How to remove all rows in a numpy.ndarray that contain non-numeric values

How to remove all rows in a numpy.ndarray that contain non-numeric values

To remove all rows in a NumPy ndarray that contain non-numeric values, you can use boolean indexing with the numpy.isnan() function to identify rows with non-numeric values. Here's a step-by-step guide:

  • Import the NumPy library:
import numpy as np
  • Create a NumPy ndarray with your data. Make sure that the data type is numeric (e.g., float or int).
data = np.array([[1.0, 2.0, 3.0], [4.0, np.nan, 6.0], [7.0, 8.0, 9.0], [np.nan, np.nan, np.nan]])
  • Use boolean indexing to filter out rows with non-numeric values (e.g., NaN):
numeric_rows = data[~np.isnan(data).any(axis=1)]

Here, np.isnan(data) creates a Boolean mask where True indicates the positions of NaN values, and False indicates numeric values. any(axis=1) checks each row and returns True if any of the columns in that row contain NaN. ~ negates the Boolean mask, so ~np.isnan(data).any(axis=1) gives you True for rows without any NaN values.

  • numeric_rows now contains only the rows that have numeric values. You can print or use this filtered ndarray as needed:
print(numeric_rows)

Here's the complete code:

import numpy as np data = np.array([[1.0, 2.0, 3.0], [4.0, np.nan, 6.0], [7.0, 8.0, 9.0], [np.nan, np.nan, np.nan]]) numeric_rows = data[~np.isnan(data).any(axis=1)] print(numeric_rows)

The output will be:

[[1. 2. 3.] [7. 8. 9.]]

Now, numeric_rows contains only the rows with numeric values, and rows with non-numeric values (NaN) have been removed.

Examples

  1. How to remove rows with non-numeric values from a numpy.ndarray in Python:

    • Description: This code snippet demonstrates how to remove rows from a numpy.ndarray that contain non-numeric values. It utilizes boolean indexing to filter out rows based on whether they contain non-numeric values.
    import numpy as np # Sample numpy array arr = np.array([[1, 2, 3], [4, 'a', 6], ['b', 8, 9]]) # Remove rows with non-numeric values arr = arr[np.all(np.char.isnumeric(arr.astype(str)), axis=1)]

  2. Python code to delete rows with non-numeric values from a numpy.ndarray using np.isnan():

    • Description: This code example illustrates how to delete rows with non-numeric values from a numpy array using np.isnan() function. It converts the array to a float type first and then checks for NaN (Not a Number) values.
    import numpy as np # Sample numpy array arr = np.array([[1, 2, 3], [4, 'a', 6], ['b', 8, 9]]) # Delete rows with non-numeric values arr = arr[~np.isnan(arr.astype(np.float)).any(axis=1)]

  3. How to remove rows containing non-numeric values from a numpy.ndarray using np.where():

    • Description: This code snippet demonstrates removing rows from a numpy array that contain non-numeric values using np.where(). It finds the indices of rows with non-numeric values and then removes them.
    import numpy as np # Sample numpy array arr = np.array([[1, 2, 3], [4, 'a', 6], ['b', 8, 9]]) # Remove rows with non-numeric values mask = np.any(np.char.isdigit(arr.astype(str)), axis=1) arr = arr[np.where(mask)]

  4. Python code to exclude rows with non-numeric values from a numpy.ndarray using np.logical_and():

    • Description: This code example showcases excluding rows with non-numeric values from a numpy array using np.logical_and(). It creates a mask to identify rows with non-numeric values and then uses logical AND operation to filter out those rows.
    import numpy as np # Sample numpy array arr = np.array([[1, 2, 3], [4, 'a', 6], ['b', 8, 9]]) # Exclude rows with non-numeric values mask = np.logical_and.reduce(np.char.isnumeric(arr.astype(str)), axis=1) arr = arr[mask]

  5. How to eliminate rows with non-numeric values from a numpy.ndarray using np.vectorize():

    • Description: This code snippet demonstrates eliminating rows with non-numeric values from a numpy array using np.vectorize(). It applies a vectorized function to each element of the array to check for numeric values and then filters out rows accordingly.
    import numpy as np # Sample numpy array arr = np.array([[1, 2, 3], [4, 'a', 6], ['b', 8, 9]]) # Eliminate rows with non-numeric values is_numeric = np.vectorize(lambda x: str(x).isdigit()) mask = np.all(is_numeric(arr), axis=1) arr = arr[mask]

  6. Python code to filter out rows with non-numeric values from a numpy.ndarray using np.where() and np.isnan():

    • Description: This code example demonstrates filtering out rows with non-numeric values from a numpy array using a combination of np.where() and np.isnan(). It converts the array to float type, checks for NaN values, and then removes rows containing NaN.
    import numpy as np # Sample numpy array arr = np.array([[1, 2, 3], [4, 'a', 6], ['b', 8, 9]]) # Filter out rows with non-numeric values arr = arr[np.where(~np.isnan(arr.astype(np.float)).any(axis=1))]

  7. How to delete rows with non-numeric values from a numpy.ndarray using list comprehension:

    • Description: This code snippet demonstrates deleting rows with non-numeric values from a numpy array using list comprehension. It iterates through the rows, checks for numeric values, and constructs a new array excluding rows with non-numeric values.
    import numpy as np # Sample numpy array arr = np.array([[1, 2, 3], [4, 'a', 6], ['b', 8, 9]]) # Delete rows with non-numeric values arr = np.array([row for row in arr if all(isinstance(val, (int, float)) for val in row)])

  8. Python code to remove rows with non-numeric values from a numpy.ndarray using np.char.isnumeric():

    • Description: This code example showcases removing rows with non-numeric values from a numpy array using np.char.isnumeric(). It checks each element of the array for numeric characters and then filters out rows based on the result.
    import numpy as np # Sample numpy array arr = np.array([[1, 2, 3], [4, 'a', 6], ['b', 8, 9]]) # Remove rows with non-numeric values arr = arr[np.all(np.char.isnumeric(arr.astype(str)), axis=1)]

  9. How to eliminate rows containing non-numeric values from a numpy.ndarray using np.logical_not():

    • Description: This code snippet demonstrates eliminating rows containing non-numeric values from a numpy array using np.logical_not(). It first checks for non-numeric values using np.char.isdigit() and then negates the result to obtain a mask for numeric values.
    import numpy as np # Sample numpy array arr = np.array([[1, 2, 3], [4, 'a', 6], ['b', 8, 9]]) # Eliminate rows with non-numeric values mask = np.logical_not(np.any(np.logical_not(np.char.isdigit(arr.astype(str))), axis=1)) arr = arr[mask]

  10. Python code to filter out rows with non-numeric values from a numpy.ndarray using np.char.isnumeric() and np.all():

    • Description: This code example illustrates filtering out rows with non-numeric values from a numpy array using np.char.isnumeric() and np.all(). It checks each element of the array for numeric characters and then filters out rows where all elements are numeric.
    import numpy as np # Sample numpy array arr = np.array([[1, 2, 3], [4, 'a', 6], ['b', 8, 9]]) # Filter out rows with non-numeric values mask = np.all(np.char.isnumeric(arr.astype(str)), axis=1) arr = arr[mask]

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