我在資料框中有一個 0,1 的串列。如何在 Pandas 或 matplotlib 中繪制條形圖的百分比，這將在圖例 1,0 和 1,0 與整個串列的百分比的書面注釋中進行比較？
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
list_1 = [1,0,1,1,1,0,0,1,0,1,1,1,0,0,1,0,1,1,1,0,0,1,0,1,1,1,0,0,1,0,1,1,1,0,0,]
list_2 = [1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,0,1,1,1,0,1,1,0,1,1,1,0,1,1,0,1,1,1,1,0,]
list_3 = [1,0,1,1,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,1,1,0,0,]
df1 = pd.DataFrame({'Data1': list_1,'Data2': list_2,'Data3': list_3})
df1 = df1.mean()
df1.columns = ['1']
df2 = pd.DataFrame(1-df1)
df2.columns = ['0']
df1 = pd.DataFrame(df1)
df = pd.concat([df1,df2], axis=1)
df.plot( kind='barh',stacked = True,mark_right = True) # this is ok
plt.text(1,2,'%', va = 'center', ha = 'center')
plt.show()
我得到這個情節：
但是，對于 3 個串列，我會得到 1 和 0 的百分比，所以是這樣的：
uj5u.com熱心網友回復：
您可以將 seabornhistplot與multiple='fill'
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
list_1 = [1,0,1,1,1,0,0,1,0,1,1,1,0,0,1,0,1,1,1,0,0,1,0,1,1,1,0,0,1,0,1,1,1,0,0]
list_2 = [1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,0,1,1,1,0,1,1,0,1,1,1,0,1,1,0,1,1,1,1,0]
list_3 = [1,0,1,1,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,1,1,0,0]
df = pd.DataFrame({'Data1': list_1, 'Data2': list_2, 'Data3': list_3})
sns.set(style='white')
ax = sns.histplot(data=df, stat='percent', multiple='fill', discrete=True, shrink=0.8)
sns.despine()
ax.set_xticks([0, 1])
對于水平條和進一步的自定義，將資料幀轉換為長格式是有幫助的。
import matplotlib.pyplot as plt
from matplotlib.ticker import PercentFormatter
import seaborn as sns
import pandas as pd
list_1 = [1,0,1,1,1,0,0,1,0,1,1,1,0,0,1,0,1,1,1,0,0,1,0,1,1,1,0,0,1,0,1,1,1,0,0]
list_2 = [1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,0,1,1,1,0,1,1,0,1,1,1,0,1,1,0,1,1,1,1,0]
list_3 = [1,0,1,1,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,1,1,0,0]
df = pd.DataFrame({'Data1': list_1, 'Data2': list_2, 'Data3': list_3})
sns.set(style='white')
fig, ax = plt.subplots(figsize=(10, 4))
sns.histplot(data=df.melt(var_name='Dataset', value_name='Value'), y='Value', hue='Dataset',
stat='percent', multiple='fill', discrete=True, shrink=0.8,
palette=['tomato', 'limegreen', 'cornflowerblue'], alpha=1, ax=ax)
sns.despine()
sns.move_legend(ax, bbox_to_anchor=(1.01, 1.02), loc='upper left')
ax.set_yticks([0, 1])
ax.xaxis.set_major_formatter(PercentFormatter(1))
for p in ax.patches:
h, w, x, y = p.get_height(), p.get_width(), p.get_x(), p.get_y()
text = f'{w * 100:0.2f} %'
ax.annotate(text=text, xy=(x w / 2, y h / 2), ha='center', va='center', color='white', size=20)
plt.tight_layout()
plt.show()
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