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class="pw-post-body-paragraph mw mx gu my b mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr ns nt gn bk">Data visualization is a crucial skill for not only data analysts but also data scientists. Many strong low code / no code platforms and business intelligence tools are available, which enables interactive data visualization for everyone. However, coding for the visual analysis of the data, especially before starting to work on a machine learning or advanced analytics task, could be more practical sometimes.</p><p id="7426" class="pw-post-body-paragraph mw mx gu my b mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr ns nt gn bk">In this article, I will provide brief information about the some essential plots and how to create them with a widely used Python Library: Matplotlib. It is a powerful tool for creating both static and interactive visualizations.</p><figure class="nx ny nz oa ob oc nu nv paragraph-image"><div role="button" tabindex="0" class="od oe fj of bh og"><div class="nu nv nw"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 1100w, https://miro.medium.com/v2/resize:fit:1400/format:webp/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 640w, https://miro.medium.com/v2/resize:fit:720/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 720w, https://miro.medium.com/v2/resize:fit:750/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 750w, https://miro.medium.com/v2/resize:fit:786/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 786w, https://miro.medium.com/v2/resize:fit:828/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 828w, https://miro.medium.com/v2/resize:fit:1100/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 1100w, https://miro.medium.com/v2/resize:fit:1400/1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px"/><img alt="" class="bh md oh c" width="700" height="507" loading="eager" role="presentation"/></picture></div></div><figcaption class="oi ff oj nu nv ok ol bf b bg z du">Photo by <a class="af om" href="https://unsplash.com/@campaign_creators?utm_content=creditCopyText&utm_medium=referral&utm_source=unsplash" rel="noopener ugc nofollow" target="_blank">Campaign Creators</a> on <a class="af om" href="https://unsplash.com/photos/person-using-macbook-pro-pypeCEaJeZY?utm_content=creditCopyText&utm_medium=referral&utm_source=unsplash" rel="noopener ugc nofollow" target="_blank">Unsplash</a></figcaption></figure><p id="850b" class="pw-post-body-paragraph mw mx gu my b mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr ns nt gn bk">In order to make this tutorial more engaging, I preferred to use a cat-themed dataset, similar to the data we used in <a class="af om" href="https://medium.com/python-in-plain-english/mastering-dataframe-indexing-in-python-with-pandas-06868808396a" rel="noopener"><em class="on">Mastering DataFrame Indexing in Python with Pandas</em></a> article.</p><pre class="nx ny nz oa ob oo op oq bp or bb bk"><span id="15cd" class="os ot gu op b bg ou ov l ow ox">import pandas as pd<br/><br/># Expanded previous cat dataframe with more cats and varied data<br/>data = {<br/> "name": ["Whiskers", "Felix", "Mittens", "Socks", "Luna", "Shadow", "Simba", "Tiger", "Oscar", "Bella"],<br/> "breed": ["Siamese", "Bengal", "Persian", "Sphynx", "Maine Coon", "British Shorthair", "Abyssinian", "Burmese", "Scottish Fold", "Ragdoll"],<br/> "weight": [4.2, 3.8, 4.5, 3.9, 5.0, 4.8, 4.0, 3.6, 4.3, 5.2],<br/> "height": [30, 28, 32, 25, 35, 31, 29, 27, 33, 34],<br/> "date_of_birth": ["2017-04-22", "2018-09-15", "2016-12-01", "2019-07-08", "2020-03-14", "2015-06-21", "2018-01-11", "2021-02-13", "2019-11-03", "2016-08-30"]<br/>}<br/><br/>cats = pd.DataFrame(data)<br/><br/># Convert date_of_birth to datetime<br/>cats["date_of_birth"] = pd.to_datetime(cats["date_of_birth"])</span></pre><h2 id="b8a0" class="oy ot gu bf oz pa pb dy pc pd pe ea pf nh pg ph pi nl pj pk pl np pm pn po pp bk">1. Histograms: Understanding Distribution</h2><p id="d3b0" class="pw-post-body-paragraph mw mx gu my b mz pq nb nc nd pr nf ng nh ps nj nk nl pt nn no np pu nr ns nt gn bk">We can use histograms to show the distribution of the numerical data. We can say that it is also bar plot where the x axis represents the bin ranges. The code below is used to visualize the distribution of the <code class="cx pv pw px op b">height</code> column using two different bin configuration:</p><pre class="nx ny nz oa ob oo op oq bp or bb bk"><span id="e1d9" class="os ot gu op b bg ou ov l ow ox">import matplotlib.pyplot as plt<br/><br/># Histogram with 10 bins<br/>cats["height"].hist(bins=10, color='skyblue', edgecolor='black', alpha=0.7)<br/>plt.title("Cat Height Distribution (10 bins)")<br/>plt.xlabel("Height (cm)")<br/>plt.ylabel("Frequency")<br/>plt.grid(True, which='both', axis='y', linestyle='--', linewidth=0.7)<br/>plt.show()<br/><br/># Histogram with 5 bins<br/>cats["height"].hist(bins=5, color='orange', edgecolor='black', alpha=0.7)<br/>plt.title("Cat Height Distribution (5 bins)")<br/>plt.xlabel("Height (cm)")<br/>plt.ylabel("Frequency")<br/>plt.grid(True, which='both', axis='y', linestyle='--', linewidth=0.7)<br/>plt.show()</span></pre><figure class="nx ny nz oa ob oc nu nv paragraph-image"><div class="nu nv py"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*PvYySv9HfuHmt1MISrajmw.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*PvYySv9HfuHmt1MISrajmw.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*PvYySv9HfuHmt1MISrajmw.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*PvYySv9HfuHmt1MISrajmw.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*PvYySv9HfuHmt1MISrajmw.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*PvYySv9HfuHmt1MISrajmw.png 1100w, https://miro.medium.com/v2/resize:fit:1256/format:webp/1*PvYySv9HfuHmt1MISrajmw.png 1256w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 628px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*PvYySv9HfuHmt1MISrajmw.png 640w, https://miro.medium.com/v2/resize:fit:720/1*PvYySv9HfuHmt1MISrajmw.png 720w, https://miro.medium.com/v2/resize:fit:750/1*PvYySv9HfuHmt1MISrajmw.png 750w, https://miro.medium.com/v2/resize:fit:786/1*PvYySv9HfuHmt1MISrajmw.png 786w, https://miro.medium.com/v2/resize:fit:828/1*PvYySv9HfuHmt1MISrajmw.png 828w, https://miro.medium.com/v2/resize:fit:1100/1*PvYySv9HfuHmt1MISrajmw.png 1100w, https://miro.medium.com/v2/resize:fit:1256/1*PvYySv9HfuHmt1MISrajmw.png 1256w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 628px"/><img alt="" class="bh md oh c" width="628" height="452" loading="lazy" role="presentation"/></picture></div></figure><figure class="nx ny nz oa ob oc nu nv paragraph-image"><div class="nu nv pz"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*3KbOF4sISLBKhD57PWBVJw.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*3KbOF4sISLBKhD57PWBVJw.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*3KbOF4sISLBKhD57PWBVJw.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*3KbOF4sISLBKhD57PWBVJw.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*3KbOF4sISLBKhD57PWBVJw.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*3KbOF4sISLBKhD57PWBVJw.png 1100w, https://miro.medium.com/v2/resize:fit:1276/format:webp/1*3KbOF4sISLBKhD57PWBVJw.png 1276w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 638px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*3KbOF4sISLBKhD57PWBVJw.png 640w, https://miro.medium.com/v2/resize:fit:720/1*3KbOF4sISLBKhD57PWBVJw.png 720w, https://miro.medium.com/v2/resize:fit:750/1*3KbOF4sISLBKhD57PWBVJw.png 750w, https://miro.medium.com/v2/resize:fit:786/1*3KbOF4sISLBKhD57PWBVJw.png 786w, https://miro.medium.com/v2/resize:fit:828/1*3KbOF4sISLBKhD57PWBVJw.png 828w, https://miro.medium.com/v2/resize:fit:1100/1*3KbOF4sISLBKhD57PWBVJw.png 1100w, https://miro.medium.com/v2/resize:fit:1276/1*3KbOF4sISLBKhD57PWBVJw.png 1276w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 638px"/><img alt="" class="bh md oh c" width="638" height="460" loading="lazy" role="presentation"/></picture></div></figure><p id="ed8d" class="pw-post-body-paragraph mw mx gu my b mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr ns nt gn bk">In the first plot, we use 10 bins and in the second plot 5 bins. As it can be seen from the graphs, bin configuration is an important factor to understand data distribution. Even if this dataset is too small, it is possible to observe the change in the visuals.</p><p id="6abc" class="pw-post-body-paragraph mw mx gu my b mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr ns nt gn bk">Moreover, you can see how to customize the color, add grid lines, or change the transparency (<code class="cx pv pw px op b">alpha=0.7</code>) in the code to make it more visually engaging.</p><h2 id="97f0" class="oy ot gu bf oz pa pb dy pc pd pe ea pf nh pg ph pi nl pj pk pl np pm pn po pp bk">2. Bar Plots: Comparing Categories</h2><p id="c6c5" class="pw-post-body-paragraph mw mx gu my b mz pq nb nc nd pr nf ng nh ps nj nk nl pt nn no np pu nr ns nt gn bk">Bar plots are generally preferred to show the comparison of aggregated values over subgroups, or in other words over categorical data. Let’s look at how we can compare the mean weight of cats based on their breed:</p><pre class="nx ny nz oa ob oo op oq bp or bb bk"><span id="280e" class="os ot gu op b bg ou ov l ow ox"># Mean weight by breed<br/>avg_weight_by_breed = cats.groupby("breed")["weight"].mean()<br/><br/># Plotting the bar chart<br/>ax = avg_weight_by_breed.plot(kind="bar", color="coral", edgecolor="black", title="Mean Weight by Cat Breed")<br/>plt.ylabel("Average Weight (kg)")<br/>plt.xticks(rotation=45)<br/><br/># Adding annotations<br/>for i in ax.patches:<br/> ax.annotate(f'{i.get_height():.2f}', (i.get_x() + i.get_width() / 2, i.get_height()),<br/> ha='center', va='baseline', fontsize=10, color='black')<br/><br/>plt.show()</span></pre><figure class="nx ny nz oa ob oc nu nv paragraph-image"><div class="nu nv qa"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*S6cqsKoxV7rDZMYLkocDNQ.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*S6cqsKoxV7rDZMYLkocDNQ.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*S6cqsKoxV7rDZMYLkocDNQ.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*S6cqsKoxV7rDZMYLkocDNQ.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*S6cqsKoxV7rDZMYLkocDNQ.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*S6cqsKoxV7rDZMYLkocDNQ.png 1100w, https://miro.medium.com/v2/resize:fit:1230/format:webp/1*S6cqsKoxV7rDZMYLkocDNQ.png 1230w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 615px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*S6cqsKoxV7rDZMYLkocDNQ.png 640w, https://miro.medium.com/v2/resize:fit:720/1*S6cqsKoxV7rDZMYLkocDNQ.png 720w, https://miro.medium.com/v2/resize:fit:750/1*S6cqsKoxV7rDZMYLkocDNQ.png 750w, https://miro.medium.com/v2/resize:fit:786/1*S6cqsKoxV7rDZMYLkocDNQ.png 786w, https://miro.medium.com/v2/resize:fit:828/1*S6cqsKoxV7rDZMYLkocDNQ.png 828w, https://miro.medium.com/v2/resize:fit:1100/1*S6cqsKoxV7rDZMYLkocDNQ.png 1100w, https://miro.medium.com/v2/resize:fit:1230/1*S6cqsKoxV7rDZMYLkocDNQ.png 1230w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 615px"/><img alt="" class="bh md oh c" width="615" height="525" loading="lazy" role="presentation"/></picture></div></figure><p id="4576" class="pw-post-body-paragraph mw mx gu my b mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr ns nt gn bk">In the code, you can see how to add annotations to the graph. In this case, I preferred to display the values on top of each bar to make the graph more informative.</p><h2 id="c519" class="oy ot gu bf oz pa pb dy pc pd pe ea pf nh pg ph pi nl pj pk pl np pm pn po pp bk">3. Line Plots: Tracking Changes Over Time</h2><p id="cb32" class="pw-post-body-paragraph mw mx gu my b mz pq nb nc nd pr nf ng nh ps nj nk nl pt nn no np pu nr ns nt gn bk">Line plots are very helpful to track changes and observe trends over time.It is so easy to understand patterns like seasonality with a line graph. Let’s see how Felix’s weight has changed in time with a line graph:</p><pre class="nx ny nz oa ob oo op oq bp or bb bk"><span id="5af2" class="os ot gu op b bg ou ov l ow ox"># Line plot for Felix's weight<br/># Lets create a new dataframe which shows Felix's weight at specified date<br/>felix_data = {<br/> "date": ["2020-01-01", "2020-06-01", "2020-12-01", "2021-06-01", "2021-12-01", "2022-06-01"],<br/> "weight": [3.8, 4.0, 4.2, 4.3, 4.5, 4.7]<br/>}<br/><br/>felix_df = pd.DataFrame(felix_data)<br/><br/># Convert date column to datetime<br/>felix_df["date"] = pd.to_datetime(felix_df["date"])<br/><br/># Plotting the line graph for Felix's weight over time<br/>plt.plot(felix_df["date"], felix_df["weight"], linestyle='--', marker='o', color='red', linewidth=2, markersize=8)<br/>plt.title("Felix's Weight Over Time (Customized)")<br/>plt.xlabel("Date")<br/>plt.ylabel("Weight (kg)")<br/>plt.grid(True)<br/>plt.xticks(rotation=45)<br/>plt.show()</span></pre><figure class="nx ny nz oa ob oc nu nv paragraph-image"><div class="nu nv qb"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*OTYRZi-Ce3Ht-A_4czswkg.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*OTYRZi-Ce3Ht-A_4czswkg.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*OTYRZi-Ce3Ht-A_4czswkg.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*OTYRZi-Ce3Ht-A_4czswkg.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*OTYRZi-Ce3Ht-A_4czswkg.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*OTYRZi-Ce3Ht-A_4czswkg.png 1100w, https://miro.medium.com/v2/resize:fit:1236/format:webp/1*OTYRZi-Ce3Ht-A_4czswkg.png 1236w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 618px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*OTYRZi-Ce3Ht-A_4czswkg.png 640w, https://miro.medium.com/v2/resize:fit:720/1*OTYRZi-Ce3Ht-A_4czswkg.png 720w, https://miro.medium.com/v2/resize:fit:750/1*OTYRZi-Ce3Ht-A_4czswkg.png 750w, https://miro.medium.com/v2/resize:fit:786/1*OTYRZi-Ce3Ht-A_4czswkg.png 786w, https://miro.medium.com/v2/resize:fit:828/1*OTYRZi-Ce3Ht-A_4czswkg.png 828w, https://miro.medium.com/v2/resize:fit:1100/1*OTYRZi-Ce3Ht-A_4czswkg.png 1100w, https://miro.medium.com/v2/resize:fit:1236/1*OTYRZi-Ce3Ht-A_4czswkg.png 1236w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 618px"/><img alt="" class="bh md oh c" width="618" height="492" loading="lazy" role="presentation"/></picture></div></figure><p id="574e" class="pw-post-body-paragraph mw mx gu my b mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr ns nt gn bk">In the code, you can see how to change line style, data marker type or the color of the line.</p><h2 id="f302" class="oy ot gu bf oz pa pb dy pc pd pe ea pf nh pg ph pi nl pj pk pl np pm pn po pp bk">4. <strong class="al">Scatter Plots: Visualizing Relationships</strong></h2><p id="ccbd" class="pw-post-body-paragraph mw mx gu my b mz pq nb nc nd pr nf ng nh ps nj nk nl pt nn no np pu nr ns nt gn bk">Scatter plots use dots to represent values of two numeric variable. They are one of the best tools to visualize non linear patterns. They often used to observe the relationship between two features. Let’s plot the height vs. weight of the cats to see the relationship between them:</p><pre class="nx ny nz oa ob oo op oq bp or bb bk"><span id="8c51" class="os ot gu op b bg ou ov l ow ox">import numpy as np<br/><br/># Scatter plot for height and weight<br/>cats.plot(x="height", y="weight", kind="scatter")<br/>plt.title("Height vs Weight of Cats")<br/>plt.xlabel("Height (cm)")<br/>plt.ylabel("Weight (kg)")<br/><br/># Adding a trend line<br/>z = np.polyfit(cats["height"], cats["weight"], 1)<br/>p = np.poly1d(z)<br/>plt.plot(cats["height"], p(cats["height"]), "r--")<br/><br/>plt.show()</span></pre><figure class="nx ny nz oa ob oc nu nv paragraph-image"><div role="button" tabindex="0" class="od oe fj of bh og"><div class="nu nv qc"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*8MFdNPkCCqQ-1vkDfeDxmA.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*8MFdNPkCCqQ-1vkDfeDxmA.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*8MFdNPkCCqQ-1vkDfeDxmA.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*8MFdNPkCCqQ-1vkDfeDxmA.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*8MFdNPkCCqQ-1vkDfeDxmA.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*8MFdNPkCCqQ-1vkDfeDxmA.png 1100w, https://miro.medium.com/v2/resize:fit:1282/format:webp/1*8MFdNPkCCqQ-1vkDfeDxmA.png 1282w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 641px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*8MFdNPkCCqQ-1vkDfeDxmA.png 640w, https://miro.medium.com/v2/resize:fit:720/1*8MFdNPkCCqQ-1vkDfeDxmA.png 720w, https://miro.medium.com/v2/resize:fit:750/1*8MFdNPkCCqQ-1vkDfeDxmA.png 750w, https://miro.medium.com/v2/resize:fit:786/1*8MFdNPkCCqQ-1vkDfeDxmA.png 786w, https://miro.medium.com/v2/resize:fit:828/1*8MFdNPkCCqQ-1vkDfeDxmA.png 828w, https://miro.medium.com/v2/resize:fit:1100/1*8MFdNPkCCqQ-1vkDfeDxmA.png 1100w, https://miro.medium.com/v2/resize:fit:1282/1*8MFdNPkCCqQ-1vkDfeDxmA.png 1282w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 641px"/><img alt="" class="bh md oh c" width="641" height="461" loading="lazy" role="presentation"/></picture></div></div></figure><h2 id="10c2" class="oy ot gu bf oz pa pb dy pc pd pe ea pf nh pg ph pi nl pj pk pl np pm pn po pp bk">5. <strong class="al">Pie Charts: Visualizing Proportions</strong></h2><p id="1c5b" class="pw-post-body-paragraph mw mx gu my b mz pq nb nc nd pr nf ng nh ps nj nk nl pt nn no np pu nr ns nt gn bk">Pie charts help us to visualize fractions / proportions. Each slice shows the proportion of a group within the whole. Since we know that, human brain is not very good at comparing the size of pies, I am not very fan of these charts. I would recommend to use of tree map or stacked bar chart to show comparison. Since it is easier to compare and grasp the difference in the size of rectangles.</p><p id="c895" class="pw-post-body-paragraph mw mx gu my b mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr ns nt gn bk">However, let’s try to understand how each breed contributes to the overall cat pack with a pie chart:</p><pre class="nx ny nz oa ob oo op oq bp or bb bk"><span id="44d6" class="os ot gu op b bg ou ov l ow ox"># Customized pie chart<br/>cat_breed_counts = cats['breed'].value_counts()<br/># Exploding the largest slice<br/>explode = [0.1 if breed == 'Maine Coon' else 0 for breed in cat_breed_counts.index] <br/><br/>cat_breed_counts.plot(kind="pie", autopct='%1.1f%%', startangle=90, explode=explode, shadow=True)<br/>plt.title("Cat Breed Distribution (Customized)")<br/>plt.ylabel("") # Hides the default y-label<br/>plt.show()</span></pre><figure class="nx ny nz oa ob oc nu nv paragraph-image"><div class="nu nv qd"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*eIo8nJ3QjEbcXgNllzsnSA.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*eIo8nJ3QjEbcXgNllzsnSA.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*eIo8nJ3QjEbcXgNllzsnSA.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*eIo8nJ3QjEbcXgNllzsnSA.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*eIo8nJ3QjEbcXgNllzsnSA.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*eIo8nJ3QjEbcXgNllzsnSA.png 1100w, https://miro.medium.com/v2/resize:fit:980/format:webp/1*eIo8nJ3QjEbcXgNllzsnSA.png 980w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 490px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*eIo8nJ3QjEbcXgNllzsnSA.png 640w, https://miro.medium.com/v2/resize:fit:720/1*eIo8nJ3QjEbcXgNllzsnSA.png 720w, https://miro.medium.com/v2/resize:fit:750/1*eIo8nJ3QjEbcXgNllzsnSA.png 750w, https://miro.medium.com/v2/resize:fit:786/1*eIo8nJ3QjEbcXgNllzsnSA.png 786w, https://miro.medium.com/v2/resize:fit:828/1*eIo8nJ3QjEbcXgNllzsnSA.png 828w, https://miro.medium.com/v2/resize:fit:1100/1*eIo8nJ3QjEbcXgNllzsnSA.png 1100w, https://miro.medium.com/v2/resize:fit:980/1*eIo8nJ3QjEbcXgNllzsnSA.png 980w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 490px"/><img alt="" class="bh md oh c" width="490" height="417" loading="lazy" role="presentation"/></picture></div></figure><p id="7d66" class="pw-post-body-paragraph mw mx gu my b mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr ns nt gn bk">You can see that the largest slice (Maine Coon) is “exploded” to draw attention. Moreover, you can see the customizations, like adding shadows in the code.</p><h2 id="0aff" class="oy ot gu bf oz pa pb dy pc pd pe ea pf nh pg ph pi nl pj pk pl np pm pn po pp bk">6. Subplots: Visualizing Multiple Graphs Together</h2><p id="6400" class="pw-post-body-paragraph mw mx gu my b mz pq nb nc nd pr nf ng nh ps nj nk nl pt nn no np pu nr ns nt gn bk">Subplots allows us to show multiple graphs in one figure. Let’s draw a histogram and scatter plot as subplots:</p><pre class="nx ny nz oa ob oo op oq bp or bb bk"><span id="2af3" class="os ot gu op b bg ou ov l ow ox"># Creating subplots<br/>fig, axs = plt.subplots(1, 2, figsize=(12, 5))<br/><br/># Histogram on the first subplot<br/>axs[0].hist(cats["height"], bins=5, color='purple', alpha=0.7)<br/>axs[0].set_title("Cat Height Distribution")<br/>axs[0].set_xlabel("Height (cm)")<br/>axs[0].set_ylabel("Frequency")<br/><br/># Scatter plot on the second subplot<br/>axs[1].scatter(cat_pack["height"], cat_pack["weight"], color='green')<br/>axs[1].set_title("Height vs Weight of Cats")<br/>axs[1].set_xlabel("Height (cm)")<br/>axs[1].set_ylabel("Weight (kg)")<br/><br/>plt.tight_layout()<br/>plt.show()</span></pre><figure class="nx ny nz oa ob oc nu nv paragraph-image"><div role="button" tabindex="0" class="od oe fj of bh og"><div class="nu nv qe"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*mrPwo7FmTbgO0x9f8iyGyg.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*mrPwo7FmTbgO0x9f8iyGyg.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*mrPwo7FmTbgO0x9f8iyGyg.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*mrPwo7FmTbgO0x9f8iyGyg.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*mrPwo7FmTbgO0x9f8iyGyg.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*mrPwo7FmTbgO0x9f8iyGyg.png 1100w, https://miro.medium.com/v2/resize:fit:1400/format:webp/1*mrPwo7FmTbgO0x9f8iyGyg.png 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*mrPwo7FmTbgO0x9f8iyGyg.png 640w, https://miro.medium.com/v2/resize:fit:720/1*mrPwo7FmTbgO0x9f8iyGyg.png 720w, https://miro.medium.com/v2/resize:fit:750/1*mrPwo7FmTbgO0x9f8iyGyg.png 750w, https://miro.medium.com/v2/resize:fit:786/1*mrPwo7FmTbgO0x9f8iyGyg.png 786w, https://miro.medium.com/v2/resize:fit:828/1*mrPwo7FmTbgO0x9f8iyGyg.png 828w, https://miro.medium.com/v2/resize:fit:1100/1*mrPwo7FmTbgO0x9f8iyGyg.png 1100w, https://miro.medium.com/v2/resize:fit:1400/1*mrPwo7FmTbgO0x9f8iyGyg.png 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px"/><img alt="" class="bh md oh c" width="700" height="285" loading="lazy" role="presentation"/></picture></div></div></figure><h2 id="0d4c" class="oy ot gu bf oz pa pb dy pc pd pe ea pf nh pg ph pi nl pj pk pl np pm pn po pp bk">7. Annotations and Legends</h2><p id="786f" class="pw-post-body-paragraph mw mx gu my b mz pq nb nc nd pr nf ng nh ps nj nk nl pt nn no np pu nr ns nt gn bk">Annotations and legends can clarify insights and make plots easier to interpret. For example, adding a legend and annotating the maximum weight in a scatter plot:</p><pre class="nx ny nz oa ob oo op oq bp or bb bk"><span id="0586" class="os ot gu op b bg ou ov l ow ox"># Scatter plot with annotation and legend<br/>ax = cats.plot(x="height", y="weight", kind="scatter", color="orange", label="Cats")<br/><br/># Annotating the maximum weight cat<br/>max_weight_idx = cats["weight"].idxmax()<br/>ax.annotate(cats.loc[max_weight_idx, "name"], <br/> (cats.loc[max_weight_idx, "height"], cats.loc[max_weight_idx, "weight"]),<br/> xytext=(5,5), textcoords="offset points", color='black', fontsize=12, weight='bold')<br/><br/>plt.title("Height vs Weight of Cats with Annotation")<br/>plt.xlabel("Height (cm)")<br/>plt.ylabel("Weight (kg)")<br/>plt.legend()<br/>plt.show()</span></pre><figure class="nx ny nz oa ob oc nu nv paragraph-image"><div class="nu nv qf"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*fg67fWWDM1XktVA8AxGf9A.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*fg67fWWDM1XktVA8AxGf9A.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*fg67fWWDM1XktVA8AxGf9A.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*fg67fWWDM1XktVA8AxGf9A.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*fg67fWWDM1XktVA8AxGf9A.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*fg67fWWDM1XktVA8AxGf9A.png 1100w, https://miro.medium.com/v2/resize:fit:1194/format:webp/1*fg67fWWDM1XktVA8AxGf9A.png 1194w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 597px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*fg67fWWDM1XktVA8AxGf9A.png 640w, https://miro.medium.com/v2/resize:fit:720/1*fg67fWWDM1XktVA8AxGf9A.png 720w, https://miro.medium.com/v2/resize:fit:750/1*fg67fWWDM1XktVA8AxGf9A.png 750w, https://miro.medium.com/v2/resize:fit:786/1*fg67fWWDM1XktVA8AxGf9A.png 786w, https://miro.medium.com/v2/resize:fit:828/1*fg67fWWDM1XktVA8AxGf9A.png 828w, https://miro.medium.com/v2/resize:fit:1100/1*fg67fWWDM1XktVA8AxGf9A.png 1100w, https://miro.medium.com/v2/resize:fit:1194/1*fg67fWWDM1XktVA8AxGf9A.png 1194w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 597px"/><img alt="" class="bh md oh c" width="597" height="458" loading="lazy" role="presentation"/></picture></div></figure><p id="bbdb" class="pw-post-body-paragraph mw mx gu my b mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr ns nt gn bk">In the graph above, we can see the cat with the maximum weight highlighted with an annotation showing its name with a legend to describe the data points.</p><p id="1b84" class="pw-post-body-paragraph mw mx gu my b mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr ns nt gn bk">In this article, we’ve explored not only basic visualizations but also customization of the plots with Matplotlib features, such as transparency in histograms, bar plots with annotations, subplots, trend lines in scatter plots, and pie charts with visual emphasis. By using these techniques, I hope you can be able to create insightful and visually appealing data stories.</p><p id="8c1d" class="pw-post-body-paragraph mw mx gu my b mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr ns nt gn bk">Stay tuned for more articles where we will dive deeper into advanced visualization techniques!</p><h1 id="fb7b" class="qg ot gu bf oz qh qi qj pc qk ql qm pf qn qo qp qq qr qs qt qu qv qw qx qy qz bk">In Plain English 🚀</h1><p id="5635" class="pw-post-body-paragraph mw mx gu my b mz pq nb nc nd pr nf ng nh ps nj nk nl pt nn no np pu nr ns nt gn bk"><em class="on">Thank you for being a part of the </em><a class="af om" href="https://plainenglish.io/" rel="noopener ugc nofollow" target="_blank"><strong class="my gv"><em class="on">In Plain English</em></strong></a><em class="on"> community! 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It is a powerful tool for creating both static and interactive visualizations.","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"ImageMetadata:1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg":{"__typename":"ImageMetadata","id":"1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg","originalHeight":2177,"originalWidth":3008,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:d76ff624cead_3":{"__typename":"Paragraph","id":"d76ff624cead_3","name":"69b3","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:1*0wYrd6ZVGkYT0uMOKr78cQ.jpeg"},"text":"Photo by Campaign Creators on Unsplash","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"A","start":9,"end":26,"href":"https:\u002F\u002Funsplash.com\u002F@campaign_creators?utm_content=creditCopyText&utm_medium=referral&utm_source=unsplash","anchorType":"LINK","userId":null,"linkMetadata":null},{"__typename":"Markup","type":"A","start":30,"end":38,"href":"https:\u002F\u002Funsplash.com\u002Fphotos\u002Fperson-using-macbook-pro-pypeCEaJeZY?utm_content=creditCopyText&utm_medium=referral&utm_source=unsplash","anchorType":"LINK","userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_4":{"__typename":"Paragraph","id":"d76ff624cead_4","name":"850b","type":"P","href":null,"layout":null,"metadata":null,"text":"In order to make this tutorial more engaging, I preferred to use a cat-themed dataset, similar to the data we used in Mastering DataFrame Indexing in Python with Pandas article.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"A","start":118,"end":168,"href":"https:\u002F\u002Fmedium.com\u002Fpython-in-plain-english\u002Fmastering-dataframe-indexing-in-python-with-pandas-06868808396a","anchorType":"LINK","userId":null,"linkMetadata":null},{"__typename":"Markup","type":"EM","start":118,"end":168,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_5":{"__typename":"Paragraph","id":"d76ff624cead_5","name":"15cd","type":"PRE","href":null,"layout":null,"metadata":null,"text":"import pandas as pd\n\n# Expanded previous cat dataframe with more cats and varied data\ndata = {\n \"name\": [\"Whiskers\", \"Felix\", \"Mittens\", \"Socks\", \"Luna\", \"Shadow\", \"Simba\", \"Tiger\", \"Oscar\", \"Bella\"],\n \"breed\": [\"Siamese\", \"Bengal\", \"Persian\", \"Sphynx\", \"Maine Coon\", \"British Shorthair\", \"Abyssinian\", \"Burmese\", \"Scottish Fold\", \"Ragdoll\"],\n \"weight\": [4.2, 3.8, 4.5, 3.9, 5.0, 4.8, 4.0, 3.6, 4.3, 5.2],\n \"height\": [30, 28, 32, 25, 35, 31, 29, 27, 33, 34],\n \"date_of_birth\": [\"2017-04-22\", \"2018-09-15\", \"2016-12-01\", \"2019-07-08\", \"2020-03-14\", \"2015-06-21\", \"2018-01-11\", \"2021-02-13\", \"2019-11-03\", \"2016-08-30\"]\n}\n\ncats = pd.DataFrame(data)\n\n# Convert date_of_birth to datetime\ncats[\"date_of_birth\"] = pd.to_datetime(cats[\"date_of_birth\"])","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"EXPLICIT","lang":"python"},"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_6":{"__typename":"Paragraph","id":"d76ff624cead_6","name":"b8a0","type":"H4","href":null,"layout":null,"metadata":null,"text":"1. Histograms: Understanding Distribution","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_7":{"__typename":"Paragraph","id":"d76ff624cead_7","name":"d3b0","type":"P","href":null,"layout":null,"metadata":null,"text":"We can use histograms to show the distribution of the numerical data. We can say that it is also bar plot where the x axis represents the bin ranges. The code below is used to visualize the distribution of the height column using two different bin configuration:","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"CODE","start":210,"end":216,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_8":{"__typename":"Paragraph","id":"d76ff624cead_8","name":"e1d9","type":"PRE","href":null,"layout":null,"metadata":null,"text":"import matplotlib.pyplot as plt\n\n# Histogram with 10 bins\ncats[\"height\"].hist(bins=10, color='skyblue', edgecolor='black', alpha=0.7)\nplt.title(\"Cat Height Distribution (10 bins)\")\nplt.xlabel(\"Height (cm)\")\nplt.ylabel(\"Frequency\")\nplt.grid(True, which='both', axis='y', linestyle='--', linewidth=0.7)\nplt.show()\n\n# Histogram with 5 bins\ncats[\"height\"].hist(bins=5, color='orange', edgecolor='black', alpha=0.7)\nplt.title(\"Cat Height Distribution (5 bins)\")\nplt.xlabel(\"Height (cm)\")\nplt.ylabel(\"Frequency\")\nplt.grid(True, which='both', axis='y', linestyle='--', linewidth=0.7)\nplt.show()","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"EXPLICIT","lang":"python"},"iframe":null,"mixtapeMetadata":null},"ImageMetadata:1*PvYySv9HfuHmt1MISrajmw.png":{"__typename":"ImageMetadata","id":"1*PvYySv9HfuHmt1MISrajmw.png","originalHeight":452,"originalWidth":628,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:d76ff624cead_9":{"__typename":"Paragraph","id":"d76ff624cead_9","name":"0687","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:1*PvYySv9HfuHmt1MISrajmw.png"},"text":"","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"ImageMetadata:1*3KbOF4sISLBKhD57PWBVJw.png":{"__typename":"ImageMetadata","id":"1*3KbOF4sISLBKhD57PWBVJw.png","originalHeight":460,"originalWidth":638,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:d76ff624cead_10":{"__typename":"Paragraph","id":"d76ff624cead_10","name":"7204","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:1*3KbOF4sISLBKhD57PWBVJw.png"},"text":"","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_11":{"__typename":"Paragraph","id":"d76ff624cead_11","name":"ed8d","type":"P","href":null,"layout":null,"metadata":null,"text":"In the first plot, we use 10 bins and in the second plot 5 bins. As it can be seen from the graphs, bin configuration is an important factor to understand data distribution. Even if this dataset is too small, it is possible to observe the change in the visuals.","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_12":{"__typename":"Paragraph","id":"d76ff624cead_12","name":"6abc","type":"P","href":null,"layout":null,"metadata":null,"text":"Moreover, you can see how to customize the color, add grid lines, or change the transparency (alpha=0.7) in the code to make it more visually engaging.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"CODE","start":94,"end":103,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_13":{"__typename":"Paragraph","id":"d76ff624cead_13","name":"97f0","type":"H4","href":null,"layout":null,"metadata":null,"text":"2. Bar Plots: Comparing Categories","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_14":{"__typename":"Paragraph","id":"d76ff624cead_14","name":"c6c5","type":"P","href":null,"layout":null,"metadata":null,"text":"Bar plots are generally preferred to show the comparison of aggregated values over subgroups, or in other words over categorical data. Let’s look at how we can compare the mean weight of cats based on their breed:","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_15":{"__typename":"Paragraph","id":"d76ff624cead_15","name":"280e","type":"PRE","href":null,"layout":null,"metadata":null,"text":"# Mean weight by breed\navg_weight_by_breed = cats.groupby(\"breed\")[\"weight\"].mean()\n\n# Plotting the bar chart\nax = avg_weight_by_breed.plot(kind=\"bar\", color=\"coral\", edgecolor=\"black\", title=\"Mean Weight by Cat Breed\")\nplt.ylabel(\"Average Weight (kg)\")\nplt.xticks(rotation=45)\n\n# Adding annotations\nfor i in ax.patches:\n ax.annotate(f'{i.get_height():.2f}', (i.get_x() + i.get_width() \u002F 2, i.get_height()),\n ha='center', va='baseline', fontsize=10, color='black')\n\nplt.show()","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"EXPLICIT","lang":"python"},"iframe":null,"mixtapeMetadata":null},"ImageMetadata:1*S6cqsKoxV7rDZMYLkocDNQ.png":{"__typename":"ImageMetadata","id":"1*S6cqsKoxV7rDZMYLkocDNQ.png","originalHeight":525,"originalWidth":615,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:d76ff624cead_16":{"__typename":"Paragraph","id":"d76ff624cead_16","name":"aa46","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:1*S6cqsKoxV7rDZMYLkocDNQ.png"},"text":"","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_17":{"__typename":"Paragraph","id":"d76ff624cead_17","name":"4576","type":"P","href":null,"layout":null,"metadata":null,"text":"In the code, you can see how to add annotations to the graph. In this case, I preferred to display the values on top of each bar to make the graph more informative.","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_18":{"__typename":"Paragraph","id":"d76ff624cead_18","name":"c519","type":"H4","href":null,"layout":null,"metadata":null,"text":"3. Line Plots: Tracking Changes Over Time","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_19":{"__typename":"Paragraph","id":"d76ff624cead_19","name":"cb32","type":"P","href":null,"layout":null,"metadata":null,"text":"Line plots are very helpful to track changes and observe trends over time.It is so easy to understand patterns like seasonality with a line graph. Let’s see how Felix’s weight has changed in time with a line graph:","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_20":{"__typename":"Paragraph","id":"d76ff624cead_20","name":"5af2","type":"PRE","href":null,"layout":null,"metadata":null,"text":"# Line plot for Felix's weight\n# Lets create a new dataframe which shows Felix's weight at specified date\nfelix_data = {\n \"date\": [\"2020-01-01\", \"2020-06-01\", \"2020-12-01\", \"2021-06-01\", \"2021-12-01\", \"2022-06-01\"],\n \"weight\": [3.8, 4.0, 4.2, 4.3, 4.5, 4.7]\n}\n\nfelix_df = pd.DataFrame(felix_data)\n\n# Convert date column to datetime\nfelix_df[\"date\"] = pd.to_datetime(felix_df[\"date\"])\n\n# Plotting the line graph for Felix's weight over time\nplt.plot(felix_df[\"date\"], felix_df[\"weight\"], linestyle='--', marker='o', color='red', linewidth=2, markersize=8)\nplt.title(\"Felix's Weight Over Time (Customized)\")\nplt.xlabel(\"Date\")\nplt.ylabel(\"Weight (kg)\")\nplt.grid(True)\nplt.xticks(rotation=45)\nplt.show()","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"EXPLICIT","lang":"python"},"iframe":null,"mixtapeMetadata":null},"ImageMetadata:1*OTYRZi-Ce3Ht-A_4czswkg.png":{"__typename":"ImageMetadata","id":"1*OTYRZi-Ce3Ht-A_4czswkg.png","originalHeight":492,"originalWidth":618,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:d76ff624cead_21":{"__typename":"Paragraph","id":"d76ff624cead_21","name":"35e8","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:1*OTYRZi-Ce3Ht-A_4czswkg.png"},"text":"","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_22":{"__typename":"Paragraph","id":"d76ff624cead_22","name":"574e","type":"P","href":null,"layout":null,"metadata":null,"text":"In the code, you can see how to change line style, data marker type or the color of the line.","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_23":{"__typename":"Paragraph","id":"d76ff624cead_23","name":"f302","type":"H4","href":null,"layout":null,"metadata":null,"text":"4. Scatter Plots: Visualizing Relationships","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":3,"end":43,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_24":{"__typename":"Paragraph","id":"d76ff624cead_24","name":"ccbd","type":"P","href":null,"layout":null,"metadata":null,"text":"Scatter plots use dots to represent values of two numeric variable. They are one of the best tools to visualize non linear patterns. They often used to observe the relationship between two features. Let’s plot the height vs. weight of the cats to see the relationship between them:","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_25":{"__typename":"Paragraph","id":"d76ff624cead_25","name":"8c51","type":"PRE","href":null,"layout":null,"metadata":null,"text":"import numpy as np\n\n# Scatter plot for height and weight\ncats.plot(x=\"height\", y=\"weight\", kind=\"scatter\")\nplt.title(\"Height vs Weight of Cats\")\nplt.xlabel(\"Height (cm)\")\nplt.ylabel(\"Weight (kg)\")\n\n# Adding a trend line\nz = np.polyfit(cats[\"height\"], cats[\"weight\"], 1)\np = np.poly1d(z)\nplt.plot(cats[\"height\"], p(cats[\"height\"]), \"r--\")\n\nplt.show()","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"EXPLICIT","lang":"python"},"iframe":null,"mixtapeMetadata":null},"ImageMetadata:1*8MFdNPkCCqQ-1vkDfeDxmA.png":{"__typename":"ImageMetadata","id":"1*8MFdNPkCCqQ-1vkDfeDxmA.png","originalHeight":460,"originalWidth":641,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:d76ff624cead_26":{"__typename":"Paragraph","id":"d76ff624cead_26","name":"ae0a","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:1*8MFdNPkCCqQ-1vkDfeDxmA.png"},"text":"","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_27":{"__typename":"Paragraph","id":"d76ff624cead_27","name":"10c2","type":"H4","href":null,"layout":null,"metadata":null,"text":"5. Pie Charts: Visualizing Proportions","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":3,"end":38,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_28":{"__typename":"Paragraph","id":"d76ff624cead_28","name":"1c5b","type":"P","href":null,"layout":null,"metadata":null,"text":"Pie charts help us to visualize fractions \u002F proportions. Each slice shows the proportion of a group within the whole. Since we know that, human brain is not very good at comparing the size of pies, I am not very fan of these charts. I would recommend to use of tree map or stacked bar chart to show comparison. Since it is easier to compare and grasp the difference in the size of rectangles.","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_29":{"__typename":"Paragraph","id":"d76ff624cead_29","name":"c895","type":"P","href":null,"layout":null,"metadata":null,"text":"However, let’s try to understand how each breed contributes to the overall cat pack with a pie chart:","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_30":{"__typename":"Paragraph","id":"d76ff624cead_30","name":"44d6","type":"PRE","href":null,"layout":null,"metadata":null,"text":"# Customized pie chart\ncat_breed_counts = cats['breed'].value_counts()\n# Exploding the largest slice\nexplode = [0.1 if breed == 'Maine Coon' else 0 for breed in cat_breed_counts.index] \n\ncat_breed_counts.plot(kind=\"pie\", autopct='%1.1f%%', startangle=90, explode=explode, shadow=True)\nplt.title(\"Cat Breed Distribution (Customized)\")\nplt.ylabel(\"\") # Hides the default y-label\nplt.show()","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"EXPLICIT","lang":"python"},"iframe":null,"mixtapeMetadata":null},"ImageMetadata:1*eIo8nJ3QjEbcXgNllzsnSA.png":{"__typename":"ImageMetadata","id":"1*eIo8nJ3QjEbcXgNllzsnSA.png","originalHeight":417,"originalWidth":490,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:d76ff624cead_31":{"__typename":"Paragraph","id":"d76ff624cead_31","name":"d72b","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:1*eIo8nJ3QjEbcXgNllzsnSA.png"},"text":"","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_32":{"__typename":"Paragraph","id":"d76ff624cead_32","name":"7d66","type":"P","href":null,"layout":null,"metadata":null,"text":"You can see that the largest slice (Maine Coon) is “exploded” to draw attention. Moreover, you can see the customizations, like adding shadows in the code.","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_33":{"__typename":"Paragraph","id":"d76ff624cead_33","name":"0aff","type":"H4","href":null,"layout":null,"metadata":null,"text":"6. Subplots: Visualizing Multiple Graphs Together","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_34":{"__typename":"Paragraph","id":"d76ff624cead_34","name":"6400","type":"P","href":null,"layout":null,"metadata":null,"text":"Subplots allows us to show multiple graphs in one figure. Let’s draw a histogram and scatter plot as subplots:","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_35":{"__typename":"Paragraph","id":"d76ff624cead_35","name":"2af3","type":"PRE","href":null,"layout":null,"metadata":null,"text":"# Creating subplots\nfig, axs = plt.subplots(1, 2, figsize=(12, 5))\n\n# Histogram on the first subplot\naxs[0].hist(cats[\"height\"], bins=5, color='purple', alpha=0.7)\naxs[0].set_title(\"Cat Height Distribution\")\naxs[0].set_xlabel(\"Height (cm)\")\naxs[0].set_ylabel(\"Frequency\")\n\n# Scatter plot on the second subplot\naxs[1].scatter(cat_pack[\"height\"], cat_pack[\"weight\"], color='green')\naxs[1].set_title(\"Height vs Weight of Cats\")\naxs[1].set_xlabel(\"Height (cm)\")\naxs[1].set_ylabel(\"Weight (kg)\")\n\nplt.tight_layout()\nplt.show()","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"EXPLICIT","lang":"python"},"iframe":null,"mixtapeMetadata":null},"ImageMetadata:1*mrPwo7FmTbgO0x9f8iyGyg.png":{"__typename":"ImageMetadata","id":"1*mrPwo7FmTbgO0x9f8iyGyg.png","originalHeight":447,"originalWidth":1101,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:d76ff624cead_36":{"__typename":"Paragraph","id":"d76ff624cead_36","name":"f5a3","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:1*mrPwo7FmTbgO0x9f8iyGyg.png"},"text":"","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_37":{"__typename":"Paragraph","id":"d76ff624cead_37","name":"0d4c","type":"H4","href":null,"layout":null,"metadata":null,"text":"7. Annotations and Legends","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_38":{"__typename":"Paragraph","id":"d76ff624cead_38","name":"786f","type":"P","href":null,"layout":null,"metadata":null,"text":"Annotations and legends can clarify insights and make plots easier to interpret. For example, adding a legend and annotating the maximum weight in a scatter plot:","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_39":{"__typename":"Paragraph","id":"d76ff624cead_39","name":"0586","type":"PRE","href":null,"layout":null,"metadata":null,"text":"# Scatter plot with annotation and legend\nax = cats.plot(x=\"height\", y=\"weight\", kind=\"scatter\", color=\"orange\", label=\"Cats\")\n\n# Annotating the maximum weight cat\nmax_weight_idx = cats[\"weight\"].idxmax()\nax.annotate(cats.loc[max_weight_idx, \"name\"], \n (cats.loc[max_weight_idx, \"height\"], cats.loc[max_weight_idx, \"weight\"]),\n xytext=(5,5), textcoords=\"offset points\", color='black', fontsize=12, weight='bold')\n\nplt.title(\"Height vs Weight of Cats with Annotation\")\nplt.xlabel(\"Height (cm)\")\nplt.ylabel(\"Weight (kg)\")\nplt.legend()\nplt.show()","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"EXPLICIT","lang":"python"},"iframe":null,"mixtapeMetadata":null},"ImageMetadata:1*fg67fWWDM1XktVA8AxGf9A.png":{"__typename":"ImageMetadata","id":"1*fg67fWWDM1XktVA8AxGf9A.png","originalHeight":458,"originalWidth":597,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:d76ff624cead_40":{"__typename":"Paragraph","id":"d76ff624cead_40","name":"d065","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:1*fg67fWWDM1XktVA8AxGf9A.png"},"text":"","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_41":{"__typename":"Paragraph","id":"d76ff624cead_41","name":"bbdb","type":"P","href":null,"layout":null,"metadata":null,"text":"In the graph above, we can see the cat with the maximum weight highlighted with an annotation showing its name with a legend to describe the data points.","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:d76ff624cead_42":{"__typename":"Paragraph","id":"d76ff624cead_42","name":"1b84","type":"P","href":null,"layout":null,"metadata":null,"text":"In this article, we’ve explored not only basic visualizations but also customization of the plots with Matplotlib features, such as transparency in histograms, bar plots with annotations, subplots, trend lines in scatter plots, and pie charts with visual emphasis. 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