python As a powerful programming language, it provides a rich for data visualization Tool box. These tools enable data scientists and analysts to transform complex data into intuitive and understandable visualizations that reveal patterns, trends and insights.
1. Matplotlib: basic and flexibleMatplotlib is one of the most popular
Python visualization libraries. It provides a range of plotting functions, including line graphs, bar graphs, scatter plots, and histograms. It allows for a high degree of customization, allowing you to create professional-grade visualizations.
import matplotlib.pyplot as plt
plt.plot(x, y)
plt.xlabel("x-axis")
plt.ylabel("y-axis")
plt.title("My Plot")
plt.show()
Seaborn is built on Matplotlib and provides a more advanced interface that focuses more on statistical data visualization. It offers pre-made themes and color schemes that simplify creating beautiful and informative visualizations.
import seaborn as sns sns.scatterplot(x, y) sns.set_theme() plt.show()
pandas
Profiling is an automateddata analysis and exploration tool. It generates an interactive html report with detailed statistics and visualizations about the columns and rows in the dataframe, which helps quickly identify patterns and outliers.
4. Plotly: interactive and dynamicPlotly is a popular interactive visualization library. It allows the creation of 2D and
3D interactive charts that can be viewed in a web browser. Plotly is especially useful for exploring complex data sets.
import plotly.express as px
fig = px.scatter_3d(df, x="x", y="y", z="z")
fig.show()
Bokeh is a visualization library focusing on
performance optimization. It uses just-in-time compiler technology to generate visualizations on the client side, enabling high frame rates and fast response times for interactive visualizations.
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure, output_notebook
output_notebook()
source = ColumnDataSource(data=df)
p = figure(x_axis_label="x", y_axis_label="y")
p.circle(source=source, x="x", y="y")
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