[C++] Parse time32 from string and infer in CSV reader #27146

Closed
Closed
[C++] Parse time32 from string and infer in CSV reader#27146
Assignees
pitrou
Labels
Component: C++Type: enhancement
Milestone
6.0.0

Description

@asfimport
asfimport
opened on Jan 13, 2021

When reading a CSV with read_csv_arrow() with date types and time types, the dates are read as datetimes rather than dates and times are read as characters rather than time.

The first problem can be fixed by supplying date32() to schema(), though better inference would be nice. However, supplying time32() to schema() causes an error.

Here is a sample dataset, also attached.

date,time,reading
2021-01-01,00:00:00,67.8
2021-01-01,00:00:00,72.4
2021-01-01,00:00:00,63.1
2021-01-01,00:05:00,67.8

Reading with readr::read_csv() results in a tibble with three columns: date, time, dbl, as expected.

samp_readr<-readr::read_csv('sampledata.csv')
samp_readr
# A tibble: 4 x 3datetimereading<date><time><dbl>12021-01-0100'00" 67.82 2021-01-01 00'00" 72.43 2021-01-01 00'00"63.142021-01-0105'00" 67.8

Reading with arrow::read_csv_arrow() without providing schema() results in a tibble with three columns: dttm, chr, dbl.

samp_arrow_plain<-arrow::read_csv_arrow('sampledata.csv')
samp_arrow_plain
# A tibble: 4 x 3datetimereading<dttm><chr><dbl>12020-12-3119:00:0000:00:0067.822020-12-3119:00:0000:00:0072.432020-12-3119:00:0000:00:0063.142020-12-3119:00:0000:05:0067.8

Reading with arrow::read_csv_arrow() and providing date=date32() via schema() to col_types results in a tibble with three columns: date, chr, dbl.

samp_arrow_date<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(date=date32()))
samp_arrow_date
# A tibble: 4 x 3datetimereading<date><chr><dbl>12021-01-0100:00:0067.822021-01-0100:00:0072.432021-01-0100:00:0063.142021-01-0100:05:0067.8

Reading with arrow::read_csv_arrow() and providing time=time32() via schema() to col_types generates an error.

samp_arrow_time<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(time=time32()))
Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported

The same error occurs when using compact string notation.

samp_arrow_string<-arrow::read_csv_arrow('sampledata.csv', col_types='DTc', col_names=c('date', 'time', 'reading'), skip=1)
Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported

This is something in the internals, so far beyond me to figure out a fix, but I saw it in action and wanted to report it.

Environment: Ubuntu 18.04, R 4.0.3
Reporter: Jared Lander
Assignee: Antoine Pitrou / @pitrou

Related issues:

Original Issue Attachments:

PRs and other links:

Note: This issue was originally created as ARROW-11243. 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 
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}
} 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
    [C++] Parse time32 from string and infer in CSV reader #27146
    Closed
    Closed
    [C++] Parse time32 from string and infer in CSV reader#27146
    Assignees
    pitrou
    Labels
    Component: C++Type: enhancement
    Milestone
    6.0.0
    Description
    @asfimport
    asfimport
    opened on Jan 13, 2021
    When reading a CSV with read_csv_arrow() with date types and time types, the dates are read as datetimes rather than dates and times are read as characters rather than time.
    The first problem can be fixed by supplying date32() to schema(), though better inference would be nice. However, supplying time32() to schema() causes an error.
    Here is a sample dataset, also attached.
    date,time,reading
    
2021-01-01,00:00:00,67.8
    
2021-01-01,00:00:00,72.4
    
2021-01-01,00:00:00,63.1
    
2021-01-01,00:05:00,67.8
    Reading with readr::read_csv() results in a tibble with three columns: date, time, dbl, as expected.
    samp_readr<-readr::read_csv('sampledata.csv')
samp_readr
    # A tibble: 4 x 3datetimereading<date><time><dbl>12021-01-0100'00" 67.82 2021-01-01 00'00" 72.43 2021-01-01 00'00"63.142021-01-0105'00" 67.8
    Reading with arrow::read_csv_arrow() without providing schema() results in a tibble with three columns: dttm, chr, dbl.
    samp_arrow_plain<-arrow::read_csv_arrow('sampledata.csv')
samp_arrow_plain
    # A tibble: 4 x 3datetimereading<dttm><chr><dbl>12020-12-3119:00:0000:00:0067.822020-12-3119:00:0000:00:0072.432020-12-3119:00:0000:00:0063.142020-12-3119:00:0000:05:0067.8
    Reading with arrow::read_csv_arrow() and providing date=date32() via schema() to col_types results in a tibble with three columns: date, chr, dbl.
    samp_arrow_date<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(date=date32()))
samp_arrow_date
    # A tibble: 4 x 3datetimereading<date><chr><dbl>12021-01-0100:00:0067.822021-01-0100:00:0072.432021-01-0100:00:0063.142021-01-0100:05:0067.8
    Reading with arrow::read_csv_arrow() and providing time=time32() via schema() to col_types generates an error.
    samp_arrow_time<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(time=time32()))
    Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
    The same error occurs when using compact string notation.
    samp_arrow_string<-arrow::read_csv_arrow('sampledata.csv', col_types='DTc', col_names=c('date', 'time', 'reading'), skip=1)
    Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
    This is something in the internals, so far beyond me to figure out a fix, but I saw it in action and wanted to report it.
    Environment: Ubuntu 18.04, R 4.0.3
    Reporter: Jared Lander
    Assignee: Antoine Pitrou / @pitrou
    Related issues:
    Original Issue Attachments:
    PRs and other links:
    Note: This issue was originally created as ARROW-11243. Please see the migration documentation for further details.
    Metadata
    Metadata
    Assignees
    Labels
    Component: C++Type: enhancement
    Type
    No type
    Projects
    No projects
      Milestone
      Relationships
      None yet
      Development
      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
      [C++] Parse time32 from string and infer in CSV reader #27146
      Closed
      Closed
      [C++] Parse time32 from string and infer in CSV reader#27146
      Assignees
      pitrou
      Labels
      Component: C++Type: enhancement
      Milestone
      6.0.0
      Description
      @asfimport
      asfimport
      opened on Jan 13, 2021
      When reading a CSV with read_csv_arrow() with date types and time types, the dates are read as datetimes rather than dates and times are read as characters rather than time.
      The first problem can be fixed by supplying date32() to schema(), though better inference would be nice. However, supplying time32() to schema() causes an error.
      Here is a sample dataset, also attached.
      date,time,reading
      
2021-01-01,00:00:00,67.8
      
2021-01-01,00:00:00,72.4
      
2021-01-01,00:00:00,63.1
      
2021-01-01,00:05:00,67.8
      Reading with readr::read_csv() results in a tibble with three columns: date, time, dbl, as expected.
      samp_readr<-readr::read_csv('sampledata.csv')
samp_readr
      # A tibble: 4 x 3datetimereading<date><time><dbl>12021-01-0100'00" 67.82 2021-01-01 00'00" 72.43 2021-01-01 00'00"63.142021-01-0105'00" 67.8
      Reading with arrow::read_csv_arrow() without providing schema() results in a tibble with three columns: dttm, chr, dbl.
      samp_arrow_plain<-arrow::read_csv_arrow('sampledata.csv')
samp_arrow_plain
      # A tibble: 4 x 3datetimereading<dttm><chr><dbl>12020-12-3119:00:0000:00:0067.822020-12-3119:00:0000:00:0072.432020-12-3119:00:0000:00:0063.142020-12-3119:00:0000:05:0067.8
      Reading with arrow::read_csv_arrow() and providing date=date32() via schema() to col_types results in a tibble with three columns: date, chr, dbl.
      samp_arrow_date<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(date=date32()))
samp_arrow_date
      # A tibble: 4 x 3datetimereading<date><chr><dbl>12021-01-0100:00:0067.822021-01-0100:00:0072.432021-01-0100:00:0063.142021-01-0100:05:0067.8
      Reading with arrow::read_csv_arrow() and providing time=time32() via schema() to col_types generates an error.
      samp_arrow_time<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(time=time32()))
      Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
      The same error occurs when using compact string notation.
      samp_arrow_string<-arrow::read_csv_arrow('sampledata.csv', col_types='DTc', col_names=c('date', 'time', 'reading'), skip=1)
      Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
      This is something in the internals, so far beyond me to figure out a fix, but I saw it in action and wanted to report it.
      Environment: Ubuntu 18.04, R 4.0.3
      Reporter: Jared Lander
      Assignee: Antoine Pitrou / @pitrou
      Related issues:
      Original Issue Attachments:
      PRs and other links:
      Note: This issue was originally created as ARROW-11243. Please see the migration documentation for further details.
      Metadata
      Metadata
      Assignees
      Labels
      Component: C++Type: enhancement
      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
        [C++] Parse time32 from string and infer in CSV reader #27146
        Closed
        Closed
        [C++] Parse time32 from string and infer in CSV reader#27146
        Assignees
        pitrou
        Labels
        Component: C++Type: enhancement
        Milestone
        6.0.0
        Description
        @asfimport
        asfimport
        opened on Jan 13, 2021
        When reading a CSV with read_csv_arrow() with date types and time types, the dates are read as datetimes rather than dates and times are read as characters rather than time.
        The first problem can be fixed by supplying date32() to schema(), though better inference would be nice. However, supplying time32() to schema() causes an error.
        Here is a sample dataset, also attached.
        date,time,reading
        
2021-01-01,00:00:00,67.8
        
2021-01-01,00:00:00,72.4
        
2021-01-01,00:00:00,63.1
        
2021-01-01,00:05:00,67.8
        Reading with readr::read_csv() results in a tibble with three columns: date, time, dbl, as expected.
        samp_readr<-readr::read_csv('sampledata.csv')
samp_readr
        # A tibble: 4 x 3datetimereading<date><time><dbl>12021-01-0100'00" 67.82 2021-01-01 00'00" 72.43 2021-01-01 00'00"63.142021-01-0105'00" 67.8
        Reading with arrow::read_csv_arrow() without providing schema() results in a tibble with three columns: dttm, chr, dbl.
        samp_arrow_plain<-arrow::read_csv_arrow('sampledata.csv')
samp_arrow_plain
        # A tibble: 4 x 3datetimereading<dttm><chr><dbl>12020-12-3119:00:0000:00:0067.822020-12-3119:00:0000:00:0072.432020-12-3119:00:0000:00:0063.142020-12-3119:00:0000:05:0067.8
        Reading with arrow::read_csv_arrow() and providing date=date32() via schema() to col_types results in a tibble with three columns: date, chr, dbl.
        samp_arrow_date<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(date=date32()))
samp_arrow_date
        # A tibble: 4 x 3datetimereading<date><chr><dbl>12021-01-0100:00:0067.822021-01-0100:00:0072.432021-01-0100:00:0063.142021-01-0100:05:0067.8
        Reading with arrow::read_csv_arrow() and providing time=time32() via schema() to col_types generates an error.
        samp_arrow_time<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(time=time32()))
        Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
        The same error occurs when using compact string notation.
        samp_arrow_string<-arrow::read_csv_arrow('sampledata.csv', col_types='DTc', col_names=c('date', 'time', 'reading'), skip=1)
        Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
        This is something in the internals, so far beyond me to figure out a fix, but I saw it in action and wanted to report it.
        Environment: Ubuntu 18.04, R 4.0.3
        Reporter: Jared Lander
        Assignee: Antoine Pitrou / @pitrou
        Related issues:
        Original Issue Attachments:
        PRs and other links:
        Note: This issue was originally created as ARROW-11243. Please see the migration documentation for further details.
        Metadata
        Metadata
        Assignees
        Labels
        Component: C++Type: enhancement
        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
          [C++] Parse time32 from string and infer in CSV reader #27146
          Closed
          Closed
          [C++] Parse time32 from string and infer in CSV reader#27146
          Assignees
          pitrou
          Labels
          Component: C++Type: enhancement
          Milestone
          6.0.0
          Description
          @asfimport
          asfimport
          opened on Jan 13, 2021
          When reading a CSV with read_csv_arrow() with date types and time types, the dates are read as datetimes rather than dates and times are read as characters rather than time.
          The first problem can be fixed by supplying date32() to schema(), though better inference would be nice. However, supplying time32() to schema() causes an error.
          Here is a sample dataset, also attached.
          date,time,reading
          
2021-01-01,00:00:00,67.8
          
2021-01-01,00:00:00,72.4
          
2021-01-01,00:00:00,63.1
          
2021-01-01,00:05:00,67.8
          Reading with readr::read_csv() results in a tibble with three columns: date, time, dbl, as expected.
          samp_readr<-readr::read_csv('sampledata.csv')
samp_readr
          # A tibble: 4 x 3datetimereading<date><time><dbl>12021-01-0100'00" 67.82 2021-01-01 00'00" 72.43 2021-01-01 00'00"63.142021-01-0105'00" 67.8
          Reading with arrow::read_csv_arrow() without providing schema() results in a tibble with three columns: dttm, chr, dbl.
          samp_arrow_plain<-arrow::read_csv_arrow('sampledata.csv')
samp_arrow_plain
          # A tibble: 4 x 3datetimereading<dttm><chr><dbl>12020-12-3119:00:0000:00:0067.822020-12-3119:00:0000:00:0072.432020-12-3119:00:0000:00:0063.142020-12-3119:00:0000:05:0067.8
          Reading with arrow::read_csv_arrow() and providing date=date32() via schema() to col_types results in a tibble with three columns: date, chr, dbl.
          samp_arrow_date<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(date=date32()))
samp_arrow_date
          # A tibble: 4 x 3datetimereading<date><chr><dbl>12021-01-0100:00:0067.822021-01-0100:00:0072.432021-01-0100:00:0063.142021-01-0100:05:0067.8
          Reading with arrow::read_csv_arrow() and providing time=time32() via schema() to col_types generates an error.
          samp_arrow_time<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(time=time32()))
          Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
          The same error occurs when using compact string notation.
          samp_arrow_string<-arrow::read_csv_arrow('sampledata.csv', col_types='DTc', col_names=c('date', 'time', 'reading'), skip=1)
          Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
          This is something in the internals, so far beyond me to figure out a fix, but I saw it in action and wanted to report it.
          Environment: Ubuntu 18.04, R 4.0.3
          Reporter: Jared Lander
          Assignee: Antoine Pitrou / @pitrou
          Related issues:
          Original Issue Attachments:
          PRs and other links:
          Note: This issue was originally created as ARROW-11243. Please see the migration documentation for further details.
          Metadata
          Metadata
          Assignees
          Labels
          Component: C++Type: enhancement
          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
            [C++] Parse time32 from string and infer in CSV reader #27146
            Closed
            Closed
            [C++] Parse time32 from string and infer in CSV reader#27146
            Assignees
            pitrou
            Labels
            Component: C++Type: enhancement
            Milestone
            6.0.0
            Description
            @asfimport
            asfimport
            opened on Jan 13, 2021
            When reading a CSV with read_csv_arrow() with date types and time types, the dates are read as datetimes rather than dates and times are read as characters rather than time.
            The first problem can be fixed by supplying date32() to schema(), though better inference would be nice. However, supplying time32() to schema() causes an error.
            Here is a sample dataset, also attached.
            date,time,reading
            
2021-01-01,00:00:00,67.8
            
2021-01-01,00:00:00,72.4
            
2021-01-01,00:00:00,63.1
            
2021-01-01,00:05:00,67.8
            Reading with readr::read_csv() results in a tibble with three columns: date, time, dbl, as expected.
            samp_readr<-readr::read_csv('sampledata.csv')
samp_readr
            # A tibble: 4 x 3datetimereading<date><time><dbl>12021-01-0100'00" 67.82 2021-01-01 00'00" 72.43 2021-01-01 00'00"63.142021-01-0105'00" 67.8
            Reading with arrow::read_csv_arrow() without providing schema() results in a tibble with three columns: dttm, chr, dbl.
            samp_arrow_plain<-arrow::read_csv_arrow('sampledata.csv')
samp_arrow_plain
            # A tibble: 4 x 3datetimereading<dttm><chr><dbl>12020-12-3119:00:0000:00:0067.822020-12-3119:00:0000:00:0072.432020-12-3119:00:0000:00:0063.142020-12-3119:00:0000:05:0067.8
            Reading with arrow::read_csv_arrow() and providing date=date32() via schema() to col_types results in a tibble with three columns: date, chr, dbl.
            samp_arrow_date<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(date=date32()))
samp_arrow_date
            # A tibble: 4 x 3datetimereading<date><chr><dbl>12021-01-0100:00:0067.822021-01-0100:00:0072.432021-01-0100:00:0063.142021-01-0100:05:0067.8
            Reading with arrow::read_csv_arrow() and providing time=time32() via schema() to col_types generates an error.
            samp_arrow_time<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(time=time32()))
            Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
            The same error occurs when using compact string notation.
            samp_arrow_string<-arrow::read_csv_arrow('sampledata.csv', col_types='DTc', col_names=c('date', 'time', 'reading'), skip=1)
            Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
            This is something in the internals, so far beyond me to figure out a fix, but I saw it in action and wanted to report it.
            Environment: Ubuntu 18.04, R 4.0.3
            Reporter: Jared Lander
            Assignee: Antoine Pitrou / @pitrou
            Related issues:
            Original Issue Attachments:
            PRs and other links:
            Note: This issue was originally created as ARROW-11243. Please see the migration documentation for further details.
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

              Skip to content
              [C++] Parse time32 from string and infer in CSV reader #27146
              Closed
              Closed
              [C++] Parse time32 from string and infer in CSV reader#27146
              Assignees
              pitrou
              Labels
              Component: C++Type: enhancement
              Milestone
              6.0.0
              Description
              @asfimport
              asfimport
              opened on Jan 13, 2021
              When reading a CSV with read_csv_arrow() with date types and time types, the dates are read as datetimes rather than dates and times are read as characters rather than time.
              The first problem can be fixed by supplying date32() to schema(), though better inference would be nice. However, supplying time32() to schema() causes an error.
              Here is a sample dataset, also attached.
              date,time,reading
              
2021-01-01,00:00:00,67.8
              
2021-01-01,00:00:00,72.4
              
2021-01-01,00:00:00,63.1
              
2021-01-01,00:05:00,67.8
              Reading with readr::read_csv() results in a tibble with three columns: date, time, dbl, as expected.
              samp_readr<-readr::read_csv('sampledata.csv')
samp_readr
              # A tibble: 4 x 3datetimereading<date><time><dbl>12021-01-0100'00" 67.82 2021-01-01 00'00" 72.43 2021-01-01 00'00"63.142021-01-0105'00" 67.8
              Reading with arrow::read_csv_arrow() without providing schema() results in a tibble with three columns: dttm, chr, dbl.
              samp_arrow_plain<-arrow::read_csv_arrow('sampledata.csv')
samp_arrow_plain
              # A tibble: 4 x 3datetimereading<dttm><chr><dbl>12020-12-3119:00:0000:00:0067.822020-12-3119:00:0000:00:0072.432020-12-3119:00:0000:00:0063.142020-12-3119:00:0000:05:0067.8
              Reading with arrow::read_csv_arrow() and providing date=date32() via schema() to col_types results in a tibble with three columns: date, chr, dbl.
              samp_arrow_date<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(date=date32()))
samp_arrow_date
              # A tibble: 4 x 3datetimereading<date><chr><dbl>12021-01-0100:00:0067.822021-01-0100:00:0072.432021-01-0100:00:0063.142021-01-0100:05:0067.8
              Reading with arrow::read_csv_arrow() and providing time=time32() via schema() to col_types generates an error.
              samp_arrow_time<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(time=time32()))
              Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
              The same error occurs when using compact string notation.
              samp_arrow_string<-arrow::read_csv_arrow('sampledata.csv', col_types='DTc', col_names=c('date', 'time', 'reading'), skip=1)
              Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
              This is something in the internals, so far beyond me to figure out a fix, but I saw it in action and wanted to report it.
              Environment: Ubuntu 18.04, R 4.0.3
              Reporter: Jared Lander
              Assignee: Antoine Pitrou / @pitrou
              Related issues:
              Original Issue Attachments:
              PRs and other links:
              Note: This issue was originally created as ARROW-11243. Please see the migration documentation for further details.
              Metadata
              Metadata
              Assignees
              Labels
              Component: C++Type: enhancement
              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)) {
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}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
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})();

                Skip to content
                [C++] Parse time32 from string and infer in CSV reader #27146
                Closed
                Closed
                [C++] Parse time32 from string and infer in CSV reader#27146
                Assignees
                pitrou
                Labels
                Component: C++Type: enhancement
                Milestone
                6.0.0
                Description
                @asfimport
                asfimport
                opened on Jan 13, 2021
                When reading a CSV with read_csv_arrow() with date types and time types, the dates are read as datetimes rather than dates and times are read as characters rather than time.
                The first problem can be fixed by supplying date32() to schema(), though better inference would be nice. However, supplying time32() to schema() causes an error.
                Here is a sample dataset, also attached.
                date,time,reading
                
2021-01-01,00:00:00,67.8
                
2021-01-01,00:00:00,72.4
                
2021-01-01,00:00:00,63.1
                
2021-01-01,00:05:00,67.8
                Reading with readr::read_csv() results in a tibble with three columns: date, time, dbl, as expected.
                samp_readr<-readr::read_csv('sampledata.csv')
samp_readr
                # A tibble: 4 x 3datetimereading<date><time><dbl>12021-01-0100'00" 67.82 2021-01-01 00'00" 72.43 2021-01-01 00'00"63.142021-01-0105'00" 67.8
                Reading with arrow::read_csv_arrow() without providing schema() results in a tibble with three columns: dttm, chr, dbl.
                samp_arrow_plain<-arrow::read_csv_arrow('sampledata.csv')
samp_arrow_plain
                # A tibble: 4 x 3datetimereading<dttm><chr><dbl>12020-12-3119:00:0000:00:0067.822020-12-3119:00:0000:00:0072.432020-12-3119:00:0000:00:0063.142020-12-3119:00:0000:05:0067.8
                Reading with arrow::read_csv_arrow() and providing date=date32() via schema() to col_types results in a tibble with three columns: date, chr, dbl.
                samp_arrow_date<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(date=date32()))
samp_arrow_date
                # A tibble: 4 x 3datetimereading<date><chr><dbl>12021-01-0100:00:0067.822021-01-0100:00:0072.432021-01-0100:00:0063.142021-01-0100:05:0067.8
                Reading with arrow::read_csv_arrow() and providing time=time32() via schema() to col_types generates an error.
                samp_arrow_time<-arrow::read_csv_arrow('sampledata.csv', col_types=schema(time=time32()))
                Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
                The same error occurs when using compact string notation.
                samp_arrow_string<-arrow::read_csv_arrow('sampledata.csv', col_types='DTc', col_names=c('date', 'time', 'reading'), skip=1)
                Errorin csv___TableReader__Read(self) :NotImplemented:CSVconversiontotime32[ms] isnotsupported
                This is something in the internals, so far beyond me to figure out a fix, but I saw it in action and wanted to report it.
                Environment: Ubuntu 18.04, R 4.0.3
                Reporter: Jared Lander
                Assignee: Antoine Pitrou / @pitrou
                Related issues:
                Original Issue Attachments:
                PRs and other links:
                Note: This issue was originally created as ARROW-11243. Please see the migration documentation for further details.
                Metadata
                Metadata
                Assignees
                Labels
                Component: C++Type: enhancement
                Type
                No type
                Projects
                No projects
                  Milestone
                  Relationships
                  None yet
                  Development
                  No branches or pull requests
                  Issue actions