How to Remove rows in Numpy array that contains non-numeric values?

Last Updated : 05 Sep, 2024

Many times NumPy arrays may contain NaN values that need to be removed to ensure the array is free from unnecessary or invalid data. This can be achieved using the np.isnan() function along with the Bitwise NOT operator. Note that this approach specifically targets NaN values and may not handle other non-numeric characters like strings.

Example 1:

Python

# Importing Numpy module
import numpy as np

# Creating a 2x3 2-D NumPy array
n_arr = np.array([[10.5, 22.5, 3.8],
                  [41, np.nan, np.nan]])

print("Given array:")
print(n_arr)

print("\nRemove all rows containing NaN values:")
cleaned_arr = n_arr[~np.isnan(n_arr).any(axis=1)]
print(cleaned_arr)

Output

Given array:
[[10.5 22.5  3.8]
 [41.   nan  nan]]

Remove all rows containing NaN values:
[[10.5 22.5  3.8]]

In this example, the row containing NaN values is removed from the 2x3 NumPy array.

Example 2:

Python

# Importing Numpy module 
import numpy as np

# Creating a 3x3 2-D NumPy array
n_arr = np.array([[10.5, 22.5, 3.8], 
                  [23.45, 50, 78.7],
                  [41, np.nan, np.nan]])

print("Given array:")
print(n_arr)

print("\nRemove all rows containing NaN values:")
cleaned_arr = n_arr[~np.isnan(n_arr).any(axis=1)]
print(cleaned_arr)

Output

Given array:
[[10.5  22.5   3.8 ]
 [23.45 50.   78.7 ]
 [41.     nan   nan]]

Remove all rows containing NaN values:
[[10.5  22.5   3.8 ]
 [23.45 50.   78.7 ]]

Here, the row with NaN values is removed from the 3x3 NumPy array.

Example 3:

Python

# Importing Numpy module
import numpy as np

# Creating a 5x4 2-D NumPy array
n_arr = np.array([[10.5, 22.5, 3.8, 5],
                  [23.45, 50, 78.7, 3.5],
                  [41, np.nan, np.nan, 0],
                  [20, 50.20, np.nan, 2.5],
                  [18.8, 50.60, 8.8, 58.6]])

print("Given array:")
print(n_arr)

print("\nRemove all rows containing NaN values:")
cleaned_arr = n_arr[~np.isnan(n_arr).any(axis=1)]
print(cleaned_arr)

Output

Given array:
[[10.5  22.5   3.8   5.  ]
 [23.45 50.   78.7   3.5 ]
 [41.     nan   nan  0.  ]
 [20.   50.2    nan  2.5 ]
 [18.8  50.6   8.8  58.6 ]]

Remove all rows containing NaN values:
[[10.5  22....

In this example, rows with NaN values are removed from the 5x4 NumPy array.

Note: This approach is effective for removing rows with NaN values but will not work for other non-numeric characters such as strings. To handle other types of non-numeric data, additional preprocessing might be required.