![]() ![]() Seaborn comes with some very important features. Also another attribute known as “alpha” is used to show the Proportional opacity of the different points. In the market attribute we can give the shape of the scatter points we wanted in the example we have used “x” mark to mark the points. We can also change the scatter point design to our desired shapes by using the attribute “marker”. Example #6įig = sns.scatterplot(x="sepal_width", y="petal_length", size="sepal_length", hue ='species', palette="muted", sizes=(40, 200), marker="x", alpha=.6, data=iris_data) The corresponding sizes of the sepal length is also shown in the legend section of the scatter plot. By giving the sizes attribute we can able to clearly identify the differentiation between the variables using a single feature or characteristic. Here we can see that the scatter points or the bubbles plotted would vary from larger to smaller in size according to the sepal length of the species. We have used a special attribute known as “sizes” to differentiate the scatter points or bubbles according to the sepal length of different species. In this example we have plotted the sepal width in x-axis and petal length in y-axis for the three different species of the iris flowers. Example #5įig = sns.scatterplot(x="sepal_width", y="petal_length", size="sepal_length", hue ='species', palette="muted", sizes=(40, 200), data=iris_data) Here virginica species has greater petal length compared to the rest of the species so it is represented in the darkest tone while days that received correspondingly lesser petal length species are represented in consequent lighter tones. This parameter allows us to plot the categorical variable in an increasing color tone where the categories are represented form lighter to a darker tone in ascending order of the numerically greater aggregate variable. In the above example we have plotted the scatterplot with unique color palette using the palette parameter. Example #4įig = sns.scatterplot(x="species", y="petal_length", hue='species', palette="flare", data=iris_data) Hue parameter allows us to individually plot the categorical values in separate colors. We have plotted the relationship between the sepal length and sepal width of different species of the flowers. We can use this feature to plot the categories inside the categorical variable. In the above example we have used a feature in seaborn scatterplot known as ‘hue’ which allows us to plot categories from a variable of the bar plot. ![]() Example #3įig = sns.scatterplot(x="sepal_length", y="sepal_width", hue = 'species', data=iris_data) For identifying individual data points of three different species we can use an attribute ‘hue’ from the seaborn library where it differentiate the categorical variable by applying different colors to them for identifying the characteristics of individual variable. Here we can see there is a wide range of points distributed along the x and y axis. ![]() In this example we have plotted the scatter plot of two features of the iris flower namely sepal width and sepal length. Example #2įig = sns.scatterplot(x="sepal_length", y="sepal_width", data=iris_data) We can see the significant difference between the petal length of the three species of the flowers where the petal length of setosa species is considerably smaller than the petal length of the other two species. We have created a scatter plot using seaborn sns.scatterplot with plotting the petal length of the given three species of the iris flower from the data set. In the above example we have loaded the iris data set which represents the iris flower’s physical characteristics such as its sepal length, sepal width, petal length and petal width for three different species of the iris flower. Example #1įig = sns.scatterplot(x="species", y="petal_length", data=iris_data) We have created multiple scatter plots using the seaborn library with different data sets. You can start by exploring the data using Pandas. You can also use the data to understand how data is used, to understand your analytics project’s business or to gain a deep understanding of the different ways customers generate data. It allows developers to plot a graphical visualization using Python’s plotting language, and the code includes a tool to load it into R or Matplotlib. ![]() Seaborn is built on top of Python’s core visualization library Matplotlib. ![]()
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