探索性數(shù)據(jù)分析，這8個流行的 Python可視化工具就夠了

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')

用 Bokeh 表示調(diào)查結(jié)果

• 安裝時要有 API 秘鑰，還要注冊，不是只用 pip 安裝就可以；

• Plotly 所繪制的數(shù)據(jù)和布局對象是獨(dú)一無二的，但并不直觀；

• 圖片布局對我來說沒有用（40 行代碼毫無意義！）

• 你可以在 Plotly 網(wǎng)站和 Python 環(huán)境中編輯圖片；

• 支持交互式圖片和商業(yè)報表；

• Plotly 與 Mapbox 合作，可以自定義地圖；

• 很有潛力繪制優(yōu)秀圖形。

#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')

• 實(shí)例化圖片；

• 用圖片目標(biāo)屬性格式化；

• 用 figure.add() 將數(shù)據(jù)添加到圖片中。

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)

-END-

往期精彩推薦 --??

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-- 2、Python 造假數(shù)據(jù)，用Faker就夠了
-- 3、在Python中玩轉(zhuǎn)Json數(shù)據(jù)



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