Get name if range values are true Python

One way to solve the problem is by creating groups using Pandas groupby method and then filtering the groups using Pandas filter method.

import pandas as pd


def is_within_range(group):
    years = sorted(list(group["Year"]))
    check_decade = {}
    for year in years:
        decade = year // 10
        if 196 <= decade <= 202:
            check_decade[decade] = True
    if len(check_decade.keys()) == (202 - 196 + 1):
        return True
    return False


data = pd.read_csv("years.csv")
filtered_data = data.groupby(['Name']).filter(lambda x: is_within_range(x))
print(list(filtered_data.Name.unique()))

Output:

['a', 'b']

years.csv:

#,Name,Description,Year
1,a,foo,1961
2,a,foo2,1977
3,a,foo3,1980
4,a,foo4,1995
5,a,foo5,2001
6,a,foo6,2011
7,a,foo7,2020
8,b,bar,1965
9,b,bar2,1970
10,b,bar3,1983
11,b,bar4,1997
12,b,bar5,2005
13,b,bar6,2016
14,b,bar7,2022
15,c,abc,1965
16,c,ab2,1970
17,c,abc3,1993
18,c,abc4,2007
19,c,abc5,2015
20,c,abc6,2020

Explanation:

• The is_with_range method check if a group has years from each decade from 1960 to 2020. Decade of an year is year // 10. E.g. year 1965 and the year 1969 have a decade value of 196 while 1996, 1998 have a decade value of 199.
• I used a dictionary to flag each decade to True and later on count the number of decades in the group.

References:

• Pandas documentation on dataframe groupby
• Pandas documentation on dataframe filter

You can use dataframe.query method to do the same like dataset.query("year">=1961, inplace=True) print(dataset) #it replace dataframe with those values whuch has year greater than 1961