filter dataframe rows based on length of column values

Question

I have a pandas dataframe as follows:

df = pd.DataFrame([ [1,2], [np.NaN,1], [\'test string1\', 5]], columns=[\'A\',\'B\'] )

df
              A  B
0                 


        

Answer 1

In [42]: df
Out[42]:
              A  B                         C          D
0             1  2                         2 2017-01-01
1           NaN  1                       NaN 2017-01-02
2  test string1  5  test string1test string1 2017-01-03

In [43]: df.dtypes
Out[43]:
A            object
B             int64
C            object
D    datetime64[ns]
dtype: object

In [44]: df.loc[~df.select_dtypes(['object']).apply(lambda x: x.str.len().gt(10)).any(1)]
Out[44]:
     A  B    C          D
0    1  2    2 2017-01-01
1  NaN  1  NaN 2017-01-02

Explanation:

df.select_dtypes(['object']) selects only columns of object (str) dtype:

In [45]: df.select_dtypes(['object'])
Out[45]:
              A                         C
0             1                         2
1           NaN                       NaN
2  test string1  test string1test string1

In [46]: df.select_dtypes(['object']).apply(lambda x: x.str.len().gt(10))
Out[46]:
       A      C
0  False  False
1  False  False
2   True   True

now we can "aggregate" it as follows:

In [47]: df.select_dtypes(['object']).apply(lambda x: x.str.len().gt(10)).any(axis=1)
Out[47]:
0    False
1    False
2     True
dtype: bool

finally we can select only those rows where value is False:

In [48]: df.loc[~df.select_dtypes(['object']).apply(lambda x: x.str.len().gt(10)).any(axis=1)]
Out[48]:
     A  B    C          D
0    1  2    2 2017-01-01
1  NaN  1  NaN 2017-01-02