typo

posts/2024-07-18-tablecloth-dplyr-pandas-polars.md

@@ -302,7 +302,7 @@ Clojure's tablecloth emphasizes functional programming concepts and immutability
 Thanks for reading :)
 
 ## Versions
-This code in this post works with the following language and library versions:
+The code in this post works with the following language and library versions:
 
 | Tool | Version |
 | --- | --- |

public/2024-07-18-tablecloth-dplyr-pandas-polars.html

@@ -133,7 +133,7 @@ <h1>
 </code></pre><h4 id="polars">Polars</h4><pre><code class="lang-python">ds.group&#95;by&#40;&quot;species&quot;&#41;.agg&#40;
     pl.col&#40;&quot;body&#95;mass&#95;g&quot;&#41;.min&#40;&#41;.alias&#40;&quot;lowest&#95;body&#95;mass&#95;g&quot;&#41;
 &#41;
-</code></pre><h2 id="conclusions">Conclusions</h2><p>As you can see, all of these libraries are perfectly suitable for accomplishing common data manipulation tasks. Choosing a language and library can impact code readability, maintainability, and performance, though, so understanding the differences between available toolkits can help us make better choices.</p><p>Clojure's tablecloth emphasizes functional programming concepts and immutability, which can lead to more predictable and re-usable code, at the cost of adopting a potentially new paradigm. Hopefully this comparison serves not only as a translation guide, but an an intro to the different philosophies underpinning these common data science tools.</p><p>Thanks for reading :)</p><h2 id="versions">Versions</h2><p>This code in this post works with the following language and library versions:</p><table><thead><tr><th>Tool</th><th>Version</th></tr></thead><tbody><tr><td>MacOS</td><td>Sonoma 14.5</td></tr><tr><td>JVM</td><td><code>21.0.2</code></td></tr><tr><td>Clojure</td><td><code>1.11.1</code></td></tr><tr><td>Tablecloth</td><td><code>7.021</code></td></tr><tr><td>R</td><td><code>4.4.1</code></td></tr><tr><td>Tidyverse</td><td><code>2.0.0</code></td></tr><tr><td>Python</td><td><code>3.12.3</code></td></tr><tr><td>Pandas</td><td><code>2.1.4</code></td></tr><tr><td>Polars</td><td><code>1.1.0</code></td></tr></tbody></table>
+</code></pre><h2 id="conclusions">Conclusions</h2><p>As you can see, all of these libraries are perfectly suitable for accomplishing common data manipulation tasks. Choosing a language and library can impact code readability, maintainability, and performance, though, so understanding the differences between available toolkits can help us make better choices.</p><p>Clojure's tablecloth emphasizes functional programming concepts and immutability, which can lead to more predictable and re-usable code, at the cost of adopting a potentially new paradigm. Hopefully this comparison serves not only as a translation guide, but an an intro to the different philosophies underpinning these common data science tools.</p><p>Thanks for reading :)</p><h2 id="versions">Versions</h2><p>The code in this post works with the following language and library versions:</p><table><thead><tr><th>Tool</th><th>Version</th></tr></thead><tbody><tr><td>MacOS</td><td>Sonoma 14.5</td></tr><tr><td>JVM</td><td><code>21.0.2</code></td></tr><tr><td>Clojure</td><td><code>1.11.1</code></td></tr><tr><td>Tablecloth</td><td><code>7.021</code></td></tr><tr><td>R</td><td><code>4.4.1</code></td></tr><tr><td>Tidyverse</td><td><code>2.0.0</code></td></tr><tr><td>Python</td><td><code>3.12.3</code></td></tr><tr><td>Pandas</td><td><code>2.1.4</code></td></tr><tr><td>Polars</td><td><code>1.1.0</code></td></tr></tbody></table>
 
 
 

public/atom.xml

@@ -3,7 +3,7 @@
   <title>Code with Kira</title>
   <link href="https://codewithkira.com/atom.xml" rel="self"/>
   <link href="https://codewithkira.com"/>
-  <updated>2024-07-18T20:46:32+00:00</updated>
+  <updated>2024-07-19T01:38:37+00:00</updated>
   <id>https://codewithkira.com</id>
   <author>
     <name>Kira McLean</name>
@@ -82,7 +82,7 @@ ds = pl.read&#95;csv&#40;&quot;https://codewithkira.com/assets/penguins.csv&quot
 </code></pre><h4 id="polars">Polars</h4><pre><code class="lang-python">ds.group&#95;by&#40;&quot;species&quot;&#41;.agg&#40;
     pl.col&#40;&quot;body&#95;mass&#95;g&quot;&#41;.min&#40;&#41;.alias&#40;&quot;lowest&#95;body&#95;mass&#95;g&quot;&#41;
 &#41;
-</code></pre><h2 id="conclusions">Conclusions</h2><p>As you can see, all of these libraries are perfectly suitable for accomplishing common data manipulation tasks. Choosing a language and library can impact code readability, maintainability, and performance, though, so understanding the differences between available toolkits can help us make better choices.</p><p>Clojure's tablecloth emphasizes functional programming concepts and immutability, which can lead to more predictable and re-usable code, at the cost of adopting a potentially new paradigm. Hopefully this comparison serves not only as a translation guide, but an an intro to the different philosophies underpinning these common data science tools.</p><p>Thanks for reading :)</p><h2 id="versions">Versions</h2><p>This code in this post works with the following language and library versions:</p><table><thead><tr><th>Tool</th><th>Version</th></tr></thead><tbody><tr><td>MacOS</td><td>Sonoma 14.5</td></tr><tr><td>JVM</td><td><code>21.0.2</code></td></tr><tr><td>Clojure</td><td><code>1.11.1</code></td></tr><tr><td>Tablecloth</td><td><code>7.021</code></td></tr><tr><td>R</td><td><code>4.4.1</code></td></tr><tr><td>Tidyverse</td><td><code>2.0.0</code></td></tr><tr><td>Python</td><td><code>3.12.3</code></td></tr><tr><td>Pandas</td><td><code>2.1.4</code></td></tr><tr><td>Polars</td><td><code>1.1.0</code></td></tr></tbody></table>]]></content>
+</code></pre><h2 id="conclusions">Conclusions</h2><p>As you can see, all of these libraries are perfectly suitable for accomplishing common data manipulation tasks. Choosing a language and library can impact code readability, maintainability, and performance, though, so understanding the differences between available toolkits can help us make better choices.</p><p>Clojure's tablecloth emphasizes functional programming concepts and immutability, which can lead to more predictable and re-usable code, at the cost of adopting a potentially new paradigm. Hopefully this comparison serves not only as a translation guide, but an an intro to the different philosophies underpinning these common data science tools.</p><p>Thanks for reading :)</p><h2 id="versions">Versions</h2><p>The code in this post works with the following language and library versions:</p><table><thead><tr><th>Tool</th><th>Version</th></tr></thead><tbody><tr><td>MacOS</td><td>Sonoma 14.5</td></tr><tr><td>JVM</td><td><code>21.0.2</code></td></tr><tr><td>Clojure</td><td><code>1.11.1</code></td></tr><tr><td>Tablecloth</td><td><code>7.021</code></td></tr><tr><td>R</td><td><code>4.4.1</code></td></tr><tr><td>Tidyverse</td><td><code>2.0.0</code></td></tr><tr><td>Python</td><td><code>3.12.3</code></td></tr><tr><td>Pandas</td><td><code>2.1.4</code></td></tr><tr><td>Polars</td><td><code>1.1.0</code></td></tr></tbody></table>]]></content>
   </entry>
   <entry>
     <id>https://codewithkira.com/2024-06-30-clojurists-together-update-may-jun-2024.html</id>

public/index.html

@@ -134,7 +134,7 @@ <h1><a href="2024-07-18-tablecloth-dplyr-pandas-polars.html">Data Manipulation i
 </code></pre><h4 id="polars">Polars</h4><pre><code class="lang-python">ds.group&#95;by&#40;&quot;species&quot;&#41;.agg&#40;
     pl.col&#40;&quot;body&#95;mass&#95;g&quot;&#41;.min&#40;&#41;.alias&#40;&quot;lowest&#95;body&#95;mass&#95;g&quot;&#41;
 &#41;
-</code></pre><h2 id="conclusions">Conclusions</h2><p>As you can see, all of these libraries are perfectly suitable for accomplishing common data manipulation tasks. Choosing a language and library can impact code readability, maintainability, and performance, though, so understanding the differences between available toolkits can help us make better choices.</p><p>Clojure's tablecloth emphasizes functional programming concepts and immutability, which can lead to more predictable and re-usable code, at the cost of adopting a potentially new paradigm. Hopefully this comparison serves not only as a translation guide, but an an intro to the different philosophies underpinning these common data science tools.</p><p>Thanks for reading :)</p><h2 id="versions">Versions</h2><p>This code in this post works with the following language and library versions:</p><table><thead><tr><th>Tool</th><th>Version</th></tr></thead><tbody><tr><td>MacOS</td><td>Sonoma 14.5</td></tr><tr><td>JVM</td><td><code>21.0.2</code></td></tr><tr><td>Clojure</td><td><code>1.11.1</code></td></tr><tr><td>Tablecloth</td><td><code>7.021</code></td></tr><tr><td>R</td><td><code>4.4.1</code></td></tr><tr><td>Tidyverse</td><td><code>2.0.0</code></td></tr><tr><td>Python</td><td><code>3.12.3</code></td></tr><tr><td>Pandas</td><td><code>2.1.4</code></td></tr><tr><td>Polars</td><td><code>1.1.0</code></td></tr></tbody></table>
+</code></pre><h2 id="conclusions">Conclusions</h2><p>As you can see, all of these libraries are perfectly suitable for accomplishing common data manipulation tasks. Choosing a language and library can impact code readability, maintainability, and performance, though, so understanding the differences between available toolkits can help us make better choices.</p><p>Clojure's tablecloth emphasizes functional programming concepts and immutability, which can lead to more predictable and re-usable code, at the cost of adopting a potentially new paradigm. Hopefully this comparison serves not only as a translation guide, but an an intro to the different philosophies underpinning these common data science tools.</p><p>Thanks for reading :)</p><h2 id="versions">Versions</h2><p>The code in this post works with the following language and library versions:</p><table><thead><tr><th>Tool</th><th>Version</th></tr></thead><tbody><tr><td>MacOS</td><td>Sonoma 14.5</td></tr><tr><td>JVM</td><td><code>21.0.2</code></td></tr><tr><td>Clojure</td><td><code>1.11.1</code></td></tr><tr><td>Tablecloth</td><td><code>7.021</code></td></tr><tr><td>R</td><td><code>4.4.1</code></td></tr><tr><td>Tidyverse</td><td><code>2.0.0</code></td></tr><tr><td>Python</td><td><code>3.12.3</code></td></tr><tr><td>Pandas</td><td><code>2.1.4</code></td></tr><tr><td>Polars</td><td><code>1.1.0</code></td></tr></tbody></table>
 
 
         <p><i>Published: 2024-07-18</i></p>

public/planetclojure.xml

@@ -3,7 +3,7 @@
   <title>Code with Kira</title>
   <link href="https://codewithkira.com/atom.xml" rel="self"/>
   <link href="https://codewithkira.com"/>
-  <updated>2024-07-18T20:46:32+00:00</updated>
+  <updated>2024-07-19T01:38:37+00:00</updated>
   <id>https://codewithkira.com</id>
   <author>
     <name>Kira McLean</name>
@@ -82,7 +82,7 @@ ds = pl.read&#95;csv&#40;&quot;https://codewithkira.com/assets/penguins.csv&quot
 </code></pre><h4 id="polars">Polars</h4><pre><code class="lang-python">ds.group&#95;by&#40;&quot;species&quot;&#41;.agg&#40;
     pl.col&#40;&quot;body&#95;mass&#95;g&quot;&#41;.min&#40;&#41;.alias&#40;&quot;lowest&#95;body&#95;mass&#95;g&quot;&#41;
 &#41;
-</code></pre><h2 id="conclusions">Conclusions</h2><p>As you can see, all of these libraries are perfectly suitable for accomplishing common data manipulation tasks. Choosing a language and library can impact code readability, maintainability, and performance, though, so understanding the differences between available toolkits can help us make better choices.</p><p>Clojure's tablecloth emphasizes functional programming concepts and immutability, which can lead to more predictable and re-usable code, at the cost of adopting a potentially new paradigm. Hopefully this comparison serves not only as a translation guide, but an an intro to the different philosophies underpinning these common data science tools.</p><p>Thanks for reading :)</p><h2 id="versions">Versions</h2><p>This code in this post works with the following language and library versions:</p><table><thead><tr><th>Tool</th><th>Version</th></tr></thead><tbody><tr><td>MacOS</td><td>Sonoma 14.5</td></tr><tr><td>JVM</td><td><code>21.0.2</code></td></tr><tr><td>Clojure</td><td><code>1.11.1</code></td></tr><tr><td>Tablecloth</td><td><code>7.021</code></td></tr><tr><td>R</td><td><code>4.4.1</code></td></tr><tr><td>Tidyverse</td><td><code>2.0.0</code></td></tr><tr><td>Python</td><td><code>3.12.3</code></td></tr><tr><td>Pandas</td><td><code>2.1.4</code></td></tr><tr><td>Polars</td><td><code>1.1.0</code></td></tr></tbody></table>]]></content>
+</code></pre><h2 id="conclusions">Conclusions</h2><p>As you can see, all of these libraries are perfectly suitable for accomplishing common data manipulation tasks. Choosing a language and library can impact code readability, maintainability, and performance, though, so understanding the differences between available toolkits can help us make better choices.</p><p>Clojure's tablecloth emphasizes functional programming concepts and immutability, which can lead to more predictable and re-usable code, at the cost of adopting a potentially new paradigm. Hopefully this comparison serves not only as a translation guide, but an an intro to the different philosophies underpinning these common data science tools.</p><p>Thanks for reading :)</p><h2 id="versions">Versions</h2><p>The code in this post works with the following language and library versions:</p><table><thead><tr><th>Tool</th><th>Version</th></tr></thead><tbody><tr><td>MacOS</td><td>Sonoma 14.5</td></tr><tr><td>JVM</td><td><code>21.0.2</code></td></tr><tr><td>Clojure</td><td><code>1.11.1</code></td></tr><tr><td>Tablecloth</td><td><code>7.021</code></td></tr><tr><td>R</td><td><code>4.4.1</code></td></tr><tr><td>Tidyverse</td><td><code>2.0.0</code></td></tr><tr><td>Python</td><td><code>3.12.3</code></td></tr><tr><td>Pandas</td><td><code>2.1.4</code></td></tr><tr><td>Polars</td><td><code>1.1.0</code></td></tr></tbody></table>]]></content>
   </entry>
   <entry>
     <id>https://codewithkira.com/2024-06-30-clojurists-together-update-may-jun-2024.html</id>