必备！25个非常优秀的可视化图形，有画法 |
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必备！25个非常优秀的可视化图形，有画法
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今天看到了一份很不错的资源，分享给大家！大家可以先收藏，在工作中可以用上时，随时拿来直接用！ 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，你希望在边界内显示一组点以强调其重要性。在此示例中，你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，你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，以便你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()10、发散型条形图 如果你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 |
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