国外大神制作的一个很棒的matplotlib 可视化教程

参考：https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/

♔一：关联

散点图
带边界的气泡图
散点图与最佳拟合线
与stripplot抖动
计数图
边缘直方图
边缘Boxplot
相关图
矩阵图

♔二：偏差

发散酒吧
分歧的文本
分散点图
用标记分散棒棒糖图表
面积图

♔三：排行

条形图
棒棒糖图表
点图
坡度图
哑铃情节

♔四：分配

连续变量的直方图
分类变量的直方图
密度图
密度曲线与直方图
Joy Plot
分布式点图
箱形图
Dot + Box Plot
小提琴剧情
人口金字塔
分类图

♔五：组成

华夫饼图
饼形图
树形图
条形图

♔六：更改

时间序列图
带有峰和谷的时间序列注释
自相关图
交叉关联图
时间序列分解图
多个时间序列
使用辅助Y轴绘制不同比例的绘图
带有误差带的时间序列
堆积面积图
区域图未拆封
日历热图
季节性情节

♔七：组

树状图
群集图
安德鲁斯曲线
平行坐标

11 散点图 Scatteplot

Scatteplot 是用于研究两个变量之间关系的经典和基本图。如果数据中有多个组，则可能需要以不同颜色可视化每个组。在Matplotlib，你可以方便地使用。

# Import dataset

%matplotlib

import pandas as pd

import numpy as np

import matplotlib as mpl

from matplotlib import patches

from matplotlib import font_manager as fm

from matplotlib import pyplot as plt

from scipy.spatial import ConvexHull

import seaborn as sns

import warnings; warnings.simplefilter('ignore')

midwest = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/midwest_filter.csv")

zhongwen_font = fm.FontProperties(fname='C:\Windows\Fonts\华文楷体.ttf') 

# Step 1: 准备数据

# 创建尽可能多的颜色，因为有独特的midwest['category']

categories = np.unique(midwest['category'])

colors = [plt.cm.tab10(i/float(len(categories)-1)) for i in range(len(categories))]

# Step 2：为每个类别绘制图形

plt.figure(figsize=(16, 10), dpi= 80, facecolor='w', edgecolor='k')

for i, category in enumerate(categories):

    plt.scatter('area', 'poptotal',

                data=midwest.loc[midwest.category==category, :],

                s=20, c=colors[i], label=str(category))

# Step 3:展示优化：设置图例等

plt.gca().set(xlim=(0.0, 0.1), ylim=(0, 90000),

              xlabel='地区', ylabel='人口')

plt.xticks(fontsize=12, fontproperties = zhongwen_font)

plt.yticks(fontsize=12, fontproperties = zhongwen_font)

plt.title("中西部地区人口分布图", fontsize=22, fontproperties = zhongwen_font)

plt.legend(fontsize=12, prop = zhongwen_font)

plt.show()

2. 带边界的气泡图

有时，您希望在边界内显示一组点以强调其重要性。在此示例中，您将从应该被环绕的数据帧中获取记录，并将其传递给下面的代码中描述的记录。encircle()

%matplotlib

import pandas as pd

import numpy as np

import matplotlib as mpl

from matplotlib import patches

from matplotlib import font_manager as fm

from matplotlib import pyplot as plt

from scipy.spatial import ConvexHull

from matplotlib import patches

import seaborn as sns

import warnings; warnings.simplefilter('ignore')

sns.set_style("white")

# S1: 准备数据

midwest = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/midwest_filter.csv")

zhongwen_font = fm.FontProperties(fname='C:\Windows\Fonts\simsun.ttc') 

# 创建尽可能多的颜色，因为有独特的midwest['category']y']

categories = np.unique(midwest['category'])

colors = [plt.cm.tab10(i/float(len(categories)-1)) for i in range(len(categories))]

# S2: 为每个类别绘制图形

fig = plt.figure(figsize=(16, 10), dpi= 80, facecolor='w', edgecolor='k')    

for i, category in enumerate(categories):

    plt.scatter('area', 'poptotal', data=midwest.loc[midwest.category==category, :], s='dot_size', c=colors[i], label=str(category), edgecolors='black', linewidths=.5)

# S3: 边界

# https://stackoverflow.com/questions/44575681/how-do-i-encircle-different-data-sets-in-scatter-plot

def encircle(x,y, ax=None, **kw):

    if not ax: ax=plt.gca()

    p = np.c_[x,y]

    hull = ConvexHull(p)

    poly = plt.Polygon(p[hull.vertices,:], **kw)

    ax.add_patch(poly)

# 选择要包围的数据

midwest_encircle_data = midwest.loc[midwest.state=='IN', :]                         

# 围绕顶点绘图

encircle(midwest_encircle_data.area, midwest_encircle_data.poptotal, ec="k", fc="gold", alpha=0.1)

encircle(midwest_encircle_data.area, midwest_encircle_data.poptotal, ec="firebrick", fc="none", linewidth=1.5)

# S4: 优化图例

plt.gca().set(xlim=(0.0, 0.1), ylim=(0, 90000),

              xlabel='Area', ylabel='Population')

plt.xticks(fontsize=12, fontproperties = zhongwen_font)

plt.yticks(fontsize=12, fontproperties = zhongwen_font)

plt.title("气泡图", fontsize=22, fontproperties = zhongwen_font)

plt.legend(fontsize=12, prop = zhongwen_font)

plt.show()

3. 带线性回归最佳拟合线的散点图

如果你想了解两个变量如何相互改变，那么最合适的线就是要走的路。下图显示了数据中各组之间最佳拟合线的差异。要禁用分组并仅为整个数据集绘制一条最佳拟合线，请从下面的调用中删除该参数。

# Import dataset

%matplotlib

import pandas as pd

import numpy as np

import matplotlib as mpl

from matplotlib import patches

from matplotlib import font_manager as fm

from matplotlib import pyplot as plt

from scipy.spatial import ConvexHull

import seaborn as sns

import warnings; warnings.simplefilter('ignore')

# S1 : 数据

df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")

zhongwen_font = fm.FontProperties(fname='C:\Windows\Fonts\simsun.ttc') 

df_select = df.loc[df.cyl.isin([4,8]), :]

# S2 : 作图

sns.set_style("white")

gridobj = sns.lmplot(x="displ", y="hwy", hue="cyl", data=df_select,

                     aspect=1.6, robust=True, palette='tab10',

                     scatter_kws=dict(s=60, linewidths=.7, edgecolors='black'))

# S3 ：优化

gridobj.set(xlim=(0.5, 7.5), ylim=(0, 50))

plt.title("带线性回归最佳拟合线的散点图", fontsize=20, fontproperties = zhongwen_font)

plt.show()

每个回归线都在自己的列中

或者，您可以在其自己的列中显示每个组的最佳拟合线。你可以通过在里面设置参数来实现这一点。

# Import Data

df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")

df_select = df.loc[df.cyl.isin([4,8]), :]

# Each line in its own column

sns.set_style("white")

gridobj = sns.lmplot(x="displ", y="hwy",

                     data=df_select,

                     height=7,

                     robust=True,

                     palette='Set1',

                     col="cyl",

                     scatter_kws=dict(s=60, linewidths=.7, edgecolors='black'))

# Decorations

gridobj.set(xlim=(0.5, 7.5), ylim=(0, 50))

plt.show()

4. 抖动图 Stripplot

通常，多个数据点具有完全相同的X和Y值。结果，多个点相互绘制并隐藏。为避免这种情况，请稍微抖动点，以便您可以直观地看到它们。这很方便使用

%matplotlib

import pandas as pd

import numpy as np

import matplotlib as mpl

from matplotlib import patches

from matplotlib import font_manager as fm

from matplotlib import pyplot as plt

from scipy.spatial import ConvexHull

from matplotlib import patches

import seaborn as sns

import warnings; warnings.simplefilter('ignore')

# S1：数据

df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")

zhongwen_font = fm.FontProperties(fname='C:\Windows\Fonts\simsun.ttc') 

# S2：作图

fig, ax = plt.subplots(figsize=(16,10), dpi= 80)

sns.stripplot(df.cty, df.hwy, jitter=0.25, size=8, ax=ax, linewidth=.5)

# S3:优化

plt.title('使用抖动图避免点重叠', fontsize=22, fontproperties = zhongwen_font)

plt.show()

相关图

Correlogram用于直观地查看给定数据帧（或2D数组）中所有可能的数值变量对之间的相关度量。

%matplotlib

import pandas as pd

import numpy as np

import matplotlib as mpl

from matplotlib import patches

from matplotlib import font_manager as fm

from matplotlib import pyplot as plt

from scipy.spatial import ConvexHull

from matplotlib import patches

import seaborn as sns

import warnings; warnings.simplefilter('ignore')

# S1： 数据

df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv")

zhongwen_font = fm.FontProperties(fname='C:\Windows\Fonts\simsun.ttc') 

# S2： plot 作图

plt.figure(figsize=(12,10), dpi= 80)

sns.heatmap(df.corr(), xticklabels=df.corr().columns, yticklabels=df.corr().columns, cmap='RdYlGn', center=0, annot=True)

# S3： 图例优化

plt.title('相关图', fontsize=22, fontproperties = zhongwen_font)

plt.xticks(fontsize=12)

plt.yticks(fontsize=12)

plt.show()

9. 矩阵图

成对图是探索性分析中的最爱，以理解所有可能的数字变量对之间的关系。它是双变量分析的必备工具。

%matplotlib

import pandas as pd

import numpy as np

import matplotlib as mpl

from matplotlib import patches

from matplotlib import font_manager as fm

from matplotlib import pyplot as plt

from scipy.spatial import ConvexHull

from matplotlib import patches

import seaborn as sns

import warnings; warnings.simplefilter('ignore')

# Load Dataset

df = sns.load_dataset('iris')

# Plot

plt.figure(figsize=(10,8), dpi= 80)

sns.pairplot(df, kind="scatter", hue="species", plot_kws=dict(s=80, edgecolor="white", linewidth=2.5))

plt.show()

23. 直方密度线图

带有直方图的密度曲线将两个图表传达的集体信息汇集在一起，这样您就可以将它们放在一个图形而不是两个图形中。

%matplotlib

import pandas as pd

import numpy as np

import matplotlib as mpl

from matplotlib import patches

from matplotlib import font_manager as fm

from matplotlib import pyplot as plt

from scipy.spatial import ConvexHull

from matplotlib import patches

import seaborn as sns

import warnings; warnings.simplefilter('ignore')

# S1:数据

df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")

zhongwen_font = fm.FontProperties(fname='C:\Windows\Fonts\simsun.ttc') 

# S2:作图

plt.figure(figsize=(13,10), dpi= 80)

sns.distplot(df.loc[df['class'] == 'compact', "cty"], color="dodgerblue", label="Compact", hist_kws={'alpha':.7}, kde_kws={'linewidth':3})

sns.distplot(df.loc[df['class'] == 'suv', "cty"], color="orange", label="SUV", hist_kws={'alpha':.7}, kde_kws={'linewidth':3})

sns.distplot(df.loc[df['class'] == 'minivan', "cty"], color="g", label="minivan", hist_kws={'alpha':.7}, kde_kws={'linewidth':3})

plt.ylim(0, 0.35)

# S3:图例

plt.title('不同车型类型城市里程密度图', fontsize=22, fontproperties = zhongwen_font)

plt.legend()

plt.show()

45. 日历热力图

与时间序列相比，日历映射是可视化基于时间的数据的备选和不太优选的选项。虽然可以在视觉上吸引人，但数值并不十分明显。然而，它可以很好地描绘极端值和假日效果。

import pandas as pd
import matplotlib as mpl
from matplotlib import pyplot as plt
import calmap as calmap

# S1:数据
df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv", parse_dates=['date'])
df.set_index('date', inplace=True)

# S2:绘图
plt.figure(figsize=(16,10), dpi= 80)
calmap.calendarplot(df['2014']['VIX.Close'], fig_kws={'figsize': (16,10)}, yearlabel_kws={'color':'black', 'fontsize':14}, subplot_kws={'title':'Yahoo Stock Prices'})
plt.show()

by : 一只阿木木