chicken_turtle_util.series

Utilities for working with pandas.Series. Contains only invert, swaps series’ index with its values

assert_equals	Assert 2 series are equal
equals	Get whether 2 series are equal
invert	Swap index with values of series
split	Split values

chicken_turtle_util.series.assert_equals(series1, series2, ignore_order=False, ignore_index=False, all_close=False)

Assert 2 series are equal

Like assert equals(series1, series2, ...), but with better hints at where the series differ. See chicken_turtle_util.series.equals() for detailed parameter doc.

Parameters:

series1, series2 : pd.Series

ignore_order : bool

ignore_index : bool

all_close : bool

chicken_turtle_util.series.equals(series1, series2, ignore_order=False, ignore_index=False, all_close=False, _return_reason=False)

Get whether 2 series are equal

NaNs are considered equal (which is consistent with pandas.Series.equals). None is considered equal to NaN.

Parameters:

series1, series2 : pd.Series

Series to compare

ignore_order : bool

Ignore order of values (and index)

ignore_index : bool

Ignore index values and name.

all_close : bool

If False, values must match exactly, if True, floats are compared as if compared with np.isclose.

_return_reason : bool

Internal. If True, equals returns a tuple containing the reason, else equals only returns a bool indicating equality (or equivalence rather).

Returns:

equal : bool

Whether they’re equal (after ignoring according to the parameters)

reason : str or None

If equal, None, otherwise short explanation of why the data frames aren’t equal. Omitted if not _return_reason.

See also

data_frame.equals

Get whether 2 data frames are equal

Notes

All values (including those of indices) must be copyable and __eq__ must be such that a copy must equal its original. A value must equal itself unless it’s np.nan. Values needn’t be orderable or hashable (however pandas requires index values to be orderable and hashable). By consequence, this is not an efficient function, but it is flexible.

chicken_turtle_util.series.invert(series)

Swap index with values of series

Parameters:

series

Series to swap on, must have a name

Returns:

pandas.Series

Series after swap

See also

pandas.Series.map

Joins series a -> b and b -> c into a -> c.

chicken_turtle_util.series.split(series)

Split values

The index is dropped, but this may change in the future.

Parameters:

series : pd.Series

Series with numpy array-like values.

Returns:

pd.Series

Series with values split across rows.

Examples

>>> series = pd.Series([[1,2],[1,2],[3,4,5]])
>>> series
0       [1, 2]
1       [1, 2]
2    [3, 4, 5]
dtype: object
>>> split(series)
0    1
1    2
2    1
3    2
4    3
5    4
6    5
dtype: object