Python | dtype object length of Numpy array of strings Last Updated : 14 Mar, 2019 Comments Improve Suggest changes Like Article Like Report In this post, we are going to see the datatype of the numpy object when the underlying data is of string type. In numpy, if the underlying data type of the given object is string then the dtype of object is the length of the longest string in the array. This is so because we cannot create variable length string in numpy since numpy needs to know how much space should be allocated for string. Problem #1 : Given a numpy array whose underlying data is of string type. Find the dtype. Solution : We will use numpy.dtype attribute to check the dtype of the given object. Python3 1== # importing the numpy library as np import numpy as np # Create a numpy array arr = np.array(['Ela', 'Ed', 'Brook', 'Sia', 'Katherine']) # Print the array print(arr) Output : Now we will check the dtype of the given array object whose underlying data is of string type. Python3 # Print the dtype print(arr.dtype) Output : As we can see in the output, the dtype of the given array object is '<U9' where 9 is the length of the longest string in the given array object. Let's verify this by checking the length of the longest string in the given object. Python3 # Use vectorize function of numpy length_checker = np.vectorize(len) # Find the length of each element arr_len = length_checker(arr) # Print the length of each element print(arr_len) # Print the maximum value print(arr_len.max()) Output : Problem #2 : Given a numpy array whose underlying data is of string type. Find the dtype. Solution : We will use numpy.dtype attribute to check the dtype of the given object. Python3 1== # importing the numpy library as np import numpy as np # Create a numpy array arr = np.array(['New York', 'Lisbon', 'Beijing', 'Quebec']) # Print the array print(arr) Output : Now we will check the dtype of the given array object whose underlying data is of string type. Python3 # Print the dtype print(arr.dtype) Output : As we can see in the output, the dtype of the given array object is '<U8' where 8 is the length of the longest string in the given array object. Let's verify this by checking the length of the longest string in the given object. Python3 # Use vectorize function of numpy length_checker = np.vectorize(len) # Find the length of each element arr_len = length_checker(arr) # Print the length of each element print(arr_len) # Print the maximum value print(arr_len.max()) Output : Comment More infoAdvertise with us Next Article Python | dtype object length of Numpy array of strings S Shubham__Ranjan Follow Improve Article Tags : Python Numpy Python-numpy Python numpy-DataType AI-ML-DS With Python +1 More Practice Tags : python Similar Reads Data type Object (dtype) in NumPy Python Every ndarray has an associated data type (dtype) object. This data type object (dtype) informs us about the layout of the array. This means it gives us information about: Type of the data (integer, float, Python object, etc.)Size of the data (number of bytes)The byte order of the data (little-endia 3 min read numpy.array_str() in Python numpy.array_str()function is used to represent the data of an array as a string. The data in the array is returned as a single string. This function is similar to array_repr, the difference being that array_repr also returns information on the kind of array and its data type. Syntax : numpy.array_st 2 min read How to find size of an object in Python? In python, the usage of sys.getsizeof() can be done to find the storage size of a particular object that occupies some space in the memory. This function returns the size of the object in bytes. It takes at most two arguments i.e Object itself. Note: Only the memory consumption directly attributed t 2 min read Store Different Datatypes In One Numpy Array Storing diverse data types in a single NumPy array presents an effective approach to handling varied datasets efficiently. Although NumPy arrays are commonly homogeneous, situations may arise where managing multiple data types within a single array becomes necessary. In this article, we will underst 3 min read NumPy - Data type Objects(dtype) Every ndarray has an associated data type (dtype) object. This data type object (dtype) informs us about the layout of the array. This means it gives us information about :Type of the data (integer, float, Python object etc.)Size of the data (number of bytes)Byte order of the data (little-endian or 3 min read Modify Numpy array to store an arbitrary length string NumPy builds on (and is a successor to) the successful Numeric array object. Its goal is to create the corner-stone for a useful environment for scientific computing. NumPy provides two fundamental objects: an N-dimensional array object (ndarray) and a universal function object (ufunc). The dtype of 4 min read Python Lists VS Numpy Arrays Here, we will understand the difference between Python List and Python Numpy array. What is a Numpy array?NumPy is the fundamental package for scientific computing in Python. Numpy arrays facilitate advanced mathematical and other types of operations on large numbers of data. Typically, such operati 7 min read Python - Built-in array vs NumPy array Let us concentrate on the built-in array module first. Built-in array module defines an object type which can efficiently represent an array of basic values: characters, integers, floating point numbers. Arrays are sequence types and behave very much like lists, except that the type of objects store 5 min read NumPy ndarray.dtype Property | Get Data Type of Elements in Array The ndarray.dtype attribute returns the data type of the arrayâs elements. This attribute is read-only and cannot be modified directly. Example Python3 import numpy as geek arr = geek.array([[0, 1], [2, 3]]) gfg = arr.dtype print (gfg) Output : int64Syntax Syntax: numpy.ndarray.dtype Parameters : No 1 min read How to Fix Python "Can't Convert np.ndarray of Type numpy.object_"? When working with NumPy we might encounter the error message "Can't Convert np.ndarray of Type numpy.object_." This error typically arises when attempting to convert or perform the operations on the NumPy array that contains mixed data types or objects that are not supported by the intended operatio 4 min read Like