2 回答

TA貢獻1853條經驗 獲得超6個贊
首先添加每個組內的序列號:
df['Order'] = df.groupby('Gender').cumcount()
然后排序:
df.sort_values('Order')
它給你:
Age Gender Country Order
0 10 Male US 0
3 40 Female Canada 0
1 20 Male UK 1
4 50 Female US 1
2 30 Male China 2
6 70 Female China 2
5 60 Male UK 3
7 80 Female Brazil 3
如果您想隨機播放,請在一開始就這樣做,例如df = df.sample(frac=1)
,請參閱:Shuffle DataFrame rows

TA貢獻1795條經驗 獲得超7個贊
使用 a 創建兩個新的數據幀,'Sort_Column'并使數據幀為df_male偶數值和數據幀為df_female奇數值。然后,使用pd.concat將它們重新組合在一起并.sort_values()在'Sort_Column'.
df = pd.DataFrame({'Age': [10, 20, 30, 40, 50, 60, 70, 80],
'Gender': ["Male", "Male", "Male", "Female", "Female", "Male", "Female", "Female"],
'Country': ["US", "UK", "China", "Canada", "US", "UK", "China", "Brazil"]})
df['Sort_Column'] = 0
df_male = df.loc[df['Gender'] == 'Male'].reset_index(drop=True)
df_male['Sort_Column'] = df_male['Sort_Column'] + df_male.index*2
df_female = df1.loc[df1['Gender'] == 'Female'].reset_index(drop=True)
df_female['Sort_Column'] = df_female['Sort_Column'] + df_female.index*2 + 1
df_sorted=pd.concat([df_male, df_female]).sort_values('Sort_Column').drop('Sort_Column', axis=1).reset_index(drop=True)
df_sorted
輸出:
Age Gender Country
0 10 Male US
1 40 Female Canada
2 20 Male UK
3 50 Female US
4 30 Male China
5 70 Female China
6 60 Male UK
7 80 Female Brazil
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