From ddebcc5b0247a430f8893ef400f0977cb56f6fe9 Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Thu, 22 Feb 2024 08:03:24 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll                       |  2 +-
 Introduction_to_python_2.html   | 12 ++++++------
 data-science-with-pandas-2.html |  2 +-
 index.html                      |  4 ++--
 search.json                     |  4 ++--
 5 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/.nojekyll b/.nojekyll
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+++ b/.nojekyll
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-b4a5ca19
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+dd038e35
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diff --git a/Introduction_to_python_2.html b/Introduction_to_python_2.html
index 9d0556a..3641a2c 100644
--- a/Introduction_to_python_2.html
+++ b/Introduction_to_python_2.html
@@ -446,12 +446,12 @@ <h2 data-number="3.3" class="anchored" data-anchor-id="if-statements-and-compari
 <p>If statements often use comparisons to check if a certain condition is met. Comparisons are done with comparison operators such as <code>&gt;</code>, <code>==</code>.</p>
 <p>Along with the <code>&gt;</code> and <code>==</code> comparison operators that we have already used for comparing values above, there are a few more options to know about:</p>
 <ul>
-<li>&gt;: greater than</li>
-<li>&lt;: less than</li>
-<li>==: equal to</li>
-<li>!=: does not equal</li>
-<li>&gt;=: greater than or equal to</li>
-<li>&lt;=: less than or equal to</li>
+<li><code>&gt;</code> greater than</li>
+<li><code>&lt;</code> less than</li>
+<li><code>==</code> equal to</li>
+<li><code>!=</code> does not equal</li>
+<li><code>&gt;=</code> greater than or equal to</li>
+<li><code>&lt;=</code> less than or equal to</li>
 </ul>
 <p>Let’s now play around with comparisons to see how they work in more detail.</p>
 <div id="57accc50-7e82-4541-ae47-02a198fa0606" class="cell" data-execution_count="9">
diff --git a/data-science-with-pandas-2.html b/data-science-with-pandas-2.html
index d48ceb2..27dc7e1 100644
--- a/data-science-with-pandas-2.html
+++ b/data-science-with-pandas-2.html
@@ -1948,7 +1948,7 @@ <h2 data-number="7.5" class="anchored" data-anchor-id="optional-grouping"><span
 <div id="b2f88c4f" class="cell" data-execution_count="27">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>grouped_data.mean()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
-<pre><code>/tmp/ipykernel_2415/1133710423.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
+<pre><code>/tmp/ipykernel_2439/1133710423.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
   grouped_data.mean()</code></pre>
 </div>
 <div class="cell-output cell-output-display" data-execution_count="27">
diff --git a/index.html b/index.html
index 49a7194..97fb036 100644
--- a/index.html
+++ b/index.html
@@ -7,7 +7,7 @@
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 <meta name="author" content="Research Data Management Support Utrecht University">
-<meta name="dcterms.date" content="2024-02-21">
+<meta name="dcterms.date" content="2024-02-22">
 
 <title>Introduction to Python &amp; Data</title>
 <style>
@@ -301,7 +301,7 @@ <h1 class="title">Introduction to Python &amp; Data</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">February 21, 2024</p>
+      <p class="date">February 22, 2024</p>
     </div>
   </div>
   
diff --git a/search.json b/search.json
index 3a3050e..9f1c6a7 100644
--- a/search.json
+++ b/search.json
@@ -264,7 +264,7 @@
     "href": "Introduction_to_python_2.html#if-statements-and-comparisons",
     "title": "3  Operators, built-in functions, if-statements",
     "section": "3.3 If-statements and comparisons",
-    "text": "3.3 If-statements and comparisons\nIn programming it is possible to perform certain tasks only when a certain condition is met: Assume we have a table where each row is a person and the table contains a column age. We want to create a new variable age_group which can have the values child and adult based on the value in the column age. We can use an if-statement to decide whether a person is younger than 18 and asign the right value to age_group.\n\nage = 17\n\nif age &lt; 18:\n    print('this person is a child')\n\nthis person is a child\n\n\nAs you can see, the line print(...) starts with 4 spaces indentation. In Python indentation is very important. Python uses indentation to determine which lines of code belong together. Lines with the same identation are called a code block. We use code blocks to define what should be done in an if-statement, for-loop or in a functions. E.g. after the if condition, all lines with indentation are only performed when the if-condition is met.\n\nnum = 99\nif num &gt; 100:\n    print('This line is only executed when num &gt; 100')\n    print('This line is only executed when num &gt; 100')\n    \n    print('This line is only executed when num &gt; 100')\n    \nprint('This line is always executed')\n\nThis line is always executed\n\n\nIt is also possible to specify a task that is performed when the condition is not met using else (note the use of indentation):\n\nage = 17\n\nif age &lt; 18:\n    print('this person is a child')\nelse:\n    print('this person is an adult')\n\nprint('done')\n\nthis person is a child\ndone\n\n\nWith an if .. else statement we can define one condition. If we need to check more conditions the if ... else statement can be extended with (one or more) elif to specify more tasks that need to be performed on other conditions. These extended if ... else statements always start with if followed by (one or more) elif. When an else statement is included it is always the last statement, it is the default which will be eceuted when all previous comparisons failed.\nOrder matters: The statements (or conditions) are checked in order from top to bottom and only the task belonging to the first condition that is met is being performed.\n\nnum = -3\n\nif num &gt; 0:\n    print(num, 'is positive')\nelif num == 0:\n    print(num, 'is zero')\nelse:\n    print(num, 'is negative')\n\n-3 is negative\n\n\nIf statements often use comparisons to check if a certain condition is met. Comparisons are done with comparison operators such as &gt;, ==.\nAlong with the &gt; and == comparison operators that we have already used for comparing values above, there are a few more options to know about:\n\n&gt;: greater than\n&lt;: less than\n==: equal to\n!=: does not equal\n&gt;=: greater than or equal to\n&lt;=: less than or equal to\n\nLet’s now play around with comparisons to see how they work in more detail.\n\n4 &gt; 3\n\nTrue\n\n\n\n4 &lt; 3\n\nFalse\n\n\n\na = 4 &gt; 3\ntype(a)\n\nbool\n\n\nAs you can see, comparisons return True or False. The data type of these values are bool which is short for boolean values.\nIf-statements work with boolean values. If the value is True, the task is performed, if the value is False, the task is not performed.\nBoolean values can also be assigned to variables:\n\na = True\nb = True\nc = False\ntype(a)\n\nbool\n\n\nand, or and not are ‘logical operators’, and are used to join two comparisons (or revert a logical expression in the case of not) to create more complex conditions.\nand will return True if both expression on either side are True.\n\na and b\n\nTrue\n\n\n\na and c\n\nFalse\n\n\n\n4 &gt; 3 and 9 &gt; 3\n\nTrue\n\n\nor is used to check if at least one of two logical expressions are True. If this is the case it will return True.\n\n3 &gt; 4 or 9 &gt; 3\n\nTrue\n\n\n\n4 &gt; 3 or 9 &gt; 3\n\nTrue\n\n\nIn the last three examples you can see that multiple expressions can be combined in a single line of Python code. Python evaluates the expressions one by one. 4 &gt; 3 would return True, 9 &gt; 3 would return True, so 4 &gt; 3 or 9 &gt; 3 would translate to True or True. Because at least one of the expressions on either side of the or operator is True, the whole expression results to True.\nThe not operator can be used to reverse the Boolean value. If you apply not to an expression that evaluates to True, then you get False as a result. If you apply not to an expression that evaluates to False, then you get True as a result:\n\nnot 4 &gt; 3\n\nFalse\n\n\nLogical operators bind variables with different strengths. The and is stronger than the or and gets evaluated first in a boolean expression. So a or b and c will be evaluated like a or (b and c), while in (a or b) and c first the value of the or is evaluated and then combined with and c. This leads to a different result.\n\na = True\nb = True\nc = False\nprint(\"This expression 'a or b and c' evalutes to \", a or b and c)\nprint(\"And this is the same as 'a or (b and c)')\", a or (b and c))\nprint(\"But this expression evaluates '(a or b) and c' first the 'or' and generates:\", (a or b) and c)\n\nThis expression 'a or b and c' evalutes to  True\nAnd this is the same as 'a or (b and c)') True\nBut this expression evaluates '(a or b) and c' first the 'or' and generates: False\n\n\nNow we know how to use comparisons and logical operators to create complex conditions. These complex conditions can be used in if-statements to perform tasks based on these conditions.\n\nif (1 &lt; 0) or (1 &gt;= 0):\n    print('at least one the above logical statements is true')\n\nat least one the above logical statements is true\n\n\nWhile and is only true if both parts are true\n\nif (1 &lt; 0) and (1 &gt;= 0):\n    print('both tests are true')\nelse:\n    print('at least one of the tests is not true')\n\nat least one of the tests is not true\n\n\n\nExercises\nNow open the morning_exercises.ipynb notebook from the place where you have stored it (see Installation and setup instruction) and do exercises 0-3.\nWhen you finished the exercises, continue to chapter Data types, if-statements and for loops",
+    "text": "3.3 If-statements and comparisons\nIn programming it is possible to perform certain tasks only when a certain condition is met: Assume we have a table where each row is a person and the table contains a column age. We want to create a new variable age_group which can have the values child and adult based on the value in the column age. We can use an if-statement to decide whether a person is younger than 18 and asign the right value to age_group.\n\nage = 17\n\nif age &lt; 18:\n    print('this person is a child')\n\nthis person is a child\n\n\nAs you can see, the line print(...) starts with 4 spaces indentation. In Python indentation is very important. Python uses indentation to determine which lines of code belong together. Lines with the same identation are called a code block. We use code blocks to define what should be done in an if-statement, for-loop or in a functions. E.g. after the if condition, all lines with indentation are only performed when the if-condition is met.\n\nnum = 99\nif num &gt; 100:\n    print('This line is only executed when num &gt; 100')\n    print('This line is only executed when num &gt; 100')\n    \n    print('This line is only executed when num &gt; 100')\n    \nprint('This line is always executed')\n\nThis line is always executed\n\n\nIt is also possible to specify a task that is performed when the condition is not met using else (note the use of indentation):\n\nage = 17\n\nif age &lt; 18:\n    print('this person is a child')\nelse:\n    print('this person is an adult')\n\nprint('done')\n\nthis person is a child\ndone\n\n\nWith an if .. else statement we can define one condition. If we need to check more conditions the if ... else statement can be extended with (one or more) elif to specify more tasks that need to be performed on other conditions. These extended if ... else statements always start with if followed by (one or more) elif. When an else statement is included it is always the last statement, it is the default which will be eceuted when all previous comparisons failed.\nOrder matters: The statements (or conditions) are checked in order from top to bottom and only the task belonging to the first condition that is met is being performed.\n\nnum = -3\n\nif num &gt; 0:\n    print(num, 'is positive')\nelif num == 0:\n    print(num, 'is zero')\nelse:\n    print(num, 'is negative')\n\n-3 is negative\n\n\nIf statements often use comparisons to check if a certain condition is met. Comparisons are done with comparison operators such as &gt;, ==.\nAlong with the &gt; and == comparison operators that we have already used for comparing values above, there are a few more options to know about:\n\n&gt; greater than\n&lt; less than\n== equal to\n!= does not equal\n&gt;= greater than or equal to\n&lt;= less than or equal to\n\nLet’s now play around with comparisons to see how they work in more detail.\n\n4 &gt; 3\n\nTrue\n\n\n\n4 &lt; 3\n\nFalse\n\n\n\na = 4 &gt; 3\ntype(a)\n\nbool\n\n\nAs you can see, comparisons return True or False. The data type of these values are bool which is short for boolean values.\nIf-statements work with boolean values. If the value is True, the task is performed, if the value is False, the task is not performed.\nBoolean values can also be assigned to variables:\n\na = True\nb = True\nc = False\ntype(a)\n\nbool\n\n\nand, or and not are ‘logical operators’, and are used to join two comparisons (or revert a logical expression in the case of not) to create more complex conditions.\nand will return True if both expression on either side are True.\n\na and b\n\nTrue\n\n\n\na and c\n\nFalse\n\n\n\n4 &gt; 3 and 9 &gt; 3\n\nTrue\n\n\nor is used to check if at least one of two logical expressions are True. If this is the case it will return True.\n\n3 &gt; 4 or 9 &gt; 3\n\nTrue\n\n\n\n4 &gt; 3 or 9 &gt; 3\n\nTrue\n\n\nIn the last three examples you can see that multiple expressions can be combined in a single line of Python code. Python evaluates the expressions one by one. 4 &gt; 3 would return True, 9 &gt; 3 would return True, so 4 &gt; 3 or 9 &gt; 3 would translate to True or True. Because at least one of the expressions on either side of the or operator is True, the whole expression results to True.\nThe not operator can be used to reverse the Boolean value. If you apply not to an expression that evaluates to True, then you get False as a result. If you apply not to an expression that evaluates to False, then you get True as a result:\n\nnot 4 &gt; 3\n\nFalse\n\n\nLogical operators bind variables with different strengths. The and is stronger than the or and gets evaluated first in a boolean expression. So a or b and c will be evaluated like a or (b and c), while in (a or b) and c first the value of the or is evaluated and then combined with and c. This leads to a different result.\n\na = True\nb = True\nc = False\nprint(\"This expression 'a or b and c' evalutes to \", a or b and c)\nprint(\"And this is the same as 'a or (b and c)')\", a or (b and c))\nprint(\"But this expression evaluates '(a or b) and c' first the 'or' and generates:\", (a or b) and c)\n\nThis expression 'a or b and c' evalutes to  True\nAnd this is the same as 'a or (b and c)') True\nBut this expression evaluates '(a or b) and c' first the 'or' and generates: False\n\n\nNow we know how to use comparisons and logical operators to create complex conditions. These complex conditions can be used in if-statements to perform tasks based on these conditions.\n\nif (1 &lt; 0) or (1 &gt;= 0):\n    print('at least one the above logical statements is true')\n\nat least one the above logical statements is true\n\n\nWhile and is only true if both parts are true\n\nif (1 &lt; 0) and (1 &gt;= 0):\n    print('both tests are true')\nelse:\n    print('at least one of the tests is not true')\n\nat least one of the tests is not true\n\n\n\nExercises\nNow open the morning_exercises.ipynb notebook from the place where you have stored it (see Installation and setup instruction) and do exercises 0-3.\nWhen you finished the exercises, continue to chapter Data types, if-statements and for loops",
     "crumbs": [
       "Python Fundamentals",
       "<span class='chapter-number'>3</span>  <span class='chapter-title'>Operators, built-in functions, if-statements</span>"
@@ -440,7 +440,7 @@
     "href": "data-science-with-pandas-2.html#optional-grouping",
     "title": "7  Grouping, Indexing, Slicing, and Subsetting DataFrames",
     "section": "7.5 Optional: Grouping",
-    "text": "7.5 Optional: Grouping\nWe often want to calculate summary statistics grouped by subsets or attributes within fields of our data. For example, we might want to calculate the average weight of all individuals per site.\nAs we have seen above we can calculate basic statistics for all records in a single column using the syntax below:\n\nsurveys_df['weight'].describe()\n\ncount    32283.000000\nmean        42.672428\nstd         36.631259\nmin          4.000000\n25%         20.000000\n50%         37.000000\n75%         48.000000\nmax        280.000000\nName: weight, dtype: float64\n\n\nIf we want to summarize by one or more variables, for example sex, we can use Pandas’ .groupby() method. Once we’ve created a groupby DataFrame, we can quickly calculate summary statistics by a group of our choice.\n\ngrouped_data = surveys_df.groupby('sex')\ngrouped_data.describe()\n\n\n\n\n\n\n\n\n\nrecord_id\nmonth\n...\nhindfoot_length\nweight\n\n\n\ncount\nmean\nstd\nmin\n25%\n50%\n75%\nmax\ncount\nmean\n...\n75%\nmax\ncount\nmean\nstd\nmin\n25%\n50%\n75%\nmax\n\n\nsex\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nF\n15690.0\n18036.412046\n10423.089000\n3.0\n8917.50\n18075.5\n27250.00\n35547.0\n15690.0\n6.587253\n...\n36.0\n64.0\n15303.0\n42.170555\n36.847958\n4.0\n20.0\n34.0\n46.0\n274.0\n\n\nM\n17348.0\n17754.835601\n10132.203323\n1.0\n8969.75\n17727.5\n26454.25\n35548.0\n17348.0\n6.396184\n...\n36.0\n58.0\n16879.0\n42.995379\n36.184981\n4.0\n20.0\n39.0\n49.0\n280.0\n\n\n\n\n2 rows × 56 columns\n\n\n\n\nThe output is a bit overwhelming. Let’s just have a look at one statistical value, the mean, to understand what is happening here:\n\ngrouped_data.mean()\n\n/tmp/ipykernel_2415/1133710423.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n  grouped_data.mean()\n\n\n\n\n\n\n\n\n\n\nrecord_id\nmonth\nday\nyear\nplot_id\nhindfoot_length\nweight\n\n\nsex\n\n\n\n\n\n\n\n\n\n\n\nF\n18036.412046\n6.587253\n15.880943\n1990.644997\n11.440854\n28.836780\n42.170555\n\n\nM\n17754.835601\n6.396184\n16.078799\n1990.480401\n11.098282\n29.709578\n42.995379\n\n\n\n\n\n\n\n\nWe see that the data is divided into two groups, one group where the value in the column sex equals “F” and another group where the value in the column sex equals “M”. The statistics is then calculated for all samples in that specific group for each of the columns in the dataframe. Note that samples annotated with sex equals NaN and column values with NaN are left out.",
+    "text": "7.5 Optional: Grouping\nWe often want to calculate summary statistics grouped by subsets or attributes within fields of our data. For example, we might want to calculate the average weight of all individuals per site.\nAs we have seen above we can calculate basic statistics for all records in a single column using the syntax below:\n\nsurveys_df['weight'].describe()\n\ncount    32283.000000\nmean        42.672428\nstd         36.631259\nmin          4.000000\n25%         20.000000\n50%         37.000000\n75%         48.000000\nmax        280.000000\nName: weight, dtype: float64\n\n\nIf we want to summarize by one or more variables, for example sex, we can use Pandas’ .groupby() method. Once we’ve created a groupby DataFrame, we can quickly calculate summary statistics by a group of our choice.\n\ngrouped_data = surveys_df.groupby('sex')\ngrouped_data.describe()\n\n\n\n\n\n\n\n\n\nrecord_id\nmonth\n...\nhindfoot_length\nweight\n\n\n\ncount\nmean\nstd\nmin\n25%\n50%\n75%\nmax\ncount\nmean\n...\n75%\nmax\ncount\nmean\nstd\nmin\n25%\n50%\n75%\nmax\n\n\nsex\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nF\n15690.0\n18036.412046\n10423.089000\n3.0\n8917.50\n18075.5\n27250.00\n35547.0\n15690.0\n6.587253\n...\n36.0\n64.0\n15303.0\n42.170555\n36.847958\n4.0\n20.0\n34.0\n46.0\n274.0\n\n\nM\n17348.0\n17754.835601\n10132.203323\n1.0\n8969.75\n17727.5\n26454.25\n35548.0\n17348.0\n6.396184\n...\n36.0\n58.0\n16879.0\n42.995379\n36.184981\n4.0\n20.0\n39.0\n49.0\n280.0\n\n\n\n\n2 rows × 56 columns\n\n\n\n\nThe output is a bit overwhelming. Let’s just have a look at one statistical value, the mean, to understand what is happening here:\n\ngrouped_data.mean()\n\n/tmp/ipykernel_2439/1133710423.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n  grouped_data.mean()\n\n\n\n\n\n\n\n\n\n\nrecord_id\nmonth\nday\nyear\nplot_id\nhindfoot_length\nweight\n\n\nsex\n\n\n\n\n\n\n\n\n\n\n\nF\n18036.412046\n6.587253\n15.880943\n1990.644997\n11.440854\n28.836780\n42.170555\n\n\nM\n17754.835601\n6.396184\n16.078799\n1990.480401\n11.098282\n29.709578\n42.995379\n\n\n\n\n\n\n\n\nWe see that the data is divided into two groups, one group where the value in the column sex equals “F” and another group where the value in the column sex equals “M”. The statistics is then calculated for all samples in that specific group for each of the columns in the dataframe. Note that samples annotated with sex equals NaN and column values with NaN are left out.",
     "crumbs": [
       "Working With Data",
       "<span class='chapter-number'>7</span>  <span class='chapter-title'>Grouping, Indexing, Slicing, and Subsetting DataFrames</span>"