Which Python Visualization Library Should You Choose? A Hands‑On Review of 8 Packages

Python developers often wonder which visualization library offers the best combination of aesthetics and functionality. This article reviews eight popular Python visualization packages, discusses their strengths, weaknesses, and suitable scenarios, and provides code examples.

Matplotlib, Seaborn and Pandas

These three packages are grouped together because Seaborn and Pandas are built on top of Matplotlib, sharing similar styling and syntax. They excel at exploratory data analysis but are less suited for polished presentations.

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))

The above code creates a bar chart of NBA teams with the highest median salary, using a custom color palette.

import matplotlib.pyplot as plt
import scipy.stats as stats

# model2 is a regression model
log_resid = model2.predict(X_test) - y_test
stats.probplot(log_resid, dist="norm", plot=plt)
plt.title("Normal Q-Q plot")
plt.show()

This snippet generates a Q‑Q plot to assess normality of regression residuals.

ggplot (Python implementation)

The Python version of ggplot mimics the R library’s grammar of graphics, relying on Pandas for data handling. Compatibility issues arise with newer Pandas releases, so the author recommends avoiding downgrading Pandas solely for ggplot.

# All Salaries
ggplot(data=df, aes(x=season_start, y=salary, colour=team)) +
  geom_point() +
  theme(legend.position="none") +
  labs(title = 'Salary Over Time', x='Year', y='Salary ($)')

Bokeh

Bokeh produces aesthetically pleasing, interactive charts suitable for dashboards. The example below visualizes responses to a masculinity survey.

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

# is_masc is a one‑hot encoded dataframe of responses to the question:
# "Do you identify as masculine?"

counts = is_masc.sum()
resps = is_masc.columns

p2 = figure(title='Do You View Yourself As Masculine?',
            x_axis_label='Response',
            y_axis_label='Count',
            x_range=list(resps))

p2.vbar(x=resps, top=counts, width=0.6, fill_color='red', line_color='black')
show(p2)

# Pandas quick bar plot for comparison
counts.plot(kind='bar')

Plotly

Plotly offers powerful interactive visualizations but requires more setup, including API keys. The author provides examples of a bar chart showing turnovers per minute by NBA team and a scatter plot of player minutes versus salary.

# plot 1 – barplot
data = [go.Bar(x=team_ave_df.team,
               y=team_ave_df.turnovers_per_mp)]

layout = go.Layout(
    title=go.layout.Title(text='Turnovers per Minute by Team', xref='paper', x=0),
    xaxis=go.layout.XAxis(title=go.layout.xaxis.Title(text='Team', font=dict(family='Courier New, monospace', size=18, color='#7f7f7f'))),
    yaxis=go.layout.YAxis(title=go.layout.yaxis.Title(text='Average Turnovers/Minute', font=dict(family='Courier New, monospace', size=18, color='#7f7f7f'))),
    autosize=True,
    hovermode='closest')
py.iplot(figure_or_data=data, layout=layout, filename='jupyter-plot', sharing='public', fileopt='overwrite')

# plot 2 – scatterplot
data = [go.Scatter(x=player_year.minutes_played,
                  y=player_year.salary,
                  marker=go.scatter.Marker(color='red', size=3))]

layout = go.Layout(title='test', xaxis=dict(title='why'), yaxis=dict(title='plotly'))
py.iplot(figure_or_data=data, layout=layout, filename='jupyter-plot2', sharing='public')

Pygal

Pygal is a lightweight library that follows the grammar‑of‑graphics approach. It is easy to use but requires rendering to a file and opening it in a browser, which can be cumbersome.

Networkx

Although built on Matplotlib, Networkx excels at graph and network visualizations. The following code demonstrates coloring nodes and drawing a sparse Facebook network.

options = {
    'node_color' : range(len(G)),
    'node_size' : 300,
    'width' : 1,
    'with_labels' : False,
    'cmap' : plt.cm.coolwarm
}
nx.draw(G, **options)

import itertools
import networkx as nx
import matplotlib.pyplot as plt

f = open('data/facebook/1684.circles', 'r')
circles = [line.split() for line in f]
f.close()

network = []
for circ in circles:
    cleaned = [int(val) for val in circ[1:]]
    network.append(cleaned)

G = nx.Graph()
for v in network:
    G.add_nodes_from(v)

edges = [itertools.combinations(net,2) for net in network]
for edge_group in edges:
    G.add_edges_from(edge_group)

options = {
    'node_color' : 'lime',
    'node_size' : 3,
    'width' : 1,
    'with_labels' : False,
}
nx.draw(G, **options)

There is no single "best" visualization package; the choice depends on the specific context and presentation goals.