Verb spread

`spread(__data, key, value, fill=None, reset_index=True)`

Reshape table by spreading it out to wide format.

Parameters:

Name	Description	Default
`__data`	The input data.	required
`key`	Column whose values will be used as new column names.	required
`value`	Column whose values will fill the new column entries.	required
`fill`	Value to set for any missing values. By default keeps them as missing values.	`None`

Examples:

>>> import pandas as pd                                                
>>> from siuba import _, gather

>>> df = pd.DataFrame({"id": ["a", "b"], "x": [1, 2], "y": [3, None]})

>>> long = gather(df, "key", "value", -_.id, drop_na=True)
>>> long
  id key  value
0  a   x    1.0
1  b   x    2.0
2  a   y    3.0

>>> spread(long, "key", "value")
  id    x    y
0  a  1.0  3.0
1  b  2.0  NaN

Source code in siuba/dply/verbs.py

@singledispatch2(pd.DataFrame)
def spread(__data, key, value, fill = None, reset_index = True):
    """Reshape table by spreading it out to wide format.

    Parameters
    ----------
    __data:
        The input data.
    key:
        Column whose values will be used as new column names.
    value:
        Column whose values will fill the new column entries.
    fill:
        Value to set for any missing values. By default keeps them as missing values.


    Examples
    --------
    >>> import pandas as pd                                                
    >>> from siuba import _, gather                                        

    >>> df = pd.DataFrame({"id": ["a", "b"], "x": [1, 2], "y": [3, None]}) 

    >>> long = gather(df, "key", "value", -_.id, drop_na=True)
    >>> long
      id key  value
    0  a   x    1.0
    1  b   x    2.0
    2  a   y    3.0

    >>> spread(long, "key", "value")
      id    x    y
    0  a  1.0  3.0
    1  b  2.0  NaN

    """
    key_col = _get_single_var_select(__data.columns, key)
    val_col = _get_single_var_select(__data.columns, value)

    id_cols = [col for col in __data.columns if col not in (key_col, val_col)]
    wide = __data.set_index(id_cols + [key_col]).unstack(level = -1)

    if fill is not None:
        wide.fillna(fill, inplace = True)

    # remove multi-index from both rows and cols
    wide.columns = wide.columns.droplevel().rename(None)
    if reset_index:
        wide.reset_index(inplace = True)

    return wide