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.