NumPy

gto76 · gto76 · commit b400d44646f0 · 2020-09-28T08:34:07.000+02:00
diff --git a/README.md b/README.md
@@ -2587,7 +2587,7 @@ with PyCallGraph(drawer):
 
 NumPy
 -----
-**Array manipulation mini-language. It can run up to one hundred times faster than the equivalent Python code.**
+**Array manipulation mini-language. It can run up to one hundred times faster than the equivalent Python code. An even faster alternative that runs on a GPU is called CuPy.**
 
 ```python
 # $ pip3 install numpy
@@ -3404,7 +3404,7 @@ def wrangle_data(covid, dow, gold, bitcoin):
     df = df.sort_index().interpolate()
     df = df.rolling(10, min_periods=1, center=True).mean()
     df = df.loc['2020-02-23':].iloc[:-2]
-    df = df / df.iloc[0] * 100
+    df = (df / df.iloc[0]) * 100
     return pd.concat([covid, df], axis=1, join='inner')
 
 def display_data(df):
diff --git a/index.html b/index.html
@@ -2235,7 +2235,7 @@
 </code></pre></div></div>
 
 
-<div><h2 id="numpy"><a href="#numpy" name="numpy">#</a>NumPy</h2><p><strong>Array manipulation mini-language. It can run up to one hundred times faster than the equivalent Python code.</strong></p><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install numpy</span>
+<div><h2 id="numpy"><a href="#numpy" name="numpy">#</a>NumPy</h2><p><strong>Array manipulation mini-language. It can run up to one hundred times faster than the equivalent Python code. An even faster alternative that runs on a GPU is called CuPy.</strong></p><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install numpy</span>
 <span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
 </code></pre></div>
 
@@ -2888,7 +2888,7 @@
     df = df.sort_index().interpolate()
     df = df.rolling(<span class="hljs-number">10</span>, min_periods=<span class="hljs-number">1</span>, center=<span class="hljs-keyword">True</span>).mean()
     df = df.loc[<span class="hljs-string">'2020-02-23'</span>:].iloc[:<span class="hljs-number">-2</span>]
-    df = df / df.iloc[<span class="hljs-number">0</span>] * <span class="hljs-number">100</span>
+    df = (df / df.iloc[<span class="hljs-number">0</span>]) * <span class="hljs-number">100</span>
     <span class="hljs-keyword">return</span> pd.concat([covid, df], axis=<span class="hljs-number">1</span>, join=<span class="hljs-string">'inner'</span>)
 
 <span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">display_data</span><span class="hljs-params">(df)</span>:</span>
