Skip to main content

python ValueError 'input contains NaN'

This is the code cell from my program where I am facing an error. Dataset used for the program is twitter.csv.

x = np.array(df["tweet"])
y = np.array(df["labels"])

cv = CountVectorizer()
x = cv.fit_transform(x)
x_train, x_test, y_train, y_test = train_test_split(x,y, test_size= 0.25, random_state= 42)
clf = DecisionTreeClassifier()
clf.fit(x_train,y_train) 

Error occured is:

ValueError                                Traceback (most recent call last)
Cell In[52], line 8
      6 x_train, x_test, y_train, y_test = train_test_split(x,y, test_size= 0.25, random_state= 42)
      7 clf = DecisionTreeClassifier()
----> 8 clf.fit(x_train,y_train)

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\sklearn\tree\_classes.py:889, in DecisionTreeClassifier.fit(self, X, y, sample_weight, check_input)
    859 def fit(self, X, y, sample_weight=None, check_input=True):
    860     """Build a decision tree classifier from the training set (X, y).
    861 
    862     Parameters
   (...)
    886         Fitted estimator.
    887     """
--> 889     super().fit(
    890         X,
    891         y,
    892         sample_weight=sample_weight,
    893         check_input=check_input,
    894     )
    895     return self

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\sklearn\tree\_classes.py:186, in BaseDecisionTree.fit(self, X, y, sample_weight, check_input)
    184 check_X_params = dict(dtype=DTYPE, accept_sparse="csc")
...
--> 111         raise ValueError("Input contains NaN")
    113 # We need only consider float arrays, hence can early return for all else.
    114 if X.dtype.kind not in "fc":

ValueError: Input contains NaN

As it is shown as input contains NaN. And I tried some of the methods shown online like fill(0), but it did not work.

What changes do I need to do to clear this error?



source https://stackoverflow.com/questions/76301529/python-valueerror-input-contains-nan

Comments

Popular posts from this blog

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...

Sorting large arrays of big numeric stings

I was solving bigSorting() problem from hackerrank: Consider an array of numeric strings where each string is a positive number with anywhere from to digits. Sort the array's elements in non-decreasing, or ascending order of their integer values and return the sorted array. I know it works as follows: def bigSorting(unsorted): return sorted(unsorted, key=int) But I didnt guess this approach earlier. Initially I tried below: def bigSorting(unsorted): int_unsorted = [int(i) for i in unsorted] int_sorted = sorted(int_unsorted) return [str(i) for i in int_sorted] However, for some of the test cases, it was showing time limit exceeded. Why is it so? PS: I dont know exactly what those test cases were as hacker rank does not reveal all test cases. source https://stackoverflow.com/questions/73007397/sorting-large-arrays-of-big-numeric-stings

How to load Javascript with imported modules?

I am trying to import modules from tensorflowjs, and below is my code. test.html <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Document</title </head> <body> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script> <script type="module" src="./test.js"></script> </body> </html> test.js import * as tf from "./node_modules/@tensorflow/tfjs"; import {loadGraphModel} from "./node_modules/@tensorflow/tfjs-converter"; const MODEL_URL = './model.json'; const model = await loadGraphModel(MODEL_URL); const cat = document.getElementById('cat'); model.execute(tf.browser.fromPixels(cat)); Besides, I run the server using python -m http.server in my command prompt(Windows 10), and this is the error prompt in the console log of my browser: Failed to loa...