25个常用Matplotlib图的Python代码，收藏收藏！

  • 2020 年 2 月 14 日
  • 笔记

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作者：zsx_yiyiyi

编辑：python大本营

大家好，小Z今天分享给大家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，可以马起来慢慢练手。

# !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()

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()

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()    








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)








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。


# 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


# 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。


# 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）。


# 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。


# 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。


# 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。


# 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. 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。


# 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。


# 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。


# 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。


# 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。


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()








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()








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。


# 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()








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。


# 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()








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。


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()








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()








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()








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()








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()








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()








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()








本文参考自：


[1]https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/







		  				

		  				

													


						
						
						


						 

							
					

					    
									

													

													
											


			


			


	

		
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