25個常用Matplotlib圖的Python代碼，收藏收藏！

  • 2020 年 2 月 14 日
  • 筆記

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作者：zsx_yiyiyi

編輯：python大本營

大家好，小Z今天分享給大家25個Matplotlib圖的匯總，在數據分析和可視化中非常有用，文章較長，可以馬起來慢慢練手。

# !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. 散點圖

Scatteplot是用於研究兩個變量之間關係的經典和基本圖。如果數據中有多個組，則可能需要以不同顏色可視化每個組。在Matplotlib，你可以方便地使用。

# Import dataset  midwest = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/midwest_filter.csv")    # Prepare Data  # Create as many colors as there are unique midwest['category']  categories = np.unique(midwest['category'])  colors = [plt.cm.tab10(i/float(len(categories)-1)) for i in range(len(categories))]    # Draw Plot for Each Category  plt.figure(figsize=(16, 10), dpi= 80, facecolor='w', edgecolor='k')    for i, category in enumerate(categories):      plt.scatter('area', 'poptotal',                  data=midwest.loc[midwest.category==category, :],                  s=20, c=colors[i], label=str(category))    # Decorations  plt.gca().set(xlim=(0.0, 0.1), ylim=(0, 90000),                xlabel='Area', ylabel='Population')    plt.xticks(fontsize=12); plt.yticks(fontsize=12)  plt.title("Scatterplot of Midwest Area vs Population", fontsize=22)  plt.legend(fontsize=12)  plt.show()

2. 帶邊界的氣泡圖

有時，您希望在邊界內顯示一組點以強調其重要性。在此示例中，您將從應該被環繞的數據幀中獲取記錄，並將其傳遞給下面的代碼中描述的記錄。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. 帶線性回歸最佳擬合線的散點圖


如果你想了解兩個變量如何相互改變，那麼最合適的線就是要走的路。下圖顯示了數據中各組之間最佳擬合線的差異。要禁用分組並僅為整個數據集繪製一條最佳擬合線，請從下面的調用中刪除該參數。


# Import Data  df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")  df_select = df.loc[df.cyl.isin([4,8]), :]    # Plot  sns.set_style("white")  gridobj = sns.lmplot(x="displ", y="hwy", hue="cyl", data=df_select,                       height=7, aspect=1.6, robust=True, palette='tab10',                       scatter_kws=dict(s=60, linewidths=.7, edgecolors='black'))    # Decorations  gridobj.set(xlim=(0.5, 7.5), ylim=(0, 50))  plt.title("Scatterplot with line of best fit grouped by number of cylinders", fontsize=20)








每個回歸線都在自己的列中


或者，您可以在其自己的列中顯示每個組的最佳擬合線。你可以通過在裏面設置參數來實現這一點。


# Import Data  df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")  df_select = df.loc[df.cyl.isin([4,8]), :]    # Each line in its own column  sns.set_style("white")  gridobj = sns.lmplot(x="displ", y="hwy",                       data=df_select,                       height=7,                       robust=True,                       palette='Set1',                       col="cyl",                       scatter_kws=dict(s=60, linewidths=.7, edgecolors='black'))    # Decorations  gridobj.set(xlim=(0.5, 7.5), ylim=(0, 50))  plt.show()








4. 抖動圖


通常，多個數據點具有完全相同的X和Y值。結果，多個點相互繪製並隱藏。為避免這種情況，請稍微抖動點，以便您可以直觀地看到它們。這很方便使用


# Import Data  df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")    # Draw Stripplot  fig, ax = plt.subplots(figsize=(16,10), dpi= 80)  sns.stripplot(df.cty, df.hwy, jitter=0.25, size=8, ax=ax, linewidth=.5)    # Decorations  plt.title('Use jittered plots to avoid overlapping of points', fontsize=22)  plt.show()








5. 計數圖


避免點重疊問題的另一個選擇是增加點的大小，這取決於該點中有多少點。因此，點的大小越大，周圍的點的集中度就越大。


# Import 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. 邊緣直方圖


邊緣直方圖具有沿X和Y軸變量的直方圖。這用於可視化X和Y之間的關係以及單獨的X和Y的單變量分佈。該圖如果經常用於探索性數據分析（EDA）。


# Import Data  df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")    # Create Fig and gridspec  fig = plt.figure(figsize=(16, 10), dpi= 80)  grid = plt.GridSpec(4, 4, hspace=0.5, wspace=0.2)    # Define the axes  ax_main = fig.add_subplot(grid[:-1, :-1])  ax_right = fig.add_subplot(grid[:-1, -1], xticklabels=[], yticklabels=[])  ax_bottom = fig.add_subplot(grid[-1, 0:-1], xticklabels=[], yticklabels=[])    # Scatterplot on main ax  ax_main.scatter('displ', 'hwy', s=df.cty*4, c=df.manufacturer.astype('category').cat.codes, alpha=.9, data=df, cmap="tab10", edgecolors='gray', linewidths=.5)    # histogram on the right  ax_bottom.hist(df.displ, 40, histtype='stepfilled', orientation='vertical', color='deeppink')  ax_bottom.invert_yaxis()    # histogram in the bottom  ax_right.hist(df.hwy, 40, histtype='stepfilled', orientation='horizontal', color='deeppink')    # Decorations  ax_main.set(title='Scatterplot with Histograms   displ vs hwy', xlabel='displ', ylabel='hwy')  ax_main.title.set_fontsize(20)  for item in ([ax_main.xaxis.label, ax_main.yaxis.label] + ax_main.get_xticklabels() + ax_main.get_yticklabels()):      item.set_fontsize(14)    xlabels = ax_main.get_xticks().tolist()  ax_main.set_xticklabels(xlabels)  plt.show()








7.邊緣箱形圖


邊緣箱圖與邊緣直方圖具有相似的用途。然而，箱線圖有助於精確定位X和Y的中位數，第25和第75百分位數。


# Import Data  df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")    # Create Fig and gridspec  fig = plt.figure(figsize=(16, 10), dpi= 80)  grid = plt.GridSpec(4, 4, hspace=0.5, wspace=0.2)    # Define the axes  ax_main = fig.add_subplot(grid[:-1, :-1])  ax_right = fig.add_subplot(grid[:-1, -1], xticklabels=[], yticklabels=[])  ax_bottom = fig.add_subplot(grid[-1, 0:-1], xticklabels=[], yticklabels=[])    # Scatterplot on main ax  ax_main.scatter('displ', 'hwy', s=df.cty*5, c=df.manufacturer.astype('category').cat.codes, alpha=.9, data=df, cmap="Set1", edgecolors='black', linewidths=.5)    # Add a graph in each part  sns.boxplot(df.hwy, ax=ax_right, orient="v")  sns.boxplot(df.displ, ax=ax_bottom, orient="h")    # Decorations ------------------  # Remove x axis name for the boxplot  ax_bottom.set(xlabel='')  ax_right.set(ylabel='')    # Main Title, Xlabel and YLabel  ax_main.set(title='Scatterplot with Histograms   displ vs hwy', xlabel='displ', ylabel='hwy')    # Set font size of different components  ax_main.title.set_fontsize(20)  for item in ([ax_main.xaxis.label, ax_main.yaxis.label] + ax_main.get_xticklabels() + ax_main.get_yticklabels()):      item.set_fontsize(14)    plt.show()








8. 相關圖


Correlogram用於直觀地查看給定數據幀（或2D數組）中所有可能的數值變量對之間的相關度量。


# Import 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. 矩陣圖


成對圖是探索性分析中的最愛，以理解所有可能的數字變量對之間的關係。它是雙變量分析的必備工具。


# Load Dataset  df = sns.load_dataset('iris')    # Plot  plt.figure(figsize=(10,8), dpi= 80)  sns.pairplot(df, kind="scatter", hue="species", plot_kws=dict(s=80, edgecolor="white", linewidth=2.5))  plt.show()








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








偏差


10. 發散型條形圖


如果您想根據單個指標查看項目的變化情況，並可視化此差異的順序和數量，那麼發散條是一個很好的工具。它有助於快速區分數據中組的性能，並且非常直觀，並且可以立即傳達這一點。


# Prepare Data  df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv")  x = df.loc[:, ['mpg']]  df['mpg_z'] = (x - x.mean())/x.std()  df['colors'] = ['red' if x < 0 else 'green' for x in df['mpg_z']]  df.sort_values('mpg_z', inplace=True)  df.reset_index(inplace=True)    # Draw plot  plt.figure(figsize=(14,10), dpi= 80)  plt.hlines(y=df.index, xmin=0, xmax=df.mpg_z, color=df.colors, alpha=0.4, linewidth=5)    # Decorations  plt.gca().set(ylabel='$Model$', xlabel='$Mileage$')  plt.yticks(df.index, df.cars, fontsize=12)  plt.title('Diverging Bars of Car Mileage', fontdict={'size':20})  plt.grid(linestyle='--', alpha=0.5)  plt.show()








11. 發散型文本


分散的文本類似於發散條，如果你想以一種漂亮和可呈現的方式顯示圖表中每個項目的價值，它更喜歡。


# Prepare Data  df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv")  x = df.loc[:, ['mpg']]  df['mpg_z'] = (x - x.mean())/x.std()  df['colors'] = ['red' if x < 0 else 'green' for x in df['mpg_z']]  df.sort_values('mpg_z', inplace=True)  df.reset_index(inplace=True)    # Draw plot  plt.figure(figsize=(14,14), dpi= 80)  plt.hlines(y=df.index, xmin=0, xmax=df.mpg_z)  for x, y, tex in zip(df.mpg_z, df.index, df.mpg_z):      t = plt.text(x, y, round(tex, 2), horizontalalignment='right' if x < 0 else 'left',                   verticalalignment='center', fontdict={'color':'red' if x < 0 else 'green', 'size':14})    # Decorations  plt.yticks(df.index, df.cars, fontsize=12)  plt.title('Diverging Text Bars of Car Mileage', fontdict={'size':20})  plt.grid(linestyle='--', alpha=0.5)  plt.xlim(-2.5, 2.5)  plt.show()








12. 發散型包點圖


發散點圖也類似於發散條。然而，與發散條相比，條的不存在減少了組之間的對比度和差異。


# Prepare Data  df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv")  x = df.loc[:, ['mpg']]  df['mpg_z'] = (x - x.mean())/x.std()  df['colors'] = ['red' if x < 0 else 'darkgreen' for x in df['mpg_z']]  df.sort_values('mpg_z', inplace=True)  df.reset_index(inplace=True)    # Draw plot  plt.figure(figsize=(14,16), dpi= 80)  plt.scatter(df.mpg_z, df.index, s=450, alpha=.6, color=df.colors)  for x, y, tex in zip(df.mpg_z, df.index, df.mpg_z):      t = plt.text(x, y, round(tex, 1), horizontalalignment='center',                   verticalalignment='center', fontdict={'color':'white'})    # Decorations  # Lighten borders  plt.gca().spines["top"].set_alpha(.3)  plt.gca().spines["bottom"].set_alpha(.3)  plt.gca().spines["right"].set_alpha(.3)  plt.gca().spines["left"].set_alpha(.3)    plt.yticks(df.index, df.cars)  plt.title('Diverging Dotplot of Car Mileage', fontdict={'size':20})  plt.xlabel('$Mileage$')  plt.grid(linestyle='--', alpha=0.5)  plt.xlim(-2.5, 2.5)  plt.show()








13. 帶標記的發散型棒棒糖圖


帶標記的棒棒糖通過強調您想要引起注意的任何重要數據點並在圖表中適當地給出推理，提供了一種可視化分歧的靈活方式。


# Prepare Data  df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv")  x = df.loc[:, ['mpg']]  df['mpg_z'] = (x - x.mean())/x.std()  df['colors'] = 'black'    # color fiat differently  df.loc[df.cars == 'Fiat X1-9', 'colors'] = 'darkorange'  df.sort_values('mpg_z', inplace=True)  df.reset_index(inplace=True)      # Draw 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.面積圖


通過對軸和線之間的區域進行着色，區域圖不僅強調峰值和低谷，而且還強調高點和低點的持續時間。高點持續時間越長，線下面積越大。


import numpy as 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. 有序條形圖


有序條形圖有效地傳達了項目的排名順序。但是，在圖表上方添加度量標準的值，用戶可以從圖表本身獲取精確信息。


# Prepare Data  df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")  df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())  df.sort_values('cty', inplace=True)  df.reset_index(inplace=True)    # Draw 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. 棒棒糖圖


棒棒糖圖表以一種視覺上令人愉悅的方式提供與有序條形圖類似的目的。


# Prepare Data  df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")  df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())  df.sort_values('cty', inplace=True)  df.reset_index(inplace=True)    # Draw plot  fig, ax = plt.subplots(figsize=(16,10), dpi= 80)  ax.vlines(x=df.index, ymin=0, ymax=df.cty, color='firebrick', alpha=0.7, linewidth=2)  ax.scatter(x=df.index, y=df.cty, s=75, color='firebrick', alpha=0.7)    # Title, Label, Ticks and Ylim  ax.set_title('Lollipop Chart for Highway Mileage', fontdict={'size':22})  ax.set_ylabel('Miles Per Gallon')  ax.set_xticks(df.index)  ax.set_xticklabels(df.manufacturer.str.upper(), rotation=60, fontdict={'horizontalalignment': 'right', 'size':12})  ax.set_ylim(0, 30)    # 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. 包點圖


點圖表傳達了項目的排名順序。由於它沿水平軸對齊，因此您可以更容易地看到點彼此之間的距離。


# Prepare Data  df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")  df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())  df.sort_values('cty', inplace=True)  df.reset_index(inplace=True)    # Draw plot  fig, ax = plt.subplots(figsize=(16,10), dpi= 80)  ax.hlines(y=df.index, xmin=11, xmax=26, color='gray', alpha=0.7, linewidth=1, linestyles='dashdot')  ax.scatter(y=df.index, x=df.cty, s=75, color='firebrick', alpha=0.7)    # Title, Label, Ticks and Ylim  ax.set_title('Dot Plot for Highway Mileage', fontdict={'size':22})  ax.set_xlabel('Miles Per Gallon')  ax.set_yticks(df.index)  ax.set_yticklabels(df.manufacturer.str.title(), fontdict={'horizontalalignment': 'right'})  ax.set_xlim(10, 27)  plt.show()








18. 坡度圖


斜率圖最適合比較給定人/項目的「之前」和「之後」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. 啞鈴圖


啞鈴圖傳達各種項目的「前」和「後」位置以及項目的排序。如果您想要將特定項目/計劃對不同對象的影響可視化，那麼它非常有用。


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. 連續變量的直方圖


直方圖顯示給定變量的頻率分佈。下面的表示基於分類變量對頻率條進行分組，從而更好地了解連續變量和串聯變量。


# Import Data  df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")    # Prepare data  x_var = 'displ'  groupby_var = 'class'  df_agg = df.loc[:, [x_var, groupby_var]].groupby(groupby_var)  vals = [df[x_var].values.tolist() for i, df in df_agg]    # Draw  plt.figure(figsize=(16,9), dpi= 80)  colors = [plt.cm.Spectral(i/float(len(vals)-1)) for i in range(len(vals))]  n, bins, patches = plt.hist(vals, 30, stacked=True, density=False, color=colors[:len(vals)])    # Decoration  plt.legend({group:col for group, col in zip(np.unique(df[groupby_var]).tolist(), colors[:len(vals)])})  plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$", fontsize=22)  plt.xlabel(x_var)  plt.ylabel("Frequency")  plt.ylim(0, 25)  plt.xticks(ticks=bins[::3], labels=[round(b,1) for b in bins[::3]])  plt.show()








21. 類型變量的直方圖


分類變量的直方圖顯示該變量的頻率分佈。通過對條形圖進行着色，您可以將分佈與表示顏色的另一個分類變量相關聯。


# Import Data  df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")    # Prepare data  x_var = 'manufacturer'  groupby_var = 'class'  df_agg = df.loc[:, [x_var, groupby_var]].groupby(groupby_var)  vals = [df[x_var].values.tolist() for i, df in df_agg]    # Draw  plt.figure(figsize=(16,9), dpi= 80)  colors = [plt.cm.Spectral(i/float(len(vals)-1)) for i in range(len(vals))]  n, bins, patches = plt.hist(vals, df[x_var].unique().__len__(), stacked=True, density=False, color=colors[:len(vals)])    # Decoration  plt.legend({group:col for group, col in zip(np.unique(df[groupby_var]).tolist(), colors[:len(vals)])})  plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$", fontsize=22)  plt.xlabel(x_var)  plt.ylabel("Frequency")  plt.ylim(0, 40)  plt.xticks(ticks=bins, labels=np.unique(df[x_var]).tolist(), rotation=90, horizontalalignment='left')  plt.show()








22. 密度圖


密度圖是一種常用工具，可視化連續變量的分佈。通過「響應」變量對它們進行分組，您可以檢查X和Y之間的關係。以下情況，如果出於代表性目的來描述城市裡程的分佈如何隨着汽缸數的變化而變化。


# Import Data  df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")    # Draw Plot  plt.figure(figsize=(16,10), dpi= 80)  sns.kdeplot(df.loc[df['cyl'] == 4, "cty"], shade=True, color="g", label="Cyl=4", alpha=.7)  sns.kdeplot(df.loc[df['cyl'] == 5, "cty"], shade=True, color="deeppink", label="Cyl=5", alpha=.7)  sns.kdeplot(df.loc[df['cyl'] == 6, "cty"], shade=True, color="dodgerblue", label="Cyl=6", alpha=.7)  sns.kdeplot(df.loc[df['cyl'] == 8, "cty"], shade=True, color="orange", label="Cyl=8", alpha=.7)    # Decoration  plt.title('Density Plot of City Mileage by n_Cylinders', fontsize=22)  plt.legend()








23. 直方密度線圖


帶有直方圖的密度曲線將兩個圖表傳達的集體信息彙集在一起，這樣您就可以將它們放在一個圖形而不是兩個圖形中。


# Import Data  df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")    # Draw Plot  plt.figure(figsize=(13,10), dpi= 80)  sns.distplot(df.loc[df['class'] == 'compact', "cty"], color="dodgerblue", label="Compact", hist_kws={'alpha':.7}, kde_kws={'linewidth':3})  sns.distplot(df.loc[df['class'] == 'suv', "cty"], color="orange", label="SUV", hist_kws={'alpha':.7}, kde_kws={'linewidth':3})  sns.distplot(df.loc[df['class'] == 'minivan', "cty"], color="g", label="minivan", hist_kws={'alpha':.7}, kde_kws={'linewidth':3})  plt.ylim(0, 0.35)    # Decoration  plt.title('Density Plot of City Mileage by Vehicle Type', fontsize=22)  plt.legend()  plt.show()








24. Joy Plot


Joy Plot允許不同組的密度曲線重疊，這是一種可視化相對於彼此的大量組的分佈的好方法。它看起來很悅目，並清楚地傳達了正確的信息。它可以使用joypy基於的包來輕鬆構建matplotlib。


# !pip install joypy  # Import Data  mpg = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")    # Draw Plot  plt.figure(figsize=(16,10), dpi= 80)  fig, axes = joypy.joyplot(mpg, column=['hwy', 'cty'], by="class", ylim='own', figsize=(14,10))    # Decoration  plt.title('Joy Plot of City and Highway Mileage by Class', fontsize=22)  plt.show()








25. 分佈式點圖


分佈點圖顯示按組分割的點的單變量分佈。點數越暗，該區域的數據點集中度越高。通過對中位數進行不同着色，組的真實定位立即變得明顯。


import matplotlib.patches as mpatches    # Prepare Data  df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")  cyl_colors = {4:'tab:red', 5:'tab:green', 6:'tab:blue', 8:'tab:orange'}  df_raw['cyl_color'] = df_raw.cyl.map(cyl_colors)    # Mean and Median city mileage by make  df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())  df.sort_values('cty', ascending=False, inplace=True)  df.reset_index(inplace=True)  df_median = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.median())    # Draw horizontal lines  fig, ax = plt.subplots(figsize=(16,10), dpi= 80)  ax.hlines(y=df.index, xmin=0, xmax=40, color='gray', alpha=0.5, linewidth=.5, linestyles='dashdot')    # Draw the 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()








本文參考自：


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







		  				

		  				

													


						
						
						


						 

							
					

					    
									

													

													
											


			


			


	

		
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