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10 Minutes to pandas — pandas 0.20.3 documentation.htm
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10 Minutes to pandas — pandas 0.20.3 documentation.htm
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<title>10 Minutes to pandas — pandas 0.20.3 documentation</title>
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<div class="related" role="navigation" aria-label="related navigation">
<h3>Navigation</h3>
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<li class="toctree-l1"><a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/whatsnew.html">What’s New</a></li>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">10 Minutes to pandas</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#object-creation">Object Creation</a></li>
<li class="toctree-l2"><a class="reference internal" href="#viewing-data">Viewing Data</a></li>
<li class="toctree-l2"><a class="reference internal" href="#selection">Selection</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="#csv">CSV</a></li>
<li class="toctree-l3"><a class="reference internal" href="#hdf5">HDF5</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#gotchas">Gotchas</a></li>
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<li class="toctree-l1"><a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/tutorials.html">Tutorials</a></li>
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<li class="toctree-l1"><a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/dsintro.html">Intro to Data Structures</a></li>
<li class="toctree-l1"><a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/basics.html">Essential Basic Functionality</a></li>
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<div class="section" id="minutes-to-pandas">
<span id="min"></span><h1>10 Minutes to pandas<a class="headerlink" href="#minutes-to-pandas" title="Permalink to this headline">¶</a></h1>
<p>This is a short introduction to pandas, geared mainly for new users.
You can see more complex recipes in the <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/cookbook.html#cookbook"><span class="std std-ref">Cookbook</span></a></p>
<p>Customarily, we import as follows:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [1]: </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="gp">In [2]: </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="gp">In [3]: </span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
</pre></div>
</div>
<div class="section" id="object-creation">
<h2>Object Creation<a class="headerlink" href="#object-creation" title="Permalink to this headline">¶</a></h2>
<p>See the <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dsintro"><span class="std std-ref">Data Structure Intro section</span></a></p>
<p>Creating a <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html#pandas.Series" title="pandas.Series"><code class="xref py py-class docutils literal"><span class="pre">Series</span></code></a> by passing a list of values, letting pandas create
a default integer index:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [4]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">8</span><span class="p">])</span>
<span class="gp">In [5]: </span><span class="n">s</span>
<span class="gh">Out[5]: </span><span class="go"></span>
<span class="go">0 1.0</span>
<span class="go">1 3.0</span>
<span class="go">2 5.0</span>
<span class="go">3 NaN</span>
<span class="go">4 6.0</span>
<span class="go">5 8.0</span>
<span class="go">dtype: float64</span>
</pre></div>
</div>
<p>Creating a <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame"><code class="xref py py-class docutils literal"><span class="pre">DataFrame</span></code></a> by passing a numpy array, with a datetime index
and labeled columns:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [6]: </span><span class="n">dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">'20130101'</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
<span class="gp">In [7]: </span><span class="n">dates</span>
<span class="gh">Out[7]: </span><span class="go"></span>
<span class="go">DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',</span>
<span class="go"> '2013-01-05', '2013-01-06'],</span>
<span class="go"> dtype='datetime64[ns]', freq='D')</span>
<span class="gp">In [8]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="n">dates</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">'ABCD'</span><span class="p">))</span>
<span class="gp">In [9]: </span><span class="n">df</span>
<span class="gh">Out[9]: </span><span class="go"></span>
<span class="go"> A B C D</span>
<span class="go">2013-01-01 0.469112 -0.282863 -1.509059 -1.135632</span>
<span class="go">2013-01-02 1.212112 -0.173215 0.119209 -1.044236</span>
<span class="go">2013-01-03 -0.861849 -2.104569 -0.494929 1.071804</span>
<span class="go">2013-01-04 0.721555 -0.706771 -1.039575 0.271860</span>
<span class="go">2013-01-05 -0.424972 0.567020 0.276232 -1.087401</span>
<span class="go">2013-01-06 -0.673690 0.113648 -1.478427 0.524988</span>
</pre></div>
</div>
<p>Creating a <code class="docutils literal"><span class="pre">DataFrame</span></code> by passing a dict of objects that can be converted to series-like.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [10]: </span><span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span> <span class="s1">'A'</span> <span class="p">:</span> <span class="mf">1.</span><span class="p">,</span>
<span class="gp"> ....: </span> <span class="s1">'B'</span> <span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'20130102'</span><span class="p">),</span>
<span class="gp"> ....: </span> <span class="s1">'C'</span> <span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)),</span><span class="n">dtype</span><span class="o">=</span><span class="s1">'float32'</span><span class="p">),</span>
<span class="gp"> ....: </span> <span class="s1">'D'</span> <span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span><span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">),</span>
<span class="gp"> ....: </span> <span class="s1">'E'</span> <span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"test"</span><span class="p">,</span><span class="s2">"train"</span><span class="p">,</span><span class="s2">"test"</span><span class="p">,</span><span class="s2">"train"</span><span class="p">]),</span>
<span class="gp"> ....: </span> <span class="s1">'F'</span> <span class="p">:</span> <span class="s1">'foo'</span> <span class="p">})</span>
<span class="gp"> ....: </span>
<span class="gp">In [11]: </span><span class="n">df2</span>
<span class="gh">Out[11]: </span><span class="go"></span>
<span class="go"> A B C D E F</span>
<span class="go">0 1.0 2013-01-02 1.0 3 test foo</span>
<span class="go">1 1.0 2013-01-02 1.0 3 train foo</span>
<span class="go">2 1.0 2013-01-02 1.0 3 test foo</span>
<span class="go">3 1.0 2013-01-02 1.0 3 train foo</span>
</pre></div>
</div>
<p>Having specific <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/basics.html#basics-dtypes"><span class="std std-ref">dtypes</span></a></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [12]: </span><span class="n">df2</span><span class="o">.</span><span class="n">dtypes</span>
<span class="gh">Out[12]: </span><span class="go"></span>
<span class="go">A float64</span>
<span class="go">B datetime64[ns]</span>
<span class="go">C float32</span>
<span class="go">D int32</span>
<span class="go">E category</span>
<span class="go">F object</span>
<span class="go">dtype: object</span>
</pre></div>
</div>
<p>If you’re using IPython, tab completion for column names (as well as public
attributes) is automatically enabled. Here’s a subset of the attributes that
will be completed:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [13]: </span><span class="n">df2</span><span class="o">.<</span><span class="n">TAB</span><span class="o">></span>
<span class="go">df2.A df2.bool</span>
<span class="go">df2.abs df2.boxplot</span>
<span class="go">df2.add df2.C</span>
<span class="go">df2.add_prefix df2.clip</span>
<span class="go">df2.add_suffix df2.clip_lower</span>
<span class="go">df2.align df2.clip_upper</span>
<span class="go">df2.all df2.columns</span>
<span class="go">df2.any df2.combine</span>
<span class="go">df2.append df2.combine_first</span>
<span class="go">df2.apply df2.compound</span>
<span class="go">df2.applymap df2.consolidate</span>
<span class="go">df2.as_blocks df2.convert_objects</span>
<span class="go">df2.asfreq df2.copy</span>
<span class="go">df2.as_matrix df2.corr</span>
<span class="go">df2.astype df2.corrwith</span>
<span class="go">df2.at df2.count</span>
<span class="go">df2.at_time df2.cov</span>
<span class="go">df2.axes df2.cummax</span>
<span class="go">df2.B df2.cummin</span>
<span class="go">df2.between_time df2.cumprod</span>
<span class="go">df2.bfill df2.cumsum</span>
<span class="go">df2.blocks df2.D</span>
</pre></div>
</div>
<p>As you can see, the columns <code class="docutils literal"><span class="pre">A</span></code>, <code class="docutils literal"><span class="pre">B</span></code>, <code class="docutils literal"><span class="pre">C</span></code>, and <code class="docutils literal"><span class="pre">D</span></code> are automatically
tab completed. <code class="docutils literal"><span class="pre">E</span></code> is there as well; the rest of the attributes have been
truncated for brevity.</p>
</div>
<div class="section" id="viewing-data">
<h2>Viewing Data<a class="headerlink" href="#viewing-data" title="Permalink to this headline">¶</a></h2>
<p>See the <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/basics.html#basics"><span class="std std-ref">Basics section</span></a></p>
<p>See the top & bottom rows of the frame</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [14]: </span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
<span class="gh">Out[14]: </span><span class="go"></span>
<span class="go"> A B C D</span>
<span class="go">2013-01-01 0.469112 -0.282863 -1.509059 -1.135632</span>
<span class="go">2013-01-02 1.212112 -0.173215 0.119209 -1.044236</span>
<span class="go">2013-01-03 -0.861849 -2.104569 -0.494929 1.071804</span>
<span class="go">2013-01-04 0.721555 -0.706771 -1.039575 0.271860</span>
<span class="go">2013-01-05 -0.424972 0.567020 0.276232 -1.087401</span>
<span class="gp">In [15]: </span><span class="n">df</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="go">Out[15]: </span>
<span class="go"> A B C D</span>
<span class="go">2013-01-04 0.721555 -0.706771 -1.039575 0.271860</span>
<span class="go">2013-01-05 -0.424972 0.567020 0.276232 -1.087401</span>
<span class="go">2013-01-06 -0.673690 0.113648 -1.478427 0.524988</span>
</pre></div>
</div>
<p>Display the index, columns, and the underlying numpy data</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [16]: </span><span class="n">df</span><span class="o">.</span><span class="n">index</span>
<span class="gh">Out[16]: </span><span class="go"></span>
<span class="go">DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',</span>
<span class="go"> '2013-01-05', '2013-01-06'],</span>
<span class="go"> dtype='datetime64[ns]', freq='D')</span>
<span class="gp">In [17]: </span><span class="n">df</span><span class="o">.</span><span class="n">columns</span>
<span class="go">Out[17]: Index(['A', 'B', 'C', 'D'], dtype='object')</span>
<span class="gp">In [18]: </span><span class="n">df</span><span class="o">.</span><span class="n">values</span>
<span class="go">Out[18]: </span>
<span class="go">array([[ 0.4691, -0.2829, -1.5091, -1.1356],</span>
<span class="go"> [ 1.2121, -0.1732, 0.1192, -1.0442],</span>
<span class="go"> [-0.8618, -2.1046, -0.4949, 1.0718],</span>
<span class="go"> [ 0.7216, -0.7068, -1.0396, 0.2719],</span>
<span class="go"> [-0.425 , 0.567 , 0.2762, -1.0874],</span>
<span class="go"> [-0.6737, 0.1136, -1.4784, 0.525 ]])</span>
</pre></div>
</div>
<p>Describe shows a quick statistic summary of your data</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [19]: </span><span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
<span class="gh">Out[19]: </span><span class="go"></span>
<span class="go"> A B C D</span>
<span class="go">count 6.000000 6.000000 6.000000 6.000000</span>
<span class="go">mean 0.073711 -0.431125 -0.687758 -0.233103</span>
<span class="go">std 0.843157 0.922818 0.779887 0.973118</span>
<span class="go">min -0.861849 -2.104569 -1.509059 -1.135632</span>
<span class="go">25% -0.611510 -0.600794 -1.368714 -1.076610</span>
<span class="go">50% 0.022070 -0.228039 -0.767252 -0.386188</span>
<span class="go">75% 0.658444 0.041933 -0.034326 0.461706</span>
<span class="go">max 1.212112 0.567020 0.276232 1.071804</span>
</pre></div>
</div>
<p>Transposing your data</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [20]: </span><span class="n">df</span><span class="o">.</span><span class="n">T</span>
<span class="gh">Out[20]: </span><span class="go"></span>
<span class="go"> 2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06</span>
<span class="go">A 0.469112 1.212112 -0.861849 0.721555 -0.424972 -0.673690</span>
<span class="go">B -0.282863 -0.173215 -2.104569 -0.706771 0.567020 0.113648</span>
<span class="go">C -1.509059 0.119209 -0.494929 -1.039575 0.276232 -1.478427</span>
<span class="go">D -1.135632 -1.044236 1.071804 0.271860 -1.087401 0.524988</span>
</pre></div>
</div>
<p>Sorting by an axis</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [21]: </span><span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="gh">Out[21]: </span><span class="go"></span>
<span class="go"> D C B A</span>
<span class="go">2013-01-01 -1.135632 -1.509059 -0.282863 0.469112</span>
<span class="go">2013-01-02 -1.044236 0.119209 -0.173215 1.212112</span>
<span class="go">2013-01-03 1.071804 -0.494929 -2.104569 -0.861849</span>
<span class="go">2013-01-04 0.271860 -1.039575 -0.706771 0.721555</span>
<span class="go">2013-01-05 -1.087401 0.276232 0.567020 -0.424972</span>
<span class="go">2013-01-06 0.524988 -1.478427 0.113648 -0.673690</span>
</pre></div>
</div>
<p>Sorting by values</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [22]: </span><span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">'B'</span><span class="p">)</span>
<span class="gh">Out[22]: </span><span class="go"></span>
<span class="go"> A B C D</span>
<span class="go">2013-01-03 -0.861849 -2.104569 -0.494929 1.071804</span>
<span class="go">2013-01-04 0.721555 -0.706771 -1.039575 0.271860</span>
<span class="go">2013-01-01 0.469112 -0.282863 -1.509059 -1.135632</span>
<span class="go">2013-01-02 1.212112 -0.173215 0.119209 -1.044236</span>
<span class="go">2013-01-06 -0.673690 0.113648 -1.478427 0.524988</span>
<span class="go">2013-01-05 -0.424972 0.567020 0.276232 -1.087401</span>
</pre></div>
</div>
</div>
<div class="section" id="selection">
<h2>Selection<a class="headerlink" href="#selection" title="Permalink to this headline">¶</a></h2>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">While standard Python / Numpy expressions for selecting and setting are
intuitive and come in handy for interactive work, for production code, we
recommend the optimized pandas data access methods, <code class="docutils literal"><span class="pre">.at</span></code>, <code class="docutils literal"><span class="pre">.iat</span></code>,
<code class="docutils literal"><span class="pre">.loc</span></code>, <code class="docutils literal"><span class="pre">.iloc</span></code> and <code class="docutils literal"><span class="pre">.ix</span></code>.</p>
</div>
<p>See the indexing documentation <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing"><span class="std std-ref">Indexing and Selecting Data</span></a> and <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/advanced.html#advanced"><span class="std std-ref">MultiIndex / Advanced Indexing</span></a></p>
<div class="section" id="getting">
<h3>Getting<a class="headerlink" href="#getting" title="Permalink to this headline">¶</a></h3>
<p>Selecting a single column, which yields a <code class="docutils literal"><span class="pre">Series</span></code>,
equivalent to <code class="docutils literal"><span class="pre">df.A</span></code></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [23]: </span><span class="n">df</span><span class="p">[</span><span class="s1">'A'</span><span class="p">]</span>
<span class="gh">Out[23]: </span><span class="go"></span>
<span class="go">2013-01-01 0.469112</span>
<span class="go">2013-01-02 1.212112</span>
<span class="go">2013-01-03 -0.861849</span>
<span class="go">2013-01-04 0.721555</span>
<span class="go">2013-01-05 -0.424972</span>
<span class="go">2013-01-06 -0.673690</span>
<span class="go">Freq: D, Name: A, dtype: float64</span>
</pre></div>
</div>
<p>Selecting via <code class="docutils literal"><span class="pre">[]</span></code>, which slices the rows.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [24]: </span><span class="n">df</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
<span class="gh">Out[24]: </span><span class="go"></span>
<span class="go"> A B C D</span>
<span class="go">2013-01-01 0.469112 -0.282863 -1.509059 -1.135632</span>
<span class="go">2013-01-02 1.212112 -0.173215 0.119209 -1.044236</span>
<span class="go">2013-01-03 -0.861849 -2.104569 -0.494929 1.071804</span>
<span class="gp">In [25]: </span><span class="n">df</span><span class="p">[</span><span class="s1">'20130102'</span><span class="p">:</span><span class="s1">'20130104'</span><span class="p">]</span>
<span class="go">Out[25]: </span>
<span class="go"> A B C D</span>
<span class="go">2013-01-02 1.212112 -0.173215 0.119209 -1.044236</span>
<span class="go">2013-01-03 -0.861849 -2.104569 -0.494929 1.071804</span>
<span class="go">2013-01-04 0.721555 -0.706771 -1.039575 0.271860</span>
</pre></div>
</div>
</div>
<div class="section" id="selection-by-label">
<h3>Selection by Label<a class="headerlink" href="#selection-by-label" title="Permalink to this headline">¶</a></h3>
<p>See more in <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-label"><span class="std std-ref">Selection by Label</span></a></p>
<p>For getting a cross section using a label</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [26]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">dates</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
<span class="gh">Out[26]: </span><span class="go"></span>
<span class="go">A 0.469112</span>
<span class="go">B -0.282863</span>
<span class="go">C -1.509059</span>
<span class="go">D -1.135632</span>
<span class="go">Name: 2013-01-01 00:00:00, dtype: float64</span>
</pre></div>
</div>
<p>Selecting on a multi-axis by label</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [27]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,[</span><span class="s1">'A'</span><span class="p">,</span><span class="s1">'B'</span><span class="p">]]</span>
<span class="gh">Out[27]: </span><span class="go"></span>
<span class="go"> A B</span>
<span class="go">2013-01-01 0.469112 -0.282863</span>
<span class="go">2013-01-02 1.212112 -0.173215</span>
<span class="go">2013-01-03 -0.861849 -2.104569</span>
<span class="go">2013-01-04 0.721555 -0.706771</span>
<span class="go">2013-01-05 -0.424972 0.567020</span>
<span class="go">2013-01-06 -0.673690 0.113648</span>
</pre></div>
</div>
<p>Showing label slicing, both endpoints are <em>included</em></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [28]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'20130102'</span><span class="p">:</span><span class="s1">'20130104'</span><span class="p">,[</span><span class="s1">'A'</span><span class="p">,</span><span class="s1">'B'</span><span class="p">]]</span>
<span class="gh">Out[28]: </span><span class="go"></span>
<span class="go"> A B</span>
<span class="go">2013-01-02 1.212112 -0.173215</span>
<span class="go">2013-01-03 -0.861849 -2.104569</span>
<span class="go">2013-01-04 0.721555 -0.706771</span>
</pre></div>
</div>
<p>Reduction in the dimensions of the returned object</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [29]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'20130102'</span><span class="p">,[</span><span class="s1">'A'</span><span class="p">,</span><span class="s1">'B'</span><span class="p">]]</span>
<span class="gh">Out[29]: </span><span class="go"></span>
<span class="go">A 1.212112</span>
<span class="go">B -0.173215</span>
<span class="go">Name: 2013-01-02 00:00:00, dtype: float64</span>
</pre></div>
</div>
<p>For getting a scalar value</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [30]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">dates</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="s1">'A'</span><span class="p">]</span>
<span class="gh">Out[30]: </span><span class="go">0.46911229990718628</span>
</pre></div>
</div>
<p>For getting fast access to a scalar (equiv to the prior method)</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [31]: </span><span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">dates</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="s1">'A'</span><span class="p">]</span>
<span class="gh">Out[31]: </span><span class="go">0.46911229990718628</span>
</pre></div>
</div>
</div>
<div class="section" id="selection-by-position">
<h3>Selection by Position<a class="headerlink" href="#selection-by-position" title="Permalink to this headline">¶</a></h3>
<p>See more in <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-integer"><span class="std std-ref">Selection by Position</span></a></p>
<p>Select via the position of the passed integers</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [32]: </span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="gh">Out[32]: </span><span class="go"></span>
<span class="go">A 0.721555</span>
<span class="go">B -0.706771</span>
<span class="go">C -1.039575</span>
<span class="go">D 0.271860</span>
<span class="go">Name: 2013-01-04 00:00:00, dtype: float64</span>
</pre></div>
</div>
<p>By integer slices, acting similar to numpy/python</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [33]: </span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
<span class="gh">Out[33]: </span><span class="go"></span>
<span class="go"> A B</span>
<span class="go">2013-01-04 0.721555 -0.706771</span>
<span class="go">2013-01-05 -0.424972 0.567020</span>
</pre></div>
</div>
<p>By lists of integer position locations, similar to the numpy/python style</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [34]: </span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">]]</span>
<span class="gh">Out[34]: </span><span class="go"></span>
<span class="go"> A C</span>
<span class="go">2013-01-02 1.212112 0.119209</span>
<span class="go">2013-01-03 -0.861849 -0.494929</span>
<span class="go">2013-01-05 -0.424972 0.276232</span>
</pre></div>
</div>
<p>For slicing rows explicitly</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [35]: </span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">,:]</span>
<span class="gh">Out[35]: </span><span class="go"></span>
<span class="go"> A B C D</span>
<span class="go">2013-01-02 1.212112 -0.173215 0.119209 -1.044236</span>
<span class="go">2013-01-03 -0.861849 -2.104569 -0.494929 1.071804</span>
</pre></div>
</div>
<p>For slicing columns explicitly</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [36]: </span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
<span class="gh">Out[36]: </span><span class="go"></span>
<span class="go"> B C</span>
<span class="go">2013-01-01 -0.282863 -1.509059</span>
<span class="go">2013-01-02 -0.173215 0.119209</span>
<span class="go">2013-01-03 -2.104569 -0.494929</span>
<span class="go">2013-01-04 -0.706771 -1.039575</span>
<span class="go">2013-01-05 0.567020 0.276232</span>
<span class="go">2013-01-06 0.113648 -1.478427</span>
</pre></div>
</div>
<p>For getting a value explicitly</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [37]: </span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
<span class="gh">Out[37]: </span><span class="go">-0.17321464905330858</span>
</pre></div>
</div>
<p>For getting fast access to a scalar (equiv to the prior method)</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [38]: </span><span class="n">df</span><span class="o">.</span><span class="n">iat</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
<span class="gh">Out[38]: </span><span class="go">-0.17321464905330858</span>
</pre></div>
</div>
</div>
<div class="section" id="boolean-indexing">
<h3>Boolean Indexing<a class="headerlink" href="#boolean-indexing" title="Permalink to this headline">¶</a></h3>
<p>Using a single column’s values to select data.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [39]: </span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">A</span> <span class="o">></span> <span class="mi">0</span><span class="p">]</span>
<span class="gh">Out[39]: </span><span class="go"></span>
<span class="go"> A B C D</span>
<span class="go">2013-01-01 0.469112 -0.282863 -1.509059 -1.135632</span>
<span class="go">2013-01-02 1.212112 -0.173215 0.119209 -1.044236</span>
<span class="go">2013-01-04 0.721555 -0.706771 -1.039575 0.271860</span>
</pre></div>
</div>
<p>Selecting values from a DataFrame where a boolean condition is met.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [40]: </span><span class="n">df</span><span class="p">[</span><span class="n">df</span> <span class="o">></span> <span class="mi">0</span><span class="p">]</span>
<span class="gh">Out[40]: </span><span class="go"></span>
<span class="go"> A B C D</span>
<span class="go">2013-01-01 0.469112 NaN NaN NaN</span>
<span class="go">2013-01-02 1.212112 NaN 0.119209 NaN</span>
<span class="go">2013-01-03 NaN NaN NaN 1.071804</span>
<span class="go">2013-01-04 0.721555 NaN NaN 0.271860</span>
<span class="go">2013-01-05 NaN 0.567020 0.276232 NaN</span>
<span class="go">2013-01-06 NaN 0.113648 NaN 0.524988</span>
</pre></div>
</div>
<p>Using the <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.isin.html#pandas.Series.isin" title="pandas.Series.isin"><code class="xref py py-func docutils literal"><span class="pre">isin()</span></code></a> method for filtering:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [41]: </span><span class="n">df2</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="gp">In [42]: </span><span class="n">df2</span><span class="p">[</span><span class="s1">'E'</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'one'</span><span class="p">,</span> <span class="s1">'one'</span><span class="p">,</span><span class="s1">'two'</span><span class="p">,</span><span class="s1">'three'</span><span class="p">,</span><span class="s1">'four'</span><span class="p">,</span><span class="s1">'three'</span><span class="p">]</span>
<span class="gp">In [43]: </span><span class="n">df2</span>
<span class="gh">Out[43]: </span><span class="go"></span>
<span class="go"> A B C D E</span>
<span class="go">2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 one</span>
<span class="go">2013-01-02 1.212112 -0.173215 0.119209 -1.044236 one</span>
<span class="go">2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two</span>
<span class="go">2013-01-04 0.721555 -0.706771 -1.039575 0.271860 three</span>
<span class="go">2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four</span>
<span class="go">2013-01-06 -0.673690 0.113648 -1.478427 0.524988 three</span>
<span class="gp">In [44]: </span><span class="n">df2</span><span class="p">[</span><span class="n">df2</span><span class="p">[</span><span class="s1">'E'</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="s1">'two'</span><span class="p">,</span><span class="s1">'four'</span><span class="p">])]</span>
<span class="go">Out[44]: </span>
<span class="go"> A B C D E</span>
<span class="go">2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two</span>
<span class="go">2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four</span>
</pre></div>
</div>
</div>
<div class="section" id="setting">
<h3>Setting<a class="headerlink" href="#setting" title="Permalink to this headline">¶</a></h3>
<p>Setting a new column automatically aligns the data
by the indexes</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [45]: </span><span class="n">s1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">'20130102'</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">6</span><span class="p">))</span>
<span class="gp">In [46]: </span><span class="n">s1</span>
<span class="gh">Out[46]: </span><span class="go"></span>
<span class="go">2013-01-02 1</span>
<span class="go">2013-01-03 2</span>
<span class="go">2013-01-04 3</span>
<span class="go">2013-01-05 4</span>
<span class="go">2013-01-06 5</span>
<span class="go">2013-01-07 6</span>
<span class="go">Freq: D, dtype: int64</span>
<span class="gp">In [47]: </span><span class="n">df</span><span class="p">[</span><span class="s1">'F'</span><span class="p">]</span> <span class="o">=</span> <span class="n">s1</span>
</pre></div>
</div>
<p>Setting values by label</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [48]: </span><span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">dates</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="s1">'A'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
</pre></div>
</div>
<p>Setting values by position</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [49]: </span><span class="n">df</span><span class="o">.</span><span class="n">iat</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
</pre></div>
</div>
<p>Setting by assigning with a numpy array</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [50]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="s1">'D'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">))</span>
</pre></div>
</div>
<p>The result of the prior setting operations</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [51]: </span><span class="n">df</span>
<span class="gh">Out[51]: </span><span class="go"></span>
<span class="go"> A B C D F</span>
<span class="go">2013-01-01 0.000000 0.000000 -1.509059 5 NaN</span>
<span class="go">2013-01-02 1.212112 -0.173215 0.119209 5 1.0</span>
<span class="go">2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0</span>
<span class="go">2013-01-04 0.721555 -0.706771 -1.039575 5 3.0</span>
<span class="go">2013-01-05 -0.424972 0.567020 0.276232 5 4.0</span>
<span class="go">2013-01-06 -0.673690 0.113648 -1.478427 5 5.0</span>
</pre></div>
</div>
<p>A <code class="docutils literal"><span class="pre">where</span></code> operation with setting.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [52]: </span><span class="n">df2</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="gp">In [53]: </span><span class="n">df2</span><span class="p">[</span><span class="n">df2</span> <span class="o">></span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="n">df2</span>
<span class="gp">In [54]: </span><span class="n">df2</span>
<span class="gh">Out[54]: </span><span class="go"></span>
<span class="go"> A B C D F</span>
<span class="go">2013-01-01 0.000000 0.000000 -1.509059 -5 NaN</span>
<span class="go">2013-01-02 -1.212112 -0.173215 -0.119209 -5 -1.0</span>
<span class="go">2013-01-03 -0.861849 -2.104569 -0.494929 -5 -2.0</span>
<span class="go">2013-01-04 -0.721555 -0.706771 -1.039575 -5 -3.0</span>
<span class="go">2013-01-05 -0.424972 -0.567020 -0.276232 -5 -4.0</span>
<span class="go">2013-01-06 -0.673690 -0.113648 -1.478427 -5 -5.0</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="missing-data">
<h2>Missing Data<a class="headerlink" href="#missing-data" title="Permalink to this headline">¶</a></h2>
<p>pandas primarily uses the value <code class="docutils literal"><span class="pre">np.nan</span></code> to represent missing data. It is by
default not included in computations. See the <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/missing_data.html#missing-data"><span class="std std-ref">Missing Data section</span></a></p>
<p>Reindexing allows you to change/add/delete the index on a specified axis. This
returns a copy of the data.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [55]: </span><span class="n">df1</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="n">dates</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">4</span><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="s1">'E'</span><span class="p">])</span>
<span class="gp">In [56]: </span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">dates</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span><span class="n">dates</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="s1">'E'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="gp">In [57]: </span><span class="n">df1</span>
<span class="gh">Out[57]: </span><span class="go"></span>
<span class="go"> A B C D F E</span>
<span class="go">2013-01-01 0.000000 0.000000 -1.509059 5 NaN 1.0</span>
<span class="go">2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0</span>
<span class="go">2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 NaN</span>
<span class="go">2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 NaN</span>
</pre></div>
</div>
<p>To drop any rows that have missing data.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [58]: </span><span class="n">df1</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">how</span><span class="o">=</span><span class="s1">'any'</span><span class="p">)</span>
<span class="gh">Out[58]: </span><span class="go"></span>
<span class="go"> A B C D F E</span>
<span class="go">2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0</span>
</pre></div>
</div>
<p>Filling missing data</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [59]: </span><span class="n">df1</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="gh">Out[59]: </span><span class="go"></span>
<span class="go"> A B C D F E</span>
<span class="go">2013-01-01 0.000000 0.000000 -1.509059 5 5.0 1.0</span>
<span class="go">2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0</span>
<span class="go">2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 5.0</span>
<span class="go">2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 5.0</span>
</pre></div>
</div>
<p>To get the boolean mask where values are <code class="docutils literal"><span class="pre">nan</span></code></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [60]: </span><span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">df1</span><span class="p">)</span>
<span class="gh">Out[60]: </span><span class="go"></span>
<span class="go"> A B C D F E</span>
<span class="go">2013-01-01 False False False False True False</span>
<span class="go">2013-01-02 False False False False False False</span>
<span class="go">2013-01-03 False False False False False True</span>
<span class="go">2013-01-04 False False False False False True</span>
</pre></div>
</div>
</div>
<div class="section" id="operations">
<h2>Operations<a class="headerlink" href="#operations" title="Permalink to this headline">¶</a></h2>
<p>See the <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/basics.html#basics-binop"><span class="std std-ref">Basic section on Binary Ops</span></a></p>
<div class="section" id="stats">
<h3>Stats<a class="headerlink" href="#stats" title="Permalink to this headline">¶</a></h3>
<p>Operations in general <em>exclude</em> missing data.</p>
<p>Performing a descriptive statistic</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [61]: </span><span class="n">df</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="gh">Out[61]: </span><span class="go"></span>
<span class="go">A -0.004474</span>
<span class="go">B -0.383981</span>
<span class="go">C -0.687758</span>
<span class="go">D 5.000000</span>
<span class="go">F 3.000000</span>
<span class="go">dtype: float64</span>
</pre></div>
</div>
<p>Same operation on the other axis</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [62]: </span><span class="n">df</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="gh">Out[62]: </span><span class="go"></span>
<span class="go">2013-01-01 0.872735</span>
<span class="go">2013-01-02 1.431621</span>
<span class="go">2013-01-03 0.707731</span>
<span class="go">2013-01-04 1.395042</span>
<span class="go">2013-01-05 1.883656</span>
<span class="go">2013-01-06 1.592306</span>
<span class="go">Freq: D, dtype: float64</span>
</pre></div>
</div>
<p>Operating with objects that have different dimensionality and need alignment.
In addition, pandas automatically broadcasts along the specified dimension.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [63]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">8</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="n">dates</span><span class="p">)</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">In [64]: </span><span class="n">s</span>
<span class="gh">Out[64]: </span><span class="go"></span>
<span class="go">2013-01-01 NaN</span>
<span class="go">2013-01-02 NaN</span>
<span class="go">2013-01-03 1.0</span>
<span class="go">2013-01-04 3.0</span>
<span class="go">2013-01-05 5.0</span>
<span class="go">2013-01-06 NaN</span>
<span class="go">Freq: D, dtype: float64</span>
<span class="gp">In [65]: </span><span class="n">df</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span>
<span class="go">Out[65]: </span>
<span class="go"> A B C D F</span>
<span class="go">2013-01-01 NaN NaN NaN NaN NaN</span>
<span class="go">2013-01-02 NaN NaN NaN NaN NaN</span>
<span class="go">2013-01-03 -1.861849 -3.104569 -1.494929 4.0 1.0</span>
<span class="go">2013-01-04 -2.278445 -3.706771 -4.039575 2.0 0.0</span>
<span class="go">2013-01-05 -5.424972 -4.432980 -4.723768 0.0 -1.0</span>
<span class="go">2013-01-06 NaN NaN NaN NaN NaN</span>
</pre></div>
</div>
</div>
<div class="section" id="apply">
<h3>Apply<a class="headerlink" href="#apply" title="Permalink to this headline">¶</a></h3>
<p>Applying functions to the data</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [66]: </span><span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">)</span>
<span class="gh">Out[66]: </span><span class="go"></span>
<span class="go"> A B C D F</span>
<span class="go">2013-01-01 0.000000 0.000000 -1.509059 5 NaN</span>
<span class="go">2013-01-02 1.212112 -0.173215 -1.389850 10 1.0</span>
<span class="go">2013-01-03 0.350263 -2.277784 -1.884779 15 3.0</span>
<span class="go">2013-01-04 1.071818 -2.984555 -2.924354 20 6.0</span>
<span class="go">2013-01-05 0.646846 -2.417535 -2.648122 25 10.0</span>
<span class="go">2013-01-06 -0.026844 -2.303886 -4.126549 30 15.0</span>
<span class="gp">In [67]: </span><span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">min</span><span class="p">())</span>
<span class="go">Out[67]: </span>
<span class="go">A 2.073961</span>
<span class="go">B 2.671590</span>
<span class="go">C 1.785291</span>
<span class="go">D 0.000000</span>
<span class="go">F 4.000000</span>
<span class="go">dtype: float64</span>
</pre></div>
</div>
</div>
<div class="section" id="histogramming">
<h3>Histogramming<a class="headerlink" href="#histogramming" title="Permalink to this headline">¶</a></h3>
<p>See more at <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/basics.html#basics-discretization"><span class="std std-ref">Histogramming and Discretization</span></a></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [68]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">))</span>
<span class="gp">In [69]: </span><span class="n">s</span>
<span class="gh">Out[69]: </span><span class="go"></span>
<span class="go">0 4</span>
<span class="go">1 2</span>
<span class="go">2 1</span>
<span class="go">3 2</span>
<span class="go">4 6</span>
<span class="go">5 4</span>
<span class="go">6 4</span>
<span class="go">7 6</span>
<span class="go">8 4</span>
<span class="go">9 4</span>
<span class="go">dtype: int64</span>
<span class="gp">In [70]: </span><span class="n">s</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
<span class="go">Out[70]: </span>
<span class="go">4 5</span>
<span class="go">6 2</span>
<span class="go">2 2</span>
<span class="go">1 1</span>
<span class="go">dtype: int64</span>
</pre></div>
</div>
</div>
<div class="section" id="string-methods">
<h3>String Methods<a class="headerlink" href="#string-methods" title="Permalink to this headline">¶</a></h3>
<p>Series is equipped with a set of string processing methods in the <cite>str</cite>
attribute that make it easy to operate on each element of the array, as in the
code snippet below. Note that pattern-matching in <cite>str</cite> generally uses <a class="reference external" href="https://docs.python.org/2/library/re.html">regular
expressions</a> by default (and in
some cases always uses them). See more at <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/text.html#text-string-methods"><span class="std std-ref">Vectorized String Methods</span></a>.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [71]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s1">'A'</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">,</span> <span class="s1">'Aaba'</span><span class="p">,</span> <span class="s1">'Baca'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="s1">'CABA'</span><span class="p">,</span> <span class="s1">'dog'</span><span class="p">,</span> <span class="s1">'cat'</span><span class="p">])</span>
<span class="gp">In [72]: </span><span class="n">s</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
<span class="gh">Out[72]: </span><span class="go"></span>
<span class="go">0 a</span>
<span class="go">1 b</span>
<span class="go">2 c</span>
<span class="go">3 aaba</span>
<span class="go">4 baca</span>
<span class="go">5 NaN</span>
<span class="go">6 caba</span>
<span class="go">7 dog</span>
<span class="go">8 cat</span>
<span class="go">dtype: object</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="merge">
<h2>Merge<a class="headerlink" href="#merge" title="Permalink to this headline">¶</a></h2>
<div class="section" id="concat">
<h3>Concat<a class="headerlink" href="#concat" title="Permalink to this headline">¶</a></h3>
<p>pandas provides various facilities for easily combining together Series,
DataFrame, and Panel objects with various kinds of set logic for the indexes
and relational algebra functionality in the case of join / merge-type
operations.</p>
<p>See the <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/merging.html#merging"><span class="std std-ref">Merging section</span></a></p>
<p>Concatenating pandas objects together with <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html#pandas.concat" title="pandas.concat"><code class="xref py py-func docutils literal"><span class="pre">concat()</span></code></a>:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [73]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="gp">In [74]: </span><span class="n">df</span>
<span class="gh">Out[74]: </span><span class="go"></span>
<span class="go"> 0 1 2 3</span>
<span class="go">0 -0.548702 1.467327 -1.015962 -0.483075</span>
<span class="go">1 1.637550 -1.217659 -0.291519 -1.745505</span>
<span class="go">2 -0.263952 0.991460 -0.919069 0.266046</span>
<span class="go">3 -0.709661 1.669052 1.037882 -1.705775</span>
<span class="go">4 -0.919854 -0.042379 1.247642 -0.009920</span>
<span class="go">5 0.290213 0.495767 0.362949 1.548106</span>
<span class="go">6 -1.131345 -0.089329 0.337863 -0.945867</span>
<span class="go">7 -0.932132 1.956030 0.017587 -0.016692</span>
<span class="go">8 -0.575247 0.254161 -1.143704 0.215897</span>
<span class="go">9 1.193555 -0.077118 -0.408530 -0.862495</span>
<span class="go"># break it into pieces</span>
<span class="gp">In [75]: </span><span class="n">pieces</span> <span class="o">=</span> <span class="p">[</span><span class="n">df</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="mi">7</span><span class="p">:]]</span>
<span class="gp">In [76]: </span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">pieces</span><span class="p">)</span>
<span class="gh">Out[76]: </span><span class="go"></span>
<span class="go"> 0 1 2 3</span>
<span class="go">0 -0.548702 1.467327 -1.015962 -0.483075</span>
<span class="go">1 1.637550 -1.217659 -0.291519 -1.745505</span>
<span class="go">2 -0.263952 0.991460 -0.919069 0.266046</span>
<span class="go">3 -0.709661 1.669052 1.037882 -1.705775</span>
<span class="go">4 -0.919854 -0.042379 1.247642 -0.009920</span>
<span class="go">5 0.290213 0.495767 0.362949 1.548106</span>
<span class="go">6 -1.131345 -0.089329 0.337863 -0.945867</span>
<span class="go">7 -0.932132 1.956030 0.017587 -0.016692</span>
<span class="go">8 -0.575247 0.254161 -1.143704 0.215897</span>
<span class="go">9 1.193555 -0.077118 -0.408530 -0.862495</span>
</pre></div>
</div>
</div>
<div class="section" id="join">
<h3>Join<a class="headerlink" href="#join" title="Permalink to this headline">¶</a></h3>
<p>SQL style merges. See the <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/merging.html#merging-join"><span class="std std-ref">Database style joining</span></a></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [77]: </span><span class="n">left</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'key'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'foo'</span><span class="p">,</span> <span class="s1">'foo'</span><span class="p">],</span> <span class="s1">'lval'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]})</span>
<span class="gp">In [78]: </span><span class="n">right</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'key'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'foo'</span><span class="p">,</span> <span class="s1">'foo'</span><span class="p">],</span> <span class="s1">'rval'</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]})</span>
<span class="gp">In [79]: </span><span class="n">left</span>
<span class="gh">Out[79]: </span><span class="go"></span>
<span class="go"> key lval</span>
<span class="go">0 foo 1</span>
<span class="go">1 foo 2</span>
<span class="gp">In [80]: </span><span class="n">right</span>
<span class="go">Out[80]: </span>
<span class="go"> key rval</span>
<span class="go">0 foo 4</span>
<span class="go">1 foo 5</span>
<span class="gp">In [81]: </span><span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s1">'key'</span><span class="p">)</span>
<span class="go">Out[81]: </span>
<span class="go"> key lval rval</span>
<span class="go">0 foo 1 4</span>
<span class="go">1 foo 1 5</span>
<span class="go">2 foo 2 4</span>
<span class="go">3 foo 2 5</span>
</pre></div>
</div>
<p>Another example that can be given is:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [82]: </span><span class="n">left</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'key'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'foo'</span><span class="p">,</span> <span class="s1">'bar'</span><span class="p">],</span> <span class="s1">'lval'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]})</span>
<span class="gp">In [83]: </span><span class="n">right</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'key'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'foo'</span><span class="p">,</span> <span class="s1">'bar'</span><span class="p">],</span> <span class="s1">'rval'</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]})</span>
<span class="gp">In [84]: </span><span class="n">left</span>
<span class="gh">Out[84]: </span><span class="go"></span>
<span class="go"> key lval</span>
<span class="go">0 foo 1</span>
<span class="go">1 bar 2</span>
<span class="gp">In [85]: </span><span class="n">right</span>
<span class="go">Out[85]: </span>
<span class="go"> key rval</span>
<span class="go">0 foo 4</span>
<span class="go">1 bar 5</span>
<span class="gp">In [86]: </span><span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s1">'key'</span><span class="p">)</span>
<span class="go">Out[86]: </span>
<span class="go"> key lval rval</span>
<span class="go">0 foo 1 4</span>
<span class="go">1 bar 2 5</span>
</pre></div>
</div>
</div>
<div class="section" id="append">
<h3>Append<a class="headerlink" href="#append" title="Permalink to this headline">¶</a></h3>
<p>Append rows to a dataframe. See the <a class="reference internal" href="https://pandas.pydata.org/pandas-docs/stable/merging.html#merging-concatenation"><span class="std std-ref">Appending</span></a></p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [87]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'A'</span><span class="p">,</span><span class="s1">'B'</span><span class="p">,</span><span class="s1">'C'</span><span class="p">,</span><span class="s1">'D'</span><span class="p">])</span>
<span class="gp">In [88]: </span><span class="n">df</span>
<span class="gh">Out[88]: </span><span class="go"></span>
<span class="go"> A B C D</span>
<span class="go">0 1.346061 1.511763 1.627081 -0.990582</span>
<span class="go">1 -0.441652 1.211526 0.268520 0.024580</span>
<span class="go">2 -1.577585 0.396823 -0.105381 -0.532532</span>
<span class="go">3 1.453749 1.208843 -0.080952 -0.264610</span>