概述
前言
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（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
複製程式碼年度條形圖
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)
複製程式碼餅圖
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)
複製程式碼條形圖
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)
複製程式碼散點圖
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)
複製程式碼六邊形圖
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)
複製程式碼環比條形圖
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)
複製程式碼元素週期表
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
複製程式碼散點圖
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()
複製程式碼餅圖
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()
複製程式碼詞雲
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()
複製程式碼樹圖
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()
複製程式碼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()
複製程式碼後記
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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