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07_std_anchor.html
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07_std_anchor.html
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<!DOCTYPE html>
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mapclassify</a>
<span class="navbar-text navbar-version pull-left"><b>2.6.1</b></span>
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<section id="Introduction-to-mapclassify">
<h1>Introduction to mapclassify<a class="headerlink" href="#Introduction-to-mapclassify" title="Link to this heading">¶</a></h1>
<p><code class="docutils literal notranslate"><span class="pre">mapclassify</span></code> implementsbins = [ ybar + trim * ystd for trim in range(-2, 2+1) ] a family of classification schemes for choropleth maps. Its focus is on the determination of the number of classes, and the assignment of observations to those classes. It is intended for use with upstream mapping and geovisualization packages (see <a class="reference external" href="https://geopandas.org/mapping.html">geopandas</a> and <a class="reference external" href="https://residentmario.github.io/geoplot/user_guide/Customizing_Plots.html">geoplot</a> for examples) that
handle the rendering of the maps.</p>
<p>In this notebook, the basic functionality of mapclassify is presented.</p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import mapclassify as mc
mc.__version__
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'2.4.2+55.g0155c6e6.dirty'
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<AxesSubplot: >
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<AxesSubplot: >
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>std = mc.StdMean(gdf.rent2008)
std
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StdMean
Interval Count
--------------------------
[ 0.00, 68.04] | 3
( 68.04, 662.61] | 0
( 662.61, 1851.75] | 45
(1851.75, 2446.32] | 2
(2446.32, 2900.00] | 5
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stda
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StdMean
Interval Count
--------------------------
[ 0.00, 68.04] | 3
( 68.04, 662.61] | 0
( 662.61, 1257.18] | 33
(1257.18, 1851.75] | 12
(1851.75, 2446.32] | 2
(2446.32, 2900.00] | 5
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y.mean()
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1257.1818181818182
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y1[0] = 5000
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StdMean
Interval Count
--------------------------
( -inf, -227.42] | 0
(-227.42, 551.24] | 3
( 551.24, 2108.58] | 45
(2108.58, 2887.24] | 5
(2887.24, 5000.00] | 2
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StdMean
Interval Count
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[ 0.00, 551.24] | 3
( 551.24, 1329.91] | 35
(1329.91, 2108.58] | 10
(2108.58, 2887.24] | 5
(2887.24, 3665.91] | 1
(3665.91, 4444.57] | 0
(4444.57, 5000.00] | 1
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1329.909090909091
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-2.1144409159637743
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68.04306411189191
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>(2446.321 - y.mean()) / y.std()
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2.0000007194253047
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(-2.1144409159637743, 2.7630386718042783)
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1257.1818181818182
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>list(map(int, (min(z), max(z))))
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[-2, 2]
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ybar = y.mean()
ystd = y.std()
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[68.04306411189191,
662.6124411468551,
1257.1818181818182,
1851.7511952167815,
2446.3205722517446]
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-2
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2
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