How to drop pandas column containing both alpha and numeric values across all records

Question

I have a pandas dataframe called df of about 2 million records. There is a column called transaction_id that might contain:

• alpha values (e.g. "abscdwew") for some records
• numeric values (e.g. "123454") for some records
• both alpha and numeric values (e.g. "asd12354") for some records
• alpha, numeric and special characters (e.g. "asd435_!") for some records
• special characters (e.g. "_-!")

I want to drop that column if ALL values (i.e. across ALL records) contain:

• combination of alpha and numeric values (e.g. "aseder345")
• combination of alpha and special characters (e.g. "asedre_!")
• combination of numeric and special characters (e.g. "123_!")
• all special characters (e.g. "!")

Is there a pythonic way of doing so?

So, if a column contains across al

Answer 1

Given the following toy dataframe, in which col1 should be removed and col2 should be kept according to your criteria:

import pandas as pd

df = pd.DataFrame(
    {
        "col1": [
            "abs@&wew",
            "123!45!4",
            "asd12354",
            "asdfzf_!",
            "123_!",
            "asd435_!",
            "_-!",
        ],
        "col2": [
            "abscdwew",
            "123454",
            "asd12354",
            "a_!sdfzf",
            "123_!",
            "asd435_!",
            "_-!",
        ],
    }
)

Here is one way to do it:

test = lambda x: True if x.isalpha() or x.isdigit() else False
cols_to_keep = df.apply(lambda x: any(test(x) for x in x))

df = df.loc[:, cols_to_keep]

print(df)
# Output
       col2
0  abscdwew
1    123454
2  asd12354
3  a_!sdfzf
4     123_!
5  asd435_!
6       _-!