50個(gè)Matplotlib圖的匯編，在數(shù)據(jù)分析和可視化中最有用。此列表允許您使用Python的Matplotlib和Seaborn庫(kù)選擇要顯示的可視化對(duì)象。

1.關(guān)聯(lián)散點(diǎn)圖
帶邊界的氣泡圖
帶線性回歸最佳擬合線的散點(diǎn)圖
抖動(dòng)圖
計(jì)數(shù)圖
邊緣直方圖
邊緣箱形圖
相關(guān)圖
矩陣圖2.偏差發(fā)散型條形圖
發(fā)散型文本
發(fā)散型包點(diǎn)圖
帶標(biāo)記的發(fā)散型棒棒糖圖
面積圖3.排序有序條形圖
棒棒糖圖
包點(diǎn)圖
坡度圖
啞鈴圖4.分布連續(xù)變量的直方圖
類(lèi)型變量的直方圖
密度圖
直方密度線圖
Joy Plot
分布式包點(diǎn)圖
包點(diǎn)+箱形圖
Dot + Box Plot
小提琴圖
人口金字塔
分類(lèi)圖5.組成華夫餅圖
餅圖
樹(shù)形圖
條形圖6.變化時(shí)間序列圖
帶波峰波谷標(biāo)記的時(shí)序圖
自相關(guān)和部分自相關(guān)圖
交叉相關(guān)圖
時(shí)間序列分解圖
多個(gè)時(shí)間序列
使用輔助Y軸來(lái)繪制不同范圍的圖形
帶有誤差帶的時(shí)間序列
堆積面積圖
未堆積的面積圖
日歷熱力圖
季節(jié)圖7.分組樹(shù)狀圖
簇狀圖
安德魯斯曲線
平行坐標(biāo)#?!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(diǎn)圖Scatteplot是用于研究?jī)蓚€(gè)變量之間關(guān)系的經(jīng)典和基本圖。如果數(shù)據(jù)中有多個(gè)組，則可能需要以不同顏色可視化每個(gè)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(shí)，您希望在邊界內(nèi)顯示一組點(diǎn)以強(qiáng)調(diào)其重要性。在此示例中，您將從應(yīng)該被環(huán)繞的數(shù)據(jù)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(diǎn)圖如果你想了解兩個(gè)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(shù)據(jù)中各組之間最佳擬合線的差異。要禁用分組并僅為整個(gè)數(shù)據(jù)集繪制一條最佳擬合線，請(qǐng)從下面的調(diào)用中刪除該參數(shù)。#?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(gè)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(gè)組的最佳擬合線。你可以通過(guò)在里面設(shè)置參數(shù)來(lái)實(shí)現(xiàn)這一點(diǎn)。#?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(dòng)圖通常，多個(gè)數(shù)據(jù)點(diǎn)具有完全相同的X和Y值。結(jié)果，多個(gè)點(diǎn)相互繪制并隱藏。為避免這種情況，請(qǐng)稍微抖動(dòng)點(diǎn)，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(jì)數(shù)圖避免點(diǎn)重疊問(wèn)題的另一個(gè)選擇是增加點(diǎn)的大小，這取決于該點(diǎn)中有多少點(diǎn)。因此，點(diǎn)的大小越大，周?chē)狞c(diǎn)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(guān)系以及單獨(dú)的X和Y的單變量分布。該圖如果經(jīng)常用于探索性數(shù)據(jù)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(shù)，第25和第75百分位數(shù)。#?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(guān)圖Correlogram用于直觀地查看給定數(shù)據(jù)幀(或2D數(shù)組)中所有可能的數(shù)值變量對(duì)之間的相關(guān)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(duì)圖是探索性分析中的最?lèi)?ài)，以理解所有可能的數(shù)字變量對(duì)之間的關(guān)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(fā)散型條形圖如果您想根據(jù)單個(gè)指標(biāo)查看項(xiàng)目的變化情況，并可視化此差異的順序和數(shù)量，那么發(fā)散條是一個(gè)很好的工具。它有助于快速區(qū)分?jǐn)?shù)據(jù)中組的性能，并且非常直觀，并且可以立即傳達(dá)這一點(diǎn)。#?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(fā)散型文本分散的文本類(lèi)似于發(fā)散條，如果你想以一種漂亮和可呈現(xiàn)的方式顯示圖表中每個(gè)項(xiàng)目的價(jià)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(fā)散型包點(diǎn)圖發(fā)散點(diǎn)圖也類(lèi)似于發(fā)散條。然而，與發(fā)散條相比，條的不存在減少了組之間的對(duì)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(biāo)記的發(fā)散型棒棒糖圖帶標(biāo)記的棒棒糖通過(guò)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(guò)對(duì)軸和線之間的區(qū)域進(jìn)行著色，區(qū)域圖不僅強(qiáng)調(diào)峰值和低谷，而且還強(qiáng)調(diào)高點(diǎn)和低點(diǎn)的持續(xù)時(shí)間。高點(diǎn)持續(xù)時(shí)間越長(zhǎng)，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(dá)了項(xiàng)目的排名順序。但是，在圖表上方添加度量標(biāo)準(zhǔn)的值，用戶(hù)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(jué)上令人愉悅的方式提供與有序條形圖類(lèi)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(diǎn)圖點(diǎn)圖表傳達(dá)了項(xiàng)目的排名順序。由于它沿水平軸對(duì)齊，因此您可以更容易地看到點(diǎn)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(xiàng)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(dá)各種項(xiàng)目的“前”和“后”位置以及項(xiàng)目的排序。如果您想要將特定項(xiàng)目/計(jì)劃對(duì)不同對(duì)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(xù)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(lèi)變量對(duì)頻率條進(jìn)行分組，從而更好地了解連續(xù)變量和串聯(lián)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(lèi)型變量的直方圖分類(lèi)變量的直方圖顯示該變量的頻率分布。通過(guò)對(duì)條形圖進(jìn)行著色，您可以將分布與表示顏色的另一個(gè)分類(lèi)變量相關(guān)聯(lián)。#?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(xù)變量的分布。通過(guò)“響應(yīng)”變量對(duì)它們進(jìn)行分組，您可以檢查X和Y之間的關(guān)系。以下情況，如果出于代表性目的來(lái)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("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(gè)圖表傳達(dá)的集體信息匯集在一起，這樣您就可以將它們放在一個(gè)圖形而不是兩個(gè)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(duì)于彼此的大量組的分布的好方法。它看起來(lái)很悅目，并清楚地傳達(dá)了正確的信息。它可以使用joypy基于的包來(lái)輕松構(gòu)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(diǎn)圖分布點(diǎn)圖顯示按組分割的點(diǎn)的單變量分布。點(diǎn)數(shù)越暗，該區(qū)域的數(shù)據(jù)點(diǎn)集中度越高。通過(guò)對(duì)中位數(shù)進(jìn)行不同著色，組的真實(shí)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/

End

總結(jié)

以上是生活随笔為你收集整理的echart关系树状图_干货 | 25个常用Matplotlib图的Python代码的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問(wèn)題。

如果覺(jué)得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。