[Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas #15664

Closed
Closed
[Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas#15664
Assignees
pitrou
Labels
Component: PythonType: bug
Milestone
0.9.0

Description

@asfimport
asfimport
opened on Feb 12, 2018

If you create a Table from a DataFrame of ints with a NaN value the NaN is improperly cast. Since pandas casts these to floats, when converted to a table the NaN is interpreted as an integer. This seems like a bug since a known limitation in pandas (the inability to have null valued integers data) is taking precedence over arrow's functionality to store these as an IntArray with nulls.

importpyarrowaspaimportpandasaspddf = pd.DataFrame({"a":[1, 2, pd.np.NaN]})
schema = pa.schema([pa.field("a", pa.int64(), nullable=True)])
table = pa.Table.from_pandas(df, schema=schema)
table[0]
<pyarrow.lib.Columnobjectat0x7f2151d19c90>
chunk0: <pyarrow.lib.Int64Arrayobjectat0x7f213bf356d8>
[
1,
2,
-9223372036854775808
]

Reporter: Matthew Gilbert
Assignee: Antoine Pitrou / @pitrou

Related issues:

PRs and other links:

Note: This issue was originally created as ARROW-2135. Please see the migration documentation for further details.

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    , '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" + '

    Skip to content
    [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas #15664
    Closed
    Closed
    [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas#15664
    Assignees
    pitrou
    Labels
    Component: PythonType: bug
    Milestone
    0.9.0
    Description
    @asfimport
    asfimport
    opened on Feb 12, 2018
    If you create a Table from a DataFrame of ints with a NaN value the NaN is improperly cast. Since pandas casts these to floats, when converted to a table the NaN is interpreted as an integer. This seems like a bug since a known limitation in pandas (the inability to have null valued integers data) is taking precedence over arrow's functionality to store these as an IntArray with nulls.
    importpyarrowaspaimportpandasaspddf = pd.DataFrame({"a":[1, 2, pd.np.NaN]})
schema = pa.schema([pa.field("a", pa.int64(), nullable=True)])
table = pa.Table.from_pandas(df, schema=schema)
table[0]
<pyarrow.lib.Columnobjectat0x7f2151d19c90>
chunk0: <pyarrow.lib.Int64Arrayobjectat0x7f213bf356d8>
[
1,
2,
-9223372036854775808
]
    Reporter: Matthew Gilbert
    Assignee: Antoine Pitrou / @pitrou
    Related issues:
    PRs and other links:
    Note: This issue was originally created as ARROW-2135. Please see the migration documentation for further details.
    Activity
    Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
    Metadata
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    Labels
    Component: PythonType: bug
    Type
    No type
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    No projects
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      None yet
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      No branches or pull requests
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, '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('^' + ".*" + '

      Skip to content
      [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas #15664
      Closed
      Closed
      [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas#15664
      Assignees
      pitrou
      Labels
      Component: PythonType: bug
      Milestone
      0.9.0
      Description
      @asfimport
      asfimport
      opened on Feb 12, 2018
      If you create a Table from a DataFrame of ints with a NaN value the NaN is improperly cast. Since pandas casts these to floats, when converted to a table the NaN is interpreted as an integer. This seems like a bug since a known limitation in pandas (the inability to have null valued integers data) is taking precedence over arrow's functionality to store these as an IntArray with nulls.
      importpyarrowaspaimportpandasaspddf = pd.DataFrame({"a":[1, 2, pd.np.NaN]})
schema = pa.schema([pa.field("a", pa.int64(), nullable=True)])
table = pa.Table.from_pandas(df, schema=schema)
table[0]
<pyarrow.lib.Columnobjectat0x7f2151d19c90>
chunk0: <pyarrow.lib.Int64Arrayobjectat0x7f213bf356d8>
[
1,
2,
-9223372036854775808
]
      Reporter: Matthew Gilbert
      Assignee: Antoine Pitrou / @pitrou
      Related issues:
      PRs and other links:
      Note: This issue was originally created as ARROW-2135. Please see the migration documentation for further details.
      Activity
      Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
      Metadata
      Metadata
      Assignees
      Labels
      Component: PythonType: bug
      Type
      No type
      Projects
      No projects
        Milestone
        Relationships
        None yet
        Development
        No branches or pull requests
        Issue actions
        
, '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('^' + ".*" + '

        Skip to content
        [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas #15664
        Closed
        Closed
        [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas#15664
        Assignees
        pitrou
        Labels
        Component: PythonType: bug
        Milestone
        0.9.0
        Description
        @asfimport
        asfimport
        opened on Feb 12, 2018
        If you create a Table from a DataFrame of ints with a NaN value the NaN is improperly cast. Since pandas casts these to floats, when converted to a table the NaN is interpreted as an integer. This seems like a bug since a known limitation in pandas (the inability to have null valued integers data) is taking precedence over arrow's functionality to store these as an IntArray with nulls.
        importpyarrowaspaimportpandasaspddf = pd.DataFrame({"a":[1, 2, pd.np.NaN]})
schema = pa.schema([pa.field("a", pa.int64(), nullable=True)])
table = pa.Table.from_pandas(df, schema=schema)
table[0]
<pyarrow.lib.Columnobjectat0x7f2151d19c90>
chunk0: <pyarrow.lib.Int64Arrayobjectat0x7f213bf356d8>
[
1,
2,
-9223372036854775808
]
        Reporter: Matthew Gilbert
        Assignee: Antoine Pitrou / @pitrou
        Related issues:
        PRs and other links:
        Note: This issue was originally created as ARROW-2135. Please see the migration documentation for further details.
        Activity
        Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
        Metadata
        Metadata
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        Labels
        Component: PythonType: bug
        Type
        No type
        Projects
        No projects
          Milestone
          Relationships
          None yet
          Development
          No branches or pull requests
          Issue actions
          
, '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" + '

          Skip to content
          [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas #15664
          Closed
          Closed
          [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas#15664
          Assignees
          pitrou
          Labels
          Component: PythonType: bug
          Milestone
          0.9.0
          Description
          @asfimport
          asfimport
          opened on Feb 12, 2018
          If you create a Table from a DataFrame of ints with a NaN value the NaN is improperly cast. Since pandas casts these to floats, when converted to a table the NaN is interpreted as an integer. This seems like a bug since a known limitation in pandas (the inability to have null valued integers data) is taking precedence over arrow's functionality to store these as an IntArray with nulls.
          importpyarrowaspaimportpandasaspddf = pd.DataFrame({"a":[1, 2, pd.np.NaN]})
schema = pa.schema([pa.field("a", pa.int64(), nullable=True)])
table = pa.Table.from_pandas(df, schema=schema)
table[0]
<pyarrow.lib.Columnobjectat0x7f2151d19c90>
chunk0: <pyarrow.lib.Int64Arrayobjectat0x7f213bf356d8>
[
1,
2,
-9223372036854775808
]
          Reporter: Matthew Gilbert
          Assignee: Antoine Pitrou / @pitrou
          Related issues:
          PRs and other links:
          Note: This issue was originally created as ARROW-2135. Please see the migration documentation for further details.
          Activity
          Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
          Metadata
          Metadata
          Assignees
          Labels
          Component: PythonType: bug
          Type
          No type
          Projects
          No projects
            Milestone
            Relationships
            None yet
            Development
            No branches or pull requests
            Issue actions
            
, '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('^' + ".*" + '

            Skip to content
            [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas #15664
            Closed
            Closed
            [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas#15664
            Assignees
            pitrou
            Labels
            Component: PythonType: bug
            Milestone
            0.9.0
            Description
            @asfimport
            asfimport
            opened on Feb 12, 2018
            If you create a Table from a DataFrame of ints with a NaN value the NaN is improperly cast. Since pandas casts these to floats, when converted to a table the NaN is interpreted as an integer. This seems like a bug since a known limitation in pandas (the inability to have null valued integers data) is taking precedence over arrow's functionality to store these as an IntArray with nulls.
            importpyarrowaspaimportpandasaspddf = pd.DataFrame({"a":[1, 2, pd.np.NaN]})
schema = pa.schema([pa.field("a", pa.int64(), nullable=True)])
table = pa.Table.from_pandas(df, schema=schema)
table[0]
<pyarrow.lib.Columnobjectat0x7f2151d19c90>
chunk0: <pyarrow.lib.Int64Arrayobjectat0x7f213bf356d8>
[
1,
2,
-9223372036854775808
]
            Reporter: Matthew Gilbert
            Assignee: Antoine Pitrou / @pitrou
            Related issues:
            PRs and other links:
            Note: This issue was originally created as ARROW-2135. Please see the migration documentation for further details.
            Activity
            Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
            Metadata
            Metadata
            Assignees
            Labels
            Component: PythonType: bug
            Type
            No type
            Projects
            No projects
              Milestone
              Relationships
              None yet
              Development
              No branches or pull requests
              Issue actions
              
, '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('^' + ".*" + '

              Skip to content
              [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas #15664
              Closed
              Closed
              [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas#15664
              Assignees
              pitrou
              Labels
              Component: PythonType: bug
              Milestone
              0.9.0
              Description
              @asfimport
              asfimport
              opened on Feb 12, 2018
              If you create a Table from a DataFrame of ints with a NaN value the NaN is improperly cast. Since pandas casts these to floats, when converted to a table the NaN is interpreted as an integer. This seems like a bug since a known limitation in pandas (the inability to have null valued integers data) is taking precedence over arrow's functionality to store these as an IntArray with nulls.
              importpyarrowaspaimportpandasaspddf = pd.DataFrame({"a":[1, 2, pd.np.NaN]})
schema = pa.schema([pa.field("a", pa.int64(), nullable=True)])
table = pa.Table.from_pandas(df, schema=schema)
table[0]
<pyarrow.lib.Columnobjectat0x7f2151d19c90>
chunk0: <pyarrow.lib.Int64Arrayobjectat0x7f213bf356d8>
[
1,
2,
-9223372036854775808
]
              Reporter: Matthew Gilbert
              Assignee: Antoine Pitrou / @pitrou
              Related issues:
              PRs and other links:
              Note: This issue was originally created as ARROW-2135. Please see the migration documentation for further details.
              Activity
              Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
              Metadata
              Metadata
              Assignees
              Labels
              Component: PythonType: bug
              Type
              No type
              Projects
              No projects
                Milestone
                Relationships
                None yet
                Development
                No branches or pull requests
                Issue actions
                
, '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); }
})();
})();

                Skip to content
                [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas #15664
                Closed
                Closed
                [Python] NaN values silently casted to int64 when passing explicit schema for conversion in Table.from_pandas#15664
                Assignees
                pitrou
                Labels
                Component: PythonType: bug
                Milestone
                0.9.0
                Description
                @asfimport
                asfimport
                opened on Feb 12, 2018
                If you create a Table from a DataFrame of ints with a NaN value the NaN is improperly cast. Since pandas casts these to floats, when converted to a table the NaN is interpreted as an integer. This seems like a bug since a known limitation in pandas (the inability to have null valued integers data) is taking precedence over arrow's functionality to store these as an IntArray with nulls.
                importpyarrowaspaimportpandasaspddf = pd.DataFrame({"a":[1, 2, pd.np.NaN]})
schema = pa.schema([pa.field("a", pa.int64(), nullable=True)])
table = pa.Table.from_pandas(df, schema=schema)
table[0]
<pyarrow.lib.Columnobjectat0x7f2151d19c90>
chunk0: <pyarrow.lib.Int64Arrayobjectat0x7f213bf356d8>
[
1,
2,
-9223372036854775808
]
                Reporter: Matthew Gilbert
                Assignee: Antoine Pitrou / @pitrou
                Related issues:
                PRs and other links:
                Note: This issue was originally created as ARROW-2135. Please see the migration documentation for further details.
                Activity
                Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
                Metadata
                Metadata
                Assignees
                Labels
                Component: PythonType: bug
                Type
                No type
                Projects
                No projects
                  Milestone
                  Relationships
                  None yet
                  Development
                  No branches or pull requests
                  Issue actions