iinfo(new_array.dtype).max
print (result)

Here is the implementation of the following given code

Numpy data type max value
Numpy data type max value

As you can see in the Screenshot the output is int32 along with the default maximum value

Example of numpy data type min value

In this program, we are going to use the same np.iinfo() function along with the np.min() method.

Source Code:

import numpy as np 

new_val = np.array([34,23,12,45,67])
print(new_val.dtype)
new_output = np.iinfo(new_val.dtype).min
print (new_output)

In the above code, we have created an array by using the np.array() method. Now create a variable ‘new_output’ and assign a function np.iinfo() in it. Once you will print ‘new_output’ then the output will display the by default minimum value that is -2147483648.

Here is the Screenshot of the following given code

Numpy data type min value
Numpy data type min value

Numpy data types bool

  • In this section, we will discuss how to use bool data type in NumPy Python.
  • In Python bool represents either the value will be true or false and this can be used as a data type and it will always return the truth values.
  • In this example, we have checked the type of True or False with the built type(bool) method.

Example:

import numpy as np

new_val = np.array([15, 0, 0])
result = new_val.astype(bool)
print(result)

In the above code, we have created an array in which we have assigned an integer value. Now use the astype(bool) method it will check the condition if the value is 0 then it will return ‘False’ otherwise ‘True’.

Here is the execution of the following given code

Numpy data types bool
Numpy data type bool

Numpy numeric data type

  • Let us see how to use the numeric data type in NumPy Python.
  • By using the dtype() method we can perform this particular task and in this method, we are going to use the np.uint8 that indicates the signed integer values.

Example:

import numpy as np

new_val = np.array([11, 56, 43],dtype=np.uint8)
print(new_val.dtype)

You can refer to the below Screenshot

Numpy numeric data type
Numpy numeric data type

As you can see in the Screenshot the output is ‘uint8’

Numpy vectorize data type not understood

  • In this section, we will discuss the error problem NumPy vectorized data type not understood in Python.
  • In this example, we are going to use the np.zeros() function for creating an array. In Python, this method will always return an array filled with zeros.

Syntax:

Here is the Syntax of np.zeros() function

np.zeros
        (
         shape,
         dtype=None,
         order='C'
        )
  • It consists of few parameters
    • shape: This parameter indicates the size of the array and it is used for the shape which we want to declare an array like(4,3).
    • dtype: It is an optional parameter and by default its value is float64.
    • order: This parameter defines the order and by default its value is ‘C’.

Source code:

import numpy as np

b= np.zeros(3,4)
print(b)
Numpy vectorize data type not understood
Numpy vectorize data type not understood

As you can see in the Screenshot the output is displaying the ‘4’ cannot interpret as datatype that represents the data type not understood

Solution

In the above code, the problem is we have not mentioned the tuple shape in an argument. Now we are going to use the () parenthesis in an argument. Once you will print ‘b’ then the output will display the new array filled with zero value.

Example:

import numpy as np

b= np.zeros((3,4))
print(b)

You can refer to the below Screenshot

Solution Numpy vectorize data type not understood
Solution Numpy vectorize data type not understood

Numpy data types memory size

  • In this Program, we will discuss how to check the size of data type in NumPy Python.
  • In this example, we are going to use the size() and itemsize() methods. We have already covered this example in ‘data type length’ and ‘numpy data type itemsize’.

Source Code:

import numpy as np

new_arr = np.array([54,82,189,765,456])

print(new_arr.size)
print(new_arr.itemsize)
result= new_arr.size * new_arr.itemsize
print(result)

Here is the Screenshot of the following given code

Numpy data types memory size
Numpy data type memory size

In this Python tutorial, we have learned how to use Data types in NumPy Python. Also, we have covered these topics.

  • NumPy data types string
  • NumPy data types size
  • NumPy data types float
  • NumPy data types list
  • NumPy data types datetime
  • NumPy data types change
  • Numpy data types range
  • Numpy data types check
  • NumPy data types with examples
  • Numpy array with different data types
  • NumPy array with mixed data types
  • NumPy empty data types
  • NumPy random data types
  • NumPy data types tuple
  • name two numpy data types for text
  • NumPy data types uint8
  • Numpy change data type uint8
  • NumPy data type string not understood
  • NumPy data types in Pandas
  • Data types in NumPy loadtxt
  • Numpy get datatype of element
  • NumPy change datatype of column
  • NumPy data types overflow
  • Numpy datatype must provide an itemsize
  • NumPy add data types
  • Numpy structured array data types
  • astypes data types numpy
  • Numpy same datatypes
  • NumPy data types float 32
  • Numpy genfromtxt multiple data types
  • NumPy data type hierarchy
  • NumPy complex data types
  • NumPy count data types
  • Numpy data type length
  • NumPy data type custom
  • Numpy data type void
  • Numpy data type max value
  • Numpy data types binary
  • Numpy data types bool
  • Numpy numeric data type
  • Numpy vectorize data type not understood
  • Numpy data types memory size