In today’s data-centric world, effectively visualizing complex data has become increasingly vital. Building on our recent exploration of R data visualization, accessible here, we focus on Python, a formidable force in data science. Join us as we uncover the top Python data visualization packages to watch in 2023.
Discussion of Python data visualization would only be complete with Matplotlib. This robust package, renowned for its longevity, enables crafting visualizations ranging from straightforward to intricate.
import matplotlib.pyplot as plt x = [1, 2, 3, 4] y = [11, 22, 25, 33] plt.plot(x, y) plt.show()
Seaborn builds upon Matplotlib’s foundation, offering a high-level interface for drawing attractive and informative statistical graphics.
import seaborn as sns sns.set_theme(style="whitegrid") tips = sns.load_dataset("tips") sns.boxplot(x=tips["total_bill"])
Plotly stands out with its ability to create interactive plots that can be embedded into web applications.
import plotly.express as px df = px.data.iris() fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species") fig.show()
For seamless integration into web applications, Bokeh is your go-to package. It facilitates the creation of interactive and real-time streaming plots.
from bokeh.plotting import figure, show p = figure(title = "simple line example") p.line([1, 2, 3, 4, 5], [6, 7, 2, 4, 7], line_width = 2) show(p)
Python remains a leading choice in data science, boasting a suite of potent visualization packages. Regardless of your experience level, these tools are indispensable for refining your data representation abilities.