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Introduction
In this article, we will look at how you can set an array element with a sequence, and then we will also learn the ways to solve the error – “ValueError: setting an array element with a sequence“.
In Python, the ValueError generally gets raised when a function gets the argument of the right type yet an improper value. e.g., when you define an integer array and insert the string values.
The ValueError: setting an array element with a sequence occurs when:
- An array does not have a proper shape, i.e., a multidimensional array has improper dimensions at different levels.
- The error also occurs when you work with the
NumPylibrary, and the NumPy array is not in sequence.
Note: The number of elements in each dimension of an array is known as its shape. The number of indices required to specify an individual array element is its dimension.
If you want to learn more about the dimensions of arrays in Python, please refer to this tutorial.
Now that you know what ValueError is let’s look at the different ways to solve ValueError: setting an array element with a sequence.
Solution 1: Using Proper Array Dimensions
Consider the following example where we have a certain NumPy array with dimensions as shown below.
Example:
# Importing the NumPy array import numpy as np print(np.array([[1, 2, 3], [4, 5, 6, 7]], dtype = int))
Output:
Traceback (most recent call last):
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\Errors\ValueError-array_sequence.py", line 4, in <module>
print(np.array([[1, 2, 3], [4, 5, 6, 7]], dtype=int))
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
Explanation: Here, the ValueError occurred because the array improper dimensions, i.e. it has a shape that is not permissible. In this case, [1, 2, 3] has a dimension of 3, while [4, 5, 6, 7] has dimension 4.
Solution: To eliminate the occurrence of the above error, you have to rectify the shape of the array. As this is a 2D array having 4 elements in the second dimension. So, you must ensure that the first dimension also has 4 elements.
# Importing the NumPy array import numpy as np print(np.array([[1, 2, 3, 0], [4, 5, 6, 7]], dtype=int))
Output:
[[1, 2, 3, 0]
[4, 5, 6, 7]]Solution 2: Dealing with Pandas Library
In Python, Pandas is an open-source library that provides high performance with easy-to-use data structures and data analysis tools. You need to import the Pandas library to utilize it. Use the following code to import it.
| import pandas as pd |
Now, consider the following example that leads to the occurrence of the ValueError:
Example:
# Importing the pandas module import pandas as pd output = pd.DataFrame(data=[[500.0]], columns=['s count'], index=['Project']) print(output.loc['Project', 's count']) output.loc['Project', 's count'] = [200.0] print(output.loc['Project', 's count'])
Output:
500.0
TypeError: float() argument must be a string or a number, not 'list'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\Errors\ValueError-array_sequence.py", line 7, in <module>
output.loc['Project', 's count'] = [200.0]
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\pandas\core\indexing.py", line 723, in __setitem__
iloc._setitem_with_indexer(indexer, value, self.name)
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\pandas\core\indexing.py", line 1732, in _setitem_with_indexer
self._setitem_single_block(indexer, value, name)
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\pandas\core\indexing.py", line 1968, in _setitem_single_block
self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\pandas\core\internals\managers.py", line 355, in setitem
return self.apply("setitem", indexer=indexer, value=value)
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\pandas\core\internals\managers.py", line 327, in apply
applied = getattr(b, f)(**kwargs)
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\pandas\core\internals\blocks.py", line 953, in setitem
values[indexer] = value
ValueError: setting an array element with a sequence.
Explanation: The rows and columns of the table are marked by file names or named strings. The above error occurred because Python was unable to recover the user input into the input list.
Solution: You can easily retrieve the input with the help of the DataFrame() function that is used to return a list of cells in a two-dimensional table. Also, DataFrame.astype() method helps us to cast a pandas object to a specified dtype that will help us to solve the above problem.
Recommended Read: Pandas DataFrame Indexing
# Importing the pandas module import pandas as pd output = pd.DataFrame(data=[[500.0]], columns=['s count'], index=['Project']) print(output.loc['Project', 's count']) output['s count'] = output['s count'].astype(object) output.loc['Project', 's count'] = [200.0] print(output)
Output:
500.0
s count
Project [200.0]Solution 3: Dealing with Sklearn
Sklearn is one of the most popular libraries in Python that is utilized to execute AI and ML strategies on a dataset. While working with ML models and datasets that involve multidimensional arrays can also cause a ValueError in the code. For example, it throws an error if the array is not uniform or if a few elements are not the same. Consider the following snippet:
Example:
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], [1]]) y = np.array([1, 2, 2, 1]) clf = make_pipeline(StandardScaler(), SVC(gamma='auto')) clf.fit(X, y)
Output:
"C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\Scripts\python.exe" "C:/Users/SHUBHAM SAYON/PycharmProjects/Finxer/Errors/ValueError-array_sequence.py"
C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\Errors\ValueError-array_sequence.py:7: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
X = np.array([[1, 1], [-2, 1], [1, -1], [1]])
TypeError: float() argument must be a string or a number, not 'list'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\Errors\ValueError-array_sequence.py", line 11, in <module>
clf.fit(X, y)
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\sklearn\pipeline.py", line 390, in fit
Xt = self._fit(X, y, **fit_params_steps)
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\sklearn\pipeline.py", line 348, in _fit
X, fitted_transformer = fit_transform_one_cached(
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\joblib\memory.py", line 349, in __call__
return self.func(*args, **kwargs)
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\sklearn\pipeline.py", line 893, in _fit_transform_one
res = transformer.fit_transform(X, y, **fit_params)
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\sklearn\base.py", line 850, in fit_transform
return self.fit(X, y, **fit_params).transform(X)
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 806, in fit
return self.partial_fit(X, y, sample_weight)
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 841, in partial_fit
X = self._validate_data(
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\sklearn\base.py", line 561, in _validate_data
X = check_array(X, **check_params)
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\venv\lib\site-packages\sklearn\utils\validation.py", line 738, in check_array
array = np.asarray(array, order=order, dtype=dtype)
ValueError: setting an array element with a sequence.
Explanation: The reason behind getting an error in this case, is once again similar to the example we discussed previously. Here, SVC() throws an error as all the elements in the array have length 2 except the last element that has length 1. Hence, to solve this error you have to ensure that all arrays have equal lengths as shown below.
Example:
# Importing the numpy and sklearn libraries 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], [1, 2]]) y = np.array([1, 2, 2, 1]) clf = make_pipeline(StandardScaler(), SVC(gamma='auto')) clf.fit(X, y) print(X) print(y)
Output:
[[ 1 1]
[-2 1]
[ 1 -1]
[ 1 2]]
[1 2 2 1]Bonus Read: ValueError: could not convert string to float: ‘Python’
Another situation which results in a similar kind of ValueError is when you feed in values that re of different type within the same Numpy array. This is not permissible and results in an error.
Example:
import numpy as np print(np.array([1.4, 1.6, 2.4, "Python"], dtype=float))
Output:
Traceback (most recent call last):
File "C:\Users\SHUBHAM SAYON\PycharmProjects\Finxer\Errors\ValueError-array_sequence.py", line 2, in <module>
print(np.array([1.4, 1.6, 2.4, "Python"], dtype = float))
ValueError: could not convert string to float: 'Python'
Solution: To solve this error, you have to set the data type (dtype) as an object instead of setting it as a particular data type like float, string, and int. This way, you will be able to access the array with different data types values as an object has an unrestricted data type.
# Importing the NumPy array import numpy as np print(np.array([1.4, 1.6, 2.4, "Python"], dtype = float))
Output:
[[1.4, 1.6, 2.4, "Python"]
Conclusion
In this tutorial, we learned how to solve ValueError: setting an array element with a sequence. I hope this discussion helped you to solve your problem. Please stay tuned and subscribe for more interesting solutions and discussions in the future. Happy learning!
Post Credits: Rashi Agarwal and Shubham Sayon
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