chatgpt python distribution fitting

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diff --git a/categories/data-analytics/index.html b/categories/data-analytics/index.html
@@ -231,6 +231,108 @@ <h1>Data Analytics</h1>
 
         <div class="posts-list">
 
+            <article class="post-preview">
+    <a href="https://www.codelooper.com/post/2023-12-12-chatgpt-python-code-for-distribution-fitting/">
+        <h2 class="post-title">ChatGPT python code for distribution fitting</h2>
+
+
+
+    </a>
+
+    <p class="post-meta">
+        <span class="post-meta">
+
+
+  <i class="fas fa-calendar"></i>&nbsp;Posted on December 12, 2023
+
+
+    &nbsp;|&nbsp;<i class="fas fa-clock"></i>&nbsp;2&nbsp;minutes
+
+
+    &nbsp;|&nbsp;<i class="fas fa-book"></i>&nbsp;234&nbsp;words
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+      &nbsp;|&nbsp;<i class="fas fa-user"></i>&nbsp;Package Build
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+</span>
+
+
+    </p>
+    <div class="post-entry">
+
+        import numpy as np import scipy.stats as stats import matplotlib.pyplot as plt from scipy.stats import norm, expon, lognorm, weibull_min, gamma, beta, gumbel_r # Your data data = np.array([0.1026, 0.284, 0.0259, 0.0364, 0.0401, 0.0297, 0.1035, 0.0533, 0.0742, 0.0246, 0.089, 0.1215, 0.1729, 0.0212, 0.0273, 0.065, 0.0853, 0.0399, 0.1926, 0.1238, 0.4324, 0.1324, 0.0486, 0.0411, 0.0414, 0.0329, 0.02, 0.0749, 0.0688, 0.1432, 0.1769, 0.0415, 0.0939, 0.2315, 0.4017, 0.0751, 0.0669, 0.242, 0.1931, 0.0839, 0.0601, 0.0794, 0.
+        <a href="https://www.codelooper.com/post/2023-12-12-chatgpt-python-code-for-distribution-fitting/" class="post-read-more">[Read More]</a>
+
+    </div>
+
+
+    <div class="blog-tags">
+
+        <a href="https://www.codelooper.com/tags/data-analytics/">Data Analytics</a>&nbsp;
+
+        <a href="https://www.codelooper.com/tags/python/">Python</a>&nbsp;
+
+    </div>
+
+
+</article>
+
+            <article class="post-preview">
+    <a href="https://www.codelooper.com/post/copula-example/">
+        <h2 class="post-title">Copula Example</h2>
+
+
+
+    </a>
+
+    <p class="post-meta">
+        <span class="post-meta">
+
+
+  <i class="fas fa-calendar"></i>&nbsp;Posted on December 8, 2023
+
+
+    &nbsp;|&nbsp;<i class="fas fa-clock"></i>&nbsp;4&nbsp;minutes
+
+
+    &nbsp;|&nbsp;<i class="fas fa-book"></i>&nbsp;654&nbsp;words
+
+
+
+      &nbsp;|&nbsp;<i class="fas fa-user"></i>&nbsp;Package Build
+
+
+
+</span>
+
+
+    </p>
+    <div class="post-entry">
+
+        Trying an introduction to copulas exercise, from (R-excerises), using the dataset (https://www.kaggle.com/datasets/gtouzin/samplestocksreturn)
+Exercise 1 We’ll start by fitting the margin. First, do a histogram of both Apple and Microsoft returns to see the shape of both distributions.
+returns &lt;- read.csv(&quot;returns_00_17.csv&quot;) hist(returns$Apple) hist(returns$Microsoft) Exercise 2 Both distributions seems symmetric and have a domain which contain positive and negative values. Knowing those facts, use the fitdist() function to see how the normal, logistic and Cauchy distribution fit the Apple returns dataset.
+        <a href="https://www.codelooper.com/post/copula-example/" class="post-read-more">[Read More]</a>
+
+    </div>
+
+
+    <div class="blog-tags">
+
+        <a href="https://www.codelooper.com/tags/math/">math</a>&nbsp;
+
+        <a href="https://www.codelooper.com/tags/data-analytics/">Data Analytics</a>&nbsp;
+
+        <a href="https://www.codelooper.com/tags/r/">R</a>&nbsp;
+
+    </div>
+
+
+</article>
+
             <article class="post-preview">
     <a href="https://www.codelooper.com/post/random-number-generator-20231108/">
         <h2 class="post-title">Random Number Generator</h2>

diff --git a/categories/data-analytics/index.xml b/categories/data-analytics/index.xml
@@ -5,7 +5,27 @@
     <link>https://www.codelooper.com/categories/data-analytics/</link>
     <description>Recent content in Data Analytics on Teddy&#39;s online desktop</description>
     <generator>Hugo -- gohugo.io</generator>
-    <lastBuildDate>Wed, 08 Nov 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://www.codelooper.com/categories/data-analytics/index.xml" rel="self" type="application/rss+xml" />
+    <lastBuildDate>Tue, 12 Dec 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://www.codelooper.com/categories/data-analytics/index.xml" rel="self" type="application/rss+xml" />
+    <item>
+      <title>ChatGPT python code for distribution fitting</title>
+      <link>https://www.codelooper.com/post/2023-12-12-chatgpt-python-code-for-distribution-fitting/</link>
+      <pubDate>Tue, 12 Dec 2023 00:00:00 +0000</pubDate>
+
+      <guid>https://www.codelooper.com/post/2023-12-12-chatgpt-python-code-for-distribution-fitting/</guid>
+      <description>import numpy as np import scipy.stats as stats import matplotlib.pyplot as plt from scipy.stats import norm, expon, lognorm, weibull_min, gamma, beta, gumbel_r # Your data data = np.array([0.1026, 0.284, 0.0259, 0.0364, 0.0401, 0.0297, 0.1035, 0.0533, 0.0742, 0.0246, 0.089, 0.1215, 0.1729, 0.0212, 0.0273, 0.065, 0.0853, 0.0399, 0.1926, 0.1238, 0.4324, 0.1324, 0.0486, 0.0411, 0.0414, 0.0329, 0.02, 0.0749, 0.0688, 0.1432, 0.1769, 0.0415, 0.0939, 0.2315, 0.4017, 0.0751, 0.0669, 0.242, 0.1931, 0.0839, 0.0601, 0.0794, 0.</description>
+    </item>
+
+    <item>
+      <title>Copula Example</title>
+      <link>https://www.codelooper.com/post/copula-example/</link>
+      <pubDate>Fri, 08 Dec 2023 00:00:00 +0000</pubDate>
+
+      <guid>https://www.codelooper.com/post/copula-example/</guid>
+      <description>Trying an introduction to copulas exercise, from (R-excerises), using the dataset (https://www.kaggle.com/datasets/gtouzin/samplestocksreturn)
+Exercise 1 We’ll start by fitting the margin. First, do a histogram of both Apple and Microsoft returns to see the shape of both distributions.
+returns &amp;lt;- read.csv(&amp;quot;returns_00_17.csv&amp;quot;) hist(returns$Apple) hist(returns$Microsoft) Exercise 2 Both distributions seems symmetric and have a domain which contain positive and negative values. Knowing those facts, use the fitdist() function to see how the normal, logistic and Cauchy distribution fit the Apple returns dataset.</description>
+    </item>
+
     <item>
       <title>Random Number Generator</title>
       <link>https://www.codelooper.com/post/random-number-generator-20231108/</link>

diff --git a/categories/index.html b/categories/index.html
@@ -239,7 +239,7 @@ <h1>Categories</h1>
                 <div class="panel-heading" id="headerprogramming">
                   <h4 class="panel-title">
                       programming
-                    <span class="badge">10</span>
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@@ -249,6 +249,8 @@ <h4 class="panel-title">
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                 <div class="list-group">
 
+                    <a href="https://www.codelooper.com/post/2023-12-12-chatgpt-python-code-for-distribution-fitting/" class="list-group-item">ChatGPT python code for distribution fitting</a>
+
                     <a href="https://www.codelooper.com/post/2022-08-06-coursera-guided-project-predicting-diabetes/" class="list-group-item">Coursera Guided Project - Predicting Diabetes</a>
 
                     <a href="https://www.codelooper.com/post/2022-08-04-completed--deeplearning-tensorflow-for-ai-course/" class="list-group-item">Completed the Deeplearning.ai Tensorflow for AI course</a>
@@ -279,7 +281,7 @@ <h4 class="panel-title">
                 <div class="panel-heading" id="headerdata-analytics">
                   <h4 class="panel-title">
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-                    <span class="badge">3</span>
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@@ -289,6 +291,10 @@ <h4 class="panel-title">
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+                    <a href="https://www.codelooper.com/post/2023-12-12-chatgpt-python-code-for-distribution-fitting/" class="list-group-item">ChatGPT python code for distribution fitting</a>
+
+                    <a href="https://www.codelooper.com/post/copula-example/" class="list-group-item">Copula Example</a>
+
                     <a href="https://www.codelooper.com/post/random-number-generator-20231108/" class="list-group-item">Random Number Generator</a>
 
                     <a href="https://www.codelooper.com/post/2022-09-19-duck-db-pandas-profiling/" class="list-group-item">Duck DB and Pandas Profiling</a>
@@ -350,6 +356,30 @@ <h4 class="panel-title">
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+          <div class="panel panel-default">
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+                    <a href="https://www.codelooper.com/post/2023-12-12-chatgpt-python-code-for-distribution-fitting/" class="list-group-item">ChatGPT python code for distribution fitting</a>
+
+                    <a href="https://www.codelooper.com/post/2022-08-03-coursera-introduction-to-tensorflow/" class="list-group-item">Coursera - introduction to tensorflow</a>
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diff --git a/categories/index.xml b/categories/index.xml
@@ -5,49 +5,49 @@
     <link>https://www.codelooper.com/categories/</link>
     <description>Recent content in Categories on Teddy&#39;s online desktop</description>
     <generator>Hugo -- gohugo.io</generator>
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+    <lastBuildDate>Tue, 12 Dec 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://www.codelooper.com/categories/index.xml" rel="self" type="application/rss+xml" />
     <item>
       <title>Data Analytics</title>
       <link>https://www.codelooper.com/categories/data-analytics/</link>
-      <pubDate>Wed, 08 Nov 2023 00:00:00 +0000</pubDate>
+      <pubDate>Tue, 12 Dec 2023 00:00:00 +0000</pubDate>
 
       <guid>https://www.codelooper.com/categories/data-analytics/</guid>
       <description></description>
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-      <link>https://www.codelooper.com/categories/economics/</link>
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+      <title>ML</title>
+      <link>https://www.codelooper.com/categories/ml/</link>
+      <pubDate>Tue, 12 Dec 2023 00:00:00 +0000</pubDate>
 
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-      <link>https://www.codelooper.com/categories/programming/</link>
-      <pubDate>Sat, 06 Aug 2022 00:00:00 +0000</pubDate>
+      <title>Economics</title>
+      <link>https://www.codelooper.com/categories/economics/</link>
+      <pubDate>Wed, 19 Jul 2023 00:00:00 +0000</pubDate>
 
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+      <guid>https://www.codelooper.com/categories/economics/</guid>
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-      <title>ML</title>
-      <link>https://www.codelooper.com/categories/ml/</link>
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+      <title>AI</title>
+      <link>https://www.codelooper.com/categories/ai/</link>
+      <pubDate>Thu, 06 Jul 2023 00:00:00 +0000</pubDate>
 
-      <guid>https://www.codelooper.com/categories/ml/</guid>
+      <guid>https://www.codelooper.com/categories/ai/</guid>
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diff --git a/categories/ml/index.html b/categories/ml/index.html
@@ -231,6 +231,55 @@ <h1>ML</h1>
 
         <div class="posts-list">
 
+            <article class="post-preview">
+    <a href="https://www.codelooper.com/post/2023-12-12-chatgpt-python-code-for-distribution-fitting/">
+        <h2 class="post-title">ChatGPT python code for distribution fitting</h2>
+
+
+
+    </a>
+
+    <p class="post-meta">
+        <span class="post-meta">
+
+
+  <i class="fas fa-calendar"></i>&nbsp;Posted on December 12, 2023
+
+
+    &nbsp;|&nbsp;<i class="fas fa-clock"></i>&nbsp;2&nbsp;minutes
+
+
+    &nbsp;|&nbsp;<i class="fas fa-book"></i>&nbsp;234&nbsp;words
+
+
+
+      &nbsp;|&nbsp;<i class="fas fa-user"></i>&nbsp;Package Build
+
+
+
+</span>
+
+
+    </p>
+    <div class="post-entry">
+
+        import numpy as np import scipy.stats as stats import matplotlib.pyplot as plt from scipy.stats import norm, expon, lognorm, weibull_min, gamma, beta, gumbel_r # Your data data = np.array([0.1026, 0.284, 0.0259, 0.0364, 0.0401, 0.0297, 0.1035, 0.0533, 0.0742, 0.0246, 0.089, 0.1215, 0.1729, 0.0212, 0.0273, 0.065, 0.0853, 0.0399, 0.1926, 0.1238, 0.4324, 0.1324, 0.0486, 0.0411, 0.0414, 0.0329, 0.02, 0.0749, 0.0688, 0.1432, 0.1769, 0.0415, 0.0939, 0.2315, 0.4017, 0.0751, 0.0669, 0.242, 0.1931, 0.0839, 0.0601, 0.0794, 0.
+        <a href="https://www.codelooper.com/post/2023-12-12-chatgpt-python-code-for-distribution-fitting/" class="post-read-more">[Read More]</a>
+
+    </div>
+
+
+    <div class="blog-tags">
+
+        <a href="https://www.codelooper.com/tags/data-analytics/">Data Analytics</a>&nbsp;
+
+        <a href="https://www.codelooper.com/tags/python/">Python</a>&nbsp;
+
+    </div>
+
+
+</article>
+
             <article class="post-preview">
     <a href="https://www.codelooper.com/post/2022-08-03-coursera-introduction-to-tensorflow/">
         <h2 class="post-title">Coursera - introduction to tensorflow</h2>

diff --git a/categories/ml/index.xml b/categories/ml/index.xml
@@ -5,7 +5,16 @@
     <link>https://www.codelooper.com/categories/ml/</link>
     <description>Recent content in ML on Teddy&#39;s online desktop</description>
     <generator>Hugo -- gohugo.io</generator>
-    <lastBuildDate>Wed, 03 Aug 2022 00:00:00 +0000</lastBuildDate><atom:link href="https://www.codelooper.com/categories/ml/index.xml" rel="self" type="application/rss+xml" />
+    <lastBuildDate>Tue, 12 Dec 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://www.codelooper.com/categories/ml/index.xml" rel="self" type="application/rss+xml" />
+    <item>
+      <title>ChatGPT python code for distribution fitting</title>
+      <link>https://www.codelooper.com/post/2023-12-12-chatgpt-python-code-for-distribution-fitting/</link>
+      <pubDate>Tue, 12 Dec 2023 00:00:00 +0000</pubDate>
+
+      <guid>https://www.codelooper.com/post/2023-12-12-chatgpt-python-code-for-distribution-fitting/</guid>
+      <description>import numpy as np import scipy.stats as stats import matplotlib.pyplot as plt from scipy.stats import norm, expon, lognorm, weibull_min, gamma, beta, gumbel_r # Your data data = np.array([0.1026, 0.284, 0.0259, 0.0364, 0.0401, 0.0297, 0.1035, 0.0533, 0.0742, 0.0246, 0.089, 0.1215, 0.1729, 0.0212, 0.0273, 0.065, 0.0853, 0.0399, 0.1926, 0.1238, 0.4324, 0.1324, 0.0486, 0.0411, 0.0414, 0.0329, 0.02, 0.0749, 0.0688, 0.1432, 0.1769, 0.0415, 0.0939, 0.2315, 0.4017, 0.0751, 0.0669, 0.242, 0.1931, 0.0839, 0.0601, 0.0794, 0.</description>
+    </item>
+
     <item>
       <title>Coursera - introduction to tensorflow</title>
       <link>https://www.codelooper.com/post/2022-08-03-coursera-introduction-to-tensorflow/</link>