[可视化]拓展：应用

1 函数：频数折线图

def freq_poly(x, bins=10, **kwargs) 
    """ Custom frequency polygon/line plot code. """
    #set bin edges if none or int specified
    if type(bins) == int:
        bins = np.linspace(x.min(), x.max(), bins+1)
    bin_centers = (bin_edges[1:] + bin_edges[:-1]) / 2

    #compute counts
    data_bins = pd.cut(x, bins, right = False, include_lowest = True)
    counts = x.groupby(data_bins).count()

    #create plot
    plt.errorbar(x=bin_centers, y=counts, **kwargs)

bin_edges = np.arange(-3, df['num_var'].max()+1/3, 1/3)
g = sb.FacetGrid(data=df, hue='cat_var', size=5)
g.map(freq_poly, 'num_var', bins=bin_edges)
g.add_legend()

折线图：多变量

2 plt.scatter没有jitter属性

def rand_jitter(arr):
    stdev = .01*(max(arr)-min(arr))
    return arr + np.random.randn(len(arr)) * stdev

def jitter(x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, hold=None, **kwargs):
    return scatter(rand_jitter(x), rand_jitter(y), s=s, c=c, marker=marker, cmap=cmap, norm=norm, vmin=vmin, vmax=vmax, alpha=alpha, linewidths=linewidths, verts=verts, hold=hold, **kwargs)

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【具体应用】

1. 绘制数学函数

# 第一步：定义求取积分的函数
def func(x):
 return 0.5 * np.exp(x) + 1 # 指数函数
# 第二步：定义积分区间，生成必须得数值
a, b = 0.5 , 1.5
x = np.linspace(0,2)
y = func(x)
# 第三步：绘制函数图像
fig, ax = plt.subplots(figsize=(7,5))
plt.plot(x,y,'b',linewidth=2)
plt.ylim(ymin=0)
# 第四步：使用Polygon函数生成阴影部分，表示积分面积
Ix = np.linspace(a, b)
Iy = func(Ix)
verts = [(a,0)] + list(zip(Ix, Iy)) + [(b, 0)]
poly = plt.Polygon(verts,facecolor='0.7',edgecolor='0.5')
ax.add_patch(poly)
# 第五步：使用plt.text和plt.figtext在图表上添加数学公式和一些坐标轴标签
plt.text(0.5 * (a + b),1,r"$\int_a^b f(x)\mathrm{d}x$",horizontalalignment='center',fontsize=20)
plt.figtext(0.9, 0.075, "$x$")
plt.figtext(0.075,0.9,"$f(x)$")
# 第六步：设置刻度标签以及添加网格
ax.set_xticks((a, b))
ax.set_xticklabels(('$a$', '$b$'))
ax.set_yticks([func(a), func(b)])
ax.set_yticklabels(('$f(a)$', '$f(b)$'))
plt.grid(True)

积分图 知乎@十维教育

2. 金融学图表
   2.1）烛柱图

import mpl_finance as mpf

import matplotlib.pyplot as plt
import mpl_finance as mpf
import tushare as ts
import pandas as pd
from matplotlib.pylab import date2num
from dateutil.parser import parse
import numpy as np
import matplotlib.dates as mdate
data = ts.get_k_data('000001') # 获取平安的k线数据
data_of = data[:60] # 只取前60份数据
fig, ax = plt.subplots(figsize=(15, 7))
__colorup__ = "r"
__colordown__ = "g"
# 图表显示中文
plt.rcParams['font.family'] = ['sans-serif']
plt.rcParams['font.sans-serif'] = ['SimHei']
qutotes = []
for index, (d, o, c, h, l) in enumerate(
 zip(data_of.date, data_of.open, data_of.close,
 data_of.high, data_of.low)):
 
 # 时间需要通过date2num转换为浮点型
 d = date2num(parse(d))
 # 日期，开盘，收盘，最高，最低组成tuple对象val
 val = (d, o, c, h, l)
 # 加val加入qutotes
 qutotes.append(val)
# 使用mpf.candlestick_ochl进行蜡烛绘制，ochl代表：open，close，high，low
mpf.candlestick_ochl(ax, qutotes, width=0.8, colorup=__colorup__,colordown=__colordown__)
#设置x轴为时间格式，否则x轴显示的将是类似于‘736268’这样的转码后的数字格式
ax.xaxis.set_major_formatter(mdate.DateFormatter('%Y-%m-%d'))
plt.xticks(pd.date_range('2016-08-01','2016-11-30',freq='W'),rotation=60)
plt.grid(True) # 网格设置
plt.title("k线图")
ax.autoscale_view()
ax.xaxis_date()

烛柱图 知乎@十维教育

2.2）3D绘图
波动率平面：同时展示许多到期日和行权价的隐含波动率

# 根据两个1维ndarray对象转换为一个二维数组
strike = np.linspace(50,150,24)
ttm = np.linspace(0.5,2.5,24)
strike,ttm = np.meshgrid(strike,ttm)
# 产生一组虚假的隐含波动率
iv = (strike - 100) ** 2/ (100 * strike) / ttm
# 生成波动率图表
fig = plt.figure(figsize=(9,6))
ax = fig.gca(projection='3d')
surf = ax.plot_surface(strike, ttm, iv, rstride=2, cstride=2, cmap=plt.cm.coolwarm, linewidth=0.5, antialiased=True)
ax.set_xlabel('strike')
ax.set_ylabel('time_to_maturity')
ax.set_zlabel('implied volatility')
fig.colorbar(surf,shrink=0.5,aspect=5)

3D绘图 知乎@十维教育

【拓展】

1. Bokeh - Python的交互式网络绘图

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References

Python技能 | Matplotlib数据可视化