python可视化分析绘制带趋势线的散点图和边缘直方图_python_

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")

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")