Setting an array element with a sequence python ошибка

In this article, we will discuss how to fix ValueError: setting array element with a sequence using Python.

Error which we basically encounter when we using Numpy library is ValueError: setting array element with a sequence. We face this error basically when we creating array or dealing with numpy.array. 

This error occurred because of when numpy.array creating array with given value but the data-type of value is not same as data-type provided to numpy. 

Steps needed to prevent this error:

• Easiest way to fix this problem is to use the data-type which support all type of data-type.
• Second way to fix this problem is to match the default data-type of array and assigning value.

Method 1: Using common data-type

Example : Program to show error code:

Output:

 File “C:UserscomputersDownloadshe.py”, line 13, in <module>

 np_array = numpy.array(array1,dtype=Data_type);

ValueError: setting an array element with a sequence.

We can fix this error if we provide the data type  which support all data-type to the element of array:

Syntax: 

numpy.array( Array ,dtype = Common_DataType );

Example: Fixed code

Output:

[1 2 4 list([5, [6, 7]])]

Method 2:  By matching default data-type of value and Array

Example: Program to show error

Output:

File “C:UserscomputersDownloadshe.py”, line 15, in <module>

np_array[1] = [“for”,”Geeks”];

ValueError: setting an array element with a sequence

Here we have seen that this error is cause because we are assigning array as a element to array which accept string data-type. we can fix this error by matching the data-type of value and array and then assign it as element of array.

Syntax: 

if np_array.dtype == type( Variable ):
      expression;

Example: Fixed code

Output:

Variable value is not the type of numpy array
['Geeks' 'For']

Introduction

In python, we have discussed many concepts and conversions. In this tutorial, we will be discussing the concept of setting an array element with a sequence. When we try to access some value with the right type but not the correct value, we encounter this type of error. In this tutorial, we will be discussing the concept of ValueError: setting an array element with a sequence in Python.

What is Value Error?

A ValueError occurs when a built-in operation or function receives an argument with the right type but an invalid value. A value is a piece of information that is stored within a certain object.

In python, we often encounter the error as ValueError: setting an array element with a sequence is when we are working with the numpy library. This error usually occurs when the Numpy array is not in sequence.

What Causes Valueerror: Setting An Array Element With A Sequence?

Python always throws this error when you are trying to create an array with a not properly multi-dimensional list in shape. The second reason for this error is the type of content in the array. For example, define the integer array and inserting the float value in it.

Examples Causing Valueerror: Setting An Array Element With A Sequence

Here, we will be discussing the different types of causes through which this type of error gets generated:

1. Array Of A Different Dimension

Let us take an example, in which we are creating an array from the list with elements of different dimensions. In the code, you can see that you have created an array of two different dimensions, which will throw an error as ValueError: setting an array element with a sequence.

import numpy as np
print(np.array([[1, 2,], [3, 4, 5]],dtype = int))

Output:

Explanation:

• Firstly, we have imported the numpy library with an alias name as np.
• Then, we will be making the array of two different dimensions with the data type of integer from the np.array() function.
• The following code will result in the error as Value Error as we cannot access the different dimensions array.
• At last, you can see the output as an error.

Solution Of An Array Of A Different Dimension

If we try to make the length of both the arrays equal, then we will not encounter any error. So the code will work fine.

import numpy as np
print(np.array([[1, 2, 5], [3, 4, 5]],dtype = int))

Output:

Explanation:

• Firstly, we have imported the numpy library with an alias name as np.
• Then, we will make the different dimension array into the same dimension array to remove the error.
• At last, we will try to print the output.
• Hence, you can see the output without any error.

Also, Read | [Solved] IndentationError: Expected An Indented Block Error

2. Different Type Of Elements In An Array

Let us take an example, in which we are creating an array from the list with elements of different data types. In the code, you can see that you have created an array of multiple data types values than the defined data type. If we do this, there will be an error generated as ValueError: setting an array element with a sequence.

import numpy as np
print(np.array([2.1, 2.2, "Ironman"], dtype=float))

Output:

Explanation:

• Firstly, we have imported the numpy library with an alias name as np.
• Then, we will be making the array of two different data types with the data type as a float from the np.array() function.
• The array contains two data types, i.e., float and string.
• The following code will result in the error as Value Error as we cannot write the different data types values as the one data type of array.
• Hence, you can see the output as Value Error.

Solution Of Different Type Of Elements In An Array

If we try to make the data type unrestricted, we should use dtype = object, which will help you remove the error.

import numpy as np
print(np.array([2.1, 2.2, "Ironman"], dtype=object))

Output:

Explanation:

• Firstly, we have imported the numpy library with an alias name as np.
• Then, if we want to access the different data types values in a single array so, we can set the dtype value as an object which is an unrestricted data type.
• Hence, you can see the correct output, and the code runs correctly without giving any error.

Also, Read | [Solved] TypeError: String Indices Must be Integers

3. Valueerror Setting An Array Element With A Sequence Pandas

In this example, we will be importing the pandas’ library. Then, we will be taking the input from the pandas dataframe function. After that, we will print the input. Then, we will update the value in the list and try to print we get an error.

import pandas as pd
output = pd.DataFrame(data = [[800.0]], columns=['Sold Count'], index=['Project1'])
print (output.loc['Project1', 'Sold Count'])

output.loc['Project1', 'Sold Count'] = [400.0]
print (output.loc['Project1', 'Sold Count'])

Output:

Solution Of Value Error From Pandas

If we dont want any error in the following code we need to make the data type as object.

import pandas as pd
output = pd.DataFrame(data = [[800.0]], columns=['Sold Count'], index=['Project1'])
print (output.loc['Project1', 'Sold Count'])

output['Sold Count'] = output['Sold Count'].astype(object)
output.loc['Project1','Sold Count'] = [1000.0,800.0]
print(output)

Output:

Also, Read | How to Solve TypeError: ‘int’ object is not Subscriptable

4. ValueError Setting An Array Element With A Sequence in Sklearn

Sklearn is a famous python library that is used to execute machine learning methods on a dataset. From regression to clustering, this module has all methods which are needed.

Using these machine learning models over the 2D arrays can sometimes cause a huge ValueError in the code. If your 2D array is not uniform, i.e., if several elements in all the sub-arrays are not the same, it’ll throw an error.

Example Code –

import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

X = np.array([[-1, 1], [2, -1], [1, -1], [2]])
y = np.array([1, 2, 2, 1])

clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
clf.fit(X, y)

Here, the last element in the X array is of length 1, whereas all other elements are of length 2. This will cause the SVC() to throw an error ValueError – Setting an element with a sequence.

Solution –

The solution to this ValueError in Sklearn would be to make the length of arrays equal. In the following code, we’ve changed all the lengths to 2.

import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

X = np.array([[-1, 1], [2, -1], [1, -1], [2, 1]])
y = np.array([1, 2, 2, 1])

clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
clf.fit(X, y)

Also, Read | Invalid literal for int() with base 10 | Error and Resolution

5. ValueError Setting An Array Element With A Sequence in Tensorflow

In Tensorflow, the input shapes have to be correct to process the data. If the shape of every element in your array is not of equal length, you’ll get a ValueError.

Example Code –

import tensorflow as tf
import numpy as np

# Initialize two arrays
x1 = tf.constant([1,2,3,[4,1]])
x2 = tf.constant([5,6,7,8])

# Multiply
result = tf.multiply(x1, x2)
tf.print(result)

Here the last element of the x1 array has length 2. This causes the tf.multiple() to throw a ValueError.

Solution –

The only solution to fix this is to ensure that all of your array elements are of equal shape. The following example will help you understand it –

import tensorflow as tf
import numpy as np

# Initialize two arrays
x1 = tf.constant([1,2,3,1])
x2 = tf.constant([5,6,7,8])

# Multiply
result = tf.multiply(x1, x2)
tf.print(result)

6. ValueError Setting An Array Element With A Sequence in Keras

Similar error in Keras can be observed when an array with different lengths of elements are passed to regression models. As the input might be a mixture of ints and lists, this error may arise.

Example Code –

model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, y, epochs=150, batch_size=10)

>>> ValueError: setting an array element with a sequence.

Here the array X contains a mixture of integers and lists. Moreover, many elements in this array are not fully filled.

Solution –

The solution to this error would be flattening your array and reshaping it to the desired shape. The following transformation will help you to achieve it. keras.layers.Flatten and pd.Series.tolist() will help you to achieve it.

model = Sequential()

model.add(Flatten(input_shape=(2,2)))

model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model

X = X.tolist()

model.fit(X, y, epochs=150, batch_size=10)

Also, Read | How to solve Type error: a byte-like object is required not ‘str’

Conclusion

In this tutorial, we have learned about the concept of ValueError: setting an array element with a sequence in Python. We have seen what value Error is? And what is ValueError: setting an array element with a sequence? And what are the causes of Value Error? We have discussed all the ways causing the value Error: setting an array element with a sequence with their solutions. All the examples are explained in detail with the help of examples. You can use any of the functions according to your choice and your requirement in the program.

However, if you have any doubts or questions, do let me know in the comment section below. I will try to help you as soon as possible.

FAQs

1. How Does ValueError Save Us From Incorrect Data Processing?

We will understand this with the help of small code snippet:

while True:
    try:
        n = input("Please enter an integer: ")
        n = int(n)
        break
    except ValueError:
        print("No valid integer! Please try again ...")
print("Great, you successfully entered an integer!")

Input:

Firstly, we will pass 10.0 as an integer and then 10 as the input. Let us see what the output comes.

Output:

Now you can see in the code. When we try to enter the float value in place of an integer value, it shows me a value error which means you can enter only the integer value in the input. Through this, ValueError saves us from incorrect data processing as we can’t enter the wrong data or input.

2. We don’t declare a data type in python, then why is this error arrises in initializing incorrect datatype?

In python, We don’t have to declare a datatype. But, when the ValueError arises, that means there is an issue with the substance of the article you attempted to allocate the incentive to. This is not to be mistaken for types in Python. Hence, Python ValueError is raised when the capacity gets a contention of the right kind; however, it an unseemly worth it.

In this Python tutorial, we will be discussing the concept of setting an array element with a sequence, and also we will see how to fix error, Valueerror: Setting an array element with a sequence:

• An array of a Different dimension
• Setting an array Element with a sequence Pandas
• Valueerror Setting An Array Element with a Sequence in Sklearn
• Valueerror Setting An Array Element with a Sequence in Tensorflow
• Valueerror Setting An Array Element with a Sequence in np.vectorize
• Setting An Array Element with a Sequence in binary text classification

What is ValueError?

ValueError is raised when a function passes an argument of the correct type but an unknown value. Also, the situation should not be prevented by a more precise exception such as Index Error.

• In Python, the error as ValueError: Setting an array element with a sequence is when we are working with numpy library mostly. This error usually occurs when you are trying to create an array with a list that is not proper multi-dimensional in shape.

valueerror setting an array element with a sequence python

An array of a Different dimension

• In this example, we will create a numpy array from the list with elements of a different dimension which will throw an error as a value error setting an array element with a sequence
• Let us see and discuss this error and its solution

Here is the code of an array of a different dimension

import numpy as np

print(np.array([[4, 5,9], [ 7, 9]],dtype = int))

Explanation

• First we will import the numpy library.
• Then, we will create the array of two different dimension by using function np.array.
• Here is the Screenshot of the following given code

You can easily see the value error in the display. This is because the structure of the numpy array is not correct.

Solution

In this solution, we will declare the size and length of both the arrays equal and fix the value error.

import numpy as np

print(np.array([[4, 5,9], [ 4,7, 9]],dtype = int))

Here is the Screenshot of the following given code

This is how to fix value error by setting an array element with a sequence python.

Setting an array Element with a sequence Pandas

In this example, we will import the Python pandas module. Then we will create a variable and use the library pandas dataframe to assign the values. Now, we will print the input, Then it will update the value in the list and got a value error.

Here is the code of value error from pandas

import pandas as pd
out = pd.DataFrame(data = [[600.0]], columns=['Sold Count'], index=['Assignment'])
print (out.loc['Assignment', 'Sold Count'])
 
out.loc['Assignment', 'Sold Count'] = [200.0]
print (out.loc['Assignment', 'Sold Count'])

Explanation

The basic issue is that I would like to set a row and a column in the dataframe to a list .loc method is used and getting a value error

Here is the Screenshot of the following given code

Solution

In this solution, if you want to solve this error, You will create a non-numeric dtype as an object since it only stores numeric values.

Here is the Code

import pandas as pd
out = pd.DataFrame(data = [[600.0]], columns=['Sold Count'], index=['Assignment'])
print (out.loc['Assignment', 'Sold Count'])
 
out['Sold Count'] = out['Sold Count'].astype(object)
out.loc['Assignment','Sold Count'] = [1000.0,600.0]
print(out)

Here is the Screenshot of the following given code

This is how to fix the error, value error: setting an array element with sequence pandas.

Read Python Pandas Drop Rows Example

ValueError Setting An Array Element with a Sequence in Sklearn

• In this method, we will discuss an error with an iterable sequence in sklearn.
• Scikit-learn is a free machine learning module for Python. It features various algorithms like SVM, random forests, and k-neighbors, and it also generates Python numerical and scientific libraries like NumPy and SciPy.
• In machine learning models sometimes numpy array got a value error in the code.
• In this method, we can easily use the function SVC() and import the sklearn library.

Here is the code of value error setting an array element with a sequence

import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
X = np.array([[-3, 4], [5, 7], [1, -1], [3]])
y = np.array([4, 5, 6, 7])
clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
clf.fit(X, y)

Explanation

• In the above code, we will import a numpy library and sklearn. Now we will create an array X and y. The end element in the numpy array X is of length 1 whereas the other value has length2.
• This will display the result of a value error for an array element with the Sequence.

Here is the Screenshot of the following given code

Solution

• In this solution, we will change the size of the end element in a given array.
• we will give all the elements the same length.

Here is the code

import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
X = np.array([[-3, 4], [5, 7], [1, -1], [3,2]])
y = np.array([4, 5, 6, 7])
clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
clf.fit(X, y)

Here is the Screenshot of the following given code

This is how to fix the error, valueerror setting an array element with a sequence sklearn.

Read Remove character from string Python

Valueerror Setting An Array Element with a Sequence in Tensorflow

• In this method, we will learn and discuss an error with a sequence in Tensorflow.
• A Tensor’s shape is the rank of the Tensor module and the length of each dimension may not always be fully known. In tf.function the shape will only be partially known.
• In this method, if the shape of every element in a given numpy array is not equal to size you got an error message.

Here is the code of value error array element with a sequence in Tensorflow.

import tensorflow as tf
import numpy as np
 

x = tf.constant([4,5,6,[4,1]])
y = tf.constant([9,8,7,6])
 

res = tf.multiply(x,y)
tf.print(res)

Explanation

In this example, we will import a TensorFlow module then create a numpy array and assign values with different sizes of lengths. Now we create a variable and use the function tf. multiply.

Here is the Screenshot of the following given code

Solution

• In this solution, we will display and change the length of the end element in a given array.
• we will give all the values the same length and all the values are of equal shape.

Here is the Code

import tensorflow as tf
import numpy as np
 

x = tf.constant([4,5,6,4])
y = tf.constant([9,8,7,6])
 

res = tf.multiply(x,y)
tf.print(res)

Here is the Screenshot of the following given code

This is how to fix the error value error by setting an array element with a sequence TensorFlow.

valueerror setting an array element with a sequence np.vectorize

• In this method, we will learn and discuss an error with a sequence in np.vectorize
• The main purpose of np.vectorize is to transform functions that are not numpy aware into functions that can provide and operate on (and return) numpy arrays.
• In this example, the given function has been vectorized so that for every value in input array t, a numpy array is an output.

Here is the Code of the array element with a sequence in np.vectorize.

import numpy as np

def Ham(t):
    d=np.array([[np.cos(t),np.sqrt(t)],[0,1]],dtype=np.complex128)
    return d
print(Ham)

Explanation

In the above code, this error happens when there are more precise and conflicts with NumPy and python. If the dtype is not given the error may display.

Here is the Screenshot of the following given code

Solution

• In this method, the problem is that np.cos(t) and np.sqrt() compute the numpy arrays with the length of t, whereas the second row ([0,1]) maintains the same size.
• To use np.vectorize with your function, you have to declare the output type.
• In this method, we can easily use hamvec as a method.

Here is the Code

import numpy as np
def Ham(t):
    d=np.array([[np.cos(t),np.sqrt(t)],[0,1]],dtype=np.complex128)
    return d

HamVec = np.vectorize(Ham, otypes=[np.ndarray])
x=np.array([1,2,3])
y=HamVec(x)
print(y)

Here is the Screenshot of the following given code

This is how to fix the error, value error setting an array element with a sequence np.vectorize.

Valueerror Setting An Array Element with a Sequence in binary text classification with tfidfvectorizer

• In this section, we will learn and discuss an error with a sequence in binary text classification with a tfidfvectorizer.
• TF-IDF stands for Term Frequency Inverse Document Frequency. This method is a numerical statistic that measures the importance of the word in a document.
• A Scikit-Learn provides the result of the TfidfVectorizer.
• I am using pandas and scikit-learn to do binary text classification using text features encoded using TfidfVectorizer on a DataFrame.

Here is the code of binary text classification with tfidfvectorizer

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from sklearn.feature_extraction.text import TfidfVectorizer
data_dict = {'tid': [0,1,2,3,4,5,6,7,8,9],
         'text':['This is the first.', 'This is the second.', 'This is the third.', 'This is the fourth.', 'This is the fourth.', 'This is the fourth.', 'This is the nintieth.', 'This is the fourth.', 'This is the fourth.', 'This is the first.'],
         'cat':[0,0,1,1,1,1,1,0,0,0]}
df = pd.DataFrame(data_dict)
tfidf = TfidfVectorizer(analyzer='word')
df['text'] = tfidf.fit_transform(df['text'])
X_train, X_test, y_train, y_test = train_test_split(df[['tid', 'text']], df[['cat']])
clf = LinearSVC()
clf.fit(X_train, y_train)

Here is the Screenshot of the following given code

Solution

• Tfidfvectorizer returns a 2-Dimension array. You can’t set the column df[‘text’] to a matrix without up the dimensions.
• Try using only the training data in the fitness routine, and try expanding out the data and set to have more values.

Here is the code

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from sklearn.feature_extraction.text import TfidfVectorizer
data_dict = {'tid': [0,1,2,3,4,5,6,7,8,9],
         'text':['This is the first.', 'This is the second.', 'This is the third.', 'This is the fourth.', 'This is the fourth.', 'This is the fourth.', 'This is the nintieth.', 'This is the fourth.', 'This is the fourth.', 'This is the first.'],
         'cat':[0,0,1,1,1,1,1,0,0,0]}
df = pd.DataFrame(data_dict)
tfidf = TfidfVectorizer(analyzer='word')
df_text = pd.DataFrame(tfidf.fit_transform(df['text']).toarray()) 
X_train, X_test, y_train, y_test = train_test_split(pd.concat([df[['tid']],df_text],axis=1), df[['cat']])
clf = LinearSVC()
clf.fit(X_train, y_train)

Here is the Screenshot of the following given code

This is how to fix the error, Valueerror Setting An Array Element with a Sequence in binary text classification with tfidfvectorizer.

You may like the following Python tutorials:

• Groupby in Python Pandas
• How to use Pandas drop() function in Python
• Python NumPy Average with Examples
• Python NumPy absolute value
• Check if a list exists in another list Python

In this tutorial, we learned how to fix the error, value error setting an array element with a sequence python.

• An array of a Different dimension
• valueerror setting an array element with a sequence python
• Setting an array Element with a sequence Pandas
• ValueError Setting An Array Element with a Sequence in Sklearn
• Valueerror Setting An Array Element with a Sequence in Tensorflow
• Valueerror Setting An Array Element with a Sequence in np.vectorize
• Setting An Array Element with a Sequence in binary text classification

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If you try to put a sequence of more than one element in the place of an array element, you will raise the error: ValueError: setting an array element with a sequence.

To solve this error, ensure that each element in the array is of consistent length and that you do not have a sequence in place of a single element.

The error can also happen if you attempt to create a numpy array with elements of different data-type than that specified with the dtype parameter. To solve this error, you can set the dtype of the numpy array to object.

This tutorial will go through the error in detail and how to solve it with the help of code examples.

What is a ValueError?

In Python, a value is the information stored within a particular object. You will encounter a ValueError in Python when you use a built-in operation or function that receives an argument with the right type but an inappropriate value.

Example #1: Setting An Array Element With A Sequence in Numpy

Let’s look at an example where we create a numpy array using a list of values. We can select the data type of the numpy array using the dtype parameter. In this example, we will set the data type to an integer. Let’s look at the code:

import numpy as np

arr = [2, 4, 5, [10, [12, 14]]]

data_type=int

np_arr = np.array(arr, dtype=data_type)

print(np_arr)

Let’s run the code to see the result:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
TypeError: int() argument must be a string, a bytes-like object or a number, not 'list'

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
      5 data_type=int
      6
  ---≻7 np_arr = np.array(arr, dtype=data_type)
      8 
      9 print(np_arr)

ValueError: setting an array element with a sequence.

The first error is a TypeError, which we throw because the int() method expects certain data types, but it received a list. The cause of the TypeError is the ValueError. The ValueError occurs because NumPy interprets [10, [12, 14]] as a list, but the data type of the numpy array to create is int. The array can only accept integers as elements.

Solution #1: Changing dtype to object

To solve this error, we can set the data type to object; the array will then support all data types, including list. Let’s look at the revised code:

import numpy as np

arr = [2, 4, 5, [10, [12, 14]]]

data_type=object

np_arr = np.array(arr, dtype=data_type)

print(np_arr)

Let’s run the code to see the result:

[2 4 5 list([10, [12, 14]])]

We have a NumPy object array, which contains references to numpy.str_ objects. Let’s look at the type of the elements in the array:

for i in np_arr:

    print(type(i))

≺class 'numpy.str_'≻
≺class 'numpy.str_'≻
≺class 'numpy.str_'≻
≺class 'numpy.str_'≻

We can still manipulate the integer elements as integers and the list element as a list

val = np_arr[0]
val_sq = val ** 2
print(val_sq)

4

lst = np_arr[3]
print(lst[0])

10

Solution #2: Fixing List Structure

Another way to resolve this is to fix the structure of the original list. If we define the list as a two-dimensional nested list, where each list has the same length, we can pass it to the np.array() method. Let’s look at the revised code:

import numpy as np

arr = [[2, 4, 5], [10, 12, 14]]

data_type=int

np_arr = np.array(arr, dtype=data_type)

print(np_arr)

Let’s run the code to see the result:

[[ 2  4  5]
 [10 12 14]]

The result is a two-dimensional numpy array, which is we can treat as a matrix. For further reading on matrices, go to the articles:

• How to Multiply Two Matrices in Python
• How to Find the Transpose of a Matrix in Python

If the dimensions of the nested list are different, the array creation will fail if the data type is not object.

import numpy as np

arr = [[2, 4], [10, 12, 14]]

data_type=int

np_arr = np.array(arr, dtype=data_type)

print(np_arr)

ValueError: setting an array element with a sequence.

To fix this, we either need to ensure the elements are of consistent length or set the data type of the array to object. The resultant array will contain numpy.str_ objects that each refer to a list:

import numpy as np

arr = [[2, 4], [10, 12, 14]]

data_type=object

np_arr = np.array(arr, dtype=data_type)

print(np_arr)

[list([2, 4]) list([10, 12, 14])]

Example #2: Setting An Array Element With A Sequence in Numpy

Let’s look at an example where we try to assign a sequence to an element of an array that only accepts a specific data type.

import numpy as np

arr = ["Python", "is", "really", "fun", "to", "learn"]

data_type = str

np_arr = np.array(arr, dtype=data_type)

np_arr[1] = ["Python", "is"]

In the above code, we attempt to assign a list of strings to a single element in the numpy array, which has the data type str. Let’s run the code to see what happens:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
----≻ 1 np_arr[1] = ["Python", "is"]

ValueError: setting an array element with a sequence

The error occurs because the array expects a string value, but it receives a list with multiple strings.

Solution

To solve this error, we need to set the data type of the numpy array to object. Let’s look at the revised code.

import numpy as np

arr = ["Python", "is", "really", "fun", "to", "learn"]

data_type = object

np_arr = np.array(arr, dtype=data_type)

np_arr[1] = ["Python", "is"]

Let’s run the code to get the result:

['Python' list(['Python', 'is']) 'really' 'fun' 'to' 'learn']

The updated array now has a list object as the first element, and the rest are string objects.

If we want the numpy array to be one specific data type, we need to use an if statement. The if statement will only assign an element if the object has the same data type as the numpy array. Let’s look at the revised code:

import numpy as np

arr = ["Python", "is", "really", "fun", "to", "learn"]

data_type = str

np_arr = np.array(arr, dtype=data_type)

variable = ["Python", "is"]

if np_arr.dtype == type(variable):

    np_arr[1] = variable

else:

    print(f'Variable value does not match the type of the numpy array {data_type}')

    print('Array:  ', np_arr)

The if statement checks if the object we want to assign to the element has the same data type as the numpy array. If it does, then the assignment happens. Otherwise, we get a print statement telling us there is a mismatch in data types. Let’s run the code to see what happens:

Variable value does not match the type of the numpy array ≺class 'str'≻
Array:   ['Python' 'is' 'really' 'fun' 'to' 'learn']

Example #3: Setting An Array Element With A Sequence in Scikit-Learn

Let’s look at another common source of the ValueError. This example will attempt to create a Scikit-Learn pipeline to fit a classifier with some training data.

import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier

# Training data

X = np.array([[-1, 1], [2, -1], [1, -1], [2]])

# Labels

y = np.array([1, 2, 2, 1])

# Pipeline

clf = make_pipeline(StandardScaler(), MLPClassifier())

# Fitting

clf.fit(X, y)

Let’s run the code to see what happens:

ValueError: setting an array element with a sequence.

We raise the ValueError because the fourth element in the array is a single value, whereas the other three elements contain two values. Therefore, the array has mismatching dimensions. If you want to manipulate multi-dimensional arrays in Python, the elements must be of consistent length.

Solution

To solve this error, we must ensure all elements have the same length. Let’s look at the revised code:

import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier

# Training data

X = np.array([[-1, 1], [2, -1], [1, -1], [2, 1]])

#Labels

y = np.array([1, 2, 2, 1])

#Pipeline

clf = make_pipeline(StandardScaler(), MLPClassifier())

# Fitting

clf.fit(X, y)

The fourth element now has two values like the rest of the elements. This code will run without any errors.

Summary

Congratulations on reading to the end of this tutorial! The error ValueError: setting an array element with a sequence occurs when you attempt to set an element to a sequence that contains more than one item. The common sources of the error are

• Attempting to create an array with a nested list of mixed dimensions
• Creating an array with a given value but the data-type of the value is not the same as the data-type of the numpy array.

To create a multi-dimensional array, ensure your elements have consistent lengths. Otherwise, if you do not need consistent element lengths, you can set the data type of the numpy array to object.

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