python - 具有两个分类变量的 Matplotlib 点图

Question

我想生成一种特定类型的可视化，包含一个相当简单的 dot plot但有一点不同:两个轴都是分类变量(即序数或非数值)。这不但没有让事情变得更容易，反而让事情变得更加复杂。

为了说明这个问题，我将使用一个小型示例数据集，该数据集是对 seaborn.load_dataset("tips") 的修改，并定义如下:

import pandas
from six import StringIO
df = """total_bill |  tip  |    sex | smoker | day |   time | size
             16.99 | 1.01  |   Male |     No | Mon | Dinner |    2
             10.34 | 1.66  |   Male |     No | Sun | Dinner |    3
             21.01 | 3.50  |   Male |     No | Sun | Dinner |    3
             23.68 | 3.31  |   Male |     No | Sun | Dinner |    2
             24.59 | 3.61  | Female |     No | Sun | Dinner |    4
             25.29 | 4.71  | Female |     No | Mon | Lunch  |    4
              8.77 | 2.00  | Female |     No | Tue | Lunch  |    2
             26.88 | 3.12  |   Male |     No | Wed | Lunch  |    4
             15.04 | 3.96  |   Male |     No | Sat | Lunch  |    2
             14.78 | 3.23  |   Male |     No | Sun | Lunch  |    2"""
df = pandas.read_csv(StringIO(df.replace(' ','')), sep="|", header=0)

生成图表的第一种方法是尝试调用 seaborn，如下所示:

import seaborn
axes = seaborn.pointplot(x="time", y="sex", data=df)

此操作失败:

ValueError: Neither the `x` nor `y` variable appears to be numeric.

等效的 seaborn.stripplot 和 seaborn.swarmplot 调用也是如此。但是，如果其中一个变量是分类变量而另一个变量是数值变量，则它确实有效。确实 seaborn.pointplot(x="total_bill", y="sex", data=df) 有效，但不是我想要的。

我还尝试了这样的散点图:

axes = seaborn.scatterplot(x="time", y="sex", size="day", data=df,
                           x_jitter=True, y_jitter=True)

这会产生以下图表，该图表不包含任何抖动，并且所有点都重叠，因此毫无用处:

你知道有什么优雅的方法或库可以解决我的问题吗？

我开始自己写一些东西，我将在下面包含它，但这种实现不是最理想的，并且受到可以在同一点重叠的点的数量的限制(目前，如果超过 4 个点重叠，它就会失败)。

# Modules #
import seaborn, pandas, matplotlib
from six import StringIO

################################################################################
def amount_to_offets(amount):
    """A function that takes an amount of overlapping points (e.g. 3)
    and returns a list of offsets (jittered) coordinates for each of the
    points.

    It follows the logic that two points are displayed side by side:

    2 ->  * *

    Three points are organized in a triangle

    3 ->   *
          * *

    Four points are sorted into a square, and so on.

    4 ->  * *
          * *
    """
    assert isinstance(amount, int)
    solutions = {
        1: [( 0.0,  0.0)],
        2: [(-0.5,  0.0), ( 0.5,  0.0)],
        3: [(-0.5, -0.5), ( 0.0,  0.5), ( 0.5, -0.5)],
        4: [(-0.5, -0.5), ( 0.5,  0.5), ( 0.5, -0.5), (-0.5,  0.5)],
    }
    return solutions[amount]

################################################################################
class JitterDotplot(object):

    def __init__(self, data, x_col='time', y_col='sex', z_col='tip'):
        self.data = data
        self.x_col = x_col
        self.y_col = y_col
        self.z_col = z_col

    def plot(self, **kwargs):
        # Load data #
        self.df = self.data.copy()

        # Assign numerical values to the categorical data #
        # So that ['Dinner', 'Lunch'] becomes [0, 1] etc. #
        self.x_values = self.df[self.x_col].unique()
        self.y_values = self.df[self.y_col].unique()
        self.x_mapping = dict(zip(self.x_values, range(len(self.x_values))))
        self.y_mapping = dict(zip(self.y_values, range(len(self.y_values))))
        self.df = self.df.replace({self.x_col: self.x_mapping, self.y_col: self.y_mapping})

        # Offset points that are overlapping in the same location #
        # So that (2.0, 3.0) becomes (2.05, 2.95) for instance #
        cols = [self.x_col, self.y_col]
        scaling_factor = 0.05
        for values, df_view in self.df.groupby(cols):
            offsets = amount_to_offets(len(df_view))
            offsets = pandas.DataFrame(offsets, index=df_view.index, columns=cols)
            offsets *= scaling_factor
            self.df.loc[offsets.index, cols] += offsets

        # Plot a standard scatter plot #
        g = seaborn.scatterplot(x=self.x_col, y=self.y_col, size=self.z_col, data=self.df, **kwargs)

        # Force integer ticks on the x and y axes #
        locator = matplotlib.ticker.MaxNLocator(integer=True)
        g.xaxis.set_major_locator(locator)
        g.yaxis.set_major_locator(locator)
        g.grid(False)

        # Expand the axis limits for x and y #
        margin = 0.4
        xmin, xmax, ymin, ymax = g.get_xlim() + g.get_ylim()
        g.set_xlim(xmin-margin, xmax+margin)
        g.set_ylim(ymin-margin, ymax+margin)

        # Replace ticks with the original categorical names #
        g.set_xticklabels([''] + list(self.x_mapping.keys()))
        g.set_yticklabels([''] + list(self.y_mapping.keys()))

        # Return for display in notebooks for instance #
        return g

################################################################################
# Graph #
graph = JitterDotplot(data=df)
axes  = graph.plot()
axes.figure.savefig('jitter_dotplot.png')

Answer 1

您可以首先将时间和性别转换为分类类型并稍微调整一下:

df.sex = pd.Categorical(df.sex)
df.time = pd.Categorical(df.time)

axes = sns.scatterplot(x=df.time.cat.codes+np.random.uniform(-0.1,0.1, len(df)), 
                       y=df.sex.cat.codes+np.random.uniform(-0.1,0.1, len(df)),
                       size=df.tip)

输出:

有了这个想法，您可以将上述代码中的偏移量(np.random)修改为相应的距离。例如:

# grouping
groups = df.groupby(['time', 'sex'])

# compute the number of samples per group
num_samples = groups.tip.transform('size')

# enumerate the samples within a group
sample_ranks = df.groupby(['time']).cumcount() * (2*np.pi) / num_samples

# compute the offset
x_offsets = np.where(num_samples.eq(1), 0, np.cos(df.sample_rank) * 0.03)
y_offsets = np.where(num_samples.eq(1), 0, np.sin(df.sample_rank) * 0.03)

# plot
axes = sns.scatterplot(x=df.time.cat.codes + x_offsets, 
                       y=df.sex.cat.codes + y_offsets,
                       size=df.tip)

输出: