# Visualizing Distributions | ](../../sketchnotes/10-Visualizing-Distributions.png)| |:---:| | Visualizing Distributions - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | In the previous lesson, you explored an interesting dataset about the birds of Minnesota. You identified some erroneous data by visualizing outliers and examined the differences between bird categories based on their maximum length. ## [Pre-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/18) ## Explore the birds dataset Another way to analyze data is by examining its distribution, or how the data is spread along an axis. For instance, you might want to understand the general distribution of maximum wingspan or maximum body mass for the birds of Minnesota in this dataset. Let’s uncover some insights about the data distributions in this dataset. In the _notebook.ipynb_ file located in the root of this lesson folder, import Pandas, Matplotlib, and your data: ```python import pandas as pd import matplotlib.pyplot as plt birds = pd.read_csv('../../data/birds.csv') birds.head() ``` | | Name | ScientificName | Category | Order | Family | Genus | ConservationStatus | MinLength | MaxLength | MinBodyMass | MaxBodyMass | MinWingspan | MaxWingspan | | ---: | :--------------------------- | :--------------------- | :-------------------- | :----------- | :------- | :---------- | :----------------- | --------: | --------: | ----------: | ----------: | ----------: | ----------: | | 0 | Black-bellied whistling-duck | Dendrocygna autumnalis | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Dendrocygna | LC | 47 | 56 | 652 | 1020 | 76 | 94 | | 1 | Fulvous whistling-duck | Dendrocygna bicolor | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Dendrocygna | LC | 45 | 53 | 712 | 1050 | 85 | 93 | | 2 | Snow goose | Anser caerulescens | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Anser | LC | 64 | 79 | 2050 | 4050 | 135 | 165 | | 3 | Ross's goose | Anser rossii | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Anser | LC | 57.3 | 64 | 1066 | 1567 | 113 | 116 | | 4 | Greater white-fronted goose | Anser albifrons | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Anser | LC | 64 | 81 | 1930 | 3310 | 130 | 165 | In general, you can quickly visualize how data is distributed by using a scatter plot, as demonstrated in the previous lesson: ```python birds.plot(kind='scatter',x='MaxLength',y='Order',figsize=(12,8)) plt.title('Max Length per Order') plt.ylabel('Order') plt.xlabel('Max Length') plt.show() ```  This provides an overview of the general distribution of body length per bird Order, but it’s not the best way to display true distributions. That’s where histograms come in. ## Working with histograms Matplotlib provides excellent tools for visualizing data distributions using histograms. A histogram is similar to a bar chart, but it shows the distribution of data through the rise and fall of the bars. To create a histogram, you need numeric data. You can plot a histogram by setting the chart type to 'hist'. This chart displays the distribution of MaxBodyMass across the dataset’s numeric range. By dividing the data into smaller bins, it reveals the distribution of values: ```python birds['MaxBodyMass'].plot(kind = 'hist', bins = 10, figsize = (12,12)) plt.show() ```  As shown, most of the 400+ birds in this dataset have a Max Body Mass under 2000. You can gain more insight by increasing the `bins` parameter to a higher value, such as 30: ```python birds['MaxBodyMass'].plot(kind = 'hist', bins = 30, figsize = (12,12)) plt.show() ```  This chart provides a more detailed view of the distribution. To create a chart that’s less skewed to the left, you can filter the data to include only birds with a body mass under 60 and set the `bins` parameter to 40: ```python filteredBirds = birds[(birds['MaxBodyMass'] > 1) & (birds['MaxBodyMass'] < 60)] filteredBirds['MaxBodyMass'].plot(kind = 'hist',bins = 40,figsize = (12,12)) plt.show() ```  ✅ Experiment with other filters and data points. To view the full distribution of the data, remove the `['MaxBodyMass']` filter to display labeled distributions. Histograms also allow for color and labeling enhancements: Create a 2D histogram to compare the relationship between two distributions. For example, compare `MaxBodyMass` and `MaxLength`. Matplotlib provides a built-in way to show convergence using brighter colors: ```python x = filteredBirds['MaxBodyMass'] y = filteredBirds['MaxLength'] fig, ax = plt.subplots(tight_layout=True) hist = ax.hist2d(x, y) ``` There seems to be a clear correlation between these two variables along an expected axis, with one particularly strong point of convergence:  Histograms are ideal for numeric data. But what if you want to analyze distributions based on text data? ## Explore the dataset for distributions using text data This dataset also contains valuable information about bird categories, genus, species, family, and conservation status. Let’s explore the conservation status. What is the distribution of birds based on their conservation status? > ✅ In the dataset, several acronyms are used to describe conservation status. These acronyms are derived from the [IUCN Red List Categories](https://www.iucnredlist.org/), which classify species' statuses: > > - CR: Critically Endangered > - EN: Endangered > - EX: Extinct > - LC: Least Concern > - NT: Near Threatened > - VU: Vulnerable Since these are text-based values, you’ll need to transform them to create a histogram. Using the filteredBirds dataframe, display its conservation status alongside its Minimum Wingspan. What do you observe? ```python x1 = filteredBirds.loc[filteredBirds.ConservationStatus=='EX', 'MinWingspan'] x2 = filteredBirds.loc[filteredBirds.ConservationStatus=='CR', 'MinWingspan'] x3 = filteredBirds.loc[filteredBirds.ConservationStatus=='EN', 'MinWingspan'] x4 = filteredBirds.loc[filteredBirds.ConservationStatus=='NT', 'MinWingspan'] x5 = filteredBirds.loc[filteredBirds.ConservationStatus=='VU', 'MinWingspan'] x6 = filteredBirds.loc[filteredBirds.ConservationStatus=='LC', 'MinWingspan'] kwargs = dict(alpha=0.5, bins=20) plt.hist(x1, **kwargs, color='red', label='Extinct') plt.hist(x2, **kwargs, color='orange', label='Critically Endangered') plt.hist(x3, **kwargs, color='yellow', label='Endangered') plt.hist(x4, **kwargs, color='green', label='Near Threatened') plt.hist(x5, **kwargs, color='blue', label='Vulnerable') plt.hist(x6, **kwargs, color='gray', label='Least Concern') plt.gca().set(title='Conservation Status', ylabel='Min Wingspan') plt.legend(); ```  There doesn’t appear to be a strong correlation between minimum wingspan and conservation status. Test other elements of the dataset using this method. Try different filters as well. Do you notice any correlations? ## Density plots You may have noticed that the histograms we’ve examined so far are 'stepped' and don’t flow smoothly. To create a smoother density chart, you can use a density plot. To work with density plots, familiarize yourself with a new plotting library, [Seaborn](https://seaborn.pydata.org/generated/seaborn.kdeplot.html). Load Seaborn and try a basic density plot: ```python import seaborn as sns import matplotlib.pyplot as plt sns.kdeplot(filteredBirds['MinWingspan']) plt.show() ```  This plot mirrors the previous one for Minimum Wingspan data but appears smoother. According to Seaborn’s documentation, "Relative to a histogram, KDE can produce a plot that is less cluttered and more interpretable, especially when drawing multiple distributions. But it has the potential to introduce distortions if the underlying distribution is bounded or not smooth. Like a histogram, the quality of the representation also depends on the selection of good smoothing parameters." [source](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) In other words, outliers can still negatively impact your charts. If you revisit the jagged MaxBodyMass line from the second chart, you can smooth it out using this method: ```python sns.kdeplot(filteredBirds['MaxBodyMass']) plt.show() ```  To create a line that’s smooth but not overly so, adjust the `bw_adjust` parameter: ```python sns.kdeplot(filteredBirds['MaxBodyMass'], bw_adjust=.2) plt.show() ```  ✅ Explore the available parameters for this type of plot and experiment! This type of chart provides visually appealing and explanatory visualizations. For instance, with just a few lines of code, you can display the max body mass density per bird Order: ```python sns.kdeplot( data=filteredBirds, x="MaxBodyMass", hue="Order", fill=True, common_norm=False, palette="crest", alpha=.5, linewidth=0, ) ```  You can also map the density of multiple variables in one chart. Compare the MaxLength and MinLength of a bird to their conservation status: ```python sns.kdeplot(data=filteredBirds, x="MinLength", y="MaxLength", hue="ConservationStatus") ```  It might be worth investigating whether the cluster of 'Vulnerable' birds based on their lengths has any significance. ## 🚀 Challenge Histograms are a more advanced type of chart compared to basic scatterplots, bar charts, or line charts. Search online for examples of histograms. How are they used, what do they reveal, and in which fields or areas of study are they commonly applied? ## [Post-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/19) ## Review & Self Study In this lesson, you used Matplotlib and began working with Seaborn to create more advanced charts. Research `kdeplot` in Seaborn, which generates a "continuous probability density curve in one or more dimensions." Read through [the documentation](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) to understand how it works. ## Assignment [Apply your skills](assignment.md) --- **Disclaimer**: This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. 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