Check pandas dataframe column for string type

Question

I have a fairly large pandas dataframe (11k rows and 20 columns). One column has a mixed data type, mostly numeric (float) with a handful of strings scattered throughout.

I subset this dataframe by querying other columns before performing some statistical analysis using the data in the mixed column (but can't do this if there's a string present). 99% of the time once subsetted this column is purely numeric, but rarely a string value will end up in the subset, which I need to trap.

What's the most efficient/pythonic way of looping through a Pandas mixed type column to check for strings (or conversely check whether the whole column is full of numeric values or not)?

If there is even a single string present in the column I want to raise an error, otherwise proceed.

Answer 1

This is one way. I'm not sure it can be vectorised.

import pandas as pd

df = pd.DataFrame({'A': [1, None, 'hello', True, 'world', 'mystr', 34.11]})

df['stringy'] = [isinstance(x, str) for x in df.A]

#        A stringy
# 0      1   False
# 1   None   False
# 2  hello    True
# 3   True   False
# 4  world    True
# 5  mystr    True
# 6  34.11   False

Answer 2

Here's a different way. It converts the values of column A to numeric, but does not fail on errors: strings are replaced by NA. The notnull() is there to remove these NA.

df = df[pd.to_numeric(df.A, errors='coerce').notnull()]

However, if there were NAs in the column already, they too will be removed.

See also: Select row from a DataFrame based on the type of the object(ie str)