所以我了解如何對條形圖進(jìn)行排序（即這里）。但我找不到的是如何按子類別之一對條形圖進(jìn)行排序。例如，給定以下數(shù)據(jù)框，我可以獲得條形圖。但是我想做的是將它從最大到最小排序，由Typeof Classic)。import pandas as pdtest_df = pd.DataFrame([['Jake', 38, 'MW', 'Classic'],['John', 38,'NW', 'Classic'],['Sam', 34, 'SE', 'Classic'],['Sam', 22, 'E' ,'Classic'],['Joe', 43, 'ESE2', 'Classic'],['Joe', 34, 'MTN2', 'Classic'],['Joe', 38, 'MTN2', 'Classic'],['Scott', 38, 'ESE2', 'Classic'],['Chris', 34, 'SSE1', 'Classic'],['Joe', 43, 'S1', 'New'],['Paul', 34, 'NE2', 'New'],['Joe', 38, 'MC1', 'New'],['Joe', 34, 'NE2', 'New'],['Nick', 38, 'MC1', 'New'],['Al', 38, 'SSE1', 'New'],['Al', 34, 'ME', 'New'],['Al', 34, 'MC1', 'New'],['Joe', 43, 'S1', 'New']], columns = ['Name','Code_A','Code_B','Type'])import seaborn as snssns.set(style="darkgrid")palette ={"Classic":"#FF9999","New":"#99CC99"}g = sns.countplot(y="Name", palette=palette, hue="Type", data=test_df)所以而不是：'Joe' 將在上面，然后是 'Sam'，等等。
2 回答
互換的青春
TA貢獻(xiàn)1797條經(jīng)驗 獲得超6個贊
添加order參數(shù)。使用pandas.crosstab和sort_values來獲得：
import pandas as pd
test_df = pd.DataFrame([
['Jake', 38, 'MW', 'Classic'],
['John', 38,'NW', 'Classic'],
['Sam', 34, 'SE', 'Classic'],
['Sam', 22, 'E' ,'Classic'],
['Joe', 43, 'ESE2', 'Classic'],
['Joe', 34, 'MTN2', 'Classic'],
['Joe', 38, 'MTN2', 'Classic'],
['Scott', 38, 'ESE2', 'Classic'],
['Chris', 34, 'SSE1', 'Classic'],
['Joe', 43, 'S1', 'New'],
['Paul', 34, 'NE2', 'New'],
['Joe', 38, 'MC1', 'New'],
['Joe', 34, 'NE2', 'New'],
['Nick', 38, 'MC1', 'New'],
['Al', 38, 'SSE1', 'New'],
['Doug', 34, 'ME', 'New'],
['Fred', 34, 'MC1', 'New'],
['Joe', 43, 'S1', 'New']], columns = ['Name','Code_A','Code_B','Type'])
import seaborn as sns
sns.set(style="darkgrid")
palette ={"Classic":"#FF9999","New":"#99CC99"}
order = pd.crosstab(test_df.Name, test_df.Type).sort_values('Classic', ascending=False).index
g = sns.countplot(y="Name",
palette=palette,
hue="Type",
data=test_df,
order=order
)
鳳凰求蠱
TA貢獻(xiàn)1825條經(jīng)驗 獲得超4個贊
import pandas as pd
test_df = pd.DataFrame([
['Jake', 38, 'MW', 'Classic'],
['John', 38,'NW', 'Classic'],
['Sam', 34, 'SE', 'Classic'],
['Sam', 22, 'E' ,'Classic'],
['Joe', 43, 'ESE2', 'Classic'],
['Joe', 34, 'MTN2', 'Classic'],
['Joe', 38, 'MTN2', 'Classic'],
['Scott', 38, 'ESE2', 'Classic'],
['Chris', 34, 'SSE1', 'Classic'],
['Joe', 43, 'S1', 'New'],
['Paul', 34, 'NE2', 'New'],
['Joe', 38, 'MC1', 'New'],
['Joe', 34, 'NE2', 'New'],
['Nick', 38, 'MC1', 'New'],
['Al', 38, 'SSE1', 'New'],
['Al', 34, 'ME', 'New'],
['Al', 34, 'MC1', 'New'],
['Joe', 43, 'S1', 'New']], columns = ['Name','Code_A','Code_B','Type'])
import seaborn as sns
sns.set(style="darkgrid")
palette ={"Classic":"#FF9999","New":"#99CC99"}
sb.countplot(y = 'Name', hue='Type', data=test_df,
order=test_df['Name'].value_counts().index)
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