可視化篇（四）——— python繪制雙y軸、箱線圖、概率分布三種圖形及案例

• 摘要
• 繪制雙y軸圖形
• 繪制箱線圖
• 繪制概率分布圖

摘要

本文演示了如何通過python繪制雙y軸、箱線圖、概率分布三種圖形，并給出了具體案例供讀者參考。

繪制雙y軸圖形

效果圖如下

代碼如下

import numpy as np import matplotlib.pyplot as plt from matplotlib.font_manager import FontProperties import datetime# 此設置為使圖形能顯示帶特殊格式的字符 plt.rcParams['axes.unicode_minus'] = False# 構建虛擬數據 """ x軸為日期 左y軸為y1，y2 右y軸為 y1-y2 及其均值 """ date = [(datetime.datetime.strptime("2019-12-08 00:00:00","%Y-%m-%d %H:%M:%S") + datetime.timedelta(days=i)).strftime("%Y-%m-%d%H:%M:%S")[:10] for i in range(300)] x = np.arange(300) x_ticks = [int(i) for i in np.linspace(0, (x.shape[0] - 1), 5)] y1 = np.random.randn(300) *2 + 75 y2 = np.random.randn(300) *2 + 70# 設置字體 font = FontProperties(fname="C:\Windows\Fonts\simsun.ttc", size=30)figure = plt.figure(figsize=[30, 16], dpi=72)ax1 = figure.add_subplot(111)fig1 = ax1.plot(x, y1, color="c", label="y1") fig2 = ax1.plot(x, y2, color="b", label="y2") plt.yticks(fontproperties=font) plt.xlabel("date", fontproperties=FontProperties(fname="C:\Windows\Fonts\simsun.ttc", size=45)) plt.xticks(x_ticks, date, fontproperties=FontProperties(fname="C:\Windows\Fonts\simsun.ttc", size=30), rotation=15) ax1.set_ylim(40, 83) ax1.set_ylabel("y", fontproperties=FontProperties(fname="C:\Windows\Fonts\simsun.ttc", size=45))ax2 = ax1.twinx()fig3 = ax2.plot(x, y1-y2, color="m", label="y1-y2") fig4 = ax2.plot(x, [(y1-y2).mean()] * (x.shape[0]), color="g", linestyle="--", linewidth=5, label="mean of y1-y2") ax2.set_ylim(-5, 40) ax2.set_ylabel("y1-y2", fontproperties=FontProperties(fname="C:\Windows\Fonts\simsun.ttc", size=45)) plt.yticks(fontproperties=font)# 顯示均值 ax2.text(x.shape[0], (y1-y2).mean() - 0.1, str((y1-y2).mean())[:4], fontdict={"size": 30, "color": "g"})# 設置圖列 legends = fig1 + fig2 + fig3 + fig4 labels = [l.get_label() for l in legends] plt.legend(legends, labels, prop=font, loc=(0.8, 0.4))plt.show()

繪制箱線圖

效果圖如下

代碼如下

import numpy as np import matplotlib.pyplot as plt from matplotlib.font_manager import FontProperties import seaborn as sns import pandas as pd# 此設置為使圖形能顯示帶特殊格式的字符 plt.rcParams['axes.unicode_minus'] = False# 構建虛擬數據 """ 繪制x1,x2,x3,x4四組數據的箱線圖 """ x1 = np.random.randn(200)*2+5 x2 = np.random.randn(200)*2+10 x3 = np.random.randn(200)*2+15 x4 = np.random.randn(200)*2+20 data = np.array([x1,x2,x3,x4]).T df = pd.DataFrame(data,columns=["x1","x2","x3","x4"])font = FontProperties(fname = "C:\Windows\Fonts\simsun.ttc",size=30) figure = plt.figure(figsize=[16,12],dpi=72)# whis為異常值在上下四分位點差值的多少倍以外 sns.boxplot(data = df, whis=3, orient="v", fliersize=15) plt.ylabel("Kpa", fontproperties=FontProperties(fname="C:\Windows\Fonts\simhei.ttf", size=45)) plt.yticks(fontproperties=font) plt.xticks(fontproperties = FontProperties(fname = "C:\Windows\Fonts\simsun.ttc",size=45)) plt.grid(linestyle="--", linewidth=1, alpha=0.5, axis="y") plt.show()

繪制概率分布圖

效果圖如下

代碼如下

import numpy as np import matplotlib.pyplot as plt from matplotlib.font_manager import FontProperties import seaborn as sns# 此設置為使圖形能顯示帶特殊格式的字符 plt.rcParams['axes.unicode_minus'] = False# 構建虛擬數據 """ 繪制x的概率分布圖 """ x = np.random.randn(1000)*2+100 figure = plt.figure(figsize=[30,26],dpi=72) sns.distplot(x,bins=30,kde_kws={"color":"red"},color="c") plt.xticks(fontproperties=FontProperties(fname="C:\Windows\Fonts\simsun.ttc",size=30)) plt.yticks(fontproperties=FontProperties( fname="C:\Windows\Fonts\simsun.ttc",size=30)) plt.xlabel(xlabel="活化能",fontproperties = FontProperties( fname="C:\Windows\Fonts\simsun.ttc",size=45)) plt.ylabel("概率密度",fontproperties = FontProperties( fname="C:\Windows\Fonts\simsun.ttc",size=45))plt.show()

by CyrusMay 2021 01 25

脫下長日的假面
奔向夢幻的疆界
南瓜馬車的午夜
換上童話的玻璃鞋
——————五月天（擁抱）——————

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