pandas.DataFrame.__add__#

DataFrame.__add__(other)[source] #

Get Addition of DataFrame and other, column-wise.

Equivalent to DataFrame.add(other).

Parameters:

otherscalar, sequence, Series, dict or DataFrame

Object to be added to the DataFrame.

Returns:

DataFrame

The result of adding other to DataFrame.

See also

DataFrame.add

Add a DataFrame and another object, with option for index- or column-oriented addition.

Examples

>>> df = pd.DataFrame({'height': [1.5, 2.6], 'weight': [500, 800]},
...  index=['elk', 'moose'])
>>> df
 height weight
elk 1.5 500
moose 2.6 800

Adding a scalar affects all rows and columns.

>>> df[['height', 'weight']] + 1.5
 height weight
elk 3.0 501.5
moose 4.1 801.5

Each element of a list is added to a column of the DataFrame, in order.

>>> df[['height', 'weight']] + [0.5, 1.5]
 height weight
elk 2.0 501.5
moose 3.1 801.5

Keys of a dictionary are aligned to the DataFrame, based on column names; each value in the dictionary is added to the corresponding column.

>>> df[['height', 'weight']] + {'height': 0.5, 'weight': 1.5}
 height weight
elk 2.0 501.5
moose 3.1 801.5

When other is a Series, the index of other is aligned with the columns of the DataFrame.

>>> s1 = pd.Series([0.5, 1.5], index=['weight', 'height'])
>>> df[['height', 'weight']] + s1
 height weight
elk 3.0 500.5
moose 4.1 800.5

Even when the index of other is the same as the index of the DataFrame, the Series will not be reoriented. If index-wise alignment is desired, DataFrame.add() should be used with axis=’index’.

>>> s2 = pd.Series([0.5, 1.5], index=['elk', 'moose'])
>>> df[['height', 'weight']] + s2
 elk height moose weight
elk NaN NaN NaN NaN
moose NaN NaN NaN NaN

>>> df[['height', 'weight']].add(s2, axis='index')
 height weight
elk 2.0 500.5
moose 4.1 801.5

When other is a DataFrame, both columns names and the index are aligned.

>>> other = pd.DataFrame({'height': [0.2, 0.4, 0.6]},
...  index=['elk', 'moose', 'deer'])
>>> df[['height', 'weight']] + other
 height weight
deer NaN NaN
elk 1.7 NaN
moose 3.0 NaN