kosiew opened a new pull request, #1019:
URL: https://github.com/apache/datafusion-python/pull/1019
# Which issue does this PR close?
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Closes #922.
# Rationale for this change
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Why are you proposing this change? If this is already explained clearly in
the issue then this section is not needed.
Explaining clearly why changes are proposed helps reviewers understand your
changes and offer better suggestions for fixes.
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DataFusion currently lacks built-in methods for handling missing values
(nulls and NaNs) in DataFrames. This functionality is commonly needed in data
processing workflows and is available in other DataFrame libraries like pandas
and PySpark.
The changes add:
- `fill_null()` method to replace NULL values with a specified value
- `fill_nan()` method to replace NaN values with a numeric value in
floating-point columns
# What changes are included in this PR?
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sometimes worth providing a summary of the individual changes in this PR.
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1. Added `fill_null()` method to DataFrame class:
- Replaces NULL values with a specified value
- Handles type casting validation
- Allows filling specific columns or entire DataFrame
- Preserves columns where casting fails
2. Added `fill_nan()` method to DataFrame class:
- Replaces NaN values with numeric values
- Only operates on floating-point columns
- Validates input types and column types
- Allows filling specific columns or all numeric columns
3. Added comprehensive test cases for both methods covering:
- Different data types
- Column subsetting
- Type validation
- Error cases
# Are there any user-facing changes?
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If there are user-facing changes then we may require documentation to be
updated before approving the PR.
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Yes, two new public methods are added to the DataFrame class:
```python
# Fill nulls with a value
df = df.fill_null(0) # Fill all nulls with 0 where possible
df = df.fill_null("missing", subset=["name"]) # Fill specific columns
# Fill NaN values in numeric columns
df = df.fill_nan(0) # Fill all NaNs with 0
df = df.fill_nan(99.9, subset=["price"]) # Fill specific columns
```
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