一、绘制带趋势线的散点图
实现功能:
在散点图上添加趋势线(线性拟合线)反映两个变量是正相关、负相关或者无相关关系。
实现代码:
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import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings(action = 'once' ) plt.style.use( 'seaborn-whitegrid' ) sns.set_style( "whitegrid" ) print (mpl.__version__) print (sns.__version__) def draw_scatter( file ): # Import Data df = pd.read_csv( file ) df_select = df.loc[df.cyl.isin([ 4 , 8 ]), :] # Plot gridobj = sns.lmplot( x = "displ" , y = "hwy" , hue = "cyl" , data = df_select, height = 7 , aspect = 1.6 , palette = 'Set1' , scatter_kws = dict (s = 60 , linewidths = . 7 , edgecolors = 'black' )) # Decorations sns. set (style = "whitegrid" , font_scale = 1.5 ) gridobj. set (xlim = ( 0.5 , 7.5 ), ylim = ( 10 , 50 )) gridobj.fig.set_size_inches( 10 , 6 ) plt.tight_layout() plt.title( "Scatterplot with line of best fit grouped by number of cylinders" ) plt.show() draw_scatter( "F:\数据杂坛\datasets\mpg_ggplot2.csv" ) |
实现效果:
在散点图上添加趋势线(线性拟合线)反映两个变量是正相关、负相关或者无相关关系。红蓝两组数据分别绘制出最佳的线性拟合线。
二、绘制边缘直方图
实现功能:
python绘制边缘直方图,用于展示X和Y之间的关系、及X和Y的单变量分布情况,常用于数据探索分析。
实现代码:
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import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings(action = 'once' ) plt.style.use( 'seaborn-whitegrid' ) sns.set_style( "whitegrid" ) print (mpl.__version__) print (sns.__version__) def draw_Marginal_Histogram( file ): # Import Data df = pd.read_csv( file ) # Create Fig and gridspec fig = plt.figure(figsize = ( 10 , 6 ), dpi = 100 ) grid = plt.GridSpec( 4 , 4 , hspace = 0.5 , wspace = 0.2 ) # Define the axes ax_main = fig.add_subplot(grid[: - 1 , : - 1 ]) ax_right = fig.add_subplot(grid[: - 1 , - 1 ], xticklabels = [], yticklabels = []) ax_bottom = fig.add_subplot(grid[ - 1 , 0 : - 1 ], xticklabels = [], yticklabels = []) # Scatterplot on main ax ax_main.scatter( 'displ' , 'hwy' , s = df.cty * 4 , c = df.manufacturer.astype( 'category' ).cat.codes, alpha = . 9 , data = df, cmap = "Set1" , edgecolors = 'gray' , linewidths = . 5 ) # histogram on the right ax_bottom.hist(df.displ, 40 , histtype = 'stepfilled' , orientation = 'vertical' , color = '#098154' ) ax_bottom.invert_yaxis() # histogram in the bottom ax_right.hist(df.hwy, 40 , histtype = 'stepfilled' , orientation = 'horizontal' , color = '#098154' ) # Decorations ax_main. set (title = 'Scatterplot with Histograms \n displ vs hwy' , xlabel = 'displ' , ylabel = 'hwy' ) ax_main.title.set_fontsize( 10 ) for item in ([ax_main.xaxis.label, ax_main.yaxis.label] + ax_main.get_xticklabels() + ax_main.get_yticklabels()): item.set_fontsize( 10 ) xlabels = ax_main.get_xticks().tolist() ax_main.set_xticklabels(xlabels) plt.show() draw_Marginal_Histogram( "F:\数据杂坛\datasets\mpg_ggplot2.csv" ) |
实现效果:
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原文链接:https://blog.csdn.net/sinat_41858359/article/details/125392091