Status
Not started
Element-wise math, matrix dot products, and value-wise comparison.
Arithmetic
df.add()ordf.__add__→ Add element-wise; supports fill_value.df.sub()ordf.__sub__→ Subtract element-wise.df.mul()ordf.__mul__→ Multiply element-wise.df.div()ordf.truediv()→ Divide element-wise (true division).df.floordiv()→ Floor division.df.mod()→ Modulo.df.pow()→ Exponentiate.df.dot()→ Matrix multiplication.df.radd(),df.rsub(),df.rmul(),df.rdiv(),df.rtruediv(),df.rfloordiv(),df.rmod(),df.rpow()→ Reverse operations.
Comparison
df.lt()→ Element-wise less than.df.gt()→ Greater than.df.le()→ Less than or equal.df.ge()→ Greater than or equal.df.eq()→ Equal to.df.ne()→ Not equal to.
Combine
df.combine(other, func)→ Combine two DataFrames element-wise using a function.df.combine_first(other)→ Fill missing values withother.
Math, Binary Operations & Comparison in Pandas DataFrames
⚠️ Vectorization Principle
Pandas operations are designed to work on entire arrays at once. These methods leverage NumPy's vectorized operations, making them dramatically faster than Python loops.
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Arithmetic Operations
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Comparison Operations
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Combine Operations
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Performance Optimization Strategies
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