From d42e21196ed87f108fc7a9d635162dbefcaa61d3 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Thu, 19 Nov 2020 23:59:22 +0000 Subject: [PATCH] [Bot] Combine APIs and create typings --- data/api.json | 666 +++++++++++++++++++++---------------------- data/typing/numpy.py | 144 +++++----- 2 files changed, 405 insertions(+), 405 deletions(-) diff --git a/data/api.json b/data/api.json index 23cc266..79bfbfb 100644 --- a/data/api.json +++ b/data/api.json @@ -1542,8 +1542,8 @@ "usage.statsmodels": 183, "usage.scipy": 160, "usage.prophet": 3, - "usage.modin": 3, "usage.matplotlib": 21, + "usage.modin": 3, "usage.dask": 15, "usage.sklearn": 35, "usage.networkx": 1 @@ -1591,8 +1591,8 @@ "usage.statsmodels": 401, "usage.scipy": 504, "usage.prophet": 6, - "usage.modin": 11, "usage.matplotlib": 188, + "usage.modin": 11, "usage.seaborn": 30, "usage.sample-usage": 4, "usage.dask": 251, @@ -5170,8 +5170,8 @@ "usage.statsmodels": 166, "usage.scipy": 803, "usage.prophet": 3, - "usage.modin": 1, "usage.matplotlib": 41, + "usage.modin": 1, "usage.seaborn": 9, "usage.sample-usage": 1, "usage.dask": 142, @@ -5211,8 +5211,8 @@ "usage.xarray": 1, "usage.statsmodels": 22, "usage.scipy": 65, - "usage.modin": 2, "usage.matplotlib": 8, + "usage.modin": 2, "usage.seaborn": 7, "usage.dask": 5, "usage.sklearn": 13, @@ -6120,8 +6120,8 @@ "usage.skimage": 14, "usage.xarray": 4, "usage.scipy": 16, - "usage.modin": 1, "usage.matplotlib": 3, + "usage.modin": 1, "usage.dask": 3, "usage.sklearn": 17 } @@ -6768,8 +6768,8 @@ "usage.xarray": 3, "usage.statsmodels": 137, "usage.scipy": 85, - "usage.modin": 1, "usage.matplotlib": 14, + "usage.modin": 1, "usage.dask": 5, "usage.sklearn": 23, "usage.networkx": 1 @@ -8337,8 +8337,8 @@ "metadata": { "usage.skimage": 2, "usage.scipy": 7, - "usage.modin": 1, "usage.matplotlib": 1, + "usage.modin": 1, "usage.seaborn": 2, "usage.sklearn": 3 } @@ -36894,169 +36894,6 @@ "usage.scipy": 2 } }, - { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": "list", - "item": { - "type": { - "module": "modin.engines.ray.pandas_on_ray.frame.partition", - "name": "PandasOnRayFramePartition" - } - } - } - } - }, - "metadata": { - "usage.modin": 30 - } - }, - { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": "list", - "item": { - "type": { - "module": "ray._raylet", - "name": "ObjectRef" - } - } - } - } - }, - "metadata": { - "usage.modin": 3 - } - }, - { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": "str", - "options": [ - "foo", - "bar" - ] - } - } - }, - "kw_only_required": { - "dtype": { - "type": "type", - "name": { - "name": "object" - } - } - }, - "metadata": { - "usage.modin": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": "str", - "options": [ - "two", - "one" - ] - } - } - }, - "kw_only_required": { - "dtype": { - "type": "type", - "name": { - "name": "object" - } - } - }, - "metadata": { - "usage.modin": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": "str", - "options": [ - "shiny", - "dull" - ] - } - } - }, - "kw_only_required": { - "dtype": { - "type": "type", - "name": { - "name": "object" - } - } - }, - "metadata": { - "usage.modin": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "int64" - } - } - } - }, - "kw_only_required": { - "dtype": { - "type": { - "module": "numpy", - "name": "dtype" - } - } - }, - "metadata": { - "usage.modin": 1, - "usage.dask": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float32" - } - } - } - }, - "kw_only_required": { - "dtype": { - "type": { - "module": "numpy", - "name": "dtype" - } - } - }, - "metadata": { - "usage.modin": 1, - "usage.dask": 1 - } - }, { "pos_only_required": { "_0": { @@ -45265,6 +45102,169 @@ "usage.matplotlib": 1 } }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": { + "module": "modin.engines.ray.pandas_on_ray.frame.partition", + "name": "PandasOnRayFramePartition" + } + } + } + } + }, + "metadata": { + "usage.modin": 30 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": { + "module": "ray._raylet", + "name": "ObjectRef" + } + } + } + } + }, + "metadata": { + "usage.modin": 3 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "str", + "options": [ + "foo", + "bar" + ] + } + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "name": "object" + } + } + }, + "metadata": { + "usage.modin": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "str", + "options": [ + "two", + "one" + ] + } + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "name": "object" + } + } + }, + "metadata": { + "usage.modin": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "str", + "options": [ + "shiny", + "dull" + ] + } + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "name": "object" + } + } + }, + "metadata": { + "usage.modin": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "int64" + } + } + } + }, + "kw_only_required": { + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" + } + } + }, + "metadata": { + "usage.modin": 1, + "usage.dask": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "float32" + } + } + } + }, + "kw_only_required": { + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" + } + } + }, + "metadata": { + "usage.modin": 1, + "usage.dask": 1 + } + }, { "pos_only_required": { "_0": { @@ -106000,8 +106000,8 @@ "usage.xarray": 21, "usage.statsmodels": 53, "usage.scipy": 257, - "usage.modin": 1, "usage.matplotlib": 27, + "usage.modin": 1, "usage.seaborn": 9, "usage.dask": 18, "usage.sklearn": 139, @@ -150945,8 +150945,8 @@ "usage.xarray": 10, "usage.statsmodels": 5, "usage.scipy": 4, - "usage.modin": 4, "usage.matplotlib": 5, + "usage.modin": 4, "usage.dask": 6, "usage.sklearn": 7 } @@ -171068,8 +171068,8 @@ "usage.pandas": 33, "usage.scipy": 163, "usage.prophet": 1, - "usage.modin": 1, "usage.matplotlib": 4, + "usage.modin": 1, "usage.seaborn": 18, "usage.dask": 6, "usage.sklearn": 39, @@ -222235,33 +222235,6 @@ } } ], - "savetxt": [ - { - "pos_or_kw_required": { - "fname": { - "type": "str", - "options": [ - "200kx99.csv" - ] - }, - "X": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "delimiter": { - "type": "str", - "options": [ - "," - ] - } - }, - "metadata": { - "usage.modin": 1 - } - } - ], "copyto": [ { "pos_only_required": { @@ -222787,6 +222760,33 @@ } } ], + "savetxt": [ + { + "pos_or_kw_required": { + "fname": { + "type": "str", + "options": [ + "200kx99.csv" + ] + }, + "X": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "delimiter": { + "type": "str", + "options": [ + "," + ] + } + }, + "metadata": { + "usage.modin": 1 + } + } + ], "save": [ { "pos_or_kw_required": { @@ -223500,8 +223500,8 @@ "usage.pandas": 894, "usage.scipy": 1173, "usage.prophet": 11, - "usage.modin": 15, "usage.matplotlib": 359, + "usage.modin": 15, "usage.seaborn": 36, "usage.sample-usage": 4, "usage.dask": 357, @@ -223622,8 +223622,8 @@ "usage.pandas": 6865, "usage.scipy": 7182, "usage.prophet": 31, - "usage.modin": 47, "usage.matplotlib": 812, + "usage.modin": 47, "usage.seaborn": 126, "usage.sample-usage": 3, "usage.dask": 529, @@ -224316,8 +224316,8 @@ "usage.statsmodels": 95, "usage.pandas": 81, "usage.scipy": 276, - "usage.modin": 1, "usage.matplotlib": 28, + "usage.modin": 1, "usage.seaborn": 11, "usage.dask": 93, "usage.sklearn": 164, @@ -226848,8 +226848,8 @@ "usage.pandas": 26, "usage.scipy": 30, "usage.prophet": 1, - "usage.modin": 4, "usage.matplotlib": 33, + "usage.modin": 4, "usage.seaborn": 1, "usage.dask": 43, "usage.sklearn": 33 @@ -228555,8 +228555,8 @@ "usage.pandas": 33, "usage.scipy": 217, "usage.prophet": 1, - "usage.modin": 2, "usage.matplotlib": 4, + "usage.modin": 2, "usage.seaborn": 25, "usage.dask": 15, "usage.sklearn": 50, @@ -233004,8 +233004,8 @@ "usage.statsmodels": 16, "usage.pandas": 8, "usage.scipy": 63, - "usage.modin": 1, "usage.matplotlib": 6, + "usage.modin": 1, "usage.seaborn": 5, "usage.dask": 1, "usage.sklearn": 29, @@ -235686,31 +235686,6 @@ "usage.scipy": 7 } }, - "savetxt": { - "pos_or_kw_required": { - "fname": { - "type": "str", - "options": [ - "200kx99.csv" - ] - }, - "X": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "delimiter": { - "type": "str", - "options": [ - "," - ] - } - }, - "metadata": { - "usage.modin": 1 - } - }, "copyto": { "pos_only_required": { "_0": { @@ -235896,6 +235871,31 @@ "usage.matplotlib": 1 } }, + "savetxt": { + "pos_or_kw_required": { + "fname": { + "type": "str", + "options": [ + "200kx99.csv" + ] + }, + "X": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "delimiter": { + "type": "str", + "options": [ + "," + ] + } + }, + "metadata": { + "usage.modin": 1 + } + }, "save": { "pos_or_kw_required": { "file": { @@ -249996,27 +249996,6 @@ "usage.matplotlib": 1 } }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "_1": { - "type": { - "module": "numpy", - "name": "int64" - } - } - }, - "metadata": { - "usage.modin": 1, - "usage.dask": 6, - "usage.sklearn": 3 - } - }, { "pos_only_required": { "_0": { @@ -250193,6 +250172,27 @@ "usage.matplotlib": 1 } }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "_1": { + "type": { + "module": "numpy", + "name": "int64" + } + } + }, + "metadata": { + "usage.modin": 1, + "usage.dask": 6, + "usage.sklearn": 3 + } + }, { "pos_only_required": { "_0": { @@ -252383,8 +252383,8 @@ "usage.pandas": 1228, "usage.scipy": 8018, "usage.prophet": 25, - "usage.modin": 1, "usage.matplotlib": 781, + "usage.modin": 1, "usage.seaborn": 43, "usage.sample-usage": 3, "usage.dask": 4506, @@ -256739,8 +256739,8 @@ "usage.xarray": 22, "usage.statsmodels": 548, "usage.scipy": 613, - "usage.modin": 3, "usage.matplotlib": 53, + "usage.modin": 3, "usage.seaborn": 14, "usage.dask": 35, "usage.sklearn": 428, @@ -256762,8 +256762,8 @@ "usage.statsmodels": 1259, "usage.scipy": 2217, "usage.prophet": 28, - "usage.modin": 20, "usage.matplotlib": 433, + "usage.modin": 20, "usage.seaborn": 74, "usage.sample-usage": 2, "usage.dask": 46, @@ -256817,8 +256817,8 @@ "usage.statsmodels": 603, "usage.scipy": 322, "usage.prophet": 7, - "usage.modin": 1, "usage.matplotlib": 45, + "usage.modin": 1, "usage.seaborn": 10, "usage.dask": 19, "usage.sklearn": 186, @@ -259677,8 +259677,8 @@ "usage.skimage": 2, "usage.statsmodels": 5, "usage.scipy": 54, - "usage.modin": 1, "usage.matplotlib": 3, + "usage.modin": 1, "usage.sklearn": 15 } }, @@ -260130,8 +260130,8 @@ "usage.skimage": 1, "usage.statsmodels": 23, "usage.scipy": 104, - "usage.modin": 1, "usage.matplotlib": 5, + "usage.modin": 1, "usage.dask": 5, "usage.sklearn": 13 } @@ -286746,8 +286746,8 @@ "usage.pandas": 181, "usage.scipy": 302, "usage.prophet": 1, - "usage.modin": 14, "usage.matplotlib": 363, + "usage.modin": 14, "usage.seaborn": 102, "usage.sample-usage": 2, "usage.dask": 6, @@ -288287,8 +288287,8 @@ "usage.xarray": 11, "usage.statsmodels": 92, "usage.scipy": 276, - "usage.modin": 1, "usage.matplotlib": 28, + "usage.modin": 1, "usage.seaborn": 2, "usage.dask": 47, "usage.sklearn": 272, @@ -333435,8 +333435,8 @@ "usage.statsmodels": 86, "usage.pandas": 122, "usage.scipy": 158, - "usage.modin": 1, "usage.matplotlib": 25, + "usage.modin": 1, "usage.seaborn": 2, "usage.sample-usage": 1, "usage.dask": 9, @@ -334421,8 +334421,8 @@ "usage.xarray": 7, "usage.statsmodels": 14, "usage.scipy": 183, - "usage.modin": 1, "usage.matplotlib": 2, + "usage.modin": 1, "usage.dask": 2, "usage.sklearn": 7, "usage.networkx": 4 @@ -334585,8 +334585,8 @@ "usage.xarray": 7, "usage.statsmodels": 14, "usage.scipy": 183, - "usage.modin": 1, "usage.matplotlib": 2, + "usage.modin": 1, "usage.dask": 2, "usage.sklearn": 7, "usage.networkx": 4 @@ -334963,8 +334963,8 @@ "usage.statsmodels": 276, "usage.scipy": 169, "usage.prophet": 4, - "usage.modin": 1, "usage.matplotlib": 14, + "usage.modin": 1, "usage.seaborn": 17, "usage.dask": 41, "usage.sklearn": 243, @@ -335512,8 +335512,8 @@ "usage.skimage": 1, "usage.statsmodels": 6, "usage.scipy": 6, - "usage.modin": 2, "usage.matplotlib": 2, + "usage.modin": 2, "usage.seaborn": 7, "usage.dask": 3, "usage.sklearn": 6 @@ -342154,8 +342154,8 @@ "usage.pandas": 1453, "usage.scipy": 1951, "usage.prophet": 1, - "usage.modin": 1, "usage.matplotlib": 77, + "usage.modin": 1, "usage.seaborn": 8, "usage.sample-usage": 1, "usage.dask": 185, @@ -342177,8 +342177,8 @@ "usage.pandas": 2206, "usage.scipy": 9090, "usage.prophet": 41, - "usage.modin": 26, "usage.matplotlib": 1402, + "usage.modin": 26, "usage.seaborn": 245, "usage.sample-usage": 5, "usage.dask": 679, @@ -342239,8 +342239,8 @@ "usage.pandas": 181, "usage.scipy": 302, "usage.prophet": 1, - "usage.modin": 14, "usage.matplotlib": 363, + "usage.modin": 14, "usage.seaborn": 102, "usage.sample-usage": 2, "usage.dask": 6, @@ -342450,8 +342450,8 @@ "usage.pandas": 895, "usage.scipy": 617, "usage.prophet": 2, - "usage.modin": 1, "usage.matplotlib": 88, + "usage.modin": 1, "usage.seaborn": 16, "usage.sample-usage": 2, "usage.dask": 194, @@ -342908,8 +342908,8 @@ "usage.statsmodels": 86, "usage.pandas": 122, "usage.scipy": 158, - "usage.modin": 1, "usage.matplotlib": 25, + "usage.modin": 1, "usage.seaborn": 2, "usage.sample-usage": 1, "usage.dask": 9, @@ -343086,8 +343086,8 @@ "usage.pandas": 90, "usage.scipy": 501, "usage.prophet": 4, - "usage.modin": 1, "usage.matplotlib": 39, + "usage.modin": 1, "usage.seaborn": 9, "usage.dask": 16, "usage.sklearn": 78, @@ -343106,8 +343106,8 @@ "usage.statsmodels": 62, "usage.pandas": 134, "usage.scipy": 441, - "usage.modin": 1, "usage.matplotlib": 41, + "usage.modin": 1, "usage.seaborn": 12, "usage.dask": 5, "usage.sklearn": 65, @@ -343239,8 +343239,8 @@ "usage.pandas": 101, "usage.scipy": 264, "usage.prophet": 4, - "usage.modin": 1, "usage.matplotlib": 16, + "usage.modin": 1, "usage.seaborn": 18, "usage.dask": 145, "usage.sklearn": 461, @@ -343304,8 +343304,8 @@ "usage.statsmodels": 12, "usage.pandas": 6, "usage.scipy": 8, - "usage.modin": 2, "usage.matplotlib": 3, + "usage.modin": 2, "usage.seaborn": 11, "usage.dask": 7, "usage.sklearn": 6 @@ -345738,8 +345738,8 @@ "usage.statsmodels": 266, "usage.pandas": 3135, "usage.scipy": 2779, - "usage.modin": 1, "usage.matplotlib": 79, + "usage.modin": 1, "usage.sample-usage": 1, "usage.dask": 454, "usage.sklearn": 1009, @@ -345773,8 +345773,8 @@ "usage.statsmodels": 114, "usage.pandas": 147, "usage.scipy": 975, - "usage.modin": 1, "usage.matplotlib": 84, + "usage.modin": 1, "usage.seaborn": 62, "usage.sample-usage": 1, "usage.dask": 21, @@ -345792,8 +345792,8 @@ "usage.pandas": 695, "usage.scipy": 4735, "usage.prophet": 10, - "usage.modin": 4, "usage.matplotlib": 323, + "usage.modin": 4, "usage.seaborn": 35, "usage.sample-usage": 2, "usage.dask": 454, @@ -345843,8 +345843,8 @@ "usage.statsmodels": 1032, "usage.pandas": 211, "usage.scipy": 1349, - "usage.modin": 3, "usage.matplotlib": 93, + "usage.modin": 3, "usage.seaborn": 37, "usage.sample-usage": 1, "usage.dask": 28, @@ -354362,8 +354362,8 @@ "usage.xarray": 2, "usage.statsmodels": 14, "usage.scipy": 15, - "usage.modin": 1, "usage.matplotlib": 16, + "usage.modin": 1, "usage.dask": 15, "usage.sklearn": 13 } @@ -355622,8 +355622,8 @@ "usage.statsmodels": 32, "usage.scipy": 49, "usage.prophet": 1, - "usage.modin": 1, "usage.matplotlib": 7, + "usage.modin": 1, "usage.dask": 19, "usage.sklearn": 37 } @@ -355642,8 +355642,8 @@ "usage.xarray": 2, "usage.statsmodels": 14, "usage.scipy": 15, - "usage.modin": 1, "usage.matplotlib": 16, + "usage.modin": 1, "usage.dask": 15, "usage.sklearn": 13 } @@ -356779,8 +356779,8 @@ "usage.statsmodels": 9, "usage.scipy": 17, "usage.prophet": 1, - "usage.modin": 1, "usage.matplotlib": 6, + "usage.modin": 1, "usage.seaborn": 2, "usage.dask": 8, "usage.sklearn": 13, @@ -356984,8 +356984,8 @@ "usage.skimage": 7, "usage.statsmodels": 15, "usage.scipy": 10, - "usage.modin": 1, "usage.matplotlib": 5, + "usage.modin": 1, "usage.seaborn": 1, "usage.dask": 3, "usage.sklearn": 10 @@ -358209,8 +358209,8 @@ "usage.pandas": 10, "usage.scipy": 83, "usage.prophet": 3, - "usage.modin": 1, "usage.matplotlib": 34, + "usage.modin": 1, "usage.dask": 39, "usage.sklearn": 53, "usage.networkx": 13 @@ -358308,8 +358308,8 @@ "usage.pandas": 24, "usage.scipy": 81, "usage.prophet": 1, - "usage.modin": 2, "usage.matplotlib": 26, + "usage.modin": 2, "usage.dask": 43, "usage.sklearn": 56 } @@ -358527,8 +358527,8 @@ "usage.pandas": 19, "usage.scipy": 34, "usage.prophet": 1, - "usage.modin": 1, "usage.matplotlib": 10, + "usage.modin": 1, "usage.seaborn": 5, "usage.dask": 21, "usage.sklearn": 22, @@ -358548,8 +358548,8 @@ "usage.pandas": 22, "usage.scipy": 52, "usage.prophet": 1, - "usage.modin": 1, "usage.matplotlib": 9, + "usage.modin": 1, "usage.seaborn": 2, "usage.dask": 6, "usage.sklearn": 25 @@ -415554,12 +415554,18 @@ "_0": { "type": "str", "options": [ - "2010" + "0001-01-01T00:00:00" + ] + }, + "_1": { + "type": "str", + "options": [ + "s" ] } }, "metadata": { - "usage.modin": 2 + "usage.matplotlib": 1 } }, { @@ -415567,12 +415573,12 @@ "_0": { "type": "str", "options": [ - "2011" + "2019-06-30" ] } }, "metadata": { - "usage.modin": 1 + "usage.matplotlib": 1 } }, { @@ -415580,12 +415586,12 @@ "_0": { "type": "str", "options": [ - "2011-06-15T00:00" + "2019-01-01" ] } }, "metadata": { - "usage.modin": 1 + "usage.matplotlib": 1 } }, { @@ -415593,12 +415599,12 @@ "_0": { "type": "str", "options": [ - "2009-01-01" + "2019-12-31" ] } }, "metadata": { - "usage.modin": 1 + "usage.matplotlib": 1 } }, { @@ -415606,7 +415612,7 @@ "_0": { "type": "str", "options": [ - "0001-01-01T00:00:00" + "NaT" ] }, "_1": { @@ -415625,7 +415631,13 @@ "_0": { "type": "str", "options": [ - "2019-06-30" + "NaT" + ] + }, + "_1": { + "type": "str", + "options": [ + "ms" ] } }, @@ -415638,7 +415650,13 @@ "_0": { "type": "str", "options": [ - "2019-01-01" + "NaT" + ] + }, + "_1": { + "type": "str", + "options": [ + "us" ] } }, @@ -415651,12 +415669,12 @@ "_0": { "type": "str", "options": [ - "2019-12-31" + "2010" ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.modin": 2 } }, { @@ -415664,18 +415682,12 @@ "_0": { "type": "str", "options": [ - "NaT" - ] - }, - "_1": { - "type": "str", - "options": [ - "s" + "2011" ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.modin": 1 } }, { @@ -415683,18 +415695,12 @@ "_0": { "type": "str", "options": [ - "NaT" - ] - }, - "_1": { - "type": "str", - "options": [ - "ms" + "2011-06-15T00:00" ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.modin": 1 } }, { @@ -415702,18 +415708,12 @@ "_0": { "type": "str", "options": [ - "NaT" - ] - }, - "_1": { - "type": "str", - "options": [ - "us" + "2009-01-01" ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.modin": 1 } }, { @@ -415767,8 +415767,8 @@ "metadata": { "usage.pandas": 37, "usage.scipy": 4, - "usage.modin": 5, "usage.matplotlib": 6, + "usage.modin": 5, "usage.seaborn": 1, "usage.dask": 1 } @@ -424593,8 +424593,8 @@ "usage.statsmodels": 414, "usage.pandas": 2265, "usage.scipy": 629, - "usage.modin": 21, "usage.matplotlib": 61, + "usage.modin": 21, "usage.seaborn": 25, "usage.dask": 128, "usage.sklearn": 162, @@ -424644,8 +424644,8 @@ "usage.statsmodels": 9, "usage.pandas": 75, "usage.scipy": 3, - "usage.modin": 3, "usage.matplotlib": 4, + "usage.modin": 3, "usage.dask": 5, "usage.sklearn": 7 }, @@ -703097,8 +703097,8 @@ "usage.statsmodels": 46, "usage.pandas": 84, "usage.scipy": 18, - "usage.modin": 32, "usage.matplotlib": 3, + "usage.modin": 32, "usage.dask": 70, "usage.sklearn": 8 }, @@ -703644,8 +703644,8 @@ "usage.skimage": 3, "usage.statsmodels": 38, "usage.scipy": 6, - "usage.modin": 22, "usage.matplotlib": 2, + "usage.modin": 22, "usage.dask": 6, "usage.sklearn": 34 } @@ -705059,8 +705059,8 @@ "usage.xarray": 97, "usage.statsmodels": 34, "usage.scipy": 140, - "usage.modin": 2, "usage.matplotlib": 4, + "usage.modin": 2, "usage.seaborn": 2, "usage.dask": 39, "usage.sklearn": 351 @@ -705079,8 +705079,8 @@ "usage.xarray": 90, "usage.statsmodels": 42, "usage.scipy": 159, - "usage.modin": 3, "usage.matplotlib": 13, + "usage.modin": 3, "usage.seaborn": 13, "usage.dask": 43, "usage.sklearn": 73 @@ -716548,8 +716548,8 @@ "usage.statsmodels": 81, "usage.pandas": 160, "usage.scipy": 66, - "usage.modin": 38, "usage.matplotlib": 3, + "usage.modin": 38, "usage.dask": 133, "usage.sklearn": 217, "usage.networkx": 7 @@ -716582,8 +716582,8 @@ "usage.statsmodels": 79, "usage.pandas": 749, "usage.scipy": 330, - "usage.modin": 5, "usage.matplotlib": 17, + "usage.modin": 5, "usage.seaborn": 15, "usage.dask": 87, "usage.sklearn": 431 diff --git a/data/typing/numpy.py b/data/typing/numpy.py index 97e8759..1c0fabf 100644 --- a/data/typing/numpy.py +++ b/data/typing/numpy.py @@ -17986,69 +17986,6 @@ def array(_0: List[List[int]], /, *, dtype: None): ... -@overload -def array( - _0: List[ - List[modin.engines.ray.pandas_on_ray.frame.partition.PandasOnRayFramePartition] - ], - /, -): - """ - usage.modin: 30 - """ - ... - - -@overload -def array(_0: List[List[ray._raylet.ObjectRef]], /): - """ - usage.modin: 3 - """ - ... - - -@overload -def array(_0: List[Literal["foo", "bar"]], /, *, dtype: Type[object]): - """ - usage.modin: 1 - """ - ... - - -@overload -def array(_0: List[Literal["two", "one"]], /, *, dtype: Type[object]): - """ - usage.modin: 1 - """ - ... - - -@overload -def array(_0: List[Literal["shiny", "dull"]], /, *, dtype: Type[object]): - """ - usage.modin: 1 - """ - ... - - -@overload -def array(_0: List[numpy.int64], /, *, dtype: numpy.dtype): - """ - usage.dask: 1 - usage.modin: 1 - """ - ... - - -@overload -def array(_0: List[numpy.float32], /, *, dtype: numpy.dtype): - """ - usage.dask: 1 - usage.modin: 1 - """ - ... - - @overload def array(_0: Tuple[int, int, int, float], /, *, dtype: Type[float]): """ @@ -20223,6 +20160,69 @@ def array(_0: Tuple[int, float, float, float], /): ... +@overload +def array( + _0: List[ + List[modin.engines.ray.pandas_on_ray.frame.partition.PandasOnRayFramePartition] + ], + /, +): + """ + usage.modin: 30 + """ + ... + + +@overload +def array(_0: List[List[ray._raylet.ObjectRef]], /): + """ + usage.modin: 3 + """ + ... + + +@overload +def array(_0: List[Literal["foo", "bar"]], /, *, dtype: Type[object]): + """ + usage.modin: 1 + """ + ... + + +@overload +def array(_0: List[Literal["two", "one"]], /, *, dtype: Type[object]): + """ + usage.modin: 1 + """ + ... + + +@overload +def array(_0: List[Literal["shiny", "dull"]], /, *, dtype: Type[object]): + """ + usage.modin: 1 + """ + ... + + +@overload +def array(_0: List[numpy.int64], /, *, dtype: numpy.dtype): + """ + usage.dask: 1 + usage.modin: 1 + """ + ... + + +@overload +def array(_0: List[numpy.float32], /, *, dtype: numpy.dtype): + """ + usage.dask: 1 + usage.modin: 1 + """ + ... + + @overload def array(_0: List[bool], _1: Type[bool], /): """ @@ -139117,15 +139117,6 @@ def __call__(self, _0: numpy.ma.core.MaskedArray, _1: numpy.ma.core.MaskedArray, """ ... - @overload - def __call__(self, _0: numpy.ndarray, _1: numpy.int64, /): - """ - usage.dask: 6 - usage.modin: 1 - usage.sklearn: 3 - """ - ... - @overload def __call__(self, _0: matplotlib.transforms.Bbox, /): """ @@ -139184,6 +139175,15 @@ def __call__(self, _0: Tuple[int, int, float, float], /): """ ... + @overload + def __call__(self, _0: numpy.ndarray, _1: numpy.int64, /): + """ + usage.dask: 6 + usage.modin: 1 + usage.sklearn: 3 + """ + ... + @overload def __call__(self, _0: Tuple[int], /): """