March 2020
Beginner to intermediate
352 pages
8h 40m
English
Content preview from Hands-On Exploratory Data Analysis with Python
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Scatter plot using seaborn
A scatter plot can also be generated using the seaborn library. Seaborn makes the graph visually better. We can illustrate the relationship between x and y for distinct subsets of the data by utilizing the size, style, and hue parameters of the scatter plot in seaborn.
Get more detailed information about the parameters from seaborn's documentation website: https://seaborn.pydata.org/generated/seaborn.scatterplot.html.
Now, let's load the Iris dataset:
df = sns.load_dataset('iris')df['species'] = df['species'].map({'setosa': 0, "versicolor": 1, "virginica": 2})sns.scatterplot(x=df["sepal_length"], y=df["sepal_width"], hue=df.species, data=df)
The scatter plot generated from the preceding code is as follows:
In the ...
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