以后你肯定会用到的，25个常用Matplotlib图的Python代码，可以不会不能没有，建议收藏

古城微笑少年丶 2022-09-01 14:48 146阅读 0赞

今天给大家分析25个Matplotlib图的汇总，在数据分析和可视化中非常有用，文章较长，可以码起来慢慢练手。

# !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

**1. 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。

# 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70]  
**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()

''' 学习中遇到问题没人解答？小编创建了一个Python学习交流QQ群：531509025 寻找有志同道合的小伙伴，互帮互助,群里还有不错的视频学习教程和PDF电子书！ '''
    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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 1]  
**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。

# 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)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 2]  
**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。

''' 学习中遇到问题没人解答？小编创建了一个Python学习交流QQ群：531509025 寻找有志同道合的小伙伴，互帮互助,群里还有不错的视频学习教程和PDF电子书！ '''
    # 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 3]  
**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

# 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 4]  
**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。

# 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 5]  
**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）。

# 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 6]**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。

''' 学习中遇到问题没人解答？小编创建了一个Python学习交流QQ群：531509025 寻找有志同道合的小伙伴，互帮互助,群里还有不错的视频学习教程和PDF电子书！ '''
    # 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 7]  
**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。

# 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 8]  
**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。

# 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 9]

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

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 10]  
**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。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 11]  
**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。

# 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 12]  
**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。

# 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 13]  
**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。

''' 学习中遇到问题没人解答？小编创建了一个Python学习交流QQ群：531509025 寻找有志同道合的小伙伴，互帮互助,群里还有不错的视频学习教程和PDF电子书！ '''
    # 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 14]  
**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。

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, 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 15]  
**15. 有序条形图**

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

# 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
    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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 16]  
**16. 棒棒糖图**

棒棒糖图表以一种视觉上令人愉悦的方式提供与有序条形图类似的目的。

''' 学习中遇到问题没人解答？小编创建了一个Python学习交流QQ群：531509025 寻找有志同道合的小伙伴，互帮互助,群里还有不错的视频学习教程和PDF电子书！ '''
    # 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)
     
    # 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 17]  
**17. 包点图**

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

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 18]  
**18. 坡度图**

斜率图最适合比较给定人/项目的“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 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 19]  
**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。

''' 学习中遇到问题没人解答？小编创建了一个Python学习交流QQ群：531509025 寻找有志同道合的小伙伴，互帮互助,群里还有不错的视频学习教程和PDF电子书！ '''
    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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 20]  
**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。

# 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 21]  
**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。

# 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 22]  
**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。

# 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 23]  
**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。

# 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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 24]  
**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。

# !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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 25]  
**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。

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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzM4NjgyODYw_size_16_color_FFFFFF_t_70 26]

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