From 31e655c0be3b0ea1f6f29e28926aba0caaf5d172 Mon Sep 17 00:00:00 2001
From: Steven Miers <steven.miers@gmail.com>
Date: Mon, 26 Feb 2024 12:09:24 -0600
Subject: [PATCH] Add installation instructions and regen docs.

---
 README.md             | 5 +++++
 docs/csv_batcher.html | 9 +++++++--
 docs/search.js        | 2 +-
 pyproject.toml        | 4 ++--
 4 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 980d578..7064520 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,13 @@
 # csv-batcher
+A python-based, multiprocess CSV batcher suitable for
+use with dataframes or other tools that deal with large CSV files (or those that require timely processing).
 
 ## Installation
 pip install csv-batcher
 
+## GitHub
+
+
 ## Documentation
 https://tangledpath.github.io/csv-batcher/csv_batcher.html
 
diff --git a/docs/csv_batcher.html b/docs/csv_batcher.html
index 7d1dfcf..d562e1c 100644
--- a/docs/csv_batcher.html
+++ b/docs/csv_batcher.html
@@ -26,6 +26,7 @@ <h2>Contents</h2>
   <li><a href="#csv-batcher">csv-batcher</a>
   <ul>
     <li><a href="#installation">Installation</a></li>
+    <li><a href="#github">GitHub</a></li>
     <li><a href="#documentation">Documentation</a></li>
     <li><a href="#usage">Usage</a></li>
     <li><a href="#development">Development</a></li>
@@ -56,10 +57,14 @@ <h1 class="modulename">
 
                         <div class="docstring"><h1 id="csv-batcher">csv-batcher</h1>
 
+<p>A python-based, multiprocess CSV batcher suitable for
+use with dataframes or other tools that deal with large CSV files (or those that require timely processing).</p>
+
 <h2 id="installation">Installation</h2>
 
-<p>TBD (python package)
-pip install csv-batcher</p>
+<p>pip install csv-batcher</p>
+
+<h2 id="github">GitHub</h2>
 
 <h2 id="documentation">Documentation</h2>
 
diff --git a/docs/search.js b/docs/search.js
index 51207bf..5491344 100644
--- a/docs/search.js
+++ b/docs/search.js
@@ -1,6 +1,6 @@
 window.pdocSearch = (function(){
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t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]<u[i]?n++:s[n]>u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();o<r.length;o++)i.add(r[o]);return i},lunr.SortedSet.prototype.toJSON=function(){return this.toArray()},function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():e.elasticlunr=t()}(this,function(){return t})}();
-    /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"csv_batcher": {"fullname": "csv_batcher", "modulename": "csv_batcher", "kind": "module", "doc": "<h1 id=\"csv-batcher\">csv-batcher</h1>\n\n<h2 id=\"installation\">Installation</h2>\n\n<p>TBD (python package)\npip install csv-batcher</p>\n\n<h2 id=\"documentation\">Documentation</h2>\n\n<p><a href=\"https://tangledpath.github.io/csv-batcher/csv_batcher.html\">https://tangledpath.github.io/csv-batcher/csv_batcher.html</a></p>\n\n<h2 id=\"usage\">Usage</h2>\n\n<p>Arguments sent to callback function can be controlled by\ncreating pooler with <code>callback_with</code> and the CallbackWith enum\nvalues:</p>\n\n<h3 id=\"as-dataframe-row\">As dataframe row</h3>\n\n<div class=\"pdoc-code codehilite\">\n<pre><span></span><code>  <span class=\"kn\">from</span> <span class=\"nn\">csv_batcher.csv_pooler</span> <span class=\"kn\">import</span> <span class=\"n\">CSVPooler</span><span class=\"p\">,</span> <span class=\"n\">CallbackWith</span>\n\n  <span class=\"c1\"># Callback function passed to pooler; accepts a dataframe row</span>\n  <span class=\"c1\">#   as a pandas Series (via apply)</span>\n  <span class=\"k\">def</span> <span class=\"nf\">process_dataframe_row</span><span class=\"p\">(</span><span class=\"n\">row</span><span class=\"p\">):</span>\n    <span class=\"k\">return</span> <span class=\"n\">row</span><span class=\"o\">.</span><span class=\"n\">iloc</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n\n  <span class=\"n\">pooler</span> <span class=\"o\">=</span> <span class=\"n\">CSVPooler</span><span class=\"p\">(</span>\n    <span class=\"s2\">&quot;5mSalesRecords.csv&quot;</span><span class=\"p\">,</span>\n    <span class=\"n\">process_dataframe_row</span><span class=\"p\">,</span>\n    <span class=\"n\">callback_with</span><span class=\"o\">=</span><span class=\"n\">CallbackWith</span><span class=\"o\">.</span><span class=\"n\">DATAFRAME_ROW</span><span class=\"p\">,</span>\n    <span class=\"n\">pool_size</span><span class=\"o\">=</span><span class=\"mi\">16</span>\n  <span class=\"p\">)</span>\n  <span class=\"n\">pooler</span><span class=\"o\">.</span><span class=\"n\">process</span><span class=\"p\">()</span>\n\n<span class=\"c1\">### As dataframe</span>\n<span class=\"err\">```</span><span class=\"n\">python</span>\n  <span class=\"kn\">from</span> <span class=\"nn\">csv_batcher.csv_pooler</span> <span class=\"kn\">import</span> <span class=\"n\">CSVPooler</span><span class=\"p\">,</span> <span class=\"n\">CallbackWith</span>\n\n  <span class=\"c1\"># Used in DataFrame.apply:</span>\n  <span class=\"k\">def</span> <span class=\"nf\">process_dataframe_row</span><span class=\"p\">(</span><span class=\"n\">row</span><span class=\"p\">):</span>\n    <span class=\"k\">return</span> <span class=\"n\">row</span><span class=\"o\">.</span><span class=\"n\">iloc</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n\n  <span class=\"c1\"># Callback function passed to pooler; accepts a dataframe:</span>\n  <span class=\"k\">def</span> <span class=\"nf\">process_dataframe</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">):</span>\n    <span class=\"n\">foo</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"n\">process_dataframe_row</span><span class=\"p\">,</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n    <span class=\"c1\"># Or do something more complicated....</span>\n    <span class=\"k\">return</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">)</span>\n\n  <span class=\"n\">pooler</span> <span class=\"o\">=</span> <span class=\"n\">CSVPooler</span><span class=\"p\">(</span>\n    <span class=\"s2\">&quot;5mSalesRecords.csv&quot;</span><span class=\"p\">,</span>\n    <span class=\"n\">process_dataframe</span><span class=\"p\">,</span>\n    <span class=\"n\">callback_with</span><span class=\"o\">=</span><span class=\"n\">CallbackWith</span><span class=\"o\">.</span><span class=\"n\">DATAFRAME</span><span class=\"p\">,</span>\n    <span class=\"n\">pool_size</span><span class=\"o\">=</span><span class=\"mi\">16</span>\n  <span class=\"p\">)</span>\n  <span class=\"n\">pooler</span><span class=\"o\">.</span><span class=\"n\">process</span><span class=\"p\">()</span>\n\n<span class=\"c1\">### As CSV filename</span>\n<span class=\"err\">```</span><span class=\"n\">python</span>\n  <span class=\"kn\">from</span> <span class=\"nn\">csv_batcher.csv_pooler</span> <span class=\"kn\">import</span> <span class=\"n\">CSVPooler</span><span class=\"p\">,</span> <span class=\"n\">CallbackWith</span>\n\n  <span class=\"k\">def</span> <span class=\"nf\">process_csv_filename</span><span class=\"p\">(</span><span class=\"n\">csv_chunk_filename</span><span class=\"p\">):</span>\n      <span class=\"c1\"># print(&quot;processing &quot;, csv_chunk_filename)</span>\n      <span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_csv</span><span class=\"p\">(</span><span class=\"n\">csv_chunk_filename</span><span class=\"p\">,</span> <span class=\"n\">skipinitialspace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">index_col</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">)</span>\n      <span class=\"n\">foo</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"n\">process_dataframe_row</span><span class=\"p\">,</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n      <span class=\"k\">return</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">)</span>\n\n  <span class=\"k\">def</span> <span class=\"nf\">process_as_dataframe</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">):</span>\n      <span class=\"n\">foo</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"n\">process_dataframe_row</span><span class=\"p\">,</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n      <span class=\"k\">return</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">)</span>\n\n  <span class=\"k\">def</span> <span class=\"nf\">process_dataframe_row</span><span class=\"p\">(</span><span class=\"n\">row</span><span class=\"p\">):</span>\n      <span class=\"k\">return</span> <span class=\"n\">row</span><span class=\"o\">.</span><span class=\"n\">iloc</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n\n  <span class=\"n\">pooler</span> <span class=\"o\">=</span> <span class=\"n\">CSVPooler</span><span class=\"p\">(</span>\n    <span class=\"s2\">&quot;5mSalesRecords.csv&quot;</span><span class=\"p\">,</span>\n    <span class=\"n\">process_dataframe</span><span class=\"p\">,</span>\n    <span class=\"n\">callback_with</span><span class=\"o\">=</span><span class=\"n\">CallbackWith</span><span class=\"o\">.</span><span class=\"n\">CSV_FILENAME</span>\n    <span class=\"n\">chunk_lines</span><span class=\"o\">=</span><span class=\"mi\">10000</span><span class=\"p\">,</span>\n    <span class=\"n\">pool_size</span><span class=\"o\">=</span><span class=\"mi\">16</span>\n  <span class=\"p\">)</span>\n  <span class=\"n\">pooler</span><span class=\"o\">.</span><span class=\"n\">process</span><span class=\"p\">()</span>\n</code></pre>\n</div>\n\n<h2 id=\"development\">Development</h2>\n\n<h3 id=\"linting\">Linting</h3>\n\n<div class=\"pdoc-code codehilite\">\n<pre><span></span><code><span class=\"w\">   </span>ruff<span class=\"w\"> </span>check<span class=\"w\"> </span>.<span class=\"w\"> </span><span class=\"c1\"># Find linting errors</span>\n<span class=\"w\">   </span>ruff<span class=\"w\"> </span>check<span class=\"w\"> </span>.<span class=\"w\"> </span>--fix<span class=\"w\"> </span><span class=\"c1\"># Auto-fix linting errors (where possible)</span>\n</code></pre>\n</div>\n\n<h3 id=\"documentation-2\">Documentation</h3>\n\n<pre><code>  # Shows in browser\n  poetry run pdoc csv_batcher\n  # Generates to ./docs\n  poetry run pdoc csv_batcher -o ./docs\n</code></pre>\n\n<h3 id=\"testing\">Testing</h3>\n\n<div class=\"pdoc-code codehilite\">\n<pre><span></span><code><span class=\"w\">  </span>clear<span class=\"p\">;</span><span class=\"w\"> </span>pytest\n</code></pre>\n</div>\n\n<h3 id=\"publishing\">Publishing</h3>\n\n<p><code>poetry publish --build -u __token__ -p $PYPI_TOKEN</code></p>\n"}, "csv_batcher.csv_pooler": {"fullname": "csv_batcher.csv_pooler", "modulename": "csv_batcher.csv_pooler", "kind": "module", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CallbackWith": {"fullname": "csv_batcher.csv_pooler.CallbackWith", "modulename": "csv_batcher.csv_pooler", "qualname": "CallbackWith", "kind": "class", "doc": "<p>CallbackWith Enum, used to control what is passed to callback function</p>\n", "bases": "enum.StrEnum"}, "csv_batcher.csv_pooler.CallbackWith.CSV_FILENAME": {"fullname": "csv_batcher.csv_pooler.CallbackWith.CSV_FILENAME", "modulename": "csv_batcher.csv_pooler", "qualname": "CallbackWith.CSV_FILENAME", "kind": "variable", "doc": "<p></p>\n", "default_value": "&lt;CallbackWith.CSV_FILENAME: &#x27;csv_filename&#x27;&gt;"}, "csv_batcher.csv_pooler.CallbackWith.DATAFRAME": {"fullname": "csv_batcher.csv_pooler.CallbackWith.DATAFRAME", "modulename": "csv_batcher.csv_pooler", "qualname": "CallbackWith.DATAFRAME", "kind": "variable", "doc": "<p></p>\n", "default_value": "&lt;CallbackWith.DATAFRAME: &#x27;dataframe&#x27;&gt;"}, "csv_batcher.csv_pooler.CallbackWith.DATAFRAME_ROW": {"fullname": "csv_batcher.csv_pooler.CallbackWith.DATAFRAME_ROW", "modulename": "csv_batcher.csv_pooler", "qualname": "CallbackWith.DATAFRAME_ROW", "kind": "variable", "doc": "<p></p>\n", "default_value": "&lt;CallbackWith.DATAFRAME_ROW: &#x27;dataframe_row&#x27;&gt;"}, "csv_batcher.csv_pooler.CSVPooler": {"fullname": "csv_batcher.csv_pooler.CSVPooler", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler", "kind": "class", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CSVPooler.__init__": {"fullname": "csv_batcher.csv_pooler.CSVPooler.__init__", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.__init__", "kind": "function", "doc": "<p>Construct <code>Pooler</code> with given <code>csv_filename</code>, <code>process_fn</code>, <code>as_dataframe</code>, `pool_size', 'chunk_size'</p>\n\n<p>Args:\n    csv_filename (str): Name of CSV file\n    process_fn (Callable): A function that accepts a single argument\n        By default, this is the path to a chunked CSV file\n        If <code>as_dataframe</code> is True, then the argument sent is a dataframe of the chunked CSV\n    callback_with (CallbackWith): Controls what is sent to callback function.\n        @see CallbackWith enumeration for details\n        Defaults to CallbackWith.CSV_FILENAME.\n    as_dataframe_rows (bool): When True, a dataframe is created as with as_dataframe.\n        that is sent to <code>process_fn</code> instead.  Defaults to False.\n    pool_size (int, optional): Number of workers to uses. Defaults to 8.\n    chunk_lines (int, optional): Target row count for each chunked CSV. Last chunk may\n        have fewer rows.  Defaults to 10000.</p>\n", "signature": "<span class=\"signature pdoc-code multiline\">(<span class=\"param\">\t<span class=\"n\">csv_filename</span><span class=\"p\">:</span> <span class=\"nb\">str</span>,</span><span class=\"param\">\t<span class=\"n\">process_fn</span><span class=\"p\">:</span> <span class=\"n\">Callable</span>,</span><span class=\"param\">\t<span class=\"n\">callback_with</span><span class=\"p\">:</span> <span class=\"n\">csv_batcher</span><span class=\"o\">.</span><span class=\"n\">csv_pooler</span><span class=\"o\">.</span><span class=\"n\">CallbackWith</span> <span class=\"o\">=</span> <span class=\"o\">&lt;</span><span class=\"n\">CallbackWith</span><span class=\"o\">.</span><span class=\"n\">CSV_FILENAME</span><span class=\"p\">:</span> <span class=\"s1\">&#39;csv_filename&#39;</span><span class=\"o\">&gt;</span>,</span><span class=\"param\">\t<span class=\"n\">pool_size</span><span class=\"p\">:</span> <span class=\"nb\">int</span> <span class=\"o\">=</span> <span class=\"mi\">5</span>,</span><span class=\"param\">\t<span class=\"n\">chunk_lines</span><span class=\"p\">:</span> <span class=\"nb\">int</span> <span class=\"o\">=</span> <span class=\"mi\">10000</span></span>)</span>"}, "csv_batcher.csv_pooler.CSVPooler.csv_filename": {"fullname": "csv_batcher.csv_pooler.CSVPooler.csv_filename", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.csv_filename", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CSVPooler.process_fn": {"fullname": "csv_batcher.csv_pooler.CSVPooler.process_fn", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.process_fn", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CSVPooler.callback_with": {"fullname": "csv_batcher.csv_pooler.CSVPooler.callback_with", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.callback_with", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CSVPooler.pool_size": {"fullname": "csv_batcher.csv_pooler.CSVPooler.pool_size", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.pool_size", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CSVPooler.chunk_lines": {"fullname": "csv_batcher.csv_pooler.CSVPooler.chunk_lines", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.chunk_lines", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CSVPooler.process": {"fullname": "csv_batcher.csv_pooler.CSVPooler.process", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.process", "kind": "function", "doc": "<p>Processes <code>self.csv_filename</code> by using <code>CSVSplitter</code> to split it\n    into multiple temporary files defined by <code>self.chunk_lines</code>.\nUse <code>multiprocessing.Pool</code> to use multiple process workers to process\nthe group of CSVs.</p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"bp\">self</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "csv_batcher.csv_splitter": {"fullname": "csv_batcher.csv_splitter", "modulename": "csv_batcher.csv_splitter", "kind": "module", "doc": "<p></p>\n"}, "csv_batcher.csv_splitter.CSVSplitter": {"fullname": "csv_batcher.csv_splitter.CSVSplitter", "modulename": "csv_batcher.csv_splitter", "qualname": "CSVSplitter", "kind": "class", "doc": "<p>Splits a CSV file into multiple files</p>\n"}, "csv_batcher.csv_splitter.CSVSplitter.__init__": {"fullname": "csv_batcher.csv_splitter.CSVSplitter.__init__", "modulename": "csv_batcher.csv_splitter", "qualname": "CSVSplitter.__init__", "kind": "function", "doc": "<p>Construct CSVSplitter</p>\n\n<p>Args:\n    csv_filename (str): path to CSV file\n    chunk_line_cnt (int, optional): Target lines for each chunk. Last chunk might\n    be smaller than this.  Defaults to 10000.</p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">csv_filename</span><span class=\"p\">:</span> <span class=\"nb\">str</span>, </span><span class=\"param\"><span class=\"n\">chunk_line_cnt</span><span class=\"p\">:</span> <span class=\"nb\">int</span> <span class=\"o\">=</span> <span class=\"mi\">10000</span></span>)</span>"}, "csv_batcher.csv_splitter.CSVSplitter.csv_filename": {"fullname": "csv_batcher.csv_splitter.CSVSplitter.csv_filename", "modulename": "csv_batcher.csv_splitter", "qualname": "CSVSplitter.csv_filename", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_splitter.CSVSplitter.chunk_line_cnt": {"fullname": "csv_batcher.csv_splitter.CSVSplitter.chunk_line_cnt", "modulename": "csv_batcher.csv_splitter", "qualname": "CSVSplitter.chunk_line_cnt", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_splitter.CSVSplitter.chunk_dir": {"fullname": 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+    /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"csv_batcher": {"fullname": "csv_batcher", "modulename": "csv_batcher", "kind": "module", "doc": "<h1 id=\"csv-batcher\">csv-batcher</h1>\n\n<p>A python-based, multiprocess CSV batcher suitable for\nuse with dataframes or other tools that deal with large CSV files (or those that require timely processing).</p>\n\n<h2 id=\"installation\">Installation</h2>\n\n<p>pip install csv-batcher</p>\n\n<h2 id=\"github\">GitHub</h2>\n\n<h2 id=\"documentation\">Documentation</h2>\n\n<p><a href=\"https://tangledpath.github.io/csv-batcher/csv_batcher.html\">https://tangledpath.github.io/csv-batcher/csv_batcher.html</a></p>\n\n<h2 id=\"usage\">Usage</h2>\n\n<p>Arguments sent to callback function can be controlled by\ncreating pooler with <code>callback_with</code> and the CallbackWith enum\nvalues:</p>\n\n<h3 id=\"as-dataframe-row\">As dataframe row</h3>\n\n<div class=\"pdoc-code codehilite\">\n<pre><span></span><code>  <span class=\"kn\">from</span> <span class=\"nn\">csv_batcher.csv_pooler</span> <span class=\"kn\">import</span> <span class=\"n\">CSVPooler</span><span class=\"p\">,</span> <span class=\"n\">CallbackWith</span>\n\n  <span class=\"c1\"># Callback function passed to pooler; accepts a dataframe row</span>\n  <span class=\"c1\">#   as a pandas Series (via apply)</span>\n  <span class=\"k\">def</span> <span class=\"nf\">process_dataframe_row</span><span class=\"p\">(</span><span class=\"n\">row</span><span class=\"p\">):</span>\n    <span class=\"k\">return</span> <span class=\"n\">row</span><span class=\"o\">.</span><span class=\"n\">iloc</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n\n  <span class=\"n\">pooler</span> <span class=\"o\">=</span> <span class=\"n\">CSVPooler</span><span class=\"p\">(</span>\n    <span class=\"s2\">&quot;5mSalesRecords.csv&quot;</span><span class=\"p\">,</span>\n    <span class=\"n\">process_dataframe_row</span><span class=\"p\">,</span>\n    <span class=\"n\">callback_with</span><span class=\"o\">=</span><span class=\"n\">CallbackWith</span><span class=\"o\">.</span><span class=\"n\">DATAFRAME_ROW</span><span class=\"p\">,</span>\n    <span class=\"n\">pool_size</span><span class=\"o\">=</span><span class=\"mi\">16</span>\n  <span class=\"p\">)</span>\n  <span class=\"n\">pooler</span><span class=\"o\">.</span><span class=\"n\">process</span><span class=\"p\">()</span>\n\n<span class=\"c1\">### As dataframe</span>\n<span class=\"err\">```</span><span class=\"n\">python</span>\n  <span class=\"kn\">from</span> <span class=\"nn\">csv_batcher.csv_pooler</span> <span class=\"kn\">import</span> <span class=\"n\">CSVPooler</span><span class=\"p\">,</span> <span class=\"n\">CallbackWith</span>\n\n  <span class=\"c1\"># Used in DataFrame.apply:</span>\n  <span class=\"k\">def</span> <span class=\"nf\">process_dataframe_row</span><span class=\"p\">(</span><span class=\"n\">row</span><span class=\"p\">):</span>\n    <span class=\"k\">return</span> <span class=\"n\">row</span><span class=\"o\">.</span><span class=\"n\">iloc</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n\n  <span class=\"c1\"># Callback function passed to pooler; accepts a dataframe:</span>\n  <span class=\"k\">def</span> <span class=\"nf\">process_dataframe</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">):</span>\n    <span class=\"n\">foo</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"n\">process_dataframe_row</span><span class=\"p\">,</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n    <span class=\"c1\"># Or do something more complicated....</span>\n    <span class=\"k\">return</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">)</span>\n\n  <span class=\"n\">pooler</span> <span class=\"o\">=</span> <span class=\"n\">CSVPooler</span><span class=\"p\">(</span>\n    <span class=\"s2\">&quot;5mSalesRecords.csv&quot;</span><span class=\"p\">,</span>\n    <span class=\"n\">process_dataframe</span><span class=\"p\">,</span>\n    <span class=\"n\">callback_with</span><span class=\"o\">=</span><span class=\"n\">CallbackWith</span><span class=\"o\">.</span><span class=\"n\">DATAFRAME</span><span class=\"p\">,</span>\n    <span class=\"n\">pool_size</span><span class=\"o\">=</span><span class=\"mi\">16</span>\n  <span class=\"p\">)</span>\n  <span class=\"n\">pooler</span><span class=\"o\">.</span><span class=\"n\">process</span><span class=\"p\">()</span>\n\n<span class=\"c1\">### As CSV filename</span>\n<span class=\"err\">```</span><span class=\"n\">python</span>\n  <span class=\"kn\">from</span> <span class=\"nn\">csv_batcher.csv_pooler</span> <span class=\"kn\">import</span> <span class=\"n\">CSVPooler</span><span class=\"p\">,</span> <span class=\"n\">CallbackWith</span>\n\n  <span class=\"k\">def</span> <span class=\"nf\">process_csv_filename</span><span class=\"p\">(</span><span class=\"n\">csv_chunk_filename</span><span class=\"p\">):</span>\n      <span class=\"c1\"># print(&quot;processing &quot;, csv_chunk_filename)</span>\n      <span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_csv</span><span class=\"p\">(</span><span class=\"n\">csv_chunk_filename</span><span class=\"p\">,</span> <span class=\"n\">skipinitialspace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">index_col</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">)</span>\n      <span class=\"n\">foo</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"n\">process_dataframe_row</span><span class=\"p\">,</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n      <span class=\"k\">return</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">)</span>\n\n  <span class=\"k\">def</span> <span class=\"nf\">process_as_dataframe</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">):</span>\n      <span class=\"n\">foo</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"n\">process_dataframe_row</span><span class=\"p\">,</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n      <span class=\"k\">return</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">)</span>\n\n  <span class=\"k\">def</span> <span class=\"nf\">process_dataframe_row</span><span class=\"p\">(</span><span class=\"n\">row</span><span class=\"p\">):</span>\n      <span class=\"k\">return</span> <span class=\"n\">row</span><span class=\"o\">.</span><span class=\"n\">iloc</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n\n  <span class=\"n\">pooler</span> <span class=\"o\">=</span> <span class=\"n\">CSVPooler</span><span class=\"p\">(</span>\n    <span class=\"s2\">&quot;5mSalesRecords.csv&quot;</span><span class=\"p\">,</span>\n    <span class=\"n\">process_dataframe</span><span class=\"p\">,</span>\n    <span class=\"n\">callback_with</span><span class=\"o\">=</span><span class=\"n\">CallbackWith</span><span class=\"o\">.</span><span class=\"n\">CSV_FILENAME</span>\n    <span class=\"n\">chunk_lines</span><span class=\"o\">=</span><span class=\"mi\">10000</span><span class=\"p\">,</span>\n    <span class=\"n\">pool_size</span><span class=\"o\">=</span><span class=\"mi\">16</span>\n  <span class=\"p\">)</span>\n  <span class=\"n\">pooler</span><span class=\"o\">.</span><span class=\"n\">process</span><span class=\"p\">()</span>\n</code></pre>\n</div>\n\n<h2 id=\"development\">Development</h2>\n\n<h3 id=\"linting\">Linting</h3>\n\n<div class=\"pdoc-code codehilite\">\n<pre><span></span><code><span class=\"w\">   </span>ruff<span class=\"w\"> </span>check<span class=\"w\"> </span>.<span class=\"w\"> </span><span class=\"c1\"># Find linting errors</span>\n<span class=\"w\">   </span>ruff<span class=\"w\"> </span>check<span class=\"w\"> </span>.<span class=\"w\"> </span>--fix<span class=\"w\"> </span><span class=\"c1\"># Auto-fix linting errors (where possible)</span>\n</code></pre>\n</div>\n\n<h3 id=\"documentation-2\">Documentation</h3>\n\n<pre><code>  # Shows in browser\n  poetry run pdoc csv_batcher\n  # Generates to ./docs\n  poetry run pdoc csv_batcher -o ./docs\n</code></pre>\n\n<h3 id=\"testing\">Testing</h3>\n\n<div class=\"pdoc-code codehilite\">\n<pre><span></span><code><span class=\"w\">  </span>clear<span class=\"p\">;</span><span class=\"w\"> </span>pytest\n</code></pre>\n</div>\n\n<h3 id=\"publishing\">Publishing</h3>\n\n<p><code>poetry publish --build -u __token__ -p $PYPI_TOKEN</code></p>\n"}, "csv_batcher.csv_pooler": {"fullname": "csv_batcher.csv_pooler", "modulename": "csv_batcher.csv_pooler", "kind": "module", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CallbackWith": {"fullname": "csv_batcher.csv_pooler.CallbackWith", "modulename": "csv_batcher.csv_pooler", "qualname": "CallbackWith", "kind": "class", "doc": "<p>CallbackWith Enum, used to control what is passed to callback function</p>\n", "bases": "enum.StrEnum"}, "csv_batcher.csv_pooler.CallbackWith.CSV_FILENAME": {"fullname": "csv_batcher.csv_pooler.CallbackWith.CSV_FILENAME", "modulename": "csv_batcher.csv_pooler", "qualname": "CallbackWith.CSV_FILENAME", "kind": "variable", "doc": "<p></p>\n", "default_value": "&lt;CallbackWith.CSV_FILENAME: &#x27;csv_filename&#x27;&gt;"}, "csv_batcher.csv_pooler.CallbackWith.DATAFRAME": {"fullname": "csv_batcher.csv_pooler.CallbackWith.DATAFRAME", "modulename": "csv_batcher.csv_pooler", "qualname": "CallbackWith.DATAFRAME", "kind": "variable", "doc": "<p></p>\n", "default_value": "&lt;CallbackWith.DATAFRAME: &#x27;dataframe&#x27;&gt;"}, "csv_batcher.csv_pooler.CallbackWith.DATAFRAME_ROW": {"fullname": "csv_batcher.csv_pooler.CallbackWith.DATAFRAME_ROW", "modulename": "csv_batcher.csv_pooler", "qualname": "CallbackWith.DATAFRAME_ROW", "kind": "variable", "doc": "<p></p>\n", "default_value": "&lt;CallbackWith.DATAFRAME_ROW: &#x27;dataframe_row&#x27;&gt;"}, "csv_batcher.csv_pooler.CSVPooler": {"fullname": "csv_batcher.csv_pooler.CSVPooler", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler", "kind": "class", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CSVPooler.__init__": {"fullname": "csv_batcher.csv_pooler.CSVPooler.__init__", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.__init__", "kind": "function", "doc": "<p>Construct <code>Pooler</code> with given <code>csv_filename</code>, <code>process_fn</code>, <code>as_dataframe</code>, `pool_size', 'chunk_size'</p>\n\n<p>Args:\n    csv_filename (str): Name of CSV file\n    process_fn (Callable): A function that accepts a single argument\n        By default, this is the path to a chunked CSV file\n        If <code>as_dataframe</code> is True, then the argument sent is a dataframe of the chunked CSV\n    callback_with (CallbackWith): Controls what is sent to callback function.\n        @see CallbackWith enumeration for details\n        Defaults to CallbackWith.CSV_FILENAME.\n    as_dataframe_rows (bool): When True, a dataframe is created as with as_dataframe.\n        that is sent to <code>process_fn</code> instead.  Defaults to False.\n    pool_size (int, optional): Number of workers to uses. Defaults to 8.\n    chunk_lines (int, optional): Target row count for each chunked CSV. Last chunk may\n        have fewer rows.  Defaults to 10000.</p>\n", "signature": "<span class=\"signature pdoc-code multiline\">(<span class=\"param\">\t<span class=\"n\">csv_filename</span><span class=\"p\">:</span> <span class=\"nb\">str</span>,</span><span class=\"param\">\t<span class=\"n\">process_fn</span><span class=\"p\">:</span> <span class=\"n\">Callable</span>,</span><span class=\"param\">\t<span class=\"n\">callback_with</span><span class=\"p\">:</span> <span class=\"n\">csv_batcher</span><span class=\"o\">.</span><span class=\"n\">csv_pooler</span><span class=\"o\">.</span><span class=\"n\">CallbackWith</span> <span class=\"o\">=</span> <span class=\"o\">&lt;</span><span class=\"n\">CallbackWith</span><span class=\"o\">.</span><span class=\"n\">CSV_FILENAME</span><span class=\"p\">:</span> <span class=\"s1\">&#39;csv_filename&#39;</span><span class=\"o\">&gt;</span>,</span><span class=\"param\">\t<span class=\"n\">pool_size</span><span class=\"p\">:</span> <span class=\"nb\">int</span> <span class=\"o\">=</span> <span class=\"mi\">5</span>,</span><span class=\"param\">\t<span class=\"n\">chunk_lines</span><span class=\"p\">:</span> <span class=\"nb\">int</span> <span class=\"o\">=</span> <span class=\"mi\">10000</span></span>)</span>"}, "csv_batcher.csv_pooler.CSVPooler.csv_filename": {"fullname": "csv_batcher.csv_pooler.CSVPooler.csv_filename", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.csv_filename", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CSVPooler.process_fn": {"fullname": "csv_batcher.csv_pooler.CSVPooler.process_fn", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.process_fn", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CSVPooler.callback_with": {"fullname": "csv_batcher.csv_pooler.CSVPooler.callback_with", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.callback_with", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CSVPooler.pool_size": {"fullname": "csv_batcher.csv_pooler.CSVPooler.pool_size", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.pool_size", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CSVPooler.chunk_lines": {"fullname": "csv_batcher.csv_pooler.CSVPooler.chunk_lines", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.chunk_lines", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_pooler.CSVPooler.process": {"fullname": "csv_batcher.csv_pooler.CSVPooler.process", "modulename": "csv_batcher.csv_pooler", "qualname": "CSVPooler.process", "kind": "function", "doc": "<p>Processes <code>self.csv_filename</code> by using <code>CSVSplitter</code> to split it\n    into multiple temporary files defined by <code>self.chunk_lines</code>.\nUse <code>multiprocessing.Pool</code> to use multiple process workers to process\nthe group of CSVs.</p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"bp\">self</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "csv_batcher.csv_splitter": {"fullname": "csv_batcher.csv_splitter", "modulename": "csv_batcher.csv_splitter", "kind": "module", "doc": "<p></p>\n"}, "csv_batcher.csv_splitter.CSVSplitter": {"fullname": "csv_batcher.csv_splitter.CSVSplitter", "modulename": "csv_batcher.csv_splitter", "qualname": "CSVSplitter", "kind": "class", "doc": "<p>Splits a CSV file into multiple files</p>\n"}, "csv_batcher.csv_splitter.CSVSplitter.__init__": {"fullname": "csv_batcher.csv_splitter.CSVSplitter.__init__", "modulename": "csv_batcher.csv_splitter", "qualname": "CSVSplitter.__init__", "kind": "function", "doc": "<p>Construct CSVSplitter</p>\n\n<p>Args:\n    csv_filename (str): path to CSV file\n    chunk_line_cnt (int, optional): Target lines for each chunk. Last chunk might\n    be smaller than this.  Defaults to 10000.</p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"n\">csv_filename</span><span class=\"p\">:</span> <span class=\"nb\">str</span>, </span><span class=\"param\"><span class=\"n\">chunk_line_cnt</span><span class=\"p\">:</span> <span class=\"nb\">int</span> <span class=\"o\">=</span> <span class=\"mi\">10000</span></span>)</span>"}, "csv_batcher.csv_splitter.CSVSplitter.csv_filename": {"fullname": "csv_batcher.csv_splitter.CSVSplitter.csv_filename", "modulename": "csv_batcher.csv_splitter", "qualname": "CSVSplitter.csv_filename", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_splitter.CSVSplitter.chunk_line_cnt": {"fullname": "csv_batcher.csv_splitter.CSVSplitter.chunk_line_cnt", "modulename": "csv_batcher.csv_splitter", "qualname": "CSVSplitter.chunk_line_cnt", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_splitter.CSVSplitter.chunk_dir": {"fullname": "csv_batcher.csv_splitter.CSVSplitter.chunk_dir", "modulename": "csv_batcher.csv_splitter", "qualname": "CSVSplitter.chunk_dir", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_splitter.CSVSplitter.chunk_files": {"fullname": "csv_batcher.csv_splitter.CSVSplitter.chunk_files", "modulename": "csv_batcher.csv_splitter", "qualname": "CSVSplitter.chunk_files", "kind": "variable", "doc": "<p></p>\n"}, "csv_batcher.csv_splitter.CSVSplitter.csv_files": {"fullname": "csv_batcher.csv_splitter.CSVSplitter.csv_files", "modulename": "csv_batcher.csv_splitter", "qualname": "CSVSplitter.csv_files", "kind": "function", "doc": "<p>Returns <code>self.chunk_files</code></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"bp\">self</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "csv_batcher.csv_splitter.CSVSplitter.cleanup": {"fullname": "csv_batcher.csv_splitter.CSVSplitter.cleanup", "modulename": "csv_batcher.csv_splitter", "qualname": "CSVSplitter.cleanup", "kind": "function", "doc": "<p>Remove temporary directory for chunk files; this must be called\nand should be called in a <code>finally</code> block</p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"param\"><span class=\"bp\">self</span></span><span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "csv_batcher.test_csv_pooler": {"fullname": "csv_batcher.test_csv_pooler", "modulename": "csv_batcher.test_csv_pooler", "kind": "module", "doc": "<p></p>\n"}, "csv_batcher.test_csv_pooler.test_big_file_as_csv": {"fullname": "csv_batcher.test_csv_pooler.test_big_file_as_csv", "modulename": "csv_batcher.test_csv_pooler", "qualname": "test_big_file_as_csv", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "csv_batcher.test_csv_pooler.test_big_file_as_dataframe": {"fullname": "csv_batcher.test_csv_pooler.test_big_file_as_dataframe", "modulename": "csv_batcher.test_csv_pooler", "qualname": "test_big_file_as_dataframe", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "csv_batcher.test_csv_pooler.test_big_file_as_dataframe_rows": {"fullname": "csv_batcher.test_csv_pooler.test_big_file_as_dataframe_rows", "modulename": "csv_batcher.test_csv_pooler", "qualname": "test_big_file_as_dataframe_rows", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature pdoc-code condensed\">(<span class=\"return-annotation\">):</span></span>", "funcdef": "def"}, "csv_batcher.test_csv_pooler.test_no_pooler": {"fullname": "csv_batcher.test_csv_pooler.test_no_pooler", "modulename": "csv_batcher.test_csv_pooler", "qualname": "test_no_pooler", "kind": "function", "doc": "<p></p>\n", "signature": "<span class=\"signature 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     // mirrored in build-search-index.js (part 1)
     // Also split on html tags. this is a cheap heuristic, but good enough.
diff --git a/pyproject.toml b/pyproject.toml
index 6ce01e4..955e1e0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [tool.poetry]
 name = "csv-batcher"
-version = "0.1.2"
-description = "A utility to split a large CSV into smaller ones, and uses multiprocessing to process the CSVs in parallel."
+version = "0.1.3"
+description = "A python utility to split a large CSV into smaller ones, and uses multiprocessing to process the CSVs in parallel."
 authors = ["Steven Miers <steven.miers@gmail.com>"]
 readme = "README.md"
 license = "The Unlicense"