Python中的分组、百分比和条形图

Question

我对 Python 很陌生，我正在尝试绘制一个显示winner_rank_status 百分比的条形图，并且在每个条形中，我想显示获胜者的百分比（颜色）。

我的数据集是这样的：

我写的代码：

Q3_df=games_df[['winner','winner_rank_status']]
Q3_df=Q3_df.groupby(['winner_rank_status','winner']).size().groupby(level=0).apply(lambda x: round(100*x/x.sum(),2))
Q3_df=Q3_df.unstack()
ax= Q3_df.plot(
    kind='bar',
    stacked=True,
    figsize=(14,7),
    rot=0,
    title='Effect of piece colour and winner rating status on the result',
    color=['black','grey','white'],
    edgecolor='black',
    
)
for c in ax.containers:
    ax.bar_label(c, label_type='center',color='b')

这是我得到的结果：

这个结果是错误的，因为它显示了所有类别的 100%！！！我需要显示每个类别（相等、更高、更低）的真实百分比，然后在每个类别中显示每种颜色的比例......

请您指导我如何实现它？

感谢您的帮助。

Answer 1

您可以为每组条形图的标签赋予不同的颜色。要获得所有 9 个值总和为 100 的百分比，您可以除以游戏总数：

from matplotlib import pyplot as plt
import pandas as pd
import numpy as np

winner_options = ['black', 'draw', 'white']
rank_options = ['lower', 'equal', 'higher']
Q3_df = pd.DataFrame({'winner_rank_status': pd.Categorical(np.random.choice(rank_options, 1000, p=[.46, .07, .47]), rank_options),
                      'winner': pd.Categorical(np.random.choice(winner_options, 1000, p=[.51, .03, .46]), winner_options)})
Q3_rank_winner_df = Q3_df.groupby(['winner_rank_status', 'winner']).size().groupby(level=0).apply(
    lambda x: np.round(100 * x / len(Q3_df), 2))
Q3_rank_winner_df = Q3_rank_winner_df.unstack()
ax = Q3_rank_winner_df.plot(
    kind='bar',
    stacked=True,
    figsize=(14, 7),
    rot=0,
    title='Effect of piece colour and winner rating status on the result',
    color=['black', 'grey', 'white'],
    edgecolor='black')
for bars, color in zip(ax.containers, ['skyblue', 'navy', 'darkblue']):
    ax.bar_label(bars, label_type='center', color=color)
ax.legend(bbox_to_anchor=[1.01, 1.02], loc='upper left')
plt.tight_layout()
plt.show()

新要求有点混乱。可以在条形顶部添加每个等级的百分比：

from matplotlib import pyplot as plt
import pandas as pd
import numpy as np

winner_options = ['black', 'draw', 'white']
rank_options = ['lower', 'equal', 'higher']
Q3_df = pd.DataFrame(
    {'winner_rank_status': pd.Categorical(np.random.choice(rank_options, 1000, p=[.65, .05, .30]), rank_options),
     'winner': pd.Categorical(np.random.choice(winner_options, 1000, p=[.46, .07, .47]), winner_options)})
Q3_rank_winner_df = Q3_df.groupby(['winner_rank_status', 'winner']).size().groupby(level=0).apply(
    lambda x: np.round(100 * x / x.sum(), 2))
Q3_rank_winner_df = Q3_rank_winner_df.unstack()
ax = Q3_rank_winner_df.plot(
    kind='bar',
    stacked=True,
    figsize=(14, 7),
    rot=0,
    title='Effect of piece colour and winner rating status on the result',
    color=['black', 'grey', 'white'],
    edgecolor='black')
for bars, color in zip(ax.containers, ['skyblue', 'navy', 'darkblue']):
    ax.bar_label(bars, label_type='center', color=color)

Q3_rank_df = Q3_df.groupby(['winner_rank_status']).size() * 100 / len(Q3_df)
for row, percent in enumerate(Q3_rank_df):
    ax.text(row, 103, f'{percent:.02f} %', color='navy', ha='center', va='center')
ax.margins(y=0.08)  # more space on top

ax.legend(bbox_to_anchor=[1.01, 1.02], loc='upper left')
plt.tight_layout()
plt.show()