Which Python Visualization Library Is Right for You? A Deep Dive into 8 Packages

Python developers often wonder which visualization library to choose. This article reviews eight popular Python visualization packages, describing their strengths, typical use cases, and providing code snippets.

Matplotlib, Seaborn, and Pandas

Matplotlib is a low‑level library offering extensive customization; Seaborn and Pandas are built on Matplotlib, providing similar aesthetics and simpler syntax for exploratory data analysis. While powerful for detailed plots, they may not be ideal for presentation‑level graphics.

import seaborn as sns
import matplotlib.pyplot as plt
color_order = ['xkcd:cerulean', 'xkcd:ocean', 'xkcd:black', 'xkcd:royal purple',
               'xkcd:royal purple', 'xkcd:navy blue', 'xkcd:powder blue',
               'xkcd:light maroon', 'xkcd:lightish blue', 'xkcd:navy']
sns.barplot(x=top10.Team, y=top10.Salary, palette=color_order).set_title('Teams with Highest Median Salary')
plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))

ggplot

ggplot (Python implementation of R's ggplot2) uses a grammar of graphics, allowing layered construction of plots. It depends on Pandas and may require older Pandas versions, which can cause compatibility issues.

from ggplot import ggplot, aes, geom_point, labs, theme

ggplot(df, aes('season_start', 'salary', colour='team')) \
    + geom_point() \
    + theme(legend_position='none') \
    + labs(title='Salary Over Time', x='Year', y='Salary ($)')

Bokeh

Bokeh provides a ggplot‑like grammar with strong support for interactive and commercial dashboards. The example shows a colored bar chart of survey responses.

import pandas as pd
from bokeh.plotting import figure, show

counts = is_masc.sum()
responses = is_masc.columns
p = figure(title='Do You View Yourself As Masculine?',
           x_axis_label='Response', y_axis_label='Count',
           x_range=list(responses))

p.vbar(x=responses, top=counts, width=0.6, fill_color='red', line_color='black')
show(p)

Plotly

Plotly is powerful and supports interactive graphics and Mapbox integration, but it requires API keys and can be verbose. The example demonstrates a bar chart of turnovers per minute.

# Plotly example
import plotly.graph_objs as go

data = [go.Bar(x=team_ave_df.team, y=team_ave_df.turnovers_per_mp)]
layout = go.Layout(title='Turnovers per Minute by Team')
py.iplot(data, layout=layout)

Pygal

Pygal is a lightweight library that follows the grammar of graphics, suitable for simple visualizations but requires rendering to files and opening them in a browser.

NetworkX

NetworkX, built on Matplotlib, excels at graph and network visualizations. The example loads a Facebook circles dataset and draws a sparse network.

import itertools, networkx as nx, matplotlib.pyplot as plt
# Load data, build graph G, then:
options = {'node_color':'lime','node_size':3,'width':1,'with_labels':False}
nx.draw(G, **options)

There is no single best library; the choice depends on the specific scenario, such as exploratory analysis, presentation quality, interactivity, or network analysis.

Original source: https://towardsdatascience.com/reviewing-python-visualization-packages-fa7fe12e622b