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@@ -212,7 +212,7 @@ <h3 class="no-anchor listing-title">
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-<div class="quarto-post image-right" data-index="1" data-categories="cli,code,geojson,json,spatial data" data-listing-date-sort="1710046800000" data-listing-file-modified-sort="1717686051257" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="5" data-listing-word-count-sort="999">
+<div class="quarto-post image-right" data-index="1" data-categories="cli,code,geojson,json,spatial data" data-listing-date-sort="1710046800000" data-listing-file-modified-sort="1718022757062" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="5" data-listing-word-count-sort="990">
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 <p><a href="./posts/awesomejq/index.html" class="no-external"></a></p><a href="./posts/awesomejq/index.html" class="no-external">
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posts/awesomejq/index.html

@@ -305,12 +305,12 @@ <h4 class="anchored" data-anchor-id="second-make-them-into-a-csv">Second make th
 </div></div><ul>
 <li><code>@csv</code> convert to a csv and we redirect the output later in <code>data/sample.csv</code></li>
 </ul>
-<p>We have just explored the surface! <code>jq</code> can help to filter some specific features: (</p>
-<pre><code>- every geometries "served" in our file?
-
-- the first node in every geometries)?
-
-- etc!</code></pre>
+<p>We have just explored the surface! <code>jq</code> can help to filter some specific features:</p>
+<ul>
+<li><p>every geometries “served” in our file?</p></li>
+<li><p>the first node in every geometries)?</p></li>
+<li><p>etc!</p></li>
+</ul>
 <p><code>jq</code> is a generic tool for filtering json and lot of people are using by following the JSON spec in geojson we can build on top of all their monumental work!</p>
 
 

search.json

@@ -46,7 +46,7 @@
     "href": "posts/awesomejq/index.html#jq-please-give-me-a-data-frame",
     "title": "Awesome jq and GeoJSON",
     "section": "jq , please give me a data frame:",
-    "text": "jq , please give me a data frame:\nBut wait what if we just want the properties?\n\nFirst let’s get their keys:\nAt the top level if we do ..\njq `keys` data/better_sample.geojson\n#[\n#  \"crs\",\n#  \"features\",\n#  \"type\"\n]\n.. we get the keys for the first array. We need to go in the features object to get properties and pass it to the keys function. Here we went a bit lazy just asking for the first feature.\njq '.features[0].properties | keys' data/better_sample.geojson\n\n\nSecond make them into a csv\nHere we will need to buckle up a bit as it would become quite a big line:\njq -r '(.features[0].properties | keys_unsorted), (.features[].properties | to_entries | map(.value))| @csv' data/better_sample.geojson > data/sample.csv\n\n(.features[0].properties | keys_unsorted) here nothing new we added parentheses to enforce precedence. We are getting the header of our csv\n(.features[].properties | to_entries | map(.value)) :\n\nwe are starting from all our properties (not the first one)\npassing it to to_entries convert our object to multiple objects with “key” / “value” (see margin)\nfinaly, map(.value) gets all “value” for every selected features\n\n\n\n\n{\n\"key\": \"state_abbr\",\n\"value\": \"VT\"\n},\n{\n\"key\": \"geoid_st\",\n\"value\": \"50\"\n},\n{\n\"key\": \"geoid_co\",\n\"value\": \"50005\"\n}\n\n@csv convert to a csv and we redirect the output later in data/sample.csv\n\nWe have just explored the surface! jq can help to filter some specific features: (\n- every geometries \"served\" in our file?\n\n- the first node in every geometries)?\n\n- etc!\njq is a generic tool for filtering json and lot of people are using by following the JSON spec in geojson we can build on top of all their monumental work!"
+    "text": "jq , please give me a data frame:\nBut wait what if we just want the properties?\n\nFirst let’s get their keys:\nAt the top level if we do ..\njq `keys` data/better_sample.geojson\n#[\n#  \"crs\",\n#  \"features\",\n#  \"type\"\n]\n.. we get the keys for the first array. We need to go in the features object to get properties and pass it to the keys function. Here we went a bit lazy just asking for the first feature.\njq '.features[0].properties | keys' data/better_sample.geojson\n\n\nSecond make them into a csv\nHere we will need to buckle up a bit as it would become quite a big line:\njq -r '(.features[0].properties | keys_unsorted), (.features[].properties | to_entries | map(.value))| @csv' data/better_sample.geojson > data/sample.csv\n\n(.features[0].properties | keys_unsorted) here nothing new we added parentheses to enforce precedence. We are getting the header of our csv\n(.features[].properties | to_entries | map(.value)) :\n\nwe are starting from all our properties (not the first one)\npassing it to to_entries convert our object to multiple objects with “key” / “value” (see margin)\nfinaly, map(.value) gets all “value” for every selected features\n\n\n\n\n{\n\"key\": \"state_abbr\",\n\"value\": \"VT\"\n},\n{\n\"key\": \"geoid_st\",\n\"value\": \"50\"\n},\n{\n\"key\": \"geoid_co\",\n\"value\": \"50005\"\n}\n\n@csv convert to a csv and we redirect the output later in data/sample.csv\n\nWe have just explored the surface! jq can help to filter some specific features:\n\nevery geometries “served” in our file?\nthe first node in every geometries)?\netc!\n\njq is a generic tool for filtering json and lot of people are using by following the JSON spec in geojson we can build on top of all their monumental work!"
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@@ -10,7 +10,7 @@
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-    <lastmod>2024-06-06T15:00:51.257Z</lastmod>
+    <lastmod>2024-06-10T12:32:37.062Z</lastmod>
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