Select columns if any of their rows contain a certain string

stack with any

df.columns[df.stack().str.contains('%').any(level=1)]

Index(['C', 'D'], dtype='object')

---

comprehension

[c for c in df if df[c].str.contains('%').any()]

['C', 'D']

---

filter

[*filter(lambda c: df[c].str.contains('%').any(), df)]

['C', 'D']

---

Numpy's find

from numpy.core.defchararray import find

df.columns[(find(df.to_numpy().astype(str), '%') >= 0).any(0)]

Index(['C', 'D'], dtype='object')

First use DataFrame.select_dtypes for filter only object columns, obviously string columns.

Then use DataFrame.applymap for elementwise check values with DataFrame.any for return True if at least one per column, so possible filter columns:

c = df.columns[df.select_dtypes(object).applymap(lambda x: '%' in str(x)).any()].tolist()
print (c)
['C', 'D']

Or use Series.str.contains per columns, na parameter should be omit if all strings columns:

f = lambda x: x.str.contains('%', na=False)
c = df.columns[df.select_dtypes(object).apply(f).any()].tolist()
print (c)
['C', 'D']

Try this:

df.columns[df.apply(lambda x: x.str.contains("\%")).any()]