python 資料視覺化利器

摘要： 前段時間有讀者向我反映，想看看資料視覺化方面的文章，這不？現在就開始寫了，如果你想看哪些方面的文章，可以通過留言或者後臺告訴我。資料視覺化的第三方庫挺多的，這裡我主要推薦兩個，分別是 bokeh、pyecharts。 如果我的文章對你有幫助，歡迎關注、點贊、轉發，這樣我會更有動力做原創分享。...

前段時間有讀者向我反映，想看看資料視覺化方面的文章，這不？現在就開始寫了，如果你想看哪些方面的文章，可以通過留言或者後臺告訴我。資料視覺化的第三方庫挺多的，這裡我主要推薦兩個，分別是 bokeh、pyecharts。 如果我的文章對你有幫助，歡迎關注、點贊、轉發，這樣我會更有動力做原創分享。

bokeh

這裡展示一下常用的圖表和比較搶眼的圖表，詳細的文件可檢視（ ofollow,noindex">bokeh.pydata.org/en/latest/d…

條形圖

這配色看著還挺舒服的，比 pyecharts 條形圖的配色好看一點。

from bokeh.io import show, output_file
from bokeh.models import ColumnDataSource
from bokeh.palettes import Spectral6
from bokeh.plotting import figure
output_file("colormapped_bars.html")#配置輸出檔名
fruits = ['Apples', '魅族', 'OPPO', 'VIVO', '小米', '華為'] # 資料
counts = [5, 3, 4, 2, 4, 6] # 資料
source = ColumnDataSource(data=dict(fruits=fruits, counts=counts, color=Spectral6))
p = figure(x_range=fruits, y_range=(0,9), plot_height=250, title="Fruit Counts",
toolbar_location=None, tools="")# 條形圖配置項
p.vbar(x='fruits', top='counts', width=0.9, color='color', legend="fruits", source=source)
p.xgrid.grid_line_color = None # 配置網格線顏色
p.legend.orientation = "horizontal" # 圖表方向為水平方向
p.legend.location = "top_center"
show(p) # 展示圖表
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年度條形圖

可以對比不同時間點的量。

from bokeh.io import show, output_file
from bokeh.models import ColumnDataSource, FactorRange
from bokeh.plotting import figure
output_file("bars.html")
fruits = ['Apples', '魅族', 'OPPO', 'VIVO', '小米', '華為']
years = ['2015', '2016', '2017']
data = {'fruits': fruits,
'2015': [2, 1, 4, 3, 2, 4],
'2016': [5, 3, 3, 2, 4, 6],
'2017': [3, 2, 4, 4, 5, 3]}
x = [(fruit, year) for fruit in fruits for year in years]
counts = sum(zip(data['2015'], data['2016'], data['2017']), ())
source = ColumnDataSource(data=dict(x=x, counts=counts))
p = figure(x_range=FactorRange(*x), plot_height=250, title="Fruit Counts by Year",
toolbar_location=None, tools="")
p.vbar(x='x', top='counts', width=0.9, source=source)
p.y_range.start = 0
p.x_range.range_padding = 0.1
p.xaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None
show(p)
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餅圖

from collections import Counter
from math import pi
import pandas as pd
from bokeh.io import output_file, show
from bokeh.palettes import Category20c
from bokeh.plotting import figure
from bokeh.transform import cumsum
output_file("pie.html")
x = Counter({
'中國': 157,
'美國': 93,
'日本': 89,
'巴西': 63,
'德國': 44,
'印度': 42,
'義大利': 40,
'澳大利亞': 35,
'法國': 31,
'西班牙': 29
})
data = pd.DataFrame.from_dict(dict(x), orient='index').reset_index().rename(index=str, columns={0:'value', 'index':'country'})
data['angle'] = data['value']/sum(x.values()) * 2*pi
data['color'] = Category20c[len(x)]
p = figure(plot_height=350, title="Pie Chart", toolbar_location=None,
tools="hover", tooltips="@country: @value")
p.wedge(x=0, y=1, radius=0.4,
start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
line_color="white", fill_color='color', legend='country', source=data)
p.axis.axis_label=None
p.axis.visible=False
p.grid.grid_line_color = None
show(p)
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條形圖

from bokeh.io import output_file, show
from bokeh.models import ColumnDataSource
from bokeh.palettes import GnBu3, OrRd3
from bokeh.plotting import figure
output_file("stacked_split.html")
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
years = ["2015", "2016", "2017"]
exports = {'fruits': fruits,
'2015': [2, 1, 4, 3, 2, 4],
'2016': [5, 3, 4, 2, 4, 6],
'2017': [3, 2, 4, 4, 5, 3]}
imports = {'fruits': fruits,
'2015': [-1, 0, -1, -3, -2, -1],
'2016': [-2, -1, -3, -1, -2, -2],
'2017': [-1, -2, -1, 0, -2, -2]}
p = figure(y_range=fruits, plot_height=250, x_range=(-16, 16), title="Fruit import/export, by year",
toolbar_location=None)
p.hbar_stack(years, y='fruits', height=0.9, color=GnBu3, source=ColumnDataSource(exports),
legend=["%s exports" % x for x in years])
p.hbar_stack(years, y='fruits', height=0.9, color=OrRd3, source=ColumnDataSource(imports),
legend=["%s imports" % x for x in years])
p.y_range.range_padding = 0.1
p.ygrid.grid_line_color = None
p.legend.location = "top_left"
p.axis.minor_tick_line_color = None
p.outline_line_color = None
show(p)
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散點圖

from bokeh.plotting import figure, output_file, show
output_file("line.html")
p = figure(plot_width=400, plot_height=400)
p.circle([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=20, color="navy", alpha=0.5)
show(p)
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六邊形圖

這兩天，馬蜂窩剛被發現資料造假，這不，與馬蜂窩應應景。

import numpy as np
from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.util.hex import axial_to_cartesian
output_file("hex_coords.html")
q = np.array([0, 0, 0, -1, -1, 1, 1])
r = np.array([0, -1, 1, 0, 1, -1, 0])
p = figure(plot_width=400, plot_height=400, toolbar_location=None) # 
p.grid.visible = False # 配置網格是否可見
p.hex_tile(q, r, size=1, fill_color=["firebrick"] * 3 + ["navy"] * 4,
line_color="white", alpha=0.5)
x, y = axial_to_cartesian(q, r, 1, "pointytop")
p.text(x, y, text=["(%d, %d)" % (q, r) for (q, r) in zip(q, r)],
text_baseline="middle", text_align="center")
show(p)
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環比條形圖

這個實現挺厲害的，看了一眼就吸引了我。我在程式碼中都做了一些註釋，希望對你理解有幫助。注：圓心為正中央，即直角座標系中標籤為（0，0）的地方。

from collections import OrderedDict
from math import log, sqrt
import numpy as np
import pandas as pd
from six.moves import cStringIO as StringIO
from bokeh.plotting import figure, show, output_file

antibiotics = """
bacteria,penicillin, streptomycin, neomycin, gram
結核分枝桿菌,800,5,2,negative
沙門氏菌,10,0.8,0.09,negative
變形桿菌,3,0.1,0.1,negative
肺炎克雷伯氏菌,850,1.2,1,negative
布魯氏菌,1,2,0.02,negative
銅綠假單胞菌,850,2,0.4,negative
大腸桿菌,100,0.4,0.1,negative
產氣桿菌,870,1,1.6,negative
白色葡萄球菌,0.007,0.1,0.001,positive
溶血性鏈球菌,0.001,14,10,positive
草綠色鏈球菌,0.005,10,40,positive
肺炎雙球菌,0.005,11,10,positive
"""

drug_color = OrderedDict([# 配置中間標籤名稱與顏色
("盤尼西林", "#0d3362"),
("鏈黴素", "#c64737"),
("新黴素", "black"),
])
gram_color = {
"positive": "#aeaeb8",
"negative": "#e69584",
}
# 讀取資料
df = pd.read_csv(StringIO(antibiotics),
skiprows=1,
skipinitialspace=True,
engine='python')
width = 800
height = 800
inner_radius = 90
outer_radius = 300 - 10

minr = sqrt(log(.001 * 1E4))
maxr = sqrt(log(1000 * 1E4))
a = (outer_radius - inner_radius) / (minr - maxr)
b = inner_radius - a * maxr


def rad(mic):
return a * np.sqrt(np.log(mic * 1E4)) + b
big_angle = 2.0 * np.pi / (len(df) + 1)
small_angle = big_angle / 7
# 整體配置
p = figure(plot_width=width, plot_height=height, title="",
x_axis_type=None, y_axis_type=None,
x_range=(-420, 420), y_range=(-420, 420),
min_border=0, outline_line_color="black",
background_fill_color="#f0e1d2")
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# annular wedges
angles = np.pi / 2 - big_angle / 2 - df.index.to_series() * big_angle#計算角度
colors = [gram_color[gram] for gram in df.gram] # 配置顏色
p.annular_wedge(
0, 0, inner_radius, outer_radius, -big_angle + angles, angles, color=colors,
)

# small wedges
p.annular_wedge(0, 0, inner_radius, rad(df.penicillin),
-big_angle + angles + 5 * small_angle, -big_angle + angles + 6 * small_angle,
color=drug_color['盤尼西林'])
p.annular_wedge(0, 0, inner_radius, rad(df.streptomycin),
-big_angle + angles + 3 * small_angle, -big_angle + angles + 4 * small_angle,
color=drug_color['鏈黴素'])
p.annular_wedge(0, 0, inner_radius, rad(df.neomycin),
-big_angle + angles + 1 * small_angle, -big_angle + angles + 2 * small_angle,
color=drug_color['新黴素'])
# 繪製大圓和標籤
labels = np.power(10.0, np.arange(-3, 4))
radii = a * np.sqrt(np.log(labels * 1E4)) + b
p.circle(0, 0, radius=radii, fill_color=None, line_color="white")
p.text(0, radii[:-1], [str(r) for r in labels[:-1]],
text_font_size="8pt", text_align="center", text_baseline="middle")
# 半徑
p.annular_wedge(0, 0, inner_radius - 10, outer_radius + 10,
-big_angle + angles, -big_angle + angles, color="black")
# 細菌標籤
xr = radii[0] * np.cos(np.array(-big_angle / 2 + angles))
yr = radii[0] * np.sin(np.array(-big_angle / 2 + angles))
label_angle = np.array(-big_angle / 2 + angles)
label_angle[label_angle < -np.pi / 2] += np.pi# easier to read labels on the left side
# 繪製各個細菌的名字
p.text(xr, yr, df.bacteria, angle=label_angle,
text_font_size="9pt", text_align="center", text_baseline="middle")
# 繪製圓形，其中數字分別為 x 軸與 y 軸標籤
p.circle([-40, -40], [-370, -390], color=list(gram_color.values()), radius=5)
# 繪製文字
p.text([-30, -30], [-370, -390], text=["Gram-" + gr for gr in gram_color.keys()],
text_font_size="7pt", text_align="left", text_baseline="middle")
# 繪製矩形，中間標籤部分。其中 -40，-40，-40 為三個矩形的 x 軸座標。18，0，-18 為三個矩形的 y 軸座標
p.rect([-40, -40, -40], [18, 0, -18], width=30, height=13,
color=list(drug_color.values()))
# 配置中間標籤文字、文字大小、文字對齊方式
p.text([-15, -15, -15], [18, 0, -18], text=list(drug_color),
text_font_size="9pt", text_align="left", text_baseline="middle")
output_file("burtin.html", title="burtin.py example")
show(p)
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元素週期表

元素週期表，這個實現好牛逼啊，距離初三剛開始學化學已經很遙遠了，想當年我還是化學課代表呢！由於基本用不到化學了，這裡就不實現了。

pyecharts

pyecharts 也是一個比較常用的資料視覺化庫，用得也是比較多的了，是百度 echarts 庫的 python 支援。這裡也展示一下常用的圖表。文件地址為（ pyecharts.org/#/zh-cn/pre…

條形圖

from pyecharts import Bar
bar = Bar("我的第一個圖表", "這裡是副標題")
bar.add("服裝", ["襯衫", "羊毛衫", "雪紡衫", "褲子", "高跟鞋", "襪子"], [5, 20, 36, 10, 75, 90])
# bar.print_echarts_options() # 該行只為了列印配置項，方便除錯時使用
bar.render()# 生成本地 HTML 檔案
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散點圖

from pyecharts import Polar
import random
data_1 = [(10, random.randint(1, 100)) for i in range(300)]
data_2 = [(11, random.randint(1, 100)) for i in range(300)]
polar = Polar("極座標系-散點圖示例", width=1200, height=600)
polar.add("", data_1, type='scatter')
polar.add("", data_2, type='scatter')
polar.render()
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餅圖

import random
from pyecharts import Pie
attr = ['A', 'B', 'C', 'D', 'E', 'F']
pie = Pie("餅圖示例", width=1000, height=600)
pie.add(
"",
attr,
[random.randint(0, 100) for _ in range(6)],
radius=[50, 55],
center=[25, 50],
is_random=True,
)
pie.add(
"",
attr,
[random.randint(20, 100) for _ in range(6)],
radius=[0, 45],
center=[25, 50],
rosetype="area",
)
pie.add(
"",
attr,
[random.randint(0, 100) for _ in range(6)],
radius=[50, 55],
center=[65, 50],
is_random=True,
)
pie.add(
"",
attr,
[random.randint(20, 100) for _ in range(6)],
radius=[0, 45],
center=[65, 50],
rosetype="radius",
)
pie.render()
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詞雲

這個是我在前面的文章中用到的圖片例項，這裡就不 po 具體資料了。

from pyecharts import WordCloud
name = ['Sam S Club'] # 詞條
value = [10000] # 權重
wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", name, value, word_size_range=[20, 100])
wordcloud.render()
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樹圖

這個是我在前面的文章中用到的圖片例項，這裡就不 po 具體資料了。

地圖

from pyecharts import Map

value = [155, 10, 66, 78, 33, 80, 190, 53, 49.6]
attr = [
"福建", "山東", "北京", "上海", "甘肅", "新疆", "河南", "廣西", "西藏"
]
map = Map("Map 結合 VisualMap 示例", width=1200, height=600)
map.add(
"",
attr,
value,
maptype="china",
is_visualmap=True,
visual_text_color="#000",
)
map.render()
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3D 散點圖

from pyecharts import Scatter3D
import random
data = [
[random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100)] for _ in range(80)
]
range_color = [
'#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
scatter3D = Scatter3D("3D 散點圖示例", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=range_color)
scatter3D.render()
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後記

大概介紹就是這樣了，兩個庫的功能都挺強大的，bokeh 的中文資料會少一點，如果閱讀英文有點難度，還是建議使用 pyecharts 就好。總體也不是很難，按照文件來修改資料都能夠直接上手使用。主要是多練習。

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