Drop rows that only relate to one value in other columns pandas

@sammywemmy's solution: df.loc[df.groupby('name').item.transform('size').ge(2)]

1. groupby to group rows with the same name together

# Get Each Group
print(df.groupby('name').apply(lambda s: s.reset_index()))

         index    item   name  gender
name                                 
jim   0      3    kiwi    jim    male
kate  0      1  banana   kate  female
      1      2   apple   kate  female
      2      6    kiwi   kate  female
kimmy 0      5  banana  kimmy  female
tom   0      0  banana    tom    male
      1      4   apple    tom    male
      2      7  banana    tom    male

2. transform to get a value in every row that represents the group size. (Number of rows)

# Turn Each Item Into The Number of Rows in The Group
df['group_size'] = df.groupby('name')['item'].transform('size')
print(df)

     item   name  gender  group_size
0  banana    tom    male           3
1  banana   kate  female           3
2   apple   kate  female           3
3    kiwi    jim    male           1
4   apple    tom    male           3
5  banana  kimmy  female           1
6    kiwi   kate  female           3
7  banana    tom    male           3

This could have been done on any column in this case:

# Turn Each Item Into The Number of Rows in The Group
df['group_size'] = df.groupby('name')['gender'].transform('size')
print(df)

     item   name  gender  group_size
0  banana    tom    male           3
1  banana   kate  female           3
2   apple   kate  female           3
3    kiwi    jim    male           1
4   apple    tom    male           3
5  banana  kimmy  female           1
6    kiwi   kate  female           3
7  banana    tom    male           3

Notice how now each row has the corresponding group size at the end. tom has 3 instances so every name == tom row has 3 in group_size.

3. ge Convert to Boolean Index based on relational operator

# Add Condition To determine if the row should be kept or not
df['should_keep'] = df.groupby('name')['item'].transform('size').ge(2)
print(df)

     item   name  gender  group_size  should_keep
0  banana    tom    male           3         True
1  banana   kate  female           3         True
2   apple   kate  female           3         True
3    kiwi    jim    male           1        False
4   apple    tom    male           3         True
5  banana  kimmy  female           1        False
6    kiwi   kate  female           3         True
7  banana    tom    male           3         True

4. loc use Boolean Index to get the desired rows

print(df.groupby('name')['item'].transform('size').ge(2))

0     True
1     True
2     True
3    False
4     True
5    False
6     True
7     True
Name: item, dtype: bool

loc will include any index that is True, any index that is False will be excluded. (indexes 3 and 5 are False so they will not be included)

All together:

import pandas as pd

df = pd.DataFrame({'item': {0: 'banana', 1: 'banana', 2: 'apple',
                            3: 'kiwi', 4: 'apple', 5: 'banana',
                            6: 'kiwi', 7: 'banana'},
                   'name': {0: 'tom', 1: 'kate', 2: 'kate',
                            3: 'jim', 4: 'tom', 5: 'kimmy',
                            6: 'kate', 7: 'tom'},
                   'gender': {0: 'male', 1: 'female',
                              2: 'female', 3: 'male',
                              4: 'male', 5: 'female',
                              6: 'female', 7: 'male'}})

print(df.loc[df.groupby('name')['name'].transform('size').ge(2)])

     item  name  gender
0  banana   tom    male
1  banana  kate  female
2   apple  kate  female
4   apple   tom    male
6    kiwi  kate  female
7  banana   tom    male