[Solved] how to know if a value is in the same range of time in python [closed]

You could group by name, next count values and filter results which have count 3 (because you have 3 years)

groups = df.groupby('name').count()
result = groups[ groups['date'] == 3 ].index.to_list()

print(result)

Or you could directly count names

counts = df['name'].value_counts()
result = counts[ counts == 3 ].index.to_list()

print('result:', result)

Minimal working example:

I use io.StringIO only to simulate file.

text=""'    date    name
0   2019    a
1   2019    b
2   2019    c
3   2020    b
4   2020    c
5   2021    b
6   2021    c
'''

import io
import pandas as pd

df = pd.read_csv(io.StringIO(text), sep='\s+')

counts = df['name'].value_counts()
result = counts[ counts == 3 ].index.to_list()
print('result:', result)

groups = df.groupby('name').count()
result = groups[ groups['date'] == 3 ].index.to_list()
print('result:', result)

BTW:

Instead of hardcoded value 3 you could count unique date

years = df['date'].unique()
print(years, len(years))

Result

[2019 2020 2021] 3

And this way you could use len(years) in place of 3

EDIT:

If values can repeate then you can use unique() in group to remove repeated values.

text=""'    date    name
0   2019    a
1   2019    b
2   2019    c
3   2020    b
4   2020    c
5   2021    b
6   2021    c
7   2019    a
8   2019    a
'''

import io
import pandas as pd

df = pd.read_csv(io.StringIO(text), sep='\s+')

groups = df.groupby('name')
#counts = groups['date'].unique().apply(len)
counts = groups['date'].nunique()
result = counts[ counts == 3 ].index.to_list()
print('result:', result)