Count number of characters in each row and drop if all are below a certain number

I have a dataframe with many columns, all of which contain text data mixed with NaNs.

I want to count the number of characters in each cell in each column and then drop any rows where all the columns have less than 5 characters (if any cells have more than 5 characters, then the row is not dropped).

I was considering making a new column with str.len for each column and then filter out rows using that, but it seems very cumbersome.

Example:

>>> df
   column_1    column_2    column_3
0   werhi      dsfhjk       dh      ---> not filtered because some columns have more than 5 characters
1   sgds        fuo         g       ---> filtered
2   wqyuio      dsklh       fhkjfj
3   fhi         d           fgho    ---> filtered
4   sadfhkj     sdjfkhs     yyisdk

>>> df_filtered
   column_1    column_2    column_3
0   werhio      dsfhjk      dh
2   wqyuio      dsfjklh     fhkjfj
4   sadfhkj     sdjfkhs     yyisdk

Solution

No need to create new columns.

OPTION 1: minimal total string length per row

just apply to all columns, aggregate the total sum and create a boolean Series for boolean indexing:

thresh = 10
out = df[df.apply(lambda x: x.str.len()).sum(axis=1).ge(thresh)]

Output:

  column_1 column_2 column_3
0   werhio   dsfhjk       dh
2   wqyuio  dsfjklh   fhkjfj
4  sadfhkj  sdjfkhs   yyisdk

OPTION 2: minimal string length for any strings in a row

If you want to ensure all values in a column have at least a given size, compare to the threshold before aggregating with any:

thresh = 5
df[df.apply(lambda x: x.str.len()).ge(thresh).any(axis=1)]

Output:

  column_1 column_2 column_3
0   werhio   dsfhjk       dh
2   wqyuio  dsfjklh   fhkjfj
4  sadfhkj  sdjfkhs   yyisdk

Intermediates

OPTION 1:

# df.apply(lambda x: x.str.len())
   column_1  column_2  column_3
0         6         6         2
1         4         3         1
2         6         7         6
3         3         1         4
4         7         7         6

# df.apply(lambda x: x.str.len()).sum(axis=1)
0    14
1     8
2    19
3     8
4    20
dtype: int64

# df.apply(lambda x: x.str.len()).sum(axis=1).ge(thresh)
0     True
1    False
2     True
3    False
4     True
dtype: bool

OPTION 2:

# df.apply(lambda x: x.str.len().ge(5))
   column_1  column_2  column_3
0      True      True     False
1     False     False     False
2      True      True      True
3     False     False     False
4      True      True      True

# df.apply(lambda x: x.str.len().ge(5)).any(axis=1)
0     True
1    False
2     True
3    False
4     True
dtype: bool