diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 3d8e3c6b..96ebdcc4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,6 +4,7 @@ repos: rev: 5df1a4bf6f04a1ed3a643167b38d502575e29aef hooks: - id: trailing-whitespace + exclude: 'docs/' - id: end-of-file-fixer - id: check-yaml - repo: local diff --git a/README.md b/README.md index b4e2c712..9e6f60f4 100644 --- a/README.md +++ b/README.md @@ -46,15 +46,15 @@ df >> mutate(z=if_else(f.x>1, 1, 0)) df >> filter(f.x>1) """# output: x y -2 2 two -3 3 three +0 2 two +1 3 three """ df >> mutate(z=if_else(f.x>1, 1, 0)) >> filter(f.z==1) """# output: x y z -2 2 two 1 -3 3 three 1 +0 2 two 1 +1 3 three 1 """ ``` @@ -65,10 +65,9 @@ from datar.base import sin, pi from plotnine import ggplot, aes, geom_line, theme_classic df = tibble(x=numpy.linspace(0, 2*pi, 500)) -( - df >> - mutate(y=sin(f.x), sign=if_else(f.y>=0, "positive", "negative")) >> - ggplot(aes(x='x', y='y')) + theme_classic() +(df >> + mutate(y=sin(f.x), sign=if_else(f.y>=0, "positive", "negative")) >> + ggplot(aes(x='x', y='y')) + theme_classic() ) + geom_line(aes(color='sign'), size=1.2) ``` @@ -79,7 +78,6 @@ df = tibble(x=numpy.linspace(0, 2*pi, 500)) # for example: klib import klib from pipda import register_verb -from datar.core.contexts import Context from datar.datasets import iris from datar.dplyr import pull diff --git a/datar/__init__.py b/datar/__init__.py index d37d98fb..4e4e9759 100644 --- a/datar/__init__.py +++ b/datar/__init__.py @@ -3,4 +3,4 @@ from .core import operator as _datar_operator from .core.defaults import f -__version__ = '0.0.2' +__version__ = '0.0.3' diff --git a/datar/dplyr/context.py b/datar/dplyr/context.py index 1d424ca5..2e3308d6 100644 --- a/datar/dplyr/context.py +++ b/datar/dplyr/context.py @@ -13,24 +13,24 @@ from .group_data import group_vars # n used directly in count -@register_func(context=Context.EVAL) +@register_func(context=Context.EVAL, summarise_prefers_input=True) def n(series: Iterable[Any]) -> int: """gives the current group size.""" return len(series) -@register_func(DataFrame, verb_arg_only=True) +@register_func(DataFrame, verb_arg_only=True, summarise_prefers_input=True) def cur_data_all(_data: DataFrame) -> DataFrame: """gives the current data for the current group (including grouping variables)""" return _data -@register_func(DataFrame, verb_arg_only=True) +@register_func(DataFrame, verb_arg_only=True, summarise_prefers_input=True) def cur_data(_data: DataFrame) -> int: """gives the current data for the current group (excluding grouping variables).""" return _data[setdiff(_data.columns, group_vars(_data))] -@register_func(DataFrame, verb_arg_only=True) +@register_func(DataFrame, verb_arg_only=True, summarise_prefers_input=True) def cur_group(_data: DataFrame) -> DataFrame: """gives the group keys, a tibble with one row and one column for each grouping variable.""" @@ -41,12 +41,12 @@ def cur_group(_data: DataFrame) -> DataFrame: gdata = _data.attrs['group_data'] return gdata.iloc[[index], :-1] -@register_func(DataFrame, verb_arg_only=True) +@register_func(DataFrame, verb_arg_only=True, summarise_prefers_input=True) def cur_group_id(_data: DataFrame) -> int: """gives a unique numeric identifier for the current group.""" return _data.attrs.get('group_index', 1) -@register_func(DataFrame, verb_arg_only=True) +@register_func(DataFrame, verb_arg_only=True, summarise_prefers_input=True) def cur_group_rows(_data: DataFrame) -> List[int]: """gives the row indices for the current group.""" index = _data.attrs.get('group_index', None) diff --git a/datar/dplyr/summarise.py b/datar/dplyr/summarise.py index 1c763a23..5b112300 100644 --- a/datar/dplyr/summarise.py +++ b/datar/dplyr/summarise.py @@ -3,6 +3,7 @@ from typing import Any, Iterable, Mapping, Optional, Union from pandas import DataFrame from pipda import register_verb, evaluate_expr +from pipda.function import Function from ..core.defaults import DEFAULT_COLUMN_PREFIX from ..core.contexts import Context @@ -35,6 +36,23 @@ def summarise( >>> # 0 10 20 >>> df >> summarise(y=sum(f.x), z=f.x+f.y) # fail + But they should not be mixed in later argument. For example: + >>> df = tibble(x=[1,2,3,4], g=list('aabb')) >> group_by(f.g) + >>> df >> summarise(n=n() + f.x) + >>> # as expected: + >>> g n + >>> # 0 a 3 + >>> # 1 a 4 + >>> # 2 b 5 + >>> # 3 b 6 + >>> # [Groups: ['g'] (n=2)] + >>> # However: + >>> df >> summarise(y=1, n=n() + f.y) + >>> # n() will be recycling output instead of input + >>> # g y n + >>> # 0 a 1 2 + >>> # 1 b 1 2 + Args: _groups: Grouping structure of the result. - "drop_last": dropping the last level of grouping. @@ -140,11 +158,15 @@ def summarise_build( out = group_keys(_data) for key, val in named.items(): + envdata = out + if out.shape[1] == 0 or ( + isinstance(val, Function) and + getattr(val.func, 'summarise_prefers_input', False) + ): + envdata = _data + try: - if out.shape[1] == 0: - val = evaluate_expr(val, _data, context) - else: - val = evaluate_expr(val, out, context) + val = evaluate_expr(val, envdata, context) except ColumnNotExistingError: # also recycle input val = evaluate_expr(val, _data, context) diff --git a/datar/stats/__init__.py b/datar/stats/__init__.py index dce35f75..573a5692 100644 --- a/datar/stats/__init__.py +++ b/datar/stats/__init__.py @@ -1,4 +1,4 @@ """APIs for stats""" -from .funcs import rnorm, rpois, runif, quantile, sd +from .funcs import rnorm, rpois, runif, quantile, sd, weighted_mean from .verbs import setNames diff --git a/datar/stats/funcs.py b/datar/stats/funcs.py index 744ea261..d9940feb 100644 --- a/datar/stats/funcs.py +++ b/datar/stats/funcs.py @@ -1,10 +1,12 @@ """Some functions ported from R-stats""" -from typing import Any, Iterable, List +from typing import Any, Iterable, List, Optional import numpy from pipda import register_func -from ..core.types import FloatOrIter, SeriesLikeType +from ..core.types import ( + FloatOrIter, NumericOrIter, NumericType, SeriesLikeType, is_scalar +) from ..core.contexts import Context # pylint: disable=redefined-builtin, redefined-outer-name @@ -86,3 +88,31 @@ def sd( numpy.nanstd(series, ddof=ddof) if na_rm else numpy.std(series, ddof=ddof) ) + +@register_func(None, context=Context.EVAL) +def weighted_mean( + # pylint: disable=invalid-name + x: NumericOrIter, + w: Optional[NumericOrIter] = None, + na_rm: bool = False +) -> NumericType: + """Calculate weighted mean""" + if is_scalar(x): + x = [x] + if w is not None and is_scalar(w): + w = [w] + x = numpy.array(x) + if w is not None: + w = numpy.array(w) + if len(x) != len(w): + raise ValueError("'x' and 'w' must have the same length") + + if na_rm: + notna = ~numpy.isnan(x) + x = x[notna] + if w is not None: + w = w[notna] + + if w is not None and sum(w) == 0: + return numpy.nan + return numpy.average(x, weights=w) diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md index 1da76ff9..9e32befe 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -1,2 +1,6 @@ +## 0.0.3 +- Add stats.weighted_mean +- Allow function to prefer recycling input or output for summarise + ## 0.0.2 - Port verbs and functions from tidyverse/dplyr and test them with original cases diff --git a/docs/TODO.md b/docs/TODO.md index eae26f46..36b517c4 100644 --- a/docs/TODO.md +++ b/docs/TODO.md @@ -1,3 +1,6 @@ - Add tests for tidyr from original tidyverse/tidyr cases - Add more tests for base/core +- Port more functions from `r-base`, `r-stats`, etc +- Port more datasets from `r-datasets` namespace +- Add more detailed documentations diff --git a/docs/notebooks/readme.ipynb b/docs/notebooks/readme.ipynb index b91da93f..433b757c 100644 --- a/docs/notebooks/readme.ipynb +++ b/docs/notebooks/readme.ipynb @@ -33,14 +33,10 @@ }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - " x y z\n", - "0 0 zero 0\n", - "1 1 one 1\n", - "2 2 two 2\n", - "3 3 three 3\n" + " x y z\n0 0 zero 0\n1 1 one 1\n2 2 two 2\n3 3 three 3\n" ] } ], @@ -66,14 +62,10 @@ }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - " x y z\n", - "0 0 zero 0\n", - "1 1 one 0\n", - "2 2 two 1\n", - "3 3 three 1\n" + " x y z\n0 0 zero 0\n1 1 one 0\n2 2 two 1\n3 3 three 1\n" ] } ], @@ -95,12 +87,10 @@ }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - " x y\n", - "0 2 two\n", - "1 3 three\n" + " x y\n0 2 two\n1 3 three\n" ] } ], @@ -122,12 +112,10 @@ }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - " x y z\n", - "0 2 two 1\n", - "1 3 three 1\n" + " x y z\n0 2 two 1\n1 3 three 1\n" ] } ], @@ -149,24 +137,23 @@ }, "outputs": [ { + "output_type": "display_data", "data": { - "image/png": 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\n" }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { + "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 1, "metadata": {}, - "output_type": "execute_result" + "execution_count": 6 } ], "source": [ @@ -197,26 +184,25 @@ }, "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "" ] }, - "execution_count": 1, "metadata": {}, - "output_type": "execute_result" + "execution_count": 7 }, { + "output_type": "display_data", "data": { - "image/png": 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\n" }, "metadata": { "needs_background": "light" - }, - "output_type": "display_data" + } } ], "source": [ @@ -224,7 +210,6 @@ "# for example: klib\n", "import klib\n", "from pipda import register_verb\n", - "from datar.core.contexts import Context\n", "from datar.datasets import iris\n", "from datar.dplyr import pull\n", "\n", diff --git a/pyproject.toml b/pyproject.toml index 097d04f4..a721bf31 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [tool.poetry] name = "datar" -version = "0.0.2" -description = "Probably the closest port of tidyr, dplyr and tibble in python" +version = "0.0.3" +description = "Port of R data packages tidyr, dplyr, tibble and so on in python" authors = ["pwwang "] license = "MIT" diff --git a/setup.py b/setup.py index 166cdc71..a7d44e02 100644 --- a/setup.py +++ b/setup.py @@ -15,8 +15,8 @@ setup( long_description=readme, name='datar', - version='0.0.2', - description='Probably the closest port of tidyr, dplyr and tibble in python', + version='0.0.3', + description='Port of R data packages tidyr, dplyr, tibble and so on in python', python_requires='==3.*,>=3.7.1', author='pwwang', author_email='pwwang@pwwang.com',