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# Row-wise Operations
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Element-wise operations across columns within rows for complex transformations and calculations.
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## Capabilities
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### Row-wise Aggregation Functions
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```python { .api }
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def rowall(*cols):
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    """Row-wise all (logical AND across columns)"""
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def rowany(*cols):
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    """Row-wise any (logical OR across columns)"""
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def rowcount(*cols):
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    """Row-wise count of non-missing values"""
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def rowsum(*cols):
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    """Row-wise sum across columns"""
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def rowmean(*cols):
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    """Row-wise mean across columns"""
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def rowmin(*cols):
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    """Row-wise minimum across columns"""
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def rowmax(*cols):
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    """Row-wise maximum across columns"""
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def rowsd(*cols):
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    """Row-wise standard deviation across columns"""
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def rowfirst(*cols):
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    """Row-wise first non-missing value"""
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def rowlast(*cols):
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    """Row-wise last non-missing value"""
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def rowargmin(*cols):
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    """Row-wise index of minimum value"""
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def rowargmax(*cols):
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    """Row-wise index of maximum value"""
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```
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## Examples
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```python
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import datatable as dt
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DT = dt.Frame({
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    'A': [1, 2, None, 4],
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    'B': [2, None, 3, 5],
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    'C': [3, 4, 5, None]
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})
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# Row-wise operations
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result = DT[:, dt.update(
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    sum_abc=dt.rowsum(f.A, f.B, f.C),
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    mean_abc=dt.rowmean(f.A, f.B, f.C),
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    min_abc=dt.rowmin(f.A, f.B, f.C),
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    max_abc=dt.rowmax(f.A, f.B, f.C),
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    count_abc=dt.rowcount(f.A, f.B, f.C)
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)]
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```