GH-36252: [Python] Add non decomposable hash aggregate UDF #36253

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Merged
icexelloss merged 11 commits into from
Jun 29, 2023
Merged

GH-36252: [Python] Add non decomposable hash aggregate UDF #36253

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57a73a2
WIP: Test passing
icexelloss Jun 20, 2023
76c2b62
Remove breakpoints(); Fix num_args
icexelloss Jun 20, 2023
1a804f1
Add more tests
icexelloss Jun 22, 2023
d1c60ec
Lint
icexelloss Jun 22, 2023
be8474b
Lint, self review and documentation
icexelloss Jun 22, 2023
1519e45
More tests for error case
icexelloss Jun 22, 2023
14b1207
Lint
icexelloss Jun 22, 2023
d2d0076
Fix doc example
icexelloss Jun 23, 2023
007221a
Apply suggestions from code review
icexelloss Jun 26, 2023
378eb48
Address PR comments
icexelloss Jun 26, 2023
c29ae72
Apply suggestions from code review
icexelloss Jun 28, 2023
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12 changes: 12 additions & 0 deletions python/pyarrow/_compute.pyx
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -2767,6 +2767,9 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
This is often used with ordered or segmented aggregation where groups
can be emit before accumulating all of the input data.

Note that currently the size of any input column can not exceed 2 GB
for a single segment (all groups combined).

Parameters
----------
func : callable
Expand DownExpand Up@@ -2823,6 +2826,15 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
>>> answer = pc.call_function(func_name, [pa.array([20, 40])])
>>> answer
<pyarrow.DoubleScalar: 30.0>
>>> table = pa.table([pa.array([1, 1, 2, 2]), pa.array([10, 20, 30, 40])], names=['k', 'v'])
>>> result = table.group_by('k').aggregate([('v', 'py_compute_median')])
>>> result
pyarrow.Table
k: int64
v_py_compute_median: double
----
k: [[1,2]]
v_py_compute_median: [[15,35]]
"""
return _register_user_defined_function(get_register_aggregate_function(),
func, function_name, function_doc, in_types,
Expand Down
10 changes: 5 additions & 5 deletions python/pyarrow/conftest.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,8 @@
from pyarrow import Codec
from pyarrow import fs

import numpy as np

groups = [
'acero',
'brotli',
Expand DownExpand Up@@ -283,15 +285,14 @@ def unary_function(ctx, x):
@pytest.fixture(scope="session")
def unary_agg_func_fixture():
"""
Register a unary aggregate function
Register a unary aggregate function (mean)
"""
from pyarrow import compute as pc
import numpy as np

def func(ctx, x):
return pa.scalar(np.nanmean(x))

func_name = "y=avg(x)"
func_name = "mean_udf"
func_doc = {"summary": "y=avg(x)",
"description": "find mean of x"}

Expand All@@ -312,15 +313,14 @@ def varargs_agg_func_fixture():
Register a unary aggregate function
"""
from pyarrow import compute as pc
import numpy as np

def func(ctx, *args):
sum = 0.0
for arg in args:
sum += np.nanmean(arg)
return pa.scalar(sum)

func_name = "y=sum_mean(x...)"
func_name = "sum_mean"
func_doc = {"summary": "Varargs aggregate",
"description": "Varargs aggregate"}

Expand Down
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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GH-36252: [Python] Add non decomposable hash aggregate UDF #36253

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 Merged

icexelloss
merged 11 commits into

from

Jun 29, 2023
 Merged


GH-36252: [Python] Add non decomposable hash aggregate UDF #36253
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57a73a2

WIP: Test passing


icexelloss Jun 20, 2023
76c2b62

Remove breakpoints(); Fix num_args


icexelloss Jun 20, 2023
1a804f1

Add more tests


icexelloss Jun 22, 2023
d1c60ec

Lint


icexelloss Jun 22, 2023
be8474b

Lint, self review and documentation


icexelloss Jun 22, 2023
1519e45

More tests for error case


icexelloss Jun 22, 2023
14b1207

Lint


icexelloss Jun 22, 2023
d2d0076

Fix doc example


icexelloss Jun 23, 2023
007221a

Apply suggestions from code review


icexelloss Jun 26, 2023
378eb48

Address PR comments


icexelloss Jun 26, 2023
c29ae72

Apply suggestions from code review


icexelloss Jun 28, 2023

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12 changes: 12 additions & 0 deletions
python/pyarrow/_compute.pyx

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2767,6 +2767,9 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 This is often used with ordered or segmented aggregation where groups
 can be emit before accumulating all of the input data.

 Note that currently the size of any input column can not exceed 2 GB
 for a single segment (all groups combined).

 Parameters
 ----------
 func : callable
Expand DownExpand Up@@ -2823,6 +2826,15 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 >>> answer = pc.call_function(func_name, [pa.array([20, 40])])
 >>> answer
 <pyarrow.DoubleScalar: 30.0>
 >>> table = pa.table([pa.array([1, 1, 2, 2]), pa.array([10, 20, 30, 40])], names=['k', 'v'])
 >>> result = table.group_by('k').aggregate([('v', 'py_compute_median')])
 >>> result
 pyarrow.Table
 k: int64
 v_py_compute_median: double
 ----
 k: [[1,2]]
 v_py_compute_median: [[15,35]]
 """
 return _register_user_defined_function(get_register_aggregate_function(),
 func, function_name, function_doc, in_types,
Expand Down

10 changes: 5 additions & 5 deletions
python/pyarrow/conftest.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,8 @@
from pyarrow import Codec
from pyarrow import fs

import numpy as np

groups = [
 'acero',
 'brotli',
Expand DownExpand Up@@ -283,15 +285,14 @@ def unary_function(ctx, x):
@pytest.fixture(scope="session")
def unary_agg_func_fixture():
 """
 Register a unary aggregate function
 Register a unary aggregate function (mean)
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, x):
 return pa.scalar(np.nanmean(x))

 func_name = "y=avg(x)"
 func_name = "mean_udf"
 func_doc = {"summary": "y=avg(x)",
 "description": "find mean of x"}

Expand All@@ -312,15 +313,14 @@ def varargs_agg_func_fixture():
 Register a unary aggregate function
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, *args):
 sum = 0.0
 for arg in args:
 sum += np.nanmean(arg)
 return pa.scalar(sum)

 func_name = "y=sum_mean(x...)"
 func_name = "sum_mean"
 func_doc = {"summary": "Varargs aggregate",
 "description": "Varargs aggregate"}

Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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GH-36252: [Python] Add non decomposable hash aggregate UDF #36253

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 Merged

icexelloss
merged 11 commits into

from

Jun 29, 2023
 Merged


GH-36252: [Python] Add non decomposable hash aggregate UDF #36253
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57a73a2

WIP: Test passing


icexelloss Jun 20, 2023
76c2b62

Remove breakpoints(); Fix num_args


icexelloss Jun 20, 2023
1a804f1

Add more tests


icexelloss Jun 22, 2023
d1c60ec

Lint


icexelloss Jun 22, 2023
be8474b

Lint, self review and documentation


icexelloss Jun 22, 2023
1519e45

More tests for error case


icexelloss Jun 22, 2023
14b1207

Lint


icexelloss Jun 22, 2023
d2d0076

Fix doc example


icexelloss Jun 23, 2023
007221a

Apply suggestions from code review


icexelloss Jun 26, 2023
378eb48

Address PR comments


icexelloss Jun 26, 2023
c29ae72

Apply suggestions from code review


icexelloss Jun 28, 2023

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12 changes: 12 additions & 0 deletions
python/pyarrow/_compute.pyx

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2767,6 +2767,9 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 This is often used with ordered or segmented aggregation where groups
 can be emit before accumulating all of the input data.

 Note that currently the size of any input column can not exceed 2 GB
 for a single segment (all groups combined).

 Parameters
 ----------
 func : callable
Expand DownExpand Up@@ -2823,6 +2826,15 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 >>> answer = pc.call_function(func_name, [pa.array([20, 40])])
 >>> answer
 <pyarrow.DoubleScalar: 30.0>
 >>> table = pa.table([pa.array([1, 1, 2, 2]), pa.array([10, 20, 30, 40])], names=['k', 'v'])
 >>> result = table.group_by('k').aggregate([('v', 'py_compute_median')])
 >>> result
 pyarrow.Table
 k: int64
 v_py_compute_median: double
 ----
 k: [[1,2]]
 v_py_compute_median: [[15,35]]
 """
 return _register_user_defined_function(get_register_aggregate_function(),
 func, function_name, function_doc, in_types,
Expand Down

10 changes: 5 additions & 5 deletions
python/pyarrow/conftest.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,8 @@
from pyarrow import Codec
from pyarrow import fs

import numpy as np

groups = [
 'acero',
 'brotli',
Expand DownExpand Up@@ -283,15 +285,14 @@ def unary_function(ctx, x):
@pytest.fixture(scope="session")
def unary_agg_func_fixture():
 """
 Register a unary aggregate function
 Register a unary aggregate function (mean)
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, x):
 return pa.scalar(np.nanmean(x))

 func_name = "y=avg(x)"
 func_name = "mean_udf"
 func_doc = {"summary": "y=avg(x)",
 "description": "find mean of x"}

Expand All@@ -312,15 +313,14 @@ def varargs_agg_func_fixture():
 Register a unary aggregate function
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, *args):
 sum = 0.0
 for arg in args:
 sum += np.nanmean(arg)
 return pa.scalar(sum)

 func_name = "y=sum_mean(x...)"
 func_name = "sum_mean"
 func_doc = {"summary": "Varargs aggregate",
 "description": "Varargs aggregate"}

Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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GH-36252: [Python] Add non decomposable hash aggregate UDF #36253

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 Merged

icexelloss
merged 11 commits into

from

Jun 29, 2023
 Merged


GH-36252: [Python] Add non decomposable hash aggregate UDF #36253
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57a73a2

WIP: Test passing


icexelloss Jun 20, 2023
76c2b62

Remove breakpoints(); Fix num_args


icexelloss Jun 20, 2023
1a804f1

Add more tests


icexelloss Jun 22, 2023
d1c60ec

Lint


icexelloss Jun 22, 2023
be8474b

Lint, self review and documentation


icexelloss Jun 22, 2023
1519e45

More tests for error case


icexelloss Jun 22, 2023
14b1207

Lint


icexelloss Jun 22, 2023
d2d0076

Fix doc example


icexelloss Jun 23, 2023
007221a

Apply suggestions from code review


icexelloss Jun 26, 2023
378eb48

Address PR comments


icexelloss Jun 26, 2023
c29ae72

Apply suggestions from code review


icexelloss Jun 28, 2023

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12 changes: 12 additions & 0 deletions
python/pyarrow/_compute.pyx

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2767,6 +2767,9 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 This is often used with ordered or segmented aggregation where groups
 can be emit before accumulating all of the input data.

 Note that currently the size of any input column can not exceed 2 GB
 for a single segment (all groups combined).

 Parameters
 ----------
 func : callable
Expand DownExpand Up@@ -2823,6 +2826,15 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 >>> answer = pc.call_function(func_name, [pa.array([20, 40])])
 >>> answer
 <pyarrow.DoubleScalar: 30.0>
 >>> table = pa.table([pa.array([1, 1, 2, 2]), pa.array([10, 20, 30, 40])], names=['k', 'v'])
 >>> result = table.group_by('k').aggregate([('v', 'py_compute_median')])
 >>> result
 pyarrow.Table
 k: int64
 v_py_compute_median: double
 ----
 k: [[1,2]]
 v_py_compute_median: [[15,35]]
 """
 return _register_user_defined_function(get_register_aggregate_function(),
 func, function_name, function_doc, in_types,
Expand Down

10 changes: 5 additions & 5 deletions
python/pyarrow/conftest.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,8 @@
from pyarrow import Codec
from pyarrow import fs

import numpy as np

groups = [
 'acero',
 'brotli',
Expand DownExpand Up@@ -283,15 +285,14 @@ def unary_function(ctx, x):
@pytest.fixture(scope="session")
def unary_agg_func_fixture():
 """
 Register a unary aggregate function
 Register a unary aggregate function (mean)
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, x):
 return pa.scalar(np.nanmean(x))

 func_name = "y=avg(x)"
 func_name = "mean_udf"
 func_doc = {"summary": "y=avg(x)",
 "description": "find mean of x"}

Expand All@@ -312,15 +313,14 @@ def varargs_agg_func_fixture():
 Register a unary aggregate function
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, *args):
 sum = 0.0
 for arg in args:
 sum += np.nanmean(arg)
 return pa.scalar(sum)

 func_name = "y=sum_mean(x...)"
 func_name = "sum_mean"
 func_doc = {"summary": "Varargs aggregate",
 "description": "Varargs aggregate"}

Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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GH-36252: [Python] Add non decomposable hash aggregate UDF #36253

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 Merged

icexelloss
merged 11 commits into

from

Jun 29, 2023
 Merged


GH-36252: [Python] Add non decomposable hash aggregate UDF #36253
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57a73a2

WIP: Test passing


icexelloss Jun 20, 2023
76c2b62

Remove breakpoints(); Fix num_args


icexelloss Jun 20, 2023
1a804f1

Add more tests


icexelloss Jun 22, 2023
d1c60ec

Lint


icexelloss Jun 22, 2023
be8474b

Lint, self review and documentation


icexelloss Jun 22, 2023
1519e45

More tests for error case


icexelloss Jun 22, 2023
14b1207

Lint


icexelloss Jun 22, 2023
d2d0076

Fix doc example


icexelloss Jun 23, 2023
007221a

Apply suggestions from code review


icexelloss Jun 26, 2023
378eb48

Address PR comments


icexelloss Jun 26, 2023
c29ae72

Apply suggestions from code review


icexelloss Jun 28, 2023

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12 changes: 12 additions & 0 deletions
python/pyarrow/_compute.pyx

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2767,6 +2767,9 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 This is often used with ordered or segmented aggregation where groups
 can be emit before accumulating all of the input data.

 Note that currently the size of any input column can not exceed 2 GB
 for a single segment (all groups combined).

 Parameters
 ----------
 func : callable
Expand DownExpand Up@@ -2823,6 +2826,15 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 >>> answer = pc.call_function(func_name, [pa.array([20, 40])])
 >>> answer
 <pyarrow.DoubleScalar: 30.0>
 >>> table = pa.table([pa.array([1, 1, 2, 2]), pa.array([10, 20, 30, 40])], names=['k', 'v'])
 >>> result = table.group_by('k').aggregate([('v', 'py_compute_median')])
 >>> result
 pyarrow.Table
 k: int64
 v_py_compute_median: double
 ----
 k: [[1,2]]
 v_py_compute_median: [[15,35]]
 """
 return _register_user_defined_function(get_register_aggregate_function(),
 func, function_name, function_doc, in_types,
Expand Down

10 changes: 5 additions & 5 deletions
python/pyarrow/conftest.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,8 @@
from pyarrow import Codec
from pyarrow import fs

import numpy as np

groups = [
 'acero',
 'brotli',
Expand DownExpand Up@@ -283,15 +285,14 @@ def unary_function(ctx, x):
@pytest.fixture(scope="session")
def unary_agg_func_fixture():
 """
 Register a unary aggregate function
 Register a unary aggregate function (mean)
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, x):
 return pa.scalar(np.nanmean(x))

 func_name = "y=avg(x)"
 func_name = "mean_udf"
 func_doc = {"summary": "y=avg(x)",
 "description": "find mean of x"}

Expand All@@ -312,15 +313,14 @@ def varargs_agg_func_fixture():
 Register a unary aggregate function
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, *args):
 sum = 0.0
 for arg in args:
 sum += np.nanmean(arg)
 return pa.scalar(sum)

 func_name = "y=sum_mean(x...)"
 func_name = "sum_mean"
 func_doc = {"summary": "Varargs aggregate",
 "description": "Varargs aggregate"}

Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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icexelloss
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Jun 29, 2023
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GH-36252: [Python] Add non decomposable hash aggregate UDF #36253
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57a73a2

WIP: Test passing


icexelloss Jun 20, 2023
76c2b62

Remove breakpoints(); Fix num_args


icexelloss Jun 20, 2023
1a804f1

Add more tests


icexelloss Jun 22, 2023
d1c60ec

Lint


icexelloss Jun 22, 2023
be8474b

Lint, self review and documentation


icexelloss Jun 22, 2023
1519e45

More tests for error case


icexelloss Jun 22, 2023
14b1207

Lint


icexelloss Jun 22, 2023
d2d0076

Fix doc example


icexelloss Jun 23, 2023
007221a

Apply suggestions from code review


icexelloss Jun 26, 2023
378eb48

Address PR comments


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c29ae72

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12 changes: 12 additions & 0 deletions
python/pyarrow/_compute.pyx

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2767,6 +2767,9 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 This is often used with ordered or segmented aggregation where groups
 can be emit before accumulating all of the input data.

 Note that currently the size of any input column can not exceed 2 GB
 for a single segment (all groups combined).

 Parameters
 ----------
 func : callable
Expand DownExpand Up@@ -2823,6 +2826,15 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 >>> answer = pc.call_function(func_name, [pa.array([20, 40])])
 >>> answer
 <pyarrow.DoubleScalar: 30.0>
 >>> table = pa.table([pa.array([1, 1, 2, 2]), pa.array([10, 20, 30, 40])], names=['k', 'v'])
 >>> result = table.group_by('k').aggregate([('v', 'py_compute_median')])
 >>> result
 pyarrow.Table
 k: int64
 v_py_compute_median: double
 ----
 k: [[1,2]]
 v_py_compute_median: [[15,35]]
 """
 return _register_user_defined_function(get_register_aggregate_function(),
 func, function_name, function_doc, in_types,
Expand Down

10 changes: 5 additions & 5 deletions
python/pyarrow/conftest.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,8 @@
from pyarrow import Codec
from pyarrow import fs

import numpy as np

groups = [
 'acero',
 'brotli',
Expand DownExpand Up@@ -283,15 +285,14 @@ def unary_function(ctx, x):
@pytest.fixture(scope="session")
def unary_agg_func_fixture():
 """
 Register a unary aggregate function
 Register a unary aggregate function (mean)
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, x):
 return pa.scalar(np.nanmean(x))

 func_name = "y=avg(x)"
 func_name = "mean_udf"
 func_doc = {"summary": "y=avg(x)",
 "description": "find mean of x"}

Expand All@@ -312,15 +313,14 @@ def varargs_agg_func_fixture():
 Register a unary aggregate function
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, *args):
 sum = 0.0
 for arg in args:
 sum += np.nanmean(arg)
 return pa.scalar(sum)

 func_name = "y=sum_mean(x...)"
 func_name = "sum_mean"
 func_doc = {"summary": "Varargs aggregate",
 "description": "Varargs aggregate"}

Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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GH-36252: [Python] Add non decomposable hash aggregate UDF #36253

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 Merged

icexelloss
merged 11 commits into

from

Jun 29, 2023
 Merged


GH-36252: [Python] Add non decomposable hash aggregate UDF #36253
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57a73a2

WIP: Test passing


icexelloss Jun 20, 2023
76c2b62

Remove breakpoints(); Fix num_args


icexelloss Jun 20, 2023
1a804f1

Add more tests


icexelloss Jun 22, 2023
d1c60ec

Lint


icexelloss Jun 22, 2023
be8474b

Lint, self review and documentation


icexelloss Jun 22, 2023
1519e45

More tests for error case


icexelloss Jun 22, 2023
14b1207

Lint


icexelloss Jun 22, 2023
d2d0076

Fix doc example


icexelloss Jun 23, 2023
007221a

Apply suggestions from code review


icexelloss Jun 26, 2023
378eb48

Address PR comments


icexelloss Jun 26, 2023
c29ae72

Apply suggestions from code review


icexelloss Jun 28, 2023

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12 changes: 12 additions & 0 deletions
python/pyarrow/_compute.pyx

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2767,6 +2767,9 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 This is often used with ordered or segmented aggregation where groups
 can be emit before accumulating all of the input data.

 Note that currently the size of any input column can not exceed 2 GB
 for a single segment (all groups combined).

 Parameters
 ----------
 func : callable
Expand DownExpand Up@@ -2823,6 +2826,15 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 >>> answer = pc.call_function(func_name, [pa.array([20, 40])])
 >>> answer
 <pyarrow.DoubleScalar: 30.0>
 >>> table = pa.table([pa.array([1, 1, 2, 2]), pa.array([10, 20, 30, 40])], names=['k', 'v'])
 >>> result = table.group_by('k').aggregate([('v', 'py_compute_median')])
 >>> result
 pyarrow.Table
 k: int64
 v_py_compute_median: double
 ----
 k: [[1,2]]
 v_py_compute_median: [[15,35]]
 """
 return _register_user_defined_function(get_register_aggregate_function(),
 func, function_name, function_doc, in_types,
Expand Down

10 changes: 5 additions & 5 deletions
python/pyarrow/conftest.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,8 @@
from pyarrow import Codec
from pyarrow import fs

import numpy as np

groups = [
 'acero',
 'brotli',
Expand DownExpand Up@@ -283,15 +285,14 @@ def unary_function(ctx, x):
@pytest.fixture(scope="session")
def unary_agg_func_fixture():
 """
 Register a unary aggregate function
 Register a unary aggregate function (mean)
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, x):
 return pa.scalar(np.nanmean(x))

 func_name = "y=avg(x)"
 func_name = "mean_udf"
 func_doc = {"summary": "y=avg(x)",
 "description": "find mean of x"}

Expand All@@ -312,15 +313,14 @@ def varargs_agg_func_fixture():
 Register a unary aggregate function
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, *args):
 sum = 0.0
 for arg in args:
 sum += np.nanmean(arg)
 return pa.scalar(sum)

 func_name = "y=sum_mean(x...)"
 func_name = "sum_mean"
 func_doc = {"summary": "Varargs aggregate",
 "description": "Varargs aggregate"}

Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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GH-36252: [Python] Add non decomposable hash aggregate UDF #36253

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 Merged

icexelloss
merged 11 commits into

from

Jun 29, 2023
 Merged


GH-36252: [Python] Add non decomposable hash aggregate UDF #36253
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57a73a2

WIP: Test passing


icexelloss Jun 20, 2023
76c2b62

Remove breakpoints(); Fix num_args


icexelloss Jun 20, 2023
1a804f1

Add more tests


icexelloss Jun 22, 2023
d1c60ec

Lint


icexelloss Jun 22, 2023
be8474b

Lint, self review and documentation


icexelloss Jun 22, 2023
1519e45

More tests for error case


icexelloss Jun 22, 2023
14b1207

Lint


icexelloss Jun 22, 2023
d2d0076

Fix doc example


icexelloss Jun 23, 2023
007221a

Apply suggestions from code review


icexelloss Jun 26, 2023
378eb48

Address PR comments


icexelloss Jun 26, 2023
c29ae72

Apply suggestions from code review


icexelloss Jun 28, 2023

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12 changes: 12 additions & 0 deletions
python/pyarrow/_compute.pyx

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2767,6 +2767,9 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 This is often used with ordered or segmented aggregation where groups
 can be emit before accumulating all of the input data.

 Note that currently the size of any input column can not exceed 2 GB
 for a single segment (all groups combined).

 Parameters
 ----------
 func : callable
Expand DownExpand Up@@ -2823,6 +2826,15 @@ def register_aggregate_function(func, function_name, function_doc, in_types, out
 >>> answer = pc.call_function(func_name, [pa.array([20, 40])])
 >>> answer
 <pyarrow.DoubleScalar: 30.0>
 >>> table = pa.table([pa.array([1, 1, 2, 2]), pa.array([10, 20, 30, 40])], names=['k', 'v'])
 >>> result = table.group_by('k').aggregate([('v', 'py_compute_median')])
 >>> result
 pyarrow.Table
 k: int64
 v_py_compute_median: double
 ----
 k: [[1,2]]
 v_py_compute_median: [[15,35]]
 """
 return _register_user_defined_function(get_register_aggregate_function(),
 func, function_name, function_doc, in_types,
Expand Down

10 changes: 5 additions & 5 deletions
python/pyarrow/conftest.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -20,6 +20,8 @@
from pyarrow import Codec
from pyarrow import fs

import numpy as np

groups = [
 'acero',
 'brotli',
Expand DownExpand Up@@ -283,15 +285,14 @@ def unary_function(ctx, x):
@pytest.fixture(scope="session")
def unary_agg_func_fixture():
 """
 Register a unary aggregate function
 Register a unary aggregate function (mean)
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, x):
 return pa.scalar(np.nanmean(x))

 func_name = "y=avg(x)"
 func_name = "mean_udf"
 func_doc = {"summary": "y=avg(x)",
 "description": "find mean of x"}

Expand All@@ -312,15 +313,14 @@ def varargs_agg_func_fixture():
 Register a unary aggregate function
 """
 from pyarrow import compute as pc
 import numpy as np

 def func(ctx, *args):
 sum = 0.0
 for arg in args:
 sum += np.nanmean(arg)
 return pa.scalar(sum)

 func_name = "y=sum_mean(x...)"
 func_name = "sum_mean"
 func_doc = {"summary": "Varargs aggregate",
 "description": "Varargs aggregate"}

Expand Down
Loading