Python数据可视化：数据分布图表可视化案例

# -*- coding: utf-8 -*- # %% from scipy import stats import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt from plotnine import * #N = 300 #x1 = np.random.normal(1.5, 0.5,N) #y1 = np.random.normal(2,0.2, N) # df=pd.DataFrame({'x':x1,'y':y1}) tips = sns.load_dataset("tips") df = pd.DataFrame({'x': tips['total_bill'], 'y': tips['tip']}) df # %% sns.set_style("ticks") sns.set_context("notebook", font_scale=1.5, rc={'font.size': 13, 'axes.labelsize': 16, 'legend.fontsize': 16, 'xtick.labelsize': 15, 'ytick.labelsize': 15}) # ---------------------------------(a) 二维散点与统计直方图 -------------------------------- sns_hist = sns.jointplot(x='x', y='y', # 设置xy轴，显示columns名称 data=df, # 设置数据 color='#D31A8A', # 设置颜色 s=60, edgecolor="w", linewidth=0.5, # 设置散点大小、边缘线颜色及宽度(只针对scatter） kind='scatter', # kind="kde","hex","reg" space=0, # 设置散点图和布局图的间距 size=5, ratio=5, # 散点图与布局图高度比，整型 xlim=(0, 60), marginal_kws=dict(bins=20, rug=False, hist_kws={'edgecolor': 'k', 'color': '#7CBC47', 'alpha': 1}) # 设置柱状图箱数，是否设置rug ) sns_hist.set_axis_labels(xlabel='total bill', ylabel='tip') # sns_hist.savefig('scatter_hist.pdf') # %% # ---------------------------------(b) 带趋势线的二维散点与统计直方图---------------------------------------------------- sns_reg = sns.jointplot(x='x', y='y', # 设置xy轴，显示columns名称 data=df, # 设置数据 color='#7CBC47', # 设置颜色 # 设置散点大小、边缘线颜色及宽度(只针对scatter） kind='reg', # kind="kde","hex","reg" space=0, # 设置散点图和布局图的间距 size=5, ratio=5, # 散点图与布局图高度比，整型 scatter_kws={"color": "#7CBC47", "alpha": 0.7, "s": 30, 'marker': "+"}, line_kws={"color": "#D31A8A", "alpha": 1, "lw": 4}, marginal_kws=dict(bins=20, rug=False, hist_kws={'edgecolor': 'k', 'color': '#7CBC47', 'alpha': 1}) # 设置柱状图箱数，是否设置rug ) sns_reg.set_axis_labels(xlabel='total bill', ylabel='tip') # sns_reg.savefig('scatter_reg.pdf') # %% # ----------------------------------(c) 二维与一维统计直方图--------------------------------------------------- sns_hex = sns.jointplot(x='x', y='y', # 设置xy轴，显示columns名称 data=df, # 设置数据 # 设置散点大小、边缘线颜色及宽度(只针对scatter） kind='hex', # kind="kde","hex","reg" color='#D31A8A', linewidth=0.1, space=0, # 设置散点图和布局图的间距 size=5, ratio=5, # 散点图与布局图高度比，整型 xlim=(0, 60), joint_kws=dict(gridsize=20, edgecolor='w'), # 主图参数设置 marginal_kws=dict(bins=20, color='#D31A8A', hist_kws={'edgecolor': 'k', 'alpha': 1}), # 边缘图设置 ) # 修改统计注释 sns_hex.set_axis_labels(xlabel='total bill', ylabel='tip') # sns_hex.savefig('sns_hex.pdf') # %% # --------------------------------(d) 二维与一维核密度估计图-------------------------------------------------- sns_kde = sns.jointplot(x="x", y="y", data=df, kind="kde", color='#D31A8A') sns_kde.plot_joint(plt.scatter, c="k", s=10, linewidth=1, marker="+") sns_kde.set_axis_labels(xlabel='total bill', ylabel='tip') # sns_kde.savefig('sns_kde.pdf') # %% # -------------------------------(e)二维散点与一维核密度估计图--------------------------------------------- g = sns.JointGrid(x="total_bill", y="tip", data=tips, size=5, ratio=5) g = g.plot_joint(plt.scatter, color="#00B1B8", s=40, edgecolor="black", linewidth=0.2) g = g.plot_marginals(sns.kdeplot, shade=True, color="#00B1B8") g = g.annotate(stats.pearsonr, loc="upper left", fontsize=12) def rsquare(a, b): return stats.pearsonr(a, b)[0] ** 2 g = g.annotate(rsquare, template="{stat}: {val:.2f}", stat="$R^2$", loc="upper left", fontsize=15) g.set_axis_labels(xlabel='total bill', ylabel='tip') # g.savefig('sns_kde_scatter.pdf')