八个流行的 Python 可视化工具包，你喜欢哪个？

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))

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()

#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 ($)')

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?"
#Dataframe Prep
counts = is_masc.sum()
resps = is_masc.columns
#Bokeh
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
counts.plot(kind='bar')

#plot 1 - barplot
# **note** - the layout lines do nothing and trip no errors
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 - attempt at a 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')
[Image: image.png]

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)