Iterating over rows of a df and check if any value in a row has the type "list"

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Consider the following dataframe with different types of elements in its columns (int and list).

>>> df

index      A         B            C
1          0        11       [13, 16, 18]
2          1         0           nan
3          34        1         [13, 16]
4          11       nan        [10, 16]

I want to iterate over the rows and for every row after dropping the NaNs, I want to check if any value of the columns is of the type "list". If no value has the type list (e.g. index 2), I create a query containing the pairs "column name" and "data (value) for that column":

for i in df.index:
  line = df.loc[i].dropna()
  *if no value in df.loc[i] is of type list:*
      q1 = ' and '.join([f'`{variable}`=={int(value)}' for variale, value in line.items()])

For index 2 we have: q1 = 'A==1' and 'B==0'

But if at least one value has the type list, I need to modify the query by adding a new condition for values of type list. Something like this:

q2 = q1 + 'variable of type list IN (list of values)'

For example, for index 3 we want to have: q2 = 'A==34' and 'B==1' and 'C in (13, 16)'

Is it possible to create such query? Any help would be greatly appreciated.

pandas dataframe list python
1 Answers

Try:

x = df.apply(
    lambda x: " and ".join(
        [
            f"'{i} in {tuple(v)}'" if isinstance(v, list) else f"'{i} == {v}'"
            for i, v in zip(x[x.notna()].index, x[x.notna()])
        ]
    ),
    axis=1,
)
print(x)

Prints:

0    'A == 0' and 'B == 11.0' and 'C in (13, 16, 18)'
1                             'A == 1' and 'B == 0.0'
2        'A == 34' and 'B == 1.0' and 'C in (13, 16)'
3                       'A == 11' and 'C in (10, 16)'
dtype: object
user_100359850
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