数据可视化实例（十一）： 矩阵图（matplotlib，pandas）

矩阵图

https://datawhalechina.github.io/pms50/#/chapter9/chapter9

导入所需要的库

import numpy as np              # 导入numpy库
import pandas as pd             # 导入pandas库
import matplotlib as mpl        # 导入matplotlib库
import matplotlib.pyplot as plt
import seaborn as sns           # 导入seaborn库
%matplotlib inline              # 在jupyter notebook显示图像

设定图像各种属性

large = 22; med = 16; small = 12

params = {'axes.titlesize': large,    # 设置子图上的标题字体
            'legend.fontsize': med,     # 设置图例的字体
            'figure.figsize': (16, 10), # 设置图像的画布
           'axes.labelsize': med,      # 设置标签的字体
            'xtick.labelsize': med,     # 设置x轴上的标尺的字体
            'ytick.labelsize': med,     # 设置整个画布的标题字体
          'figure.titlesize': large}
#plt.rcParams.update(params)           # 更新默认属性
plt.style.use('seaborn-whitegrid')    # 设定整体风格
sns.set_style("white")                # 设定整体背景风格

程序代码

# step1:导入数据

df = sns.load_dataset('iris')

# step2: 绘制矩阵图

    # 画布
plt.figure(figsize = (12, 10),    # 画布尺寸_(12, 10)
           dpi = 80)             # 分辨率_80
    # 矩阵图
sns.pairplot(df,                                     # 使用的数据
            kind = 'scatter',                        # 绘制图像的类型_scatter
            hue = 'species',                         # 类别的列，让不同类别具有不谈的颜色
            plot_kws = dict(s = 50,                  # 点的尺寸
                           edgecolor = 'white',      # 边缘颜色
                           linewidth = 2.5))         # 线宽

# step1:导入数据

df = sns.load_dataset('iris')

# step2: 绘制矩阵图

    # 画布
plt.figure(figsize = (12, 10),    # 画布尺寸_(12, 10)
           dpi = 80)             # 分辨率_80
    # 矩阵图(带有拟合线的散点图)
sns.pairplot(df,                                     # 使用的数据
            kind = 'reg',                            # 绘制图像的类型_reg
            hue = 'species')                         # 类别的列，让不同类别具有不谈的颜色

博文总结

seaborn.pairplot

seaborn.pairplot(data, hue=None, hue_order=None,
 palette=None, vars=None, x_vars=None, y_vars=None, kind='scatter', 
diag_kind='auto', markers=None, height=2.5, aspect=1, 
dropna=True, plot_kws=None, diag_kws=None, grid_kws=None, size=None)

Plot pairwise relationships in a dataset.

By default, this function will create a grid of Axes such that each variable in data will by shared in the y-axis across a single row and in the x-axis across a single column.

The diagonal Axes are treated differently, drawing a plot to show the univariate distribution of the data for the variable in that column.

It is also possible to show a subset of variables or plot different variables on the rows and columns.

This is a high-level interface for PairGrid that is intended to make it easy to draw a few common styles. You should use PairGriddirectly if you need more flexibility.

参数：data：DataFrame

Tidy (long-form) dataframe where each column is a variable and each row is an observation.

hue：string (variable name), optional

Variable in data to map plot aspects to different colors.

hue_order：list of strings

Order for the levels of the hue variable in the palette

palette：dict or seaborn color palette

Set of colors for mapping the hue variable. If a dict, keys should be values in the hue variable.

vars：list of variable names, optional

Variables within data to use, otherwise use every column with a numeric datatype.

{x, y}_vars：lists of variable names, optional

Variables within data to use separately for the rows and columns of the figure; i.e. to make a non-square plot.

kind：{‘scatter’, ‘reg’}, optional

Kind of plot for the non-identity relationships.

diag_kind：{‘auto’, ‘hist’, ‘kde’}, optional

Kind of plot for the diagonal subplots. The default depends on whether "hue" is used or not.

markers：single matplotlib marker code or list, optional

Either the marker to use for all datapoints or a list of markers with a length the same as the number of levels in the hue variable so that differently colored points will also have different scatterplot markers.

height：scalar, optional

Height (in inches) of each facet.

aspect：scalar, optional

Aspect * height gives the width (in inches) of each facet.

dropna：boolean, optional

Drop missing values from the data before plotting.

{plot, diag, grid}_kws：dicts, optional

Dictionaries of keyword arguments.

返回值：grid：PairGrid

Returns the underlying PairGrid instance for further tweaking.

seaborn.load_dataset

seaborn.load_dataset(name, cache=True, data_home=None, **kws)

从在线库中获取数据集（需要联网）。

参数：name：字符串

数据集的名字 (<cite>name</cite>.csv on https://github.com/mwaskom/seaborn-data)。 您可以通过 get_dataset_names() 获取可用的数据集。

cache：boolean, 可选

如果为True，则在本地缓存数据并在后续调用中使用缓存。

data_home：string, 可选

用于存储缓存数据的目录。 默认情况下使用 ~/seaborn-data/

kws：dict, 可选

传递给 pandas.read_csv