filter on pandas array - pandas

Question

I'm doing this kind of code to find if a value belongs to the array a inside a dataframe:
Solution 1
df = pd.DataFrame([{'a':[1,2,3], 'b':4},{'a':[5,6], 'b':7},])
df = df.explode('a')
df[df['a'] == 1]
will give the output:
a b
0 1 4
Problem
This can go worst if there are repetitions:
df = pd.DataFrame([{'a':[1,2,1,3], 'b':4},{'a':[5,6], 'b':7},])
df = df.explode('a')
df[df['a'] == 1]
will give the output:
a b
0 1 4
0 1 4
Solution 2
Another solution could go like:
df = pd.DataFrame([{'a':[1,2,1,3], 'b':4},{'a':[5,6], 'b':7},])
df = df[df['a'].map(lambda row: 1 in row)]
Problem
That Lambda can't go fast if the Dataframe is Big.
Question
As a first goal, I want all the lines where the value 1 belongs to a:
without using Python, since it is slow
with high performance
avoiding memory issues
...
So I'm trying to understand what may I do with the arrays inside Pandas. Is there some documentation on how to use this type efficiently?

Answer 1

IIUC, you are trying to do:
df[df['a'].eq(1).groupby(level=0).transform('any')
Output:
a b
0 1 4
0 2 4
0 3 4

Answer 2

Nothing is wrong. This is normal behavior of pandas.explode().
To check whether a value belongs to values in a you may use this:
if x in df.a.explode()
where x is what you test for.

Answer 3

I think you can convert arrays to scalars with DataFrame constructor and then test value with DataFrame.eq and DataFrame.any:
df = df[pd.DataFrame(df['a'].tolist()).eq(1).any(axis=1)]
print (df)
a b
0 [1, 2, 1, 3] 4
Details:
print (pd.DataFrame(df['a'].tolist()))
0 1 2 3
0 1 2 1.0 3.0
1 5 6 NaN NaN
print (pd.DataFrame(df['a'].tolist()).eq(1))
0 1 2 3
0 True False True False
1 False False False False
So I'm trying to understand what may I do with the arrays inside Pandas. Is there some documentation on how to use this type efficiently?
I think working with lists in pandas is not good idea.