50個Matplotlib圖的匯編，在數據分析和可視化中最有用。此列表允許您使用Python的Matplotlib和Seaborn庫選擇要顯示的可視化對象。

1.關聯散點圖
帶邊界的氣泡圖
帶線性回歸最佳擬合線的散點圖
抖動圖
計數圖
邊緣直方圖
邊緣箱形圖
相關圖
矩陣圖2.偏差發散型條形圖
發散型文本
發散型包點圖
帶標記的發散型棒棒糖圖
面積圖3.排序有序條形圖
棒棒糖圖
包點圖
坡度圖
啞鈴圖4.分布連續變量的直方圖
類型變量的直方圖
密度圖
直方密度線圖
Joy Plot
分布式包點圖
包點+箱形圖
Dot + Box Plot
小提琴圖
人口金字塔
分類圖5.組成華夫餅圖
餅圖
樹形圖
條形圖6.變化時間序列圖
帶波峰波谷標記的時序圖
自相關和部分自相關圖
交叉相關圖
時間序列分解圖
多個時間序列
使用輔助Y軸來繪制不同范圍的圖形
帶有誤差帶的時間序列
堆積面積圖
未堆積的面積圖
日歷熱力圖
季節圖7.分組樹狀圖
簇狀圖
安德魯斯曲線
平行坐標#?!pip?install?brewer2mplimport?numpy?as?npimport?pandas?as?pdimport?matplotlib?as?mplimport?matplotlib.pyplot?as?pltimport?seaborn?as?snsimport?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#?Versionprint(mpl.__version__)??#>?3.0.0print(sns.__version__)??#>?0.9.01. 散點圖Scatteplot是用于研究兩個變量之間關系的經典和基本圖。如果數據中有多個組，則可能需要以不同顏色可視化每個組。在Matplotlib，你可以方便地使用。#?Import?dataset?
midwest?=?pd.read_csv("https://raw.githubusercontent.com/selva86/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. 帶邊界的氣泡圖有時，您希望在邊界內顯示一組點以強調其重要性。在此示例中，您將從應該被環繞的數據幀中獲取記錄，并將其傳遞給下面的代碼中描述的記錄。encircle()from?matplotlib?import?patchesfrom?scipy.spatial?import?ConvexHullimport?warnings;?warnings.simplefilter('ignore')
sns.set_style("white")#?Step?1:?Prepare?Data
midwest?=?pd.read_csv("https://raw.githubusercontent.com/selva86/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-plotdef?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圖如果你想了解兩個變量如何相互改變，那么最合適的線就是要走的路。下圖顯示了數據中各組之間最佳擬合線的差異。要禁用分組并僅為整個數據集繪制一條最佳擬合線，請從下面的調用中刪除該參數。#?Import?Data
df?=?pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")
df_select?=?df.loc[df.cyl.isin([4,8]),?:]#?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)每個回歸線都在自己的列中或者，您可以在其自己的列中顯示每個組的最佳擬合線。你可以通過在里面設置參數來實現這一點。#?Import?Data
df?=?pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")
df_select?=?df.loc[df.cyl.isin([4,8]),?:]#?Each?line?in?its?own?column
sns.set_style("white")
gridobj?=?sns.lmplot(x="displ",?y="hwy",?
?????????????????????data=df_select,?
?????????????????????height=7,?
?????????????????????robust=True,?
?????????????????????palette='Set1',?
?????????????????????col="cyl",
?????????????????????scatter_kws=dict(s=60,?linewidths=.7,?edgecolors='black'))#?Decorations
gridobj.set(xlim=(0.5,?7.5),?ylim=(0,?50))
plt.show()4. 抖動圖通常，多個數據點具有完全相同的X和Y值。結果，多個點相互繪制并隱藏。為避免這種情況，請稍微抖動點，以便您可以直觀地看到它們。這很方便使用#?Import?Data
df?=?pd.read_csv("https://raw.githubusercontent.com/selva86/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. 計數圖避免點重疊問題的另一個選擇是增加點的大小，這取決于該點中有多少點。因此，點的大小越大，周圍的點的集中度就越大。#?Import?Datadf?=?pd.read_csv("https://raw.githubusercontent.com/selva86/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. 邊緣直方圖邊緣直方圖具有沿X和Y軸變量的直方圖。這用于可視化X和Y之間的關系以及單獨的X和Y的單變量分布。該圖如果經常用于探索性數據分析(EDA)。#?Import?Data
df?=?pd.read_csv("https://raw.githubusercontent.com/selva86/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?
?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.邊緣箱形圖邊緣箱圖與邊緣直方圖具有相似的用途。然而，箱線圖有助于精確定位X和Y的中位數，第25和第75百分位數。#?Import?Data
df?=?pd.read_csv("https://raw.githubusercontent.com/selva86/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?
?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. 相關圖Correlogram用于直觀地查看給定數據幀(或2D數組)中所有可能的數值變量對之間的相關度量。#?Import?Datasetdf?=?pd.read_csv("https://github.com/selva86/datasets/raw/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. 矩陣圖成對圖是探索性分析中的最愛，以理解所有可能的數字變量對之間的關系。它是雙變量分析的必備工具。#?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()偏差10. 發散型條形圖如果您想根據單個指標查看項目的變化情況，并可視化此差異的順序和數量，那么發散條是一個很好的工具。它有助于快速區分數據中組的性能，并且非常直觀，并且可以立即傳達這一點。#?Prepare?Data
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)#?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. 發散型文本分散的文本類似于發散條，如果你想以一種漂亮和可呈現的方式顯示圖表中每個項目的價值，它更喜歡。#?Prepare?Data
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)#?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. 發散型包點圖發散點圖也類似于發散條。然而，與發散條相比，條的不存在減少了組之間的對比度和差異。#?Prepare?Data
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)#?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. 帶標記的發散型棒棒糖圖帶標記的棒棒糖通過強調您想要引起注意的任何重要數據點并在圖表中適當地給出推理，提供了一種可視化分歧的靈活方式。#?Prepare?Data
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'#?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?plotimport?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.面積圖通過對軸和線之間的區域進行著色，區域圖不僅強調峰值和低谷，而且還強調高點和低點的持續時間。高點持續時間越長，線下面積越大。import?numpy?as?npimport?pandas?as?pd#?Prepare?Data
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#?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?
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')#?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()排序15. 有序條形圖有序條形圖有效地傳達了項目的排名順序。但是，在圖表上方添加度量標準的值，用戶可以從圖表本身獲取精確信息。#?Prepare?Data
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)#?Draw?plotimport?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?Textfor?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。#?Prepare?Data
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)#?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)#?Annotatefor?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. 包點圖點圖表傳達了項目的排名順序。由于它沿水平軸對齊，因此您可以更容易地看到點彼此之間的距離。#?Prepare?Data
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)#?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. 坡度圖斜率圖最適合比較給定人/項目的“之前”和“之后”位置。import?matplotlib.lines?as?mlines#?Import?Data
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'])]
#?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?Annotationfor?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. 啞鈴圖啞鈴圖傳達各種項目的“前”和“后”位置以及項目的排序。如果您想要將特定項目/計劃對不同對象的影響可視化，那么它非常有用。import?matplotlib.lines?as?mlines#?Import?Data
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)#?Func?to?draw?line?segmentdef?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?Segmentsfor?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()分配20. 連續變量的直方圖直方圖顯示給定變量的頻率分布。下面的表示基于分類變量對頻率條進行分組，從而更好地了解連續變量和串聯變量。#?Import?Data
df?=?pd.read_csv("https://github.com/selva86/datasets/raw/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. 類型變量的直方圖分類變量的直方圖顯示該變量的頻率分布。通過對條形圖進行著色，您可以將分布與表示顏色的另一個分類變量相關聯。#?Import?Data
df?=?pd.read_csv("https://github.com/selva86/datasets/raw/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. 密度圖密度圖是一種常用工具，可視化連續變量的分布。通過“響應”變量對它們進行分組，您可以檢查X和Y之間的關系。以下情況，如果出于代表性目的來描述城市里程的分布如何隨著汽缸數的變化而變化。#?Import?Data
df?=?pd.read_csv("https://github.com/selva86/datasets/raw/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()23. 直方密度線圖帶有直方圖的密度曲線將兩個圖表傳達的集體信息匯集在一起，這樣您就可以將它們放在一個圖形而不是兩個圖形中。#?Import?Data
df?=?pd.read_csv("https://github.com/selva86/datasets/raw/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 PlotJoy Plot允許不同組的密度曲線重疊，這是一種可視化相對于彼此的大量組的分布的好方法。它看起來很悅目，并清楚地傳達了正確的信息。它可以使用joypy基于的包來輕松構建matplotlib。#?!pip?install?joypy#?Import?Data
mpg?=?pd.read_csv("https://github.com/selva86/datasets/raw/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. 分布式點圖分布點圖顯示按組分割的點的單變量分布。點數越暗，該區域的數據點集中度越高。通過對中位數進行不同著色，組的真實定位立即變得明顯。import?matplotlib.patches?as?mpatches#?Prepare?Data
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)#?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?Dotsfor?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()本文參考自：[1]https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/

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