其實(shí)特簡(jiǎn)單！Python 可輕松制作這樣酷炫的可視化圖形！
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對(duì)于從事數(shù)據(jù)的人來(lái)說(shuō)，想要闡述自己的觀點(diǎn)時(shí)，單調(diào)乏味的文本和數(shù)字，很難抓住別人的眼球。很多時(shí)候，一張炫酷圖就足以勝過(guò)千言萬(wàn)語(yǔ)。
本文基于 Python 的 Plotly 圖形庫(kù)，介紹幾種工作中常用的動(dòng)畫(huà)圖和交互式圖標(biāo)。在使用之前看一下是否安裝了 Plotly。
朝陽(yáng)圖
層次結(jié)構(gòu)數(shù)據(jù)通常存儲(chǔ)為矩形數(shù)據(jù)框，其中不同的列對(duì)應(yīng)于層次結(jié)構(gòu)的不同級(jí)別。px.sunburst可以采用path與列列表相對(duì)應(yīng)的參數(shù)。請(qǐng)注意，如果給出id，則parent不應(yīng)提供path。
import plotly.express as px
df = px.data.tips()
fig = px.sunburst(df, path=['day', 'time', 'sex'], values='total_bill')
fig.show()效果圖
桑基圖
桑基圖通過(guò)定義可視化到流動(dòng)的貢獻(xiàn)源來(lái)表示源節(jié)點(diǎn)，目標(biāo)為目標(biāo)節(jié)點(diǎn)，數(shù)值以設(shè)置流volum，和標(biāo)簽，顯示了節(jié)點(diǎn)名稱,在流量分析中常用。
import plotly.graph_objects as go
import urllib, json
url = 'https://raw.githubusercontent.com/plotly/plotly.js/master/test/image/mocks/sankey_energy.json'
response = urllib.request.urlopen(url)
data = json.loads(response.read())
# override gray link colors with 'source' colors
opacity = 0.4
# change 'magenta' to its 'rgba' value to add opacity
data['data'][0]['node']['color'] = ['rgba(255,0,255, 0.8)' if color == "magenta" else color for color in data['data'][0]['node']['color']]
data['data'][0]['link']['color'] = [data['data'][0]['node']['color'][src].replace("0.8", str(opacity))
for src in data['data'][0]['link']['source']]
fig = go.Figure(data=[go.Sankey(
valueformat = ".0f",
valuesuffix = "TWh",
# Define nodes
node = dict(
pad = 15,
thickness = 15,
line = dict(color = "black", width = 0.5),
label = data['data'][0]['node']['label'],
color = data['data'][0]['node']['color']
),
# Add links
link = dict(
source = data['data'][0]['link']['source'],
target = data['data'][0]['link']['target'],
value = data['data'][0]['link']['value'],
label = data['data'][0]['link']['label'],
color = data['data'][0]['link']['color']
))])
fig.update_layout(title_text="Energy forecast for 2050<br>Source: Department of Energy & Climate Change, Tom Counsell via <a ,
font_size=10)
fig.show()
效果圖
雷達(dá)圖
雷達(dá)圖（也稱為蜘蛛情節(jié)或情節(jié)星）顯示器在從中心軸始發(fā)表示定量變量的二維圖的形式多變量數(shù)據(jù)。軸的相對(duì)位置和角度通常是無(wú)用的。它等效于軸沿徑向排列的平行坐標(biāo)圖。
import plotly.graph_objects as go
categories = ['processing cost','mechanical properties','chemical stability',
'thermal stability', 'device integration']
fig = go.Figure()
fig.add_trace(go.Scatterpolar(
r=[1, 5, 2, 2, 3],
theta=categories,
fill='toself',
name='Product A'
))
fig.add_trace(go.Scatterpolar(
r=[4, 3, 2.5, 1, 2],
theta=categories,
fill='toself',
name='Product B'
))
fig.update_layout(
polar=dict(
radialaxis=dict(
visible=True,
range=[0, 5]
)),
showlegend=False
)
fig.show()
效果圖
漏斗圖
漏斗圖通常用于表示業(yè)務(wù)流程不同階段的數(shù)據(jù)。在商業(yè)智能中，這是識(shí)別流程潛在問(wèn)題區(qū)域的重要機(jī)制。例如，它用于觀察銷售過(guò)程中每個(gè)階段的收入或損失，并顯示逐漸減小的值。每個(gè)階段均以占所有值的百分比表示。
from plotly import graph_objects as go
fig = go.Figure()
fig.add_trace(go.Funnel(
name = 'Montreal',
y = ["Website visit", "Downloads", "Potential customers", "Requested price"],
x = [120, 60, 30, 20],
textinfo = "value+percent initial"))
fig.add_trace(go.Funnel(
name = 'Toronto',
orientation = "h",
y = ["Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent"],
x = [100, 60, 40, 30, 20],
textposition = "inside",
textinfo = "value+percent previous"))
fig.add_trace(go.Funnel(
name = 'Vancouver',
orientation = "h",
y = ["Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent", "Finalized"],
x = [90, 70, 50, 30, 10, 5],
textposition = "outside",
textinfo = "value+percent total"))
fig.show()
效果圖
3D表面圖
具有輪廓的曲面圖，使用contours屬性顯示和自定義每個(gè)軸的輪廓數(shù)據(jù)。
import plotly.graph_objects as go
import pandas as pd
# Read data from a csv
z_data = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/api_docs/mt_bruno_elevation.csv')
fig = go.Figure(data=[go.Surface(z=z_data.values)])
fig.update_traces(contours_z=dict(show=True, usecolormap=True,
highlightcolor="limegreen", project_z=True))
fig.update_layout(title='Mt Bruno Elevation', autosize=False,
scene_camera_eye=dict(x=1.87, y=0.88, z=-0.64),
width=500, height=500,
margin=dict(l=65, r=50, b=65, t=90)
)
fig.show()
效果圖
動(dòng)畫(huà)圖
一些Plotly Express函數(shù)支持通過(guò)animation_frame和animation_group參數(shù)創(chuàng)建動(dòng)畫(huà)人物。這是使用Plotly Express創(chuàng)建的動(dòng)畫(huà)散點(diǎn)圖的示例。請(qǐng)注意，您應(yīng)始終修復(fù)x_range和，y_range以確保您的數(shù)據(jù)在整個(gè)動(dòng)畫(huà)中始終可見(jiàn)。
import plotly.express as px
df = px.data.gapminder()
px.scatter(df, x="gdpPercap", y="lifeExp", animation_frame="year", animation_group="country",
size="pop", color="continent", hover_name="country",
log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90])
效果圖
結(jié)論
可視化的圖形在日常工作中經(jīng)常實(shí)用，其中 Plotly 是用過(guò)的體驗(yàn)比較好的，本篇文章分享給大家一些案例，Plotly可 視化遠(yuǎn)不止這些，在后續(xù)的文章中，涉及可視化部分的，將介紹更多酷炫的可視化圖形，喜歡點(diǎn)個(gè)在看分享，收藏以備不時(shí)之需。
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