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1.2 Introduction to Matplotlib.html
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Machine Learning
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Matplotlib: Visualization with Python
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plt.pie()
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<section class="tex2jax_ignore mathjax_ignore" id="matplotlib-visualization-with-python">
<h1>Matplotlib: Visualization with Python<a class="headerlink" href="#matplotlib-visualization-with-python" title="Permalink to this headline">¶</a></h1>
<p>Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Below are some of the example plots that can be made using the Matplotlib library.</p>
<p><img alt="" src="_images/matplotlib1.png" /></p>
<section id="installation-using-pip">
<h2>Installation Using pip<a class="headerlink" href="#installation-using-pip" title="Permalink to this headline">¶</a></h2>
<div class="cell docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip install matplotlib
</pre></div>
</div>
</div>
</div>
<p>Matplotlib is a very vast library, but in this module we’ll just talk about plotting the 2-D graphs. Most of the matplotlib utilities lies under the pyplot submodule and it is usually impoterd under the <em>plt</em> alias, so for 2-D plots we will use the pyplot submodule. So let’s import the module and start plotting a very simple graph using the plot function, which takes two iterables as the input and returns a plotted graph using the given coordinates.</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Importing pyplot submodule from matplotlib as plt</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
</pre></div>
</div>
</div>
</div>
</section>
<section id="plotting">
<h2>Plotting<a class="headerlink" href="#plotting" title="Permalink to this headline">¶</a></h2>
<section id="plt-plot">
<h3><code class="docutils literal notranslate"><span class="pre">plt.plot()</span></code><a class="headerlink" href="#plt-plot" title="Permalink to this headline">¶</a></h3>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
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<img alt="_images/1.2 Introduction to Matplotlib_7_0.png" src="_images/1.2 Introduction to Matplotlib_7_0.png" />
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</section>
<section id="plt-scatter">
<h3><code class="docutils literal notranslate"><span class="pre">plt.scatter()</span></code><a class="headerlink" href="#plt-scatter" title="Permalink to this headline">¶</a></h3>
<p>Now if we want to plot individual points rather than connecting them using a line, we can use another function called scatter which takes the same input format.</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<p>If you’ve noticed every time we plot a graph, we call <em>plt.show()</em> function. This is because everytime, we start plotting a graph, matplotlib maintains a graph buffer so that we can plot multiple graphs in a single plane, and whenever <em>plt.show()</em> is called, it flushes the maintained buffer</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Plotting 4 Graphs in one plane</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">"lightblue"</span><span class="p">)</span> <span class="c1"># c parameter is for defining color</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">"lightgreen"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">"darkblue"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">"darkgreen"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>We can also provide a list of colors in the <em>c parameter</em>, to define color of every seperate point.</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">],</span> <span class="n">c</span> <span class="o">=</span><span class="p">[</span><span class="s2">"red"</span><span class="p">,</span><span class="s2">"blue"</span><span class="p">,</span><span class="s2">"green"</span><span class="p">,</span><span class="s2">"red"</span><span class="p">,</span><span class="s2">"black"</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<section id="plt-bar-plt-pie">
<h3><code class="docutils literal notranslate"><span class="pre">plt.bar()</span> <span class="pre">&</span> <span class="pre">plt.pie()</span></code><a class="headerlink" href="#plt-bar-plt-pie" title="Permalink to this headline">¶</a></h3>
<p>Similarly, we have some other data visualisation functions like : BarGraphs, PieCharts, etc. So let’s try and plot each of them and we can use another function called subplot to plot multiple graphs in a single plot.</p>
<p>There are tons of other parameters too in these plots, that can make the representation more representative and useful. For eg:</p>
<p><code class="docutils literal notranslate"><span class="pre">plt.xlabel("X</span> <span class="pre">-</span> <span class="pre">Axis")</span></code> –> Used to represent X-Axis label</p>
<p><code class="docutils literal notranslate"><span class="pre">plt.ylabel("Y</span> <span class="pre">-</span> <span class="pre">Axis")</span></code> –> Used to represent Y-Axis label</p>
<p><code class="docutils literal notranslate"><span class="pre">plt.title("Graph</span> <span class="pre">Title")</span></code> –> Used to give graphs a Title</p>
<p><code class="docutils literal notranslate"><span class="pre">plt.legends()</span></code> –> Used to define a legend for graph.</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># subplot signature => subplot(nrows, ncols, index, **kwargs)</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="c1"># ---Bar Graph at 1st index of subplot---</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="n">Products</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">"P1"</span><span class="p">,</span><span class="s2">"P2"</span><span class="p">,</span><span class="s2">"P3"</span><span class="p">,</span><span class="s2">"P4"</span><span class="p">,</span><span class="s2">"P5"</span><span class="p">])</span>
<span class="n">Sale2020</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">200</span><span class="p">,</span><span class="mi">100</span><span class="p">,</span><span class="mi">400</span><span class="p">,</span><span class="mi">100</span><span class="p">,</span><span class="mi">400</span><span class="p">])</span>
<span class="n">Sale2021</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">300</span><span class="p">,</span><span class="mi">200</span><span class="p">,</span><span class="mi">300</span><span class="p">,</span><span class="mi">400</span><span class="p">,</span><span class="mi">300</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Product Sales in 2020 v/s 2021"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Product Names"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Sale Quantity"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">Products</span><span class="p">,</span><span class="n">Sale2020</span><span class="p">,</span> <span class="n">align</span> <span class="o">=</span> <span class="s1">'edge'</span> <span class="p">,</span><span class="n">width</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"2020 Sales"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">Products</span><span class="p">,</span><span class="n">Sale2021</span><span class="p">,</span> <span class="n">align</span> <span class="o">=</span> <span class="s1">'center'</span><span class="p">,</span><span class="n">width</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"2021 Sales"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="c1"># --- Pie Chart at 2nd index of subplot ---</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Market Share of the Investors"</span><span class="p">)</span>
<span class="n">Investors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"A"</span><span class="p">,</span><span class="s2">"B"</span><span class="p">,</span><span class="s2">"C"</span><span class="p">,</span><span class="s2">"D"</span><span class="p">,</span><span class="s2">"E"</span><span class="p">]</span>
<span class="n">Share</span> <span class="o">=</span> <span class="p">[</span><span class="mi">40</span><span class="p">,</span><span class="mi">25</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">Share</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">Investors</span><span class="p">,</span><span class="n">explode</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mf">0.2</span><span class="p">],</span> <span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<section id="plotting-some-data">
<h3>Plotting some data<a class="headerlink" href="#plotting-some-data" title="Permalink to this headline">¶</a></h3>
<p>Now instead of plotting these small no. of points, let’s plot some good amount of data. So let’s first import the data using <code class="docutils literal notranslate"><span class="pre">np.load()</span></code> function and plot them.</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"./Data/Matplotlib/a.npy"</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"./Data/Matplotlib/b.npy"</span><span class="p">)</span>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>Now consider a dataset which contains 2 different classes.</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"./Data/Matplotlib/X_data.npy"</span><span class="p">)</span>
<span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"./Data/Matplotlib/Y_data.npy"</span><span class="p">)</span>
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<p>Here X contains the x & y co’ordinates of the data and y contains the information of which class every corresponding instance belongs to.</p>
<p>X looks like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span> <span class="mf">8.28200531</span><span class="p">,</span> <span class="o">-</span><span class="mf">4.84599105</span><span class="p">],</span>
<span class="p">[</span><span class="o">-</span><span class="mf">1.34017991</span><span class="p">,</span> <span class="mf">5.32077278</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">8.04211253</span><span class="p">,</span> <span class="o">-</span><span class="mf">3.94098662</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">8.69884399</span><span class="p">,</span> <span class="o">-</span><span class="mf">3.50222611</span><span class="p">],</span>
<span class="p">[</span><span class="o">-</span><span class="mf">3.61081635</span><span class="p">,</span> <span class="mf">7.61528245</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">8.09647278</span><span class="p">,</span> <span class="o">-</span><span class="mf">4.52234241</span><span class="p">],</span> <span class="o">...</span> <span class="mi">100</span> <span class="n">instances</span><span class="p">]</span>
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<p>y looks like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">...</span> <span class="mi">100</span> <span class="n">instances</span><span class="p">]</span>
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<p>So Let’s plot the graph between using X</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">])</span>
<span class="c1"># Here [:] is used to correspond to every element in the nd_array. It is an advanced loop provided by numpy.</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>Now to provide colors to these instances we can make use to Y. There can be a lot of ways to do this. One way can be to prepare a list of color and give it to <em>c</em> like this</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">colors</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">Y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"red"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">Y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"blue"</span><span class="p">)</span>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span><span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span><span class="n">c</span> <span class="o">=</span> <span class="n">colors</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>But this approach will only work if we know how many classes my data has, so another way is instead of providing color names to the color list, we assign a correspoding number, and prepare a list like</p>
<p>Instead of colors = <code class="docutils literal notranslate"><span class="pre">["red","blue","red","red","blue",...]</span></code></p>
<p>we make colors = <code class="docutils literal notranslate"><span class="pre">[0,1,0,0,1,...]</span></code></p>
<p>but notice this new list is same as Y, so we can give Y in my c parameter and it will automatically assign different colors, to different classes and plot them.</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span> <span class="o">=</span> <span class="n">Y</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>But notice here we doesn’t have freedom to choose colors for the classes, it will automatically assign different colors to them. There can be a lot of different ways too, to achieve these results.</p>
</section>
</section>
<section id="further-readings">
<h2>Further Readings<a class="headerlink" href="#further-readings" title="Permalink to this headline">¶</a></h2>
<p>We’ve seen the most commonly used functions of Matplotlib that are most commonly used in the field of Machine Learning and Data Analysis.</p>
<p>However there is a variety of other plots that matplotlib.pyplot provides us.
For Example: Box Plot, Step Plot, Time Series, histogram, etc., which can be used depending on the type of data and other factors. All these Plots also contains a variety of parameters to make them more beautiful and useful.</p>
<p>Other submodules of Matplotlib can also be used for plotting 3-D plots, in a very beautiful and appealing manner. To explore more such type of plots, one can always refer to the matplotlib official Docs at:
<a class="reference external" href="https://matplotlib.org/stable/contents.html">https://matplotlib.org/stable/contents.html</a></p>
</section>
</section>
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