My DataFrame looks like this:

What I would like to do is: if weight is once less than 70, drop all rows that have the same name. So, if Thomas' weight was once less than 70, drop all his data and repeat this for all the other names. So in my case the result would be:

Code to rebuild data:

data = {'date': {0: Timestamp('2014-01-01 00:00:00'), 
                 1: Timestamp('2014-01-02 00:00:00'), 
                 2: Timestamp('2014-01-03 00:00:00'), 
                 3: Timestamp('2014-01-04 00:00:00'), 
                 4: Timestamp('2014-01-05 00:00:00'), 
                 5: Timestamp('2014-01-06 00:00:00'), 
                 6: Timestamp('2014-01-07 00:00:00'), 
                 7: Timestamp('2014-01-08 00:00:00')}, 
        'name': {0: 'Thomas', 1: 'Thomas', 2: 'Thomas', 3: 'Max', 
                 4: 'Max', 5: 'Paul', 6: 'Paul', 7: 'Paul'}, 
        'size': {0: 130, 1: 132, 2: 132, 3: 143, 4: 150, 5: 140, 
                 6: 140, 7: 141}, 
        'weight': {0: 60, 1: 65, 2: 80, 3: 75, 4: 56, 5: 75, 6: 76, 7: 74}}

df = pd.DataFrame(data)

CodePudding user response：

names = list(df[df['weight']<70]['name'])
df_new = df[~(df['name'].isin(names))]

CodePudding user response：

Try as follows:

• Select column name from the df based on Series.lt and turn into a list with Series.tolist. Feed the resulting list to Series.isin and combine with unary operator (~) for selection from the df.

res = df[~df.name.isin(df[df.weight.lt(70)].name.tolist())]

print(res)

        date  name  size  weight
5 2014-01-06  Paul   140      75
6 2014-01-07  Paul   140      76
7 2014-01-08  Paul   141      74

Or as a variant on this answer to a similar question, try as follows:

• Use df.groupby on column name and apply filter with a lambda function, keeping the group only if Series.ge is True for all its values.

res = df.groupby('name').filter(lambda x: x.weight.ge(70).all())

# same result