remove all rows in pandas dataframe with mixed data types that contain a specific string in multiple columns

Question

How can I remove all rows in a dataframe if a row contains '9999-Don't Know' in any column?

I have been able to find solutions that delete rows based on format of value (string, numerical, etc.) in the entire dataframe, or delete rows based on values in a specific column, or delete rows from a dataframe that has few columns by using their names.

This is the closest thing I found but this solution doesn't work for me because I cannot enter all the column names due to sheer volume (76+ columns).

Below is a sample dataset

pd.DataFrame.from_items([('RespondentId', ['1ghi3g','335hduu','4vlsiu4','5nnvkkt','634deds','7kjng']), ('Satisfaction - Timing', ['9-Excellent','9-Excellent','9999-Don\'t Know','8-Very Good','1-Very Unsatisfied','9999-Don\'t Know']),('Response Speed - Time',['9999-Don\'t Know','9999-Don\'t Know','9-Excellent','9-Excellent','9-Excellent','9-Excellent'])])

After removing the 4 rows that contain '9999-Don't Know', the output should look like this so I can write a new Excel file with the cleaned up data.

pd.DataFrame.from_items([('RespondentId', ['5nnvkkt','634deds']), ('Satisfaction - Timing', ['8-Very Good','1-Very Unsatisfied']),('Response Speed - Time',['9-Excellent','9-Excellent'])]) 

Answer 1

Use

In [677]: df[~(df == "9999-Don't Know").any(axis=1)]
Out[677]:
  RespondentId Satisfaction - Timing Response Speed - Time
3      5nnvkkt           8-Very Good           9-Excellent
4      634deds    1-Very Unsatisfied           9-Excellent

Or

In [683]: df[(df != "9999-Don't Know").all(axis=1)]
Out[683]:
  RespondentId Satisfaction - Timing Response Speed - Time
3      5nnvkkt           8-Very Good           9-Excellent
4      634deds    1-Very Unsatisfied           9-Excellent

Same as

In [686]: df[~df.eq("9999-Don't Know").any(axis=1)]
Out[686]:
  RespondentId Satisfaction - Timing Response Speed - Time
3      5nnvkkt           8-Very Good           9-Excellent
4      634deds    1-Very Unsatisfied           9-Excellent

Or

In [687]: df[df.ne("9999-Don't Know").all(axis=1)]
Out[687]:
  RespondentId Satisfaction - Timing Response Speed - Time
3      5nnvkkt           8-Very Good           9-Excellent
4      634deds    1-Very Unsatisfied           9-Excellent

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With mixed column types, see @PiR's comment df.astype(object)

In [695]: df[df.astype(object).ne("9999-Don't Know").all(axis=1)]
Out[695]:
  RespondentId Satisfaction - Timing Response Speed - Time
3      5nnvkkt           8-Very Good           9-Excellent
4      634deds    1-Very Unsatisfied           9-Excellent