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<div class="section" id="create-and-store-dask-dataframes">
<h1>Create and Store Dask DataFrames<a class="headerlink" href="#create-and-store-dask-dataframes" title="Permalink to this headline">¶</a></h1>
<p>Dask can create dataframes from various data storage formats like CSV, HDF,
Apache Parquet, and others. For most formats this data can live on various
storage systems including local disk, network file systems (NFS), the Hadoop
File System (HDFS), and Amazon’s S3 (excepting HDF, which is only available on
POSIX like file systems).</p>
<p>See the <a class="reference external" href="http://dask.pydata.org/en/latest/dataframe-overview.html">Overview section</a> for an in depth
discussion of <code class="docutils literal"><span class="pre">dask.dataframe</span></code> scope, use, limitations.</p>
<div class="section" id="api">
<h2>API<a class="headerlink" href="#api" title="Permalink to this headline">¶</a></h2>
<p>The following functions provide access to convert between Dask Dataframes,
file formats, and other Dask or Python collections.</p>
<p>File Formats:</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.read_csv" title="dask.dataframe.read_csv"><code class="xref py py-obj docutils literal"><span class="pre">read_csv</span></code></a>(urlpath[, blocksize, collection, …])</td>
<td>Read CSV files into a Dask.DataFrame</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.read_parquet" title="dask.dataframe.read_parquet"><code class="xref py py-obj docutils literal"><span class="pre">read_parquet</span></code></a>(path[, columns, filters, …])</td>
<td>Read ParquetFile into a Dask DataFrame</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.read_hdf" title="dask.dataframe.read_hdf"><code class="xref py py-obj docutils literal"><span class="pre">read_hdf</span></code></a>(pattern, key[, start, stop, …])</td>
<td>Read HDF files into a Dask DataFrame</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.read_sql_table" title="dask.dataframe.read_sql_table"><code class="xref py py-obj docutils literal"><span class="pre">read_sql_table</span></code></a>(table, uri, index_col[, …])</td>
<td>Create dataframe from an SQL table.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.from_bcolz" title="dask.dataframe.from_bcolz"><code class="xref py py-obj docutils literal"><span class="pre">from_bcolz</span></code></a>(x[, chunksize, categorize, …])</td>
<td>Read BColz CTable into a Dask Dataframe</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.from_array" title="dask.dataframe.from_array"><code class="xref py py-obj docutils literal"><span class="pre">from_array</span></code></a>(x[, chunksize, columns])</td>
<td>Read any slicable array into a Dask Dataframe</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.to_csv" title="dask.dataframe.to_csv"><code class="xref py py-obj docutils literal"><span class="pre">to_csv</span></code></a>(df, filename[, name_function, …])</td>
<td>Store Dask DataFrame to CSV files</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.to_parquet" title="dask.dataframe.to_parquet"><code class="xref py py-obj docutils literal"><span class="pre">to_parquet</span></code></a>(df, path[, engine, compression, …])</td>
<td>Store Dask.dataframe to Parquet files</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.to_hdf" title="dask.dataframe.to_hdf"><code class="xref py py-obj docutils literal"><span class="pre">to_hdf</span></code></a>(df, path, key[, mode, append, get, …])</td>
<td>Store Dask Dataframe to Hierarchical Data Format (HDF) files</td>
</tr>
</tbody>
</table>
<p>Dask Collections:</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.from_delayed" title="dask.dataframe.from_delayed"><code class="xref py py-obj docutils literal"><span class="pre">from_delayed</span></code></a>(dfs[, meta, divisions, prefix])</td>
<td>Create Dask DataFrame from many Dask Delayed objects</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.from_dask_array" title="dask.dataframe.from_dask_array"><code class="xref py py-obj docutils literal"><span class="pre">from_dask_array</span></code></a>(x[, columns])</td>
<td>Create a Dask DataFrame from a Dask Array.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="bag-creation.html#dask.bag.core.Bag.to_dataframe" title="dask.bag.core.Bag.to_dataframe"><code class="xref py py-obj docutils literal"><span class="pre">dask.bag.core.Bag.to_dataframe</span></code></a>([meta, columns])</td>
<td>Create Dask Dataframe from a Dask Bag.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.to_delayed" title="dask.dataframe.to_delayed"><code class="xref py py-obj docutils literal"><span class="pre">to_delayed</span></code></a>(df)</td>
<td>Create Dask Delayed objects from a Dask Dataframe</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.to_records" title="dask.dataframe.to_records"><code class="xref py py-obj docutils literal"><span class="pre">to_records</span></code></a>(df)</td>
<td>Create Dask Array from a Dask Dataframe</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.to_bag" title="dask.dataframe.to_bag"><code class="xref py py-obj docutils literal"><span class="pre">to_bag</span></code></a>(df[, index])</td>
<td>Create Dask Bag from a Dask DataFrame</td>
</tr>
</tbody>
</table>
<p>Pandas:</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="dataframe-api.html#dask.dataframe.from_pandas" title="dask.dataframe.from_pandas"><code class="xref py py-obj docutils literal"><span class="pre">from_pandas</span></code></a>(data[, npartitions, chunksize, …])</td>
<td>Construct a Dask DataFrame from a Pandas DataFrame</td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="locations">
<h2>Locations<a class="headerlink" href="#locations" title="Permalink to this headline">¶</a></h2>
<p>For text, CSV, and Apache Parquet formats data can come from local disk, from
the Hadoop File System, from S3FS, or others, by prepending the filenames with
a protocol.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">dd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'my-data-*.csv'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">dd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'hdfs:///path/to/my-data-*.csv'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">dd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'s3://bucket-name/my-data-*.csv'</span><span class="p">)</span>
</pre></div>
</div>
<p>For remote systems like HDFS or S3 credentials may be an issue. Usually these
are handled by configuration files on disk (such as a <code class="docutils literal"><span class="pre">.boto</span></code> file for S3)
but in some cases you may want to pass storage-specific options through to the
storage backend. You can do this with the <code class="docutils literal"><span class="pre">storage_options=</span></code> keyword.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">dd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'s3://bucket-name/my-data-*.csv'</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">storage_options</span><span class="o">=</span><span class="p">{</span><span class="s1">'anon'</span><span class="p">:</span> <span class="bp">True</span><span class="p">})</span>
</pre></div>
</div>
</div>
<div class="section" id="dask-delayed">
<h2>Dask Delayed<a class="headerlink" href="#dask-delayed" title="Permalink to this headline">¶</a></h2>
<p>For more complex situations not covered by the functions above you may want to
use <a class="reference internal" href="delayed-overview.html"><span class="doc">dask.delayed</span></a> , which lets you construct
Dask.dataframes out of arbitrary Python function calls that load dataframes.
This can allow you to handle new formats easily, or bake in particular logic
around loading data if, for example, your data is stored with some special</p>
<p>See <a class="reference internal" href="delayed-collections.html"><span class="doc">documentation on using dask.delayed with
collections</span></a> or an <a class="reference external" href="https://gist.github.com/mrocklin/e7b7b3a65f2835cda813096332ec73ca">example notebook</a> showing
how to create a Dask DataFrame from a nested directory structure of Feather
files (as a stand in for any custom file format).</p>
<p>Dask.delayed is particularly useful when simple <code class="docutils literal"><span class="pre">map</span></code> operations aren’t
sufficient to capture the complexity of your data layout.</p>
</div>
<div class="section" id="from-raw-dask-graphs">
<h2>From Raw Dask Graphs<a class="headerlink" href="#from-raw-dask-graphs" title="Permalink to this headline">¶</a></h2>
<p>This section is mainly for developers wishing to extend dask.dataframe. It
discusses internal API not normally needed by users. Everything below can be
done just as effectively with <a class="reference internal" href="delayed-overview.html"><span class="doc">dask.delayed</span></a> described
just above. You should never need to create a dataframe object by hand.</p>
<p>To construct a DataFrame manually from a dask graph you need the following
information:</p>
<ol class="arabic simple">
<li>dask: a dask graph with keys like <code class="docutils literal"><span class="pre">{(name,</span> <span class="pre">0):</span> <span class="pre">...,</span> <span class="pre">(name,</span> <span class="pre">1):</span> <span class="pre">...}</span></code> as
well as any other tasks on which those tasks depend. The tasks
corresponding to <code class="docutils literal"><span class="pre">(name,</span> <span class="pre">i)</span></code> should produce <code class="docutils literal"><span class="pre">pandas.DataFrame</span></code> objects
that correspond to the columns and divisions information discussed below.</li>
<li>name: The special name used above</li>
<li>columns: A list of column names</li>
<li>divisions: A list of index values that separate the different partitions.
Alternatively, if you don’t know the divisions (this is common) you can
provide a list of <code class="docutils literal"><span class="pre">[None,</span> <span class="pre">None,</span> <span class="pre">None,</span> <span class="pre">...]</span></code> with as many partitions as
you have plus one. For more information see the Partitions section in the
<a class="reference internal" href="dataframe.html"><span class="doc">dataframe documentation</span></a>.</li>
</ol>
<p>As an example, we build a DataFrame manually that reads several CSV files that
have a datetime index separated by day. Note, you should never do this. The
<code class="docutils literal"><span class="pre">dd.read_csv</span></code> function does this for you.</p>
<div class="highlight-Python"><div class="highlight"><pre><span></span><span class="n">dsk</span> <span class="o">=</span> <span class="p">{(</span><span class="s1">'mydf'</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span> <span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">,</span> <span class="s1">'data/2000-01-01.csv'</span><span class="p">),</span>
<span class="p">(</span><span class="s1">'mydf'</span><span class="p">,</span> <span class="mi">1</span><span class="p">):</span> <span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">,</span> <span class="s1">'data/2000-01-02.csv'</span><span class="p">),</span>
<span class="p">(</span><span class="s1">'mydf'</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span> <span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">,</span> <span class="s1">'data/2000-01-03.csv'</span><span class="p">)}</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'mydf'</span>
<span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'price'</span><span class="p">,</span> <span class="s1">'name'</span><span class="p">,</span> <span class="s1">'id'</span><span class="p">]</span>
<span class="n">divisions</span> <span class="o">=</span> <span class="p">[</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'2000-01-01 00:00:00'</span><span class="p">),</span>
<span class="n">Timestamp</span><span class="p">(</span><span class="s1">'2000-01-02 00:00:00'</span><span class="p">),</span>
<span class="n">Timestamp</span><span class="p">(</span><span class="s1">'2000-01-03 00:00:00'</span><span class="p">),</span>
<span class="n">Timestamp</span><span class="p">(</span><span class="s1">'2000-01-03 23:59:59'</span><span class="p">)]</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">dd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">dsk</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">columns</span><span class="p">,</span> <span class="n">divisions</span><span class="p">)</span>
</pre></div>
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