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)