CSV, Plotly

README.md

@@ -1832,6 +1832,7 @@ import csv
 <writer>.writerows(<coll_of_coll>)  # Appends multiple rows.
 ```
 * **File must be opened with a `'newline=""'` argument, or '\r' will be added in front of every '\n' on platforms that use '\r\n' line endings!**
+* **Open existing file with `'mode="w"'` to overwrite it or `'mode="a"'` to append to it.**
 
 ### Parameters
 * **`'dialect'` - Master parameter that sets the default values. String or a 'csv.Dialect' object.**
@@ -3466,8 +3467,8 @@ def scrape_data():
     return map(pd.Series.rename, out, ['Total Cases', 'Bitcoin', 'Gold', 'Dow Jones'])
 
 def wrangle_data(covid, bitcoin, gold, dow):
-    df = pd.concat([bitcoin, gold, dow], axis=1)  # Joins columns on dates.
-    df = df.sort_index().interpolate()            # Sorts by date and interpolates NaN-s.
+    df = pd.concat([bitcoin, gold, dow], axis=1)  # Creates table by joining columns on dates.
+    df = df.sort_index().interpolate()            # Sorts table by date and interpolates NaN-s.
     df = df.loc['2020-02-23':]                    # Discards rows before '2020-02-23'.
     df = (df / df.iloc[0]) * 100                  # Calculates percentages relative to day 1.
     df = df.join(covid)                           # Adds column with covid cases.

index.html

@@ -1526,6 +1526,7 @@ CompletedProcess(args=[<span class="hljs-string">'bc'</span>, <span class="hljs-
 
 <ul>
 <li><strong>File must be opened with a <code class="python hljs"><span class="hljs-string">'newline=""'</span></code> argument, or '\r' will be added in front of every '\n' on platforms that use '\r\n' line endings!</strong></li>
+<li><strong>Open existing file with <code class="python hljs"><span class="hljs-string">'mode="w"'</span></code> to overwrite it or <code class="python hljs"><span class="hljs-string">'mode="a"'</span></code> to append to it.</strong></li>
 </ul>
 <div><h3 id="parameters">Parameters</h3><ul>
 <li><strong><code class="python hljs"><span class="hljs-string">'dialect'</span></code> - Master parameter that sets the default values. String or a 'csv.Dialect' object.</strong></li>
@@ -2824,8 +2825,8 @@ ex.line(df, x=<span class="hljs-string">'Date'</span>, y=<span class="hljs-strin
     <span class="hljs-keyword">return</span> map(pd.Series.rename, out, [<span class="hljs-string">'Total Cases'</span>, <span class="hljs-string">'Bitcoin'</span>, <span class="hljs-string">'Gold'</span>, <span class="hljs-string">'Dow Jones'</span>])
 
 <span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">wrangle_data</span><span class="hljs-params">(covid, bitcoin, gold, dow)</span>:</span>
-    df = pd.concat([bitcoin, gold, dow], axis=<span class="hljs-number">1</span>)  <span class="hljs-comment"># Joins columns on dates.</span>
-    df = df.sort_index().interpolate()            <span class="hljs-comment"># Sorts by date and interpolates NaN-s.</span>
+    df = pd.concat([bitcoin, gold, dow], axis=<span class="hljs-number">1</span>)  <span class="hljs-comment"># Creates table by joining columns on dates.</span>
+    df = df.sort_index().interpolate()            <span class="hljs-comment"># Sorts table by date and interpolates NaN-s.</span>
     df = df.loc[<span class="hljs-string">'2020-02-23'</span>:]                    <span class="hljs-comment"># Discards rows before '2020-02-23'.</span>
     df = (df / df.iloc[<span class="hljs-number">0</span>]) * <span class="hljs-number">100</span>                  <span class="hljs-comment"># Calculates percentages relative to day 1.</span>
     df = df.join(covid)                           <span class="hljs-comment"># Adds column with covid cases.</span>