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Methods that can be shared by many array-like classes or subclasses:
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Get Addition of DataFrame and other, column-wise.
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Equivalent to ``DataFrame.add(other)``.
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Parameters
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----------
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other : scalar, sequence, Series, dict or DataFrame
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Object to be added to the DataFrame.
|
|
Returns
|
-------
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DataFrame
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The result of adding ``other`` to DataFrame.
|
|
See Also
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--------
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DataFrame.add : Add a DataFrame and another object, with option for index-
|
or column-oriented addition.
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Examples
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--------
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>>> df = pd.DataFrame({'height': [1.5, 2.6], 'weight': [500, 800]},
|
... index=['elk', 'moose'])
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>>> df
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height weight
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elk 1.5 500
|
moose 2.6 800
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Adding a scalar affects all rows and columns.
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>>> df[['height', 'weight']] + 1.5
|
height weight
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elk 3.0 501.5
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moose 4.1 801.5
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Each element of a list is added to a column of the DataFrame, in order.
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>>> df[['height', 'weight']] + [0.5, 1.5]
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height weight
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elk 2.0 501.5
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moose 3.1 801.5
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Keys of a dictionary are aligned to the DataFrame, based on column names;
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each value in the dictionary is added to the corresponding column.
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>>> df[['height', 'weight']] + {'height': 0.5, 'weight': 1.5}
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height weight
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elk 2.0 501.5
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moose 3.1 801.5
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|
When `other` is a :class:`Series`, the index of `other` is aligned with the
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columns of the DataFrame.
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>>> s1 = pd.Series([0.5, 1.5], index=['weight', 'height'])
|
>>> df[['height', 'weight']] + s1
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height weight
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elk 3.0 500.5
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moose 4.1 800.5
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Even when the index of `other` is the same as the index of the DataFrame,
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the :class:`Series` will not be reoriented. If index-wise alignment is desired,
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:meth:`DataFrame.add` should be used with `axis='index'`.
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>>> s2 = pd.Series([0.5, 1.5], index=['elk', 'moose'])
|
>>> df[['height', 'weight']] + s2
|
elk height moose weight
|
elk NaN NaN NaN NaN
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moose NaN NaN NaN NaN
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|
>>> df[['height', 'weight']].add(s2, axis='index')
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height weight
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elk 2.0 500.5
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moose 4.1 801.5
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|
When `other` is a :class:`DataFrame`, both columns names and the
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index are aligned.
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|
>>> other = pd.DataFrame({'height': [0.2, 0.4, 0.6]},
|
... index=['elk', 'moose', 'deer'])
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>>> df[['height', 'weight']] + other
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height weight
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deer NaN NaN
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elk 1.7 NaN
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moose 3.0 NaN
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numpy.org/doc/stable/reference/arrays.classes.html#numpy.class.__array_ufunc__
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