* **All operations operate on columns by default. Use `'axis=1'` parameter to process the rows instead. Transform passes DF to a function if it raises an error after receiving a Sr.**
* **All operations operate on columns by default. Pass `'axis=1'` to process the rows instead.**
<li><strong>All operations operate on columns by default. Use<codeclass="python hljs"><spanclass="hljs-string">'axis=1'</span></code>parameter to process the rows instead. Transform passes DF to a function if it raises an error after receiving a Sr.</strong></li>
<li><strong>All operations operate on columns by default. Pass<codeclass="python hljs"><spanclass="hljs-string">'axis=1'</span></code> to process the rows instead.</strong></li>
@ -2666,8 +2666,8 @@ b <span class="hljs-number">3</span> <span class="hljs-number">4</span>
┃ df.agg(…) │ x <spanclass="hljs-number">4</span> │ sum <spanclass="hljs-number">4</span><spanclass="hljs-number">6</span> │ x <spanclass="hljs-number">4</span> ┃
┃ df.agg(…) │ x <spanclass="hljs-number">4</span> │ sum <spanclass="hljs-number">4</span><spanclass="hljs-number">6</span> │ x <spanclass="hljs-number">4</span> ┃
@ -2679,7 +2679,7 @@ b <span class="hljs-number">3</span> <span class="hljs-number">4</span>
<ul>
<ul>
<li><strong>Use <codeclass="python hljs"><spanclass="hljs-string">'<DF>[col_key_1, col_key_2][row_key]'</span></code> to get the fifth result's values.</strong></li>
<li><strong>Use <codeclass="python hljs"><spanclass="hljs-string">'<DF>[col_key_1, col_key_2][row_key]'</span></code> to get the fifth result's values.</strong></li>
<div><h3id="groupby">GroupBy</h3><p><strong>Object that groups together rows of a dataframe based on the value of the passed column.</strong></p><pre><codeclass="python language-python hljs"><spanclass="hljs-meta">>>></span>df = DataFrame([[<spanclass="hljs-number">1</span>, <spanclass="hljs-number">2</span>, <spanclass="hljs-number">3</span>], [<spanclass="hljs-number">4</span>, <spanclass="hljs-number">5</span>, <spanclass="hljs-number">6</span>], [<spanclass="hljs-number">7</span>, <spanclass="hljs-number">8</span>, <spanclass="hljs-number">6</span>]], index=list(<spanclass="hljs-string">'abc'</span>), columns=list(<spanclass="hljs-string">'xyz'</span>))
<div><h3id="groupby">GroupBy</h3><p><strong>Object that groups together rows of a dataframe based on the value of the passed column.</strong></p><pre><codeclass="python language-python hljs"><spanclass="hljs-meta">>>></span>df = DataFrame([[<spanclass="hljs-number">1</span>, <spanclass="hljs-number">2</span>, <spanclass="hljs-number">3</span>], [<spanclass="hljs-number">4</span>, <spanclass="hljs-number">5</span>, <spanclass="hljs-number">6</span>], [<spanclass="hljs-number">7</span>, <spanclass="hljs-number">8</span>, <spanclass="hljs-number">6</span>]], index=list(<spanclass="hljs-string">'abc'</span>), columns=list(<spanclass="hljs-string">'xyz'</span>))
@ -2699,11 +2702,10 @@ c <span class="hljs-number">7</span> <span class="hljs-number">8</span>
<pre><codeclass="python language-python hljs"><GB> = <DF>.groupby(column_key/s) <spanclass="hljs-comment"># DF is split into groups based on passed column.</span>
<pre><codeclass="python language-python hljs"><GB> = <DF>.groupby(column_key/s) <spanclass="hljs-comment"># DF is split into groups based on passed column.</span>
<DF> = <GB>.get_group(group_key/s) <spanclass="hljs-comment"># Selects a group by value of grouping column.</span>
<DF> = <GB>.apply(<func>) <spanclass="hljs-comment"># Maps each group. Func can return DF, Sr or el.</span>
<DF> = <GB>.apply(<func>) <spanclass="hljs-comment"># Maps each group. Func can return DF, Sr or el.</span>
<GB> = <GB>[column_key] <spanclass="hljs-comment"># A single column GB. All operations return a Sr.</span>
<GB> = <GB>[column_key] <spanclass="hljs-comment"># A single column GB. All operations return a Sr.</span>