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

**Table of Contents**show

## 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.

## 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 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**.

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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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