How to count rows in pandas DF on condition, if column value is a list of dates?

Question

There is a DF with column Views, which contains lists of dates. I need to count not-empty rows of this DF, ie rows where Views:= [1970-01-01 00:00:00] (type: list of datetimes)

What I tried:

a = datetime.strptime('1970-01-01 00:00:00', '%Y-%m-%d %H:%M:%S')
result.loc[result.Views[0] != a ]
result.loc[result.Views[0][0] != a ]
result.loc[result.Views[0][0] != [a] ]
result.loc[result.Views[0] != [a] ]

also tried isin, unique funcs, but all it gives either KeyValue error or 'list not hashable' error

Please, help

UPDATE

The code that works:

a = datetime.strptime('1970-01-01 00:00:00', '%Y-%m-%d %H:%M:%S')
test = result.explode('Views')
out = test[test.Views != a]
result['Count'] = result.Views.apply(lambda x: sum(y != a for y in x))
viewed = len(result.loc[result.Count > 0]) #Total rows with not empty views

But I suspect there is a simpler and quicker way to count such stuff. And nevertheless, how can I achieve items in lists by their index, if this list is a value in DF? - still unanswered

UPDATE

Shortest solution:

print(len(df.loc[df["Views"].apply(lambda l: pd.Series(l).explode().ne("1970-01-01").all())]))

Answer 1

Use DataFrame.explode for convert lists to scalars - flatten, so possible compare:

df = result.explode('Views')
a = datetime.strptime('1970-01-01 00:00:00', '%Y-%m-%d %H:%M:%S')
out = df[df.Views != a ]

If need count values use generator with sum :

result['count'] = result.Views.apply(lambda x: sum(y != a for y in x))

Answer 2

• given your embedded list is always one row long
• explode() and logical operator as mask
• filter and len

import numpy as np
import pandas as pd
import random

df = pd.DataFrame(
    {
        "ObjectClass": np.repeat("flats", 20),
        "Views": [
            np.random.choice([pd.to_datetime("1-jan-1970")]+list(pd.date_range("1-aug-2021", periods=3)), random.randint(1,3)) for i in range(20)
        ],
    }
)

print(len(df.loc[df["Views"].apply(lambda l: pd.Series(l).explode().ne("1970-01-01").all())]))
df