Top 48 Expected 2D Array Got 1D Array Instead Trust The Answer

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Table of Contents

How do you convert a 1D array to a 2D array?

Use reshape() Function to Transform 1d Array to 2d Array

To modify the layout of a NumPy ndarray, we will be using the reshape() method.

Is 1D array faster than 2D?

Unless you are talking about static arrays, 1D is faster. Clearly the 2D case loses the cache locality and uses more memory. It also introduces an extra indirection (and thus an extra pointer to follow) but the first array has the overhead of calculating the indices so these even out more or less.

How do you represent a 2D array?

Two-dimensional array example in C

#include<stdio.h>
int main(){
int i=0,j=0;
int arr[4][3]={{1,2,3},{2,3,4},{3,4,5},{4,5,6}};
//traversing 2D array.
for(i=0;i<4;i++){
for(j=0;j<3;j++){
printf(“arr[%d] [%d] = %d \n”,i,j,arr[i][j]);

How do you treat 1D arrays as 2D?

Treating a 1D data structure as 2D grid

Get the value at x,y coordinates (in this example, 1,2 would give l )
Get any sub-grid using x,y,width,height ( 1,2,2,2 would give [l, m, q, r] )
Set the value at any x,y coordinate (etc.)

How do you convert a 1D array to a 2D array in Matlab?

Direct link to this answer

[i,j] = size(img);
x1 = mod(32 + 19* (1:i*i), i);
x1 = reshape(x1, 8, 8);
y1 = mod(16 + 13* (1:j*j), j);
y1 = reshape(y1, 8, 8);

Which is complicated between 1D array and 2D array?

The main difference between 1D and 2D array is that the 1D array represents multiple data items as a list while 2D array represents multiple data items as a table consisting of rows and columns. A variable is a memory location to store data of a specific type.

What is difference between 1D and 2D array?

A one-dimensional array stores a single list of various elements having a similar data type. A two-dimensional array stores an array of various arrays, or a list of various lists, or an array of various one-dimensional arrays. It represents multiple data items in the form of a list.

What is the difference between 1D and 2D?

The difference between 1D, 2D and 3D geophysical measurements is related to how you measure and process the data you collect. For 1D measurements data are only collected beneath a single point at the surface, for 2D a profile is measured and, for 3D, data from across a volume of ground is collected.

What is an 1D array?

Definition. A One-Dimensional Array is the simplest form of an Array in which the elements are stored linearly and can be accessed individually by specifying the index value of each element stored in the array.

What is 1D array in C?

A one-dimensional array is a structured collection of components (often called array elements) that can be accessed individually by specifying the position of a component with a single index value.

How 2D array is represented in memory?

Row-major order and column-major order are methods for storing multidimensional arrays in linear storage such as random access memory.

How do you convert a 1D array to a 2D array in Python?

Let’s use this to convert our 1D numpy array to 2D numpy array,

arr = np. array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
# Convert 1D array to a 2D numpy array of 2 rows and 3 columns.
arr_2d = np. reshape(arr, (2, 5))
print(arr_2d)

How do you create a dynamic 2D array in Python?

“python create dynamic 2d array” Code Answer

def build_matrix(rows, cols):
matrix = []
for r in range(0, rows):
matrix. append([0 for c in range(0, cols)])
return matrix.

How do you accept a 2D array in Python?

Elements in a 2D array can be inserted using the insert() function specifying the index/position of the element to be inserted.

How do you convert a 1D matrix to a 2D matrix?

Let’s use this to convert our 2D array or matrix to a 1D array,

# Create a 2D Numpy Array.
arr = np. array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
# convert 2D array to a 1D array of size 9.
flat_arr = np. reshape(arr, 9)
print(‘1D Numpy Array:’)
print(flat_arr)

How do you convert 1D to 2D list in Python?

Python – Convert 1D list to 2D list of variable length

Using append and index. In this approach we will create a for loop to loop through each element in the 2D list and use it as an index for the new list to be created. …
Example. …
Output. …
Using islice. …
Example. …
Output.

What is the difference between 1D and 2D array?

1D arrays are just one row of values, while 2D arrays contain a grid of values that has several rows/columns. 1D: 2D: An ArrayList is just like a 1D Array except it’s length is unbounded and you can add as many elements as you need.

PYTHON : Error in Python script \”Expected 2D array, got 1D array instead:\”?

Error in Python script “Expected 2D array, got 1D array instead:”? – Stack Overflow

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Saturday January 1 2022

[FIXED] How to fix “ValueError: Expected 2D array, got 1D array instead” in sklearn/python?

Issue

I there. I just started with the machine learning with a simple example to try and learn. So, I want to classify the files in my disk based on the file type by making use of a classifier. The code I have written is,

import sklearn import numpy as np #Importing a local data set from the desktop import pandas as pd mydata = pd.read_csv(‘file_format.csv’,skipinitialspace=True) print mydata x_train = mydata.script y_train = mydata.label #print x_train #print y_train x_test = mydata.script from sklearn import tree classi = tree.DecisionTreeClassifier() classi.fit(x_train, y_train) predictions = classi.predict(x_test) print predictions

And I am getting the error as,

script class div label 0 5 6 7 html 1 0 0 0 python 2 1 1 1 csv Traceback (most recent call last): File “newtest.py”, line 21, in classi.fit(x_train, y_train) File “/home/initiouser2/.local/lib/python2.7/site- packages/sklearn/tree/tree.py”, line 790, in fit X_idx_sorted=X_idx_sorted) File “/home/initiouser2/.local/lib/python2.7/site- packages/sklearn/tree/tree.py”, line 116, in fit X = check_array(X, dtype=DTYPE, accept_sparse=”csc”) File “/home/initiouser2/.local/lib/python2.7/site- packages/sklearn/utils/validation.py”, line 410, in check_array “if it contains a single sample.”.format(array)) ValueError: Expected 2D array, got 1D array instead: array=[ 5. 0. 1.]. Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

If anyone can help me with the code, it would be so helpful to me !!

Solution

When passing your input to the classifiers, pass 2D arrays (of shape (M, N) where N >= 1), not 1D arrays (which have shape (N,) ). The error message is pretty clear,

Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

from sklearn.model_selection import train_test_split # X.shape should be (N, M) where M >= 1 X = mydata[[‘script’]] # y.shape should be (N, 1) y = mydata[‘label’] # perform label encoding if “label” contains strings # y = pd.factorize(mydata[‘label’])[0].reshape(-1, 1) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42) … clf.fit(X_train, y_train) print(clf.score(X_test, y_test))

Some other helpful tips –

split your data into valid train and test portions. Do not use your training data to test – that leads to inaccurate estimations of your classifier’s strength I’d recommend factorizing your labels, so you’re dealing with integers. It’s just easier.

Answered By – cs95

Convert 1d Array to 2d Array Python

NumPy provides a wide range of effective and fast methods for declaring arrays and handling numerical information within them. Although several data types exist within a particular Python list, every member in a NumPy array will be homogeneous. If the arrays have not been homogenous, the arithmetic operations that are intended to be executed on them might be highly ineffective.

NumPy arrays are much more concise and efficient than Python lists. NumPy stores information in a substantially smaller amount of storage and also contains a method for defining the type of data. The NumPy library’s centralized data structure is an array. An array is a set of attributes that provides data about the original information, where and how to find items, as well as how to understand them. It also has a framework of components that will be organized by using different approaches.

The array data type relates to the fact that all of the items are from the identical type. The array’s form is a set of integers indicating the array’s dimensions for every element. In this article, we will explain numerous methodologies that are used to transform a one-dimensional array into a two-dimensional array.

Use reshape() Function to Transform 1d Array to 2d Array

Modifying the layout of an array is referred to as reshaping. The number of components within every dimension defines the form of the array. We may add or delete parameters or adjust the number of items within every dimension by using reshaping.

To modify the layout of a NumPy ndarray, we will be using the reshape() method. Any form transition is accessible, even switching from a one-dimensional into a two-dimensional array. The measurement of the dimension is immediately computed when we have to use -1.

import numpy as np

import matplotlib. pyplot as plt

x = np. arange ( 6 )

print ( x )

print ( x. reshape ( 2 , 3 ) )

print ( x. reshape ( – 1 , 3 ) )

print ( x. reshape ( 2 , – 1 ) )

When we are handling the numeric values, then we have to import the NumPy library as np in the code so that we can easily perform the numeric functions and also we manage the figures and graphs by using the matplotlib.pyplot library as plt. The ‘plt’ is one of the sub-library of the main ‘matplot’ library because we need some specific functions, not all libraries. The whole library takes more space than the sub-library, also the same case for NumPy as np.

After that, we get a variable and initialize this variable termed ‘x,’ and we assign a value by using a function np.arrange(). This function is from the ‘np’ library called arrange, and we pass a value as the parameters of the function. We employ this method to create the array based on numeric values. It constructs an illustration of ndarray with equally spaced elements and provides access to it. After that, we just print the array, and the result of this array is shown in the output.

Next, we are going to call the reshape() function to change the array. The reshape() function takes a single array that is also called a one-dimensional array and transforms it into a two-dimensional array with one column. The argument of this function is determined by the data shape, and the next is for the second dimension.

Use np.array() Function to Transform 1d Array to 2d Array

In Python language, the np.array() function can be utilized for this purpose. We may transform a list into a NumPy.ndarray, modify it by using the reshape() function and afterward restore this to a set with NumPy.

import numpy as np

import matplotlib. pyplot as plt

list = [ 2 , 4 , 6 , 8 , 10 , 12 ]
print ( np. array ( list ) . reshape ( – 1 , 3 ) . tolist ( ) )

print ( np. array ( list ) . reshape ( 3 , – 1 ) . tolist ( ) )

In the first two lines of our code, we have included the required libraries NumPy as np and matplotlib.pyplot as plt. Now we start the main code where we define the elements of the 1d array, and this list contains even numbers from two to twelve. Then we have utilized two functions np.array() and reshape() in two lines with different parameters.

In the first line, we pass -1 and 3 as a parameter to the function reshape(). It means that each array contains three elements. On the other hand, 3 and -1 are provided as an argument of the reshape() function, and this shows that there are three sets of elements.

Use List Comprehensions to Transfer 1d Array to 2d Array

We can transform the one-dimensional array to a two-dimensional array in Python instead of utilizing NumPy and applying list comprehensions.

import numpy as np

import matplotlib. pyplot as plt

def convert_1d_to_2d ( l , cols ) :

return [ list [ j:j + cols ] for j in range ( 0 , len ( list ) , cols ) ]
list = [ 10 , 20 , 30 , 40 , 50 , 60 ]
print ( convert_1d_to_2d ( list , 2 ) )

print ( convert_1d_to_2d ( list , 3 ) )

print ( convert_1d_to_2d ( list , 4 ) )

After importing the ‘NumPy’ and ‘matplotlib.pyplot’ libraries we define a function ‘convert_1d_to_2d()’. The purpose of using this function is to convert one- dimensional array to a two – dimensional array, and here we pass one row and one column. And we returned a list of where columns are arranged by calling the list() function. We enlist the elements by passing parameters in the len() function.

Then we initialized a list and printed it in three different ways by using a print statement. Firstly, we make three arrays with two elements. In the second, we make two arrays having three elements. However, in the last, the arrays have four and two elements.

The initial list is the first parameter, and the series of entries in the innermost list is the second parameter. When there is a remnant, like in the preceding example, an array including a distinctive set of items will be retained.

Conclusion

We looked at three distinct techniques for transforming the one-dimensional array into a two-dimensional array in Python in this article. NumPy array provides high computational formats that perform better than Python’s native array dataset for numerical calculations. When a one-dimensional array is molded into a two-dimensional array, it is divided into an array of arrays with the required set of numbers.

1D or 2D array, what’s faster?

tl;dr : You should probably use a one-dimensional approach.

Note: One cannot dig into detail affecting performance when comparing dynamic 1d or dynamic 2d storage patterns without filling books since the performance of code is dependent one a very large number of parameters. Profile if possible.

1. What’s faster?

For dense matrices the 1D approach is likely to be faster since it offers better memory locality and less allocation and deallocation overhead.

2. What’s smaller?

Dynamic-1D consumes less memory than the 2D approach. The latter also requires more allocations.

Remarks

I laid out a pretty long answer beneath with several reasons but I want to make some remarks on your assumptions first.

I can imagine, that recalculating indices for 1D arrays (y + x*n) could be slower than using 2D array (x, y)

Let’s compare these two functions:

int get_2d (int **p, int r, int c) { return p[r][c]; } int get_1d (int *p, int r, int c) { return p[c + C*r]; }

The (non-inlined) assembly generated by Visual Studio 2015 RC for those functions (with optimizations turned on) is:

?get_1d@@YAHPAHII@Z PROC push ebp mov ebp, esp mov eax, DWORD PTR _c$[ebp] lea eax, DWORD PTR [eax+edx*4] mov eax, DWORD PTR [ecx+eax*4] pop ebp ret 0 ?get_2d@@YAHPAPAHII@Z PROC push ebp mov ebp, esp mov ecx, DWORD PTR [ecx+edx*4] mov eax, DWORD PTR _c$[ebp] mov eax, DWORD PTR [ecx+eax*4] pop ebp ret 0

The difference is mov (2d) vs. lea (1d). The former has a latency of 3 cycles and a a maximum throughput of 2 per cycle while the latter has a latency of 2 cycles and a maximum throughput of 3 per cycle. (According to Instruction tables – Agner Fog Since the differences are minor, I think there should not be a big performance difference arising from index recalculation. I expect it to be very unlikely to identify this difference itself to be the bottleneck in any program.

This brings us to the next (and more interesting) point:

… but I could image that 1D could be in CPU cache …

True, but 2d could be in CPU cache, too. See The Downsides: Memory locality for an explanation why 1d is still better.

The long answer, or why dynamic 2 dimensional data storage (pointer-to-pointer or vector-of-vector) is “bad” for simple / small matrices.

Note: This is about dynamic arrays/allocation schemes [malloc/new/vector etc.]. A static 2d array is a contiguous block of memory and therefore not subject to the downsides I’m going to present here.

The Problem

To be able to understand why a dynamic array of dynamic arrays or a vector of vectors is most likely not the data storage pattern of choice, you are required to understand the memory layout of such structures.

Example case using pointer to pointer syntax

int main (void) { // allocate memory for 4×4 integers; quick & dirty int ** p = new int*[4]; for (size_t i=0; i<4; ++i) p[i] = new int[4]; // do some stuff here, using p[x][y] // deallocate memory for (size_t i=0; i<4; ++i) delete[] p[i]; delete[] p; } The downsides Memory locality For this “matrix” you allocate one block of four pointers and four blocks of four integers. All of the allocations are unrelated and can therefore result in an arbitrary memory position. The following image will give you an idea of how the memory may look like. For the real 2d case: The violet square is the memory position occupied by p itself. itself. The green squares assemble the memory region p points to (4 x int* ). points to (4 x ). The 4 regions of 4 contiguous blue squares are the ones pointed to by each int* of the green region For the 2d mapped on 1d case: The green square is the only required pointer int * The blue squares ensemble the memory region for all matrix elements (16 x int ). This means that (when using the left layout) you will probably observe worse performance than for a contiguous storage pattern (as seen on the right), due to caching for instance. Let's say a cache line is "the amount of data transfered into the cache at once" and let's imagine a program accessing the whole matrix one element after another. If you have a properly aligned 4 times 4 matrix of 32 bit values, a processor with a 64 byte cache line (typical value) is able to "one-shot" the data (4*4*4 = 64 bytes). If you start processing and the data isn't already in the cache you'll face a cache miss and the data will be fetched from main memory. This load can fetch the whole matrix at once since it fits into a cache line, if and only if it is contiguously stored (and properly aligned). There will probably not be any more misses while processing that data. In case of a dynamic, "real two-dimensional" system with unrelated locations of each row/column, the processor needs to load every memory location seperately. Eventhough only 64 bytes are required, loading 4 cache lines for 4 unrelated memory positions would -in a worst case scenario- actually transfer 256 bytes and waste 75% throughput bandwidth. If you process the data using the 2d-scheme you'll again (if not already cached) face a cache miss on the first element. But now, only the first row/colum will be in the cache after the first load from main memory because all other rows are located somewhere else in memory and not adjacent to the first one. As soon as you reach a new row/column there will again be a cache miss and the next load from main memory is performed. Long story short: The 2d pattern has a higher chance of cache misses with the 1d scheme offering better potential for performance due to locality of the data. Frequent Allocation / Deallocation As many as N + 1 (4 + 1 = 5) allocations (using either new, malloc, allocator::allocate or whatever) are necessary to create the desired NxM (4×4) matrix. (4 + 1 = 5) allocations (using either new, malloc, allocator::allocate or whatever) are necessary to create the desired NxM (4×4) matrix. The same number of proper, respective deallocation operations must be applied as well. Therefore, it is more costly to create/copy such matrices in contrast to a single allocation scheme. This is getting even worse with a growing number of rows. Memory consumption overhead I'll asumme a size of 32 bits for int and 32 bits for pointers. (Note: System dependency.) Let's remember: We want to store a 4×4 int matrix which means 64 bytes. For a NxM matrix, stored with the presented pointer-to-pointer scheme we consume N*M*sizeof(int) [the actual blue data] + [the actual blue data] + N*sizeof(int*) [the green pointers] + [the green pointers] + sizeof(int**) [the violet variable p] bytes. That makes 4*4*4 + 4*4 + 4 = 84 bytes in case of the present example and it gets even worse when using std::vector> . It will require N * M * sizeof(int) + N * sizeof(vector) + sizeof(vector>) bytes, that is 4*4*4 + 4*16 + 16 = 144 bytes in total, intead of 64 bytes for 4 x 4 int.

In addition -depending on the used allocator- each single allocation may well (and most likely will) have another 16 bytes of memory overhead. (Some “Infobytes” which store the number of allocated bytes for the purpose of proper deallocation.)

This means the worst case is:

N*(16+M*sizeof(int)) + 16+N*sizeof(int*) + sizeof(int**)

= 4*(16+4*4) + 16+4*4 + 4 = 164 bytes ! _Overhead: 156%_

The share of the overhead will reduce as the size of the matrix grows but will still be present.

Risk of memory leaks

The bunch of allocations requires an appropriate exception handling in order to avoid memory leaks if one of the allocations will fail! You’ll need to keep track of allocated memory blocks and you must not forget them when deallocating the memory.

If new runs of of memory and the next row cannot be allocated (especially likely when the matrix is very large), a std::bad_alloc is thrown by new .

Example:

In the above mentioned new/delete example, we’ll face some more code if we want to avoid leaks in case of bad_alloc exceptions.

// allocate memory for 4×4 integers; quick & dirty size_t const N = 4; // we don’t need try for this allocation // if it fails there is no leak int ** p = new int*[N]; size_t allocs(0U); try { // try block doing further allocations for (size_t i=0; i #include #include #include namespace matrices { template class simple { public: // misc types using data_type = std::vector; using value_type = typename std::vector::value_type; using size_type = typename std::vector::size_type; // ref using reference = typename std::vector::reference; using const_reference = typename std::vector::const_reference; // iter using iterator = typename std::vector::iterator; using const_iterator = typename std::vector::const_iterator; // reverse iter using reverse_iterator = typename std::vector::reverse_iterator; using const_reverse_iterator = typename std::vector::const_reverse_iterator; // empty construction simple() = default; // default-insert rows*cols values simple(size_type rows, size_type cols) : m_rows(rows), m_cols(cols), m_data(rows*cols) {} // copy initialized matrix rows*cols simple(size_type rows, size_type cols, const_reference val) : m_rows(rows), m_cols(cols), m_data(rows*cols, val) {} // 1d-iterators iterator begin() { return m_data.begin(); } iterator end() { return m_data.end(); } const_iterator begin() const { return m_data.begin(); } const_iterator end() const { return m_data.end(); } const_iterator cbegin() const { return m_data.cbegin(); } const_iterator cend() const { return m_data.cend(); } reverse_iterator rbegin() { return m_data.rbegin(); } reverse_iterator rend() { return m_data.rend(); } const_reverse_iterator rbegin() const { return m_data.rbegin(); } const_reverse_iterator rend() const { return m_data.rend(); } const_reverse_iterator crbegin() const { return m_data.crbegin(); } const_reverse_iterator crend() const { return m_data.crend(); } // element access (row major indexation) reference operator() (size_type const row, size_type const column) { return m_data[m_cols*row + column]; } const_reference operator() (size_type const row, size_type const column) const { return m_data[m_cols*row + column]; } reference at() (size_type const row, size_type const column) { return m_data.at(m_cols*row + column); } const_reference at() (size_type const row, size_type const column) const { return m_data.at(m_cols*row + column); } // resizing void resize(size_type new_rows, size_type new_cols) { // new matrix new_rows times new_cols simple tmp(new_rows, new_cols); // select smaller row and col size auto mc = std::min(m_cols, new_cols); auto mr = std::min(m_rows, new_rows); for (size_type i(0U); i < mr; ++i) { // iterators to begin of rows auto row = begin() + i*m_cols; auto tmp_row = tmp.begin() + i*new_cols; // move mc elements to tmp std::move(row, row + mc, tmp_row); } // move assignment to this *this = std::move(tmp); } // size and capacity size_type size() const { return m_data.size(); } size_type max_size() const { return m_data.max_size(); } bool empty() const { return m_data.empty(); } // dimensionality size_type rows() const { return m_rows; } size_type cols() const { return m_cols; } // data swapping void swap(simple &rhs) { using std::swap; m_data.swap(rhs.m_data); swap(m_rows, rhs.m_rows); swap(m_cols, rhs.m_cols); } private: // content size_type m_rows{ 0u }; size_type m_cols{ 0u }; data_type m_data{}; }; template void swap(simple & lhs, simple & rhs) { lhs.swap(rhs); } template bool operator== (simple const &a, simple const &b) { if (a.rows() != b.rows() || a.cols() != b.cols()) { return false; } return std::equal(a.begin(), a.end(), b.begin(), b.end()); } template bool operator!= (simple const &a, simple const &b) { return !(a == b); } }

Note several things here:

T needs to fulfill the requirements of the used std::vector member functions

needs to fulfill the requirements of the used member functions operator() doesn’t do any “of of range” checks

doesn’t do any “of of range” checks No need to manage data on your own

No destructor, copy constructor or assignment operators required

So you don’t have to bother about proper memory handling for each application but only once for the class you write.

Restrictions

There may be cases where a dynamic “real” two dimensional structure is favourable. This is for instance the case if

So you have finished reading the expected 2d array got 1d array instead topic article, if you find this article useful, please share it. Thank you very much. See more: Expected 2d array got 1d array instead linear regression, Y should be a 1d array, got an array of shape instead, Expected 1d or 2D array, got 0D array instead, Expected 2D array got 1D array instead OneHotEncoder, Convert 1D array to 2D Python, Expected 2D array, got scalar array instead, Reshape your data either using array reshape 1, 1, Unknown label type: ‘continuous

Top 48 Expected 2D Array Got 1D Array Instead Trust The Answer

How do you convert a 1D array to a 2D array?

Is 1D array faster than 2D?

How do you represent a 2D array?

How do you treat 1D arrays as 2D?

How do you convert a 1D array to a 2D array in Matlab?

Which is complicated between 1D array and 2D array?

What is difference between 1D and 2D array?

What is the difference between 1D and 2D?

What is an 1D array?

What is 1D array in C?

How 2D array is represented in memory?

How do you convert a 1D array to a 2D array in Python?

How do you create a dynamic 2D array in Python?

How do you accept a 2D array in Python?

How do you convert a 1D matrix to a 2D matrix?

How do you convert 1D to 2D list in Python?

What is the difference between 1D and 2D array?

Error in Python script “Expected 2D array, got 1D array instead:”? – Stack Overflow

How to solve ValueError: Expected 2D array, got 1D array instead error in Python – Quora

[FIXED] How to fix “ValueError: Expected 2D array, got 1D array instead” in sklearn/python? ~ PythonFixing

Convert 1d Array to 2d Array Python

c++ – 1D or 2D array, what’s faster? – Stack Overflow

Two Dimensional Array in C – javatpoint

Two Dimensional Arrays (2D Arrays) With Examples From Python | Prepare For Coding Interview – YouTube

valueerror expected 2d array got 1d array instead sklearn for MinMaxScaler | Data Science and Machine Learning | Kaggle

Error in Python script Expected 2D array got 1D array instead | Edureka Community

Valueerror: expected 2d array, got 1d array instead:- how can i circumvent the error? – ITtutoria

Error in Python script Expected 2D array, got 1D array instead:? – Tech Notes Help

How To Fix Valueerror Expected 2d Array Got 1d Array Instead In Sklearn Pyth

Use sklearn to report ValueError: Expected 2D array, got 1D array instead – actorsfit

Error in Python script “Expected 2D array, got …anycodings

[FIXED] How to fix “ValueError: Expected 2D array, got 1D array instead” in sklearn/python?

Convert 1d Array to 2d Array Python

1D or 2D array, what’s faster?

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