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[DOC] Typo in documentation, fixed monotonous to monotonic (#5946)
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Reference Issues/PRs

Fixes issues #5878 

What does this implement/fix? Explain your changes.
I have addressed the typo in the structure convention. Instead of
'monotonous,' it now correctly states 'monotonic.' I made this
adjustment in the section detailing the structure convention for time
series objects, specifically in the line: 'obj.index must be monotonous,
and one of... I have push just the correct file this time.

Does your contribution introduce a new dependency? If yes, which one?

No

What should a reviewer concentrate their feedback on?

Did you add any tests for the change?

no

Any other comments?

no

PR checklist

- [ ] I've added myself to the [list of
contributors](https://github.com/sktime/sktime/blob/main/CONTRIBUTORS.md)
with any new badges I've earned :-)

- [ ] Optionally, I've added myself and possibly others to the
[CODEOWNERS]

- [X] The PR title starts with either [ENH], [MNT], [DOC], or [BUG].
[BUG] - bugfix, [MNT] - CI, test framework, [ENH] - adding or improving
code, [DOC] - writing or improving documentation or docstrings.

For new estimators

 

- [ ] I've added the estimator to the API reference - in
docs/source/api_reference/taskname.rst, follow the pattern.

- [ ] I've added one or more illustrative usage examples to the
docstring, in a pydocstyle compliant Examples section.

- [ ] If the estimator relies on a soft dependency, I've set the
python_dependencies tag and ensured

dependency isolation, see the [estimator dependencies guide]
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@eduardojp26
eduardojp26 authored Feb 15, 2024
1 parent fdb3754 commit 82759af
Showing 1 changed file with 50 additions and 50 deletions.
100 changes: 50 additions & 50 deletions examples/AA_datatypes_and_datasets.ipynb
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"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Section 1.1.1: Time series - the `\"pd.DataFrame\"` mtype\n",
"\n",
"In the `\"pd.DataFrame\"` mtype, time series are represented by an in-memory container `obj: pandas.DataFrame` as follows.\n",
"\n",
"* structure convention: `obj.index` must be monotonous, and one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex`.\n",
"* variables: columns of `obj` correspond to different variables\n",
"* variable names: column names `obj.columns`\n",
"* time points: rows of `obj` correspond to different, distinct time points\n",
"* time index: `obj.index` is interpreted as a time index.\n",
"* capabilities: can represent multivariate series; can represent unequally spaced series"
]
"source": [
"### Section 1.1.1: Time series - the `\"pd.DataFrame\"` mtype\n",
"\n",
"In the `\"pd.DataFrame\"` mtype, time series are represented by an in-memory container `obj: pandas.DataFrame` as follows.\n",
"\n",
"* structure convention: `obj.index` must be monotonic, and one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex`.\n",
"* variables: columns of `obj` correspond to different variables\n",
"* variable names: column names `obj.columns`\n",
"* time points: rows of `obj` correspond to different, distinct time points\n",
"* time index: `obj.index` is interpreted as a time index.\n",
"* capabilities: can represent multivariate series; can represent unequally spaced series"
]
},
{
"attachments": {},
Expand Down Expand Up @@ -279,18 +279,18 @@
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Section 1.1.2: Time series - the `\"pd.Series\"` mtype\n",
"\n",
"In the `\"pd.Series\"` mtype, time series are represented by an in-memory container `obj: pandas.Series` as follows.\n",
"\n",
"* structure convention: `obj.index` must be monotonous, and one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex`.\n",
"* variables: there is a single variable, corresponding to the values of `obj`. Only univariate series can be represented.\n",
"* variable names: by default, there is no column name. If needed, a variable name can be provided as `obj.name`.\n",
"* time points: entries of `obj` correspond to different, distinct time points\n",
"* time index: `obj.index` is interpreted as a time index.\n",
"* capabilities: cannot represent multivariate series; can represent unequally spaced series"
]
"source": [
"### Section 1.1.2: Time series - the `\"pd.Series\"` mtype\n",
"\n",
"In the `\"pd.Series\"` mtype, time series are represented by an in-memory container `obj: pandas.Series` as follows.\n",
"\n",
"* structure convention: `obj.index` must be monotonic, and one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex`.\n",
"* variables: there is a single variable, corresponding to the values of `obj`. Only univariate series can be represented.\n",
"* variable names: by default, there is no column name. If needed, a variable name can be provided as `obj.name`.\n",
"* time points: entries of `obj` correspond to different, distinct time points\n",
"* time index: `obj.index` is interpreted as a time index.\n",
"* capabilities: cannot represent multivariate series; can represent unequally spaced series"
]
},
{
"attachments": {},
Expand Down Expand Up @@ -435,20 +435,20 @@
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Section 1.2.1: Time series panels - the `\"pd-multiindex\"` mtype\n",
"\n",
"In the `\"pd-multiindex\"` mtype, time series panels are represented by an in-memory container `obj: pandas.DataFrame` as follows.\n",
"\n",
"* structure convention: `obj.index` must be a pair multi-index of type `(Index, t)`, where `t` is one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex` and monotonous. `obj.index` must have two levels (can be named or not).\n",
"* instance index: the first element of pairs in `obj.index` (0-th level value) is interpreted as an instance index, we call it \"instance index\" below.\n",
"* instances: rows with the same \"instance index\" index value correspond to the same instance; rows with different \"instance index\" values correspond to different instances. \n",
"* time index: the second element of pairs in `obj.index` (1-st level value) is interpreted as a time index, we call it \"time index\" below. \n",
"* time points: rows of `obj` with the same \"time index\" value correspond correspond to the same time point; rows of `obj` with different \"time index\" index correspond correspond to the different time points.\n",
"* variables: columns of `obj` correspond to different variables\n",
"* variable names: column names `obj.columns`\n",
"* capabilities: can represent panels of multivariate series; can represent unequally spaced series; can represent panels of unequally supported series; cannot represent panels of series with different sets of variables."
]
"source": [
"### Section 1.2.1: Time series panels - the `\"pd-multiindex\"` mtype\n",
"\n",
"In the `\"pd-multiindex\"` mtype, time series panels are represented by an in-memory container `obj: pandas.DataFrame` as follows.\n",
"\n",
"* structure convention: `obj.index` must be a pair multi-index of type `(Index, t)`, where `t` is one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex` and monotonic. `obj.index` must have two levels (can be named or not).\n",
"* instance index: the first element of pairs in `obj.index` (0-th level value) is interpreted as an instance index, we call it \"instance index\" below.\n",
"* instances: rows with the same \"instance index\" index value correspond to the same instance; rows with different \"instance index\" values correspond to different instances. \n",
"* time index: the second element of pairs in `obj.index` (1-st level value) is interpreted as a time index, we call it \"time index\" below. \n",
"* time points: rows of `obj` with the same \"time index\" value correspond correspond to the same time point; rows of `obj` with different \"time index\" index correspond correspond to the different time points.\n",
"* variables: columns of `obj` correspond to different variables\n",
"* variable names: column names `obj.columns`\n",
"* capabilities: can represent panels of multivariate series; can represent unequally spaced series; can represent panels of unequally supported series; cannot represent panels of series with different sets of variables."
]
},
{
"attachments": {},
Expand Down Expand Up @@ -702,18 +702,18 @@
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Hierarchical time series - the `\"pd_multiindex_hier\"` mtype\n",
"\n",
"* structure convention: `obj.index` must be a 3 or more level multi-index of type `(Index, ..., Index, t)`, where `t` is one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex` and monotonous. We call the last index the \"time-like\" index.\n",
"* hierarchy level: rows with the same non-time-like index values correspond to the same hierarchy unit; rows with different non-time-like index combination correspond to different hierarchy unit.\n",
"* hierarchy: the non-time-like indices in `obj.index` are interpreted as a hierarchy identifying index. \n",
"* time index: the last element of tuples in `obj.index` is interpreted as a time index. \n",
"* time points: rows of `obj` with the same `\"timepoints\"` index correspond correspond to the same time point; rows of `obj` with different `\"timepoints\"` index correspond correspond to the different time points.\n",
"* variables: columns of `obj` correspond to different variables\n",
"* variable names: column names `obj.columns`\n",
"* capabilities: can represent hierarchical series; can represent unequally spaced series; can represent unequally supported hierarchical series; cannot represent hierarchical series with different sets of variables."
]
"source": [
"#### Hierarchical time series - the `\"pd_multiindex_hier\"` mtype\n",
"\n",
"* structure convention: `obj.index` must be a 3 or more level multi-index of type `(Index, ..., Index, t)`, where `t` is one of `Int64Index`, `RangeIndex`, `DatetimeIndex`, `PeriodIndex` and monotonic. We call the last index the \"time-like\" index.\n",
"* hierarchy level: rows with the same non-time-like index values correspond to the same hierarchy unit; rows with different non-time-like index combination correspond to different hierarchy unit.\n",
"* hierarchy: the non-time-like indices in `obj.index` are interpreted as a hierarchy identifying index. \n",
"* time index: the last element of tuples in `obj.index` is interpreted as a time index. \n",
"* time points: rows of `obj` with the same `\"timepoints\"` index correspond correspond to the same time point; rows of `obj` with different `\"timepoints\"` index correspond correspond to the different time points.\n",
"* variables: columns of `obj` correspond to different variables\n",
"* variable names: column names `obj.columns`\n",
"* capabilities: can represent hierarchical series; can represent unequally spaced series; can represent unequally supported hierarchical series; cannot represent hierarchical series with different sets of variables."
]
},
{
"cell_type": "code",
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