Python数据可视化-基于Python-matplotlib

这里写自定义目录标题

• 相关类-Correlation
  *
• 1.相关类-散点图-Scatter plot
• 2.相关类-带边界的气泡图-Bubble plot with Encircling
• 3.相关类-带线性回归最佳拟合线的散点图-Scatter plot with linear regression line of best fit
• 4.相关类-抖动图-Jittering with stripplot
• 5.相关类-计数图-Counts Plot
• 6.相关类-边缘直方图-Marginal Histogram
• 7.相关类-边缘箱形图-Marginal Boxplot
• 8.相关类-相关图-Correllogram
• 9.相关类-成对图-Pairwise Plot
• 偏差类
  *
• 10.偏差类-发散型条形图-Diverging Bars
• 11.偏差类-发散型文本-Diverging Texts
• 12-偏差类-发散型包点图-Diverging Dot Plot
• 13.偏差类-带标记的发散型棒棒糖图-Diverging Lollipop Chart with Markers
• 14.偏差类-面积图-Area Chart
• 排序类
  *
• 15排序类-有序条形图-Ordered Bar Chart
• 16排序类-棒棒糖图-Lollipop Chart
• 17排序类-包点图-Dot Plot
• 18排序类-坡度图图-Slope Chart
• 19排序类-哑铃图-Dumbbell Plot
• 分布类-Distribution
  *
• 20-连续变量的直方图-Histogram for Continuous Variable
• 21-类型变量的直方图-Histogram for Categorical Variable
• 22-密度图-Density Plot
• 23-直方密度线图-Density Curves with Histogram
• 24-Joy Plot-Joy Plot
• 25-分布式点图-Distributed Dot Plot
• 26-箱形图-Box Plot
• 27-包点+箱形图-Dot + Box Plot
• 28-小提琴图-Violin Plot
• 29-人口金字塔-Population Pyramid
• 30-分类图-Categorical Plots
• 组成类-Composition
  *
• 补充-组成类-雷达图（Radar Chart）
• 31组成类-华夫饼图（Waffle Chart）
• 32组成类- 饼图（Pie Chart）
• 33组成类-树形图（Treemap）
• 34组成类-条形图（Bar Chart）
• 变化类-Change
  *
• 35变化类-时间序列图（Time Series Plot）
• 36变化类-带波峰波谷标记的时序图（Time Series with Peaks and Troughs Annotated）
• 37变化类-自相关和部分自相关图（Autocorrelation (ACF) and Partial Autocorrelation (PACF) Plot）
• 38变化类-交叉相关图（Cross Correlation plot）
• 39变化类-时间序列分解图（Time Series Decomposition Plot）
• 40变化类-多个时间序列（Multiple Time Series）
• 41变化类-使用辅助 Y 轴来绘制不同范围的图形（Plotting with different scales using secondary Y axis）
• 42变化类-带有误差带的时间序列（Time Series with Error Bands）
• 43变化类-堆积面积图（Stacked Area Chart）
• 44变化类-未堆积的面积图（Area Chart UnStacked）
• 45变化类-日历热力图（Calendar Heat Map）
• 46变化类- 季节图（Seasonal Plot）
• 分组类-Groups
  *
• 47-分组类-树状图（Dendrogram）
• 48-分组类-簇状图（Cluster Plot）
• 49-分组类-安德鲁斯曲线（Andrews Curve）
• 50-分组类-平行坐标（Parallel Coordinates）

相较于之前的加了雷达图和多层饼图

预先设置

import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import warnings; warnings.filterwarnings(action='once')

large = 22; med = 16; small = 12
params = {'axes.titlesize': large,
          'legend.fontsize': med,
          'figure.figsize': (16, 10),
          'axes.labelsize': med,
          'axes.titlesize': med,
          'xtick.labelsize': med,
          'ytick.labelsize': med,
          'figure.titlesize': large}
plt.rcParams.update(params)
plt.style.use('seaborn-whitegrid')
sns.set_style("white")
%matplotlib inline

print(mpl.__version__)
print(sns.__version__)

相关类-Correlation

1.相关类-散点图-Scatter plot

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。

代码

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

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

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))

plt.gca().set(xlim=(0.0, 0.1), ylim=(0, 90000),
              xlabel='Area', ylabel='Population')

plt.xticks(fontsize=12); plt.yticks(fontsize=12)
plt.title("Scatterplot of Midwest Area vs Population", fontsize=22)
plt.legend(fontsize=12)
plt.show()

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2.相关类-带边界的气泡图-Bubble plot with Encircling

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。

代码

from matplotlib import patches
from scipy.spatial import ConvexHull
import warnings; warnings.simplefilter('ignore')
sns.set_style("white")

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

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

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)

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)

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

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)

plt.gca().set(xlim=(0.0, 0.1), ylim=(0, 90000),
              xlabel='Area', ylabel='Population')

plt.xticks(fontsize=12); plt.yticks(fontsize=12)
plt.title("Bubble Plot with Encircling", fontsize=22)
plt.legend(fontsize=12)
plt.show()

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3.相关类-带线性回归最佳拟合线的散点图-Scatter plot with linear regression line of best fit

最佳拟合线

代码

df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")
df_select = df.loc[df.cyl.isin([4,8]), :]

sns.set_style("white")
gridobj = sns.lmplot(x="displ", y="hwy", hue="cyl", data=df_select,
                     height=7, aspect=1.6, robust=True, palette='tab10',
                     scatter_kws=dict(s=60, linewidths=.7, edgecolors='black'))

gridobj.set(xlim=(0.5, 7.5), ylim=(0, 50))
plt.title("Scatterplot with line of best fit grouped by number of cylinders", fontsize=20)
plt.show()

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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。

代码

df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")
df_select = df.loc[df.cyl.isin([4,8]), :]

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'))

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

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4.相关类-抖动图-Jittering with 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

代码

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

fig, ax = plt.subplots(figsize=(16,10), dpi= 80)
sns.stripplot(df.cty, df.hwy, jitter=0.25, size=8, ax=ax, linewidth=.5)

plt.title('Use jittered plots to avoid overlapping of points', fontsize=22)
plt.show()

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5.相关类-计数图-Counts Plot

避免点重叠问题的另一个选择是增加点的大小，这取决于该点中有多少点。因此，点的大小越大，周围的点的集中度就越大。

代码

df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")
df_counts = df.groupby(['hwy', 'cty']).size().reset_index(name='counts')

fig, ax = plt.subplots(figsize=(16,10), dpi= 80)
sns.stripplot(df_counts.cty, df_counts.hwy, size=df_counts.counts*2, ax=ax)

plt.title('Counts Plot - Size of circle is bigger as more points overlap', fontsize=22)
plt.show()

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6.相关类-边缘直方图-Marginal Histogram

边缘直方图具有沿X和Y轴变量的直方图。这用于可视化X和Y之间的关系以及单独的X和Y的单变量分布。该图如果经常用于探索性数据分析（EDA）

代码

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

fig = plt.figure(figsize=(16, 10), dpi= 80)
grid = plt.GridSpec(4, 4, hspace=0.5, wspace=0.2)

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=[])

ax_main.scatter('displ', 'hwy', s=df.cty*4, c=df.manufacturer.astype('category').cat.codes, alpha=.9, data=df, cmap="tab10", edgecolors='gray', linewidths=.5)

ax_bottom.hist(df.displ, 40, histtype='stepfilled', orientation='vertical', color='deeppink')
ax_bottom.invert_yaxis()

ax_right.hist(df.hwy, 40, histtype='stepfilled', orientation='horizontal', color='deeppink')

ax_main.set(title='Scatterplot with Histograms
 displ vs hwy', xlabel='displ', ylabel='hwy')
ax_main.title.set_fontsize(20)
for item in ([ax_main.xaxis.label, ax_main.yaxis.label] + ax_main.get_xticklabels() + ax_main.get_yticklabels()):
    item.set_fontsize(14)

xlabels = ax_main.get_xticks().tolist()
ax_main.set_xticklabels(xlabels)
plt.show()

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7.相关类-边缘箱形图-Marginal Boxplot

边缘箱图与边缘直方图具有相似的用途。然而，箱线图有助于精确定位X和Y的中位数，第25和第75百分位数

代码

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

fig = plt.figure(figsize=(16, 10), dpi= 80)
grid = plt.GridSpec(4, 4, hspace=0.5, wspace=0.2)

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=[])

ax_main.scatter('displ', 'hwy', s=df.cty*5, c=df.manufacturer.astype('category').cat.codes, alpha=.9, data=df, cmap="Set1", edgecolors='black', linewidths=.5)

sns.boxplot(df.hwy, ax=ax_right, orient="v")
sns.boxplot(df.displ, ax=ax_bottom, orient="h")

ax_bottom.set(xlabel='')
ax_right.set(ylabel='')

ax_main.set(title='Scatterplot with Histograms \n displ vs hwy', xlabel='displ', ylabel='hwy')

ax_main.title.set_fontsize(20)
for item in ([ax_main.xaxis.label, ax_main.yaxis.label] + ax_main.get_xticklabels() + ax_main.get_yticklabels()):
    item.set_fontsize(14)

plt.show()

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8.相关类-相关图-Correllogram

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。

代码

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

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)

plt.title('Correlogram of mtcars', fontsize=22)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.show()

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9.相关类-成对图-Pairwise Plot

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。

代码

df = sns.load_dataset('iris')

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()

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代码

df = sns.load_dataset('iris')

plt.figure(figsize=(10,8), dpi= 80)
sns.pairplot(df, kind="reg", hue="species")
plt.show()

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偏差类

10.偏差类-发散型条形图-Diverging Bars

如果您想根据单个指标查看项目的变化情况，并可视化此差异的顺序和数量，那么发散条是一个很好的工具。它有助于快速区分数据中组的性能，并且非常直观，并且可以立即传达这一点。

代码

df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv")
x = df.loc[:, ['mpg']]
df['mpg_z'] = (x - x.mean())/x.std()
df['colors'] = ['red' if x < 0 else 'green' for x in df['mpg_z']]
df.sort_values('mpg_z', inplace=True)
df.reset_index(inplace=True)

plt.figure(figsize=(14,10), dpi= 80)
plt.hlines(y=df.index, xmin=0, xmax=df.mpg_z, color=df.colors, alpha=0.4, linewidth=5)

plt.gca().set(ylabel='$Model$', xlabel='$Mileage$')
plt.yticks(df.index, df.cars, fontsize=12)
plt.title('Diverging Bars of Car Mileage', fontdict={'size':20})
plt.grid(linestyle='--', alpha=0.5)
plt.show()

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11.偏差类-发散型文本-Diverging Texts

分散的文本类似于发散条，如果你想以一种漂亮和可呈现的方式显示图表中每个项目的价值，它更喜欢。

代码

df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv")
x = df.loc[:, ['mpg']]
df['mpg_z'] = (x - x.mean())/x.std()
df['colors'] = ['red' if x < 0 else 'green' for x in df['mpg_z']]
df.sort_values('mpg_z', inplace=True)
df.reset_index(inplace=True)

plt.figure(figsize=(14,14), dpi= 80)
plt.hlines(y=df.index, xmin=0, xmax=df.mpg_z)
for x, y, tex in zip(df.mpg_z, df.index, df.mpg_z):
    t = plt.text(x, y, round(tex, 2), horizontalalignment='right' if x < 0 else 'left',
                 verticalalignment='center', fontdict={'color':'red' if x < 0 else 'green', 'size':14})

plt.yticks(df.index, df.cars, fontsize=12)
plt.title('Diverging Text Bars of Car Mileage', fontdict={'size':20})
plt.grid(linestyle='--', alpha=0.5)
plt.xlim(-2.5, 2.5)
plt.show()

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12-偏差类-发散型包点图-Diverging Dot Plot

发散点图也类似于发散条。然而，与发散条相比，条的不存在减少了组之间的对比度和差异

代码

df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv")
x = df.loc[:, ['mpg']]
df['mpg_z'] = (x - x.mean())/x.std()
df['colors'] = ['red' if x < 0 else 'darkgreen' for x in df['mpg_z']]
df.sort_values('mpg_z', inplace=True)
df.reset_index(inplace=True)

plt.figure(figsize=(14,16), dpi= 80)
plt.scatter(df.mpg_z, df.index, s=450, alpha=.6, color=df.colors)
for x, y, tex in zip(df.mpg_z, df.index, df.mpg_z):
    t = plt.text(x, y, round(tex, 1), horizontalalignment='center',
                 verticalalignment='center', fontdict={'color':'white'})

plt.gca().spines["top"].set_alpha(.3)
plt.gca().spines["bottom"].set_alpha(.3)
plt.gca().spines["right"].set_alpha(.3)
plt.gca().spines["left"].set_alpha(.3)

plt.yticks(df.index, df.cars)
plt.title('Diverging Dotplot of Car Mileage', fontdict={'size':20})
plt.xlabel('$Mileage$')
plt.grid(linestyle='--', alpha=0.5)
plt.xlim(-2.5, 2.5)
plt.show()

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13.偏差类-带标记的发散型棒棒糖图-Diverging Lollipop Chart with Markers

带标记的棒棒糖通过强调您想要引起注意的任何重要数据点并在图表中适当地给出推理，提供了一种可视化分歧的灵活方式。

代码

df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv")
x = df.loc[:, ['mpg']]
df['mpg_z'] = (x - x.mean())/x.std()
df['colors'] = 'black'

df.loc[df.cars == 'Fiat X1-9', 'colors'] = 'darkorange'
df.sort_values('mpg_z', inplace=True)
df.reset_index(inplace=True)

import matplotlib.patches as patches

plt.figure(figsize=(14,16), dpi= 80)
plt.hlines(y=df.index, xmin=0, xmax=df.mpg_z, color=df.colors, alpha=0.4, linewidth=1)
plt.scatter(df.mpg_z, df.index, color=df.colors, s=[600 if x == 'Fiat X1-9' else 300 for x in df.cars], alpha=0.6)
plt.yticks(df.index, df.cars)
plt.xticks(fontsize=12)

plt.annotate('Mercedes Models', xy=(0.0, 11.0), xytext=(1.0, 11), xycoords='data',
            fontsize=15, ha='center', va='center',
            bbox=dict(boxstyle='square', fc='firebrick'),
            arrowprops=dict(arrowstyle='-[, widthB=2.0, lengthB=1.5', lw=2.0, color='steelblue'), color='white')

p1 = patches.Rectangle((-2.0, -1), width=.3, height=3, alpha=.2, facecolor='red')
p2 = patches.Rectangle((1.5, 27), width=.8, height=5, alpha=.2, facecolor='green')
plt.gca().add_patch(p1)
plt.gca().add_patch(p2)

plt.title('Diverging Bars of Car Mileage', fontdict={'size':20})
plt.grid(linestyle='--', alpha=0.5)
plt.show()

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14.偏差类-面积图-Area Chart

通过对轴和线之间的区域进行着色，区域图不仅强调峰值和低谷，而且还强调高点和低点的持续时间。高点持续时间越长，线下面积越大。

代码

import numpy as np
import pandas as pd

df = pd.read_csv("https://github.com/selva86/datasets/raw/master/economics.csv", parse_dates=['date']).head(100)
x = np.arange(df.shape[0])
y_returns = (df.psavert.diff().fillna(0)/df.psavert.shift(1)).fillna(0) * 100

plt.figure(figsize=(16,10), dpi= 80)
plt.fill_between(x[1:], y_returns[1:], 0, where=y_returns[1:] >= 0, facecolor='green', interpolate=True, alpha=0.7)
plt.fill_between(x[1:], y_returns[1:], 0, where=y_returns[1:]  0, facecolor='red', interpolate=True, alpha=0.7)

plt.annotate('Peak
1975', xy=(94.0, 21.0), xytext=(88.0, 28),
             bbox=dict(boxstyle='square', fc='firebrick'),
             arrowprops=dict(facecolor='steelblue', shrink=0.05), fontsize=15, color='white')

xtickvals = [str(m)[:3].upper()+"-"+str(y) for y,m in zip(df.date.dt.year, df.date.dt.month_name())]
plt.gca().set_xticks(x[::6])
plt.gca().set_xticklabels(xtickvals[::6], rotation=90, fontdict={'horizontalalignment': 'center', 'verticalalignment': 'center_baseline'})
plt.ylim(-35,35)
plt.xlim(1,100)
plt.title("Month Economics Return %", fontsize=22)
plt.ylabel('Monthly returns %')
plt.grid(alpha=0.5)
plt.show()

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排序类

15排序类-有序条形图-Ordered Bar Chart

有序条形图有效地传达了项目的排名顺序。但是，在图表上方添加度量标准的值，用户可以从图表本身获取精确信息

代码

df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")
df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())
df.sort_values('cty', inplace=True)
df.reset_index(inplace=True)

import matplotlib.patches as patches

fig, ax = plt.subplots(figsize=(16,10), facecolor='white', dpi= 80)
ax.vlines(x=df.index, ymin=0, ymax=df.cty, color='firebrick', alpha=0.7, linewidth=20)

for i, cty in enumerate(df.cty):
    ax.text(i, cty+0.5, round(cty, 1), horizontalalignment='center')

ax.set_title('Bar Chart for Highway Mileage', fontdict={'size':22})
ax.set(ylabel='Miles Per Gallon', ylim=(0, 30))
plt.xticks(df.index, df.manufacturer.str.upper(), rotation=60, horizontalalignment='right', fontsize=12)

p1 = patches.Rectangle((.57, -0.005), width=.33, height=.13, alpha=.1, facecolor='green', transform=fig.transFigure)
p2 = patches.Rectangle((.124, -0.005), width=.446, height=.13, alpha=.1, facecolor='red', transform=fig.transFigure)
fig.add_artist(p1)
fig.add_artist(p2)
plt.show()

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16排序类-棒棒糖图-Lollipop Chart

棒棒糖图表以一种视觉上令人愉悦的方式提供与有序条形图类似的目的

代码

df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")
df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())
df.sort_values('cty', inplace=True)
df.reset_index(inplace=True)

fig, ax = plt.subplots(figsize=(16,10), dpi= 80)
ax.vlines(x=df.index, ymin=0, ymax=df.cty, color='firebrick', alpha=0.7, linewidth=2)
ax.scatter(x=df.index, y=df.cty, s=75, color='firebrick', alpha=0.7)

ax.set_title('Lollipop Chart for Highway Mileage', fontdict={'size':22})
ax.set_ylabel('Miles Per Gallon')
ax.set_xticks(df.index)
ax.set_xticklabels(df.manufacturer.str.upper(), rotation=60, fontdict={'horizontalalignment': 'right', 'size':12})
ax.set_ylim(0, 30)

for row in df.itertuples():
    ax.text(row.Index, row.cty+.5, s=round(row.cty, 2), horizontalalignment= 'center', verticalalignment='bottom', fontsize=14)

plt.show()

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17排序类-包点图-Dot Plot

点图表传达了项目的排名顺序。由于它沿水平轴对齐，因此您可以更容易地看到点彼此之间的距离。

代码

df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")
df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())
df.sort_values('cty', inplace=True)
df.reset_index(inplace=True)

fig, ax = plt.subplots(figsize=(16,10), dpi= 80)
ax.hlines(y=df.index, xmin=11, xmax=26, color='gray', alpha=0.7, linewidth=1, linestyles='dashdot')
ax.scatter(y=df.index, x=df.cty, s=75, color='firebrick', alpha=0.7)

ax.set_title('Dot Plot for Highway Mileage', fontdict={'size':22})
ax.set_xlabel('Miles Per Gallon')
ax.set_yticks(df.index)
ax.set_yticklabels(df.manufacturer.str.title(), fontdict={'horizontalalignment': 'right'})
ax.set_xlim(10, 27)
plt.show()

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18排序类-坡度图图-Slope Chart

斜率图最适合比较给定人/项目的”之前”和”之后”位置。

代码

import matplotlib.lines as mlines

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

left_label = [str(c) + ', '+ str(round(y)) for c, y in zip(df.continent, df['1952'])]
right_label = [str(c) + ', '+ str(round(y)) for c, y in zip(df.continent, df['1957'])]
klass = ['red' if (y1-y2) < 0 else 'green' for y1, y2 in zip(df['1952'], df['1957'])]

def newline(p1, p2, color='black'):
    ax = plt.gca()
    l = mlines.Line2D([p1[0],p2[0]], [p1[1],p2[1]], color='red' if p1[1]-p2[1] > 0 else 'green', marker='o', markersize=6)
    ax.add_line(l)
    return l

fig, ax = plt.subplots(1,1,figsize=(14,14), dpi= 80)

ax.vlines(x=1, ymin=500, ymax=13000, color='black', alpha=0.7, linewidth=1, linestyles='dotted')
ax.vlines(x=3, ymin=500, ymax=13000, color='black', alpha=0.7, linewidth=1, linestyles='dotted')

ax.scatter(y=df['1952'], x=np.repeat(1, df.shape[0]), s=10, color='black', alpha=0.7)
ax.scatter(y=df['1957'], x=np.repeat(3, df.shape[0]), s=10, color='black', alpha=0.7)

for p1, p2, c in zip(df['1952'], df['1957'], df['continent']):
    newline([1,p1], [3,p2])
    ax.text(1-0.05, p1, c + ', ' + str(round(p1)), horizontalalignment='right', verticalalignment='center', fontdict={'size':14})
    ax.text(3+0.05, p2, c + ', ' + str(round(p2)), horizontalalignment='left', verticalalignment='center', fontdict={'size':14})

ax.text(1-0.05, 13000, 'BEFORE', horizontalalignment='right', verticalalignment='center', fontdict={'size':18, 'weight':700})
ax.text(3+0.05, 13000, 'AFTER', horizontalalignment='left', verticalalignment='center', fontdict={'size':18, 'weight':700})

ax.set_title("Slopechart: Comparing GDP Per Capita between 1952 vs 1957", fontdict={'size':22})
ax.set(xlim=(0,4), ylim=(0,14000), ylabel='Mean GDP Per Capita')
ax.set_xticks([1,3])
ax.set_xticklabels(["1952", "1957"])
plt.yticks(np.arange(500, 13000, 2000), fontsize=12)

plt.gca().spines["top"].set_alpha(.0)
plt.gca().spines["bottom"].set_alpha(.0)
plt.gca().spines["right"].set_alpha(.0)
plt.gca().spines["left"].set_alpha(.0)
plt.show()

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19排序类-哑铃图-Dumbbell Plot

哑铃图传达各种项目的”前”和”后”位置以及项目的排序。如果您想要将特定项目/计划对不同对象的影响可视化，那么它非常有用。

代码

import matplotlib.lines as mlines

df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/health.csv")
df.sort_values('pct_2014', inplace=True)
df.reset_index(inplace=True)

def newline(p1, p2, color='black'):
    ax = plt.gca()
    l = mlines.Line2D([p1[0],p2[0]], [p1[1],p2[1]], color='skyblue')
    ax.add_line(l)
    return l

fig, ax = plt.subplots(1,1,figsize=(14,14), facecolor='#f7f7f7', dpi= 80)

ax.vlines(x=.05, ymin=0, ymax=26, color='black', alpha=1, linewidth=1, linestyles='dotted')
ax.vlines(x=.10, ymin=0, ymax=26, color='black', alpha=1, linewidth=1, linestyles='dotted')
ax.vlines(x=.15, ymin=0, ymax=26, color='black', alpha=1, linewidth=1, linestyles='dotted')
ax.vlines(x=.20, ymin=0, ymax=26, color='black', alpha=1, linewidth=1, linestyles='dotted')

ax.scatter(y=df['index'], x=df['pct_2013'], s=50, color='#0e668b', alpha=0.7)
ax.scatter(y=df['index'], x=df['pct_2014'], s=50, color='#a3c4dc', alpha=0.7)

for i, p1, p2 in zip(df['index'], df['pct_2013'], df['pct_2014']):
    newline([p1, i], [p2, i])

ax.set_facecolor('#f7f7f7')
ax.set_title("Dumbell Chart: Pct Change - 2013 vs 2014", fontdict={'size':22})
ax.set(xlim=(0,.25), ylim=(-1, 27), ylabel='Mean GDP Per Capita')
ax.set_xticks([.05, .1, .15, .20])
ax.set_xticklabels(['5%', '15%', '20%', '25%'])
ax.set_xticklabels(['5%', '15%', '20%', '25%'])
plt.show()

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分布类-Distribution

20-连续变量的直方图-Histogram for Continuous Variable

直方图显示给定变量的频率分布。下面的表示基于分类变量对频率条进行分组，从而更好地了解连续变量和串联变量。

代码

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

x_var = 'displ'
groupby_var = 'class'
df_agg = df.loc[:, [x_var, groupby_var]].groupby(groupby_var)
vals = [df[x_var].values.tolist() for i, df in df_agg]

plt.figure(figsize=(16,9), dpi= 80)
colors = [plt.cm.Spectral(i/float(len(vals)-1)) for i in range(len(vals))]
n, bins, patches = plt.hist(vals, 30, stacked=True, density=False, color=colors[:len(vals)])

plt.legend({group:col for group, col in zip(np.unique(df[groupby_var]).tolist(), colors[:len(vals)])})
plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$", fontsize=22)
plt.xlabel(x_var)
plt.ylabel("Frequency")
plt.ylim(0, 25)
plt.xticks(ticks=bins[::3], labels=[round(b,1) for b in bins[::3]])
plt.show()

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21-类型变量的直方图-Histogram for Categorical Variable

分类变量的直方图显示该变量的频率分布。通过对条形图进行着色，您可以将分布与表示颜色的另一个分类变量相关联。

代码

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

x_var = 'manufacturer'
groupby_var = 'class'
df_agg = df.loc[:, [x_var, groupby_var]].groupby(groupby_var)
vals = [df[x_var].values.tolist() for i, df in df_agg]

plt.figure(figsize=(16,9), dpi= 80)
colors = [plt.cm.Spectral(i/float(len(vals)-1)) for i in range(len(vals))]
n, bins, patches = plt.hist(vals, df[x_var].unique().__len__(), stacked=True, density=False, color=colors[:len(vals)])

plt.legend({group:col for group, col in zip(np.unique(df[groupby_var]).tolist(), colors[:len(vals)])})
plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$", fontsize=22)
plt.xlabel(x_var)
plt.ylabel("Frequency")
plt.ylim(0, 40)
plt.xticks(ticks=bins, labels=np.unique(df[x_var]).tolist(), rotation=90, horizontalalignment='left')
plt.show()

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22-密度图-Density Plot

密度图是一种常用工具，可视化连续变量的分布。通过”响应”变量对它们进行分组，您可以检查X和Y之间的关系。以下情况，如果出于代表性目的来描述城市里程的分布如何随着汽缸数的变化而变化

代码

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

plt.figure(figsize=(16,10), dpi= 80)
sns.kdeplot(df.loc[df['cyl'] == 4, "cty"], shade=True, color="g", label="Cyl=4", alpha=.7)
sns.kdeplot(df.loc[df['cyl'] == 5, "cty"], shade=True, color="deeppink", label="Cyl=5", alpha=.7)
sns.kdeplot(df.loc[df['cyl'] == 6, "cty"], shade=True, color="dodgerblue", label="Cyl=6", alpha=.7)
sns.kdeplot(df.loc[df['cyl'] == 8, "cty"], shade=True, color="orange", label="Cyl=8", alpha=.7)

plt.title('Density Plot of City Mileage by n_Cylinders', fontsize=22)
plt.legend()
plt.show()

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23-直方密度线图-Density Curves with Histogram

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。

代码

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

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)

plt.title('Density Plot of City Mileage by Vehicle Type', fontsize=22)
plt.legend()
plt.show()

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24-Joy Plot-Joy Plot

Joy Plot允许不同组的密度曲线重叠，这是一种可视化相对于彼此的大量组的分布的好方法。它看起来很悦目，并清楚地传达了正确的信息。它可以使用joypy基于的包来轻松构建matplotlib

代码

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

plt.figure(figsize=(16,10), dpi= 80)
fig, axes = joypy.joyplot(mpg, column=['hwy', 'cty'], by="class", ylim='own', figsize=(14,10))

plt.title('Joy Plot of City and Highway Mileage by Class', fontsize=22)
plt.show()

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25-分布式点图-Distributed Dot Plot

分布点图显示按组分割的点的单变量分布。点数越暗，该区域的数据点集中度越高。通过对中位数进行不同着色，组的真实定位立即变得明显。

代码

import matplotlib.patches as mpatches

df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")
cyl_colors = {4:'tab:red', 5:'tab:green', 6:'tab:blue', 8:'tab:orange'}
df_raw['cyl_color'] = df_raw.cyl.map(cyl_colors)

df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())
df.sort_values('cty', ascending=False, inplace=True)
df.reset_index(inplace=True)
df_median = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.median())

fig, ax = plt.subplots(figsize=(16,10), dpi= 80)
ax.hlines(y=df.index, xmin=0, xmax=40, color='gray', alpha=0.5, linewidth=.5, linestyles='dashdot')

for i, make in enumerate(df.manufacturer):
    df_make = df_raw.loc[df_raw.manufacturer==make, :]
    ax.scatter(y=np.repeat(i, df_make.shape[0]), x='cty', data=df_make, s=75, edgecolors='gray', c='w', alpha=0.5)
    ax.scatter(y=i, x='cty', data=df_median.loc[df_median.index==make, :], s=75, c='firebrick')

ax.text(33, 13, "$red \; dots \; are \; the \: median$", fontdict={'size':12}, color='firebrick')

red_patch = plt.plot([],[], marker="o", ms=10, ls="", mec=None, color='firebrick', label="Median")
plt.legend(handles=red_patch)
ax.set_title('Distribution of City Mileage by Make', fontdict={'size':22})
ax.set_xlabel('Miles Per Gallon (City)', alpha=0.7)
ax.set_yticks(df.index)
ax.set_yticklabels(df.manufacturer.str.title(), fontdict={'horizontalalignment': 'right'}, alpha=0.7)
ax.set_xlim(1, 40)
plt.xticks(alpha=0.7)
plt.gca().spines["top"].set_visible(False)
plt.gca().spines["bottom"].set_visible(False)
plt.gca().spines["right"].set_visible(False)
plt.gca().spines["left"].set_visible(False)
plt.grid(axis='both', alpha=.4, linewidth=.1)
plt.show()

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26-箱形图-Box Plot

箱形图是一种可视化分布的好方法，记住中位数、第 25 个第 45 个四分位数和异常值。但是，您需要注意解释可能会扭曲该组中包含的点数的框的大小。因此，手动提供每个框中的观察数量可以帮助克服这个缺点。

代码

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

plt.figure(figsize=(13,10), dpi= 80)
sns.boxplot(x='class', y='hwy', data=df, notch=False)

def add_n_obs(df,group_col,y):
    medians_dict = {grp[0]:grp[1][y].median() for grp in df.groupby(group_col)}
    xticklabels = [x.get_text() for x in plt.gca().get_xticklabels()]
    n_obs = df.groupby(group_col)[y].size().values
    for (x, xticklabel), n_ob in zip(enumerate(xticklabels), n_obs):
        plt.text(x, medians_dict[xticklabel]*1.01, "#obs : "+str(n_ob), horizontalalignment='center', fontdict={'size':14}, color='white')

add_n_obs(df,group_col='class',y='hwy')

plt.title('Box Plot of Highway Mileage by Vehicle Class', fontsize=22)
plt.ylim(10, 40)
plt.show()

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27-包点+箱形图-Dot + Box Plot

包点+箱形图（Dot+Box Plot）传达类似于分组的箱形图信息。此外，这些点可以了解每组中有多少数据点

代码

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

plt.figure(figsize=(13,10), dpi= 80)
sns.boxplot(x='class', y='hwy', data=df, hue='cyl')
sns.stripplot(x='class', y='hwy', data=df, color='black', size=3, jitter=1)

for i in range(len(df['class'].unique())-1):
    plt.vlines(i+.5, 10, 45, linestyles='solid', colors='gray', alpha=0.2)

plt.title('Box Plot of Highway Mileage by Vehicle Class', fontsize=22)
plt.legend(title='Cylinders')
plt.show()

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28-小提琴图-Violin Plot

小提琴图是箱形图在视觉上令人愉悦的替代品。小提琴的形状或面积取决于它所持有的观察次数。但是，小提琴图可能更难以阅读，并且在专业设置中不常用

代码

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

plt.figure(figsize=(13,10), dpi= 80)
sns.violinplot(x='class', y='hwy', data=df, scale='width', inner='quartile')

plt.title('Violin Plot of Highway Mileage by Vehicle Class', fontsize=22)
plt.show()

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29-人口金字塔-Population Pyramid

人口金字塔可用于显示由数量排序的组的分布。或者它也可以用于显示人口的逐级过滤，因为它在下面用于显示有多少人通过营销渠道的每个阶段

代码

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

plt.figure(figsize=(13,10), dpi= 80)
group_col = 'Gender'
order_of_bars = df.Stage.unique()[::-1]
colors = [plt.cm.Spectral(i/float(len(df[group_col].unique())-1)) for i in range(len(df[group_col].unique()))]

for c, group in zip(colors, df[group_col].unique()):
    sns.barplot(x='Users', y='Stage', data=df.loc[df[group_col]==group, :], order=order_of_bars, color=c, label=group)

plt.xlabel("$Users$")
plt.ylabel("Stage of Purchase")
plt.yticks(fontsize=12)
plt.title("Population Pyramid of the Marketing Funnel", fontsize=22)
plt.legend()
plt.show()

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30-分类图-Categorical Plots

由 seaborn 库 提供的分类图可用于可视化彼此相关的 2 个或更多分类变量的计数分布

代码

titanic = sns.load_dataset("titanic")

g = sns.catplot("alive", col="deck", col_wrap=4,
                data=titanic[titanic.deck.notnull()],
                kind="count", height=3.5, aspect=.8,
                palette='tab20')

fig.suptitle('sf')
plt.show()

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还有其他形式

titanic = sns.load_dataset("titanic")

sns.catplot(x="age", y="embark_town",
            hue="sex", col="class",
            data=titanic[titanic.embark_town.notnull()],
            orient="h", height=5, aspect=1, palette="tab10",
            kind="violin", dodge=True, cut=0, bw=.2)

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组成类-Composition

补充-组成类-雷达图（Radar Chart）

可以使用 雷达图 表明各个能力

代码

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.font_manager import FontProperties

labels=np.array(["A","B","C","D","E","F"])
stats=[83, 61, 95, 67, 76, 88]

angles=np.linspace(0, 2*np.pi, len(labels), endpoint=False)
stats=np.concatenate((stats,[stats[0]]))
angles=np.concatenate((angles,[angles[0]]))

fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
ax.plot(angles, stats, 'o-', linewidth=2)
ax.fill(angles, stats, alpha=0.25)

ax.set_thetagrids(angles * 180/np.pi, labels)
plt.show()

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31组成类-华夫饼图（Waffle Chart）

可以使用 pywaffle 包 创建华夫饼图，并用于显示更大群体中的组的组成

代码

from pywaffle import Waffle

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

df = df_raw.groupby('class').size().reset_index(name='counts')
n_categories = df.shape[0]
colors = [plt.cm.inferno_r(i/float(n_categories)) for i in range(n_categories)]

fig = plt.figure(
    FigureClass=Waffle,
    plots={
        '111': {
            'values': df['counts'],
            'labels': ["{0} ({1})".format(n[0], n[1]) for n in df[['class', 'counts']].itertuples()],
            'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 12},
            'title': {'label': '# Vehicles by Class', 'loc': 'center', 'fontsize':18}
        },
    },
    rows=7,
    colors=colors,
    figsize=(16, 9)
)

图片

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代码

from pywaffle import Waffle

df_class = df_raw.groupby('class').size().reset_index(name='counts_class')
n_categories = df_class.shape[0]
colors_class = [plt.cm.Set3(i/float(n_categories)) for i in range(n_categories)]

df_cyl = df_raw.groupby('cyl').size().reset_index(name='counts_cyl')
n_categories = df_cyl.shape[0]
colors_cyl = [plt.cm.Spectral(i/float(n_categories)) for i in range(n_categories)]

df_make = df_raw.groupby('manufacturer').size().reset_index(name='counts_make')
n_categories = df_make.shape[0]
colors_make = [plt.cm.tab20b(i/float(n_categories)) for i in range(n_categories)]

fig = plt.figure(
    FigureClass=Waffle,
    plots={
        '311': {
            'values': df_class['counts_class'],
            'labels': ["{1}".format(n[0], n[1]) for n in df_class[['class', 'counts_class']].itertuples()],
            'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 12, 'title':'Class'},
            'title': {'label': '# Vehicles by Class', 'loc': 'center', 'fontsize':18},
            'colors': colors_class
        },
        '312': {
            'values': df_cyl['counts_cyl'],
            'labels': ["{1}".format(n[0], n[1]) for n in df_cyl[['cyl', 'counts_cyl']].itertuples()],
            'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 12, 'title':'Cyl'},
            'title': {'label': '# Vehicles by Cyl', 'loc': 'center', 'fontsize':18},
            'colors': colors_cyl
        },
        '313': {
            'values': df_make['counts_make'],
            'labels': ["{1}".format(n[0], n[1]) for n in df_make[['manufacturer', 'counts_make']].itertuples()],
            'legend': {'loc': 'upper left', 'bbox_to_anchor': (1.05, 1), 'fontsize': 12, 'title':'Manufacturer'},
            'title': {'label': '# Vehicles by Make', 'loc': 'center', 'fontsize':18},
            'colors': colors_make
        }
    },
    rows=9,
    figsize=(16, 14)
)

图片

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32组成类- 饼图（Pie Chart）

饼图是显示组成的经典方式。然而，现在通常不建议使用它，因为馅饼部分的面积有时会变得误导。因此，如果您要使用饼图，强烈建议明确记下饼图每个部分的百分比或数字。

代码

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

df = df_raw.groupby('class').size()

df.plot(kind='pie', subplots=True, figsize=(8, 8), dpi= 80)
plt.title("Pie Chart of Vehicle Class - Bad")
plt.ylabel("")
plt.show()

图片

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分离的饼图，常突出重点

代码

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

df = df_raw.groupby('class').size().reset_index(name='counts')

fig, ax = plt.subplots(figsize=(12, 7), subplot_kw=dict(aspect="equal"), dpi= 80)

data = df['counts']
categories = df['class']
explode = [0,0,0,0,0,0.1,0]

def func(pct, allvals):
    absolute = int(pct/100.*np.sum(allvals))
    return "{:.1f}% ({:d} )".format(pct, absolute)

wedges, texts, autotexts = ax.pie(data,
                                  autopct=lambda pct: func(pct, data),
                                  textprops=dict(color="w"),
                                  colors=plt.cm.Dark2.colors,
                                 startangle=140,
                                 explode=explode)

ax.legend(wedges, categories, title="Vehicle Class", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))
plt.setp(autotexts, size=10, weight=700)
ax.set_title("Class of Vehicles: Pie Chart")
plt.show()

图片

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多层饼图，常用来表达深层次细节关系

代码

from matplotlib import pyplot as plt
import numpy as np

size = 0.25
base = 50

plt.rcParams['font.family'] = 'SimHei'
fig, ax = plt.subplots(figsize = (10, 10))

vals_middle = np.array([
    [47.5,11.7,15.2,9.6],
    [0,44.8,7.5,0],
    [9.2, 68.5 , 0, 0],
    [1.2, 7.2, 0, 0],
    [80,0, 0, 0],
    [1.7, 18.9, 0, 0]
])

vals_outer = np.array([
    [47.5,11.7,15.2,9.6],
    [0,36.6,8.2,7.5],
    [9.2,38.1,30.4, 0],
    [1.2, 5.8, 1.4, 0],
    [80,0, 0, 0],
    [1.7, 18.9, 0, 0]
])

vals_inner = vals_middle.sum(axis=1)

vals_first = vals_inner + base

'''
第二圈使用的数值, 因为最内圈每个类别都加上了base, 所以为了确保第二圈的数值和内圈相匹配, 第二圈的各类别要按照各自所占的比例分配各类的总数值.

'''
vals_second = np.zeros((6, 4))
for i in range(6):
    s_a = vals_first[i]
    s_b = vals_middle[i].sum()

    if s_b == 0.0:
        vals_second[i][1] = base
        continue
    for j in range(4):
        vals_second[i][j] = (vals_middle[i][j] / s_b) * s_a

vals_third = np.zeros((6, 4))
for i in range(6):
    s_a = vals_first[i]
    s_b = vals_outer[i].sum()
    if s_b == 0.0:
        vals_third[i][1] = base
        continue
    for j in range(4):
        vals_third[i][j] = (vals_outer[i][j] / s_b) * s_a

cmap_c = plt.get_cmap("tab20c")
cmap_b = plt.get_cmap("tab20b")

cmap_1 = cmap_c(np.arange(20))
cmap_2 = cmap_b(np.array([4, 5, 6, 7]))

inner_colors = np.vstack((cmap_1[::4], cmap_2[0]))

outer_colors = np.vstack((cmap_1, cmap_2))

labels_first=["餐厨废弃物\n{}万吨".format(vals_inner[0]),
        "农业秸秆\n{}万吨".format(vals_inner[1]),
        "水草\n151.2万吨",
        "园林绿化\n废弃物\n{}万吨".format(vals_inner[3]),
        "淤泥\n432.0万吨",
        "畜禽粪便\n21.6万吨"
        ]

labels_seocnd=[
    "未分类收集\n67.6万吨",
    "生物干化\n3.7万吨",
    "厌氧发酵\n10.2万吨",
    "油水分离\n2.6万吨",

    "",
    "粉碎\n46.8万吨",
    "好氧发酵\n3.5万吨",
    "",

    "未处理\n4.2万吨",
    "藻水分离\n147.0万吨",
    "",
    "",

    "未处理\n1.2万吨",
    "粉碎\n7.2万吨",
    "",
    "",

    "堆放\n432.0万吨",
    "",
    "",
    "",

    "未处理\n0.7万吨",
    "好氧发酵\n19.9万吨",
    "",
    "",
]

labels_third=[
    "未处理\n67.5万吨",
    "肥料化、发电\n3.7万吨",
    "沼气、沼渣发电\n10.2万吨",
    "焚烧\n2.6万吨",

    "",
    "还田\n42.6万吨",
    "燃料化\n4.2万吨",
    "肥料化\n3.5万吨",

    "未利用\n4.2万吨",
    "燃料化\n80.2万吨",
    "肥料化\n66.8万吨",
    "",

    "未利用\n1.2万吨",
    "肥料化\n5.8万吨",
    "燃料化\n1.4万吨",
    "",

    "未利用\n432.0万吨",
    "",
    "",
    "",

    "未利用\n0.7万吨",
    "肥料化\n19.9万吨",
    "",
    "",
]

handles, labels =  ax.pie(vals_first, radius=1-size-size,
                labels=labels_first,
                labeldistance=0.5,  rotatelabels=True, textprops={'fontsize': 11},
                colors=inner_colors, wedgeprops=dict(width=size, edgecolor='w'))

ax.pie(vals_second.flatten(),   radius=1-size, colors=outer_colors,
    labels=labels_seocnd,
    labeldistance=0.7,  rotatelabels=True, textprops={'fontsize': 11},
    wedgeprops=dict(width=size, edgecolor='w'))

ax.pie(vals_third.flatten(), radius=1, colors=outer_colors,
    labels=labels_third,
    labeldistance=0.8,  rotatelabels=True, textprops={'fontsize': 11},
    wedgeprops=dict(width=size, edgecolor='w'))

plt.title('某市有机废弃物产生、处理、利用情况', fontsize=25)
plt.legend(handles=handles, labels=[
        "餐厨废弃物",
        "农业秸秆",
        "水草",
        "园林绿化废弃物",
        "淤泥",
        "畜禽粪便"],
        loc = 1
        )
plt.show()

图片

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33组成类-树形图（Treemap）

树形图类似于饼图，它可以更好地完成工作而不会误导每个组的贡献。

代码

import squarify

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

df = df_raw.groupby('class').size().reset_index(name='counts')
labels = df.apply(lambda x: str(x[0]) + "\n (" + str(x[1]) + ")", axis=1)
sizes = df['counts'].values.tolist()
colors = [plt.cm.Spectral(i/float(len(labels))) for i in range(len(labels))]

plt.figure(figsize=(12,8), dpi= 80)
squarify.plot(sizes=sizes, label=labels, color=colors, alpha=.8)

plt.title('Treemap of Vechile Class')
plt.axis('off')
plt.show()

图片

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34组成类-条形图（Bar Chart）

条形图是基于计数或任何给定指标可视化项目的经典方式。在下面的图表中，我为每个项目使用了不同的颜色，但您通常可能希望为所有项目选择一种颜色，除非您按组对其进行着色

代码

import random

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

df = df_raw.groupby('manufacturer').size().reset_index(name='counts')
n = df['manufacturer'].unique().__len__()+1
all_colors = list(plt.cm.colors.cnames.keys())
random.seed(100)
c = random.choices(all_colors, k=n)

plt.figure(figsize=(16,10), dpi= 80)
plt.bar(df['manufacturer'], df['counts'], color=c, width=.5)
for i, val in enumerate(df['counts'].values):
    plt.text(i, val, float(val), horizontalalignment='center', verticalalignment='bottom', fontdict={'fontweight':500, 'size':12})

plt.gca().set_xticklabels(df['manufacturer'], rotation=60, horizontalalignment= 'right')
plt.title("Number of Vehicles by Manaufacturers", fontsize=22)
plt.ylabel('# Vehicles')
plt.ylim(0, 45)
plt.show()

图片

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变化类-Change

35变化类-时间序列图（Time Series Plot）

时间序列图用于显示给定度量随时间变化的方式。在这里，您可以看到 1949 年 至 1969 年间航空客运量的变化情况

代码

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

plt.figure(figsize=(16,10), dpi= 80)
plt.plot('date', 'traffic', data=df, color='tab:red')

plt.ylim(50, 750)
xtick_location = df.index.tolist()[::12]
xtick_labels = [x[-4:] for x in df.date.tolist()[::12]]
plt.xticks(ticks=xtick_location, labels=xtick_labels, rotation=0, fontsize=12, horizontalalignment='center', alpha=.7)
plt.yticks(fontsize=12, alpha=.7)
plt.title("Air Passengers Traffic (1949 - 1969)", fontsize=22)
plt.grid(axis='both', alpha=.3)

plt.gca().spines["top"].set_alpha(0.0)
plt.gca().spines["bottom"].set_alpha(0.3)
plt.gca().spines["right"].set_alpha(0.0)
plt.gca().spines["left"].set_alpha(0.3)
plt.show()

图片

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36变化类-带波峰波谷标记的时序图（Time Series with Peaks and Troughs Annotated）

下面的时间序列绘制了所有峰值和低谷，并注释了所选特殊事件的发生。

代码

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

data = df['traffic'].values
doublediff = np.diff(np.sign(np.diff(data)))
peak_locations = np.where(doublediff == -2)[0] + 1

doublediff2 = np.diff(np.sign(np.diff(-1*data)))
trough_locations = np.where(doublediff2 == -2)[0] + 1

plt.figure(figsize=(16,10), dpi= 80)
plt.plot('date', 'traffic', data=df, color='tab:blue', label='Air Traffic')
plt.scatter(df.date[peak_locations], df.traffic[peak_locations], marker=mpl.markers.CARETUPBASE, color='tab:green', s=100, label='Peaks')
plt.scatter(df.date[trough_locations], df.traffic[trough_locations], marker=mpl.markers.CARETDOWNBASE, color='tab:red', s=100, label='Troughs')

for t, p in zip(trough_locations[1::5], peak_locations[::3]):
    plt.text(df.date[p], df.traffic[p]+15, df.date[p], horizontalalignment='center', color='darkgreen')
    plt.text(df.date[t], df.traffic[t]-35, df.date[t], horizontalalignment='center', color='darkred')

plt.ylim(50,750)
xtick_location = df.index.tolist()[::6]
xtick_labels = df.date.tolist()[::6]
plt.xticks(ticks=xtick_location, labels=xtick_labels, rotation=90, fontsize=12, alpha=.7)
plt.title("Peak and Troughs of Air Passengers Traffic (1949 - 1969)", fontsize=22)
plt.yticks(fontsize=12, alpha=.7)

plt.gca().spines["top"].set_alpha(.0)
plt.gca().spines["bottom"].set_alpha(.3)
plt.gca().spines["right"].set_alpha(.0)
plt.gca().spines["left"].set_alpha(.3)

plt.legend(loc='upper left')
plt.grid(axis='y', alpha=.3)
plt.show()

图片

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37变化类-自相关和部分自相关图（Autocorrelation (ACF) and Partial Autocorrelation (PACF) Plot）

自相关图（ACF图）显示时间序列与其自身滞后的相关性。每条垂直线（在自相关图上）表示系列与滞后 0 之间的滞后之间的相关性。图中的蓝色阴影区域是显着性水平。那些位于蓝线之上的滞后是显着的滞后。

代码

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

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

fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(16,6), dpi= 80)
plot_acf(df.traffic.tolist(), ax=ax1, lags=50)
plot_pacf(df.traffic.tolist(), ax=ax2, lags=20)

ax1.spines["top"].set_alpha(.3); ax2.spines["top"].set_alpha(.3)
ax1.spines["bottom"].set_alpha(.3); ax2.spines["bottom"].set_alpha(.3)
ax1.spines["right"].set_alpha(.3); ax2.spines["right"].set_alpha(.3)
ax1.spines["left"].set_alpha(.3); ax2.spines["left"].set_alpha(.3)

ax1.tick_params(axis='both', labelsize=12)
ax2.tick_params(axis='both', labelsize=12)
plt.show()

图片

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38变化类-交叉相关图（Cross Correlation plot）

交叉相关图显示了两个时间序列相互之间的滞后。

代码

import statsmodels.tsa.stattools as stattools

df = pd.read_csv('https://github.com/selva86/datasets/raw/master/mortality.csv')
x = df['mdeaths']
y = df['fdeaths']

ccs = stattools.ccf(x, y)[:100]
nlags = len(ccs)

conf_level = 2 / np.sqrt(nlags)

plt.figure(figsize=(12,7), dpi= 80)

plt.hlines(0, xmin=0, xmax=100, color='gray')
plt.hlines(conf_level, xmin=0, xmax=100, color='gray')
plt.hlines(-conf_level, xmin=0, xmax=100, color='gray')

plt.bar(x=np.arange(len(ccs)), height=ccs, width=.3)

plt.title('$Cross\; Correlation\; Plot:\; mdeaths\; vs\; fdeaths$', fontsize=22)
plt.xlim(0,len(ccs))
plt.show()

图片

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39变化类-时间序列分解图（Time Series Decomposition Plot）

时间序列分解图显示时间序列分解为趋势，季节和残差分量。

代码

from statsmodels.tsa.seasonal import seasonal_decompose
from dateutil.parser import parse

df = pd.read_csv('https://github.com/selva86/datasets/raw/master/AirPassengers.csv')
dates = pd.DatetimeIndex([parse(d).strftime('%Y-%m-01') for d in df['date']])
df.set_index(dates, inplace=True)

result = seasonal_decompose(df['traffic'], model='multiplicative')

plt.rcParams.update({'figure.figsize': (10,10)})
result.plot().suptitle('Time Series Decomposition of Air Passengers')
plt.show()

图片

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40变化类-多个时间序列（Multiple Time Series）

您可以绘制多个时间序列，在同一图表上测量相同的值，如下所示。

代码

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

y_LL = 100
y_UL = int(df.iloc[:, 1:].max().max()*1.1)
y_interval = 400
mycolors = ['tab:red', 'tab:blue', 'tab:green', 'tab:orange']

fig, ax = plt.subplots(1,1,figsize=(16, 9), dpi= 80)

columns = df.columns[1:]
for i, column in enumerate(columns):
    plt.plot(df.date.values, df[column].values, lw=1.5, color=mycolors[i])
    plt.text(df.shape[0]+1, df[column].values[-1], column, fontsize=14, color=mycolors[i])

for y in range(y_LL, y_UL, y_interval):
    plt.hlines(y, xmin=0, xmax=71, colors='black', alpha=0.3, linestyles="--", lw=0.5)

plt.tick_params(axis="both", which="both", bottom=False, top=False,
                labelbottom=True, left=False, right=False, labelleft=True)

plt.gca().spines["top"].set_alpha(.3)
plt.gca().spines["bottom"].set_alpha(.3)
plt.gca().spines["right"].set_alpha(.3)
plt.gca().spines["left"].set_alpha(.3)

plt.title('Number of Deaths from Lung Diseases in the UK (1974-1979)', fontsize=22)
plt.yticks(range(y_LL, y_UL, y_interval), [str(y) for y in range(y_LL, y_UL, y_interval)], fontsize=12)
plt.xticks(range(0, df.shape[0], 12), df.date.values[::12], horizontalalignment='left', fontsize=12)
plt.ylim(y_LL, y_UL)
plt.xlim(-2, 80)
plt.show()

图片

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41变化类-使用辅助 Y 轴来绘制不同范围的图形（Plotting with different scales using secondary Y axis）

如果要显示在同一时间点测量两个不同数量的两个时间序列，则可以在右侧的辅助 Y 轴上再绘制第二个系列。

代码

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

x = df['date']
y1 = df['psavert']
y2 = df['unemploy']

fig, ax1 = plt.subplots(1,1,figsize=(16,9), dpi= 80)
ax1.plot(x, y1, color='tab:red')

ax2 = ax1.twinx()
ax2.plot(x, y2, color='tab:blue')

ax1.set_xlabel('Year', fontsize=20)
ax1.tick_params(axis='x', rotation=0, labelsize=12)
ax1.set_ylabel('Personal Savings Rate', color='tab:red', fontsize=20)
ax1.tick_params(axis='y', rotation=0, labelcolor='tab:red' )
ax1.grid(alpha=.4)

ax2.set_ylabel("# Unemployed (1000's)", color='tab:blue', fontsize=20)
ax2.tick_params(axis='y', labelcolor='tab:blue')
ax2.set_xticks(np.arange(0, len(x), 60))
ax2.set_xticklabels(x[::60], rotation=90, fontdict={'fontsize':10})
ax2.set_title("Personal Savings Rate vs Unemployed: Plotting in Secondary Y Axis", fontsize=22)
fig.tight_layout()
plt.show()

图片

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42变化类-带有误差带的时间序列（Time Series with Error Bands）

如果您有一个时间序列数据集，每个时间点（日期/时间戳）有多个观测值，则可以构建带有误差带的时间序列。您可以在下面看到一些基于每天不同时间订单的示例。另一个关于 45 天持续到达的订单数量的例子。

代码

from scipy.stats import sem

df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/user_orders_hourofday.csv")
df_mean = df.groupby('order_hour_of_day').quantity.mean()
df_se = df.groupby('order_hour_of_day').quantity.apply(sem).mul(1.96)

plt.figure(figsize=(16,10), dpi= 80)
plt.ylabel("# Orders", fontsize=16)
x = df_mean.index
plt.plot(x, df_mean, color="white", lw=2)
plt.fill_between(x, df_mean - df_se, df_mean + df_se, color="#3F5D7D")

plt.gca().spines["top"].set_alpha(0)
plt.gca().spines["bottom"].set_alpha(1)
plt.gca().spines["right"].set_alpha(0)
plt.gca().spines["left"].set_alpha(1)
plt.xticks(x[::2], [str(d) for d in x[::2]] , fontsize=12)
plt.title("User Orders by Hour of Day (95% confidence)", fontsize=22)
plt.xlabel("Hour of Day")

s, e = plt.gca().get_xlim()
plt.xlim(s, e)

for y in range(8, 20, 2):
    plt.hlines(y, xmin=s, xmax=e, colors='black', alpha=0.5, linestyles="--", lw=0.5)

plt.show()

图片

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代码

from dateutil.parser import parse
from scipy.stats import sem

df_raw = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/orders_45d.csv',
                     parse_dates=['purchase_time', 'purchase_date'])

df_mean = df_raw.groupby('purchase_date').quantity.mean()
df_se = df_raw.groupby('purchase_date').quantity.apply(sem).mul(1.96)

plt.figure(figsize=(16,10), dpi= 80)
plt.ylabel("# Daily Orders", fontsize=16)
x = [d.date().strftime('%Y-%m-%d') for d in df_mean.index]
plt.plot(x, df_mean, color="white", lw=2)
plt.fill_between(x, df_mean - df_se, df_mean + df_se, color="#3F5D7D")

plt.gca().spines["top"].set_alpha(0)
plt.gca().spines["bottom"].set_alpha(1)
plt.gca().spines["right"].set_alpha(0)
plt.gca().spines["left"].set_alpha(1)
plt.xticks(x[::6], [str(d) for d in x[::6]] , fontsize=12)
plt.title("Daily Order Quantity of Brazilian Retail with Error Bands (95% confidence)", fontsize=20)

s, e = plt.gca().get_xlim()
plt.xlim(s, e-2)
plt.ylim(4, 10)

for y in range(5, 10, 1):
    plt.hlines(y, xmin=s, xmax=e, colors='black', alpha=0.5, linestyles="--", lw=0.5)

plt.show()

图片

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43变化类-堆积面积图（Stacked Area Chart）

堆积面积图可以直观地显示多个时间序列的贡献程度，因此很容易相互比较。

代码

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

mycolors = ['tab:red', 'tab:blue', 'tab:green', 'tab:orange', 'tab:brown', 'tab:grey', 'tab:pink', 'tab:olive']

fig, ax = plt.subplots(1,1,figsize=(16, 9), dpi= 80)
columns = df.columns[1:]
labs = columns.values.tolist()

x  = df['yearmon'].values.tolist()
y0 = df[columns[0]].values.tolist()
y1 = df[columns[1]].values.tolist()
y2 = df[columns[2]].values.tolist()
y3 = df[columns[3]].values.tolist()
y4 = df[columns[4]].values.tolist()
y5 = df[columns[5]].values.tolist()
y6 = df[columns[6]].values.tolist()
y7 = df[columns[7]].values.tolist()
y = np.vstack([y0, y2, y4, y6, y7, y5, y1, y3])

labs = columns.values.tolist()
ax = plt.gca()
ax.stackplot(x, y, labels=labs, colors=mycolors, alpha=0.8)

ax.set_title('Night Visitors in Australian Regions', fontsize=18)
ax.set(ylim=[0, 100000])
ax.legend(fontsize=10, ncol=4)
plt.xticks(x[::5], fontsize=10, horizontalalignment='center')
plt.yticks(np.arange(10000, 100000, 20000), fontsize=10)
plt.xlim(x[0], x[-1])

plt.gca().spines["top"].set_alpha(0)
plt.gca().spines["bottom"].set_alpha(.3)
plt.gca().spines["right"].set_alpha(0)
plt.gca().spines["left"].set_alpha(.3)

plt.show()

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44变化类-未堆积的面积图（Area Chart UnStacked）

未堆积面积图用于可视化两个或更多个系列相对于彼此的进度（起伏）。在下面的图表中，您可以清楚地看到随着失业中位数持续时间的增加，个人储蓄率会下降。未堆积面积图表很好地展示了这种现象。

代码

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

x = df['date'].values.tolist()
y1 = df['psavert'].values.tolist()
y2 = df['uempmed'].values.tolist()
mycolors = ['tab:red', 'tab:blue', 'tab:green', 'tab:orange', 'tab:brown', 'tab:grey', 'tab:pink', 'tab:olive']
columns = ['psavert', 'uempmed']

fig, ax = plt.subplots(1, 1, figsize=(16,9), dpi= 80)
ax.fill_between(x, y1=y1, y2=0, label=columns[1], alpha=0.5, color=mycolors[1], linewidth=2)
ax.fill_between(x, y1=y2, y2=0, label=columns[0], alpha=0.5, color=mycolors[0], linewidth=2)

ax.set_title('Personal Savings Rate vs Median Duration of Unemployment', fontsize=18)
ax.set(ylim=[0, 30])
ax.legend(loc='best', fontsize=12)
plt.xticks(x[::50], fontsize=10, horizontalalignment='center')
plt.yticks(np.arange(2.5, 30.0, 2.5), fontsize=10)
plt.xlim(-10, x[-1])

for y in np.arange(2.5, 30.0, 2.5):
    plt.hlines(y, xmin=0, xmax=len(x), colors='black', alpha=0.3, linestyles="--", lw=0.5)

plt.gca().spines["top"].set_alpha(0)
plt.gca().spines["bottom"].set_alpha(.3)
plt.gca().spines["right"].set_alpha(0)
plt.gca().spines["left"].set_alpha(.3)
plt.show()

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45变化类-日历热力图（Calendar Heat Map）

与时间序列相比，日历地图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。

代码

import matplotlib as mpl
import calmap

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

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()

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46变化类- 季节图（Seasonal Plot）

季节图可用于比较上一季中同一天（年/月/周等）的时间序列。

代码

from dateutil.parser import parse

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

df['year'] = [parse(d).year for d in df.date]
df['month'] = [parse(d).strftime('%b') for d in df.date]
years = df['year'].unique()

mycolors = ['tab:red', 'tab:blue', 'tab:green', 'tab:orange', 'tab:brown', 'tab:grey', 'tab:pink', 'tab:olive', 'deeppink', 'steelblue', 'firebrick', 'mediumseagreen']
plt.figure(figsize=(16,10), dpi= 80)

for i, y in enumerate(years):
    plt.plot('month', 'traffic', data=df.loc[df.year==y, :], color=mycolors[i], label=y)
    plt.text(df.loc[df.year==y, :].shape[0]-.9, df.loc[df.year==y, 'traffic'][-1:].values[0], y, fontsize=12, color=mycolors[i])

plt.ylim(50,750)
plt.xlim(-0.3, 11)
plt.ylabel('$Air Traffic$')
plt.yticks(fontsize=12, alpha=.7)
plt.title("Monthly Seasonal Plot: Air Passengers Traffic (1949 - 1969)", fontsize=22)
plt.grid(axis='y', alpha=.3)

plt.gca().spines["top"].set_alpha(0.0)
plt.gca().spines["bottom"].set_alpha(0.5)
plt.gca().spines["right"].set_alpha(0.0)
plt.gca().spines["left"].set_alpha(0.5)

plt.show()

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分组类-Groups

47-分组类-树状图（Dendrogram）

树形图基于给定的距离度量将相似的点组合在一起，并基于点的相似性将它们组织在树状链接中

代码

import scipy.cluster.hierarchy as shc

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

plt.figure(figsize=(16, 10), dpi= 80)
plt.title("USArrests Dendograms", fontsize=22)
dend = shc.dendrogram(shc.linkage(df[['Murder', 'Assault', 'UrbanPop', 'Rape']], method='ward'), labels=df.State.values, color_threshold=100)
plt.xticks(fontsize=12)
plt.show()

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48-分组类-簇状图（Cluster Plot）

簇状图（Cluster Plot）可用于划分属于同一群集的点。下面是根据 USArrests 数据集将美国各州分为 5 组的代表性示例。此图使用”谋杀”和”攻击”列作为 X 和 Y 轴。或者，您可以将第一个到主要组件用作 X 轴和 Y 轴。

代码

from sklearn.cluster import AgglomerativeClustering
from scipy.spatial import ConvexHull

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

cluster = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward')
cluster.fit_predict(df[['Murder', 'Assault', 'UrbanPop', 'Rape']])

plt.figure(figsize=(14, 10), dpi= 80)
plt.scatter(df.iloc[:,0], df.iloc[:,1], c=cluster.labels_, cmap='tab10')

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)

encircle(df.loc[cluster.labels_ == 0, 'Murder'], df.loc[cluster.labels_ == 0, 'Assault'], ec="k", fc="gold", alpha=0.2, linewidth=0)
encircle(df.loc[cluster.labels_ == 1, 'Murder'], df.loc[cluster.labels_ == 1, 'Assault'], ec="k", fc="tab:blue", alpha=0.2, linewidth=0)
encircle(df.loc[cluster.labels_ == 2, 'Murder'], df.loc[cluster.labels_ == 2, 'Assault'], ec="k", fc="tab:red", alpha=0.2, linewidth=0)
encircle(df.loc[cluster.labels_ == 3, 'Murder'], df.loc[cluster.labels_ == 3, 'Assault'], ec="k", fc="tab:green", alpha=0.2, linewidth=0)
encircle(df.loc[cluster.labels_ == 4, 'Murder'], df.loc[cluster.labels_ == 4, 'Assault'], ec="k", fc="tab:orange", alpha=0.2, linewidth=0)

plt.xlabel('Murder'); plt.xticks(fontsize=12)
plt.ylabel('Assault'); plt.yticks(fontsize=12)
plt.title('Agglomerative Clustering of USArrests (5 Groups)', fontsize=22)
plt.show()

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49-分组类-安德鲁斯曲线（Andrews Curve）

安德鲁斯曲线有助于可视化是否存在基于给定分组的数字特征的固有分组。如果要素（数据集中的列）无法区分组（cyl），那么这些线将不会很好地隔离，如下所示。

代码

from pandas.plotting import andrews_curves

df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv")
df.drop(['cars', 'carname'], axis=1, inplace=True)

plt.figure(figsize=(12,9), dpi= 80)
andrews_curves(df, 'cyl', colormap='Set1')

plt.gca().spines["top"].set_alpha(0)
plt.gca().spines["bottom"].set_alpha(.3)
plt.gca().spines["right"].set_alpha(0)
plt.gca().spines["left"].set_alpha(.3)

plt.title('Andrews Curves of mtcars', fontsize=22)
plt.xlim(-3,3)
plt.grid(alpha=0.3)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.show()

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50-分组类-平行坐标（Parallel Coordinates）

平行坐标有助于可视化特征是否有助于有效地隔离组。如果实现隔离，则该特征可能在预测该组时非常有用。

代码

from pandas.plotting import parallel_coordinates

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

plt.figure(figsize=(12,9), dpi= 80)
parallel_coordinates(df_final, 'cut', colormap='Dark2')

plt.gca().spines["top"].set_alpha(0)
plt.gca().spines["bottom"].set_alpha(.3)
plt.gca().spines["right"].set_alpha(0)
plt.gca().spines["left"].set_alpha(.3)

plt.title('Parallel Coordinated of Diamonds', fontsize=22)
plt.grid(alpha=0.3)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.show()

图片

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参考：

英文

中文

Original: https://blog.csdn.net/qq1226317595/article/details/119569452
Author: 袁一白
Title: Python数据可视化-基于Python-matplotlib