pandas group by ALL functionality?
<p>I'm using the <code>pandas groupby+agg</code> functionality to generate nice reports</p>

<pre><code>aggs_dict = {'a':['mean', 'std'], 'b': 'size'}
df.groupby('year').agg(aggs_dict)
</code></pre>

<p>I would like to use the same <code>aggs_dict</code> on the entire dataframe as a single group, with no division to years, something like:</p>

<pre><code>df.groupall().agg(aggs_dict)
</code></pre>

<p>or:</p>

<pre><code>df.agg(aggs_dict)
</code></pre>

<p>But couldn't find any elegant way to do it.. Note that in my real code <code>aggs_dict</code> is quite complex so it's rather cumbersome to do:</p>

<pre><code>df.a.mean()
df.a.std()
df.b.size()
....
</code></pre>

<p>am I missing something simple and nice?</p>

<p>Ami Tavory's answer is a great way to do it but just in case you wanted a solution that doesn't require creating new columns and deleting them afterwards you could do something like:</p>

<pre><code>df.groupby([True]*len(df)).agg(aggs_dict) 
</code></pre>

python pandas group-by