根据一列的计数对数据框进行排序并绘图

Question

我的数据框中有两列：

winner    opening_shortname
0      White         Slav Defense
1      Black  Nimzowitsch Defense
2      White     King's Pawn Game
3      White    Queen's Pawn Game
4      White     Philidor Defense
...      ...                  ...
20053  White        Dutch Defense
20054  Black         Queen's Pawn
20055  White    Queen's Pawn Game
20056  White         Pirc Defense
20057  Black    Queen's Pawn Game

我想创建下面的图，前 10 名开局及其获胜者颜色比例 (%)。

Answer 1

topk = 10
z = df.groupby(['opening_shortname', 'winner']).size().unstack()
ax = z.loc[z.sum(1).sort_values().tail(topk).index].plot.barh(color=['black', 'white'], edgecolor='black')
ax.xaxis.set_visible(False)

这按打开 和 的流行程度排序，限制在顶部 k（例如 OP 问题中的 10）。问题中提到的“比例 (%)”不明确：提供的图表清楚地显示了从顶部开口到下一个开口的总数递减，并且水平轴被移除。

无论如何，根据您提供的示例数据：

Answer 2

假设您的数据框名称为df，您可以groupby+count+unstack。然后对总和进行排序，取前 10 个进行绘图：

df2 = (df.assign(count=1)
         .groupby(['winner', 'opening_shortname'])
         .count()
         .unstack(level=0)
         .droplevel(0, axis=1)
      )

# plot part
idx = df2.sum(axis=1).sort_values().head(10).index
(df2.div(df2.sum(axis=1), axis=0) # calculate the proportion
    .fillna(0)
    .loc[idx, ['White', 'Black']]
    .plot.barh(color=['w', 'k'], edgecolor='k')
)

输出：

Answer 3

首先，您应该通过以下方式重新塑造您的数据框：

df = df.groupby(by = ['opening_shortname', 'winner']).size().reset_index().rename(columns = {'opening_shortname': 'opening_shortname', 'winner': 'winner', 0: 'count'}).sort_values(['count', 'opening_shortname', 'winner'], ascending = False, ignore_index = True)

所以你会得到一个类似（假数据）的数据框：

      opening_shortname winner  count
0     Queen's Pawn Game  White    141
1     Queen's Pawn Game  Black    132
2          Queen's Pawn  White     57
3          Queen's Pawn  Black     57
4      King's Pawn Game  Black     57
5         Dutch Defense  Black     53
6      Sicilian Defense  White     51
7      Sicilian Defense  Black     50
8   Nimzowitsch Defense  White     46
9   Nimzowitsch Defense  Black     45
10     Philidor Defense  Black     44
11         Slav Defense  White     43
12         Pirc Defense  White     42
13         Slav Defense  Black     39
14         Pirc Defense  Black     38
15     King's Pawn Game  White     38
16        Dutch Defense  White     36
17     Philidor Defense  White     31

然后你可以绘制你的数据，例如使用seaborn.barplot:

sns.barplot(ax = ax, data = df, x = 'count', y = 'opening_shortname', hue = 'winner', palette = ['white', 'black'], edgecolor = 'black')

完整代码

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv(r'data/data.csv')

df = df.groupby(by = ['opening_shortname', 'winner']).size().reset_index().rename(columns = {'opening_shortname': 'opening_shortname', 'winner': 'winner', 0: 'count'}).sort_values(['count', 'opening_shortname', 'winner'], ascending = False, ignore_index = True)


fig, ax = plt.subplots()

sns.barplot(ax = ax, data = df, x = 'count', y = 'opening_shortname', hue = 'winner', palette = ['white', 'black'], edgecolor = 'black')

plt.show()

---

如果你想绘制相对比例来代替count，那么你可以在上面的代码中添加一行：

df['count'] = df['count']/df.groupby('opening_shortname')['count'].transform('sum')

完整代码

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv(r'data/data.csv')

df = df.groupby(by = ['opening_shortname', 'winner']).size().reset_index().rename(columns = {'opening_shortname': 'opening_shortname', 'winner': 'winner', 0: 'count'}).sort_values(['count', 'opening_shortname', 'winner'], ascending = False, ignore_index = True)
df['count'] = df['count']/df.groupby('opening_shortname')['count'].transform('sum')


fig, ax = plt.subplots()

sns.barplot(ax = ax, data = df, x = 'count', y = 'opening_shortname', hue = 'winner', palette = ['white', 'black'], edgecolor = 'black')

plt.show()