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数据分析最有用的 Top 50 Matplotlib 图（附完整的Python代码）(上)
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作者：zsx_yiyiyi 来源：python大本营 50个Matplotlib图的汇编，在数据分析和可视化中最有用。此列表允许您使用Python的Matplotlib和Seaborn库选择要显示的可视化对象。 1.关联 散点图 带边界的气泡图 带线性回归最佳拟合线的散点图 抖动图 计数图 边缘直方图 边缘箱形图 相关图 矩阵图 2.偏差 发散型条形图 发散型文本 发散型包点图 带标记的发散型棒棒糖图 面积图 3.排序 有序条形图 棒棒糖图 包点图 坡度图 哑铃图 4.分布 连续变量的直方图 类型变量的直方图 密度图 直方密度线图 Joy Plot 分布式包点图 包点+箱形图 Dot + Box Plot 小提琴图 人口金字塔 分类图 5.组成 华夫饼图 饼图 树形图 条形图 6.变化 时间序列图 带波峰波谷标记的时序图 自相关和部分自相关图 交叉相关图 时间序列分解图 多个时间序列 使用辅助Y轴来绘制不同范围的图形 带有误差带的时间序列 堆积面积图 未堆积的面积图 日历热力图 季节图 7.分组 树状图 簇状图 安德鲁斯曲线 平行坐标 代码语言：javascript复制# !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__) #> 0.9.01. 散点图 Scatteplot是用于研究两个变量之间关系的经典和基本图。如果数据中有多个组，则可能需要以不同颜色可视化每个组。在Matplotlib，你可以方便地使用。 代码语言：javascript复制# 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. 带边界的气泡图 有时，您希望在边界内显示一组点以强调其重要性。在此示例中，您将从应该被环绕的数据帧中获取记录，并将其传递给下面的代码中描述的记录。encircle() 代码语言：javascript复制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("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-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() 代码语言：javascript复制 3. 带线性回归最佳拟合线的散点图 如果你想了解两个变量如何相互改变，那么最合适的线就是要走的路。下图显示了数据中各组之间最佳拟合线的差异。要禁用分组并仅为整个数据集绘制一条最佳拟合线，请从下面的调用中删除该参数。 代码语言：javascript复制# 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) 每个回归线都在自己的列中 或者，您可以在其自己的列中显示每个组的最佳拟合线。你可以通过在里面设置参数来实现这一点。 代码语言：javascript复制# Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv") df_select = df.loc[df.cyl.isin([4,8]), :] # Each line in its own column sns.set_style("white") gridobj = sns.lmplot(x="displ", y="hwy", data=df_select, height=7, robust=True, palette= Set1 , col="cyl", scatter_kws=dict(s=60, linewidths=.7, edgecolors= black )) # Decorations gridobj.set(xlim=(0.5, 7.5), ylim=(0, 50)) plt.show() 4. 抖动图 通常，多个数据点具有完全相同的X和Y值。结果，多个点相互绘制并隐藏。为避免这种情况，请稍微抖动点，以便您可以直观地看到它们。这很方便使用 代码语言：javascript复制# 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. 计数图 避免点重叠问题的另一个选择是增加点的大小，这取决于该点中有多少点。因此，点的大小越大，周围的点的集中度就越大。 代码语言：javascript复制# Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv") df_counts = df.groupby([ hwy , cty ]).size().reset_index(name= counts ) # 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. 边缘直方图 边缘直方图具有沿X和Y轴变量的直方图。这用于可视化X和Y之间的关系以及单独的X和Y的单变量分布。该图如果经常用于探索性数据分析（EDA）。 代码语言：javascript复制# 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.边缘箱形图 边缘箱图与边缘直方图具有相似的用途。然而，箱线图有助于精确定位X和Y的中位数，第25和第75百分位数。 代码语言：javascript复制# 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. 相关图 Correlogram用于直观地查看给定数据帧（或2D数组）中所有可能的数值变量对之间的相关度量。 代码语言：javascript复制# Import Dataset df = 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. 矩阵图 成对图是探索性分析中的最爱，以理解所有可能的数字变量对之间的关系。它是双变量分析的必备工具。 代码语言：javascript复制# 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() 代码语言：javascript复制# 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. 发散型条形图 如果您想根据单个指标查看项目的变化情况，并可视化此差异的顺序和数量，那么发散条是一个很好的工具。它有助于快速区分数据中组的性能，并且非常直观，并且可以立即传达这一点。 代码语言：javascript复制# 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. 发散型文本 分散的文本类似于发散条，如果你想以一种漂亮和可呈现的方式显示图表中每个项目的价值，它更喜欢。 代码语言：javascript复制# 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. 发散型包点图 发散点图也类似于发散条。然而，与发散条相比，条的不存在减少了组之间的对比度和差异。 代码语言：javascript复制# 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. 带标记的发散型棒棒糖图 带标记的棒棒糖通过强调您想要引起注意的任何重要数据点并在图表中适当地给出推理，提供了一种可视化分歧的灵活方式。 代码语言：javascript复制# 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 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.面积图 通过对轴和线之间的区域进行着色，区域图不仅强调峰值和低谷，而且还强调高点和低点的持续时间。高点持续时间越长，线下面积越大。 代码语言：javascript复制import numpy as np import 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 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. 哑铃图 哑铃图传达各种项目的“前”和“后”位置以及项目的排序。如果您想要将特定项目/计划对不同对象的影响可视化，那么它非常有用。 代码语言：javascript复制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 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() 分配 20. 连续变量的直方图 直方图显示给定变量的频率分布。下面的表示基于分类变量对频率条进行分组，从而更好地了解连续变量和串联变量。 代码语言：javascript复制# 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. 类型变量的直方图 分类变量的直方图显示该变量的频率分布。通过对条形图进行着色，您可以将分布与表示颜色的另一个分类变量相关联。 代码语言：javascript复制# 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. 密度图 密度图是一种常用工具，可视化连续变量的分布。通过“响应”变量对它们进行分组，您可以检查X和Y之间的关系。以下情况，如果出于代表性目的来描述城市里程的分布如何随着汽缸数的变化而变化。 代码语言：javascript复制# 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. 直方密度线图 带有直方图的密度曲线将两个图表传达的集体信息汇集在一起，这样您就可以将它们放在一个图形而不是两个图形中。 代码语言：javascript复制# 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 Plot Joy Plot允许不同组的密度曲线重叠，这是一种可视化相对于彼此的大量组的分布的好方法。它看起来很悦目，并清楚地传达了正确的信息。它可以使用joypy基于的包来轻松构建matplotlib。 代码语言：javascript复制# !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. 分布式点图 分布点图显示按组分割的点的单变量分布。点数越暗，该区域的数据点集中度越高。通过对中位数进行不同着色，组的真实定位立即变得明显。 代码语言：javascript复制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 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() 本文参考自： https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/ |
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