A null column in a 2D array means that *all the elements in that column have null or missing values*. Checking for null columns is important for identifying incomplete or inconsistent data in datasets. Overall, using NumPy to check for null columns in a 2D array is an efficient and convenient way to perform data cleaning and preprocessing operations. Following are the few functions that can be used to check null columns:

#### 1. Using “np.any()” and “np.all()” functions:

Here, we first import the NumPy library and create a 2D array. Then, we check if all elements in each column of the array are equal to zero using `np.all(arr == 0, axis=0)`

. This returns a boolean array indicating which columns have all zero elements. Then, we use `np.any()`

to check if any of the columns have all zero elements. If there are any null columns, “has_null_columns” will be `True`

; otherwise, it will be `False`

.

#### 2. Using “np.count_nonzero()” function:

Here, we first import the NumPy library and create a 2D array. Then, we use `np.count_nonzero(arr, axis=0)`

to count the number of nonzero elements in each column of the array. This returns a one-dimensional array with the number of nonzero elements in each column. Then, we check if any of the columns have nonzero elements using `np.any(np.count_nonzero(arr, axis=0) == 0)`

. If there are any null columns, “has_null_columns” will be `True`

; otherwise, it will be `False`

.

#### 3. Using “np.sum()” function:

Here, we first import the NumPy library and create a 2D array. Then, we use `np.sum(arr, axis=0)`

to sum the elements in each column of the array. This returns a one-dimensional array with the sum of elements in each column. Then, we check if any of the columns have a sum of zero using `np.any(np.sum(arr, axis=0) == 0)`

. If there are any null columns, “has_null_columns” will be `True`

; otherwise, it will be `False`

.