Python 如何在matplotlib中为嵌套数据框创建计数图

我想使用

matplotlib

可视化下面的

pandas

dataframe，如草图所示

草图只显示了一般情况下需要的内容-不需要像图中所示的那样有精确的布局

如何使用

matplotlib

实现此任务

import pandas as pd
df = pd.DataFrame({'a': [0, 0, 0, 0, 0 , 1, 1,], 'b': [7, 7, 3, 3, 1, 2, 3, ], 'c': [102, 102, -50, -50, 30, 10, 10], })
df
   a  b    c
0  0  7  102
1  0  7  102
2  0  3  -50
3  0  3  -50
4  0  1   30
5  1  2   10
6  1  3   10

---

在开始可视化之前，我建议重新塑造数据，以明确嵌套级别，并预先计算频率。比如：

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.gridspec as gridspec

temp_df = pd.concat([
    df.groupby(["a"])["b"].value_counts().reset_index(name="count").rename(columns={"b":"value"}).assign(level_2="b"),
    df.groupby(["a"])["c"].value_counts().reset_index(name="count").rename(columns={"c":"value"}).assign(level_2="c")
])

final_df = (temp_df
            .rename(columns={"a":"level_1"})
            [["level_1", "level_2", "value", "count"]]
            .sort_values(["level_1", "level_2"]))

生成的数据帧如下所示：

   level_1 level_2  value  count
0        0       b      3      2
1        0       b      7      2
2        0       b      1      1
0        0       c    -50      2
1        0       c    102      2
2        0       c     30      1
3        1       b      2      1
4        1       b      3      1
3        1       c     10      2

现在，要以这种嵌套方式打印值及其计数，可以使用根据每个嵌套级别下的值数量定义布局。为了演示这个玩具数据集，我对这些值进行了硬编码，但是您希望以编程方式处理实际数据

您有9个值，因此

GridSpec

将有9列。您有两个嵌套级别，因此我们保留了两个底部行用于嵌套标签，并添加了几行以“承载”条形图

f = plt.figure(figsize=(10,4), dpi=300)

grid = gridspec.GridSpec(10, 9, figure=f)
mpl.rcParams["axes.edgecolor"] = "gainsboro"

# Use context manager to set mpl parameters for nested axs
with mpl.rc_context({"xtick.major.bottom": False, "ytick.major.left": False}):

    # Level 1 axs (label, ax)
    ax_level_1_0 = ("0", f.add_subplot(grid[9, 0:6]))
    ax_level_1_1 = ("1", f.add_subplot(grid[9, 6:]))
    level_1_axs = [ax_level_1_0, ax_level_1_1]

    # Level 2 axs (label, ax)
    ax_level_2_0b = ("B", f.add_subplot(grid[8, 0:3]))
    ax_level_2_0c = ("C", f.add_subplot(grid[8, 3:6]))
    ax_level_2_1b = ("B", f.add_subplot(grid[8, 6:8]))
    ax_level_2_1c = ("C", f.add_subplot(grid[8, 8:]))
    level_2_axs = [ax_level_2_0b, ax_level_2_0c, ax_level_2_1b, ax_level_2_1c]

# Actual count plot axs (level_1, level_2, ax)
ax_0b = (0, "b", f.add_subplot(grid[0:8, 0:3]))
ax_0b[2].set_ylabel("Frequency")

# Hide y-ticks
with mpl.rc_context({"ytick.major.left": False}):
    ax_0c = (0, "c", f.add_subplot(grid[0:8, 3:6]))
    ax_1b = (1, "b", f.add_subplot(grid[0:8, 6:8]))
    ax_1c = (1, "c", f.add_subplot(grid[0:8, 8:]))

count_axs = [ax_0b, ax_0c, ax_1b, ax_1c]

# Remove white space between subplots
plt.subplots_adjust(wspace=0, hspace=0)

# Add label text to Level 1 and 2 axs
for label, ax in level_1_axs + level_2_axs:
    ax.text(0.5, 0.5, label, horizontalalignment='center',
            verticalalignment='center', transform=ax.transAxes)

    
for l1, l2, ax in count_axs:
    y = final_df.query(f'(level_1 == {l1}) & (level_2 == "{l2}")')["count"]
    labels = final_df.query(f'(level_1 == {l1}) & (level_2 == "{l2}")')["value"]
    x = range(len(y))
    ax.bar(x, y, color="steelblue")
    ax.set_xticks(x)
    ax.set_xticklabels(labels)
    ax.tick_params(
        axis="x", direction="in", bottom=False, pad=-20,
        colors="white", labelsize=15)