NumPy

README.md

@@ -2641,19 +2641,19 @@ import numpy as np
 ```python
 <array> = np.array(<list/list_of_lists>)                # Returns a 1d/2d NumPy array.
 <array> = np.zeros/ones(<shape>)                        # Also np.full(<shape>, <el>).
-<array> = np.arange(from_inc, to_exc, ±step)            # Also np.linspace(start, stop, num).
+<array> = np.arange(from_inc, to_exc, ±step)            # Also np.linspace(start, stop, len).
 <array> = np.random.randint(from_inc, to_exc, <shape>)  # Also np.random.random(<shape>).
 ```
 
 ```python
 <view>  = <array>.reshape(<shape>)                      # Also `<array>.shape = <shape>`.
-<array> = <array>.flatten()                             # Collapses array into one dimension.
-<view>  = <array>.squeeze()                             # Removes dimensions of length one.
+<array> = <array>.flatten()                             # Also `<view> = <array>.ravel()`.
+<view>  = <array>.transpose()                           # Also `<view> = <array>.T`.
 ```
 
 ```python
-<array> = <array>.sum/min/mean/var/std([axis])          # Passed dimension gets aggregated.
-<array> = <array>.argmin([axis])                        # Returns indexes of smallest elements.
+<array> = np.copy/int64/float64(<array>)                # Returns new array of the same shape.
+<array> = <array>.sum/max/mean/argmax/all([axis])       # Passed dimension gets aggregated.
 <array> = np.apply_along_axis(<func>, axis, <array>)    # Func can return a scalar or array.
 ```
 

index.html

@@ -2163,15 +2163,15 @@ drawer = cg.output.GraphvizOutput(output_file=filename)
 
 <pre><code class="python language-python hljs">&lt;array&gt; = np.array(&lt;list/list_of_lists&gt;)                <span class="hljs-comment"># Returns a 1d/2d NumPy array.</span>
 &lt;array&gt; = np.zeros/ones(&lt;shape&gt;)                        <span class="hljs-comment"># Also np.full(&lt;shape&gt;, &lt;el&gt;).</span>
-&lt;array&gt; = np.arange(from_inc, to_exc, ±step)            <span class="hljs-comment"># Also np.linspace(start, stop, num).</span>
+&lt;array&gt; = np.arange(from_inc, to_exc, ±step)            <span class="hljs-comment"># Also np.linspace(start, stop, len).</span>
 &lt;array&gt; = np.random.randint(from_inc, to_exc, &lt;shape&gt;)  <span class="hljs-comment"># Also np.random.random(&lt;shape&gt;).</span>
 </code></pre>
 <pre><code class="python language-python hljs">&lt;view&gt;  = &lt;array&gt;.reshape(&lt;shape&gt;)                      <span class="hljs-comment"># Also `&lt;array&gt;.shape = &lt;shape&gt;`.</span>
-&lt;array&gt; = &lt;array&gt;.flatten()                             <span class="hljs-comment"># Collapses array into one dimension.</span>
-&lt;view&gt;  = &lt;array&gt;.squeeze()                             <span class="hljs-comment"># Removes dimensions of length one.</span>
+&lt;array&gt; = &lt;array&gt;.flatten()                             <span class="hljs-comment"># Also `&lt;view&gt; = &lt;array&gt;.ravel()`.</span>
+&lt;view&gt;  = &lt;array&gt;.transpose()                           <span class="hljs-comment"># Also `&lt;view&gt; = &lt;array&gt;.T`.</span>
 </code></pre>
-<pre><code class="python language-python hljs">&lt;array&gt; = &lt;array&gt;.sum/min/mean/var/std([axis])          <span class="hljs-comment"># Passed dimension gets aggregated.</span>
-&lt;array&gt; = &lt;array&gt;.argmin([axis])                        <span class="hljs-comment"># Returns indexes of smallest elements.</span>
+<pre><code class="python language-python hljs">&lt;array&gt; = np.copy/int64/float64(&lt;array&gt;)                <span class="hljs-comment"># Returns new array of the same shape.</span>
+&lt;array&gt; = &lt;array&gt;.sum/max/mean/argmax/all([axis])       <span class="hljs-comment"># Passed dimension gets aggregated.</span>
 &lt;array&gt; = np.apply_along_axis(&lt;func&gt;, axis, &lt;array&gt;)    <span class="hljs-comment"># Func can return a scalar or array.</span>
 </code></pre>
 <ul>