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?

Python ValueError is raised when a function receives an argument of the correct type but an inappropriate value. Also, the situation should not be described by a more precise exception such as IndexError.

## Setting an array element with a sequence

In Python, the error as **ValueError: Setting an array element with a sequence** is when we are working with Python numpy library mostly. This error is usually occurs when you are trying to create an array with the list which 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 first, we will create an array from the list with elements of a different dimension which will throw an error as a **valueerror setting an array element with a sequence**

Let us see the details of 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 see the value error is raised. This is because the structure of the array is not correct.

## Solution

In this solution, we will create the length of both the arrays equal and solve 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 **valueerror setting an array element with a sequence python**.

## Setting an array Element with a sequence Pandas

In this example first, we will import the Python pandas library. Then we will create a variable and use the function pandas dataframe to assign the values. After that, we will print the input, Then we 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 certain row a column in the dataframe to a list .loc function and getting a value error

Here is the Screenshot of the following given code

## Solution

In this solution, if we want to solve this error, we need to create a non-numeric data type as an object since it only contains 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 error, valueerror: setting an array element with a sequence pandas.

Read Python Pandas Drop Rows Example

## ValueError Setting An Array Element with a Sequence in Sklearn

In this section, we will discuss an error with a sequence in sklearn.

**Scikit-learn** is a free machine learning library for **Python**. It features various algorithms like support vector machine, random forests, and k-neighbours, and it also supports **Python** numerical and scientific libraries like NumPy and SciPy .

In machine learning models sometime numpy array got an value error in the code.

In this method we can easily use the function SVC() and import 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 example first, we will import a numpy library and sklearn. After that create an array X and y. The last element in the array X is of length 1 whereas the other element has length2.

This will get an 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 length of the last element in a given array.

we will give all the elements 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 section, we will discuss an error with a sequence in Tensorflow.

A Tensor’s shape (that is, the rank of the Tensor and the size of each dimension) may not always be fully known. In **tf.function** definitions, the shape may only be partially known.

In this method if the shape of every element in an given array is not equal length the you got an value 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 first, we will import a TensorFlow library then create an array and assign values with different sizes of lengths. After that create a variable and use the function tf.multiply.

Here is the Screenshot of the following given code

## Solution

In this solution, we will change the length of the last element in a given array.

we will give all the elements same length and all the elements 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 **valueerror setting an array element with a sequence tensorflow**.

## valueerror setting an array element with a sequence np.vectorize

In this section, we will discuss an error with a sequence in np.vectorize

The purpose of **np**.**vectorize** is to transform functions which are not **numpy**-aware (e.g. take floats as input and return floats as output) into functions that can operate on (and return) **numpy** arrays.

In this method the following function that has been vectorized so that for every element in input array t, an array is 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 example, this error happens when there are conflicts with NumPy and python. If the data type 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()** generate 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 define the output type.

In this method we can also use hamvec as a function.

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, **valueerror 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 discuss an error with a sequence in binary text classification with tfidfvectorizer.

**TF-IDF** stands for **“Term frequency -Inverse document Frequency ”**. TF-IDF is a numerical statistic which measures the importance of the word in a document.

A Scikit-Learn provides the implementation of the TfidfVectorizer.

I am using pandas and scikti-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 (sparse) 2-D array or a matrix. You can’t set the column df[‘text’] to a matrix without up the dimensions.

Try using only the training data in the fit routine, and try expanding out the toy data 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**.

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In this tutorial, we learned how to fix error, **valueerror 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