mutate, transmute

mutate(__data, *args, **kwargs)

Assign new variables to a DataFrame, while keeping existing ones.

Parameters:

Name	Type	Description	Default
__data	pd.DataFrame		required
**kwargs		new_col_name=value pairs, where value can be a function taking a singledispatch2 argument for the data being operated on.	{}

Examples:

>>> from siuba import _, mutate, head
>>> from siuba.data import cars
>>> cars >> mutate(cyl2 = _.cyl * 2, cyl4 = _.cyl2 * 2) >> head(2)
   cyl   mpg   hp  cyl2  cyl4
0    6  21.0  110    12    24
1    6  21.0  110    12    24

Source code in siuba/dply/verbs.py

@singledispatch2(pd.DataFrame)
def mutate(__data, *args, **kwargs):
    """Assign new variables to a DataFrame, while keeping existing ones.

    Parameters
    ----------
    __data: pd.DataFrame
    **kwargs:
        new_col_name=value pairs, where value can be a function taking a singledispatch2
        argument for the data being operated on.

    See Also
    --------
    transmute : Returns a DataFrame with only the newly created columns.

    Examples
    --------
    >>> from siuba import _, mutate, head
    >>> from siuba.data import cars
    >>> cars >> mutate(cyl2 = _.cyl * 2, cyl4 = _.cyl2 * 2) >> head(2)
       cyl   mpg   hp  cyl2  cyl4
    0    6  21.0  110    12    24
    1    6  21.0  110    12    24

    """

    new_names, df_res = _mutate_cols(__data, args, kwargs)
    return df_res

transmute(__data, *args, **kwargs)

Assign new columns to a DataFrame, while dropping previous columns.

Parameters:

Name	Type	Description	Default
__data		The input data.	required
**kwargs		Each keyword argument is the name of a new column, and an expression.	{}

Examples:

>>> from siuba import _, transmute, mutate, head
>>> from siuba.data import cars

Notice that transmute results in a table with only the new column:

>>> cars >> transmute(cyl2 = _.cyl + 1) >> head(2)
   cyl2
0     7
1     7

By contrast, mutate adds the new column to the end of the table:

>>> cars >>  mutate(cyl2 = _.cyl + 1) >> head(2)
   cyl   mpg   hp  cyl2
0    6  21.0  110     7
1    6  21.0  110     7

Source code in siuba/dply/verbs.py

@singledispatch2(DataFrame)
def transmute(__data, *args, **kwargs):
    """Assign new columns to a DataFrame, while dropping previous columns.

    Parameters
    ----------
    __data:
        The input data.
    **kwargs:
        Each keyword argument is the name of a new column, and an expression.

    See Also
    --------
    mutate : Assign new columns, or modify existing ones.

    Examples
    --------

    >>> from siuba import _, transmute, mutate, head
    >>> from siuba.data import cars

    Notice that transmute results in a table with only the new column:

    >>> cars >> transmute(cyl2 = _.cyl + 1) >> head(2)
       cyl2
    0     7
    1     7

    By contrast, mutate adds the new column to the end of the table:

    >>> cars >>  mutate(cyl2 = _.cyl + 1) >> head(2)
       cyl   mpg   hp  cyl2
    0    6  21.0  110     7
    1    6  21.0  110     7


    """
    arg_vars = list(map(simple_varname, args))

    col_names, df_res = _mutate_cols(__data, args, kwargs)
    return df_res[col_names]