Beyond IQR: Alternative Techniques for Outlier Removal in NumPy

1. Calculate Quartiles:

2. Compute IQR:

3. Set Thresholds:

4. Filter Data:

Here's an example code demonstrating this approach:

import numpy as np

def iqr_outlier_removal(data):
  """
  This function removes outliers from a list using Interquartile Range (IQR).

  Args:
      data: A NumPy array containing the data.

  Returns:
      A NumPy array with outliers removed.
  """
  q1 = np.percentile(data, 25)
  q3 = np.percentile(data, 75)
  iqr = q3 - q1
  lower_bound = q1 - (1.5 * iqr)
  upper_bound = q3 + (1.5 * iqr)
  return data[(data >= lower_bound) & (data <= upper_bound)]

# Sample data with outliers
data = np.array([1, 2, 3, 4, 5, 100, 200])

# Remove outliers using IQR
filtered_data = iqr_outlier_removal(data)

print("Original data:", data)
print("Outliers removed:", filtered_data)

This code defines a function iqr_outlier_removal that takes a NumPy array data as input and returns a new array with outliers filtered out. The function calculates the IQR and thresholds, then uses boolean indexing to select only the in-range values.

Important points to consider:

• IQR-based outlier detection is a common method, but it might not be suitable for all scenarios. Depending on your data distribution, you might need to explore other outlier detection techniques.
• This approach assumes your data is numerical. You'll need different methods to handle categorical data.

import numpy as np

def iqr_outlier_removal(data):
  """
  This function removes outliers from a list using Interquartile Range (IQR).

  Args:
      data: A NumPy array containing the data.

  Returns:
      A NumPy array with outliers removed.
  """
  q1 = np.percentile(data, 25)
  q3 = np.percentile(data, 75)
  iqr = q3 - q1
  lower_bound = q1 - (1.5 * iqr)
  upper_bound = q3 + (1.5 * iqr)
  return data[(data >= lower_bound) & (data <= upper_bound)]

# Sample data with outliers
data = np.array([1, 2, 3, 4, 5, 100, 200])

# Remove outliers using IQR
filtered_data = iqr_outlier_removal(data)

print("Original data:", data)
print("Outliers removed:", filtered_data)

This code defines a function iqr_outlier_removal that:

1. Takes a NumPy array data as input.
2. Calculates the first quartile (Q1) and third quartile (Q3) using np.percentile.
3. Computes the IQR (Q3 - Q1).
4. Defines upper and lower bounds based on IQR with a factor of 1.5.
5. Uses boolean indexing with & (and) to create a mask selecting in-range values.
6. Returns a new array containing only the filtered data.

This method identifies data points that deviate significantly from the mean by a certain number of standard deviations.

import numpy as np

def sd_outlier_removal(data, threshold=3):
  """
  This function removes outliers from a list based on standard deviation.

  Args:
      data: A NumPy array containing the data.
      threshold: The number of standard deviations to consider as outliers (default 3).

  Returns:
      A NumPy array with outliers removed.
  """
  mean = np.mean(data)
  std = np.std(data)
  lower_bound = mean - threshold * std
  upper_bound = mean + threshold * std
  return data[(data >= lower_bound) & (data <= upper_bound)]

This function takes a threshold (default 3) as input, which determines how many standard deviations away from the mean a value is considered an outlier. It calculates the mean and standard deviation, then defines thresholds based on these values multiplied by the threshold. Finally, it uses boolean indexing to filter the data.

Z-score based:

Similar to the SD approach, this method uses Z-scores to identify outliers. Z-scores represent the number of standard deviations a specific point is away from the mean. Here, values with absolute Z-scores exceeding a certain threshold are considered outliers.

import numpy as np

def zscore_outlier_removal(data, threshold=3):
  """
  This function removes outliers from a list based on Z-scores.

  Args:
      data: A NumPy array containing the data.
      threshold: The absolute Z-score threshold for outliers (default 3).

  Returns:
      A NumPy array with outliers removed.
  """
  mean = np.mean(data)
  std = np.std(data)
  zscores = np.abs(stats.zscore(data))  # Assuming stats module is imported
  return data[zscores <= threshold]

This function utilizes the stats.zscore function (assuming the stats module is imported) to calculate absolute Z-scores. It then selects only the data points with Z-scores less than or equal to the defined threshold.

Choosing the right method:

• IQR is a good choice for data with potential outliers on both ends of the distribution.
• Standard deviation based methods work well for normally distributed data.
• Z-score is similar to SD-based methods but focuses on the absolute deviation from the mean.