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ADDITION
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SUBTRACTION
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MULTIPLICATION
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FLOOR DIVISION
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MODULO
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DESCRIBE
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MAX - MIN
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IDXMAX - IDXMIN
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QUANTILE
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SUM
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</li>
<li class="md-nav__item">
<a href="#mean" class="md-nav__link">
<span class="md-ellipsis">
MEAN
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#median" class="md-nav__link">
<span class="md-ellipsis">
MEDIAN
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#mad-mean-absolute-deviation" class="md-nav__link">
<span class="md-ellipsis">
MAD (mean absolute deviation)
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#var-variance" class="md-nav__link">
<span class="md-ellipsis">
VAR (variance)
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#std-standard-deviation" class="md-nav__link">
<span class="md-ellipsis">
STD (standard deviation)
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#skew" class="md-nav__link">
<span class="md-ellipsis">
SKEW
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#kurt" class="md-nav__link">
<span class="md-ellipsis">
KURT
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#cumsum-cumulative-sum" class="md-nav__link">
<span class="md-ellipsis">
CUMSUM (cumulative sum)
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#cummax-cummin-cumulative-maximum-minimum" class="md-nav__link">
<span class="md-ellipsis">
CUMMAX - CUMMIN (cumulative maximum - minimum)
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#cumprod-cumulative-product" class="md-nav__link">
<span class="md-ellipsis">
CUMPROD (cumulative product)
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#diff" class="md-nav__link">
<span class="md-ellipsis">
DIFF
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#pct_change" class="md-nav__link">
<span class="md-ellipsis">
PCT_CHANGE
</span>
</a>
</li>
</ul>
</nav>
</li>
<li class="md-nav__item">
<a href="#handling-missing-data" class="md-nav__link">
<span class="md-ellipsis">
HANDLING MISSING DATA
</span>
</a>
<nav class="md-nav" aria-label="HANDLING MISSING DATA">
<ul class="md-nav__list">
<li class="md-nav__item">
<a href="#filtering-out-missing-data" class="md-nav__link">
<span class="md-ellipsis">
FILTERING OUT MISSING DATA
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#filling-in-missing-data" class="md-nav__link">
<span class="md-ellipsis">
FILLING IN MISSING DATA
</span>
</a>
</li>
</ul>
</nav>
</li>
<li class="md-nav__item">
<a href="#hierarchical-indexing-multiindex" class="md-nav__link">
<span class="md-ellipsis">
HIERARCHICAL INDEXING (MultiIndex)
</span>
</a>
<nav class="md-nav" aria-label="HIERARCHICAL INDEXING (MultiIndex)">
<ul class="md-nav__list">
<li class="md-nav__item">
<a href="#multiiindex-creation" class="md-nav__link">
<span class="md-ellipsis">
MULTIIINDEX CREATION
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#multiindex-levels" class="md-nav__link">
<span class="md-ellipsis">
MULTIINDEX LEVELS
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#partial-and-cross-section-selection" class="md-nav__link">
<span class="md-ellipsis">
PARTIAL AND CROSS-SECTION SELECTION
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#indexing-slicing" class="md-nav__link">
<span class="md-ellipsis">
INDEXING, SLICING
</span>
</a>
</li>
<li class="md-nav__item">
<a href="#reordering-and-sorting-levels" class="md-nav__link">
<span class="md-ellipsis">
REORDERING AND SORTING LEVELS
</span>
</a>
</li>
</ul>
</nav>
</li>
<li class="md-nav__item">
<a href="#data-loading-storage-file-formats" class="md-nav__link">
<span class="md-ellipsis">
DATA LOADING, STORAGE FILE FORMATS
</span>
</a>
</li>
</ul>
</nav>
</div>
</div>
</div>
<div class="md-content" data-md-component="content">
<article class="md-content__inner md-typeset">
<h1 id="pandas">Pandas</h1>
<h2 id="basic-pandas-imports">Basic Pandas Imports</h2>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-1">1</a></span>
<span class="normal"><a href="#__codelineno-0-2">2</a></span>
<span class="normal"><a href="#__codelineno-0-3">3</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1"></a><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<a id="__codelineno-0-2" name="__codelineno-0-2"></a><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<a id="__codelineno-0-3" name="__codelineno-0-3"></a><span class="kn">from</span> <span class="nn">pandas</span> <span class="kn">import</span> <span class="n">Series</span><span class="p">,</span> <span class="n">DataFrame</span>
</code></pre></div></td></tr></table></div>
<h2 id="series">SERIES</h2>
<p>1-dimensional labelled array, axis label referred as INDEX.<br />
Index can contain repetitions.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-1-1">1</a></span>
<span class="normal"><a href="#__codelineno-1-2">2</a></span>
<span class="normal"><a href="#__codelineno-1-3">3</a></span>
<span class="normal"><a href="#__codelineno-1-4">4</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1"></a><span class="n">s</span> <span class="o">=</span> <span class="n">Series</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;name&#39;</span><span class="p">)</span>
<a id="__codelineno-1-2" name="__codelineno-1-2"></a><span class="c1"># DATA: {python dict, ndarray, scalar value}</span>
<a id="__codelineno-1-3" name="__codelineno-1-3"></a><span class="c1"># NAME: {string}</span>
<a id="__codelineno-1-4" name="__codelineno-1-4"></a><span class="n">s</span> <span class="o">=</span> <span class="n">Series</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span> <span class="c1"># Series created from python dict, dict keys become index values</span>
</code></pre></div></td></tr></table></div>
<h3 id="indexing-selection-slicing">INDEXING / SELECTION / SLICING</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-2-1">1</a></span>
<span class="normal"><a href="#__codelineno-2-2">2</a></span>
<span class="normal"><a href="#__codelineno-2-3">3</a></span>
<span class="normal"><a href="#__codelineno-2-4">4</a></span>
<span class="normal"><a href="#__codelineno-2-5">5</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-2-1" name="__codelineno-2-1"></a><span class="n">s</span><span class="p">[</span><span class="s1">&#39;index&#39;</span><span class="p">]</span> <span class="c1"># selection by index label</span>
<a id="__codelineno-2-2" name="__codelineno-2-2"></a><span class="n">s</span><span class="p">[</span><span class="n">condition</span><span class="p">]</span> <span class="c1"># return slice selected by condition</span>
<a id="__codelineno-2-3" name="__codelineno-2-3"></a><span class="n">s</span><span class="p">[</span> <span class="p">:</span> <span class="p">]</span> <span class="c1"># slice endpoint included</span>
<a id="__codelineno-2-4" name="__codelineno-2-4"></a><span class="n">s</span><span class="p">[</span> <span class="p">:</span> <span class="p">]</span> <span class="o">=</span> <span class="o">*</span><span class="n">value</span> <span class="c1"># modify value of entire slice</span>
<a id="__codelineno-2-5" name="__codelineno-2-5"></a><span class="n">s</span><span class="p">[</span><span class="n">condition</span><span class="p">]</span> <span class="o">=</span> <span class="o">*</span><span class="n">value</span> <span class="c1"># modify slice by condition</span>
</code></pre></div></td></tr></table></div>
<h2 id="missing-data">MISSING DATA</h2>
<p>Missing data appears as NaN (Not a Number).</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-3-1">1</a></span>
<span class="normal"><a href="#__codelineno-3-2">2</a></span>
<span class="normal"><a href="#__codelineno-3-3">3</a></span>
<span class="normal"><a href="#__codelineno-3-4">4</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-3-1" name="__codelineno-3-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">array</span><span class="p">)</span> <span class="c1"># return a Series index-bool indicating which indexes don&#39;t have data</span>
<a id="__codelineno-3-2" name="__codelineno-3-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">notnull</span><span class="p">(</span><span class="n">array</span><span class="p">)</span> <span class="c1"># return a Series index-bool indicating which indexes have data</span>
<a id="__codelineno-3-3" name="__codelineno-3-3"></a><span class="n">array</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span>
<a id="__codelineno-3-4" name="__codelineno-3-4"></a><span class="n">array</span><span class="o">.</span><span class="n">notnull</span><span class="p">()</span>
</code></pre></div></td></tr></table></div>
<h3 id="series-attributes">SERIES ATTRIBUTES</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-4-1">1</a></span>
<span class="normal"><a href="#__codelineno-4-2">2</a></span>
<span class="normal"><a href="#__codelineno-4-3">3</a></span>
<span class="normal"><a href="#__codelineno-4-4">4</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-4-1" name="__codelineno-4-1"></a><span class="n">s</span><span class="o">.</span><span class="n">values</span> <span class="c1"># NumPy representation of Series</span>
<a id="__codelineno-4-2" name="__codelineno-4-2"></a><span class="n">s</span><span class="o">.</span><span class="n">index</span> <span class="c1"># index object of Series</span>
<a id="__codelineno-4-3" name="__codelineno-4-3"></a><span class="n">s</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Series name&quot;</span> <span class="c1"># renames Series object</span>
<a id="__codelineno-4-4" name="__codelineno-4-4"></a><span class="n">s</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;index name&quot;</span> <span class="c1"># renames index</span>
</code></pre></div></td></tr></table></div>
<h3 id="series-methods">SERIES METHODS</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-5-1"> 1</a></span>
<span class="normal"><a href="#__codelineno-5-2"> 2</a></span>
<span class="normal"><a href="#__codelineno-5-3"> 3</a></span>
<span class="normal"><a href="#__codelineno-5-4"> 4</a></span>
<span class="normal"><a href="#__codelineno-5-5"> 5</a></span>
<span class="normal"><a href="#__codelineno-5-6"> 6</a></span>
<span class="normal"><a href="#__codelineno-5-7"> 7</a></span>
<span class="normal"><a href="#__codelineno-5-8"> 8</a></span>
<span class="normal"><a href="#__codelineno-5-9"> 9</a></span>
<span class="normal"><a href="#__codelineno-5-10">10</a></span>
<span class="normal"><a href="#__codelineno-5-11">11</a></span>
<span class="normal"><a href="#__codelineno-5-12">12</a></span>
<span class="normal"><a href="#__codelineno-5-13">13</a></span>
<span class="normal"><a href="#__codelineno-5-14">14</a></span>
<span class="normal"><a href="#__codelineno-5-15">15</a></span>
<span class="normal"><a href="#__codelineno-5-16">16</a></span>
<span class="normal"><a href="#__codelineno-5-17">17</a></span>
<span class="normal"><a href="#__codelineno-5-18">18</a></span>
<span class="normal"><a href="#__codelineno-5-19">19</a></span>
<span class="normal"><a href="#__codelineno-5-20">20</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-5-1" name="__codelineno-5-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">values</span><span class="p">)</span> <span class="c1"># boolean Series showing whether elements in Series matches elements in values exactly</span>
<a id="__codelineno-5-2" name="__codelineno-5-2"></a>
<a id="__codelineno-5-3" name="__codelineno-5-3"></a><span class="c1"># Conform Series to new index, new object produced unless the new index is equivalent to current one and copy=False</span>
<a id="__codelineno-5-4" name="__codelineno-5-4"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<a id="__codelineno-5-5" name="__codelineno-5-5"></a><span class="c1"># INDEX: {array} -- new labels / index</span>
<a id="__codelineno-5-6" name="__codelineno-5-6"></a><span class="c1"># METHOD: {none (don&#39;t fill gaps), pad (fill or carry values forward), backfill (fill or carry values backward)}-- hole filling method</span>
<a id="__codelineno-5-7" name="__codelineno-5-7"></a><span class="c1"># COPY: {bool} -- return new object even if index is same -- DEFAULT True</span>
<a id="__codelineno-5-8" name="__codelineno-5-8"></a><span class="c1"># FILLVALUE: {scalar} --value to use for missing values. DEFAULT NaN</span>
<a id="__codelineno-5-9" name="__codelineno-5-9"></a>
<a id="__codelineno-5-10" name="__codelineno-5-10"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="c1"># return Series with specified index labels removed</span>
<a id="__codelineno-5-11" name="__codelineno-5-11"></a><span class="c1"># INPLACE: {bool} -- if true do operation in place and return None -- DEFAULT False</span>
<a id="__codelineno-5-12" name="__codelineno-5-12"></a><span class="c1"># ERRORS: {ignore, raise} -- If &quot;ignore&quot;, suppress error and existing labels are dropped</span>
<a id="__codelineno-5-13" name="__codelineno-5-13"></a><span class="c1"># KeyError raised if not all of the labels are found in the selected axis</span>
<a id="__codelineno-5-14" name="__codelineno-5-14"></a>
<a id="__codelineno-5-15" name="__codelineno-5-15"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">normalize</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sort</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dropna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<a id="__codelineno-5-16" name="__codelineno-5-16"></a><span class="c1"># NORMALIZE: {bool} -- if True then object returned will contain relative frequencies of unique values</span>
<a id="__codelineno-5-17" name="__codelineno-5-17"></a><span class="c1"># SORT: {bool} -- sort by frequency -- DEFAULT True</span>
<a id="__codelineno-5-18" name="__codelineno-5-18"></a><span class="c1"># ASCENDING: {bool} -- sort in ascending order -- DEFAULT False</span>
<a id="__codelineno-5-19" name="__codelineno-5-19"></a><span class="c1"># BINS: {int} -- group values into half-open bins, only works with numeric data</span>
<a id="__codelineno-5-20" name="__codelineno-5-20"></a><span class="c1"># DROPNA: {bool} -- don&#39;t include counts of NaN</span>
</code></pre></div></td></tr></table></div>
<h2 id="dataframe">DATAFRAME</h2>
<p>2-dimensional labeled data structure with columns of potentially different types.
Index and columns can contain repetitions.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-6-1"> 1</a></span>
<span class="normal"><a href="#__codelineno-6-2"> 2</a></span>
<span class="normal"><a href="#__codelineno-6-3"> 3</a></span>
<span class="normal"><a href="#__codelineno-6-4"> 4</a></span>
<span class="normal"><a href="#__codelineno-6-5"> 5</a></span>
<span class="normal"><a href="#__codelineno-6-6"> 6</a></span>
<span class="normal"><a href="#__codelineno-6-7"> 7</a></span>
<span class="normal"><a href="#__codelineno-6-8"> 8</a></span>
<span class="normal"><a href="#__codelineno-6-9"> 9</a></span>
<span class="normal"><a href="#__codelineno-6-10">10</a></span>
<span class="normal"><a href="#__codelineno-6-11">11</a></span>
<span class="normal"><a href="#__codelineno-6-12">12</a></span>
<span class="normal"><a href="#__codelineno-6-13">13</a></span>
<span class="normal"><a href="#__codelineno-6-14">14</a></span>
<span class="normal"><a href="#__codelineno-6-15">15</a></span>
<span class="normal"><a href="#__codelineno-6-16">16</a></span>
<span class="normal"><a href="#__codelineno-6-17">17</a></span>
<span class="normal"><a href="#__codelineno-6-18">18</a></span>
<span class="normal"><a href="#__codelineno-6-19">19</a></span>
<span class="normal"><a href="#__codelineno-6-20">20</a></span>
<span class="normal"><a href="#__codelineno-6-21">21</a></span>
<span class="normal"><a href="#__codelineno-6-22">22</a></span>
<span class="normal"><a href="#__codelineno-6-23">23</a></span>
<span class="normal"><a href="#__codelineno-6-24">24</a></span>
<span class="normal"><a href="#__codelineno-6-25">25</a></span>
<span class="normal"><a href="#__codelineno-6-26">26</a></span>
<span class="normal"><a href="#__codelineno-6-27">27</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-6-1" name="__codelineno-6-1"></a><span class="n">df</span> <span class="o">=</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">row_labels</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">column_labels</span><span class="p">)</span>
<a id="__codelineno-6-2" name="__codelineno-6-2"></a><span class="c1"># DATA: {list, dict (of lists), nested dicts, series, dict of 1D ndarray, 2D ndarray, DataFrame}</span>
<a id="__codelineno-6-3" name="__codelineno-6-3"></a><span class="c1"># INDEX: {list of row_labels}</span>
<a id="__codelineno-6-4" name="__codelineno-6-4"></a><span class="c1"># COLUMNS: {list of column_labels}</span>
<a id="__codelineno-6-5" name="__codelineno-6-5"></a><span class="c1"># outer dict keys interpreted as index labels, inner dict keys interpreted as column labels</span>
<a id="__codelineno-6-6" name="__codelineno-6-6"></a><span class="c1"># INDEXING / SELECTION / SLICING</span>
<a id="__codelineno-6-7" name="__codelineno-6-7"></a><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="c1"># column selection</span>
<a id="__codelineno-6-8" name="__codelineno-6-8"></a><span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span> <span class="c1"># access a single value for a row/column label pair</span>
<a id="__codelineno-6-9" name="__codelineno-6-9"></a><span class="n">df</span><span class="o">.</span><span class="n">iat</span><span class="p">[</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span> <span class="c1"># access a single value for a row/column pair by integer position</span>
<a id="__codelineno-6-10" name="__codelineno-6-10"></a>
<a id="__codelineno-6-11" name="__codelineno-6-11"></a><span class="n">df</span><span class="o">.</span><span class="n">column_label</span> <span class="c1"># column selection</span>
<a id="__codelineno-6-12" name="__codelineno-6-12"></a>
<a id="__codelineno-6-13" name="__codelineno-6-13"></a><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">label</span><span class="p">]</span> <span class="c1"># row selection by label</span>
<a id="__codelineno-6-14" name="__codelineno-6-14"></a><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">loc</span><span class="p">]</span> <span class="c1"># row selection by integer location</span>
<a id="__codelineno-6-15" name="__codelineno-6-15"></a>
<a id="__codelineno-6-16" name="__codelineno-6-16"></a><span class="n">df</span><span class="p">[</span> <span class="p">:</span> <span class="p">]</span> <span class="c1"># slice rows</span>
<a id="__codelineno-6-17" name="__codelineno-6-17"></a><span class="n">df</span><span class="p">[</span><span class="n">bool_vec</span><span class="p">]</span> <span class="c1"># slice rows by boolean vector</span>
<a id="__codelineno-6-18" name="__codelineno-6-18"></a><span class="n">df</span><span class="p">[</span><span class="n">condition</span><span class="p">]</span> <span class="c1"># slice rows by condition</span>
<a id="__codelineno-6-19" name="__codelineno-6-19"></a>
<a id="__codelineno-6-20" name="__codelineno-6-20"></a><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s2">&quot;column_1&quot;</span><span class="p">,</span> <span class="s2">&quot;column_2&quot;</span><span class="p">]]</span> <span class="c1"># slice columns by names</span>
<a id="__codelineno-6-21" name="__codelineno-6-21"></a><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="n">bool_vector</span><span class="p">]]</span> <span class="c1"># slice columns by names</span>
<a id="__codelineno-6-22" name="__codelineno-6-22"></a>
<a id="__codelineno-6-23" name="__codelineno-6-23"></a><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="o">*</span><span class="n">value</span> <span class="c1"># modify column contents, if colon is missing it will be created</span>
<a id="__codelineno-6-24" name="__codelineno-6-24"></a><span class="n">df</span><span class="p">[</span> <span class="p">:</span> <span class="p">]</span> <span class="o">=</span> <span class="o">*</span><span class="n">value</span> <span class="c1"># modify rows contents</span>
<a id="__codelineno-6-25" name="__codelineno-6-25"></a><span class="n">df</span><span class="p">[</span><span class="n">condition</span><span class="p">]</span> <span class="o">=</span> <span class="o">*</span><span class="n">value</span> <span class="c1"># modify contents</span>
<a id="__codelineno-6-26" name="__codelineno-6-26"></a>
<a id="__codelineno-6-27" name="__codelineno-6-27"></a><span class="k">del</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="c1"># delete column</span>
</code></pre></div></td></tr></table></div>
<h3 id="dataframe-attributes">DATAFRAME ATTRIBUTES</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-7-1">1</a></span>
<span class="normal"><a href="#__codelineno-7-2">2</a></span>
<span class="normal"><a href="#__codelineno-7-3">3</a></span>
<span class="normal"><a href="#__codelineno-7-4">4</a></span>
<span class="normal"><a href="#__codelineno-7-5">5</a></span>
<span class="normal"><a href="#__codelineno-7-6">6</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-7-1" name="__codelineno-7-1"></a><span class="n">df</span><span class="o">.</span><span class="n">index</span> <span class="c1"># row labels</span>
<a id="__codelineno-7-2" name="__codelineno-7-2"></a><span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="c1"># column labels</span>
<a id="__codelineno-7-3" name="__codelineno-7-3"></a><span class="n">df</span><span class="o">.</span><span class="n">values</span> <span class="c1"># NumPy representation of DataFrame</span>
<a id="__codelineno-7-4" name="__codelineno-7-4"></a><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;index name&quot;</span>
<a id="__codelineno-7-5" name="__codelineno-7-5"></a><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;columns name&quot;</span>
<a id="__codelineno-7-6" name="__codelineno-7-6"></a><span class="n">df</span><span class="o">.</span><span class="n">T</span> <span class="c1"># transpose</span>
</code></pre></div></td></tr></table></div>
<h3 id="dataframe-methods">DATAFRAME METHODS</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-8-1"> 1</a></span>
<span class="normal"><a href="#__codelineno-8-2"> 2</a></span>
<span class="normal"><a href="#__codelineno-8-3"> 3</a></span>
<span class="normal"><a href="#__codelineno-8-4"> 4</a></span>
<span class="normal"><a href="#__codelineno-8-5"> 5</a></span>
<span class="normal"><a href="#__codelineno-8-6"> 6</a></span>
<span class="normal"><a href="#__codelineno-8-7"> 7</a></span>
<span class="normal"><a href="#__codelineno-8-8"> 8</a></span>
<span class="normal"><a href="#__codelineno-8-9"> 9</a></span>
<span class="normal"><a href="#__codelineno-8-10">10</a></span>
<span class="normal"><a href="#__codelineno-8-11">11</a></span>
<span class="normal"><a href="#__codelineno-8-12">12</a></span>
<span class="normal"><a href="#__codelineno-8-13">13</a></span>
<span class="normal"><a href="#__codelineno-8-14">14</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-8-1" name="__codelineno-8-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="bp">self</span> <span class="p">,</span> <span class="n">values</span><span class="p">)</span> <span class="c1"># boolean DataFrame showing whether elements in DataFrame matches elements in values exactly</span>
<a id="__codelineno-8-2" name="__codelineno-8-2"></a>
<a id="__codelineno-8-3" name="__codelineno-8-3"></a><span class="c1"># Conform DataFrame to new index, new object produced unless the new index is equivalent to current one and copy=False</span>
<a id="__codelineno-8-4" name="__codelineno-8-4"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<a id="__codelineno-8-5" name="__codelineno-8-5"></a><span class="c1"># INDEX: {array} -- new labels / index</span>
<a id="__codelineno-8-6" name="__codelineno-8-6"></a><span class="c1"># COLUMNS: {array} -- new labels / columns</span>
<a id="__codelineno-8-7" name="__codelineno-8-7"></a><span class="c1"># METHOD: {none (don&#39;t fill gaps), pad (fill or carry values forward), backfill (fill or carry values backward)}-- hole filling method</span>
<a id="__codelineno-8-8" name="__codelineno-8-8"></a><span class="c1"># COPY: {bool} -- return new object even if index is same -- DEFAULT True</span>
<a id="__codelineno-8-9" name="__codelineno-8-9"></a><span class="c1"># FILLVALUE: {scalar} --value to use for missing values. DEFAULT NaN</span>
<a id="__codelineno-8-10" name="__codelineno-8-10"></a>
<a id="__codelineno-8-11" name="__codelineno-8-11"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="c1"># Remove rows or columns by specifying label names</span>
<a id="__codelineno-8-12" name="__codelineno-8-12"></a><span class="c1"># INPLACE: {bool} -- if true do operation in place and return None -- DEFAULT False</span>
<a id="__codelineno-8-13" name="__codelineno-8-13"></a><span class="c1"># ERRORS: {ignore, raise} -- If &quot;ignore&quot;, suppress error and existing labels are dropped</span>
<a id="__codelineno-8-14" name="__codelineno-8-14"></a><span class="c1"># KeyError raised if not all of the labels are found in the selected axis</span>
</code></pre></div></td></tr></table></div>
<h2 id="index-objects">INDEX OBJECTS</h2>
<p>Holds axis labels and metadata, immutable.</p>
<h3 id="index-types">INDEX TYPES</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-9-1">1</a></span>
<span class="normal"><a href="#__codelineno-9-2">2</a></span>
<span class="normal"><a href="#__codelineno-9-3">3</a></span>
<span class="normal"><a href="#__codelineno-9-4">4</a></span>
<span class="normal"><a href="#__codelineno-9-5">5</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-9-1" name="__codelineno-9-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span> <span class="c1"># immutable ordered ndarray, sliceable. stores axis labels</span>
<a id="__codelineno-9-2" name="__codelineno-9-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Int64Index</span> <span class="c1"># special case of Index with purely integer labels</span>
<a id="__codelineno-9-3" name="__codelineno-9-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span> <span class="c1"># multi-level (hierarchical) index object for pandas objects</span>
<a id="__codelineno-9-4" name="__codelineno-9-4"></a><span class="n">pd</span><span class="o">.</span><span class="n">PeriodINdex</span> <span class="c1"># immutable ndarray holding ordinal values indicating regular periods in time</span>
<a id="__codelineno-9-5" name="__codelineno-9-5"></a><span class="n">pd</span><span class="o">.</span><span class="n">DatetimeIndex</span> <span class="c1"># nanosecond timestamps (uses Numpy datetime64)</span>
</code></pre></div></td></tr></table></div>
<h3 id="index-attributers">INDEX ATTRIBUTERS</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-10-1">1</a></span>
<span class="normal"><a href="#__codelineno-10-2">2</a></span>
<span class="normal"><a href="#__codelineno-10-3">3</a></span>
<span class="normal"><a href="#__codelineno-10-4">4</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-10-1" name="__codelineno-10-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="o">.</span><span class="n">is_monotonic_increasing</span> <span class="c1"># Return True if the index is monotonic increasing (only equal or increasing) values</span>
<a id="__codelineno-10-2" name="__codelineno-10-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="o">.</span><span class="n">is_monotonic_decreasing</span> <span class="c1"># Return True if the index is monotonic decreasing (only equal or decreasing) values</span>
<a id="__codelineno-10-3" name="__codelineno-10-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="o">.</span><span class="n">is_unique</span> <span class="c1"># Return True if the index has unique values.</span>
<a id="__codelineno-10-4" name="__codelineno-10-4"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="o">.</span><span class="n">hasnans</span> <span class="c1"># Return True if the index has NaNs</span>
</code></pre></div></td></tr></table></div>
<h3 id="index-methods">INDEX METHODS</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-11-1"> 1</a></span>
<span class="normal"><a href="#__codelineno-11-2"> 2</a></span>
<span class="normal"><a href="#__codelineno-11-3"> 3</a></span>
<span class="normal"><a href="#__codelineno-11-4"> 4</a></span>
<span class="normal"><a href="#__codelineno-11-5"> 5</a></span>
<span class="normal"><a href="#__codelineno-11-6"> 6</a></span>
<span class="normal"><a href="#__codelineno-11-7"> 7</a></span>
<span class="normal"><a href="#__codelineno-11-8"> 8</a></span>
<span class="normal"><a href="#__codelineno-11-9"> 9</a></span>
<span class="normal"><a href="#__codelineno-11-10">10</a></span>
<span class="normal"><a href="#__codelineno-11-11">11</a></span>
<span class="normal"><a href="#__codelineno-11-12">12</a></span>
<span class="normal"><a href="#__codelineno-11-13">13</a></span>
<span class="normal"><a href="#__codelineno-11-14">14</a></span>
<span class="normal"><a href="#__codelineno-11-15">15</a></span>
<span class="normal"><a href="#__codelineno-11-16">16</a></span>
<span class="normal"><a href="#__codelineno-11-17">17</a></span>
<span class="normal"><a href="#__codelineno-11-18">18</a></span>
<span class="normal"><a href="#__codelineno-11-19">19</a></span>
<span class="normal"><a href="#__codelineno-11-20">20</a></span>
<span class="normal"><a href="#__codelineno-11-21">21</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-11-1" name="__codelineno-11-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="c1"># append a collection of Index options together</span>
<a id="__codelineno-11-2" name="__codelineno-11-2"></a>
<a id="__codelineno-11-3" name="__codelineno-11-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">sort</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># set difference of two Index objects</span>
<a id="__codelineno-11-4" name="__codelineno-11-4"></a><span class="c1"># SORT: {None (attempt sorting), False (don&#39;t sort)}</span>
<a id="__codelineno-11-5" name="__codelineno-11-5"></a>
<a id="__codelineno-11-6" name="__codelineno-11-6"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">sort</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># set intersection of two Index objects</span>
<a id="__codelineno-11-7" name="__codelineno-11-7"></a><span class="c1"># SORT: {None (attempt sorting), False (don&#39;t sort)}</span>
<a id="__codelineno-11-8" name="__codelineno-11-8"></a>
<a id="__codelineno-11-9" name="__codelineno-11-9"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">sort</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># set union of two Index objects</span>
<a id="__codelineno-11-10" name="__codelineno-11-10"></a><span class="c1"># SORT: {None (attempt sorting), False (don&#39;t sort)}</span>
<a id="__codelineno-11-11" name="__codelineno-11-11"></a>
<a id="__codelineno-11-12" name="__codelineno-11-12"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># boolean array indicating where the index values are in values</span>
<a id="__codelineno-11-13" name="__codelineno-11-13"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loc</span><span class="p">,</span> <span class="n">item</span><span class="p">)</span> <span class="c1"># make new Index inserting new item at location</span>
<a id="__codelineno-11-14" name="__codelineno-11-14"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loc</span><span class="p">)</span> <span class="c1"># make new Index with passed location(-s) deleted</span>
<a id="__codelineno-11-15" name="__codelineno-11-15"></a>
<a id="__codelineno-11-16" name="__codelineno-11-16"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">errors</span><span class="o">=</span><span class="s1">&#39;raise&#39;</span><span class="p">)</span> <span class="c1"># Make new Index with passed list of labels deleted</span>
<a id="__codelineno-11-17" name="__codelineno-11-17"></a><span class="c1"># ERRORS: {ignore, raise} -- If &#39;ignore&#39;, suppress error and existing labels are dropped</span>
<a id="__codelineno-11-18" name="__codelineno-11-18"></a><span class="c1"># KeyError raised if not all of the labels are found in the selected axis</span>
<a id="__codelineno-11-19" name="__codelineno-11-19"></a>
<a id="__codelineno-11-20" name="__codelineno-11-20"></a><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="c1"># create index with target&#39;s values (move/add/delete values as necessary)</span>
<a id="__codelineno-11-21" name="__codelineno-11-21"></a><span class="c1"># METHOD: {none (don&#39;t fill gaps), pad (fill or carry values forward), backfill (fill or carry values backward)}-- hole filling method</span>
</code></pre></div></td></tr></table></div>
<h2 id="arithmetic-operations">ARITHMETIC OPERATIONS</h2>
<p>NumPy arrays operations preserve labels-value link.<br />
Arithmetic operations automatically align differently indexed data.<br />
Missing values propagate in arithmetic computations (NaN <code>&lt;operator&gt;</code> value = NaN)</p>
<h3 id="addition">ADDITION</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-12-1">1</a></span>
<span class="normal"><a href="#__codelineno-12-2">2</a></span>
<span class="normal"><a href="#__codelineno-12-3">3</a></span>
<span class="normal"><a href="#__codelineno-12-4">4</a></span>
<span class="normal"><a href="#__codelineno-12-5">5</a></span>
<span class="normal"><a href="#__codelineno-12-6">6</a></span>
<span class="normal"><a href="#__codelineno-12-7">7</a></span>
<span class="normal"><a href="#__codelineno-12-8">8</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-12-1" name="__codelineno-12-1"></a><span class="bp">self</span> <span class="o">+</span> <span class="n">other</span>
<a id="__codelineno-12-2" name="__codelineno-12-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># add(), supports substitution of NaNs</span>
<a id="__codelineno-12-3" name="__codelineno-12-3"></a><span class="n">pd</span><span class="p">,</span><span class="n">Series</span><span class="o">.</span><span class="n">radd</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># radd(), supports substitution of NaNs</span>
<a id="__codelineno-12-4" name="__codelineno-12-4"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># add(), supports substitution of NaNs</span>
<a id="__codelineno-12-5" name="__codelineno-12-5"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">radd</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># radd(), supports substitution of NaNs</span>
<a id="__codelineno-12-6" name="__codelineno-12-6"></a><span class="c1"># OTHER: {scalar, sequence, Series, DataFrame}</span>
<a id="__codelineno-12-7" name="__codelineno-12-7"></a><span class="c1"># AXIS: {0, 1, index, columns} -- whether to compare by the index or columns</span>
<a id="__codelineno-12-8" name="__codelineno-12-8"></a><span class="c1"># FILLVALUE: {None, float} -- fill missing value</span>
</code></pre></div></td></tr></table></div>
<h3 id="subtraction">SUBTRACTION</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-13-1">1</a></span>
<span class="normal"><a href="#__codelineno-13-2">2</a></span>
<span class="normal"><a href="#__codelineno-13-3">3</a></span>
<span class="normal"><a href="#__codelineno-13-4">4</a></span>
<span class="normal"><a href="#__codelineno-13-5">5</a></span>
<span class="normal"><a href="#__codelineno-13-6">6</a></span>
<span class="normal"><a href="#__codelineno-13-7">7</a></span>
<span class="normal"><a href="#__codelineno-13-8">8</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-13-1" name="__codelineno-13-1"></a><span class="bp">self</span> <span class="o">-</span> <span class="n">other</span>
<a id="__codelineno-13-2" name="__codelineno-13-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># sub(), supports substitution of NaNs</span>
<a id="__codelineno-13-3" name="__codelineno-13-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">radd</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># radd(), supports substitution of NaNs</span>
<a id="__codelineno-13-4" name="__codelineno-13-4"></a><span class="n">ps</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># sub(), supports substitution of NaNs</span>
<a id="__codelineno-13-5" name="__codelineno-13-5"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">rsub</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># rsub(), supports substitution of NaNs</span>
<a id="__codelineno-13-6" name="__codelineno-13-6"></a><span class="c1"># OTHER: {scalar, sequence, Series, DataFrame}</span>
<a id="__codelineno-13-7" name="__codelineno-13-7"></a><span class="c1"># AXIS: {0, 1, index, columns} -- whether to compare by the index or columns</span>
<a id="__codelineno-13-8" name="__codelineno-13-8"></a><span class="c1"># FILLVALUE: {None, float} -- fill missing value</span>
</code></pre></div></td></tr></table></div>
<h3 id="multiplication">MULTIPLICATION</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-14-1">1</a></span>
<span class="normal"><a href="#__codelineno-14-2">2</a></span>
<span class="normal"><a href="#__codelineno-14-3">3</a></span>
<span class="normal"><a href="#__codelineno-14-4">4</a></span>
<span class="normal"><a href="#__codelineno-14-5">5</a></span>
<span class="normal"><a href="#__codelineno-14-6">6</a></span>
<span class="normal"><a href="#__codelineno-14-7">7</a></span>
<span class="normal"><a href="#__codelineno-14-8">8</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-14-1" name="__codelineno-14-1"></a><span class="bp">self</span> <span class="o">*</span> <span class="n">other</span>
<a id="__codelineno-14-2" name="__codelineno-14-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># mul(), supports substitution of NaNs</span>
<a id="__codelineno-14-3" name="__codelineno-14-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">rmul</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># rmul(), supports substitution of NaNs</span>
<a id="__codelineno-14-4" name="__codelineno-14-4"></a><span class="n">ps</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># mul(), supports substitution of NaNs</span>
<a id="__codelineno-14-5" name="__codelineno-14-5"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">rmul</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># rmul(), supports substitution of NaNs</span>
<a id="__codelineno-14-6" name="__codelineno-14-6"></a><span class="c1"># OTHER: {scalar, sequence, Series, DataFrame}</span>
<a id="__codelineno-14-7" name="__codelineno-14-7"></a><span class="c1"># AXIS: {0, 1, index, columns} -- whether to compare by the index or columns</span>
<a id="__codelineno-14-8" name="__codelineno-14-8"></a><span class="c1"># FILLVALUE: {None, float} -- fill missing value</span>
</code></pre></div></td></tr></table></div>
<h3 id="division-float-division">DIVISION (float division)</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-15-1"> 1</a></span>
<span class="normal"><a href="#__codelineno-15-2"> 2</a></span>
<span class="normal"><a href="#__codelineno-15-3"> 3</a></span>
<span class="normal"><a href="#__codelineno-15-4"> 4</a></span>
<span class="normal"><a href="#__codelineno-15-5"> 5</a></span>
<span class="normal"><a href="#__codelineno-15-6"> 6</a></span>
<span class="normal"><a href="#__codelineno-15-7"> 7</a></span>
<span class="normal"><a href="#__codelineno-15-8"> 8</a></span>
<span class="normal"><a href="#__codelineno-15-9"> 9</a></span>
<span class="normal"><a href="#__codelineno-15-10">10</a></span>
<span class="normal"><a href="#__codelineno-15-11">11</a></span>
<span class="normal"><a href="#__codelineno-15-12">12</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-15-1" name="__codelineno-15-1"></a><span class="bp">self</span> <span class="o">/</span> <span class="n">other</span>
<a id="__codelineno-15-2" name="__codelineno-15-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># div(), supports substitution of NaNs</span>
<a id="__codelineno-15-3" name="__codelineno-15-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">rdiv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># rdiv(), supports substitution of NaNs</span>
<a id="__codelineno-15-4" name="__codelineno-15-4"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">truediv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># truediv(), supports substitution of NaNs</span>
<a id="__codelineno-15-5" name="__codelineno-15-5"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">rtruediv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># rtruediv(), supports substitution of NaNs</span>
<a id="__codelineno-15-6" name="__codelineno-15-6"></a><span class="n">ps</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># div(), supports substitution of NaNs</span>
<a id="__codelineno-15-7" name="__codelineno-15-7"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">rdiv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># rdiv(), supports substitution of NaNs</span>
<a id="__codelineno-15-8" name="__codelineno-15-8"></a><span class="n">ps</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">truediv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># truediv(), supports substitution of NaNs</span>
<a id="__codelineno-15-9" name="__codelineno-15-9"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">rtruediv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># rtruediv(), supports substitution of NaNs</span>
<a id="__codelineno-15-10" name="__codelineno-15-10"></a><span class="c1"># OTHER: {scalar, sequence, Series, DataFrame}</span>
<a id="__codelineno-15-11" name="__codelineno-15-11"></a><span class="c1"># AXIS: {0, 1, index, columns} -- whether to compare by the index or columns</span>
<a id="__codelineno-15-12" name="__codelineno-15-12"></a><span class="c1"># FILLVALUE: {None, float} -- fill missing value</span>
</code></pre></div></td></tr></table></div>
<h3 id="floor-division">FLOOR DIVISION</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-16-1">1</a></span>
<span class="normal"><a href="#__codelineno-16-2">2</a></span>
<span class="normal"><a href="#__codelineno-16-3">3</a></span>
<span class="normal"><a href="#__codelineno-16-4">4</a></span>
<span class="normal"><a href="#__codelineno-16-5">5</a></span>
<span class="normal"><a href="#__codelineno-16-6">6</a></span>
<span class="normal"><a href="#__codelineno-16-7">7</a></span>
<span class="normal"><a href="#__codelineno-16-8">8</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-16-1" name="__codelineno-16-1"></a><span class="bp">self</span> <span class="o">//</span> <span class="n">other</span>
<a id="__codelineno-16-2" name="__codelineno-16-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">floordiv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># floordiv(), supports substitution of NaNs</span>
<a id="__codelineno-16-3" name="__codelineno-16-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">rfloordiv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># rfloordiv(), supports substitution of NaNs</span>
<a id="__codelineno-16-4" name="__codelineno-16-4"></a><span class="n">ps</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">floordiv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># floordiv(), supports substitution of NaNs</span>
<a id="__codelineno-16-5" name="__codelineno-16-5"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">rfloordiv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># rfloordiv(), supports substitution of NaNs</span>
<a id="__codelineno-16-6" name="__codelineno-16-6"></a><span class="c1"># OTHER: {scalar, sequence, Series, DataFrame}</span>
<a id="__codelineno-16-7" name="__codelineno-16-7"></a><span class="c1"># AXIS: {0, 1, index, columns} -- whether to compare by the index or columns</span>
<a id="__codelineno-16-8" name="__codelineno-16-8"></a><span class="c1"># FILLVALUE: {None, float} -- fill missing value</span>
</code></pre></div></td></tr></table></div>
<h3 id="modulo">MODULO</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-17-1">1</a></span>
<span class="normal"><a href="#__codelineno-17-2">2</a></span>
<span class="normal"><a href="#__codelineno-17-3">3</a></span>
<span class="normal"><a href="#__codelineno-17-4">4</a></span>
<span class="normal"><a href="#__codelineno-17-5">5</a></span>
<span class="normal"><a href="#__codelineno-17-6">6</a></span>
<span class="normal"><a href="#__codelineno-17-7">7</a></span>
<span class="normal"><a href="#__codelineno-17-8">8</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-17-1" name="__codelineno-17-1"></a><span class="bp">self</span> <span class="o">%</span> <span class="n">other</span>
<a id="__codelineno-17-2" name="__codelineno-17-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">mod</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># mod(), supports substitution of NaNs</span>
<a id="__codelineno-17-3" name="__codelineno-17-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">rmod</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># rmod(), supports substitution of NaNs</span>
<a id="__codelineno-17-4" name="__codelineno-17-4"></a><span class="n">ps</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">mod</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># mod(), supports substitution of NaNs</span>
<a id="__codelineno-17-5" name="__codelineno-17-5"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">rmod</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># rmod(), supports substitution of NaNs</span>
<a id="__codelineno-17-6" name="__codelineno-17-6"></a><span class="c1"># OTHER: {scalar, sequence, Series, DataFrame}</span>
<a id="__codelineno-17-7" name="__codelineno-17-7"></a><span class="c1"># AXIS: {0, 1, index, columns} -- whether to compare by the index or columns</span>
<a id="__codelineno-17-8" name="__codelineno-17-8"></a><span class="c1"># FILLVALUE: {None, float} -- fill missing value</span>
</code></pre></div></td></tr></table></div>
<h3 id="power">POWER</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-18-1">1</a></span>
<span class="normal"><a href="#__codelineno-18-2">2</a></span>
<span class="normal"><a href="#__codelineno-18-3">3</a></span>
<span class="normal"><a href="#__codelineno-18-4">4</a></span>
<span class="normal"><a href="#__codelineno-18-5">5</a></span>
<span class="normal"><a href="#__codelineno-18-6">6</a></span>
<span class="normal"><a href="#__codelineno-18-7">7</a></span>
<span class="normal"><a href="#__codelineno-18-8">8</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-18-1" name="__codelineno-18-1"></a><span class="n">other</span> <span class="o">**</span> <span class="bp">self</span>
<a id="__codelineno-18-2" name="__codelineno-18-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># pow(), supports substitution of NaNs</span>
<a id="__codelineno-18-3" name="__codelineno-18-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">rpow</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># rpow(), supports substitution of NaNs</span>
<a id="__codelineno-18-4" name="__codelineno-18-4"></a><span class="n">ps</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># pow(), supports substitution of NaNs</span>
<a id="__codelineno-18-5" name="__codelineno-18-5"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">rpow</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># rpow(), supports substitution of NaNs</span>
<a id="__codelineno-18-6" name="__codelineno-18-6"></a><span class="c1"># OTHER: {scalar, sequence, Series, DataFrame}</span>
<a id="__codelineno-18-7" name="__codelineno-18-7"></a><span class="c1"># AXIS: {0, 1, index, columns} -- whether to compare by the index or columns</span>
<a id="__codelineno-18-8" name="__codelineno-18-8"></a><span class="c1"># FILLVALUE: {None, float} -- fill missing value</span>
</code></pre></div></td></tr></table></div>
<h2 id="essential-functionality">ESSENTIAL FUNCTIONALITY</h2>
<h3 id="function-application-and-mapping">FUNCTION APPLICATION AND MAPPING</h3>
<p>NumPy ufuncs work fine with pandas objects.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-19-1"> 1</a></span>
<span class="normal"><a href="#__codelineno-19-2"> 2</a></span>
<span class="normal"><a href="#__codelineno-19-3"> 3</a></span>
<span class="normal"><a href="#__codelineno-19-4"> 4</a></span>
<span class="normal"><a href="#__codelineno-19-5"> 5</a></span>
<span class="normal"><a href="#__codelineno-19-6"> 6</a></span>
<span class="normal"><a href="#__codelineno-19-7"> 7</a></span>
<span class="normal"><a href="#__codelineno-19-8"> 8</a></span>
<span class="normal"><a href="#__codelineno-19-9"> 9</a></span>
<span class="normal"><a href="#__codelineno-19-10">10</a></span>
<span class="normal"><a href="#__codelineno-19-11">11</a></span>
<span class="normal"><a href="#__codelineno-19-12">12</a></span>
<span class="normal"><a href="#__codelineno-19-13">13</a></span>
<span class="normal"><a href="#__codelineno-19-14">14</a></span>
<span class="normal"><a href="#__codelineno-19-15">15</a></span>
<span class="normal"><a href="#__codelineno-19-16">16</a></span>
<span class="normal"><a href="#__codelineno-19-17">17</a></span>
<span class="normal"><a href="#__codelineno-19-18">18</a></span>
<span class="normal"><a href="#__codelineno-19-19">19</a></span>
<span class="normal"><a href="#__codelineno-19-20">20</a></span>
<span class="normal"><a href="#__codelineno-19-21">21</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-19-1" name="__codelineno-19-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">)</span> <span class="c1"># apply function element-wise</span>
<a id="__codelineno-19-2" name="__codelineno-19-2"></a>
<a id="__codelineno-19-3" name="__codelineno-19-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">())</span> <span class="c1"># apply a function along an axis of a DataFrame</span>
<a id="__codelineno-19-4" name="__codelineno-19-4"></a><span class="c1"># FUNC: {function} -- function to apply</span>
<a id="__codelineno-19-5" name="__codelineno-19-5"></a><span class="c1"># AXIS: {O, 1, index, columns} -- axis along which the function is applied</span>
<a id="__codelineno-19-6" name="__codelineno-19-6"></a><span class="c1"># ARGS: {tuple} -- positional arguments to pass to func in addition to the array/series</span>
<a id="__codelineno-19-7" name="__codelineno-19-7"></a><span class="c1"># SORTING AND RANKING</span>
<a id="__codelineno-19-8" name="__codelineno-19-8"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">sort_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">True</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="c1"># sort Series by index labels</span>
<a id="__codelineno-19-9" name="__codelineno-19-9"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="c1"># sort series by the values</span>
<a id="__codelineno-19-10" name="__codelineno-19-10"></a><span class="c1"># ASCENDING: {bool} -- if True, sort values in ascending order, otherwise descending -- DEFAULT True</span>
<a id="__codelineno-19-11" name="__codelineno-19-11"></a><span class="c1"># INPALCE: {bool} -- if True, perform operation in-place</span>
<a id="__codelineno-19-12" name="__codelineno-19-12"></a><span class="c1"># KIND: {quicksort, mergesort, heapsort} -- sorting algorithm</span>
<a id="__codelineno-19-13" name="__codelineno-19-13"></a><span class="c1"># NA_POSITION {first, last} -- &#39;first&#39; puts NaNs at the beginning, &#39;last&#39; puts NaNs at the end</span>
<a id="__codelineno-19-14" name="__codelineno-19-14"></a>
<a id="__codelineno-19-15" name="__codelineno-19-15"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">sort_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="c1"># sort object by labels along an axis</span>
<a id="__codelineno-19-16" name="__codelineno-19-16"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="c1"># sort object by values along an axis</span>
<a id="__codelineno-19-17" name="__codelineno-19-17"></a><span class="c1"># AXIS: {0, 1, index, columns} -- the axis along which to sort</span>
<a id="__codelineno-19-18" name="__codelineno-19-18"></a><span class="c1"># ASCENDING: {bool} -- if True, sort values in ascending order, otherwise descending -- DEFAULT True</span>
<a id="__codelineno-19-19" name="__codelineno-19-19"></a><span class="c1"># INPALCE: {bool} -- if True, perform operation in-place</span>
<a id="__codelineno-19-20" name="__codelineno-19-20"></a><span class="c1"># KIND: {quicksort, mergesort, heapsort} -- sorting algorithm</span>
<a id="__codelineno-19-21" name="__codelineno-19-21"></a><span class="c1"># NA_POSITION {first, last} -- &#39;first&#39; puts NaNs at the beginning, &#39;last&#39; puts NaNs at the end</span>
</code></pre></div></td></tr></table></div>
<h2 id="descriptive-and-summary-statistics">DESCRIPTIVE AND SUMMARY STATISTICS</h2>
<h3 id="count">COUNT</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-20-1">1</a></span>
<span class="normal"><a href="#__codelineno-20-2">2</a></span>
<span class="normal"><a href="#__codelineno-20-3">3</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-20-1" name="__codelineno-20-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="c1"># return number of non-NA/null observations in the Series</span>
<a id="__codelineno-20-2" name="__codelineno-20-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="c1"># count non-NA cells for each column or row</span>
<a id="__codelineno-20-3" name="__codelineno-20-3"></a><span class="c1"># NUMERIC_ONLY: {bool} -- Include only float, int or boolean data -- DEFAULT False</span>
</code></pre></div></td></tr></table></div>
<h3 id="describe">DESCRIBE</h3>
<p>Generate descriptive statistics summarizing central tendency, dispersion and shape of dataset's distribution (exclude NaN).</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-21-1">1</a></span>
<span class="normal"><a href="#__codelineno-21-2">2</a></span>
<span class="normal"><a href="#__codelineno-21-3">3</a></span>
<span class="normal"><a href="#__codelineno-21-4">4</a></span>
<span class="normal"><a href="#__codelineno-21-5">5</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-21-1" name="__codelineno-21-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">percentiles</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">include</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<a id="__codelineno-21-2" name="__codelineno-21-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">percentiles</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">include</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<a id="__codelineno-21-3" name="__codelineno-21-3"></a><span class="c1"># PERCENTILES: {list-like of numbers} -- percentiles to include in output,between 0 and 1 -- DEFAULT [.25, .5, .75]</span>
<a id="__codelineno-21-4" name="__codelineno-21-4"></a><span class="c1"># INCLUDE: {all, None, list of dtypes} -- white list of dtypes to include in the result. ignored for Series</span>
<a id="__codelineno-21-5" name="__codelineno-21-5"></a><span class="c1"># EXCLUDE: {None, list of dtypes} -- black list of dtypes to omit from the result. ignored for Series</span>
</code></pre></div></td></tr></table></div>
<h3 id="max-min">MAX - MIN</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-22-1">1</a></span>
<span class="normal"><a href="#__codelineno-22-2">2</a></span>
<span class="normal"><a href="#__codelineno-22-3">3</a></span>
<span class="normal"><a href="#__codelineno-22-4">4</a></span>
<span class="normal"><a href="#__codelineno-22-5">5</a></span>
<span class="normal"><a href="#__codelineno-22-6">6</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-22-1" name="__codelineno-22-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># maximum of the values for the requested axis</span>
<a id="__codelineno-22-2" name="__codelineno-22-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># minimum of the values for the requested axis</span>
<a id="__codelineno-22-3" name="__codelineno-22-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># maximum of the values for the requested axis</span>
<a id="__codelineno-22-4" name="__codelineno-22-4"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># minimum of the values for the requested axis</span>
<a id="__codelineno-22-5" name="__codelineno-22-5"></a><span class="c1"># SKIPNA: {bool} -- exclude NA/null values when computing the result</span>
<a id="__codelineno-22-6" name="__codelineno-22-6"></a><span class="c1"># NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series</span>
</code></pre></div></td></tr></table></div>
<h3 id="idxmax-idxmin">IDXMAX - IDXMIN</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-23-1">1</a></span>
<span class="normal"><a href="#__codelineno-23-2">2</a></span>
<span class="normal"><a href="#__codelineno-23-3">3</a></span>
<span class="normal"><a href="#__codelineno-23-4">4</a></span>
<span class="normal"><a href="#__codelineno-23-5">5</a></span>
<span class="normal"><a href="#__codelineno-23-6">6</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-23-1" name="__codelineno-23-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">idxmax</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># row label of the maximum value</span>
<a id="__codelineno-23-2" name="__codelineno-23-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">idxmin</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># row label of the minimum value</span>
<a id="__codelineno-23-3" name="__codelineno-23-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">idxmax</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># Return index of first occurrence of maximum over requested axis</span>
<a id="__codelineno-23-4" name="__codelineno-23-4"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">idxmin</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># Return index of first occurrence of minimum over requested axis</span>
<a id="__codelineno-23-5" name="__codelineno-23-5"></a><span class="c1"># AXIS:{0, 1, index, columns} -- row-wise or column-wise</span>
<a id="__codelineno-23-6" name="__codelineno-23-6"></a><span class="c1"># SKIPNA: {bool} -- exclude NA/null values. ff an entire row/column is NA, result will be NA</span>
</code></pre></div></td></tr></table></div>
<h3 id="quantile">QUANTILE</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-24-1">1</a></span>
<span class="normal"><a href="#__codelineno-24-2">2</a></span>
<span class="normal"><a href="#__codelineno-24-3">3</a></span>
<span class="normal"><a href="#__codelineno-24-4">4</a></span>
<span class="normal"><a href="#__codelineno-24-5">5</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-24-1" name="__codelineno-24-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">)</span> <span class="c1"># return values at the given quantile</span>
<a id="__codelineno-24-2" name="__codelineno-24-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">)</span> <span class="c1"># return values at the given quantile over requested axis</span>
<a id="__codelineno-24-3" name="__codelineno-24-3"></a><span class="c1"># Q: {flaot, array} -- value between 0 &lt;= q &lt;= 1, the quantile(s) to compute -- DEFAULT 0.5 (50%)</span>
<a id="__codelineno-24-4" name="__codelineno-24-4"></a><span class="c1"># NUMERIC_ONLY: {bool} -- if False, quantile of datetime and timedelta data will be computed as well</span>
<a id="__codelineno-24-5" name="__codelineno-24-5"></a><span class="c1"># INTERPOLATION: {linear, lower, higher, midpoint, nearest} -- SEE DOCS</span>
</code></pre></div></td></tr></table></div>
<h3 id="sum">SUM</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-25-1">1</a></span>
<span class="normal"><a href="#__codelineno-25-2">2</a></span>
<span class="normal"><a href="#__codelineno-25-3">3</a></span>
<span class="normal"><a href="#__codelineno-25-4">4</a></span>
<span class="normal"><a href="#__codelineno-25-5">5</a></span>
<span class="normal"><a href="#__codelineno-25-6">6</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-25-1" name="__codelineno-25-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">min_count</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># sum of the values</span>
<a id="__codelineno-25-2" name="__codelineno-25-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">min_count</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># sum of the values for the requested axis</span>
<a id="__codelineno-25-3" name="__codelineno-25-3"></a><span class="c1"># AXIS: {0, 1, index, columns} -- axis for the function to be applied on</span>
<a id="__codelineno-25-4" name="__codelineno-25-4"></a><span class="c1"># SKIPNA: {bool} -- exclude NA/null values when computing the result</span>
<a id="__codelineno-25-5" name="__codelineno-25-5"></a><span class="c1"># NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series</span>
<a id="__codelineno-25-6" name="__codelineno-25-6"></a><span class="c1"># MIN_COUNT: {int} -- required number of valid values to perform the operation. if fewer than min_count non-NA values are present the result will be NA</span>
</code></pre></div></td></tr></table></div>
<h3 id="mean">MEAN</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-26-1">1</a></span>
<span class="normal"><a href="#__codelineno-26-2">2</a></span>
<span class="normal"><a href="#__codelineno-26-3">3</a></span>
<span class="normal"><a href="#__codelineno-26-4">4</a></span>
<span class="normal"><a href="#__codelineno-26-5">5</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-26-1" name="__codelineno-26-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># mean of the values</span>
<a id="__codelineno-26-2" name="__codelineno-26-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># mean of the values for the requested axis</span>
<a id="__codelineno-26-3" name="__codelineno-26-3"></a><span class="c1"># AXIS: {0, 1, index, columns} -- axis for the function to be applied on</span>
<a id="__codelineno-26-4" name="__codelineno-26-4"></a><span class="c1"># SKIPNA: {bool} -- exclude NA/null values when computing the result</span>
<a id="__codelineno-26-5" name="__codelineno-26-5"></a><span class="c1"># NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series</span>
</code></pre></div></td></tr></table></div>
<h3 id="median">MEDIAN</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-27-1">1</a></span>
<span class="normal"><a href="#__codelineno-27-2">2</a></span>
<span class="normal"><a href="#__codelineno-27-3">3</a></span>
<span class="normal"><a href="#__codelineno-27-4">4</a></span>
<span class="normal"><a href="#__codelineno-27-5">5</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-27-1" name="__codelineno-27-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># median of the values</span>
<a id="__codelineno-27-2" name="__codelineno-27-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># median of the values for the requested axis</span>
<a id="__codelineno-27-3" name="__codelineno-27-3"></a><span class="c1"># AXIS: {0, 1, index, columns} -- axis for the function to be applied on</span>
<a id="__codelineno-27-4" name="__codelineno-27-4"></a><span class="c1"># SKIPNA: {bool} -- exclude NA/null values when computing the result</span>
<a id="__codelineno-27-5" name="__codelineno-27-5"></a><span class="c1"># NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series</span>
</code></pre></div></td></tr></table></div>
<h3 id="mad-mean-absolute-deviation">MAD (mean absolute deviation)</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-28-1">1</a></span>
<span class="normal"><a href="#__codelineno-28-2">2</a></span>
<span class="normal"><a href="#__codelineno-28-3">3</a></span>
<span class="normal"><a href="#__codelineno-28-4">4</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-28-1" name="__codelineno-28-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">mad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># mean absolute deviation</span>
<a id="__codelineno-28-2" name="__codelineno-28-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">mad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># mean absolute deviation of the values for the requested axis</span>
<a id="__codelineno-28-3" name="__codelineno-28-3"></a><span class="c1"># AXIS: {0, 1, index, columns} -- axis for the function to be applied on</span>
<a id="__codelineno-28-4" name="__codelineno-28-4"></a><span class="c1"># SKIPNA: {bool} -- exclude NA/null values when computing the result</span>
</code></pre></div></td></tr></table></div>
<h3 id="var-variance">VAR (variance)</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-29-1">1</a></span>
<span class="normal"><a href="#__codelineno-29-2">2</a></span>
<span class="normal"><a href="#__codelineno-29-3">3</a></span>
<span class="normal"><a href="#__codelineno-29-4">4</a></span>
<span class="normal"><a href="#__codelineno-29-5">5</a></span>
<span class="normal"><a href="#__codelineno-29-6">6</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-29-1" name="__codelineno-29-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># unbiased variance</span>
<a id="__codelineno-29-2" name="__codelineno-29-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ddof</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># unbiased variance over requested axis</span>
<a id="__codelineno-29-3" name="__codelineno-29-3"></a><span class="c1"># AXIS: {0, 1, index, columns} -- axis for the function to be applied on</span>
<a id="__codelineno-29-4" name="__codelineno-29-4"></a><span class="c1"># SKIPNA: {bool} -- exclude NA/null values. if an entire row/column is NA, the result will be NA</span>
<a id="__codelineno-29-5" name="__codelineno-29-5"></a><span class="c1"># DDOF: {int} -- Delta Degrees of Freedom. divisor used in calculations is N - ddof (N represents the number of elements) -- DEFAULT 1</span>
<a id="__codelineno-29-6" name="__codelineno-29-6"></a><span class="c1"># NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series</span>
</code></pre></div></td></tr></table></div>
<h3 id="std-standard-deviation">STD (standard deviation)</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-30-1">1</a></span>
<span class="normal"><a href="#__codelineno-30-2">2</a></span>
<span class="normal"><a href="#__codelineno-30-3">3</a></span>
<span class="normal"><a href="#__codelineno-30-4">4</a></span>
<span class="normal"><a href="#__codelineno-30-5">5</a></span>
<span class="normal"><a href="#__codelineno-30-6">6</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-30-1" name="__codelineno-30-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ddof</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># sample standard deviation</span>
<a id="__codelineno-30-2" name="__codelineno-30-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Dataframe</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ddof</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># sample standard deviation over requested axis</span>
<a id="__codelineno-30-3" name="__codelineno-30-3"></a><span class="c1"># AXIS: {0, 1, index, columns} -- axis for the function to be applied on</span>
<a id="__codelineno-30-4" name="__codelineno-30-4"></a><span class="c1"># SKIPNA: {bool} -- exclude NA/null values. if an entire row/column is NA, the result will be NA</span>
<a id="__codelineno-30-5" name="__codelineno-30-5"></a><span class="c1"># DDOF: {int} -- Delta Degrees of Freedom. divisor used in calculations is N - ddof (N represents the number of elements) -- DEFAULT 1</span>
<a id="__codelineno-30-6" name="__codelineno-30-6"></a><span class="c1"># NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series</span>
</code></pre></div></td></tr></table></div>
<h3 id="skew">SKEW</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-31-1">1</a></span>
<span class="normal"><a href="#__codelineno-31-2">2</a></span>
<span class="normal"><a href="#__codelineno-31-3">3</a></span>
<span class="normal"><a href="#__codelineno-31-4">4</a></span>
<span class="normal"><a href="#__codelineno-31-5">5</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-31-1" name="__codelineno-31-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">skew</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># unbiased skew Normalized bt N-1</span>
<a id="__codelineno-31-2" name="__codelineno-31-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">skew</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># unbiased skew over requested axis Normalized by N-1</span>
<a id="__codelineno-31-3" name="__codelineno-31-3"></a><span class="c1"># AXIS: {0, 1, index, columns} -- axis for the function to be applied on</span>
<a id="__codelineno-31-4" name="__codelineno-31-4"></a><span class="c1"># SKIPNA: {bool} -- exclude NA/null values when computing the result</span>
<a id="__codelineno-31-5" name="__codelineno-31-5"></a><span class="c1"># NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series</span>
</code></pre></div></td></tr></table></div>
<h3 id="kurt">KURT</h3>
<p>Unbiased kurtosis over requested axis using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-32-1">1</a></span>
<span class="normal"><a href="#__codelineno-32-2">2</a></span>
<span class="normal"><a href="#__codelineno-32-3">3</a></span>
<span class="normal"><a href="#__codelineno-32-4">4</a></span>
<span class="normal"><a href="#__codelineno-32-5">5</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-32-1" name="__codelineno-32-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">kurt</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<a id="__codelineno-32-2" name="__codelineno-32-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Dataframe</span><span class="o">.</span><span class="n">kurt</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_only</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<a id="__codelineno-32-3" name="__codelineno-32-3"></a><span class="c1"># AXIS: {0, 1, index, columns} -- axis for the function to be applied on</span>
<a id="__codelineno-32-4" name="__codelineno-32-4"></a><span class="c1"># SKIPNA: {bool} -- exclude NA/null values when computing the result</span>
<a id="__codelineno-32-5" name="__codelineno-32-5"></a><span class="c1"># NUMERIC_ONLY: {bool} -- include only float, int, boolean columns, not implemented for Series</span>
</code></pre></div></td></tr></table></div>
<h3 id="cumsum-cumulative-sum">CUMSUM (cumulative sum)</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-33-1">1</a></span>
<span class="normal"><a href="#__codelineno-33-2">2</a></span>
<span class="normal"><a href="#__codelineno-33-3">3</a></span>
<span class="normal"><a href="#__codelineno-33-4">4</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-33-1" name="__codelineno-33-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># cumulative sum</span>
<a id="__codelineno-33-2" name="__codelineno-33-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Dataframe</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># cumulative sum over requested axis</span>
<a id="__codelineno-33-3" name="__codelineno-33-3"></a><span class="c1"># AXIS: {0, 1, index, columns} -- axis for the function to be applied on</span>
<a id="__codelineno-33-4" name="__codelineno-33-4"></a><span class="c1"># SKIPNA: {bool} -- exclude NA/null values. if an entire row/column is NA, the result will be NA</span>
</code></pre></div></td></tr></table></div>
<h3 id="cummax-cummin-cumulative-maximum-minimum">CUMMAX - CUMMIN (cumulative maximum - minimum)</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-34-1">1</a></span>
<span class="normal"><a href="#__codelineno-34-2">2</a></span>
<span class="normal"><a href="#__codelineno-34-3">3</a></span>
<span class="normal"><a href="#__codelineno-34-4">4</a></span>
<span class="normal"><a href="#__codelineno-34-5">5</a></span>
<span class="normal"><a href="#__codelineno-34-6">6</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-34-1" name="__codelineno-34-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">cummax</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># cumulative maximum</span>
<a id="__codelineno-34-2" name="__codelineno-34-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">cummin</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># cumulative minimum</span>
<a id="__codelineno-34-3" name="__codelineno-34-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">Dataframe</span><span class="o">.</span><span class="n">cummax</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># cumulative maximum over requested axis</span>
<a id="__codelineno-34-4" name="__codelineno-34-4"></a><span class="n">pd</span><span class="o">.</span><span class="n">Dataframe</span><span class="o">.</span><span class="n">cummin</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># cumulative minimum over requested axis</span>
<a id="__codelineno-34-5" name="__codelineno-34-5"></a><span class="c1"># AXIS: {0, 1, index, columns} -- axis for the function to be applied on</span>
<a id="__codelineno-34-6" name="__codelineno-34-6"></a><span class="c1"># SKIPNA: {bool} -- exclude NA/null values. if an entire row/column is NA, the result will be NA</span>
</code></pre></div></td></tr></table></div>
<h3 id="cumprod-cumulative-product">CUMPROD (cumulative product)</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-35-1">1</a></span>
<span class="normal"><a href="#__codelineno-35-2">2</a></span>
<span class="normal"><a href="#__codelineno-35-3">3</a></span>
<span class="normal"><a href="#__codelineno-35-4">4</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-35-1" name="__codelineno-35-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">cumprod</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># cumulative product</span>
<a id="__codelineno-35-2" name="__codelineno-35-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Dataframe</span><span class="o">.</span><span class="n">cumprod</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># cumulative product over requested axis</span>
<a id="__codelineno-35-3" name="__codelineno-35-3"></a><span class="c1"># AXIS: {0, 1, index, columns} -- axis for the function to be applied on</span>
<a id="__codelineno-35-4" name="__codelineno-35-4"></a><span class="c1"># SKIPNA: {bool} -- exclude NA/null values. if an entire row/column is NA, the result will be NA</span>
</code></pre></div></td></tr></table></div>
<h3 id="diff">DIFF</h3>
<p>Calculates the difference of a DataFrame element compared with another element in the DataFrame.<br />
(default is the element in the same column of the previous row)</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-36-1">1</a></span>
<span class="normal"><a href="#__codelineno-36-2">2</a></span>
<span class="normal"><a href="#__codelineno-36-3">3</a></span>
<span class="normal"><a href="#__codelineno-36-4">4</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-36-1" name="__codelineno-36-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<a id="__codelineno-36-2" name="__codelineno-36-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<a id="__codelineno-36-3" name="__codelineno-36-3"></a><span class="c1"># PERIODS: {int} -- Periods to shift for calculating difference, accepts negative values -- DEFAULT 1</span>
<a id="__codelineno-36-4" name="__codelineno-36-4"></a><span class="c1"># AXIS: {0, 1, index, columns} -- Take difference over rows or columns</span>
</code></pre></div></td></tr></table></div>
<h3 id="pct_change">PCT_CHANGE</h3>
<p>Percentage change between the current and a prior element.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-37-1">1</a></span>
<span class="normal"><a href="#__codelineno-37-2">2</a></span>
<span class="normal"><a href="#__codelineno-37-3">3</a></span>
<span class="normal"><a href="#__codelineno-37-4">4</a></span>
<span class="normal"><a href="#__codelineno-37-5">5</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-37-1" name="__codelineno-37-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">Pct_change</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fill_method</span><span class="o">=</span><span class="s1">&#39;pad&#39;</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<a id="__codelineno-37-2" name="__codelineno-37-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Dataframe</span><span class="o">.</span><span class="n">pct_change</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">fill_method</span><span class="o">=</span><span class="s1">&#39;pad&#39;</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<a id="__codelineno-37-3" name="__codelineno-37-3"></a><span class="c1"># PERIODS:{int} -- periods to shift for forming percent change</span>
<a id="__codelineno-37-4" name="__codelineno-37-4"></a><span class="c1"># FILL_METHOD: {str, pda} -- How to handle NAs before computing percent changes -- DEFAULT pad</span>
<a id="__codelineno-37-5" name="__codelineno-37-5"></a><span class="c1"># LIMIT: {int} -- number of consecutive NAs to fill before stopping -- DEFAULT None</span>
</code></pre></div></td></tr></table></div>
<h2 id="handling-missing-data">HANDLING MISSING DATA</h2>
<h3 id="filtering-out-missing-data">FILTERING OUT MISSING DATA</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-38-1">1</a></span>
<span class="normal"><a href="#__codelineno-38-2">2</a></span>
<span class="normal"><a href="#__codelineno-38-3">3</a></span>
<span class="normal"><a href="#__codelineno-38-4">4</a></span>
<span class="normal"><a href="#__codelineno-38-5">5</a></span>
<span class="normal"><a href="#__codelineno-38-6">6</a></span>
<span class="normal"><a href="#__codelineno-38-7">7</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-38-1" name="__codelineno-38-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="c1"># return a new Series with missing values removed</span>
<a id="__codelineno-38-2" name="__codelineno-38-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;any&#39;</span><span class="p">,</span> <span class="n">tresh</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">subset</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="c1"># return a new DataFrame with missing values removed</span>
<a id="__codelineno-38-3" name="__codelineno-38-3"></a><span class="c1"># AXIS: {tuple, list} -- tuple or list to drop on multiple axes. only a single axis is allowed</span>
<a id="__codelineno-38-4" name="__codelineno-38-4"></a><span class="c1"># HOW: {any, all} -- determine if row or column is removed from DataFrame (ANY = if any NA present, ALL = if all values are NA). DEFAULT any</span>
<a id="__codelineno-38-5" name="__codelineno-38-5"></a><span class="c1"># TRESH: {int} -- require that many non-NA values</span>
<a id="__codelineno-38-6" name="__codelineno-38-6"></a><span class="c1"># SUBSET: {array} -- labels along other axis to consider</span>
<a id="__codelineno-38-7" name="__codelineno-38-7"></a><span class="c1"># INPLACE: {bool} -- if True, do operation inplace and return None -- DEFAULT False</span>
</code></pre></div></td></tr></table></div>
<h3 id="filling-in-missing-data">FILLING IN MISSING DATA</h3>
<p>Fill NA/NaN values using the specified method.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-39-1">1</a></span>
<span class="normal"><a href="#__codelineno-39-2">2</a></span>
<span class="normal"><a href="#__codelineno-39-3">3</a></span>
<span class="normal"><a href="#__codelineno-39-4">4</a></span>
<span class="normal"><a href="#__codelineno-39-5">5</a></span>
<span class="normal"><a href="#__codelineno-39-6">6</a></span>
<span class="normal"><a href="#__codelineno-39-7">7</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-39-1" name="__codelineno-39-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<a id="__codelineno-39-2" name="__codelineno-39-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<a id="__codelineno-39-3" name="__codelineno-39-3"></a><span class="c1"># VALUE: {scalar, dict, Series, DataFrame} -- value to use to fill holes, dict/Series/DataFrame specifying which value to use for each index or column</span>
<a id="__codelineno-39-4" name="__codelineno-39-4"></a><span class="c1"># METHOD: {backfill, pad, None} -- method to use for filling holes -- DEFAULT None</span>
<a id="__codelineno-39-5" name="__codelineno-39-5"></a><span class="c1"># AXIS: {0, 1, index, columns} -- axis along which to fill missing values</span>
<a id="__codelineno-39-6" name="__codelineno-39-6"></a><span class="c1"># INPLACE: {bool} -- if true fill in-place (will modify views of object) -- DEFAULT False</span>
<a id="__codelineno-39-7" name="__codelineno-39-7"></a><span class="c1"># LIMIT: {int} -- maximum number of consecutive NaN values to forward/backward fill -- DEFAULT None</span>
</code></pre></div></td></tr></table></div>
<h2 id="hierarchical-indexing-multiindex">HIERARCHICAL INDEXING (MultiIndex)</h2>
<p>Enables storing and manipulation of data with an arbitrary number of dimensions.<br />
In lower dimensional data structures like Series (1d) and DataFrame (2d).</p>
<h3 id="multiiindex-creation">MULTIIINDEX CREATION</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-40-1">1</a></span>
<span class="normal"><a href="#__codelineno-40-2">2</a></span>
<span class="normal"><a href="#__codelineno-40-3">3</a></span>
<span class="normal"><a href="#__codelineno-40-4">4</a></span>
<span class="normal"><a href="#__codelineno-40-5">5</a></span>
<span class="normal"><a href="#__codelineno-40-6">6</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-40-1" name="__codelineno-40-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span><span class="o">*</span><span class="n">arrays</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># convert arrays to MultiIndex</span>
<a id="__codelineno-40-2" name="__codelineno-40-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="o">*</span><span class="n">arrays</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># convert tuples to MultiIndex</span>
<a id="__codelineno-40-3" name="__codelineno-40-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_frame</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># convert DataFrame to MultiIndex</span>
<a id="__codelineno-40-4" name="__codelineno-40-4"></a><span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_product</span><span class="p">(</span><span class="o">*</span><span class="n">iterables</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="c1"># MultiIndex from cartesian product of iterables</span>
<a id="__codelineno-40-5" name="__codelineno-40-5"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="o">*</span><span class="n">arrays</span><span class="p">)</span> <span class="c1"># Index constructor makes MultiIndex from Series</span>
<a id="__codelineno-40-6" name="__codelineno-40-6"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="o">*</span><span class="n">arrays</span><span class="p">)</span> <span class="c1"># Index constructor makes MultiINdex from DataFrame</span>
</code></pre></div></td></tr></table></div>
<h3 id="multiindex-levels">MULTIINDEX LEVELS</h3>
<p>Vector of label values for requested level, equal to the length of the index.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-41-1">1</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-41-1" name="__codelineno-41-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">get_level_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">level</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
<h3 id="partial-and-cross-section-selection">PARTIAL AND CROSS-SECTION SELECTION</h3>
<p>Partial selection "drops" levels of the hierarchical index in the result in a completely analogous way to selecting a column in a regular DataFrame.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-42-1">1</a></span>
<span class="normal"><a href="#__codelineno-42-2">2</a></span>
<span class="normal"><a href="#__codelineno-42-3">3</a></span>
<span class="normal"><a href="#__codelineno-42-4">4</a></span>
<span class="normal"><a href="#__codelineno-42-5">5</a></span>
<span class="normal"><a href="#__codelineno-42-6">6</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-42-1" name="__codelineno-42-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">xs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">drop_level</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># cross-section from Series</span>
<a id="__codelineno-42-2" name="__codelineno-42-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">xs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">drop_level</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># cross-section from DataFrame</span>
<a id="__codelineno-42-3" name="__codelineno-42-3"></a><span class="c1"># KEY: {label, tuple of label} -- label contained in the index, or partially in a MultiIndex</span>
<a id="__codelineno-42-4" name="__codelineno-42-4"></a><span class="c1"># AXIS: {0, 1, index, columns} -- axis to retrieve cross-section on -- DEFAULT 0</span>
<a id="__codelineno-42-5" name="__codelineno-42-5"></a><span class="c1"># LEVEL: -- in case of key partially contained in MultiIndex, indicate which levels are used. Levels referred by label or position</span>
<a id="__codelineno-42-6" name="__codelineno-42-6"></a><span class="c1"># DROP_LEVEL: {bool} -- If False, returns object with same levels as self -- DEFAULT True</span>
</code></pre></div></td></tr></table></div>
<h3 id="indexing-slicing">INDEXING, SLICING</h3>
<p>Multi index keys take the form of tuples.</p>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-43-1">1</a></span>
<span class="normal"><a href="#__codelineno-43-2">2</a></span>
<span class="normal"><a href="#__codelineno-43-3">3</a></span>
<span class="normal"><a href="#__codelineno-43-4">4</a></span>
<span class="normal"><a href="#__codelineno-43-5">5</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-43-1" name="__codelineno-43-1"></a><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="s1">&#39;lvl_1&#39;</span><span class="p">,</span> <span class="s1">&#39;lvl_2&#39;</span><span class="p">,</span> <span class="o">...</span><span class="p">)]</span> <span class="c1"># selection of single row</span>
<a id="__codelineno-43-2" name="__codelineno-43-2"></a><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="s1">&#39;idx_lvl_1&#39;</span><span class="p">,</span> <span class="s1">&#39;idx_lvl_2&#39;</span><span class="p">,</span> <span class="o">...</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;col_lvl_1&#39;</span><span class="p">,</span> <span class="s1">&#39;col_lvl_2&#39;</span><span class="p">,</span> <span class="o">...</span><span class="p">)]</span> <span class="c1"># selection of single value</span>
<a id="__codelineno-43-3" name="__codelineno-43-3"></a>
<a id="__codelineno-43-4" name="__codelineno-43-4"></a><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;idx_lvl_1&#39;</span><span class="p">:</span><span class="s1">&#39;idx_lvl_1&#39;</span><span class="p">]</span> <span class="c1"># slice of rows (aka partial selection)</span>
<a id="__codelineno-43-5" name="__codelineno-43-5"></a><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="s1">&#39;idx_lvl_1&#39;</span><span class="p">,</span> <span class="s1">&#39;idx_lvl_2&#39;</span><span class="p">)</span> <span class="p">:</span> <span class="p">(</span><span class="s1">&#39;idx_lvl_1&#39;</span><span class="p">,</span> <span class="s1">&#39;idx_lvl_2&#39;</span><span class="p">)]</span> <span class="c1"># slice of rows with levels</span>
</code></pre></div></td></tr></table></div>
<h3 id="reordering-and-sorting-levels">REORDERING AND SORTING LEVELS</h3>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-44-1">1</a></span>
<span class="normal"><a href="#__codelineno-44-2">2</a></span>
<span class="normal"><a href="#__codelineno-44-3">3</a></span>
<span class="normal"><a href="#__codelineno-44-4">4</a></span>
<span class="normal"><a href="#__codelineno-44-5">5</a></span>
<span class="normal"><a href="#__codelineno-44-6">6</a></span>
<span class="normal"><a href="#__codelineno-44-7">7</a></span>
<span class="normal"><a href="#__codelineno-44-8">8</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-44-1" name="__codelineno-44-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">swaplevel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">j</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># swap level i with level j</span>
<a id="__codelineno-44-2" name="__codelineno-44-2"></a><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">swaplevel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">j</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># swap levels i and j in a MultiIndex</span>
<a id="__codelineno-44-3" name="__codelineno-44-3"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">swaplevel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">j</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># swap levels i and j in a MultiIndex on a partivular axis</span>
<a id="__codelineno-44-4" name="__codelineno-44-4"></a>
<a id="__codelineno-44-5" name="__codelineno-44-5"></a><span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">sortlevel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sort_remaining</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># sort MultiIndex at requested level</span>
<a id="__codelineno-44-6" name="__codelineno-44-6"></a><span class="c1"># LEVEL: {str, int, list-like} -- DEFAULT 0</span>
<a id="__codelineno-44-7" name="__codelineno-44-7"></a><span class="c1"># ASCENDING: {bool} -- if True, sort values in ascending order, otherwise descending -- DEFAULT True</span>
<a id="__codelineno-44-8" name="__codelineno-44-8"></a><span class="c1"># SORT_REMAINING: {bool} -- sort by the remaining levels after level</span>
</code></pre></div></td></tr></table></div>
<h2 id="data-loading-storage-file-formats">DATA LOADING, STORAGE FILE FORMATS</h2>
<div class="highlight"><table class="highlighttable"><tr><th colspan="2" class="filename"><span class="filename">Python</span></th></tr><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-45-1"> 1</a></span>
<span class="normal"><a href="#__codelineno-45-2"> 2</a></span>
<span class="normal"><a href="#__codelineno-45-3"> 3</a></span>
<span class="normal"><a href="#__codelineno-45-4"> 4</a></span>
<span class="normal"><a href="#__codelineno-45-5"> 5</a></span>
<span class="normal"><a href="#__codelineno-45-6"> 6</a></span>
<span class="normal"><a href="#__codelineno-45-7"> 7</a></span>
<span class="normal"><a href="#__codelineno-45-8"> 8</a></span>
<span class="normal"><a href="#__codelineno-45-9"> 9</a></span>
<span class="normal"><a href="#__codelineno-45-10">10</a></span>
<span class="normal"><a href="#__codelineno-45-11">11</a></span>
<span class="normal"><a href="#__codelineno-45-12">12</a></span>
<span class="normal"><a href="#__codelineno-45-13">13</a></span>
<span class="normal"><a href="#__codelineno-45-14">14</a></span>
<span class="normal"><a href="#__codelineno-45-15">15</a></span>
<span class="normal"><a href="#__codelineno-45-16">16</a></span>
<span class="normal"><a href="#__codelineno-45-17">17</a></span>
<span class="normal"><a href="#__codelineno-45-18">18</a></span>
<span class="normal"><a href="#__codelineno-45-19">19</a></span>
<span class="normal"><a href="#__codelineno-45-20">20</a></span>
<span class="normal"><a href="#__codelineno-45-21">21</a></span>
<span class="normal"><a href="#__codelineno-45-22">22</a></span>
<span class="normal"><a href="#__codelineno-45-23">23</a></span>
<span class="normal"><a href="#__codelineno-45-24">24</a></span>
<span class="normal"><a href="#__codelineno-45-25">25</a></span>
<span class="normal"><a href="#__codelineno-45-26">26</a></span>
<span class="normal"><a href="#__codelineno-45-27">27</a></span>
<span class="normal"><a href="#__codelineno-45-28">28</a></span>
<span class="normal"><a href="#__codelineno-45-29">29</a></span>
<span class="normal"><a href="#__codelineno-45-30">30</a></span>
<span class="normal"><a href="#__codelineno-45-31">31</a></span>
<span class="normal"><a href="#__codelineno-45-32">32</a></span>
<span class="normal"><a href="#__codelineno-45-33">33</a></span>
<span class="normal"><a href="#__codelineno-45-34">34</a></span>
<span class="normal"><a href="#__codelineno-45-35">35</a></span>
<span class="normal"><a href="#__codelineno-45-36">36</a></span>
<span class="normal"><a href="#__codelineno-45-37">37</a></span>
<span class="normal"><a href="#__codelineno-45-38">38</a></span></pre></div></td><td class="code"><div><pre><span></span><code><a id="__codelineno-45-1" name="__codelineno-45-1"></a><span class="n">pd</span><span class="o">.</span><span class="n">read_fwf</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">colspecs</span><span class="o">=</span><span class="s1">&#39;infer&#39;</span><span class="p">,</span> <span class="n">widths</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">infer_nrows</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span> <span class="c1"># read a table of fixed-width formatted lines into DataFrame</span>
<a id="__codelineno-45-2" name="__codelineno-45-2"></a><span class="c1"># FILEPATH: {str, path object} -- any valid string path is acceptable, could be a URL. Valid URLs: http, ftp, s3, and file</span>
<a id="__codelineno-45-3" name="__codelineno-45-3"></a><span class="c1"># COLSPECS: {list of tuple (int, int), &#39;infer&#39;} -- list of tuples giving extents of fixed-width fields of each line as half-open intervals { [from, to) }</span>
<a id="__codelineno-45-4" name="__codelineno-45-4"></a><span class="c1"># WIDTHS: {list of int} -- list of field widths which can be used instead of &quot;colspecs&quot; if intervals are contiguous</span>
<a id="__codelineno-45-5" name="__codelineno-45-5"></a><span class="c1"># INFER_ROWS: {int} -- number of rows to consider when letting parser determine colspecs -- DEFAULT 100</span>
<a id="__codelineno-45-6" name="__codelineno-45-6"></a>
<a id="__codelineno-45-7" name="__codelineno-45-7"></a><span class="n">pd</span><span class="o">.</span><span class="n">read_excel</span><span class="p">()</span> <span class="c1"># read an Excel file into a pandas DataFrame</span>
<a id="__codelineno-45-8" name="__codelineno-45-8"></a><span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">()</span> <span class="c1"># convert a JSON string to pandas object</span>
<a id="__codelineno-45-9" name="__codelineno-45-9"></a><span class="n">pd</span><span class="o">.</span><span class="n">read_html</span><span class="p">()</span> <span class="c1"># read HTML tables into a list of DataFrame objects</span>
<a id="__codelineno-45-10" name="__codelineno-45-10"></a><span class="n">pd</span><span class="o">.</span><span class="n">read_sql</span><span class="p">()</span> <span class="c1"># read SQL query or database table into a DataFrame</span>
<a id="__codelineno-45-11" name="__codelineno-45-11"></a>
<a id="__codelineno-45-12" name="__codelineno-45-12"></a><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">&#39;,&#39;</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span> <span class="c1"># read a comma-separated values (csv) file into DataFrame</span>
<a id="__codelineno-45-13" name="__codelineno-45-13"></a><span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="c1"># read general delimited file into DataFrame</span>
<a id="__codelineno-45-14" name="__codelineno-45-14"></a><span class="c1"># FILEPATH: {str, path object} -- any valid string path is acceptable, could be a URL. Valid URLs: http, ftp, s3, and file</span>
<a id="__codelineno-45-15" name="__codelineno-45-15"></a><span class="c1"># SEP: {str} -- delimiter to use -- DEFAULT \t (tab)</span>
<a id="__codelineno-45-16" name="__codelineno-45-16"></a><span class="c1"># HEADER {int, list of int, &#39;infer&#39;} -- row numbers to use as column names, and the start of the data -- DEFAULT &#39;infer&#39;</span>
<a id="__codelineno-45-17" name="__codelineno-45-17"></a><span class="c1"># NAMES:{array} -- list of column names to use -- DEFAULT None</span>
<a id="__codelineno-45-18" name="__codelineno-45-18"></a><span class="c1"># INDEX_COL: {int, str, False, sequnce of int/str, None} -- Columns to use as row labels of DataFrame, given as string name or column index -- DEFAULT None</span>
<a id="__codelineno-45-19" name="__codelineno-45-19"></a><span class="c1"># SKIPROWS: {list-like, int, callable} -- Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file</span>
<a id="__codelineno-45-20" name="__codelineno-45-20"></a><span class="c1"># NA_VALUES: {scalar, str, list-like, dict} -- additional strings to recognize as NA/NaN. if dict passed, specific per-column NA values</span>
<a id="__codelineno-45-21" name="__codelineno-45-21"></a><span class="c1"># THOUSANDS: {str} -- thousand separator</span>
<a id="__codelineno-45-22" name="__codelineno-45-22"></a><span class="c1"># *ARGS, **KWARGS -- SEE DOCS</span>
<a id="__codelineno-45-23" name="__codelineno-45-23"></a>
<a id="__codelineno-45-24" name="__codelineno-45-24"></a><span class="c1"># write object to a comma-separated values (csv) file</span>
<a id="__codelineno-45-25" name="__codelineno-45-25"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path_or_buf</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">&#39;,&#39;</span><span class="p">,</span> <span class="n">na_rep</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">,</span> <span class="n">line_terminator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">decimal</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<a id="__codelineno-45-26" name="__codelineno-45-26"></a><span class="c1"># SEP: {str len 1} -- Field delimiter for the output file</span>
<a id="__codelineno-45-27" name="__codelineno-45-27"></a><span class="c1"># NA_REP: {str} -- missing data representation</span>
<a id="__codelineno-45-28" name="__codelineno-45-28"></a><span class="c1"># COLUMNS: {sequence} -- colums to write</span>
<a id="__codelineno-45-29" name="__codelineno-45-29"></a><span class="c1"># HEADER: {bool, list of str} -- write out column names. if list of strings is given its assumed to be aliases for column names</span>
<a id="__codelineno-45-30" name="__codelineno-45-30"></a><span class="c1"># INDEX: {bool, list of str} -- write out row names (index)</span>
<a id="__codelineno-45-31" name="__codelineno-45-31"></a><span class="c1"># ENCODING: {str} -- string representing encoding to use -- DEFAULT &quot;utf-8&quot;</span>
<a id="__codelineno-45-32" name="__codelineno-45-32"></a><span class="c1"># LINE_TERMINATOR: {str} -- newline character or character sequence to use in the output file -- DEFAULT os.linesep</span>
<a id="__codelineno-45-33" name="__codelineno-45-33"></a><span class="c1"># DECIMAL: {str} -- character recognized as decimal separator (in EU ,)</span>
<a id="__codelineno-45-34" name="__codelineno-45-34"></a>
<a id="__codelineno-45-35" name="__codelineno-45-35"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">to_excel</span><span class="p">()</span>
<a id="__codelineno-45-36" name="__codelineno-45-36"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">to_json</span><span class="p">()</span>
<a id="__codelineno-45-37" name="__codelineno-45-37"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">to_html</span><span class="p">()</span>
<a id="__codelineno-45-38" name="__codelineno-45-38"></a><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">to_sql</span><span class="p">()</span>
</code></pre></div></td></tr></table></div>
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