dtoolkit.accessor.series.equal#

dtoolkit.accessor.series.equal(s: Series, /, other, align: bool = True, **kwargs) → Series[source]#

Return a boolean Series containing the result of comparing with other.

A sugar syntax for np.equal(s, other, **kwargs).

Parameters:

otherscalar or 1d array-like

The value(s) to compare with the Series.

scalar : compare each element with the scalar value.
1d array-like : compare each element with the corresponding value.

alignbool, default True

If True, align s and other before comparing. Only works while other is a Series.

**kwargs

Additional keyword arguments are passed to numpy.equal().

Returns:

Series(bool)

Raises:

ValueError: If the lengths of s and other are not equal.

Warns:

UserWarning: If align is True and other is a Series, but its indexes are not equal to s.

See also

pandas.Series.eq: Compare two Series.
numpy.equal: Return (x1 == x2) element-wise.
dtoolkit.accessor.dataframe.equal: DataFrame version of .equal.

Examples

>>> import dtoolkit
>>> import pandas as pd
>>> s = pd.Series([1, 2, 3])
>>> s
0    1
1    2
2    3
dtype: int64

Like Series.eq method to compare scalar or array-like via ==, Series.equal could also do that.

Compare with scalar.

>>> s == 2
0    False
1     True
2    False
dtype: bool
>>> s.equal(2)
0    False
1     True
2    False
dtype: bool

Compare with array-like.

>>> s.equal(pd.Series([3, 2, 1]))
0    False
1     True
2    False
dtype: bool
>>> s.equal(pd.Series([3, 2, 1]))
0    False
1     True
2    False
dtype: bool

s.equal can also compare with array-like based on index.

>>> s == pd.Series([3, 2, 1], index=[2, 1, 0])  
ValueError: Can only compare identically-labeled Series objects
>>> s.equal(pd.Series([3, 2, 1], index=[2, 1, 0]))
0    True
1    True
2    True
dtype: bool
>>> s.equal(pd.Series([3, 2, 1], index=[2, 1, 0]), align=False)
0    False
1     True
2    False
dtype: bool