get non numerical rows in a column pandas python

Question

I checked this post: finding non-numeric rows in dataframe in pandas? but it doesn't really answer my question.

my sample data:

import pandas as pd   d = {  'unit': ['UD', 'UD', 'UD', 'UD', 'UD','UD'],  'N-D': [ 'Q1', 'Q2', 'Q3', 'Q4','Q5','Q6'],  'num' : [ -1.48, 1.7, -6.18, 0.25, 'sum(d)', 0.25]  } df = pd.DataFrame(d) 

it looks like this:

  N-D   num   unit 0  Q1  -1.48   UD 1  Q2   1.70   UD 2  Q3  -6.18   UD 3  Q4   0.25   UD 4  Q5   sum(d) UD 5  Q6   0.25   UD 

I want to filter out only the rows in column 'num' that are NON-NUMERIC. I want all of the columns for only the rows that contain non-numeric values for column 'num'.

desired output:

  N-D   num   unit 4  Q5   sum(d) UD 

my attempts:

nonnumeric=df[~df.applymap(np.isreal).all(1)] #didn't work, it pulled out everything, besides i want the condition to check only column 'num'.   nonnumeric=df['num'][~df.applymap(np.isreal).all(1)] #didn't work, it pulled out all the rows for column 'num' only. 

Answer 1

Use boolean indexing with mask created by to_numeric + isnull
Note: This solution does not find or filter numbers saved as strings: like '1' or '22'

print (pd.to_numeric(df['num'], errors='coerce')) 0   -1.48 1    1.70 2   -6.18 3    0.25 4     NaN 5    0.25 Name: num, dtype: float64  print (pd.to_numeric(df['num'], errors='coerce').isnull()) 0    False 1    False 2    False 3    False 4     True 5    False Name: num, dtype: bool  print (df[pd.to_numeric(df['num'], errors='coerce').isnull()])   N-D     num unit 4  Q5  sum(d)   UD 

Another solution with isinstance and apply:

print (df[df['num'].apply(lambda x: isinstance(x, str))])   N-D     num unit 4  Q5  sum(d)   UD