Handle Null Values | Clean Incomplete Data Without Code
Missing values distort analysis, skew aggregations, and break logic in downstream workflows. Whether you're preparing a dataset for modeling or dashboarding, unresolved nulls compromise your outputs.
Edilitics solves this with a governed, no-code Null Values operation - offering flexible options to drop incomplete records or impute missing values based on data type. No scripts, no guesswork - just clean, reliable data in minutes.
Why Null Handling Matters
Datasets with unresolved nulls lead to:
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Invalid aggregations (e.g., sum of a column with
null
returnsnull
) -
Broken joins or filters due to missing key values
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Skewed statistical results when nulls bias averages
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Dashboard and export errors from incomplete rows
Edilitics gives you full control to:
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Impute missing values based on data type and context
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Drop incomplete rows with full column-level precision
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Apply operations across multiple columns at once
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Preview all changes before applying
How to Handle Nulls in Edilitics
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Select columns with missing values
Edilitics automatically highlights fields that contain nulls. You can select one or more to apply the operation.
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Choose a handling method
For each selected column, choose one of the following strategies:
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Drop – Exclude rows where this column is null
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Impute – Replace nulls using a defined rule
-
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Select imputation logic (if applicable)
Based on column type, choose from:
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For numerical fields:
Mean
,Median
,Min
,Max
,Constant
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For categorical fields:
Mode
or aCustom Constant
-
-
Submit the operation
Edilitics applies the transformation and shows the updated schema with imputation results.
Supported Null Handling Methods
Method | Description | Recommended For |
---|---|---|
Drop | Removes rows where selected column(s) are null | Rows with non-recoverable gaps |
Mean | Replaces nulls with the column’s average | Numerical fields with normal distribution |
Median | Uses the midpoint value to replace nulls | Numerical fields with outliers |
Mode | Fills with the most frequent value in the column | Categorical fields |
Min / Max | Replaces with minimum or maximum value | Numerical fields in performance metrics |
Constant | Fills nulls with a user-defined static value | Default fill-ins for reporting |
(No Interpolation) | Not supported in Edilitics at this time | - |
Best Practices for Null Handling
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Analyze null patterns first – Understand if missingness is random or systematic
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Choose type-appropriate methods – Don't use mean for categories or mode for decimals
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Validate post-imputation impact – Watch for statistical drift in aggregates
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Apply domain knowledge – Use constants or medians that make contextual sense
Common Use Cases for Null Handling
Industry | Scenario | Recommended Action |
---|---|---|
Retail | Missing values in Age or Location | Use Median for age, Mode for location |
Healthcare | Gaps in Vitals or Lab Results | Use Median or Min for physiological fields |
Finance | Missing Transaction Amount or Transaction Date | Use Mean or Constant for amount, drop or filter for invalid dates |
Manufacturing | Nulls in Quality Score for some batches | Impute using Mean or Median based on production run |
Education | Gaps in Exam Scores or Attendance | Use Mean for scores, Mode or Constant for categorical attendance status |
Manual Equivalent – SQL & Pandas Examples
SQL Example – Redshift (Impute and Drop)
-- Impute with meanSELECT COALESCE(age, AVG(age) OVER()) AS imputed_age FROM customers;-- Drop rows with nullsSELECT * FROM orders WHERE transaction_amount IS NOT NULL;
Pandas Example
# Imputedf['age'] = df['age'].fillna(df['age'].median())# Drop rowsdf = df.dropna(subset=['transaction_amount'])
In Edilitics, this takes a few dropdowns - no queries, no syntax, and fully governed.
Reliable, Schema-Aware, Risk-Free
Edilitics' Null Values operation is:
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Data-type aware – Imputation options vary by field type
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Safe – Null drops are column-scoped, not table-wide
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Previewable – Verify before applying
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Governed – All changes are tracked within transformation flows
Cleaning up nulls is a prerequisite for trustworthy analytics. With Edilitics, you can drop bad rows, intelligently fill gaps, and prepare datasets for reliable decision-making - without writing a single line of code.
Next: Structure Cleaned Data for Analysis
Once nulls are resolved, continue your transformation with:
Enterprise Support & Technical Assistance