valueerror

问题 I am making a simple definition to return a list of colors based on some intervals. Unfortunately I keep getting the following error: ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). I have seen the following discussion on stackoverflow but only had failed attempts to make it work. Truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all() This is my code: y = 5 std = pd.Series([1,1,1,1]) values = pd

问题 I am trying to use a LinearRegression from sklearn and I am getting a 'Could not convert a string to float'. All columns of the dataframe are float and the output y is also float. I have looked at other posts and the suggestions are to convert to float which I have done. <class 'pandas.core.frame.DataFrame'> Int64Index: 789 entries, 158 to 684 Data columns (total 8 columns): f1 789 non-null float64 f2 789 non-null float64 f3 789 non-null float64 f4 789 non-null float64 f5 789 non-null float64

问题 i am a newbie I want to prevent the duplicate usernames every time people do registration. Here is my snipped code: def submit(self): username_info = username.get() username_password = password.get() #connect to db db = mysql.connector.connect(host = 'localhost', user = 'root', password = '', database = 'user') #create a cursor mycursor = db.cursor() #insert to db sql = ("INSERT INTO useraccess (user_type, password) VALUES (%s, %s)") query = (username_info, username_password) mycursor.execute

问题 i am a newbie I want to prevent the duplicate usernames every time people do registration. Here is my snipped code: def submit(self): username_info = username.get() username_password = password.get() #connect to db db = mysql.connector.connect(host = 'localhost', user = 'root', password = '', database = 'user') #create a cursor mycursor = db.cursor() #insert to db sql = ("INSERT INTO useraccess (user_type, password) VALUES (%s, %s)") query = (username_info, username_password) mycursor.execute

问题 i am a newbie I want to prevent the duplicate usernames every time people do registration. Here is my snipped code: def submit(self): username_info = username.get() username_password = password.get() #connect to db db = mysql.connector.connect(host = 'localhost', user = 'root', password = '', database = 'user') #create a cursor mycursor = db.cursor() #insert to db sql = ("INSERT INTO useraccess (user_type, password) VALUES (%s, %s)") query = (username_info, username_password) mycursor.execute

问题 I've gotten this error before where the cause was obvious, but I'm having trouble with this snippet below. #!/usr/bin/python ACL = 'group:troubleshooters:r,user:auto:rx,user:nrpe:r' for e in ACL.split(','): print 'e = "%s"' % e print 'type during split = %s' % type(e.split(':')) print 'value during split: %s' % e.split(':') print 'number of elements: %d' % len(e.split(':')) for (one, two, three) in e.split(':'): print 'one = "%s", two = "%s"' % (one, two) I've added those print statements for

问题 I'm trying to read my text file and extract 3 main parameters and put them in separate list and apply normalizing on lists of parameters which are (Temperature, Speed, Acceleration) after assigning Gaussian distribution function. For getting good result I split up positive and negative numbers of each parameters' list and apply gaussian distribution function and pick mean value of negative numbers as the real Minimum and pick mean value of positive numbers as the real Maximum instead of

问题 I'm trying to calculate the dot product between a scipy parse matrix and a numpy array. First I was using a numpy matrix, which you can see in the following code: def power_iteration(matrix, n): b_k = np.random.rand(matrix.shape[1]) for _ in range(n): b_k = np.dot(matrix, b_k) return b_k Here the matrix is a numpy matrix and no error occurs. If you pass a scipy sparse matrix as a parameter, the following error occurs: ValueError: shapes (6762,6762) and (1,6762) not aligned: 6762 (dim 1) != 1

问题 I'm trying to calculate the dot product between a scipy parse matrix and a numpy array. First I was using a numpy matrix, which you can see in the following code: def power_iteration(matrix, n): b_k = np.random.rand(matrix.shape[1]) for _ in range(n): b_k = np.dot(matrix, b_k) return b_k Here the matrix is a numpy matrix and no error occurs. If you pass a scipy sparse matrix as a parameter, the following error occurs: ValueError: shapes (6762,6762) and (1,6762) not aligned: 6762 (dim 1) != 1

问题 This code works fine... forest1 = RandomForestClassifier() forest1.fit(train[['Random Forest Score','lr','neural']],train['target']) But when I try to predict , test['target'] = forest1.predict_proba(test[['Random Forest Score','lr','neural']]) It shows an error.. ValueError: Wrong number of items passed 2, placement implies 1 回答1: forest1.predict_proba(...) predicts class probabilities for X . It returns an array of shape = [n_samples, n_classes] , or a list of n_outputs such arrays if n