pandas 中的行洗牌但有序

Question

Let's say that I have a data frame of three columns: age, gender, and country.

I want to randomly shuffle this data but in an ordered fashion according to gender. There are n males and m females, where n could be less than, greater than, or equal to m. The shuffling should happen in such a way that we get the following results for a size of 8 people:

male, female, male, female, male, female, female, female,.... (if there are more females: m > n) male, female, male, female, male, male, male, male (if there are more males: n > m) male, female, male, female, male, female, male, female, male, female (if equal males and females: n = m)

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"]})

Answer 1

First add the sequence numbers within each group:

df['Order'] = df.groupby('Gender').cumcount()

Then sort:

df.sort_values('Order')

It gives you:

   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

If you want to shuffle, do that at the very beginning, eg df = df.sample(frac=1) , see: Shuffle DataFrame rows

Answer 2

Create two new dataframes with a 'Sort_Column' and make the df_male dataframe even values and the df_female dataframe odd values. Then, use pd.concat to bring them back together and use .sort_values() on the '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

Ouput:

    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