最熱門的50個matplotlib圖

關(guān)聯(lián) Correlation

1. 散點(diǎn)圖 Scatter plot
2. 帶邊界的氣泡圖 Bubble plot with Encircling
3. 帶線性回歸最佳擬合線的散點(diǎn)圖 Scatter plot with line of best fit
4. 抖動圖 Jittering with stripplot
5. 計數(shù)圖 Counts Plot
6. 邊緣直方圖 Marginal Histogram
7. 邊緣箱線圖 Marginal Boxplot
8. 相關(guān)圖 Correlogram
9. 矩陣圖 Pairwise Plot

偏差 Deviation

1. 發(fā)散型條形圖 Diverging Bars
2. 發(fā)散型文本 Diverging Texts
3. 發(fā)散型包點(diǎn)圖 Diverging Dot Plot
4. 帶標(biāo)記的發(fā)散型 棒棒糖圖 Diverging Lollipop Chart with Markers
5. 面積圖 Area Chart

排序 Ranking

1. 有序條形圖 Ordered Bar Chart
2. 棒棒糖圖 Lollipop Chart
3. 包點(diǎn)圖 Dot Plot
4. 坡度圖 Slope Chart
5. 啞鈴圖 Dumbbell Plot

分布 Distribution

1. 連續(xù)變量的直方圖 Histogram for Continuous Variable
2. 類型變量的直方圖 Histogram for Categorical Variable
3. 密度圖 Density Plot
4. 直方密度圖 Density Curves with Histogram
5. Joy Plot
6. 分布式包點(diǎn)圖 Distributed Dot Plot
7. 箱型圖 Box Plot
8. 包點(diǎn)+箱型圖 Dot + Box Plot
9. 小提琴圖 Violin Plot
10. 人口金字塔 Population Pyramid
11. 分類圖 Categorical Plots

組成 Composition

1. 華夫餅圖 Waffle Chart
2. 餅圖 Pie Chart
3. 樹形圖 Treemap
4. 條形圖 Bar Chart

變化 Change

1. 時間序列圖 Time Series Plot
2. 帶波峰波谷標(biāo)記的時序圖 Time Series with Peaks and Troughs Annotated
3. 自相關(guān)圖 Autocorrelation Plot
4. 交叉相關(guān)圖 Cross Correlation Plot
5. 時間序列分解圖 Time Series Decomposition Plot
6. 多個時間序列 Multiple Time Series
7. 使用輔助 Y 軸來繪制不同范圍的圖形 Plotting with different scales using secondary Y axis
8. 帶有誤差的時間序列 Time Series with Error Bands
9. 堆積面積圖 Stacked Area Chart
10. 未堆積面積圖 Area Chart Unstacked
11. 日歷熱力圖 Calendar Heat Map
12. 季節(jié)圖 Seasonal Plot

分組 Groups

1. 樹狀圖 Dendrogram
2. 簇狀圖 Cluster Plot
3. 安德魯斯曲線 Andrews Curve
4. 平行坐標(biāo) Parallel Coordinates

# !pip install brewer2mpl
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

# Version
print(mpl.__version__)  #> 3.0.0
print(sns.__version__)  #> 0.9.0

數(shù)據(jù)源1

數(shù)據(jù)源2

數(shù)據(jù)源3

關(guān)聯(lián) Correlation

1.散點(diǎn)圖 Scatter plot

散點(diǎn)圖是用于研究兩個變量之間關(guān)系的經(jīng)典基礎(chǔ)圖表煌贴。如果數(shù)據(jù)中有多個組陵叽，則可能需要以不同的顏色可視化每個組宾娜。在matplotlib中妙色，您可以使用plt.scatterplot()方便地執(zhí)行此操作。

# Import dataset 
midwest = pd.read_csv("datasets-master/midwest_filter.csv")

# Prepare Data 
# Create as many colors as there are unique midwest['category']
categories = np.unique(midwest['category'])
colors = [plt.cm.tab10(i/float(len(categories)-1)) for i in range(len(categories))]

# Draw Plot for Each Category
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))

# Decorations
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()  

2. 帶邊界的氣泡圖(Bubble plot with Encircling)

有時奔浅，您想在邊界內(nèi)顯示一組點(diǎn)以強(qiáng)調(diào)其重要性情屹。在此示例中呆贿，您從應(yīng)該環(huán)繞的數(shù)據(jù)框中獲取記錄消请，并用encircle()來使邊界顯示出來栏笆。

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

# Step 1: Prepare Data
midwest = pd.read_csv("datasets-master/midwest_filter.csv")

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

# Step 2: Draw Scatterplot with unique color for each category
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)

# Step 3: Encircling
# 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)

# Select data to be encircled
midwest_encircle_data = midwest.loc[midwest.state=='IN', :]                         

# Draw polygon surrounding vertices    
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)

# Step 4: Decorations
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()    

3. 帶線性回歸最佳擬合線的散點(diǎn)圖（Scatter plot with linear regression line of best fit）

如果你想了解兩個變量如何相互改變，那么最佳擬合線就是常用的方法臊泰。下圖顯示了數(shù)據(jù)中各組之間最佳擬合線的差異蛉加。要禁用分組并僅為整個數(shù)據(jù)集繪制一條最佳擬合線，請從 sns.lmplot() 調(diào)用中刪除 hue ='cyl' 參數(shù)。

# Import Data
df = pd.read_csv("datasets-master/mpg_ggplot2.csv")
df_select = df.loc[df.cyl.isin([4,8]), :]

# Plot
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'))

# Decorations
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()

# Import Data
df = pd.read_csv("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. 抖動圖（Jittering with stripplot）

通常筷凤，多個數(shù)據(jù)點(diǎn)具有完全相同的 X 和 Y 值。結(jié)果户盯，多個點(diǎn)繪制會重疊并隱藏嵌施。為避免這種情況饲化，請將數(shù)據(jù)點(diǎn)稍微抖動莽鸭，以便您可以直觀地看到它們。使用 seaborn 的 stripplot() 很方便實(shí)現(xiàn)這個功能吃靠。

# Import Data
df = pd.read_csv("datasets-master/mpg_ggplot2.csv")

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

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

5. 計數(shù)圖（Counts Plot）

避免點(diǎn)重疊問題的另一個選擇是增加點(diǎn)的大小硫眨，這取決于該點(diǎn)中有多少點(diǎn)。因此巢块，點(diǎn)的大小越大礁阁，其周圍的點(diǎn)的集中度越高。

# Import Data
df = pd.read_csv("datasets-master/mpg_ggplot2.csv")
df_counts = df.groupby(['hwy', 'cty']).size().reset_index(name='counts')

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

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

6. 邊緣直方圖（Marginal Histogram）

邊緣直方圖具有沿 X 和 Y 軸變量的直方圖族奢。這用于可視化 X 和 Y 之間的關(guān)系以及單獨(dú)的 X 和 Y 的單變量分布姥闭。這種圖經(jīng)常用于探索性數(shù)據(jù)分析（EDA）。

# Import Data
df = pd.read_csv("datasets-master/mpg_ggplot2.csv")

# Create Fig and gridspec
fig = plt.figure(figsize=(16, 10), dpi= 80)
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="tab10", edgecolors='gray', linewidths=.5)

# histogram on the right
ax_bottom.hist(df.displ, 40, histtype='stepfilled', orientation='vertical', color='deeppink')
ax_bottom.invert_yaxis()

# histogram in the bottom
ax_right.hist(df.hwy, 40, histtype='stepfilled', orientation='horizontal', color='deeppink')

# Decorations
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)

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

7. 邊緣箱形圖（Marginal Boxplot）

邊緣箱圖與邊緣直方圖具有相似的用途越走。然而棚品，箱線圖有助于精確定位 X 和 Y 的中位數(shù)、第 25 和第 75 百分位數(shù)廊敌。

# Import Data
df = pd.read_csv("datasets-master/mpg_ggplot2.csv")

# Create Fig and gridspec
fig = plt.figure(figsize=(16, 10), dpi= 80)
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*5, c=df.manufacturer.astype('category').cat.codes, alpha=.9, data=df, cmap="Set1", edgecolors='black', linewidths=.5)

# Add a graph in each part
sns.boxplot(df.hwy, ax=ax_right, orient="v")
sns.boxplot(df.displ, ax=ax_bottom, orient="h")

# Decorations ------------------
# Remove x axis name for the boxplot
ax_bottom.set(xlabel='')
ax_right.set(ylabel='')

# Main Title, Xlabel and YLabel
ax_main.set(title='Scatterplot with Histograms \n displ vs hwy', xlabel='displ', ylabel='hwy')

# Set font size of different components
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()

8. 相關(guān)圖（Correllogram）

相關(guān)圖用于直觀地查看給定數(shù)據(jù)框（或二維數(shù)組）中所有可能的數(shù)值變量對之間的相關(guān)度量铜跑。

# Import Dataset
df = pd.read_csv("datasets-master/mtcars.csv")

# Plot
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)

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

9. 矩陣圖（Pairwise Plot）

矩陣圖是探索性分析中的最愛，用于理解所有可能的數(shù)值變量對之間的關(guān)系骡澈。它是雙變量分析的必備工具锅纺。

# 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()

# Load Dataset
df = sns.load_dataset('iris')

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

02 偏差（Deviation）

10. 發(fā)散型條形圖（Diverging Bars）

如果您想根據(jù)單個指標(biāo)查看項目的變化情況，并可視化此差異的順序和數(shù)量肋殴，那么散型條形圖（Diverging Bars）是一個很好的工具囤锉。它有助于快速區(qū)分?jǐn)?shù)據(jù)中組的性能，并且非常直觀护锤，并且可以立即傳達(dá)這一點(diǎn)官地。

# Prepare Data
df = pd.read_csv("datasets-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)

# Draw plot
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)

# Decorations
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()

11. 發(fā)散型文本（Diverging Texts）

發(fā)散型文本（Diverging Texts）與發(fā)散型條形圖（Diverging Bars）相似，如果你想以一種漂亮和可呈現(xiàn)的方式顯示圖表中每個項目的價值蔽豺，就可以使用這種方法区丑。

# Prepare Data
df = pd.read_csv("datasets-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)

# Draw plot
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})

# Decorations    
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()

12. 發(fā)散型包點(diǎn)圖（Diverging Dot Plot）

發(fā)散型包點(diǎn)圖（Diverging Dot Plot）也類似于發(fā)散型條形圖（Diverging Bars）。然而，與發(fā)散型條形圖（Diverging Bars）相比沧侥，條的缺失減少了組之間的對比度和差異可霎。##

# Prepare Data
df = pd.read_csv("datasets-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)

# Draw plot
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'})

# Decorations
# Lighten borders
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()

13. 帶標(biāo)記的發(fā)散型棒棒糖圖（Diverging Lollipop Chart with Markers）

帶標(biāo)記的棒棒糖圖通過強(qiáng)調(diào)您想要引起注意的任何重要數(shù)據(jù)點(diǎn)并在圖表中適當(dāng)?shù)亟o出推理，提供了一種對差異進(jìn)行可視化的靈活方式宴杀。

# Prepare Data
df = pd.read_csv("datasets-master/mtcars.csv")
x = df.loc[:, ['mpg']]
df['mpg_z'] = (x - x.mean())/x.std()
df['colors'] = 'black'

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


# Draw plot
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)

# Annotate
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')

# Add Patches
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)

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

14. 面積圖（Area Chart）

通過對軸和線之間的區(qū)域進(jìn)行著色癣朗，面積圖不僅強(qiáng)調(diào)峰和谷，而且還強(qiáng)調(diào)高點(diǎn)和低點(diǎn)的持續(xù)時間旺罢。高點(diǎn)持續(xù)時間越長旷余，線下面積越大。

import numpy as np
import pandas as pd

# Prepare Data
df = pd.read_csv("datasets-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

# Plot
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)

# Annotate
plt.annotate('Peak \n1975', 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')


# Decorations
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()

03 排序（Ranking）

15. 有序條形圖（Ordered Bar Chart）

有序條形圖有效地傳達(dá)了項目的排名順序扁达。但是正卧，在圖表上方添加度量標(biāo)準(zhǔn)的值，用戶可以從圖表本身獲取精確信息跪解。

# Prepare Data
df_raw = pd.read_csv("datasets-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)

# Draw plot
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)

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


# Title, Label, Ticks and Ylim
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)

# Add patches to color the X axis labels
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()

16. 棒棒糖圖（Lollipop Chart）

棒棒糖圖表以一種視覺上令人愉悅的方式提供與有序條形圖類似的目的炉旷。

# Prepare Data
df_raw = pd.read_csv("datasets-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)

# Draw plot
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)

# Title, Label, Ticks and Ylim
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)

# Annotate
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()

17. 包點(diǎn)圖（Dot Plot）

包點(diǎn)圖表傳達(dá)了項目的排名順序，并且由于它沿水平軸對齊叉讥，因此您可以更容易地看到點(diǎn)彼此之間的距離窘行。

# Prepare Data
df_raw = pd.read_csv("datasets-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)

# Draw plot
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)

# Title, Label, Ticks and Ylim
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()

18. 坡度圖（Slope Chart）

坡度圖最適合比較給定人/項目的“前”和“后”位置。

import matplotlib.lines as mlines
# Import Data
df = pd.read_csv("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'])]

# draw line
# https://stackoverflow.com/questions/36470343/how-to-draw-a-line-with-matplotlib/36479941
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)

# Vertical Lines
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')

# Points
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)

# Line Segmentsand Annotation
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})

# 'Before' and 'After' Annotations
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})

# Decoration
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)

# Lighten borders
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()

19. 啞鈴圖（Dumbbell Plot）

啞鈴圖表傳達(dá)了各種項目的“前”和“后”位置以及項目的等級排序图仓。如果您想要將特定項目/計劃對不同對象的影響可視化罐盔，那么它非常有用。

import matplotlib.lines as mlines

# Import Data
df = pd.read_csv("datasets-master/health.csv")
df.sort_values('pct_2014', inplace=True)
df.reset_index(inplace=True)

# Func to draw line segment
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

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

# Vertical Lines
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')

# Points
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)

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

# Decoration
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()

04 分布（Distribution）

20. 連續(xù)變量的直方圖（Histogram for Continuous Variable）

直方圖顯示給定變量的頻率分布救崔。下面的圖表示基于類型變量對頻率條進(jìn)行分組惶看，從而更好地了解連續(xù)變量和類型變量。

# Import Data
df = pd.read_csv("datasets-master/mpg_ggplot2.csv")

# Prepare data
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]

# Draw
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)])

# Decoration
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()

21. 類型變量的直方圖（Histogram for Categorical Variable）

類型變量的直方圖顯示該變量的頻率分布帚豪。通過對條形圖進(jìn)行著色碳竟，可以將分布與表示顏色的另一個類型變量相關(guān)聯(lián)。

# Import Data
df = pd.read_csv("datasets-master/mpg_ggplot2.csv")

# Prepare data
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]

# Draw
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)])

# Decoration
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()

22. 密度圖（Density Plot）

密度圖是一種常用工具狸臣，用于可視化連續(xù)變量的分布莹桅。通過“響應(yīng)”變量對它們進(jìn)行分組，您可以檢查 X 和 Y 之間的關(guān)系烛亦。以下情況用于表示目的诈泼，以描述城市里程的分布如何隨著汽缸數(shù)的變化而變化。

# Import Data
df = pd.read_csv("datasets-master/mpg_ggplot2.csv")

# Draw Plot
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)

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

23. 直方密度線圖（Density Curves with Histogram）

帶有直方圖的密度曲線匯集了兩個圖所傳達(dá)的集體信息煤禽，因此您可以將它們放在一個圖中而不是兩個圖中铐达。

# Import Data
df = pd.read_csv("datasets-master/mpg_ggplot2.csv")

# Draw Plot
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)

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

24. Joy Plot

Joy Plot 允許不同組的密度曲線重疊，這是一種可視化大量分組數(shù)據(jù)的彼此關(guān)系分布的好方法檬果。它看起來很悅目瓮孙，并清楚地傳達(dá)了正確的信息唐断。它可以使用基于 matplotlib 的 joypy 包輕松構(gòu)建。
注：需要安裝 joypy 庫

# !pip install joypy
# Import Data
mpg = pd.read_csv("datasets-master/mpg_ggplot2.csv")

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

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

25. 分布式包點(diǎn)圖（Distributed Dot Plot）

分布式包點(diǎn)圖顯示按組分割的點(diǎn)的單變量分布杭抠。點(diǎn)數(shù)越暗脸甘，該區(qū)域的數(shù)據(jù)點(diǎn)集中度越高。通過對中位數(shù)進(jìn)行不同著色偏灿，組的真實(shí)定位立即變得明顯丹诀。

import matplotlib.patches as mpatches

# Prepare Data
df_raw = pd.read_csv("datasets-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)

# Mean and Median city mileage by make
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())

# Draw horizontal lines
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')

# Draw the Dots
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')

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

# Decorations
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()

26. 箱形圖（Box Plot）

箱形圖是一種可視化分布的好方法，記住中位數(shù)翁垂、第 25 個第 45 個四分位數(shù)和異常值铆遭。但是，您需要注意解釋可能會扭曲該組中包含的點(diǎn)數(shù)的框的大小沿猜。因此枚荣，手動提供每個框中的觀察數(shù)量可以幫助克服這個缺點(diǎn)。

例如邢疙，左邊的前兩個框具有相同大小的框棍弄，即使它們的值分別是 5 和 47。因此疟游，寫入該組中的觀察數(shù)量是必要的。

# Import Data
df = pd.read_csv("datasets-master/mpg_ggplot2.csv")

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

# Add N Obs inside boxplot (optional)
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')    

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

27. 包點(diǎn)+箱形圖（Dot+Box Plot）

包點(diǎn)+箱形圖（Dot+Box Plot）傳達(dá)類似于分組的箱形圖信息痕支。此外颁虐，這些點(diǎn)可以了解每組中有多少數(shù)據(jù)點(diǎn)。

# Import Data
df = pd.read_csv("datasets-master/mpg_ggplot2.csv")

# Draw Plot
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)

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

28. 小提琴圖（Violin Plot）

小提琴圖是箱形圖在視覺上令人愉悅的替代品卧须。小提琴的形狀或面積取決于它所持有的觀察次數(shù)另绩。但是，小提琴圖可能更難以閱讀花嘶，并且在專業(yè)設(shè)置中不常用笋籽。

# Import Data
df = pd.read_csv("datasets-master/mpg_ggplot2.csv")

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

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

29. 人口金字塔（Population Pyramid）

人口金字塔可用于顯示由數(shù)量排序的組的分布⊥衷保或者它也可以用于顯示人口的逐級過濾车海，因?yàn)樗谙旅嬗糜陲@示有多少人通過營銷渠道的每個階段。

 # Read data
df = pd.read_csv("datasets-master/email_campaign_funnel.csv")

# Draw Plot
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)

# Decorations    
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()

30. 分類圖（Categorical Plots）

由 seaborn 庫 提供的分類圖可用于可視化彼此相關(guān)的 2 個或更多分類變量的計數(shù)分布隘击。

# Load Dataset
titanic = sns.load_dataset("titanic")

# Plot
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()

# Load Dataset
titanic = sns.load_dataset("titanic")

# Plot
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)

05 組成（Composition）

31. 華夫餅圖（Waffle Chart）

可以使用 pywaffle 包 創(chuàng)建華夫餅圖侍芝，并用于顯示更大群體中的組的組成。

注：需要安裝 pywaffle 庫

 #! pip install pywaffle
# Reference: https://stackoverflow.com/questions/41400136/how-to-do-waffle-charts-in-python-square-piechart
from pywaffle import Waffle

# Import
df_raw = pd.read_csv("datasets-master/mpg_ggplot2.csv")

# Prepare Data
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)]

# Draw Plot and Decorate
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)
)

#! pip install pywaffle
from pywaffle import Waffle

# Import
# df_raw = pd.read_csv("datasets-master/mpg_ggplot2.csv")

# Prepare Data
# By Class Data
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)]

# By Cylinders Data
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)]

# By Make Data
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)]


# Draw Plot and Decorate
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)
)

32. 餅圖（Pie Chart）

餅圖是顯示組成的經(jīng)典方式埋同。然而州叠，現(xiàn)在通常不建議使用它，因?yàn)轲W餅部分的面積有時會變得誤導(dǎo)凶赁。因此咧栗，如果您要使用餅圖逆甜，強(qiáng)烈建議明確記下餅圖每個部分的百分比或數(shù)字。

# Import
df_raw = pd.read_csv("datasets-master/mpg_ggplot2.csv")

# Prepare Data
df = df_raw.groupby('class').size()

# Make the plot with pandas
df.plot(kind='pie', subplots=True, figsize=(8, 8))
plt.title("Pie Chart of Vehicle Class - Bad")
plt.ylabel("")
plt.show()

# Import
df_raw = pd.read_csv("datasets-master/mpg_ggplot2.csv")

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

# Draw Plot
fig, ax = plt.subplots(figsize=(12, 7), subplot_kw=dict(aspect="equal"))

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)

# Decoration
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()

33. 樹形圖（Treemap）

樹形圖類似于餅圖致板，它可以更好地完成工作而不會誤導(dǎo)每個組的貢獻(xiàn)绸贡。
注：需要安裝 squarify 庫

# !pip install squarify
import squarify 

# Import Data
df_raw = pd.read_csv("datasets-master/mpg_ggplot2.csv")

# Prepare Data
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))]

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

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

34. 條形圖（Bar Chart）

條形圖是基于計數(shù)或任何給定指標(biāo)可視化項目的經(jīng)典方式。在下面的圖表中叉谜，我為每個項目使用了不同的顏色践樱，但您通常可能希望為所有項目選擇一種顏色缕粹，除非您按組對其進(jìn)行著色稚茅。

import random

# Import Data
df_raw = pd.read_csv("datasets-master/mpg_ggplot2.csv")

# Prepare Data
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)

# Plot Bars
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})

# Decoration
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()

06 變化（Change）

35. 時間序列圖（Time Series Plot）

時間序列圖用于顯示給定度量隨時間變化的方式。在這里平斩，您可以看到 1949 年 至 1969 年間航空客運(yùn)量的變化情況亚享。

# Import Data
df = pd.read_csv('datasets-master/AirPassengers.csv')

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

# Decoration
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)

# Remove borders
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()

36. 帶波峰波谷標(biāo)記的時序圖（Time Series with Peaks and Troughs Annotated）

下面的時間序列繪制了所有峰值和低谷，并注釋了所選特殊事件的發(fā)生绘面。

# Import Data
df = pd.read_csv('datasets-master/AirPassengers.csv')

# Get the Peaks and Troughs
data = df['value'].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

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

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

# Decoration
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)

# Lighten borders
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()

37. 自相關(guān)和部分自相關(guān)圖（Autocorrelation (ACF) and Partial Autocorrelation (PACF) Plot）

自相關(guān)圖（ACF圖）顯示時間序列與其自身滯后的相關(guān)性欺税。每條垂直線（在自相關(guān)圖上）表示系列與滯后 0 之間的滯后之間的相關(guān)性。圖中的藍(lán)色陰影區(qū)域是顯著性水平揭璃。那些位于藍(lán)線之上的滯后是顯著的滯后晚凿。

那么如何解讀呢?

對于空乘旅客，我們看到多達(dá) 14 個滯后跨越藍(lán)線瘦馍，因此非常重要歼秽。這意味著，14 年前的航空旅客交通量對今天的交通狀況有影響情组。

PACF 在另一方面顯示了任何給定滯后（時間序列）與當(dāng)前序列的自相關(guān)燥筷，但是刪除了滯后的貢獻(xiàn)。

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

# Import Data
df = pd.read_csv('datasets-master/AirPassengers.csv')

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

# Decorate
# lighten the borders
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)

# font size of tick labels
ax1.tick_params(axis='both', labelsize=12)
ax2.tick_params(axis='both', labelsize=12)
plt.show()

38. 交叉相關(guān)圖（Cross Correlation plot）

交叉相關(guān)圖顯示了兩個時間序列相互之間的滯后院崇。

import statsmodels.tsa.stattools as stattools

# Import Data
df = pd.read_csv('datasets-master/mortality.csv')
x = df['mdeaths']
y = df['fdeaths']

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

# Compute the Significance level
# ref: https://stats.stackexchange.com/questions/3115/cross-correlation-significance-in-r/3128#3128
conf_level = 2 / np.sqrt(nlags)

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

plt.hlines(0, xmin=0, xmax=100, color='gray')  # 0 axis
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)

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

39. 時間序列分解圖（Time Series Decomposition Plot）

時間序列分解圖顯示時間序列分解為趨勢肆氓，季節(jié)和殘差分量。

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

# Import Data
df = pd.read_csv('datasets-master/AirPassengers.csv')
dates = pd.DatetimeIndex([parse(d).strftime('%Y-%m-01') for d in df['date']])
df.set_index(dates, inplace=True)

# Decompose 
result = seasonal_decompose(df['value'], model='multiplicative')

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

40. 多個時間序列（Multiple Time Series）

您可以繪制多個時間序列底瓣，在同一圖表上測量相同的值谢揪，如下所示。

# Import Data
df = pd.read_csv('datasets-master/mortality.csv')

# Define the upper limit, lower limit, interval of Y axis and colors
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']    

# Draw Plot and Annotate
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])

# Draw Tick lines  
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)

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

# Lighten borders
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()

41. 使用輔助 Y 軸來繪制不同范圍的圖形（Plotting with different scales using secondary Y axis）

如果要顯示在同一時間點(diǎn)測量兩個不同數(shù)量的兩個時間序列濒持，則可以在右側(cè)的輔助 Y 軸上再繪制第二個系列键耕。

# Import Data
df = pd.read_csv("datasets-master/economics.csv")

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

# Plot Line1 (Left Y Axis)
fig, ax1 = plt.subplots(1,1,figsize=(16,9), dpi= 80)
ax1.plot(x, y1, color='tab:red')

# Plot Line2 (Right Y Axis)
ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis
ax2.plot(x, y2, color='tab:blue')

# Decorations
# ax1 (left Y axis)
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 (right Y axis)
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()

42. 帶有誤差帶的時間序列（Time Series with Error Bands）

如果您有一個時間序列數(shù)據(jù)集，每個時間點(diǎn)（日期/時間戳）有多個觀測值柑营，則可以構(gòu)建帶有誤差帶的時間序列屈雄。您可以在下面看到一些基于每天不同時間訂單的示例。另一個關(guān)于 45 天持續(xù)到達(dá)的訂單數(shù)量的例子官套。

在該方法中酒奶，訂單數(shù)量的平均值由白線表示蚁孔。并且計算 95% 置信區(qū)間并圍繞均值繪制。

from scipy.stats import sem

# Import Data
df = pd.read_csv("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)

# Plot
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")  

# Decorations
# Lighten borders
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)

# Draw Horizontal Tick lines  
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()

"Data Source: https://www.kaggle.com/olistbr/brazilian-ecommerce#olist_orders_dataset.csv"
from dateutil.parser import parse
from scipy.stats import sem

# Import Data
df_raw = pd.read_csv('datasets-master/orders_45d.csv', 
                     parse_dates=['purchase_time', 'purchase_date'])

# Prepare Data: Daily Mean and SE Bands
df_mean = df_raw.groupby('purchase_date').quantity.mean()
df_se = df_raw.groupby('purchase_date').quantity.apply(sem).mul(1.96)

# Plot
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")  

# Decorations
# Lighten borders
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)

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

# Draw Horizontal Tick lines  
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()

43. 堆積面積圖（Stacked Area Chart）

堆積面積圖可以直觀地顯示多個時間序列的貢獻(xiàn)程度惋嚎，因此很容易相互比較杠氢。

# Import Data
df = pd.read_csv('datasets-master/nightvisitors.csv')

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

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

# Prepare data
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])

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

# Decorations
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])

# Lighten borders
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()

44. 未堆積的面積圖（Area Chart UnStacked）

未堆積面積圖用于可視化兩個或更多個系列相對于彼此的進(jìn)度（起伏）。在下面的圖表中另伍，您可以清楚地看到隨著失業(yè)中位數(shù)持續(xù)時間的增加鼻百，個人儲蓄率會下降。未堆積面積圖表很好地展示了這種現(xiàn)象摆尝。

# Import Data
df = pd.read_csv("datasets-master/economics.csv")

# Prepare Data
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']

# Draw Plot 
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)

# Decorations
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])

# Draw Tick lines  
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)

# Lighten borders
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()

45. 日歷熱力圖（Calendar Heat Map）

與時間序列相比温艇，日歷地圖是可視化基于時間的數(shù)據(jù)的備選和不太優(yōu)選的選項。雖然可以在視覺上吸引人堕汞，但數(shù)值并不十分明顯勺爱。然而，它可以很好地描繪極端值和假日效果讯检。

注：需要安裝 calmap 庫

#!pip install calmap
import matplotlib as mpl
import calmap

# Import Data
df = pd.read_csv("datasets-master/yahoo.csv", parse_dates=['date'])
df.set_index('date', inplace=True)

# Plot
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()

46. 季節(jié)圖（Seasonal Plot）

季節(jié)圖可用于比較上一季中同一天（年/月/周等）的時間序列琐鲁。

from dateutil.parser import parse 

# Import Data
df = pd.read_csv('datasets-master/AirPassengers.csv')

# Prepare data
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()

# Draw Plot
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', 'value', 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, 'value'][-1:].values[0], y, fontsize=12, color=mycolors[i])

# Decoration
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)

# Remove borders
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.legend(loc='upper right', ncol=2, fontsize=12)
plt.show()

07 分組（Groups）

47. 樹狀圖（Dendrogram）

樹形圖基于給定的距離度量將相似的點(diǎn)組合在一起，并基于點(diǎn)的相似性將它們組織在樹狀鏈接中人灼。

import scipy.cluster.hierarchy as shc

# Import Data
df = pd.read_csv('datasets-master/USArrests.csv')

# Plot
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()

48. 簇狀圖（Cluster Plot）

簇狀圖（Cluster Plot）可用于劃分屬于同一群集的點(diǎn)围段。下面是根據(jù) USArrests 數(shù)據(jù)集將美國各州分為 5 組的代表性示例挡毅。此圖使用“謀殺”和“攻擊”列作為 X 和 Y 軸跪呈【停或者拇派，您可以將第一個到主要組件用作 X 軸和 Y 軸博助。

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

# Import Data
df = pd.read_csv('datasets-master/USArrests.csv')

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

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

# Encircle
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)

# Draw polygon surrounding vertices    
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)

# Decorations
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()

49. 安德魯斯曲線（Andrews Curve）

安德魯斯曲線有助于可視化是否存在基于給定分組的數(shù)字特征的固有分組。如果要素（數(shù)據(jù)集中的列）無法區(qū)分組（cyl）关噪，那么這些線將不會很好地隔離鸟蟹，如下所示。

from pandas.plotting import andrews_curves

# Import
df = pd.read_csv("datasets-master/mtcars.csv")
df.drop(['cars', 'carname'], axis=1, inplace=True)

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

# Lighten borders
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()

50. 平行坐標(biāo)（Parallel Coordinates）

平行坐標(biāo)有助于可視化特征是否有助于有效地隔離組使兔。如果實(shí)現(xiàn)隔離建钥，則該特征可能在預(yù)測該組時非常有用。

from pandas.plotting import parallel_coordinates

# Import Data
df_final = pd.read_csv("datasets-master/diamonds_filter.csv")

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

# Lighten borders
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()

學(xué)習(xí)來源