Handling Identifiers in EDA

how to Identify and remove columns that are likely to be identifiers or have very limited predictive power while performing Exploratory Data Analysis

Identifying and removing columns with limited predictive power or those acting as identifiers is a crucial step in Exploratory Data Analysis (EDA) and feature engineering. These columns can introduce noise, increase dimensionality, and hinder model performance. Here’s a breakdown of common techniques:

1. Identifying Potential Identifier Columns:

Identifier columns are unique for each record and provide little to no predictive information. Examples include IDs, unique codes, or sequential numbers.

High Cardinality Check: Columns with a very high number of unique values compared to the total number of rows are strong candidates for identifiers.
Python

def identify_high_cardinality(df, threshold=0.9):
  """Identifies columns with cardinality exceeding a threshold."""
  high_cardinality_cols = [col for col in df.columns if df[col].nunique() / len(df) > threshold]
  return high_cardinality_cols

high_cardinality_columns = identify_high_cardinality(df)
print(f"High cardinality columns: {high_cardinality_columns}")

Adjust the threshold based on your dataset size and domain knowledge. A threshold close to 1 indicates that almost every value in the column is unique.

Unique Value Count: Directly check the number of unique values. If it equals the number of rows (or is very close), it’s likely an identifier.
Python

def identify_unique_columns(df):
  """Identifies columns where the number of unique values equals the number of rows."""
  unique_cols = [col for col in df.columns if df[col].nunique() == len(df)]
  return unique_cols

unique_columns = identify_unique_columns(df)
print(f"Unique columns: {unique_columns}")

Domain Knowledge: Your understanding of the data is invaluable. You might know beforehand which columns are meant to be unique identifiers.

2. Identifying Columns with Very Limited Predictive Power:

These columns might have low variance, be dominated by a single value, or show little to no correlation with the target variable (if you have one defined for your analysis).

Low Variance Columns: Columns where the values are almost constant provide little discriminatory information.
Python

def identify_low_variance_columns(df, threshold=0.01):
  """Identifies columns with variance below a threshold."""
  low_variance_cols = [col for col in df.columns if df[col].var() < threshold]
  return low_variance_cols

# For boolean or categorical columns, you can check the proportion of the dominant category
def identify_dominant_category_columns(df, threshold=0.95):
  """Identifies categorical columns where one category dominates."""
  dominant_category_cols = []
  for col in df.select_dtypes(include=['object', 'category', 'bool']):
    value_counts = df[col].value_counts(normalize=True)
    if value_counts.iloc[0] > threshold:
      dominant_category_cols.append(col)
  return dominant_category_cols

low_variance_columns = identify_low_variance_columns(df.select_dtypes(include=np.number)) # Apply to numerical columns
dominant_category_columns = identify_dominant_category_columns(df)
print(f"Low variance columns (numerical): {low_variance_columns}")
print(f"Dominant category columns: {dominant_category_columns}")

Adjust the threshold for variance and dominant category proportion as needed.

Columns with Many Missing Values: While not strictly low predictive power, columns with a very high percentage of missing values might be difficult to impute reliably and could introduce bias. You might consider removing them if the information they could provide is outweighed by the amount of missing data.
Python

def identify_high_missing_value_columns(df, threshold=0.8):
  """Identifies columns with a high percentage of missing values."""
  high_missing_cols = [col for col in df.columns if df[col].isnull().sum() / len(df) > threshold]
  return high_missing_cols

high_missing_columns = identify_high_missing_value_columns(df)
print(f"Columns with high missing values: {high_missing_columns}")

Low Correlation with the Target Variable (for supervised tasks): If you have a defined target variable, examine the correlation (for numerical targets) or other association metrics (for categorical targets) between each feature and the target. Features with very low or no correlation might not be helpful for prediction.
Python

# For numerical target
if 'target_column' in df.columns and df['target_column'].dtype in ['int64', 'float64']:
  correlations = df.corr()['target_column'].abs().sort_values()
  low_correlation_columns = correlations[correlations < 0.05].index.tolist() # Example threshold
  print(f"Columns with low correlation to target: {low_correlation_columns}")

# For categorical target, you can use techniques like Chi-squared test or ANOVA (after encoding)
# This is more involved and depends on the nature of your categorical features.

Visually Inspecting Distributions: Plotting the distribution of each feature (histograms for numerical, bar plots for categorical) can reveal columns with very skewed distributions or those dominated by a single category.

3. Removing Identified Columns:

Once you've identified the columns you want to remove, you can use the drop() method in Pandas:

Python

columns_to_drop = high_cardinality_columns + unique_columns + low_variance_columns + dominant_category_columns + high_missing_columns # Combine lists

df_cleaned = df.drop(columns=columns_to_drop, axis=1, errors='ignore')
print(f"Shape of original DataFrame: {df.shape}")
print(f"Shape of cleaned DataFrame: {df_cleaned.shape}")

The errors='ignore' argument is useful to prevent errors if some of the columns in columns_to_drop don't actually exist in the DataFrame.

Important Considerations and Best Practices:

• Domain Knowledge is Key: Always combine automated checks with your understanding of the data. A high cardinality column might be important in certain contexts (e.g., zip codes for location-based analysis).
• Iterative Process: EDA is often iterative. You might identify and remove some columns, then perform further analysis that reveals other candidates for removal.
• Impact on Other Features: Removing a column might indirectly affect the predictive power of other features. Consider the relationships between variables.
• Feature Engineering First: Sometimes, seemingly uninformative columns can be transformed or combined to create valuable features. Consider feature engineering before outright removal.
• Test Set Considerations: If you are preparing data for modeling, ensure that any columns removed from the training set are also removed from the test set to maintain consistency.
• Document Your Steps: Keep track of the columns you remove and the reasons why. This is crucial for reproducibility and understanding your data cleaning process.

By systematically applying these techniques and using your domain expertise, you can effectively identify and remove columns that are likely to be identifiers or have limited predictive power, leading to a cleaner and more effective dataset for further analysis and modeling.