results = executor.map(lambda x, y: x + y, 'abc', '123') # ('a1', 'b2', 'c3')
```
* **CPython interpreter can only run a single thread at the time. That is why this map() won't be faster than the standard map() unless passed function contains an I/O operation.**
results = executor.map(<spanclass="hljs-keyword">lambda</span> x, y: x + y, <spanclass="hljs-string">'abc'</span>, <spanclass="hljs-string">'123'</span>) <spanclass="hljs-comment"># ('a1', 'b2', 'c3')</span>
</code></pre></div>
<ul>
<li><strong>CPython interpreter can only run a single thread at the time. That is why this map() won't be faster than the standard map() unless passed function contains an I/O operation.</strong></li>
<div><h2id="introspection"><ahref="#introspection"name="introspection">#</a>Introspection</h2><p><strong>Inspecting code at runtime.</strong></p><div><h3id="variables">Variables</h3><pre><codeclass="python language-python hljs"><list> = dir() <spanclass="hljs-comment"># Names of variables in current scope.</span>
<dict> = locals() <spanclass="hljs-comment"># Dict of local variables. Also vars().</span>
<dict> = globals() <spanclass="hljs-comment"># Dict of global variables.</span>
@ -1854,6 +1848,15 @@ MyMetaClass.__base__ == type <span class="hljs-comment"># MyMetaClass is a
<li><strong>Similar to generator, but generator pulls data through the pipe with iteration, while coroutine pushes data into the pipeline with send().</strong></li>
<li><strong>Coroutines provide more powerful data routing possibilities than iterators.</strong></li>
Jure Šorn
5 years ago