import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
# 不发出警告
from bokeh.io import output_notebook
output_notebook()
# 导入notebook绘图模块
from bokeh.plotting import figure,show
from bokeh.models import ColumnDataSource
# 导入图表绘制、图标展示模块
# 导入ColumnDataSource模块
1、单系列柱状图-vbar
p = figure(plot_width=400, plot_height=400)
p.vbar(x=[1, 2, 3], width=0.5, bottom=0,top=[1.2, 2.5, 3.7], # x：横轴坐标，width：宽度，bottom：底高度，top：顶高度
#color = ['red','blue','green'], alpha = 0.8 # 整体颜色设置，也可单独设置 → color="firebrick"
line_width = 1,line_alpha = 0.8,line_color = 'black', line_dash = [5,2], # 单独设置线参数
fill_color = 'red',fill_alpha = 0.6 # 单独设置填充颜色参数
)
# 绘制竖向柱状图
show(p)
image.png
2.单系列柱状图 - 分类设置标签-ColumnDataSource
from bokeh.palettes import Spectral6
from bokeh.transform import factor_cmap
# 导入相关模块
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
counts = [5, 3, 4, 2, 4, 6]
source = ColumnDataSource(data=dict(fruits=fruits, counts=counts))
colors = [ "salmon", "olive", "darkred", "goldenrod", "skyblue", "orange"]
# 创建一个包含标签的data，对象类型为ColumnDataSource
p = figure(x_range=fruits, y_range=(0,9), plot_height=350, title="Fruit Counts",tools="")
#plot_height=350高度
p.vbar(x='fruits', top='counts', source=source, # 加载数据另一个方式
width=0.9, alpha = 0.8,
color = factor_cmap('fruits', palette=Spectral6, factors=fruits), # 设置颜色
legend="fruits")
# 绘制柱状图，横轴直接显示标签
# factor_cmap(field_name, palette, factors, start=0, end=None, nan_color='gray')：颜色转换模块，生成一个颜色转换对象
# field_name：分类名称
# palette：调色盘
# factors：用于在调色盘中分颜色的参数
# 参考文档：http://bokeh.pydata.org/en/latest/docs/reference/transform.html
p.xgrid.grid_line_color = None
p.legend.orientation = "horizontal"
p.legend.location = "top_center"
# 其他参数设置
show(p)
image.png
2、多系列柱状图-vbar
from bokeh.transform import dodge
from bokeh.core.properties import value
# 导入dodge、value模块
df = pd.DataFrame({'2015':[2, 1, 4, 3, 2, 4],'2016':[5, 3, 3, 2, 4, 6], '2017':[3, 2, 4, 4, 5, 3]},
index = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'])
# 创建数据
fruits = df.index.tolist() # 横坐标
years = df.columns.tolist() # 系列名
data = {'index':fruits}
for year in years:
data[year] = df[year].tolist()
print(data)
# 生成数据，数据格式为dict
source = ColumnDataSource(data=data)
# 将数据转化为ColumnDataSource对象
p = figure(x_range=fruits, y_range=(0, 10), plot_height=350, title="Fruit Counts by Year",tools="")
p.vbar(x=dodge('index', -0.25, range=p.x_range), top='2015', width=0.2, source=source,color="#c9d9d3", legend=value("2015"))
p.vbar(x=dodge('index', 0.0, range=p.x_range), top='2016', width=0.2, source=source,color="#718dbf", legend=value("2016"))
p.vbar(x=dodge('index', 0.25, range=p.x_range), top='2017', width=0.2, source=source,color="#e84d60", legend=value("2017"))
# 绘制多系列柱状图
# dodge(field_name, value, range=None) → 转换成一个可分组的对象，value为元素的位置（配合width设置）
# value(val, transform=None) → 按照年份分为dict
p.xgrid.grid_line_color = None
p.legend.location = "top_left"
p.legend.orientation = "horizontal"
# 其他参数设置
show(p)
#结果：
{'index': ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'], '2015': [2, 1, 4, 3, 2, 4], '2016': [5, 3, 3, 2, 4, 6], '2017': [3, 2, 4, 4, 5, 3]}
image.png
3、堆叠图
from bokeh.core.properties import value
# 导入value模块
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
years = ["2015", "2016", "2017"]
colors = ["#c9d9d3", "#718dbf", "#e84d60"]
data = {'fruits' : fruits,
'2015' : [2, 1, 4, 3, 2, 4],
'2016' : [5, 3, 4, 2, 4, 6],
'2017' : [3, 2, 4, 4, 5, 3]}
source = ColumnDataSource(data=data)
# 创建数据
p = figure(x_range=fruits, plot_height=350, title="Fruit Counts by Year",tools="")
renderers = p.vbar_stack(years, # 设置堆叠值，这里source中包含了不同年份的值，years变量用于识别不同堆叠层
x='fruits', # 设置x坐标
source=source,
width=0.9, color=colors,
legend=[value(x) for x in years], name=years)
# 绘制堆叠图
# 注意第一个参数需要放years
p.xgrid.grid_line_color = None
p.axis.minor_tick_line_color = None
p.outline_line_color = None
p.legend.location = "top_left"
p.legend.orientation = "horizontal"
# 设置其他参数
show(p)
image.png
4.堆叠图
from bokeh.palettes import GnBu3, OrRd3
# 导入颜色模块
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=350, x_range=(-16, 16), title="Fruit import/export, by year")
p.hbar_stack(years, y='fruits', height=0.9, color=GnBu3, source=ColumnDataSource(exports),
legend=["%s exports" % x for x in years]) # 绘制出口数据堆叠图
p.hbar_stack(years, y='fruits', height=0.9, color=OrRd3, source=ColumnDataSource(imports),
legend=["%s imports" % x for x in years]) # 绘制进口数据堆叠图，这里值为负值
p.y_range.range_padding = 0.2 # 调整边界间隔
p.ygrid.grid_line_color = None
p.legend.location = "top_left"
p.axis.minor_tick_line_color = None
p.outline_line_color = None
# 设置其他参数
show(p)
image.png
5.直方图-np.histogram + figure.quad()，不需要构建ColumnDataSource对象
df = pd.DataFrame({'value': np.random.randn(1000)*100})
df.index.name = 'index'
print(df.head())
# 创建数据
hist, edges = np.histogram(df['value'],bins=20)
print(hist[:5])
print(edges)
# 将数据解析成直方图统计格式
# 高阶函数np.histogram(a, bins=10, range=None, weights=None, density=None)
# a：数据
# bins：箱数
# range：最大最小值的范围，如果不设定则为(a.min(), a.max())
# weights：权重
# density：为True则返回“频率”，为False则返回“计数”
# 返回值1 - hist：每个箱子的统计值（top）
# 返回值2 - edges：每个箱子的位置坐标，这里n个bins将会有n+1个edges
p = figure(title="HIST", tools="save",background_fill_color="#E8DDCB")
p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], # 分别代表每个柱子的四边值
fill_color="#036564", line_color="#033649")
# figure.quad绘制直方图
show(p)
#结果：
value
index
0 -6.749140
1 29.988627
2 -8.200213
3 -107.498925
4 -10.983780
[1 0 0 2 5]
[-4.15892896e+02 -3.81201622e+02 -3.46510349e+02 -3.11819075e+02
-2.77127802e+02 -2.42436528e+02 -2.07745254e+02 -1.73053981e+02
-1.38362707e+02 -1.03671433e+02 -6.89801597e+01 -3.42888861e+01
4.02387569e-01 3.50936612e+01 6.97849349e+01 1.04476208e+02
1.39167482e+02 1.73858756e+02 2.08550029e+02 2.43241303e+02
2.77932577e+02]
image.png
