![]() ![]() ValueError: setting an array element with a sequence. > 1993 return np.asarray(self._values, dtype=dtype) A triangular number Tn is a figurative number that can be represented in the form of an equilateral triangular grid of elements such that every subsequent row. usr/local/lib/python3.7/dist-packages/pandas/core/generic.py in _array_(self, dtype)ġ992 def _array_(self, dtype: NpDtype | None = None) -> np.ndarray: Output: Traceback (most recent call last): File 'C:UsersSHUBHAM SAYONPycharmProjectsFinxerErrorsValueError-arraysequence. > 2 clf.fit(pomt_X_train.squeeze(), pomt_Y_train)ĥ X_train_prediction = clf.predict(pomt_X_train) The solution is to check and fix your input data. ![]() When you convert to a numpy array it becomes an array of shape (3,) with dtypeObject. ValueError Traceback (most recent call last) There are 59 elements in row 1, and 58 in rows 2 & 3. The above exception was the direct cause of the following exception: ![]() TypeError: only size-1 arrays can be converted to Python scalars I am trying to run logistic regression on my y and x, however I keep getting the error Setting an array element with a sequence. TypeError Traceback (most recent call last) Pomt_Y_train = pomt_Y_train.astype("int")Ĭlf = OneVsOneClassifier(SVC(C = 1, verbose=True)) Pomt_X_train, pomt_X_test, pomt_Y_train, pomt_Y_test = train_test_split(pomt_train_x, pomt_train_y, test_size= (count / pomt_train_x.shape), stratify=pomt_train_y) Pomt_train_x = pd.DataFrame(columns=)įeature_dict = from testing") # These are used to map the data to their appropriate column on each pass arr np.array( 1, 2, 3) arr0 np.array( 4, 5) print(arr) Output: ValueError: setting an array. Another reason why you might see the ValueError: setting array element with a sequence is if you try to replace a singular array element with an array. Model = om_pretrained('bert-base-uncased') Replacing a single element with an array won’t work. If you have a list of lists that you want to convert to a DataFrame, you can use the pd.DataFrame constructor to create a new DataFrame. Tokenizer = om_pretrained('bert-base-uncased',do_lower_case=True,max_length=1024) To fix the error, you can use the at or iat accessor methods to assign a scalar value to a single cell of the column, or you can use the pd.Series constructor to create a new column from a list or an array. Testing_data = pd.read_csv('/dev.tsv', sep='\t') You can find the data I'm using here: training_data = pd.read_csv('/train.tsv', sep='\t') Asking for help, clarification, or responding to other answers. How do I work around this? I'd really appreciate some help. Thanks for contributing an answer to Stack Overflow Please be sure to answer the question.Provide details and share your research But avoid. Now that I have added an additional value to that array, the dimensionality matched for all the 1D arrays and Xtrain was able to form a 2D array. However, I get the error "setting an array element with a sequence," presumably because I am trying to call fit on a dataframe of arrays rather than scalar values. The problem was that Xtrain did not have the same number of elements in all the arrays. Python Dictionaries Access Items Change Items Add Items Remove Items Loop Dictionaries Copy Dictionaries Nested Dictionaries Dictionary Methods Dictionary Exercise Python If.Else Python While Loops Python For Loops Python Functions Python Lambda Python Arrays Python Classes/Objects Python Inheritance Python Iterators Python Polymorphism Python Scope Python Modules Python Dates Python Math Python JSON Python RegEx Python PIP Python Try.I am trying to call scikit learn fit functions on dataframes where the elements of each column are numpy arrays. ![]()
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